biomolecules

Review
Molecular Biomarkers in Cancer
Virinder Kaur Sarhadi 1 and Gemma Armengol 2, *

1 Department of Oral and Maxillofacial Diseases, Helsinki University Hospital and University of Helsinki,
00290 Helsinki, Finland; virinder.sarhadi@helsinki.fi
2 Department of Animal Biology, Plant Biology, and Ecology, Faculty of Biosciences,
Universitat Autònoma de Barcelona, 08193 Barcelona, Catalonia, Spain
* Correspondence: gemma.armengol@uab.cat; Tel.: +34-935811503

Abstract: Molecular cancer biomarkers are any measurable molecular indicator of risk of cancer,
occurrence of cancer, or patient outcome. They may include germline or somatic genetic variants,
epigenetic signatures, transcriptional changes, and proteomic signatures. These indicators are based
on biomolecules, such as nucleic acids and proteins, that can be detected in samples obtained from
tissues through tumor biopsy or, more easily and non-invasively, from blood (or serum or plasma),
saliva, buccal swabs, stool, urine, etc. Detection technologies have advanced tremendously over
the last decades, including techniques such as next-generation sequencing, nanotechnology, or
methods to study circulating tumor DNA/RNA or exosomes. Clinical applications of biomarkers are
extensive. They can be used as tools for cancer risk assessment, screening and early detection of cancer,
accurate diagnosis, patient prognosis, prediction of response to therapy, and cancer surveillance
and monitoring response. Therefore, they can help to optimize making decisions in clinical practice.
Moreover, precision oncology is needed for newly developed targeted therapies, as they are functional
only in patients with specific cancer genetic mutations, and biomarkers are the tools used for the
identification of these subsets of patients. Improvement in the field of cancer biomarkers is, however,
needed to overcome the scientific challenge of developing new biomarkers with greater sensitivity,
specificity, and positive predictive value.

Citation: Sarhadi, V.K.; Armengol, G. Keywords: cancer biomarkers; biomolecules; risk assessment; diagnostic biomarkers; predictive
Molecular Biomarkers in Cancer. biomarkers
Biomolecules 2022, 12, 1021. https://
doi.org/10.3390/biom12081021

Academic Editors: Alessandro

1. Introduction
Alaimo and Valentina Vaira
A cancer biomarker is a characteristic that is measured as an indicator of risk of cancer,
Received: 3 June 2022 occurrence of cancer, or patient outcome. These characteristics can be either molecular,
Accepted: 20 July 2022 cellular, physiologic, or imaging-based. The present review focuses on molecular (and cellu-
Published: 23 July 2022 lar) cancer biomarkers. These biomolecules, found in tissues or body fluids, are present or
Publisher’s Note: MDPI stays neutral produced by cancer cells or normal cells in response to cancer. On the one hand, biomarker
with regard to jurisdictional claims in testing in cancer involves profiling tumor or body fluids to detect changes in DNA, RNA,
published maps and institutional affil- proteins, or other biomolecules that provide information for cancer diagnosis, prognosis,
iations. precision medicine/guiding cancer treatment, predicting drug response, or cancer monitor-
ing. On the other hand, genetic testing, which is different from cancer biomarker testing, is
used for detecting germline genetic variations associated with cancer susceptibility, heredi-
tary cancer, or cancer-associated syndromes [1]. Germline genetic markers can, in addition
Copyright: © 2022 by the authors. to providing cancer susceptibility information, also provide useful information regarding
Licensee MDPI, Basel, Switzerland.
treatment options [2]. They can also be included as cancer biomarkers in a broader sense.
This article is an open access article
In the following sections, we discuss the numerous molecular changes that are useful as
distributed under the terms and
cancer biomarkers, explaining the different types of biomolecules, types of samples, and
conditions of the Creative Commons
the techniques used for detecting cancer biomarkers. We also discuss different applications
Attribution (CC BY) license (https://
of cancer biomarkers in clinics and the steps involved in the process from cancer biomarker
creativecommons.org/licenses/by/
4.0/).
discovery to their clinical implementation.

Biomolecules 2022, 12, 1021. https://doi.org/10.3390/biom12081021 https://www.mdpi.com/journal/biomolecules

Biomolecules 2022, 12, 1021 2 of 39

2. Cancer-Associated Alterations
2.1. Germline Genetic Variants
There are certain inherited or germline variants that render individuals carrying them
a higher risk of developing cancer. Germline variants can be classified into three groups
according to their frequency and their effect size to cause disease: rare variants with
high penetrance, moderately frequent variants with moderate penetrance, and common
variants with low penetrance. The first ones correspond to cancer-predisposing syndromes
and hereditary cancers and are good candidates to be used as cancer risk assessment
biomarkers, because of their strong effect. For example, it is well-known that BRCA1
and BRCA2 high-penetrance variants are strongly linked to breast and ovarian cancer.
Germline variants can have different penetrance for different cancer types; for example,
Lynch syndrome associated variants in genes EPCAM, MLH1, MLH2, MSH6, and PMS2
have higher penetrance for colorectal cancer (CRC) than for pancreatic cancer [3]. Moreover,
the risk of cancer can be different for different genes [3]. Germline genetic markers are
not only useful for identifying cancer susceptibility but are also important prognostic and
predictive markers for targeted therapies. For example, poly (ADP-ribose) polymerase
inhibitors are effective for germline BRCA mutant breast and ovarian cancer [4].
A large cancer study on 10,389 cases and 33 cancer types reported pathogenic germline
variants frequency of 8% among cancer patients and discovered 853 pathogenic variants [5].
Next-generation sequencing (NGS) of the tumor sample can be used to detect germline
variants, besides somatic mutations. A recent study on more than 21,000 cancer patients
using a Food and Drug Administration (FDA)-approved NGS panel and pipeline, showed
that tumor-only sequencing identified 89.5% of pathogenic germline variants, while miss-
ing mainly germline copy number variations, intronic variants, and repetitive element
insertions [6]. Recent studies highlight the relevance of studying germline markers, along
with somatic tumor markers, as germline pathological variants have been found in patients
with no family history of cancer [3]. Moreover, it increases the germline variant detection
in familial cancer patients, which would otherwise not have been identified [7]. However,
it also increases the frequency of variants of unknown significance, which can make the
interpretation of results difficult.

2.2. Somatic Genetic Mutations

Genomic instability is an important feature of cancer cells, driving cancer evolution and
its adaptation to changing microenvironment. Most cancers result from the accumulation
of somatic mutations, some of which are specific to a cancer type, while others are shared
with other cancers. The alterations can involve a wide range of sizes, from a large part or
a whole chromosome to single base pair changes. Chromosomal abnormalities comprise
numerical abnormalities (aneuploidies and polyploidies) and structural abnormalities
(translocations; inversions; and copy number alterations (CNAs), including insertions and
deletions, as well as chromothripsis, which can result in massive rearrangements [8]). Those
chromosomal abnormalities that are specific and recurrent are relevant cancer biomarkers,
and they are used mostly in hematologic malignancies. For example, the Philadelphia
chromosome was the first chromosomal abnormality to be detected in cancer, involving
translocation of chromosomes 22 and 9, resulting in BCR–ABL fusion. It is commonly seen
in chronic myeloid leukemia, but can occur in acute myeloid leukemia, too, and is used for
their diagnosis. A comprehensive list of gene fusions obtained from an analysis of RNA
sequencing and DNA copy number data from The Cancer Genome Atlas is available at
https://tumorfusions.org/ (accessed on 22 February 2022).
However, the more common genetic alterations used as cancer biomarkers are muta-
tions involving a single nucleotide (single nucleotide variants or SNVs) or a few nucleotides
(small insertions and deletions or indels), e.g., driver mutations in EGFR, KRAS, BRAF,
TP53, KITK, and other genes. Results from the Pan-Cancer Gene Atlas sequencing project [9]
on 3281 tumors from 12 cancer types identified 127 recurrently mutated genes. The muta-
tion frequency was found to depend on tumor types ranging from 0.28 mutations/Mb in
Biomolecules 2022, 12, 1021 3 of 39

acute myeloid leukemia to 8.15 in lung squamous cell carcinoma. Despite the large number
of mutations seen in each tumor, only four or five mutations are thought to be drivers of
cancer development [10]. A more recent and detailed analysis of Pan-Cancer data from
33 different cancer types identified 299 driver genes and around 3400 driver mutations
based on in silico methods together with experimental validation [11]. Mutations in TP53
were found to be the most commonly shared among 27 different cancer types, followed
by PIK3CA, KRAS, PTEN, and ARID1A (in 15 or more). However, the majority of the
genes (142) were found mutated in only one cancer type. Interestingly, they reported that
57% of tumors had mutations that could be targeted by known cancer treatments [11]. A
detailed description of somatic mutations in tumor tissue in different cancers can be found
in a manually curated COSMIC (Catalogue of Somatic Mutations in Cancer) database
(https://cancer.sanger.ac.uk/cosmic, accessed on 22 February 2022), which also includes
separate datasets for gene mutations with causal implications in cancer, cancer-driving
gene mutations, and mutations actionable with precision oncology.
In addition to mutations seen in tumor tissue, cancer patients have cell-free DNA
originating from cancer cell lysis/death or active secretion, which is referred to as circu-
lating tumor DNA (ctDNA). It can be found in body fluids such as blood, urine, stool,
saliva, sputum, and exhaled breath, and reflects genomic alterations similar to those seen
in tumor DNA [12–14]. Moreover, healthy and cancer patients can show differences in
ctDNA concentration, fragment size, and the relative ratio of mitochondrial/nuclear DNA,
making ctDNA a good potential cancer biomarker. The main challenge associated with
the detection of ctDNA in plasma or other body fluids is its very low concentration, thus
requiring very sensitive methods for its detection.
Overall, it is important to consider the factors that can affect DNA mutation detection,
which include tumor DNA content in total DNA, sample type (e.g., fresh frozen or formalin-
fixed paraffin-embedded (FFPE) tumor tissue, or body fluids, which can harbor inhibitors
or factors affecting detection efficiency), as well as the detection technique used. Most of
the small DNA alterations are readily studied by DNA sequencing or polymerase chain
reaction (PCR)-based methods, while those involving larger DNA fragments are studied
by methods such as fluorescent in situ hybridization (FISH), array comparative genomic
hybridization, or similar methods. In the case of gene fusions, the RNA transcribed from
the gene fusion can be used for PCR- or NGS-based diagnosis. RNA fusion panels are
now available to test the most common fusions in tumors by using NGS, while single gene
fusions can be tested by reverse transcription PCR [15]. Finally, it is important to consider
that tumors can be highly heterogeneous; therefore, developing good cancer biomarkers
requires a multiple gene approach. Furthermore, cancer patients have their specific tumor
mutation profiles, and individualized approaches to treatment based on tumor profiling
are being increasingly carried out (see sections below).

2.3. Epigenetic Variants

Epigenetic variants cause changes in DNA methylation or histone protein modifica-
tions, without affecting the coding sequence of DNA. However, they affect the structure and
stability of DNA and play an important role in carcinogenesis. These epigenetic changes in
cancer cells are therefore useful as cancer biomarkers, especially since DNA methylation
changes occur in the early stages of tumorigenesis [16]. Loss of global DNA methylation
is common in many tumors and is associated with genomic instability, DNA damage,
and reactivation of transposons and retroviruses. Moreover, more localized changes in
DNA methylation at the CpG-rich promoter regions of the genes can inactivate tumor
suppressor genes. For example, the CpG island methylator phenotype, characterized by
hypermethylation of multiple sites, is a feature associated with genomic instability and
cancer [16]. Methylation of CACN3A1G, IGF2, NEUROG1, RUNX3, and SOCS1 promoters
is common in this phenotype and is associated with MLH1 methylation and microsatellite
instability. It is noteworthy that MLH1 methylation is a biomarker used for cancer-prone
Lynch syndrome testing in clinics [16]. In addition, DNA methylation biomarkers are
Biomolecules 2022, 12, 1021 4 of 39

also useful for predicting cancer treatment response; for example, tumor MGMT (gene
important in DNA repair) promotor hypermethylation is associated with good response to
alkylating drugs and used in clinical testing in glioblastomas [17].
Interestingly, these epigenetic variants can also be detected in minimally invasive
samples such as plasma, where the DNA-methylation-based screening biomarkers have
advantages compared to mutation detection [18]. These include their higher sensitivity
and specificity in detecting early stages of cancer and in detecting residual disease, as
these changes occur early on and are tissue specific. A recently developed plasma DNA
methylation panel “PanSeer” comprising 477 differentially methylated regions (10,613 CpG
sites) in overall cancer showed high sensitivity (88%) and specificity (96%) in detecting
five common cancer types, up to four years before conventional diagnosis, in a Taizhou
Longitudinal Study on 123,115 individuals who had donated plasma for long-term storage
and study [19]. FDA-approved methylation-based biomarkers include SEPT9 from plasma
(Epi ProColon) and a combination of NDRG4 and BMP3 from stool samples in CRC.
While most methods dedicated to identifying epigenetic variants rely on bisulfite conver-
sion of unmethylated cytosines into uracils, such as methylation-specific PCR, methylation-
sensitive high-resolution melting, pyrosequencing, methylation-specific droplet digital PCR,
microarray, and NGS, other non-bisulfite treatment methods are also in use, such as methy-
lated DNA immunoprecipitation or the use of methylation-sensitive restriction enzymes.
Even though genome-wide methylome studies have identified numerous differentially
methylated genes in cancer patients, it is worth mentioning that these studies have been
usually carried out on small sample sizes and that they may require a standardization of
methods and proper bioinformatics analysis, especially when sequencing techniques are
used [20]. Validation in larger sets of patients and the development of new assays can bring
many more assays to be used in clinical settings.

2.4. Transcriptional Alterations

The human transcriptome includes both coding or messenger RNA (mRNA) and
non-coding RNA (ncRNA). Among the ncRNA, long ncRNA (lncRNA), with transcript
size greater than 200 nucleotides, forms the largest group and includes long intergenic
RNA, antisense RNA, pseudogenes, and circular RNAs (circRNA), while small ncRNAs
include microRNA (miRNA), small interfering RNA, small nucleolar RNA, ribosomal RNA,
transfer RNA, and piwi-interacting RNA. Both coding and ncRNA have been found to be
differentially expressed in cancer and play a significant role in carcinogenesis. Moreover,
some of these cancer-related RNA molecules can be found cell-free, and then they are
called ctRNA.

2.4.1. mRNAs
Tumor mRNA profiling has shown differential expression of genes in tumors compared
to normal tissue and also between various histological subtypes, stages of cancer, and other
tumor features. Tumors can be classified into molecular subtypes based on the RNA
profile, and these molecular subtypes, irrespective of tumor type, can predict treatment
response. Tumor immune profile or expression of immune-related genes are also important
biomarkers for immunotherapy response. Moreover, expressions of tissue- or tumor-
specific genes, mutated genes, amplified genes, or gene fusions are all useful RNA-based
cancer biomarkers. In a recent transcriptome-wide analysis of plasma samples of cancer
and non-cancer patients, 23 tissue- and cancer-specific ctRNA biomarkers were identified
after filtering out transcripts expressed in non-cancer patients [21]. The study found that
RNA expression in plasma correlated with that in matched tissue and could predict the
origin of tumor tissue and cancer type.

2.4.2. miRNAs
MiRNAs are small ncRNAs, around 22 nucleotides long, which regulate post-transcrip-
tional gene expression. It is reported that each tissue expresses around 1000 miRNAs, 143 of
Biomolecules 2022, 12, 1021 5 of 39

which are found in all tissues [22]. There is enormous amount of data available related to
differentially expressed miRNAs in tumor tissues (reviewed in Reference [23]), as well as
in the body fluids of cancer patients, compared to normal tissue or healthy individuals,
indicating their usefulness in diagnosis/differential diagnosis, prognosis, or as predictive
cancer biomarkers. Moreover, miRNAs can be oncogenic, as, for example, miR-21 and
miR-155 are overexpressed in many cancers; or they can be tumor suppressive, e.g., let-7,
miR-128b, miR-15, and miR-16, which are under-expressed as a result of deletion, methyla-
tion, or other mechanisms (reviewed in Reference [23]). Moreover, miRNAs also play a role
in metastasis; for example, miR-10b and miR-655 can affect the tumor microenvironment by
modulating immune cells or angiogenesis [24].
However, miRNA analysis can be affected by factors such as sample collection/RNA
stabilization, RNA isolation method (miRNA or total RNA), analysis method (reverse
transcription PCR, microarray, and sequencing), and selection of reference gene [25], all of
which can affect their expression profile.
The main advantage of miRNAs as cancer biomarkers is their small size, which
makes them suitable for samples with low RNA quality, such as archival FFPE samples or
body fluids. Interestingly, miRNAs with differential expression in tumor tissue can also
be detected in ctRNA in body fluids. Moreover, miRNAs constitute the main cargo of
extracellular vesicles (EVs), where they are protected from RNases and are thus increasingly
being studied for their utility as cancer biomarkers in blood (Table 1). For example, miR-122
is found to be highly expressed in tumor tissue and serum EVs of CRC patients, especially
with liver metastasis [26]. The miRNA expression profile of serum/plasma-EVs can be
different from that of plasma/serum-ctRNA, and the EV-miRNA profile is found to be more
informative as a cancer biomarker [27]; however, some studies suggest studying both [28].
Overall, EV-associated miRNAs are promising liquid-biopsy-based cancer biomarkers [29].

Table 1. Circulating extracellular-vesicles-associated miRNAs as potential cancer biomarkers.

miRNA Sample Cancer Application Reference

Predictive markers of drug
response
Resistance to
miR-494-3p Plasma EVs NSCLC Kaźmierczak et al., 2022 [30]
osimertinib
miR-323-3p, miR-1468-3p, Resistance to
Plasma EVs NSCLC Janpipatkul et al., 2021 [31]
miR-5189-5p and miR-6513-5p osimertinib
Resistance to
miR-184, miR-3913-5p Serum EVs NSCLC Li et al., 2021 [32]
osimertinib
EGFR mutated Response to
miR-21 Plasma ctRNA Leonetti et al., 2021 [33]
NSCLC EGFR_TKIs
Predictive of
miR-125b-5p Serum EVs NSCLC Zhang et al., 2020 [34]
chemotherapy response
Predictive of
miR-620 Serum EVs NSCLC chemotherapy Tang et al., 2020 [35]
response/Diagnosis
Predictive of
miR-30b, miR-328, and miR-423 Plasma EVs Breast Cancer Todorova et al., 2022 [36]
chemotherapy response
Diagnostic, prognostic,
miR-30a Serum EVs Oral Cancer Kulkarni et al., 2020 [37]
cisplatin-resistance
Biomolecules 2022, 12, 1021 6 of 39

Table 1. Cont.

miRNA Sample Cancer Application Reference

Predictive markers of response
to immunotherapy
Overall survival after
miR-625-5p Plasma EVs NSCLC immune checkpoint Pantano et al.2022 [38]
inhibitor treatment
Response/prognosis
miR-4649-3p, miR-615-3p, and after immune
Plasma EVs Melanoma Bustos et al., 2020 [39]
miR-1234-3p checkpoint inhibitor
treatment
Predictive markers of response
to radiotherapy
Resistance to
miR-92a-3p Plasma EVs NSCLC radiotherapy Zeng et al., 2022 [40]
(upregulation)
miR-96 Plasma EVs NSCLC Radioresistant Zheng et al., 2021 [41]
Diagnostic/prognostic markers
* (Let-7b-5p and miR-22-3p and Plasma EVs
NSCLC Diagnostic Vadla et al., 2022 [42]
miR-184)
miR-125b-5p and miR-5684 Serum EVs NSCLC Diagnostic Zhang et al., 2020 [34]
miR-378 Serum EVs NSCLC Prognostic/monitoring Zhang et al., 2020 [43]
miR-382 Serum EVs NSCLC Prognostic Luo et al., 2021 [44]
miR-1260b Plasma EVs NSCLC Prognostic Kim et al., 2021 [45]
miR-486-5p and miR-146a-5p Serum EVs NSCLC Diagnostic Wu et al., 2020 [28]
Diagnostic and
miR-1246 Serum EVs NSCLC Huang et al., 2020 [46]
prognostic
miR-1246 and miR-96 Plasma EVs NSCLC Diagnostic Zheng et al., 2021 [41]
Diagnostic,
* (miR-206, miR-24, miR-1246,
Plasma EVs Breast cancer combination 98% Jang et al., 2021 [47]
and miR-373)
accuracy
miR-148a Serum EVs Breast cancer Prognosis Li et al., 2020 [48]
miR-138-5p Serum EVs Breast cancer Prognosis Xun et al. (2021) [49]
miR-363-5p Plasma EVs Breast cancer Prognostic Wang X et al., 2021 [50]
Diagnostic (with
miR-1910-3p Serum EVs Breast cancer Wang B et al. (2020) [51]
CA153)
miR-17-5p Serum EVs Breast cancer Prognostic Sueta et al., 2017 [52]
miR-423, miR-424, let7-i, and
Urine EVs Breast cancer Diagnostic Hirschfeld et al., 2020 [53]
miR-660
miR-21-5p Plasma EVs Breast cancer Diagnostic Liu et al., 2021 [54]
miR-3662, miR-146a, and
Serum EVs Breast cancer Diagnostic Li et al., 2021 [55]
miR-1290
Predictive of
miR-423-3p Plasma EVs Prostate cancer Guo et al., 2021 [56]
castration-resistance
miR-532-5p Urine EVs Prostate cancer Prognostic Kim et al., 2021 [57]
Biomolecules 2022, 12, 1021 7 of 39

Table 1. Cont.

miRNA Sample Cancer Application Reference

Diagnostic of
miR-425-5p Plasma EVs Prostate cancer metastatic prostate Rode et al., 2021 [58]
cancer
miR-16-5p, miR-451a,
miR-142-3p, miR-21-5p, and Urine EVs Prostate cancer Prognostic Shin et al., 2021 [59]
miR-636
miR-125a-5p and miR-141-5p Plasma EVs Prostate cancer Diagnostic Li et al., 2020 [60]
miR-375 and miR-451a Urine EVs Prostate cancer Diagnostic Li et al., 2021 [61]
iR-24-3p Saliva EVs Oral cancer Diagnostic He et al., 2020 [62]
Diagnostic and
miR-130a Plasma EVs Oral cancer He et al., 2021 [63]
prognostic
Diagnostic and
miR-126, miR-155, and miR-21 Serum EVs Oral cancer Chen et al., 2021 [64]
prognostic
Let7, miR-16, and miR-23 Serum EVs CRC Diagnostic Dohmen et al., 2022 [27]
miR-139-3p Plasma EVs CRC Diagnosis Li et al., 2020 [65]
miR-126, miR-1290, miR-23a,
Serum EVs CRC Diagnostic Shi et al., 2021 [66]
and miR-940
miR-4323, miR-4284, miR-1290,
Serum EVs CRC Diagnostic Handa et al., 2021 [67]
and miR-1246
Diagnostic and
miR-106b-3p Serum EVs CRC Liu et al., 2020 [68]
prognostic
Diagnostic and
miR-874 Serum EVs CRC Zhang et al. (2020) [69]
prognostic
Diagnostic and
let-7g and miR-193a Plasma EVs CRC Cho et al. (2021) [70]
prognostic
Diagnostic of
miR-122 Serum EVs CRC Sun et al., 2020 [26]
metastatic CRC (liver)
Esophageal ade- Prognostic,
miR-375 Plasma van Zweeden et al.2021 [71]
nocarcinoma Overall survival
miR-17-5p, miR-25-3p,
miR-27a, miR-27b, miR-191, Diagnostic (Higher
Meta-analysis by Gao et al.,
miR-199a-5p, miR-211, Plasma/serum Osteosarcoma expression of
2020 [72]
miR-300, miR-542-3p, miR-586, individual miRNAs)
miR-663a
miR-34a, miR-101, miR-124,
miR-125b, miR-139-5p,
Diagnostic (Lower
miR-144, miR-148a, miR-152, Meta-analysis by Gao et al.,
Plasma/serum Osteosarcoma expression of
miR-194, miR-195, miR-222, 2020 [72]
individual miRNAs)
miR-223, miR-326, miR-375,
miR-491-5p
Diagnostic (Higher
* (miR-21, miR-199a-3p, Meta-analysis by Gao et al.,
Plasma/serum Osteosarcoma expression,
miR-143) 2020 [72]
miRNA group)
* (miR-199b-5p/miR-124);
Diagnostic (Lower
(miR-195-5p, miR-199a-3p, Meta-analysis by Gao et al.,
Plasma/serum Osteosarcoma expression,
miR-320a, miR-374a-5p); 2020 [72]
miRNA group)
(miR-586, miR-223)
Biomolecules 2022, 12, 1021 8 of 39

Table 1. Cont.

miRNA Sample Cancer Application Reference

Diffuse large Meta-analysis
Diagnostic
miR-21 Plasma B-cell Lopez-Santillan et al.,
(upregulation)
lymphoma 2018 [73]
Acute
miR-92a and miR-638 Plasma lymphoblastic Diagnostic Fayed et al., 2021 [74]
leukemia
Hepatocellular Diagnostic
miR-23b-3p Plasma Manganelli et al., 2021 [75]
carcinoma (downregulation)
Compiled from the
EVmiRNA database
Top differentially expressed
(http://bioinfo.life.hust.edu.
miRNAs in plasma EVs of
cn/EVmiRNA, accessed on
cancer patients
20 June 2022) (Liu et al.,
2019) [76]
Breast adenocar-
miR-17-5p, miR-3960, miR-4488 Plasma Exo Diagnostic
cinoma
miR-3168, miR-3178, Breast adenocar-
Plasma EVs Diagnostic
miR-425-3p cinoma
miR-10a-3p, miR-10a-5p,
miR-1290, miR-141-3p,
miR-183-5p, miR-191-5p,
miR-192-5p, miR-194-5p,
Plasma Exo CRC Diagnostic
miR-182-5p, miR-19b-3p,
miR-200a-5p, miR-200b-3p,
miR-215-5p, miR-19a-3p,
miR-429
miR-1224-5p, miR-451a Plasma EVs CRC Diagnostic
let-7f-5p, let-7g-5p,
miR-106b-3p, miR-1246,
miR-1260b, miR-1290,
Chronic
miR-146a-5p, miR-155-5p,
Plasma Exo lymphocytic Diagnostic
miR-16-2-3p, miR-17-5p,
leukemia
miR-181a-2-3p, miR-20a-5p,
miR-30e-3p, miR-339-5p,
miR-4488.
Chronic
miR-126-5p, miR-182-5p,
Plasma EVs lymphocytic Diagnostic
miR-183-5p
leukemia
miR-103a-3p, miR-106b-3p,
miR-10b-5p, miR-1307-5p,
miR-130b-3p, miR-142-5p,
miR-181a-3p, miR-186-5p,
Plasma Exo Lymphoma Diagnostic
miR-191-5p, miR-25-3p,
miR-423-3p, miR-4767,
miR-877-5p, miR-92a-3p,
miR-92b-3p
miR-151a-5p, miR-3195,
miR-3960, miR-4792, miR-7641, Plasma Exo Oral cancer Diagnostic
miR-7704.
miR-1224-5p, miR-9-5p Plasma Exo Prostate cancer Diagnostic
miR-1224-5p, miR-9-5p Plasma EVs Prostate cancer Diagnostic
Exo, exosomes; EVs, extracellular vesicles, including exosomes; NSCLC, non-small cell lung cancer; CRC,
colorectal cancer; * combination of miRNAs.
Biomolecules 2022, 12, 1021 9 of 39

2.4.3. CircRNAs
CircRNAs are endogenous lncRNAs acting as miRNA sponges and regulating tran-
scription and splicing. They are single-stranded RNAs lacking cap and poly-A tail, with
ends joined covalently to form a circular structure. Their high stability, tissue-specific
expression, and association with tumor progression make them suitable candidates for
cancer biomarkers. Recent studies have shown their possibility as diagnostic biomarkers,
especially from EVs in body fluids (reviewed in Reference [77]).

2.4.4. Other lncRNAs

Tissue specificity and association of certain lncRNAs’ expression with stage, metastasis,
and survival make them candidate cancer biomarkers. Some of the lncRNA with good
potential in cancer diagnosis or prognosis include PCA3, MALAT1, HOTAIR, H19, and
CCAT1 (reviewed in Reference [78]). For example, high expression of HOTAIR, MALAT1,
and CCAT2 is related to poor prognosis in various cancer types. PCA3 is a clinically
approved biomarker with high sensitivity and specificity for the detection of early prostate
cancer from urine samples, while the MALAT1 assay is more useful in patients with
borderline prostate-specific antigen (PSA) levels. Plasma levels of H19 in breast cancer,
and H19, HOTAIR, and MALAT1 in gastric cancer have shown high diagnostic potential,
while single nucleotide polymorphisms in the H19 gene have applications in cancer risk
prediction. Some current clinical trials are looking for their future potential application in
cancer diagnosis.

2.4.5. Summary of Transcriptional Alterations

According to a manually curated database “CRMarker”, created for cancer RNA
biomarker discovery [79], the top RNA diagnostic/prognostic candidate biomarkers iden-
tified from different cancer studies include the following mRNAs: TP53, EGFR, ERBB2,
WT1, CDKN2A, MK167, TERT, PCA3, PTEN, CD44, BCL2, ERCC1, CCND1, MET, and
BIRC5. Among the ncRNA, the following miRNAs are included: miR-21, miR-155, miR-221,
miR-210, miR-145, miR-375, miR-205, miR-126, miR-223, miR-200c, miR-141, and miR-31.
The lncRNAs are MALAT1, HOTAIR, UCA1, PVT1, H19, NEAT1, GASS, lnc-FOXB2, lnc-
BMP6-106, lnc-FGF1-9, CYTOR, TUG1, and CDKN2B-AS1; and the circRNAs are circ_002059,
circ-PVT1, ciRS-7, circ_0001649, circ_0005075, and circ_100338. In addition, levels of BIRC5
mRNA are also increased in the serum of CRC patients, and very high levels of this mRNA
are correlated with poor prognosis [80].

2.5. Proteomic Changes

Cancer-associated alterations at DNA and RNA levels are also observed at the protein
level (Figure 1), although gene expression does not necessarily correlate with protein
expression. Proteins are more difficult to study than nucleic acids, as they are complex and
sensitive to physiological changes, and their function is dependent on post-translational
modifications. Protein biomarkers in cancer include overexpressed proteins (e.g., HER2),
mutated proteins (including neoantigens and products of gene fusions), or proteins with
tumor-specific post-translational modifications (e.g., glycoproteins), all of which can be
detected in tumor tissue. On the other hand, protein biomarkers detectable in blood
or other body fluids, in addition to those detected in tumors, also include tissue/cell-
specific proteins that have increased levels in body fluids compared to normal, e.g., PSA
in the plasma of prostate cancer patients. However, the challenge for protein biomarkers
from plasma/blood is the dominant expression of certain normal proteins that mask the
very low expression of cancer-related proteins or protein modifications, making their
characterization difficult.
Biomolecules 2022, 12, 1021 10 of 40
Biomolecules 2022, 12, 1021 10 of 39

Figure 1. Different biomolecules for detecting cancer biomarkers. ALK fusions in lung tumor
tissue detected
Figure as (a)
1. Different DNA by fluorescence
biomolecules incancer
for detecting situ hybridization, (b) RNA
biomarkers. ALK by reverse-transcription
fusions in lung tumor tissue
polymerase chain reaction, and (c) protein by immunohistochemistry. Figure modified from Tuonen
detected as (a) DNA by fluorescence in situ hybridization, (b) RNA by reverse-transcription pol-
et al. [15],chain
ymerase (open access) under
reaction, and (c)Creative
protein Commons Attribution License.
by immunohistochemistry. Figure modified from Tuonen
et al. [15], (open access) under Creative Commons Attribution License.
Protein biomarkers were among the first to be used in cancer diagnostics. Most
of them are biomarkers
Protein based on cancer antigens,
were among theenzymes,
first to beand
usedhormones, but also on Most
in cancer diagnostics. changes
of
in protein glycosylation profile, which is a characteristic feature of cancer.
them are based on cancer antigens, enzymes, and hormones, but also on changes in pro- Glycans are
polymers
tein of monosaccharides
glycosylation profile, whichthat
is acan conjugate with
characteristic proteins
feature to form
of cancer. glycoproteins.
Glycans are poly-
These differentially expressed glycans or glycoproteins are useful cancer biomarkers
mers of monosaccharides that can conjugate with proteins to form glycoproteins. These in
tumor tissue,expressed
differentially as well asglycans
in blood.or The alterationsare
glycoproteins in protein glycosylation
useful cancer can be
biomarkers due to
in tumor
Biomolecules 2022, 12, 1021 11 of 39

the altered expression of glycoproteins or changes in the glycans or glycotransferases.

Some of the glycans/glycoproteins used as cancer biomarkers include AFP, β-hCG, cancer
antigen (CA)15-3, CA19-9, CA27.29, CA125, CA549, Carcinoembryonic Antigen (CEA),
CEACAMS HER2, onfFN, PLAP, PSA, sTn antigen, TAG-72, TG, and Tn antigen (reviewed
in Reference [81]).
Two initiatives have been launched to gain insight into proteome characterization in
cancer. First, Clinical Proteomic Tumor Analysis Consortium (https://proteomics.cancer.
gov, accessed on 22 February 2022) was set up to increase the understanding of cancer pro-
teomics in relation to cancer genomics, as well as to standardize proteomic assays for clinical
use. Second, the Human Protein Atlas (https://www.proteinatlas.org/humanproteome/
tissue/cancer, accessed on 28 February 2022) documents all human proteins expressed
in different healthy cells and tissues, as well as in tumor tissue (pathological conditions).
However, although numerous proteomic studies have been carried out on tumor tissue and
body fluids from different cancer types and many candidate proteins have been identified
as potential cancer biomarkers, relatively few have finally received FDA approval. More re-
cently, tumor immune cell infiltration or immune profiling using RNA/protein biomarkers
have proved to be useful as prognostic biomarkers or as predictive biomarkers for selecting
patients that could benefit from immunotherapy (see Section 5.5). Anti-programmed cell
death-1/programmed cell death-ligand 1 (PD1/PD-L1) antibody is approved for the first
or second line of treatment for various cancers. For example, in gastroesophageal cancers, a
combined positive score for microsatellite instability or mismatch repair, tumor mutational
burden, and PD-L1 expression is used for immunotherapy selection [82].
Proteins from tumor tissues are mainly studied by immunohistochemistry (IHC),
while enzyme-linked immunosorbent assay (ELISA) is commonly used for body fluid
protein biomarkers. Proteomic technologies applied for liquid biopsies in cancer diagnosis
are reviewed in Ding et al. [83]. Moreover, glycomic profiling can be performed by mass
spectrometry, but lectin microarrays are increasingly being used [84].

2.6. Cellular Phenotype

Changes in gene and protein expression can result in changes in morphology and
function of cells, which is known as cellular phenotype. These phenotypic features are
reflecting the variety of pathways involved in the expression of that particular phenotype.
Therefore, cellular responses, such as DNA damage response (including DNA repair),
levels of oxidative stress, or cell death (apoptosis), among others, can be used as cellu-
lar biomarkers for cancer. Notably, if the phenotypic assay used to analyze the cellular
biomarker is high throughput, it may be easier to implement in a clinical setting than gene
or protein assays.
Since many cancers are due to genomic instability, which can be related to alterations
in DNA damage response pathways, this can be a sign of tumor growth and progression.
Moreover, genomic instability and DNA damage response alterations are potential biomark-
ers of success for new immunotherapy drugs [85] (see Section 5.5). Finally, as many cancer
treatments are targeting DNA damage response pathways, alterations of these pathways
can be a biomarker of response to cancer treatment.
A recent meta-analysis of 55 case-control studies evaluated the association between
DNA repair phenotype and risk for 12 different cancers [86]. According to this study,
individuals with lower DNA repair capacity have a higher risk of developing cancer.
Moreover, these results were obtained for all studied cancer types, suggesting that the DNA
repair phenotype is a good candidate to be used as a cancer biomarker.
Another example of a phenotype assay would be the analysis of levels of cellular
oxidative stress in body fluids as a biomarker of response to cancer treatment. One of
the best methods to assess oxidative stress is to study levels of 8-hydroxydeoxyguanosine
(8-oxo-dG). Interestingly, several studies have observed that levels of 8-oxo-dG in urine or
blood may be a useful biomarker of therapy response in cancer patients [87–89].
Biomolecules 2022, 12, 1021 12 of 39

In addition, phenotypic cellular apoptosis measured in blood after in vitro irradiation

has been used as a predictor of radiation-induced cancer [90] and as a biomarker of late
radiotherapy toxicity in breast cancer patients (see Section 5.5).

3. Sources of Molecular Cancer Biomarkers

Cancer biomarkers can be studied from a variety of sample types, with the tumor
tissue being the most widely analyzed so far. An alternative to tumor biopsies is liquid
biopsies, which are predominantly non-invasive. The most common non-tumor sample
types used for cancer biomarker analysis are blood, urine, stool, and, less commonly,
exhaled breath, saliva/buccal swabs, cerebrospinal fluid, sputum, and other body fluids
(Table 2).

Table 2. Commonly studied cancer biomarkers from different sample types. (source: National Cancer
Institute [91], and references [16,17].

Tumor
Cerebrospinal Saliva/Buccal
Biomarker Cancer Application Tissue/Bone Blood Urine Stool
Fluid Swab
Marrow
ALK gene NSCLC, anaplastic To help determine
rearrangements and large cell lymphoma, treatment and X
overexpression and histiocytosis prognosis
To help diagnose
liver cancer and
follow response to
Alpha-fetoprotein Liver cancer and treatment; to assess
X
(AFP) germ cell tumors stage, prognosis,
and response to
treatment of germ
cell tumors
To help in
diagnosis, to
B-cell evaluate
immunoglobulin B-cell lymphoma effectiveness of X X
gene rearrangement treatment, and to
check for
recurrence
BCL2 gene Lymphomas and For diagnosis and
X X
rearrangement leukemias planning therapy
To confirm
Chronic myeloid diagnosis, predict
leukemia, acute response to
BCR–ABL fusion lymphoblastic targeted therapy,
X X
gene leukemia, and acute help determine
myelogenous treatment, and
leukemia monitor disease
status
Multiple myeloma, To determine
Beta-2-microglobulin chronic lymphocytic prognosis and
X X X
(B2M) leukemia, and some follow response to
lymphomas treatment
Beta-human To assess stage,
chorionic Choriocarcinoma and prognosis, and
X X
gonadotropin germ cell tumors response to
(Beta-hCG) treatment
As surveillance
with cytology and
Bladder cancer and
Bladder Tumor cystoscopy of
cancer of the kidney X
Antigen (BTA) patients already
or ureter
known to have
bladder cancer
Biomolecules 2022, 12, 1021 13 of 39

Table 2. Cont.

Tumor
Cerebrospinal Saliva/Buccal
Biomarker Cancer Application Tissue/Bone Blood Urine Stool
Fluid Swab
Marrow
Cutaneous
melanoma,
BRAF gene V600 Erdheim–Chester To help determine
X
mutations disease, Langerhans treatment
cell histiocytosis,
CRC, and NSCLC
BRCA1 and BRCA2 Ovarian and breast To help determine
X X
gene mutations cancers treatment
To assess whether
treatment is
CA15-3/CA27.29 Breast cancer X
working or if
cancer has recurred
Pancreatic, To assess whether
CA19-9 gallbladder, bile duct, treatment is X
and gastric cancers working
To help in
diagnosis,
assessment of
CA-125 Ovarian cancer response to X
treatment, and
evaluation of
recurrence
To detect
CA27.29 Breast cancer metastasis or X
recurrence
To help in
diagnosis, check
Medullary thyroid
Calcitonin whether treatment X
cancer
is working, and
assess recurrence
To monitor the
effectiveness of
Carcinoembryonic CRC and some other
treatment and to X
antigen (CEA) cancers
detect recurrence
or spread
To help in
B-cell lymphomas diagnosis and to
CD19 X X
and leukemias help determine
treatment
Non-Hodgkin To help determine
CD20 X
lymphoma treatment
To help in
B-cell lymphomas diagnosis and to
CD22 X X
and leukemias help determine
treatment
Non-Hodgkin (T-cell) To help determine
CD25 X
lymphoma treatment
Classic Hodgkin
lymphoma, and To help determine
CD30 X
B-cell and T-cell treatment
lymphomas
Acute myeloid To help determine
CD33 X
leukemia treatment
Biomolecules 2022, 12, 1021 14 of 39

Table 2. Cont.

Tumor
Cerebrospinal Saliva/Buccal
Biomarker Cancer Application Tissue/Bone Blood Urine Stool
Fluid Swab
Marrow
To help in
diagnosis,
Chromogranin A Neuroendocrine assessment of
X
(CgA) tumors treatment response,
and evaluation of
recurrence
Chromosome 17p Chronic lymphocytic To help determine
X
deletion leukemia treatment
To help in
Chromosomes 3, 7,
Bladder cancer monitoring for X
17, and 9p21
tumor recurrence
Circulating tumor To inform clinical
cells of epithelial Metastatic breast, decision-making,
X
origin prostate, and CRC and to assess
(CELLSEARCH) prognosis
Gastrointestinal
stromal tumor, To help in
mucosal melanoma, diagnosis and to
C-kit/CD117 X X
acute myeloid help determine
leukemia, and mast treatment
cell disease
Cyclin D1 (CCND1)
Lymphoma and To help in
gene rearrangement X
myeloma diagnosis
or expression
To help in
Cytokeratin fragment
Lung cancer monitoring for X
21-1
recurrence
To monitor the
Des-gamma-carboxy Hepatocellular effectiveness of
X
prothrombin (DCP) carcinoma treatment and to
detect recurrence
To predict the risk
Breast, CRC, gastric,
of a toxic reaction
DPD gene mutation and pancreatic X
to 5-fluorouracil
cancers
therapy
To help determine
EGFR gene mutation NSCLC treatment and X
prognosis
Estrogen receptor
To help determine
(ER)/progesterone Breast cancer X
treatment
receptor (PR)
FGFR2 and FGFR3 To help determine
Bladder cancer X
gene mutations treatment
To monitor
progression and
Fibrin/fibrinogen Bladder cancer X
response to
treatment
Acute myeloid To help determine
FLT3 gene mutations X
leukemia treatment
As a companion
FoundationOne CDx diagnostic test to
Any solid tumor X X
(F1CDx) genomic test determine
treatment
Biomolecules 2022, 12, 1021 15 of 39

Table 2. Cont.

Tumor
Cerebrospinal Saliva/Buccal
Biomarker Cancer Application Tissue/Bone Blood Urine Stool
Fluid Swab
Marrow
To help in
diagnosis, monitor
Gastrin-producing
Gastrin the effectiveness of X
tumor (gastrinoma)
treatment, and
detect recurrence
As a companion
diagnostic test to
Guardant360 CDx determine
Any solid tumor X
genomic test treatment and for
general tumor
mutation profiling
To plan cancer
treatment, assess
disease
HE4 Ovarian cancer X
progression, and
monitor for
recurrence
HER2/neu gene
Breast, ovarian,
amplification or To help determine
bladder, pancreatic, X
protein treatment
and stomach cancers
overexpression
To help in
5-HIAA Carcinoid tumors diagnosis and to X
monitor disease
IDH1 and IDH2 gene Acute myeloid To help determine
X X
mutations leukemia treatment
To help diagnose
Multiple myeloma disease, assess
Immunoglobulins and Waldenström response to X X
macroglobulinemia treatment, and look
for recurrence
IRF4 gene To help in
Lymphoma X
rearrangement diagnosis
Certain types of To help in
JAK2 gene mutation X X
leukemia diagnosis
To help determine
KRAS gene mutation CRC and NSCLC X
treatment
Germ cell tumors, To assess stage,
Lactate lymphoma, leukemia, prognosis, and
X
dehydrogenase melanoma, and response to
neuroblastoma treatment
Mammaprint test To evaluate risk of
Breast cancer X
(70-gene signature) recurrence
To guide treatment
Microsatellite
and to identify
instability (MSI)
CRC and other solid those at high risk
and/or deficient X
tumors of certain cancer-
mismatch repair
predisposing
(dMMR)
syndromes
To help in
Lymphomas and diagnosis and to
MYC gene expression X
leukemias help determine
treatment
To help in
Lymphoma and
MYD88 gene diagnosis and to
Waldenström X
mutation help determine
macroglobulinemia
treatment
Biomolecules 2022, 12, 1021 16 of 39

Table 2. Cont.

Tumor
Cerebrospinal Saliva/Buccal
Biomarker Cancer Application Tissue/Bone Blood Urine Stool
Fluid Swab
Marrow
Myeloperoxidase To help in
Leukemia X
(MPO) diagnosis
To help in
Neuron-specific Small cell lung cancer diagnosis and to
X
enolase (NSE) and neuroblastoma assess response to
treatment
To help determine
NTRK gene fusion Any solid tumor X
treatment
To monitor
Nuclear matrix
Bladder cancer response to X
protein 22
treatment
To evaluate risk of
Oncotype DX Breast
distant recurrence
Recurrence Score test Breast cancer X
and to help plan
(21-gene signature)
treatment
To predict the
Oncotype DX
aggressiveness of
Genomic Prostate
Prostate cancer prostate cancer and X
Score test (17-gene
to help manage
signature)
treatment
To pre-operatively
OVA1 test (5-protein assess pelvic mass
Ovarian cancer X
signature) for suspected
ovarian cancer
To determine need
for repeating
PCA3 mRNA Prostate cancer X
biopsy after a
negative biopsy
To diagnose, to
predict response to
all-trans-retinoic
acid or arsenic
trioxide therapy, to
PML/RARα Acute promyelocytic
assess effectiveness X X
fusion gene leukemia
of therapy, monitor
minimal residual
disease, and
predict early
relapse
NSCLC, liver cancer,
stomach cancer,
gastroesophageal
Programmed death junction cancer, To help determine
X
ligand 1 (PD-L1) classical Hodgkin treatment
lymphoma, and other
aggressive
lymphoma subtypes
To predict the
aggressiveness of
Prolaris test (46-gene
Prostate cancer prostate cancer and X
signature)
to help manage
treatment
To help in
diagnosis, to assess
Prostate-specific
Prostate cancer response to X
antigen (PSA)
treatment, and to
look for recurrence
Biomolecules 2022, 12, 1021 17 of 39

Table 2. Cont.

Tumor
Cerebrospinal Saliva/Buccal
Biomarker Cancer Application Tissue/Bone Blood Urine Stool
Fluid Swab
Marrow
To help in
Prostatic Acid Metastatic prostate diagnosing poorly
X
Phosphatase (PAP) cancer differentiated
carcinomas
ROS1 gene To help determine
NSCLC X
rearrangement treatment
Soluble To monitor
mesothelin-related Mesothelioma progression or X
peptides (SMRP) recurrence
Neuroendocrine
Somatostatin tumors affecting the To help determine
X
receptor pancreas or treatment
gastrointestinal tract
To help in
diagnosis;
T-cell receptor gene
T-cell lymphoma sometimes to X X
rearrangement
detect and evaluate
residual disease
Terminal transferase Leukemia and To help in
X X
(TdT) lymphoma diagnosis
To predict the risk
Thiopurine of severe bone
S-methyltransferase marrow toxicity
Acute lymphoblastic
(TPMT) enzyme (myelosuppres- X X
leukemia
activity or TPMT sion) with
genetic test thiopurine
treatment
To evaluate
response to
Thyroglobulin Thyroid cancer x
treatment and to
look for recurrence
To predict toxicity
UGT1A1*28 variant
CRC from irinotecan X X
homozygosity
therapy
Urine
To help in
catecholamines: Neuroblastoma X
diagnosis
VMA and HVA
Urokinase
plasminogen To determine the
activator (uPA) and aggressiveness of
Breast cancer X
plasminogen cancer and guide
activator inhibitor treatment
(PAI-1)
DNA methylation
markers based on
References [16,17]
Methylation of Drug response to
Glioblastoma X
MGMT promoter chemotherapy
Methylation of MLH1 Lynch syndrome Treatment decision X
Methylation of
Colorectal Cancer Diagnostic X
NDRG4 and BMP3
Methylation of
Colorectal Cancer Diagnostic X
SEPT9 promoter
NSCLC, non-small cell lung cancer; CRC, colorectal cancer; UGT1A1*28, variant with seven (TA) repeats; X,
detected in sample.
Biomolecules 2022, 12, 1021 18 of 39

3.1. Blood
Different parts of the blood, such as white blood cells (WBCs), circulating tumor cells
(CTCs), plasma, serum, or EV can be used for biomarker testing.

3.1.1. WBCs
DNA from WBCs is used for germline genetic variant detection; for example, BRAC-
Analysis CDx (Myriad Genetics, Inc., Salt Lake City, UT, USA) is used to screen for changes
in coding regions of BRCA1 and BRCA2 genes. The germline genetic variant detection is
performed by using PCR and Sanger sequencing/NGS for SNV and indel detection and by
using multiplex PCR for large deletions and duplications in precision medicine for breast,
ovarian, pancreatic, and prostate cancer.
In addition, for hematological cancers, such as leukemias, lymphomas, or myelo-
mas, WBCs are the primary specimen for diagnosis, from basic morphology studies to
immunophenotyping, using panels of fluorochrome-labeled antibodies to analyze anti-
gen expression by flow cytometry. Moreover, in these types of cancer, WBCs are used in
conventional cytogenetics/molecular cytogenetics/FISH to detect mostly aneuploidy or
translocations; in addition, WBC DNA is used in molecular genetic tests to detect sequence
mutations. These WBC-based biomarkers are useful in diagnosis, prognosis, detection of
residual disease, and prediction of response to treatment or relapse.

3.1.2. CTCs
Some cancer cells are shed in circulation by solid tumors and have a promising future
in liquid-biopsy-based cancer diagnostics and monitoring [92]. The biggest hurdle in their
detection is their extremely low number, of approximately 1 CTC/mL in blood. Despite the
low number, technical improvements have made it possible to capture, identify, and count
them. The detection methods include those based on their physical characteristics, such as
large cell size, electrical properties (e.g., CTCs can have a more negative charge compared to
leukocytes [93]), or immunological properties, which can help to identify surface proteins
on CTCs. Currently, nanomaterials are being tested to increase the efficiency of immuno-
capture and detection [94]. As their number is increased in metastatic disease, they have
use in monitoring metastatic breast cancer, CRC, and prostate cancer.

3.1.3. Plasma/Serum
Tumor-associated ctDNA, ctRNA, and protein changes can also be observed in plasma
or serum, and nowadays plasma-based cancer diagnosis is at the forefront of cancer
biomarker development. Protein biomarkers have been widely used for decades for cancer
diagnosis and monitoring from plasma samples, and some examples are mentioned in
Sections 5.2 and 5.4.
Moreover, a few NGS panels for detecting cancer-associated genetic alterations in
plasma/serum are now available and approved for use in the clinic. For example, Founda-
tionOne Liquid CDx (Foundation Medicine, Inc., Beverly, MA, USA) uses targeted high-
throughput hybridization-based capture technology and detects mutations in 311 genes,
including four gene rearrangements and CNAs in three genes, in ctDNA from plasma
samples for use as companion diagnostics for targeted therapies. Another similar NGS-
based plasma ctDNA test approved is Guardant360 CDx (Guardant Health, Inc., Palo Alto,
CA, USA), which detects mutations in fifty-five genes, fusions in four genes, and CNAs in
two genes, and is used as a companion diagnostics for lung cancer. Other plasma-based
tests approved for clinics are mentioned next. The Therascreen PIK3CA RGQ PCR Kit
(Qiagen Gmbh, Hilden, Germany), which is based on real-time qualitative PCR, is used
for the detection of 11 mutations in the PIK3CA gene in FFPE tumor DNA or ctDNA
of patients with breast cancer to identify patients for targeted treatment with PIQRAY®
(Alpelisib). Epi proColon (Epigenomics AG, Berlin, Germany) is used for screening CRC in
individuals older than 50 years and that cannot be screened by standard methods. It detects
methylation in the promoter region of the Septin 9 gene from plasma DNA, using real-time
Biomolecules 2022, 12, 1021 19 of 39

methylation-specific PCR. Finally, COBAS EGFR mutation test V2 (Roche Molecular Sys-
tems, Indianapolis, IN, USA) detects somatic mutations in the EGFR gene from FFPE tumor
DNA or plasma ctDNA and is used as a companion diagnostics method for the selection of
targeted therapies in lung cancer patients [95]. It is noteworthy that the main limitation of
plasma-based tests is the low concentration of tumor-associated biomolecules present in
this fluid, and most NGS-based tests are, therefore, approved as single-site assays carried
out at specific laboratories.

3.2. Urine
Urine is mainly used for biomarker testing in bladder or prostate cancer. An in vitro
RNA-based assay (Progensa PCA3 assay, from Hologic, Inc., Marlborough, MA, USA),
which calculates the ratio of PCA3/PSA RNA molecules (PCA3 score), is approved for
urine samples collected after digital rectal exam to aid in the decision for prostate biopsy
(score of <25 is associated with a low probability of positive prostate biopsy). Another
urine-based diagnostic test for suspected bladder cancer is the Urovision Bladder Cancer
kit, which detects aneuploidies for chromosomes 3, 7, and 17 and loss of 9p21 locus by
FISH. The test is used for initial diagnosis and tumor recurrence monitoring. Finally, a
simple protein-based diagnostic and monitoring test for bladder cancer, the Alere Nmp22
Bladderchek test, is an enzyme immunoassay-based quantitative testing of nuclear matrix
protein (NMP22) in urine samples for bladder cancer diagnosis.

3.3. Stool
Stool samples are mainly used for CRC screening; these tests are simple to perform,
and some of them can even be performed at home. The basic test for CRC screening is
fecal immunochemical testing, which detects blood released in small amounts by tumors
and polyps into the stool. However, the test is non-specific, as other conditions such as
hemorrhoids can also give positive results. On the other hand, CRC-related gene mutations
can also be detected in the stool DNA of patients. We detected gene mutations in stool
DNA of CRC patients, using a targeted amplicon gene panel by NGS; these mutations
correlated to those seen in matched FFPE tumor tissues [14]. Interestingly, a CRC screening
test, Cologuard (Exact Sciences, Inc., Marlborough, MA, USA), which is based on detecting
CRC-associated DNA mutations in stool samples, is approved by FDA. It has a sensitivity
of 92% for CRC detection and 42% for large polyps, compared to 74% and 24%, respectively,
when using fecal immunochemical testing.
Stool samples are also useful for microbiota profiling, which has also gained relevance
as a biomarker, not only for CRC but also for other gastrointestinal cancers, including
pancreatic cancer [96,97]. Moreover, the bacterial profile can also predict response to
cancer therapy.

3.4. Exhaled Breath

Cancer gene mutations can also be detected from the exhaled breath of lung cancer
patients (extensively reviewed in Reference [12]).

3.5. Saliva/Buccal Swabs

The main advantage of saliva/buccal swabs as a source of cancer biomarkers is their
ease of collection. DNA isolated from saliva/buccal swabs is mostly used for genotyping
germline genetic variants, e.g., TMPT variant detection for risk evaluation before treatment
with thiopurines in leukemias. Moreover, and similarly to plasma samples, saliva is also
a source of tumor-associated nucleic acids, proteins, metabolites, and EVS to be used as
cancer biomarkers, for example, in oral cancer or head and neck cancers [98]. In this regard,
two meta-analyses have shown hypermethylation biomarkers with high specificity [99]
and IL1β, IL6, and IL8 as early interleukin biomarkers [100] in saliva samples for diagnosis
and early detection of oral cancer. In addition, changes in the oral microbiome can also be
useful biomarkers not only in head and neck cancer [98] but also in pancreatic cancer [96].
Biomolecules 2022, 12, 1021 20 of 39

Finally, human papillomavirus DNA in saliva can be a useful biomarker for treatment
response and recurrence in human papillomavirus-associated head and neck cancers [101].

3.6. EVs
EVs are small lipid-bound vesicles released by all cells that contain different types
of biomolecules, such as proteins and nucleic acids, and that play an important role in
intercellular communication. Depending upon their origin, they can be grouped into
exosomes (endocytic origin), microvesicles (formed from plasma membrane budding), or
apoptotic bodies. The content or the type of cargo they carry depend on their cell of origin,
and, thus, their analysis in body fluids can contribute to identifying new cancer biomarkers.
Differential expression of membrane-bound and intra-vesicular proteins and miRNAs of
EVs has been reported in plasma, urine, or other body fluids of cancer patients compared
to healthy individuals (reviewed in Reference [102]). Moreover, in addition to protein and
miRNAs, DNA alterations, such as cancer gene mutations can also be detected in EVs of
cancer patients [103].
However, the limitation of these EV-associated biomarkers is that EVs are heteroge-
neous vesicles with a range of different sizes and types, and, therefore, the results can vary
depending upon the isolation method or kits used. The methods generally applied are
based on density-gradient ultracentrifugation, filtration/size exclusion chromatography,
EV-precipitation, affinity interactions (by antibodies, lipid-binding proteins, and lectins), or
microfluidic separation (based on immunoaffinity, microporous filtration, acoustic nanofil-
tration, and porous micropillars) [104]. Notably, miRNAs are found to be more concentrated
in serum EVs than as free circulating serum molecules, and their EV concentration is found
to increase with increasing malignancy in cervical cancer and better discriminate cancer
from controls [27]. A manually curated database named EVAtlas, created by Liu et al. [105],
provides a comprehensive compilation of ncRNA expression in EVs from 2030 small se-
quencing datasets. Using the dataset, the authors identified miR-451a as a potential lung
cancer biomarker in plasma EVs [105]; however, they observed that miRNA EV expression
does not necessarily correlate with tumor expression. MiR-451a was found to be highly
expressed in plasma EVs of lung cancer patients compared to controls, while its expression
was lower in lung tumor tissue compared to normal lung tissue. The authors postulated
that miR-451, which acts as a tumor suppressor and is expressed in normal lung tissue,
might be packed in EVs and removed from tumor cells.

4. Techniques Used to Detect Molecular Cancer Biomarkers

4.1. FISH
It employs a fluorescently labeled probe that hybridizes with DNA to detect gene
copy number changes (e.g., HER2 amplification) or gene fusions in tumor sections or
cells (Figure 1a). Some variants of FISH include multiplex FISH, spectral karyotyping,
and comparative genomic hybridization. Spectral karyotyping is a 24-color chromosome
painting assay, which detects chromosomal abnormalities with high sensitivity. It can be
used to detect chromosomal biomarkers of cancer diagnosis and prognosis, especially in
hematological malignancies, sarcomas, carcinomas, and brain tumors [106].

4.2. PCR/Real-Time PCR/Digital PCR

PCR-based targeted genetic profiling is the most common technology used in cancer
diagnostics for both DNA- and RNA-based applications. It is used for the detection of
small DNA mutations (e.g., EGFR mutations), gene fusions (e.g., RNA-based testing for
ALK), or DNA methylation analysis using methylation-specific PCR (e.g., MGMT promoter
methylation in glioblastoma or Septin9 gene methylation in CRC). Many modifications of
this basic method are continuously being developed to increase the sensitivity of detecting
biomarkers from trace sources.
Biomolecules 2022, 12, 1021 21 of 39

4.3. NGS
NGS is finding application in genetic screening of both germline variants and somatic
mutations, including SNVs, indels, and CNAs. It is also being used for RNA-based
biomarkers, such as gene fusions and RNA sequencing. The approaches include both
amplicon-based screening using primer panels to amplify regions of interest harboring
driver gene mutations, or targeted capture and hybridization for selecting fragments of
interest for sequencing using capture probes. Different kinds of NGS gene panels have been
developed: cancer-specific panels (e.g., for lung cancer, CRC, and breast cancer), general
pan-cancer panels for solid tumors or hematological cancers, or panels designed to detect
genomic changes for targeted therapies. Noteworthy, NGS-based tests are sensitive to
the platform and methods used, and they are therefore mainly approved for testing at
a specific site. Other challenges in NGS-based methods relate to differentiating cancer
driver mutations from passenger mutations and setting a minimum threshold mutant allele
frequency for variant calling.
Nowadays, new machine learning and computation methods are being developed
to relate NGS mutations to clinical significance or therapy response. One such approach,
named TARGETS (TreAtment Response Generalized Elastic-neT Signatures), is shown to
predict the response to specific drugs, based on NGS DNA/RNA profiles [107].

4.4. Flow Cytometry

It is often applied in leukemia and lymphoma diagnostics to identify and count cells
by using a panel of fluorescently labeled antibodies. It is also deployed to quantitate DNA
in cancer cells by treating them with DNA-binding, light-sensitive dyes. Changes in DNA
quantity indicate cancer recurrence in breast, prostate, or bladder cancer. What is more, it
has application in CTC-based biomarkers, as well.

4.5. Gene Expression Microarrays

They are used to study differentially expressed genes in tumor samples and to classify
tumors into molecular subtypes, both of which can be predictive of prognosis or treatment
response. For example, MammaPrint, a microarray-based prognostic test, uses a 70-gene
expression profile from FFPE tissue to predict early-stage breast cancer patients with a
high/low risk of recurrence. The implementation of such microarray-based molecular clas-
sifications of breast cancer by MammaPrint, TargetPrint, or BluePrint has shown that these
tests are useful for better management of this cancer [108]. Other gene-expression-based
tests, such as Oncotype DX, have applications as predictive biomarkers of chemotherapy
response and cancer prognosis (see Section 5.5).

4.6. IHC
IHC is a routinely used method in cancer diagnostics/pathology for detecting pro-
teins expressed by cancer cells in tumor tissues. Developments in this technology include
(i) multiplex IHC, with cycles of antibody staining, imaging, and quenching, which can be
repeated by using different antibodies on the same tissue section; and (ii) other technolo-
gies, such as tyramide signal amplification, MultiOmyxTM , and fluorescent quantum dot
nanocrystals, with more sensitivity for detecting low-abundance proteins and with a high
signal-to-noise ratio.

4.7. ELISA
It is the most commonly used protein-analysis method in clinical practice, especially
in body fluids. New developments, such as electrochemical ELISA assays, increase the sen-
sitivity of ELISA for protein biomarkers at low concentrations in body fluids by amplifying
the signal. They are more cost-effective and easier to use [109].
Biomolecules 2022, 12, 1021 22 of 39

4.8. Lectin Microarrays

Lectin microarrays are used for high-throughput profiling of glycans, especially for
studying differences in glycomic profiles between cancer and normal tissue or for identify-
ing new biomarkers in plasma or EVs [110].

4.9. Other Proteomic Tools

Mass spectrometry (MS) and reverse-phase protein arrays (RPPA) are two other
proteomic tools that can be used to detect a large number of proteins in cancer samples.
MS can be used either as a global profiling tool for cancer biomarker discovery or as a
targeted approach (reviewed in Reference [111]), whereas RPPA is a targeted antibody-
based proteomics platform. Sometimes, RPPA is utilized to validate candidate protein
biomarkers detected by MS profiling. Both can detect and quantify proteins and their
post-translational modifications in tumor tissues or biological fluids. RPPA has a higher
throughput and lower cost than MS; moreover, it has a lower limit of detection and higher
sensitivity. A recently optimized RPPA platform analyzed 240 validated antibodies and
detected important proteins in cancer [112].

4.10. Biosensors/Nanotechnology
The biggest hurdle in the cancer biomarker field is the very low concentration of
analytes in non-tumor tissue samples, such as blood or other body fluids. The use of
biosensors and nanotechnology is being tested to increase the sensitivity and specificity
of detection. Biosensors are devices that detect a biomarker by a chemical process which
is converted into an electric signal by a transducer, and the signal is then processed and
amplified [113]. Moreover, gold nanoparticles, quantum dots, nanotubes, and nanoribbons
provide a high surface-to-volume ratio, allowing different molecules (antibodies, linkers,
small molecules, etc.) to be densely attached to their surface, thereby increasing the
sensitivity of detection by biosensors. They can be applied to capture and detect some
cancer biomarkers, such as ctDNA/RNA/miRNAs (e.g., miR-141 in serum of prostate
cancer patients [114], or DNA methylation in ctDNA for cancer detection [115]), proteins,
CTCs, and EVs in body fluids [116]. Some of the proteins expressed by CTCs and used
for detecting these cells by nanotechnology include EpCAM, PTK7, HER2, and Cd2/Cd3.
Moreover, although not yet in clinical use, some nanoribbon sensor chips can detect
circRNA in gliomas [117] or miR-17-3p in CRC [118], employing oligonucleotide molecular
probes complementary to the target sequence.

4.11. Microfluidics
Microfluidic chips are being developed in combination with other biomarker detection
techniques for use in clinical applications (reviewed in Reference [119]). For example,
microfluidic chips have been optimized to detect cancer-associated proteins in oral can-
cer [120] or to capture CTCs by using new methods combining cell size and immunoaffinity
in prostate cancer [93]. Moreover, Chu et al. [116] developed a nanomaterial-based mi-
crofluidic chip for ultra-sensitive detection of miRNAs at attomole levels for use in cancer
diagnosis. Similarly, microfluidic chips combined with digital PCR have also been devel-
oped to analyze ncRNA or DNA methylation from liquid biopsy [121].

4.12. CRISPR-Based ctDNA/RNA Detection

Another promising technique that could simplify the detection of ctDNA/RNA and
increase its sensitivity and specificity has been recently developed based on clustered
regularly interspaced short palindromic repeats (CRISPR) technology. It is known that
different CRISPR-associated (Cas) enzymes can be used to detect different nucleic acids.
Therefore, by combining an RNA-guided RNase Cas13a, a target-specific CRISPR RNA,
and a labeled reporter RNA, RNA signals can be detected without the need for nucleic acid
amplification steps. Interestingly, the CRISPR/Cas13a system integrated into microfluidic
chips with biosensors has been successful in detecting miRNA biomarkers in serum samples
Biomolecules 2022, 12, 1021 23 of 39

of brain cancer patients with remarkable sensitivity of detection (10 pM in a volume

of less than 0.6 µL and in less than 4 h of processing time) [122]. Similarly, an assay
using the CRISPR-Cas14a system and strand displacement amplification for detecting
miR-21 expression in blood was shown to discriminate cholangiocarcinoma patients from
controls [123].

4.13. Synthetic Biomarker Technology

To overcome some of the challenges associated with cancer biomarkers, such as low
sensitivity or specificity and technical limitations, a new class of synthetic biomarkers is
being developed (reviewed in Reference [124]). The theory behind activity-based synthetic
biomarkers is to administer an exogenous agent that includes a bioengineered sensor
component. This agent is targeted to specific activity/physiology characteristics of cancer
cells and leads the tumor to shed synthetic biomarkers, resulting in the production of a
signal that can be detected. Examples include proteases-activated synthetic biomarkers
and small-molecule probes.
A summary of technologies, their applications, and their advantages and disadvan-
tages is presented in Table 3.

Table 3. List of technologies used for biomarker discovery and detection, and their applications,
advantages, and disadvantages.

Technology Applications Advantages Disadvantages

Cell-based genetic results,
Detection of chromosomal Unable to detect sequence
FISH specificity, simplicity, and
abnormalities mutations
reliability
High sensitivity and specificity,
Detection of targeted sequence
simplicity, good reproducibility, Restricted to targeted mutations
PCR/real-time PCR/digital PCR mutations, gene fusions, or DNA
suitable in a clinical setting, and and limited throughput
methylation
low cost
Results depend on the platform.
Difficult to interpret the
High-throughput tool; can be
Detection of somatic and germline significance of low-frequency
targeted or genome-wide, and can
NGS alterations in a large number of variants. Genome-wide
detect different types of genetic
genes approaches require bioinformatic
alterations at the same time
analysis. Site-specific testing for
clinical applications
Cell count and identification, High sensitivity and rapid
Flow cytometry Restricted to specific parameters
DNA quantification analysis
Differences in gene expression
between tumor subtypes or
Bioinformatic analysis is required.
Gene expression microarrays between tumor and normal tissue High-throughput tool
Not all targets are identified
or in tumor tissue before and after
treatment, etc.
Restricted to proteins with
Localization of protein expression
IHC Detection of protein expression available antibodies. Subjective
in the tumor tissue
interpretation
Restricted to proteins with
Detection of protein expression, available antibodies. Limited
ELISA Easy procedure and quantitative
primarily in body fluids detection sensitivity in body
fluids
Can be useful in tumor tissues
Inconsistencies due to variation
and body fluids, high-throughput
Lectin microarrays Glycomic profiling between batches and between
tool, high sensitivity, and rapid
purification procedures
analysis
Can be used for targeted assays or
Protein profiling of tumor tissues Procedure complexity, low
MS biomarker discovery, and highly
or body fluids sensitivity, and throughput
multiplex
 available prognostic biomarkers have also been designed to be predictive of chemother-
apy benefits (see below), as this is preferable in a clinical setting.

Biomolecules 2022, 12, 1021 5.5. Biomarkers Predicting Response to Cancer Therapy 24 of 39

It is well-known that treatment decisions are critical in cancer patient management,

and often there is uncertainty in the levels of response, as well as lack of precision, side
effects,
Table unnecessary overtreatment, etc. However, considerable progress is being made
3. Cont.
and predictive biomarkers are increasingly playing a key role in the optimization of can-
Technology Applications Advantages Disadvantages
cer treatment, based on the idea that specific tumor alterations or specific germline ge-
netic variants (pharmacogenetics) Highyield
reproducibility,
a certain high
pattern of Need for special
sensitivity to devices,
cancer therapy
Targeted detection of protein
RPPA throughput, and lower cost than restricted to proteins with
agents.
levels
MS validated antibodies
Predictive biomarkers aim to estimate the effect of a specific therapy on a cancer pa-
Low stability, poor reproducibility,
Detection
tient beforeof low concentration
treatment has started. High According
sensitivity andtospecificity,
the results of the biomarker
problems assay, can-
in miniaturizing
Biosensors/nanotechnology biomarkers primarily in body
cer patients can be classified and
as probable devices, and low performance
ease of use responders or non-responders to in
a specific
fluids
human whole blood samples
therapy, either chemotherapy, endocrine therapy, radiotherapy, or the emerging target-
Detection of low concentration High sensitivity, high throughput,
ed strategies and immunotherapy. Some of these biomarkersNeeds can improvement
also identify those pa-
in accuracy
Microfluidics biomarkers primarily in body cost-effective tool, and can be
tients and efficiency
fluids that will likely show severe toxicity
combined after therapy. Predictive biomarkers can be
with biosensors
very useful for adjusting
Detection of low concentration treatment doses or guiding alternative
Complicatedtherapies
multi-stepin patients
CRISPR-based ctDNA/RNA
detection
classified
biomarkersas non-responders
primarily in body orHigh
with sensitivity and specificity;
a high risk of toxicity.
can be combined with biosensors
procedure; lack of
fluids
Biomarkers to predict tumor response to classical chemotherapy high-throughput design
or endocrine ther-
apy are offew, despite activity Significant
being the most extensively used to treat cancer patients [142,143]. noise from off-target
Sensing dysregulated
Molecular amplification of tumor activity; need for better
Synthetic biomarker technology Traditional
of tumor cellsexamples
or tumor of cancer predictive biomarkers are pharmacogenetic-based ones,
biomarker knowledge on early stage cancer
microenvironment
such as germline variants on TPMT or TYMS genes that estimate the effective-
pathogenesis
ness/toxicity of treatment with mercaptopurine for leukemia or
FISH, fluorescence in situ hybridization; PCR, polymerase chain reaction; NGS, next-generation with fluorouracil for co-
sequencing;
lon, immunohistochemistry;
IHC, bladder, and gastricELISA, carcinoma, respectively
Enzyme-Linked (reviewed
ImmunoSorbent by MS,
Assay; Reference [144]). Moreo-
mass spectrometry; RPPA,
reverse-phase protein arrays; CRISPR, clustered regularly interspaced short palindromic repeats; ctDNA/RNA,
ver, somatic cancer mutations can also help to predict tumor drug response. In this re-
circulating tumor DNA/RNA.
gard, data related to the sensitivity of 1000 human cancer cell lines to different drugs is
compiled
5. in the database
Clinical Applications Genomics Examples
of Cancer Biomarkers: of Drug sensitivity in Cancer
(www.cancerrxgene.org, accessed on 1 March 2022). More information about currently
The clinical applications of cancer biomarkers are extensive, and their ultimate goal is
used and potential future biomarkers of this kind can be found at the Phar-
to achieve precision medicine to optimize prevention, screening, and treatment strategies of
macogenomics Knowledgebase [145]. Recently, multi-gene predictive biomarkers have
cancer. These applications include risk assessment; screening and early detection; accurate
been developed, such as the Oncotype DX Breast Recurrence Score test, which measures
diagnosis; patient prognosis; prediction of response to therapy; and cancer surveillance
the expression of 21 genes on breast cancer samples. This test allows clinicians to select
and monitoring response.
the therapy that will be optimal for women with hormone receptor+ and HER2− early
Updated lists of biomarkers currently being used in cancer patients can be found
stage invasive breast cancer according to their score (prognostic biomarker): either
in the National Comprehensive Cancer Network Compendium [125] and the Table of
chemotherapy plus endocrine therapy or endocrine therapy alone [146,147]. Additional-
Pharmacogenomics Biomarkers in Drug Labelling from the FDA (therapeutic area = oncol-
ly, the
ogy) test as
[126], giveswellinformation
as in the Tumor about distant
Marker recurrence
List from National (predictive
Cancerbiomarker),
Institute [91]and it has
(Table 2).
been incorporated by the American Joint Committee on
Moreover, a knowledge base, OncoMX, has recently been developed for exploring cancer Cancer into the breast cancer
staging system
biomarkers in the[148].
context of related evidence [127].
Radiotherapy effects on cancer patients vary greatly, even in patients with similar
tumor
5.1. types
Cancer Risk and treated with
Assessment similar radiation schemes, both in terms of tumoral re-
Biomarkers
sponse and of early or late adverse
A cancer risk or susceptibility biomarker reactions is in
used non-tumoral tissues [149].
to identify individuals withSeveral
a higher bi-
omarkers to assess tumor radiosensitivity have been studied,
probability to develop cancer compared to the standard risk in the general population. including tumor molecular
signatures,
Cancer expression tests
risk biomarker of specific
includemRNA DNA repair molecules
phenotypeor proteins,
assays, as mutations at specific
well as genotyping
genes involved in DNA repair, or the less studied EVs and CTCs
assays for germline variants. DNA repair has shown clear interindividual variability related [150–152]. Concerning
biomarkers
to aimed to predict
cancer susceptibility [128]. the risk of
Several radiation-induced
technologies have been toxicity in normal
reported tissues,
to quantify DNA the
research
repair has focused
capacity (and alsoon DNA
the assessment
damage and of DNA
DNA damage
damage response
response):(comet assay, ɣH2AX
comet assay, H2AX
foci formation,
foci formation,host or micronucleus
cell reactivation formation)
assay, DNA and, more
repair recently,
beacons, andon apoptosis,
others (reviewed all inof
which are studied after in vitro irradiation of peripheral blood
Reference [128]). However, in the last decades, genotyping assays have gained importance lymphocytes from pa-
tients [151,153].
thanks Other well-studied
to the development predictive biomarkers
of high-throughput NGS toolsof(see toxicity
Sectionare 2.1).
germline genet-
In a recent
ic variants
study from patients
performed with a largewith multicenter
breast cancer, prostate
cohort, cancer, and
the personal riskother cancers [154].
for hereditary cancerIn
addition, protein
syndromes, among biomarkers presentwas
other disorders, in blood frominbreast
evaluated cancer
healthy patients[129].
individuals have Disease-
been re-
lated to cardiotoxicity
predisposing variants after
related radiotherapy [151,155]. were present in 7.7% of individuals
to cancer syndromes
analyzed. Clinically significant variants were commonly detected in MUTYH, CHEK2,
APC, ATM, BRCA1, BRCA2, MITF, HOXB13, PMS2, PALB2, NBN, BRIP1, MSH6, SDHA,
and BARD1. These findings would prove the utility of using genetic screening as part of
regular medical care [129], although there are doubts about the interpretation of variants of
uncertain effect [130].
Biomolecules 2022, 12, 1021 25 of 39

Once a biomarker of cancer risk is identified and validated, it should be incorporated

into a comprehensive risk assessment model, which should include also other risk factors,
such as environment and lifestyle. Individuals identified to be at high risk of develop-
ing cancer could engage in changes in their lifestyle and could benefit from enhanced
surveillance, prophylactic surgery, and/or some sort of chemoprevention.

5.2. Screening and Early Cancer Detection Biomarkers

The purpose of these biomarkers is to detect cancer in otherwise healthy patients, and
without having shown any signs of disease. Actually, they are suggesting the presence of
cancer, which has to be diagnosed with other medical approaches. The main justification for
their use is the fact that if cancer is detected in an early and asymptomatic stage, the survival
rate increases, and the probabilities of complications or morbidities decrease. However, in
some cases, the use of these biomarkers leads to overdiagnosis, that is, detection of a cancer
that would never cause any symptoms.
A good screening biomarker assay must be highly specific, i.e., with a very low false-
positive rate, and, ideally, it should be also noninvasive and inexpensive. Some of the
problems caused by overdiagnosis and false-positive rate are further invasive medical
procedures, patient psychological distress, and high costs for the healthcare system, which
are otherwise unnecessary.
Several blood-based screening biomarkers have been used or are currently used in
clinical practice, e.g., alpha-fetoprotein for liver cancer, PSA for prostate cancer, CA19-9
for pancreatic cancer, and CA125 for ovarian cancer, among others [131]. However, some
of these biomarkers do not meet the requirements of high specificity and sensitivity; for
example, this is the case for blood PSA screening for prostate cancer. Furthermore, this
biomarker does not allow us to distinguish individuals with benign prostatic hyperplasia
from those with malignant prostate cancer. Therefore, the current prostate cancer screening
program using PSA would not be desirable in a clinical setting, and it would only be
recommended for men who express a specific interest in screening [132]. New tests are
under evaluation, such as a recently developed biomarker panel combining filamin-A gene,
age, and prostate volume which has a better performance than PSA alone, especially in
men with benign hyperplasia [133].
Interestingly, promising multi-cancer early detection tests on liquid biopsies are now
being designed to complement single-cancer screening tests, and they seem to perform
better [134–136]. However, concerns have risen about their clinical utility, as well as about
overdiagnosis, overtreatment, and the accuracy of identification of tissue of origin [137].

5.3. Accurate Cancer Diagnosis Biomarkers

Diagnostic biomarkers are used to confirm the presence of cancer or to identify a
subtype of cancer. The usefulness of these biomarkers is that proper diagnosis can lead
to proper treatment and, therefore, best chances of survival. Some screening biomarkers
are also used as diagnostic biomarkers; however, the latter is only applied to symptomatic
patients, whereas screening biomarkers are applied to asymptomatic individuals. Despite
diagnostic biomarkers can help to detect cancer or to classify patients into subtypes, they
are not sufficient for a final diagnosis and need to be combined with other diagnostic
procedures, such as imaging or biopsies.

5.4. Patient Prognosis Biomarker

Once a tumor has been diagnosed, a prognostic biomarker provides information about
the probable course of the disease, including its recurrence, progression, and patient’s
overall survival, regardless of the treatment. Sometimes, these biomarkers can reflect
tumor burden, and then they can help in determining the stage of cancer (e.g., the tumor–
node–metastasis classification). Some examples of prognostic biomarkers widely used
are protein biomarkers, such as CEA for CRC, CA19-9 for pancreatic cancer, and CA125
for ovarian cancer; and some tests based on gene expression signatures for breast cancer,
 Biomolecules 2022, 12, 1021 26 of 39

022, 12, 1021 27 of 40

such as MammaPrint and Prosigna [125,134,138,139]. Other examples would be the genetic
alterations that facilitate an accurate risk-stratification of patients with acute leukemia that
available prognosticarebiomarkers
associatedhave with also been
patient designed
outcomes to be predictive of chemother-
[140,141].
apy benefits (see below),The as this is preferable in a clinical setting.
information obtained with these biomarkers can be useful for clinicians to make
decisions for aggressive or prolonged treatments. However, some of the currently available
5.5. Biomarkers Predicting Response
prognostic to Cancerhave
biomarkers Therapyalso been designed to be predictive of chemotherapy benefits
It is well-known
(see below), as this is preferablecritical
that treatment decisions are in cancer
in a clinical patient management,
setting.
and often there is uncertainty in the levels of response, as well as lack of precision, side
effects, unnecessary5.5. Biomarkers Predicting
overtreatment, Response
etc. However, to Cancer Therapy
considerable progress is being made
and predictive biomarkers It isare increasingly
well-known thatplaying
treatmenta keydecisions
role in the areoptimization
critical in cancer of can-patient management,
cer treatment, based andonoften
the ideatherethat specific tumor
is uncertainty in thealterations
levels of or specific as
response, germline
well as ge- lack of precision, side
effects, unnecessary
netic variants (pharmacogenetics) yield overtreatment,
a certain patternetc. However, considerable
of sensitivity to cancer therapy progress is being made
agents. and predictive biomarkers are increasingly playing a key role in the optimization of cancer
treatment,
Predictive biomarkers aimbased on thethe
to estimate idea that of
effect specific
a specific tumor alterations
therapy on a canceror specific
pa- germline genetic
variants
tient before treatment (pharmacogenetics)
has started. According toyield a certain
the results of pattern of sensitivity
the biomarker assay,tocan- cancer therapy agents.
Predictive
cer patients can be classified biomarkers
as probable aim to estimate
responders the effect oftoa specific
or non-responders a specific therapy on a cancer
patient before
therapy, either chemotherapy, treatment
endocrine has started.
therapy, According
radiotherapy, or the to emerging
the results of the biomarker assay,
target-
cancer patients can
ed strategies and immunotherapy. Some beofclassified as probable
these biomarkers canresponders
also identify or non-responders
those pa- to a specific
therapy, either chemotherapy, endocrine therapy,
tients that will likely show severe toxicity after therapy. Predictive biomarkers can be radiotherapy, or the emerging targeted
strategies and immunotherapy. Some of these
very useful for adjusting treatment doses or guiding alternative therapies in patients biomarkers can also identify those patients
that will likely
classified as non-responders or with show severe
a high risktoxicity after therapy. Predictive biomarkers can be very useful
of toxicity.
Biomarkers to for adjusting
predict tumortreatment
response doses or guiding
to classical chemotherapyalternative therapies ther-
or endocrine in patients classified as
non-responders or with a high risk of toxicity.
apy are few, despite being the most extensively used to treat cancer patients [142,143].
Traditional examples ofBiomarkers
cancer predictive to predict tumor response
biomarkers to classical chemotherapy
are pharmacogenetic-based ones,or endocrine therapy
such as germline variants on TPMT or TYMS genes that estimate the effective- [142,143]. Tradi-
are few, despite being the most extensively used to treat cancer patients
tional examples
ness/toxicity of treatment of cancer predictive
with mercaptopurine for leukemia biomarkers
or withare pharmacogenetic-based
fluorouracil for co- ones, such
as germline variants on TPMT or TYMS genes
lon, bladder, and gastric carcinoma, respectively (reviewed by Reference [144]). Moreo- that estimate the effectiveness/toxicity of
ver, somatic cancertreatment
mutationswith can mercaptopurine
also help to predict for leukemia
tumor drug or with fluorouracil
response. In thisfor re-colon, bladder, and
gastric carcinoma, respectively (reviewed by
gard, data related to the sensitivity of 1000 human cancer cell lines to different drugs is Reference [144]). Moreover, somatic cancer
mutations can also help to predict tumor drug response. In this regard, data related to the
compiled in the database Genomics of Drug sensitivity in Cancer
sensitivity of 1000 human cancer cell lines to different drugs is compiled in the database
(www.cancerrxgene.org, accessed on 1 March 2022). More information about currently
Genomics of Drug sensitivity in Cancer (www.cancerrxgene.org, accessed on 1 March 2022).
used and potential future biomarkers of this kind can be found at the Phar-
More information about currently used and potential future biomarkers of this kind can
macogenomics Knowledgebase [145]. Recently, multi-gene predictive biomarkers have
be found at the Pharmacogenomics Knowledgebase [145]. Recently, multi-gene predictive
been developed, such as the Oncotype DX Breast Recurrence Score test, which measures
biomarkers have been developed, such as the Oncotype DX Breast Recurrence Score test,
the expression of 21 genes on breast cancer samples. This test allows clinicians to select
which measures the expression of 21 genes on breast cancer samples. This test allows
the therapy that will be optimal for women with hormone receptor+ and HER2− early
clinicians to select the therapy that will be optimal for women with hormone receptor+ and
stage invasive breast cancer according to their score (prognostic biomarker): either
HER2− early stage invasive breast cancer according to their score (prognostic biomarker):
chemotherapy plus endocrine therapy or endocrine therapy alone [146,147]. Additional-
either chemotherapy plus endocrine therapy or endocrine therapy alone [146,147]. Addi-
ly, the test gives information about distant recurrence (predictive biomarker), and it has
tionally, the test gives information about distant recurrence (predictive biomarker), and it
been incorporated has by the
beenAmerican
incorporated Jointby Committee
the American on Cancer into the breast
Joint Committee on Cancer cancerinto the breast cancer
staging system [148].staging system [148].
Radiotherapy effects on cancer patients
Radiotherapy effects vary greatly,
on cancer even in
patients varypatients
greatly, with
even similar
in patients with similar
tumor types and treated with similar radiation schemes, both
tumor types and treated with similar radiation schemes, both in terms in terms of tumoral re-of tumoral response
sponse and of early or late adverse reactions in non-tumoral tissues
and of early or late adverse reactions in non-tumoral tissues [149]. Several [149]. Several bi- biomarkers to
omarkers to assess assess
tumor tumor
radiosensitivity have been studied, including tumor molecular
radiosensitivity have been studied, including tumor molecular signatures,
signatures, expression of specific
expression mRNA
of specific mRNAmolecules
molecules or proteins,
or proteins, mutations
mutations at atspecific
specific genes involved in
genes involved in DNADNA repair,
repair, or or the
the less
less studied
studiedEVs EVsand andCTCs CTCs[150–152].
[150–152].Concerning
Concerningbiomarkers aimed to
biomarkers aimed to predict
predict thetherisk riskof of radiation-induced
radiation-induced toxicity
toxicity in in normal
normal tissues,
tissues, thethe
research has focused
research has focused on the assessment of DNA damage response (comet
on the assessment of DNA damage response (comet assay, H2AX foci formation, or assay, ɣH2AX
foci formation, or micronucleus
micronucleus formation)formation)and, and,more more recently,
recently, on apoptosis,
on apoptosis, all ofallwhich
of are studied after
which are studied in after in vitro irradiation of peripheral blood lymphocytes
vitro irradiation of peripheral blood lymphocytes from patients [151,153]. Other well- from pa-
tients [151,153]. Other well-studied
studied predictive predictive
biomarkers biomarkers
of toxicity ofare
toxicity
germlineare germline genet- from patients with
genetic variants
ic variants from patients
breast with
cancer, breast cancer,
prostate prostate
cancer, andcancer, and other
other cancers [154].cancers [154]. Inprotein biomarkers
In addition,
addition, protein biomarkers present in blood from breast cancer patients have been re-
lated to cardiotoxicity after radiotherapy [151,155].
Biomolecules 2022, 12, 1021 27 of 39

present in blood from breast cancer patients have been related to cardiotoxicity after
radiotherapy [151,155].
In the case of targeted cancer treatments, first, the alteration that is driving tumor
growth in that particular cancer is identified. Then a treatment strategy is developed to
specifically target that alteration. Therefore, targeted therapies only work in a subset of
cancers, those that have the specific alteration for which the therapy was designed. It is im-
portant, if not essential, to perform a biomarker assay to identify those individuals who will
benefit from therapy in order to increase efficacy and diminish costs [142]. These kinds of
assays are often called companion diagnostics and are usually approved by the regulatory
agencies in conjunction with the drug they are paired with. The information provided by
the companion diagnostic tool is essential for the safe and effective use of the therapeutic
product [156]. An example is an immunohistochemistry test for increased expression of
HER2 receptor to select patients that would benefit from therapy with trastuzumab, an
anti-HER2-targeted agent used to treat breast, gastric, and gastroesophageal cancers [157].
Another example is EGFR therapies, mainly used in metastatic NSCLC, and which target
cancerous cells with EGFR mutations at exon 19 or 21. More recently, NGS-based com-
panion diagnostics have been developed to detect cancer-associated genetic alterations in
plasma/serum ctDNA (see Section 3.1.3).
Moreover, some cancer biomarkers help in the assessment of the risks and benefits
of a particular drug, even though the information provided by these biomarkers is not
required for the use of that drug. This kind of assay is referred to as complementary
diagnostics since it provides additional information to the physicians [156]. For example,
the FoundationFocus CDx BRCA loss of heterozygosity (LOH) assay is an FDA-approved
complementary diagnostic test for rucaparib, a poly (ADP-ribose) polymerase inhibitor
used to treat recurrent ovarian carcinoma. While rucaparib improves the progression-free
survival rate in patients with high genomic LOH, those with lower genomic LOH may also
benefit [158].
In the last decade, immunotherapy, especially immune checkpoint inhibitors, have
demonstrated high efficacy against some cancers by enhancing anti-tumoral immunity,
while many others do not respond or even show serious side effects. The discovery of
biomarkers to predict sensitive or refractory tumors to this kind of therapy is urgently
needed. Moreover, there is a need for optimization of the currently FDA-approved biomark-
ers of response to immunotherapy, including expression of PD-L1, microsatellite instability,
and tumor mutational burden [159,160]. Of these, PD-L1 is the most commonly used in
clinical practice, especially in NSCLC. Cancer cells that express PD-L1 can attenuate or
inhibit the activity of tumor-infiltrating lymphocytes, which express the receptor of PD-L1.
This is a mechanism used by tumor cells to escape immune surveillance. The blockade of
this interaction by using antibodies, either against PD-L1 or its receptor, makes lympho-
cytes reactivate and enhance their antitumoral effect. Therefore, tumors overexpressing
PD-L1 are more likely to respond to these antibodies, even though those with lower PD-L1
expression may also benefit. However, the accuracy and clinical utility of this biomarker
need to be improved [159,160].
A good summary of key cancer predictive biomarkers clinically adopted, as well as
those showing potential for clinical translation, can be found in Reference [142].

5.6. Biomarkers for Cancer Surveillance and Monitoring Response

A monitoring biomarker is assessed serially over time, e.g., during treatment with
curative intent or after it has finished. This can allow for comparisons to observe, for
example, real-time overall disease burden, to detect worsening of the disease, or to follow
disease response to treatment.
Liquid-biopsy-based biomarkers are the best option for minimal residual disease
monitoring and cancer surveillance. In this sense, several studies have reported ctDNA as
a promising good monitoring biomarker, since it is believed that ctDNA levels correlate
with tumor burden over time. Therefore, monitoring ctDNA in cancer patients could help
Biomolecules 2022, 12, 1021 28 of 39

Biomolecules 2022, 12, 1021 29 of 40

to detect early recurrences or residual cancer that would otherwise remain undetected by
other methods, such as imaging [161]. Other monitoring biomarkers are blood proteins
proteins
(e.g., CEA,(e.g., CEA,. CA19-9…),
CA19-9 . . ), althoughalthough theysome
they have havedisadvantages
some disadvantages
compared compared
to ctDNA,to
ctDNA, such as
such as lower lower tumor-specificity
tumor-specificity and longerand longer As
half-life. half-life. Asexample,
a recent a recent driver
example, driver
mutations
mutations in ctDNA
in ctDNA (EGFR, KRAS,(EGFR, KRAS,and
or BRAF) or serum
BRAF)concentrations
and serum concentrations
of Cyfra21-1,of andCyfra21-1,
possibly
and possibly
CA125, have CA125, have been
been described as described as relevant
relevant useful useful for
biomarkers biomarkers for therapy
therapy response re-
moni-
sponse monitoring and early detection of progression during therapy
toring and early detection of progression during therapy in lung cancer [162]. However, in lung cancer
[162].
clinicalHowever, clinical of
implementation implementation of monitoring
monitoring biomarkers biomarkers
is challenging, is challenging,
as there as
are still some
there are still some
methodological andmethodological and biological
biological limitations [163]. limitations [163].

6. Steps
6. Steps in
in the
the Search
Search for
for New
New Biomarkers
Biomarkers
Even though
Even though considerable
considerable progress
progress has
has been
been made,
made, there
there is
is an
an urgent
urgent need
need forfor the
the
discovery and development of new effective biomarkers in the field of oncology.
discovery and development of new effective biomarkers in the field of oncology. The The steps
involved
steps in thein
involved pipeline of development
the pipeline of cancer
of development biomarkers
of cancer are asare
biomarkers follows: discovery,
as follows: dis-
assay development/analytical
covery, validation,
assay development/analytical clinicalclinical
validation, validation, clinicalclinical
validation, utility, utility,
and finally
and
clinicalclinical
finally implementation [164–166]
implementation (Figure(Figure
[164–166] 2). 2).

Discovery Assay Clinical Clinical utility Clinical

development / validation implementation
Analytical
validation

•identification •selection of •analysis of •analysis of •analysis of

of the assay biomarker health application
potential •analysis of performance improvement in clinical
biomarker assay for purpose with use of care
reliability the biomarker

Figure 2. Schematic steps on the search for new biomarkers.

Figure 2. Schematic steps on the search for new biomarkers.

6.1. Discovery
6.1. Discovery
It is the initial step
It step based
basedon onthe
theidentification,
identification,selection,
selection,and
andprioritization
prioritization ofof
potential
poten-
individual
tial or a or
individual group of biomarkers
a group of biomarkers (biomarker signature)
(biomarker through
signature) exploratory
through preclinical
exploratory pre-
studies.studies.
clinical Ideally,Ideally,
beforebefore
starting, researchers
starting, should
researchers clearly
should define
clearly the purpose
define the purpose of the
of
biomarker
the biomarkerandandthe the
specific clinical
specific context
clinical [164,167].
context [164,167].
The advent of
The of new
newtechniques,
techniques,such suchasas NGS,
NGS, gene
geneexpression
expressionarrays, protein
arrays, MS, MS,
protein and
other high-throughput technologies, has provided researchers
and other high-throughput technologies, has provided researchers with an enormous with an enormous amount
of data inofadata
amount shortintime andtime
a short at a low
andcost, which
at a low haswhich
cost, sometimes led to the generation
has sometimes of data-
led to the genera-
tion of data-driven hypotheses [168]. However, a correct study design and proper must
driven hypotheses [168]. However, a correct study design and proper data analysis data
be employed
analysis must to
bebe able to select
employed to berelevant
able to data
selectfrom which
relevant reliable
data from candidate biomarkers
which reliable candi-
can be
date identified.can be identified.
biomarkers
A correct study
A correct study design
design includes,
includes, among
among other
other things,
things, aa good
good selection
selection ofof the
the target
target
population, enough statistical power, consideration of possible confounding
population, enough statistical power, consideration of possible confounding factors, and factors, and
randomization and blinding to avoid bias [167]. Later, appropriate
randomization and blinding to avoid bias [167]. Later, appropriate data analysis is data analysis is equally
important,
equally as it canas
important, influence the reproducibility
it can influence and robustness
the reproducibility of results.ofFor
and robustness example,
results. For
careful handling of missing data should be implemented, as well as
example, careful handling of missing data should be implemented, as well as statistical statistical correction of
multiple comparisons.
correction of multipleThe latter is especially
comparisons. useful
The latter is when analyzing
especially a group
useful whenofanalyzing
biomarkers, a
which usually perform better than individual ones [167,169].
group of biomarkers, which usually perform better than individual ones [167,169].
6.2. Assay Development and Analytical Validation
6.2. Assay Development and Analytical Validation
After a potential cancer biomarker (or a biomarker signature) has been identified, an assay
After a potential
is developed cancer
to detect or biomarker
quantify (or a biomarker
the biomarker in a patientsignature)
specimen.has been identified,
A technical protocol
an assay is developed to detect or quantify the biomarker in a patient specimen.
must be specified, including sample collection, processing, and storage procedures [170]. A tech-
nical The
protocol must be specified, including sample collection, processing, and storage
next step is to establish whether the selected biomarker assay can detect or measure
procedures [170]. to detect or measure, which is called analytical validation. It is defined as a
what it is intended
The next step is to establish whether the selected biomarker assay can detect or
measure what it is intended to detect or measure, which is called analytical validation. It
Biomolecules 2022, 12, 1021 29 of 39

process to establish that the performance characteristics of the assay are acceptable in terms of
its analytical sensitivity, specificity, accuracy, and precision, which includes repeatability and
reproducibility [170]. Definitions of these terms are shown in Table 4.

Table 4. Definitions of terms assessed in analytical validation, according to References [171,172].

Term Definition
The smallest concentration of a substance in a biological specimen that can be reliably
Analytical Sensitivity
measured by an analytical procedure
The ability of an assay to measure the specific substance (intended target), rather than
Analytical Specificity
others, in a biological specimen
The closeness of agreement between the value which is accepted either as a conventional
Analytical Accuracy true value or an accepted reference value and the value found. Usually, there is a
comparison with another measurement technique
A measure of the extent to which a test conducted multiple times on the same subject, in the
Analytical Repeatability same laboratory, using the same equipment, by the same operator, over a short period of
time, gives the same result
A measure of the extent to which a test conducted multiple times in different laboratories,
Analytical Reproducibility using different equipment, by different operators, or over different periods of time, gives
comparable results

6.3. Clinical Validation

Clinical validation is defined as a process to establish that the biomarker (through its
assay) can acceptably identify, measure, or predict the relevant clinical concept [170]; that is,
the assay reliably divides the population of interest into two or more groups of individuals
that have significant differences [173]. There is not necessarily evidence that the biomarker
improves clinical care [174].
The performance of the biomarker is estimated in terms of diagnostic sensitivity,
diagnostic specificity, positive predictive value (PPV) and negative predictive value (NPV),
receiver operating characteristics (ROC) curve, and area under the ROC curve (AUCROC )
(Table 5). Diagnostic sensitivity and specificity terms are distinct from the previously
mentioned analytical terms, e.g., high analytical sensitivity does not necessarily correlate
with acceptable diagnostic sensitivity [172]. It is generally accepted that a good biomarker
should have values of diagnostic sensitivity and specificity of at least 90% and AUC
values higher than 75% (values of AUC above 90% would correspond to an excellent
biomarker) [175,176]. However, for clinicians, PPV and NPV represent more interesting
probabilities than diagnostic sensitivity and specificity. PPV is the proportion of individuals
that are positive among all the test-positive individuals, and NPV is the proportion of
individuals that are negative among all the test-negative individuals.
Clinical validation of a cancer biomarker usually needs external validation with an
independent set of samples, entirely different from the one used in the discovery step. This
can be performed either in a retrospective or prospective study [164,167].
Biomolecules 2022, 12, 1021 30 of 39

Table 5. Definitions of terms assessed in clinical validation, according to References [167,171,172,175].

Term Definition
The measure of how often a binary biomarker test correctly indicates the
presence of a particular characteristic in individuals that truly have the
Diagnostic Sensitivity
characteristic. Biomarker sensitivity is the number of true positive results
divided by the number of true-positive plus false-negative results.
The measure of how often a binary biomarker test correctly indicates the absence
of a particular characteristic in individuals who truly do not have the
Diagnostic Specificity
characteristic. Biomarker specificity is the number of true-negative results
divided by the number of true-negative plus false-positive results.
The measure of how often a binary biomarker test correctly indicates the
presence of a particular characteristic in individuals that have a positive test
Positive predictive value
result. Biomarker positive predictive value is the number of true positive results
divided by the number of true-positive plus false-positive results.
The measure of how often a binary biomarker test correctly indicates the absence
of a particular characteristic in individuals that have a negative test result.
Negative predictive value
Biomarker negative predictive value is the number of true negative results
divided by the number of true-negative plus false-negative results.
Plot showing the relationship between sensitivity (true positive) and 1-specificity
Receiver operating characteristics (ROC) curve (true negative). It is a graphical way of describing likelihood ratios at various
values of the biomarker test.
The ability of a binary biomarker to distinguish two or more groups of
Area under the ROC curve (AUCROC ) individuals. It is a measure of discrimination. Values range from 0 to 1, and
1 corresponds to perfect discriminative power.

6.4. Clinical Utility

A cancer biomarker must have high levels of evidence of clinical utility, besides analyt-
ical and clinical validity, if it is meant to be applied to routine practice to guide healthcare.
The clinical utility of a cancer biomarker is the demonstration that the use of the biomarker
in an individual will lead to a net improvement in his/her health outcome (or to a decrease
in treatment toxicity or healthcare costs) compared with that of an individual whose care
is managed without the use of that biomarker [171,173]. Therefore, the outcome benefit
of using the biomarker must be clinically and/or financially meaningful. Factors to be
considered to determine the clinical utility of a cancer biomarker are extensively addressed
in the [173] review. It is important to assess both the effectiveness of the biomarker and the
benefit-to-risk ratio.
The assessment of clinical utility usually requires a prospective clinical trial or a
prospective–retrospective study with a completely independent dataset and performing a
controlled comparison with standard options of clinical management [173].

6.5. Clinical Implementation

Some key aspects of clinical implementation of a cancer biomarker are regulatory
approval by authorities (e.g., FDA or European Medicines Agency), commercialization,
health insurance coverage, and incorporation into clinical practice guidelines [164]. These
factors are highly dependent on each national health system and particular national reg-
ulations. Moreover, regulatory processes may be different if the biomarker assay is an
in vitro diagnostic device or a laboratory-developed test [164]. Finally, acceptability to
physicians and patients is also essential when implementing a biomarker assay in the clinic.
Moreover, healthcare providers should know what biomarker assay to use, when, and how
to interpret the results to take advantage of the full potential of the biomarker. Therefore,
good clinical guidelines are critical and must be updated regularly. Equally important is
the need that the healthcare facilities are equipped with the appropriate infrastructure for
efficient sample collection and handling, as well as testing, if no subcontract is applied.
Biomolecules 2022, 12, 1021 31 of 39

Big challenges remain in the process of cancer biomarker development, especially the
need to generate high levels of evidence of a cancer biomarker value. Recently, Parker
et al. [177] performed the first extensive analysis of the outcomes of cancer biomarker use.
Interestingly, they found statistical evidence that biomarker usage has a substantial clinical
benefit in cancer patients, even when analyzing biomarkers not yet approved by regulatory
authorities. However, Ou et al. [167] “urge oncologists to resist the temptation of adopting
unvalidated biomarker findings into practice”. Similarly, Dr. Hayes [178] expressed his
concern about biomarker assays not approved by regulatory agencies but extensively used,
assuming accuracy and reliability. He often says, “a bad tumor biomarker test is as bad
as a bad drug”. Other authors propose what is called an adaptive assessment approach
for cancer screening biomarkers based on new high-throughput technologies [137]. In
this case, after condensed randomized controlled trials with surrogate endpoints, there
would be conditional approval by regulatory authorities and patient access to the screening
intervention. Then trials would continue with the definitive endpoint generating more
evidence, which would lead to final approval or disapproval. The objection to this approach
is that patients would be exposed to the risks associated with premature biomarker test
application [137].
In summary, a good cancer biomarker should fulfill all the previously mentioned
requirements (Figure 2). Moreover, an ideal biomarker assay should be rapid, preferably
binary, easily measurable in an accessible biological specimen, easily adaptable to routine
clinical practice, and with a short processing time [167]. Unfortunately, up to now, very
few cancer biomarkers, out of all promising biomarkers that have been discovered, have
satisfied these rigorous characteristics and therefore have been approved by regulatory
agencies. Major impediments in the confirmation of claimed discovered biomarkers and
translation to the clinic are, among others, (1) the lack of standardization methods in sample
collection, handling, and storage; and (2) the lack of large sample sizes for validation trials,
causing a lack of statistical power [179]. These aspects could be overcome by collaborative
approaches with multidisciplinary teams involving industry and science, with experts in
clinics, biology, epidemiology, statistics, regulation, and healthcare economics [167,179].

7. Conclusions and Future Perspectives

Cancer cells undergo multiple changes, and these alterations have been used for
decades as cancer biomarkers, mainly tested in tumor tissue. Recent research in the cancer
biomarker field has helped in the development of new DNA, RNA, and protein-based
cancer biomarkers that can be detected from easily available body fluids. NGS has opened
up possibilities for analyzing all cancer-associated genetic alterations in a single assay.
Moreover, increased benefits of including analysis of both germline and somatic mutations
in a single panel are being recognized for precision oncology. However, most NGS-based
tests are optimized for panel, sequencing platform, and site. In addition, although high-
throughput transcriptomic and proteomic studies have identified new candidate cancer
biomarkers, only very few have been clinically implemented. The biggest challenge in
cancer biomarker detection from body fluids is their very low concentration. To overcome
this, highly sensitive detection technologies are being developed. For example, nanoparti-
cles with a high surface-to-volume ratio make it possible to attach different molecules to
their surface; this, combined with sensor technology, for signal amplification and detection,
offers opportunities for the development of more sensitive cancer biomarkers from the body
fluids. Finally, it is important to consider all the steps needed to develop a new biomarker.
Before clinical implementation, requirements related to analytical and clinical validation, as
well as clinical utility, must be fulfilled in order to obtain the necessary regulatory approval
by authorities.

Author Contributions: V.K.S. and G.A. both wrote the article. All authors have read and agreed to
the published version of the manuscript.
Biomolecules 2022, 12, 1021 32 of 39

Funding: G.A. belongs to an SGR research group recognized by Generalitat de Catalunya (SGR
2017SGR0255).
Institutional Review Board Statement: Not applicable.
Informed Consent Statement: Not applicable.
Data Availability Statement: Not applicable.
Conflicts of Interest: The authors declare no conflict of interest.

References
1. Available online: https://www.Cancer.Gov/Publications/Dictionaries/Cancer-Terms/Def/Tumor-Marker-Test (accessed on
22 February 2022).
2. Schienda, J.; Church, A.J.; Corson, L.B.; Decker, B.; Clinton, C.M.; Manning, D.K.; Imamovic-Tuco, A.; Reidy, D.; Strand, G.R.;
Applebaum, M.A.; et al. Germline Sequencing Improves Tumor-Only Sequencing Interpretation in a Precision Genomic Study of
Patients With Pediatric Solid Tumor. JCO Precis. Oncol. 2021, 5, PO.21.00281. [CrossRef] [PubMed]
3. Akras, Z.; Bungo, B.; Leach, B.H.; Marquard, J.; Ahluwalia, M.; Carraway, H.; Grivas, P.; Sohal, D.P.S.; Funchain, P. Primer on
Hereditary Cancer Predisposition Genes Included within Somatic Next-Generation Sequencing Panels. JCO Precis. Oncol. 2019, 3,
1–11. [CrossRef]
4. Slade, D. PARP and PARG Inhibitors in Cancer Treatment. Genes Dev. 2020, 34, 360–394. [CrossRef]
5. Huang, K.-L.; Mashl, R.J.; Wu, Y.; Ritter, D.I.; Wang, J.; Oh, C.; Paczkowska, M.; Reynolds, S.; Wyczalkowski, M.A.; Oak, N.; et al.
Pathogenic Germline Variants in 10,389 Adult Cancers. Cell 2018, 173, 355–370.e14. [CrossRef] [PubMed]
6. Terraf, P.; Pareja, F.; Brown, D.N.; Ceyhan-Birsoy, O.; Misyura, M.; Rana, S.; O’Reilly, E.; Carlo, M.I.; Aghajanian, C.; Liu, Y.; et al.
Comprehensive Assessment of Germline Pathogenic Variant Detection in Tumor-Only Sequencing. Ann. Oncol. 2022, 33, 426–433.
[CrossRef] [PubMed]
7. Velázquez, C.; Lastra, E.; Avila Cobos, F.; Abella, L.; de la Cruz, V.; Hernando, B.A.; Hernández, L.; Martínez, N.; Infante, M.;
Durán, M. A Comprehensive Custom Panel Evaluation for Routine Hereditary Cancer Testing: Improving the Yield of Germline
Mutation Detection. J. Transl. Med. 2020, 18, 232. [CrossRef]
8. Cortés-Ciriano, I.; Lee, J.J.-K.; Xi, R.; Jain, D.; Jung, Y.L.; Yang, L.; Gordenin, D.; Klimczak, L.J.; Zhang, C.-Z.; Pellman, D.S.; et al.
Comprehensive Analysis of Chromothripsis in 2658 Human Cancers Using Whole-Genome Sequencing. Nat. Genet. 2020, 52,
331–341. [CrossRef]
9. Kandoth, C.; McLellan, M.D.; Vandin, F.; Ye, K.; Niu, B.; Lu, C.; Xie, M.; Zhang, Q.; McMichael, J.F.; Wyczalkowski, M.A.; et al.
Mutational Landscape and Significance across 12 Major Cancer Types. Nature 2013, 502, 333–339. [CrossRef]
10. Pan-Cancer Analysis of Whole Genomes. Nature 2020, 578, 82–93. [CrossRef]
11. Bailey, M.H.; Tokheim, C.; Porta-Pardo, E.; Sengupta, S.; Bertrand, D.; Weerasinghe, A.; Colaprico, A.; Wendl, M.C.; Kim, J.;
Reardon, B.; et al. Comprehensive Characterization of Cancer Driver Genes and Mutations. Cell 2018, 173, 371–385.e18. [CrossRef]
12. Youssef, O.; Sarhadi, V.K.; Armengol, G.; Piirilä, P.; Knuuttila, A.; Knuutila, S. Exhaled Breath Condensate as a Source of
Biomarkers for Lung Carcinomas. A Focus on Genetic and Epigenetic Markers—A Mini-Review. Genes Chromosomes Cancer 2016,
55, 905–914. [CrossRef] [PubMed]
13. Sarhadi, V.; Lahti, L.; Saberi, F.; Youssef, O.; Kokkola, A.; Karla, T.; Tikkanen, M.; Rautelin, H.; Puolakkainen, P.; Salehi, R.; et al.
Gut Microbiota and Host Gene Mutations in Colorectal Cancer Patients and Controls of Iranian and Finnish Origin. Anticancer.
Res. 2020, 40, 1325–1334. [CrossRef] [PubMed]
14. Armengol, G.; Sarhadi, V.K.; Ghanbari, R.; Doghaei-Moghaddam, M.; Ansari, R.; Sotoudeh, M.; Puolakkainen, P.; Kokkola,
A.; Malekzadeh, R.; Knuutila, S. Driver Gene Mutations in Stools of Colorectal Carcinoma Patients Detected by Targeted
Next-Generation Sequencing. J. Mol. Diagn. 2016, 18, 471–479. [CrossRef] [PubMed]
15. Tuononen, K.; Sarhadi, V.K.; Wirtanen, A.; Rönty, M.; Salmenkivi, K.; Knuuttila, A.; Remes, S.; Telaranta-Keerie, A.I.; Bloor,
S.; Ellonen, P.; et al. Targeted Resequencing Reveals ALK Fusions in Non-Small Cell Lung Carcinomas Detected by FISH,
Immunohistochemistry, and Real-Time RT-PCR: A Comparison of Four Methods. Biomed. Res. Int. 2013, 2013, 757490. [CrossRef]
16. Müller, D.; Győrffy, B. DNA Methylation-Based Diagnostic, Prognostic, and Predictive Biomarkers in Colorectal Cancer. Biochim.
Biophys. Acta Rev. Cancer 2022, 1877, 188722. [CrossRef]
17. Poon, M.T.C.; Keni, S.; Vimalan, V.; Ip, C.; Smith, C.; Erridge, S.; Weir, C.J.; Brennan, P.M. Extent of MGMT Promoter Methylation
Modifies the Effect of Temozolomide on Overall Survival in Patients with Glioblastoma: A Regional Cohort Study. Neurooncol.
Adv. 2021, 3, vdab171. [CrossRef]
18. Constâncio, V.; Nunes, S.P.; Henrique, R.; Jerónimo, C. DNA Methylation-Based Testing in Liquid Biopsies as Detection and
Prognostic Biomarkers for the Four Major Cancer Types. Cells 2020, 9, 624. [CrossRef]
19. Chen, X.; Gole, J.; Gore, A.; He, Q.; Lu, M.; Min, J.; Yuan, Z.; Yang, X.; Jiang, Y.; Zhang, T.; et al. Non-Invasive Early Detection of
Cancer Four Years before Conventional Diagnosis Using a Blood Test. Nat. Commun. 2020, 11, 3475. [CrossRef]
20. Mio, C.; Damante, G. Challenges in Promoter Methylation Analysis in the New Era of Translational Oncology: A Focus on Liquid
Biopsy. Biochim. Biophys. Acta Mol. Basis Dis. 2022, 1868, 166390. [CrossRef]
Biomolecules 2022, 12, 1021 33 of 39

21. Larson, M.H.; Pan, W.; Kim, H.J.; Mauntz, R.E.; Stuart, S.M.; Pimentel, M.; Zhou, Y.; Knudsgaard, P.; Demas, V.; Aravanis, A.M.;
et al. A Comprehensive Characterization of the Cell-Free Transcriptome Reveals Tissue- and Subtype-Specific Biomarkers for
Cancer Detection. Nat. Commun. 2021, 12, 2357. [CrossRef]
22. Ludwig, N.; Leidinger, P.; Becker, K.; Backes, C.; Fehlmann, T.; Pallasch, C.; Rheinheimer, S.; Meder, B.; Stähler, C.; Meese, E.; et al.
Distribution of MiRNA Expression across Human Tissues. Nucleic Acids Res. 2016, 44, 3865–3877. [CrossRef] [PubMed]
23. Mishra, S.; Yadav, T.; Rani, V. Exploring MiRNA Based Approaches in Cancer Diagnostics and Therapeutics. Crit. Rev. Oncol.
/Hematol. 2016, 98, 12–23. [CrossRef] [PubMed]
24. Inoue, J.; Inazawa, J. Cancer-Associated MiRNAs and Their Therapeutic Potential. J. Hum. Genet. 2021, 66, 937–945. [CrossRef]
[PubMed]
25. Cacheux, J.; Bancaud, A.; Leichlé, T.; Cordelier, P. Technological Challenges and Future Issues for the Detection of Circulating
MicroRNAs in Patients with Cancer. Front. Chem. 2019, 7, 815. [CrossRef]
26. Sun, L.; Liu, X.; Pan, B.; Hu, X.; Zhu, Y.; Su, Y.; Guo, Z.; Zhang, G.; Xu, M.; Xu, X.; et al. Serum Exosomal MiR-122 as a Potential
Diagnostic and Prognostic Biomarker of Colorectal Cancer with Liver Metastasis. J. Cancer 2020, 11, 630–637. [CrossRef]
27. Dohmen, J.; Semaan, A.; Kobilay, M.; Zaleski, M.; Branchi, V.; Schlierf, A.; Hettwer, K.; Uhlig, S.; Hartmann, G.; Kalff, J.C.; et al.
Diagnostic Potential of Exosomal MicroRNAs in Colorectal Cancer. Diagnostics 2022, 12, 1413. [CrossRef]
28. Wu, Q.; Yu, L.; Lin, X.; Zheng, Q.; Zhang, S.; Chen, D.; Pan, X.; Huang, Y. Combination of Serum MiRNAs with Serum Exosomal
MiRNAs in Early Diagnosis for Non-Small-Cell Lung Cancer. Cancer Manag. Res. 2020, 12, 485–495. [CrossRef]
29. Preethi, K.A.; Selvakumar, S.C.; Ross, K.; Jayaraman, S.; Tusubira, D.; Sekar, D. Liquid Biopsy: Exosomal MicroRNAs as Novel
Diagnostic and Prognostic Biomarkers in Cancer. Mol. Cancer 2022, 21, 54. [CrossRef]
30. Kaźmierczak, D.; Eide, I.J.Z.; Gencheva, R.; Lai, Y.; Lewensohn, R.; Tsakonas, G.; Grundberg, O.; de Petris, L.; McGowan, M.;
Brustugun, O.T.; et al. Elevated Expression of MiR-494-3p Is Associated with Resistance to Osimertinib in EGFR T790M-Positive
Non-Small Cell Lung Cancer. Transl. Lung Cancer Res. 2022, 11, 722–734. [CrossRef]
31. Janpipatkul, K.; Trachu, N.; Watcharenwong, P.; Panvongsa, W.; Worakitchanon, W.; Metheetrairut, C.; Oranratnachai, S.;
Reungwetwattana, T.; Chairoungdua, A. Exosomal MicroRNAs as Potential Biomarkers for Osimertinib Resistance of Non-Small
Cell Lung Cancer Patients. Cancer Biomark. 2021, 31, 281–294. [CrossRef]
32. Li, X.; Chen, C.; Wang, Z.; Liu, J.; Sun, W.; Shen, K.; Lv, Y.; Zhu, S.; Zhan, P.; Lv, T.; et al. Elevated Exosome-Derived MiRNAs
Predict Osimertinib Resistance in Non-Small Cell Lung Cancer. Cancer Cell Int. 2021, 21, 428. [CrossRef] [PubMed]
33. Leonetti, A.; Capula, M.; Minari, R.; Mazzaschi, G.; Gregori, A.; el Hassouni, B.; Papini, F.; Bordi, P.; Verzè, M.; Avan, A.; et al.
Dynamic Evaluation of Circulating MiRNA Profile in EGFR-Mutated NSCLC Patients Treated with EGFR-TKIs. Cells 2021,
10, 1520. [CrossRef] [PubMed]
34. Zhang, Z.; Tang, Y.; Song, X.; Xie, L.; Zhao, S.; Song, X. Tumor-Derived Exosomal MiRNAs as Diagnostic Biomarkers in Non-Small
Cell Lung Cancer. Front. Oncol. 2020, 10, 560025. [CrossRef]
35. Tang, Y.; Zhang, Z.; Song, X.; Yu, M.; Niu, L.; Zhao, Y.; Wang, L.; Song, X.; Xie, L. Tumor-Derived Exosomal MiR-620 as a
Diagnostic Biomarker in Non-Small-Cell Lung Cancer. J. Oncol. 2020, 2020, 6691211. [CrossRef] [PubMed]
36. Todorova, V.K.; Byrum, S.D.; Gies, A.J.; Haynie, C.; Smith, H.; Reyna, N.S.; Makhoul, I. Circulating Exosomal MicroRNAs as
Predictive Biomarkers of Neoadjuvant Chemotherapy Response in Breast Cancer. Curr. Oncol. 2022, 29, 613–630. [CrossRef]
[PubMed]
37. Kulkarni, B.; Gondaliya, P.; Kirave, P.; Rawal, R.; Jain, A.; Garg, R.; Kalia, K. Exosome-Mediated Delivery of MiR-30a Sensitize
Cisplatin-Resistant Variant of Oral Squamous Carcinoma Cells via Modulating Beclin1 and Bcl2. Oncotarget 2020, 11, 1832–1845.
[CrossRef] [PubMed]
38. Pantano, F.; Zalfa, F.; Iuliani, M.; Simonetti, S.; Manca, P.; Napolitano, A.; Tiberi, S.; Russano, M.; Citarella, F.; Foderaro, S.;
et al. Large-Scale Profiling of Extracellular Vesicles Identified MiR-625-5p as a Novel Biomarker of Immunotherapy Response in
Advanced Non-Small-Cell Lung Cancer Patients. Cancers 2022, 14, 2435. [CrossRef]
39. Bustos, M.A.; Gross, R.; Rahimzadeh, N.; Cole, H.; Tran, L.T.; Tran, K.D.; Takeshima, L.; Stern, S.L.; O’Day, S.; Hoon, D.S.B. A
Pilot Study Comparing the Efficacy of Lactate Dehydrogenase Levels Versus Circulating Cell-Free MicroRNAs in Monitoring
Responses to Checkpoint Inhibitor Immunotherapy in Metastatic Melanoma Patients. Cancers 2020, 12, 3361. [CrossRef]
40. Zeng, L.; Zeng, G.; Ye, Z. Bioinformatics Analysis for Identifying Differentially Expressed MicroRNAs Derived from Plasma
Exosomes Associated with Radiotherapy Resistance in Non-Small-Cell Lung Cancer. Appl. Bionics Biomech. 2022, 2022, 9268206.
[CrossRef]
41. Zheng, Q.; Ding, H.; Wang, L.; Yan, Y.; Wan, Y.; Yi, Y.; Tao, L.; Zhu, C. Circulating Exosomal MiR-96 as a Novel Biomarker for
Radioresistant Non-Small-Cell Lung Cancer. J. Oncol. 2021, 2021, 5893981. [CrossRef]
42. Vadla, G.P.; Daghat, B.; Patterson, N.; Ahmad, V.; Perez, G.; Garcia, A.; Manjunath, Y.; Kaifi, J.T.; Li, G.; Chabu, C.Y. Combining
Plasma Extracellular Vesicle Let-7b-5p, MiR-184 and Circulating MiR-22-3p Levels for NSCLC Diagnosis and Drug Resistance
Prediction. Sci. Rep. 2022, 12, 6693. [CrossRef] [PubMed]
43. Zhang, Y.; Xu, H. Serum Exosomal MiR-378 Upregulation Is Associated with Poor Prognosis in Non–Small-cell Lung Cancer
Patients. J. Clin. Lab. Anal. 2020, 34, e23237. [CrossRef] [PubMed]
44. Luo, R.; Liu, H.; Chen, J. Reduced Circulating Exosomal MiR-382 Predicts Unfavorable Outcome in Non-Small Cell Lung Cancer.
Int. J. Clin. Exp. Pathol. 2021, 14, 469–474. [PubMed]
Biomolecules 2022, 12, 1021 34 of 39

45. Kim, D.H.; Park, H.; Choi, Y.J.; Kang, M.-H.; Kim, T.-K.; Pack, C.-G.; Choi, C.-M.; Lee, J.C.; Rho, J.K. Exosomal MiR-1260b Derived
from Non-Small Cell Lung Cancer Promotes Tumor Metastasis through the Inhibition of HIPK2. Cell Death Dis. 2021, 12, 747.
[CrossRef]
46. Huang, D.; Qu, D. Early Diagnostic and Prognostic Value of Serum Exosomal MiR-1246 in Non-Small Cell Lung Cancer. Int. J.
Clin. Exp. Pathol. 2020, 13, 1601–1607.
47. Jang, J.; Kim, Y.; Kang, K.; Kim, K.; Park, Y.; Kim, C. Multiple MicroRNAs as Biomarkers for Early Breast Cancer Diagnosis. Mol.
Clin. Oncol. 2020, 14, 31. [CrossRef]
48. Li, D.; Wang, J.; Ma, L.-J.; Yang, H.-B.; Jing, J.-F.; Jia, M.-M.; Zhang, X.-J.; Guo, F.; Gao, J.-N. Identification of Serum Exosomal
MiR-148a as a Novel Prognostic Biomarker for Breast Cancer. Eur. Rev. Med. Pharmacol. Sci. 2020, 24, 7303–7309. [CrossRef]
49. Xun, J.; Du, L.; Gao, R.; Shen, L.; Wang, D.; Kang, L.; Chen, C.; Zhang, Z.; Zhang, Y.; Yue, S.; et al. Cancer-Derived Exosomal
MiR-138-5p Modulates Polarization of Tumor-Associated Macrophages through Inhibition of KDM6B. Theranostics 2021, 11,
6847–6859. [CrossRef]
50. Wang, X.; Qian, T.; Bao, S.; Zhao, H.; Chen, H.; Xing, Z.; Li, Y.; Zhang, M.; Meng, X.; Wang, C.; et al. Circulating Exosomal
MiR-363-5p Inhibits Lymph Node Metastasis by Downregulating PDGFB and Serves as a Potential Noninvasive Biomarker for
Breast Cancer. Mol. Oncol. 2021, 15, 2466–2479. [CrossRef]
51. Wang, B.; Mao, J.-H.; Wang, B.-Y.; Wang, L.-X.; Wen, H.-Y.; Xu, L.-J.; Fu, J.-X.; Yang, H. Exosomal MiR-1910-3p Promotes
Proliferation, Metastasis, and Autophagy of Breast Cancer Cells by Targeting MTMR3 and Activating the NF-KB Signaling
Pathway. Cancer Lett. 2020, 489, 87–99. [CrossRef]
52. Sueta, A.; Yamamoto, Y.; Tomiguchi, M.; Takeshita, T.; Yamamoto-Ibusuki, M.; Iwase, H. Differential expression of exosomal
miRNAs between breast cancer patients with and without recurrence. Oncotarget 2017, 8, 69934–69944. [CrossRef] [PubMed]
53. Hirschfeld, M.; Rücker, G.; Weiß, D.; Berner, K.; Ritter, A.; Jäger, M.; Erbes, T. Urinary Exosomal MicroRNAs as Potential
Non-Invasive Biomarkers in Breast Cancer Detection. Mol. Diagn. Ther. 2020, 24, 215–232. [CrossRef] [PubMed]
54. Liu, M.; Mo, F.; Song, X.; He, Y.; Yuan, Y.; Yan, J.; Yang, Y.; Huang, J.; Zhang, S. Exosomal Hsa-MiR-21-5p Is a Biomarker for Breast
Cancer Diagnosis. PeerJ 2021, 9, e12147. [CrossRef] [PubMed]
55. Li, S.; Zhang, M.; Xu, F.; Wang, Y.; Leng, D. Detection Significance of MiR-3662, MiR-146a, and MiR-1290 in Serum Exosomes of
Breast Cancer Patients. J. Cancer Res. Ther. 2021, 17, 749–755. [CrossRef]
56. Guo, T.; Wang, Y.; Jia, J.; Mao, X.; Stankiewicz, E.; Scandura, G.; Burke, E.; Xu, L.; Marzec, J.; Davies, C.R.; et al. The Identification
of Plasma Exosomal MiR-423-3p as a Potential Predictive Biomarker for Prostate Cancer Castration-Resistance Development by
Plasma Exosomal MiRNA Sequencing. Front. Cell Dev. Biol. 2021, 8, 602493. [CrossRef]
57. Kim, M.Y.; Shin, H.; Moon, H.W.; Park, Y.H.; Park, J.; Lee, J.Y. Urinary Exosomal MicroRNA Profiling in Intermediate-Risk
Prostate Cancer. Sci. Rep. 2021, 11, 7355. [CrossRef]
58. Rode, M.P.; Silva, A.H.; Cisilotto, J.; Rosolen, D.; Creczynski-Pasa, T.B. MiR-425-5p as an Exosomal Biomarker for Metastatic
Prostate Cancer. Cell. Signal. 2021, 87, 110113. [CrossRef]
59. Shin, S.; Park, Y.H.; Jung, S.-H.; Jang, S.-H.; Kim, M.Y.; Lee, J.Y.; Chung, Y. Urinary Exosome MicroRNA Signatures as a
Noninvasive Prognostic Biomarker for Prostate Cancer. NPJ Genom. Med. 2021, 6, 45. [CrossRef]
60. Li, W.; Dong, Y.; Wang, K.J.; Deng, Z.; Zhang, W.; Shen, H.F. Plasma Exosomal MiR-125a-5p and MiR-141-5p as Non-Invasive
Biomarkers for Prostate Cancer. Neoplasma 2021, 67, 1314–1318. [CrossRef]
61. Li, Z.; Li, L.-X.; Diao, Y.-J.; Wang, J.; Ye, Y.; Hao, X.-K. Identification of Urinary Exosomal MiRNAs for the Non-Invasive Diagnosis
of Prostate Cancer. Cancer Manag. Res. 2021, 13, 25–35. [CrossRef]
62. He, L.; Ping, F.; Fan, Z.; Zhang, C.; Deng, M.; Cheng, B.; Xia, J. Salivary Exosomal MiR-24-3p Serves as a Potential Detective
Biomarker for Oral Squamous Cell Carcinoma Screening. Biomed. Pharmacother. 2020, 121, 109553. [CrossRef] [PubMed]
63. He, T.; Guo, X.; Li, X.; Liao, C.; Wang, X.; He, K. Plasma-Derived Exosomal MicroRNA-130a Serves as a Noninvasive Biomarker
for Diagnosis and Prognosis of Oral Squamous Cell Carcinoma. J. Oncol. 2021, 2021, 5547911. [CrossRef]
64. Chen, C.-M.; Chu, T.-H.; Chou, C.-C.; Chien, C.-Y.; Wang, J.-S.; Huang, C.-C. Exosome-Derived MicroRNAs in Oral Squamous
Cell Carcinomas Impact Disease Prognosis. Oral Oncol. 2021, 120, 105402. [CrossRef] [PubMed]
65. Liu, W.; Yang, D.; Chen, L.; Liu, Q.; Wang, W.; Yang, Z.; Shang, A.; Quan, W.; Li, D. Plasma Exosomal MiRNA-139-3p Is a Novel
Biomarker of Colorectal Cancer. J. Cancer 2020, 11, 4899–4906. [CrossRef] [PubMed]
66. Shi, Y.; Zhuang, Y.; Zhang, J.; Chen, M.; Wu, S. Four Circulating Exosomal MiRNAs as Novel Potential Biomarkers for the Early
Diagnosis of Human Colorectal Cancer. Tissue Cell 2021, 70, 101499. [CrossRef]
67. Handa, T.; Kuroha, M.; Nagai, H.; Shimoyama, Y.; Naito, T.; Moroi, R.; Kanazawa, Y.; Shiga, H.; Kakuta, Y.; Kinouchi, Y.; et al.
Liquid Biopsy for Colorectal Adenoma: Is the Exosomal MiRNA Derived from Organoid a Potential Diagnostic Biomarker? Clin.
Transl. Gastroenterol. 2021, 12, e00356. [CrossRef] [PubMed]
68. Liu, H.; Liu, Y.; Sun, P.; Leng, K.; Xu, Y.; Mei, L.; Han, P.; Zhang, B.; Yao, K.; Li, C.; et al. Colorectal Cancer-Derived Exosomal
MiR-106b-3p Promotes Metastasis by down-Regulating DLC-1 Expression. Clin. Sci. 2020, 134, 419–434. [CrossRef]
69. Zhang, N.; Zhang, P.P.; Huang, J.J.; Wang, Z.Y.; Zhang, Z.H.; Yuan, J.Z.; Ma, E.M.; Liu, X.; Bai, J. Reduced Serum Exosomal
MiR-874 Expression Predicts Poor Prognosis in Colorectal Cancer. Eur. Rev. Med. Pharmacol. Sci. 2020, 24, 664–672.
70. Cho, W.-C.; Kim, M.; Park, J.W.; Jeong, S.-Y.; Ku, J.-L. Exosomal MiR-193a and Let-7g Accelerate Cancer Progression on Primary
Colorectal Cancer and Paired Peritoneal Metastatic Cancer. Transl. Oncol. 2021, 14, 101000. [CrossRef]
Biomolecules 2022, 12, 1021 35 of 39

71. van Zweeden, A.A.; Opperman, R.C.M.; Honeywell, R.J.; Peters, G.J.; Verheul, H.M.W.; van der Vliet, H.J.; Poel, D. The Prognostic
Impact of Circulating MiRNAs in Patients with Advanced Esophagogastric Cancer during Palliative Chemotherapy. Cancer Treat.
Res. Commun. 2021, 27, 100371. [CrossRef]
72. Gao, S.-S.; Wang, Y.-J.; Zhang, G.-X.; Zhang, W.-T. Potential Diagnostic Value of MiRNAs in Peripheral Blood for Osteosarcoma: A
Meta-Analysis. J. Bone Oncol. 2020, 23, 100307. [CrossRef] [PubMed]
73. Lopez-Santillan, M.; Larrabeiti-Etxebarria, A.; Arzuaga-Mendez, J.; Lopez-Lopez, E.; Garcia-Orad, A. Circulating MiRNAs as
Biomarkers in Diffuse Large B-Cell Lymphoma: A Systematic Review. Oncotarget 2018, 9, 22850–22861. [CrossRef] [PubMed]
74. Fayed, D.; Donia, T.; El-Shanshory, M.; Ali, E.; Mohamed, T. Evaluation of MicroRNA92, MicroRNA638 in Acute Lymphoblastic
Leukemia of Egyptian Children. Asian Pac. J. Cancer Prev. 2021, 22, 1567–1572. [CrossRef]
75. Manganelli, M.; Grossi, I.; Ferracin, M.; Guerriero, P.; Negrini, M.; Ghidini, M.; Senti, C.; Ratti, M.; Pizzo, C.; Passalacqua, R.;
et al. Longitudinal Circulating Levels of MiR-23b-3p, MiR-126-3p and LncRNA GAS5 in HCC Patients Treated with Sorafenib.
Biomedicines 2021, 9, 813. [CrossRef] [PubMed]
76. Liu, T.; Zhang, Q.; Zhang, J.; Li, C.; Miao, Y.-R.; Lei, Q.; Li, Q.; Guo, A.-Y. EVmiRNA: A Database of MiRNA Profiling in
Extracellular Vesicles. Nucleic Acids Res. 2019, 47, D89–D93. [CrossRef] [PubMed]
77. Lei, B.; Tian, Z.; Fan, W.; Ni, B. Circular RNA: A Novel Biomarker and Therapeutic Target for Human Cancers. Int. J. Med. Sci.
2019, 16, 292–301. [CrossRef]
78. Qian, Y.; Shi, L.; Luo, Z. Long Non-Coding RNAs in Cancer: Implications for Diagnosis, Prognosis, and Therapy. Front. Med.
2020, 7, 902. [CrossRef]
79. Zhang, J.; Yan, S.; Li, R.; Wang, G.; Kang, S.; Wang, Y.; Hou, W.; Wang, C.; Tian, W. CRMarker: A Manually Curated Comprehensive
Resource of Cancer RNA Markers. Int. J. Biol. Macromol. 2021, 174, 263–269. [CrossRef]
80. Wang, S.; Zhang, K.; Tan, S.; Xin, J.; Yuan, Q.; Xu, H.; Xu, X.; Liang, Q.; Christiani, D.C.; Wang, M.; et al. Circular RNAs in Body
Fluids as Cancer Biomarkers: The New Frontier of Liquid Biopsies. Mol. Cancer 2021, 20, 13. [CrossRef]
81. Stowell, S.R.; Ju, T.; Cummings, R.D. Protein Glycosylation in Cancer. Annu. Rev. Pathol. 2015, 10, 473–510. [CrossRef]
82. Park, R.; da Silva, L.L.; Saeed, A. Immunotherapy Predictive Molecular Markers in Advanced Gastroesophageal Cancer: MSI and
Beyond. Cancers 2021, 13, 1715. [CrossRef] [PubMed]
83. Ding, Z.; Wang, N.; Ji, N.; Chen, Z.-S. Proteomics Technologies for Cancer Liquid Biopsies. Mol. Cancer 2022, 21, 53. [CrossRef]
84. Bertok, T.; Bertokova, A.; Jane, E.; Hires, M.; Aguedo, J.; Potocarova, M.; Lukac, L.; Vikartovska, A.; Kasak, P.; Borsig, L.; et al.
Identification of Whole-Serum Glycobiomarkers for Colorectal Carcinoma Using Reverse-Phase Lectin Microarray. Front Oncol.
2021, 11, 735338. [CrossRef] [PubMed]
85. André, T.; Shiu, K.-K.; Kim, T.W.; Jensen, B.V.; Jensen, L.H.; Punt, C.; Smith, D.; Garcia-Carbonero, R.; Benavides, M.; Gibbs,
P.; et al. Pembrolizumab in Microsatellite-Instability–High Advanced Colorectal Cancer. N. Engl. J. Med. 2020, 383, 2207–2218.
[CrossRef]
86. Wu, H.-C.; Kehm, R.; Santella, R.M.; Brenner, D.J.; Terry, M.B. DNA Repair Phenotype and Cancer Risk: A Systematic Review and
Meta-Analysis of 55 Case–Control Studies. Sci. Rep. 2022, 12, 3405. [CrossRef]
87. Grupińska, J.; Budzyń, M.; Brzeziński, J.; Gryszczyńska, B.; Kasprzak, M.; Kycler, W.; Leporowska, E.; Iskra, M. Association
between Clinicopathological Features of Breast Cancer with Adipocytokine Levels and Oxidative Stress Markers before and after
Chemotherapy. Biomed. Rep. 2021, 14, 30. [CrossRef] [PubMed]
88. Chao, M.-R.; Evans, M.D.; Hu, C.-W.; Ji, Y.; Møller, P.; Rossner, P.; Cooke, M.S. Biomarkers of Nucleic Acid Oxidation—A Summary
State-of-the-Art. Redox Biol. 2021, 42, 101872. [CrossRef]
89. Pour Khavari, A.; Liu, Y.; He, E.; Skog, S.; Haghdoost, S. Serum 8-Oxo-DG as a Predictor of Sensitivity and Outcome of
Radiotherapy and Chemotherapy of Upper Gastrointestinal Tumours. Oxidative Med. Cell. Longev. 2018, 2018, 4153574. [CrossRef]
90. Mirjolet, C.; Merlin, J.L.; Truc, G.; Noël, G.; Thariat, J.; Domont, J.; Sargos, P.; Renard-Oldrini, S.; Ray-Coquard, I.; Liem, X.; et al.
RILA Blood Biomarker as a Predictor of Radiation-Induced Sarcoma in a Matched Cohort Study. EBioMedicine 2019, 41, 420–426.
[CrossRef]
91. National Cancer InstituteTumor Markers in Common Use Was Originally Published by National Cancer Institute. Available
online: https://www.cancer.gov/about-cancer/diagnosis-staging/diagnosis/tumor-markers-list (accessed on 22 February 2022).
92. Zhang, H.; Lin, X.; Huang, Y.; Wang, M.; Cen, C.; Tang, S.; Dique, M.R.; Cai, L.; Luis, M.A.; Smollar, J.; et al. Detection Methods
and Clinical Applications of Circulating Tumor Cells in Breast Cancer. Front. Oncol. 2021, 11, 652253. [CrossRef]
93. Ren, X.; Foster, B.M.; Ghassemi, P.; Strobl, J.S.; Kerr, B.A.; Agah, M. Entrapment of Prostate Cancer Circulating Tumor Cells with a
Sequential Size-Based Microfluidic Chip. Anal. Chem. 2018, 90, 7526–7534. [CrossRef] [PubMed]
94. Liu, Y.; Li, R.; Zhang, L.; Guo, S. Nanomaterial-Based Immunocapture Platforms for the Recognition, Isolation, and Detection of
Circulating Tumor Cells. Front. Bioeng. Biotechnol. 2022, 10, 850241. [CrossRef] [PubMed]
95. List of Cleared or Approved Companion Diagnostic Devices (In Vitro and Imaging Tools). Available online: https:
//Www.Fda.Gov/Medical-Devices/in-Vitro-Diagnostics/List-Cleared-or-Approved-Companion-Diagnostic-Devices-in-
Vitro-and-Imaging-Tools (accessed on 22 February 2022).
96. Sammallahti, H.; Kokkola, A.; Rezasoltani, S.; Ghanbari, R.; Asadzadeh Aghdaei, H.; Knuutila, S.; Puolakkainen, P.; Sarhadi, V.K.
Microbiota Alterations and Their Association with Oncogenomic Changes in Pancreatic Cancer Patients. Int. J. Mol. Sci. 2021,
22, 12978. [CrossRef]
Biomolecules 2022, 12, 1021 36 of 39

97. Youssef, O.; Lahti, L.; Kokkola, A.; Karla, T.; Tikkanen, M.; Ehsan, H.; Carpelan-Holmström, M.; Koskensalo, S.; Böhling, T.;
Rautelin, H.; et al. Stool Microbiota Composition Differs in Patients with Stomach, Colon, and Rectal Neoplasms. Dig. Dis. Sci.
2018, 63, 2950–2958. [CrossRef]
98. Patel, A.; Patel, S.; Patel, P.; Tanavde, V. Saliva Based Liquid Biopsies in Head and Neck Cancer: How Far Are We from the Clinic?
Front. Oncol. 2022, 12, 828434. [CrossRef]
99. Gaw, G.; Gribben, M. Can We Detect Biomarkers of Oral Squamous Cell Carcinoma from Saliva or Mouth Swabs? Evid. Based
Dent. 2022, 23, 32–33. [CrossRef] [PubMed]
100. Elmahgoub, F. Could Salivary Biomarkers Be Useful in the Early Detection of Oral Cancer and Oral Potentially Malignant
Disorders, and Is There a Relationship between These Biomarkers and Risk Factors? Evid.-Based Dent. 2022, 23, 30–31. [CrossRef]
[PubMed]
101. Mattox, A.K.; Fakhry, C.; Agrawal, N. Biomarker-Based Evaluation of Treatment Response and Surveillance of HPV-Associated
Squamous Cell Carcinoma. Curr. Otorhinolaryngol. Rep. 2022, 10, 85–95. [CrossRef]
102. Xu, R.; Rai, A.; Chen, M.; Suwakulsiri, W.; Greening, D.W.; Simpson, R.J. Extracellular Vesicles in Cancer—Implications for Future
Improvements in Cancer Care. Nat. Rev. Clin. Oncol. 2018, 15, 617–638. [CrossRef]
103. Allenson, K.; Castillo, J.; San Lucas, F.A.; Scelo, G.; Kim, D.U.; Bernard, V.; Davis, G.; Kumar, T.; Katz, M.; Overman, M.J.; et al.
High prevalence of mutant KRAS in circulating exosome-derived DNA from early-stage pancreatic cancer patients. Ann Oncol.
2017, 28, 741–747. [CrossRef]
104. Konoshenko, M.Y.; Lekchnov, E.A.; Vlassov, A.V.; Laktionov, P.P. Isolation of Extracellular Vesicles: General Methodologies and
Latest Trends. BioMed Res. Int. 2018, 2018, 8545347. [CrossRef] [PubMed]
105. Liu, C.-J.; Xie, G.-Y.; Miao, Y.-R.; Xia, M.; Wang, Y.; Lei, Q.; Zhang, Q.; Guo, A.-Y. EVAtlas: A Comprehensive Database for NcRNA
Expression in Human Extracellular Vesicles. Nucleic Acids Res. 2022, 50, D111–D117. [CrossRef] [PubMed]
106. Bayani, J.M.; Squire, J.A. Applications of SKY in Cancer Cytogenetics. Cancer Investig. 2002, 20, 373–386. [CrossRef] [PubMed]
107. Rydzewski, N.R.; Peterson, E.; Lang, J.M.; Yu, M.; Laura Chang, S.; Sjöström, M.; Bakhtiar, H.; Song, G.; Helzer, K.T.; Bootsma,
M.L.; et al. Predicting Cancer Drug TARGETS-TreAtment Response Generalized Elastic-NeT Signatures. NPJ Genom. Med. 2021,
6, 76. [CrossRef]
108. Nguyen, B.; Cusumano, P.G.; Deck, K.; Kerlin, D.; Garcia, A.A.; Barone, J.L.; Rivera, E.; Yao, K.; de Snoo, F.A.; van den Akker, J.;
et al. Comparison of Molecular Subtyping with BluePrint, MammaPrint, and TargetPrint to Local Clinical Subtyping in Breast
Cancer Patients. Ann. Surg. Oncol. 2012, 19, 3257–3263. [CrossRef]
109. Arya, S.; Estrela, P. Recent Advances in Enhancement Strategies for Electrochemical ELISA-Based Immunoassays for Cancer
Biomarker Detection. Sensors 2018, 18, 2010. [CrossRef]
110. Dang, K.; Zhang, W.; Jiang, S.; Lin, X.; Qian, A. Application of Lectin Microarrays for Biomarker Discovery. ChemistryOpen 2020,
9, 285–300. [CrossRef]
111. Kowalczyk, T.; Ciborowski, M.; Kisluk, J.; Kretowski, A.; Barbas, C. Mass Spectrometry Based Proteomics and Metabolomics in
Personalized Oncology. Biochim. Biophys. Acta Mol. Basis Dis. 2020, 1866, 165690. [CrossRef]
112. Coarfa, C.; Grimm, S.L.; Rajapakshe, K.; Perera, D.; Lu, H.-Y.; Wang, X.; Christensen, K.R.; Mo, Q.; Edwards, D.P.; Huang, S.
Reverse-Phase Protein Array: Technology, Application, Data Processing, and Integration. J. Biomol. Tech. JBT 2021, 32, 15–29.
[CrossRef]
113. Hasan, M.R.; Ahommed, M.S.; Daizy, M.; Bacchu, M.S.; Ali, M.R.; Al-Mamun, M.R.; Saad Aly, M.A.; Khan, M.Z.H.; Hossain,
S.I. Recent Development in Electrochemical Biosensors for Cancer Biomarkers Detection. Biosens. Bioelectron. X 2021, 8, 100075.
[CrossRef]
114. Jou, A.F.; Lu, C.-H.; Ou, Y.-C.; Wang, S.-S.; Hsu, S.-L.; Willner, I.; Ho, J.A. Diagnosing the MiR-141 Prostate Cancer Biomarker
Using Nucleic Acid-Functionalized CdSe/ZnS QDs and Telomerase. Chem. Sci. 2015, 6, 659–665. [CrossRef] [PubMed]
115. Sina, A.A.I.; Carrascosa, L.G.; Liang, Z.; Grewal, Y.S.; Wardiana, A.; Shiddiky, M.J.A.; Gardiner, R.A.; Samaratunga, H.; Gandhi,
M.K.; Scott, R.J.; et al. Epigenetically Reprogrammed Methylation Landscape Drives the DNA Self-Assembly and Serves as a
Universal Cancer Biomarker. Nat. Commun. 2018, 9, 4915. [CrossRef] [PubMed]
116. Chu, Y.; Gao, Y.; Tang, W.; Qiang, L.; Han, Y.; Gao, J.; Zhang, Y.; Liu, H.; Han, L. Attomolar-Level Ultrasensitive and Multiplex
MicroRNA Detection Enabled by a Nanomaterial Locally Assembled Microfluidic Biochip for Cancer Diagnosis. Anal. Chem.
2021, 93, 5129–5136. [CrossRef] [PubMed]
117. Ivanov, Y.D.; Malsagova, K.A.; Popov, V.P.; Pleshakova, T.O.; Kozlov, A.F.; Galiullin, R.A.; Shumov, I.D.; Kapustina, S.I.;
Tikhonenko, F.V.; Ziborov, V.S.; et al. Nanoribbon-Based Electronic Detection of a Glioma-Associated Circular MiRNA. Biosensors
2021, 11, 237. [CrossRef]
118. Ivanov, Y.D.; Goldaeva, K.V.; Malsagova, K.A.; Pleshakova, T.O.; Galiullin, R.A.; Popov, V.P.; Kushlinskii, N.E.; Alferov, A.A.;
Enikeev, D.V.; Potoldykova, N.V.; et al. Nanoribbon Biosensor in the Detection of MiRNAs Associated with Colorectal Cancer.
Micromachines 2021, 12, 1581. [CrossRef]
119. Li, C.; He, W.; Wang, N.; Xi, Z.; Deng, R.; Liu, X.; Kang, R.; Xie, L.; Liu, X. Application of Microfluidics in Detection of Circulating
Tumor Cells. Front. Bioeng. Biotechnol. 2022, 10, 907232. [CrossRef]
120. Malhotra, R.; Patel, V.; Chikkaveeraiah, B.V.; Munge, B.S.; Cheong, S.C.; Zain, R.B.; Abraham, M.T.; Dey, D.K.; Gutkind, J.S.;
Rusling, J.F. Ultrasensitive Detection of Cancer Biomarkers in the Clinic by Use of a Nanostructured Microfluidic Array. Anal.
Chem. 2012, 84, 6249–6255. [CrossRef]
Biomolecules 2022, 12, 1021 37 of 39

121. Wu, Z.; Bai, Y.; Cheng, Z.; Liu, F.; Wang, P.; Yang, D.; Li, G.; Jin, Q.; Mao, H.; Zhao, J. Absolute Quantification of DNA Methylation
Using Microfluidic Chip-Based Digital PCR. Biosens. Bioelectron. 2017, 96, 339–344. [CrossRef]
122. Bruch, R.; Baaske, J.; Chatelle, C.; Meirich, M.; Madlener, S.; Weber, W.; Dincer, C.; Urban, G.A. CRISPR/Cas13a-Powered
Electrochemical Microfluidic Biosensor for Nucleic Acid Amplification-Free MiRNA Diagnostics. Adv. Mater. 2019, 31, 1905311.
[CrossRef]
123. Chi, Z.; Wu, Y.; Chen, L.; Yang, H.; Khan, M.R.; Busquets, R.; Huang, N.; Lin, X.; Deng, R.; Yang, W.; et al. CRISPR-Cas14a-
Integrated Strand Displacement Amplification for Rapid and Isothermal Detection of Cholangiocarcinoma Associated Circulating
MicroRNAs. Anal. Chim. Acta 2022, 1205, 339763. [CrossRef]
124. Kwong, G.A.; Ghosh, S.; Gamboa, L.; Patriotis, C.; Srivastava, S.; Bhatia, S.N. Synthetic Biomarkers: A Twenty-First Century Path
to Early Cancer Detection. Nat. Rev. Cancer 2021, 21, 655–668. [CrossRef]
125. National Comprehensive Cancer Network National Comprehensive Cancer Network Compendium. Available online: https:
//www.nccn.org/compendia-templates/compendia/biomarkers-compendium (accessed on 22 February 2022).
126. Food and Drug Administration Table of Pharmacogenomic Biomarkers in Drug Labeling. Available online: https://www.fda.
gov/drugs/scienceresearch/researchareas/pharmacogenetics/ucm083378.htm (accessed on 22 February 2022).
127. Dingerdissen, H.M.; Bastian, F.; Vijay-Shanker, K.; Robinson-Rechavi, M.; Bell, A.; Gogate, N.; Gupta, S.; Holmes, E.; Kahsay, R.;
Keeney, J.; et al. OncoMX: A Knowledgebase for Exploring Cancer Biomarkers in the Context of Related Cancer and Healthy
Data. JCO Clin. Cancer Inform. 2020, 4, 210–220. [CrossRef] [PubMed]
128. Nagel, Z.D.; Engelward, B.P.; Brenner, D.J.; Begley, T.J.; Sobol, R.W.; Bielas, J.H.; Stambrook, P.J.; Wei, Q.; Hu, J.J.; Terry, M.B.; et al.
Towards Precision Prevention: Technologies for Identifying Healthy Individuals with High Risk of Disease. Mutat. Res. Fundam.
Mol. Mech. Mutagenesis 2017, 800–802, 14–28. [CrossRef] [PubMed]
129. Haverfield, E.V.; Esplin, E.D.; Aguilar, S.J.; Hatchell, K.E.; Ormond, K.E.; Hanson-Kahn, A.; Atwal, P.S.; Macklin-Mantia, S.; Hines,
S.; Sak, C.W.M.; et al. Physician-Directed Genetic Screening to Evaluate Personal Risk for Medically Actionable Disorders: A
Large Multi-Center Cohort Study. BMC Med. 2021, 19, 199. [CrossRef]
130. Turnbull, C.; Sud, A.; Houlston, R.S. Cancer Genetics, Precision Prevention and a Call to Action. Nat. Genet. 2018, 50, 1212–1218.
[CrossRef]
131. Louie, A.D.; Huntington, K.; Carlsen, L.; Zhou, L.; El-Deiry, W.S. Integrating Molecular Biomarker Inputs Into Development and
Use of Clinical Cancer Therapeutics. Front. Pharmacol. 2021, 12, 2850. [CrossRef]
132. Grossman, D.C.; Curry, S.J.; Owens, D.K.; Bibbins-Domingo, K.; Caughey, A.B.; Davidson, K.W.; Doubeni, C.A.; Ebell, M.; Epling,
J.W.; Kemper, A.R.; et al. Screening for Prostate Cancer: US Preventive Services Task Force Recommendation Statement. JAMA
2018, 319, 1901–1913. [CrossRef]
133. Kiebish, M.A.; Tekumalla, P.; Ravipaty, S.; Dobi, A.; Srivastava, S.; Wu, W.; Patil, S.; Friss, T.; Klotz, A.; Srinivasan, A.; et al.
Clinical Utility of a Serum Biomarker Panel in Distinguishing Prostate Cancer from Benign Prostate Hyperplasia. Sci. Rep. 2021,
11, 15052. [CrossRef]
134. Liu, M.C.; Oxnard, G.R.; Klein, E.A.; Swanton, C.; Seiden, M.V.; Liu, M.C.; Oxnard, G.R.; Klein, E.A.; Smith, D.; Richards, D.; et al.
Sensitive and Specific Multi-Cancer Detection and Localization Using Methylation Signatures in Cell-Free DNA. Ann. Oncol.
2020, 31, 745–759. [CrossRef]
135. Lennon, A.M.; Buchanan, A.H.; Kinde, I.; Warren, A.; Honushefsky, A.; Cohain, A.T.; Ledbetter, D.H.; Sanfilippo, F.; Sheridan, K.;
Rosica, D.; et al. Feasibility of Blood Testing Combined with PET-CT to Screen for Cancer and Guide Intervention. Science 2020,
369, eabb9601. [CrossRef]
136. Cohen, J.D.; Li, L.; Wang, Y.; Thoburn, C.; Afsari, B.; Danilova, L.; Douville, C.; Javed, A.A.; Wong, F.; Mattox, A.; et al. Detection
and Localization of Surgically Resectable Cancers with a Multi-Analyte Blood Test. Science 2018, 359, 926–930. [CrossRef]
[PubMed]
137. Raoof, S.; Kennedy, C.J.; Wallach, D.A.; Bitton, A.; Green, R.C. Molecular Cancer Screening: In Search of Evidence. Nat. Med. 2021,
27, 1139–1142. [CrossRef] [PubMed]
138. Hequet, D.; Harrissart, G.; Krief, D.; Maumy, L.; Lerebours, F.; Menet, E.; Callens, C.; Rouzier, R. Prosigna Test in Breast Cancer:
Real-Life Experience. Breast Cancer Res. Treat. 2021, 188, 141–147. [CrossRef] [PubMed]
139. Piccart, M.; van’t Veer, L.J.; Poncet, C.; Lopes Cardozo, J.M.N.; Delaloge, S.; Pierga, J.-Y.; Vuylsteke, P.; Brain, E.; Vrijaldenhoven,
S.; Neijenhuis, P.A.; et al. 70-Gene Signature as an Aid for Treatment Decisions in Early Breast Cancer: Updated Results of the
Phase 3 Randomised MINDACT Trial with an Exploratory Analysis by Age. Lancet Oncol. 2021, 22, 476–488. [CrossRef]
140. Herold, T.; Rothenberg-Thurley, M.; Grunwald, V.V.; Janke, H.; Goerlich, D.; Sauerland, M.C.; Konstandin, N.P.; Dufour, A.;
Schneider, S.; Neusser, M.; et al. Validation and Refinement of the Revised 2017 European LeukemiaNet Genetic Risk Stratification
of Acute Myeloid Leukemia. Leukemia 2020, 34, 3161–3172. [CrossRef]
141. Inaba, H.; Mullighan, C.G. Pediatric Acute Lymphoblastic Leukemia. Haematologica 2020, 105, 2524–2539. [CrossRef]
142. Batis, N.; Brooks, J.M.; Payne, K.; Sharma, N.; Nankivell, P.; Mehanna, H. Lack of Predictive Tools for Conventional and Targeted
Cancer Therapy: Barriers to Biomarker Development and Clinical Translation. Adv. Drug Deliv. Rev. 2021, 176, 113854. [CrossRef]
143. Gerhards, N.M.; Rottenberg, S. New Tools for Old Drugs: Functional Genetic Screens to Optimize Current Chemotherapy. Drug
Resist. Updates 2018, 36, 30. [CrossRef]
144. Miteva-Marcheva, N.N.; Ivanov, H.Y.; Dimitrov, D.K.; Stoyanova, V.K. Application of Pharmacogenetics in Oncology. Biomark.
Res. 2020, 8, 32. [CrossRef]
Biomolecules 2022, 12, 1021 38 of 39

145. Whirl-Carrillo, M.; Huddart, R.; Gong, L.; Sangkuhl, K.; Thorn, C.F.; Whaley, R.; Klein, T.E. An Evidence-Based Framework for
Evaluating Pharmacogenomics Knowledge for Personalized Medicine. Clin. Pharmacol. Ther. 2021, 110, 563–572. [CrossRef]
146. Syed, Y.Y. Oncotype DX Breast Recurrence Score® : A Review of Its Use in Early-Stage Breast Cancer. Mol. Diagn. Ther. 2020, 24,
621–632. [CrossRef] [PubMed]
147. Schaafsma, E.; Zhang, B.; Schaafsma, M.; Tong, C.-Y.; Zhang, L.; Cheng, C. Impact of Oncotype DX Testing on ER+ Breast Cancer
Treatment and Survival in the First Decade of Use. Breast Cancer Res. 2021, 23, 74. [CrossRef] [PubMed]
148. Giuliano, A.E.; Edge, S.B.; Hortobagyi, G.N. Eighth Edition of the AJCC Cancer Staging Manual: Breast Cancer. Ann. Surg. Oncol.
2018, 25, 1783–1785. [CrossRef] [PubMed]
149. Averbeck, D.; Candéias, S.; Chandna, S.; Foray, N.; Friedl, A.A.; Haghdoost, S.; Jeggo, P.A.; Lumniczky, K.; Paris, F.; Quintens, R.;
et al. Establishing Mechanisms Affecting the Individual Response to Ionizing Radiation. Int. J. Radiat. Biol. 2020, 96, 297–323.
[CrossRef]
150. Boelens, M.C.; Wu, T.J.; Nabet, B.Y.; Xu, B.; Qiu, Y.; Yoon, T.; Azzam, D.J.; Twyman-Saint Victor, C.; Wiemann, B.Z.; Ishwaran, H.;
et al. Exosome Transfer from Stromal to Breast Cancer Cells Regulates Therapy Resistance Pathways. Cell 2014, 159, 499–513.
[CrossRef]
151. Meehan, J.; Gray, M.; Martínez-Pérez, C.; Kay, C.; Pang, L.Y.; Fraser, J.A.; Poole, A.V.; Kunkler, I.H.; Langdon, S.P.; Argyle, D.; et al.
Precision Medicine and the Role of Biomarkers of Radiotherapy Response in Breast Cancer. Front. Oncol. 2020, 10, 628. [CrossRef]
152. Goodman, C.R.; Seagle, B.-L.L.; Friedl, T.W.P.; Rack, B.; Lato, K.; Fink, V.; Cristofanilli, M.; Donnelly, E.D.; Janni, W.; Shahabi, S.;
et al. Association of Circulating Tumor Cell Status with Benefit of Radiotherapy and Survival in Early-Stage Breast Cancer. JAMA
Oncol. 2018, 4, e180163. [CrossRef]
153. Veldwijk, M.R.; Seibold, P.; Botma, A.; Helmbold, I.; Sperk, E.; Giordano, F.A.; Gürth, N.; Kirchner, A.; Behrens, S.; Wenz, F.; et al.
Association of CD4+ Radiation-Induced Lymphocyte Apoptosis with Fibrosis and Telangiectasia after Radiotherapy in 272 Breast
Cancer Patients with >10-Year Follow-Up. Clin. Cancer Res. 2019, 25, 562–572. [CrossRef]
154. Benitez, C.M.; Knox, S.J. Harnessing Genome-Wide Association Studies to Minimize Adverse Radiation-Induced Side Effects.
Radiat. Oncol. J. 2020, 38, 226. [CrossRef]
155. Chalubinska-Fendler, J.; Graczyk, L.; Piotrowski, G.; Wyka, K.; Nowicka, Z.; Tomasik, B.; Fijuth, J.; Kozono, D.; Fendler, W.
Lipopolysaccharide-Binding Protein Is an Early Biomarker of Cardiac Function After Radiation Therapy for Breast Cancer. Int. J.
Radiat. Oncol. Biol. Phys. 2019, 104, 1074–1083. [CrossRef]
156. Scheerens, H.; Malong, A.; Bassett, K.; Boyd, Z.; Gupta, V.; Harris, J.; Mesick, C.; Simnett, S.; Stevens, H.; Gilbert, H.; et al. Current
Status of Companion and Complementary Diagnostics: Strategic Considerations for Development and Launch. Clin. Transl. Sci.
2017, 10, 84–92. [CrossRef]
157. Bradley, R.; Braybrooke, J.; Gray, R.; Hills, R.; Liu, Z.; Peto, R.; Davies, L.; Dodwell, D.; McGale, P.; Pan, H.; et al. Trastuzumab for
Early-Stage, HER2-Positive Breast Cancer: A Meta-Analysis of 13,864 Women in Seven Randomised Trials. Lancet Oncol. 2021, 22,
1139–1150. [CrossRef]
158. Coleman, R.L.; Oza, A.M.; Lorusso, D.; Aghajanian, C.; Oaknin, A.; Dean, A.; Colombo, N.; Weberpals, J.I.; Clamp, A.; Scambia,
G.; et al. Rucaparib Maintenance Treatment for Recurrent Ovarian Carcinoma after Response to Platinum Therapy (ARIEL3): A
Randomised, Double-Blind, Placebo-Controlled, Phase 3 Trial. Lancet 2017, 390, 1949. [CrossRef]
159. Lei, Y.; Li, X.; Huang, Q.; Zheng, X.; Liu, M. Progress and Challenges of Predictive Biomarkers for Immune Checkpoint Blockade.
Front. Oncol. 2021, 11, 609. [CrossRef]
160. Wang, Y.; Tong, Z.; Zhang, W.; Zhang, W.; Buzdin, A.; Mu, X.; Yan, Q.; Zhao, X.; Chang, H.H.; Duhon, M.; et al. FDA-Approved
and Emerging Next Generation Predictive Biomarkers for Immune Checkpoint Inhibitors in Cancer Patients. Front. Oncol. 2021,
11, 2115. [CrossRef] [PubMed]
161. Kilgour, E.; Rothwell, D.G.; Brady, G.; Dive, C. Liquid Biopsy-Based Biomarkers of Treatment Response and Resistance. Cancer
Cell 2020, 37, 485–495. [CrossRef]
162. de Kock, R.; van den Borne, B.; Youssef-El Soud, M.; Belderbos, H.; Stege, G.; de Saegher, M.; van Dongen-Schrover, C.; Genet,
S.; Brunsveld, L.; Scharnhorst, V.; et al. Circulating Biomarkers for Monitoring Therapy Response and Detection of Disease
Progression in Lung Cancer Patients. Cancer Treat. Res. Commun. 2021, 28, 100410. [CrossRef] [PubMed]
163. Heidrich, I.; Ačkar, L.; Mossahebi Mohammadi, P.; Pantel, K. Liquid Biopsies: Potential and Challenges. Int. J. Cancer 2021, 148,
528–545. [CrossRef] [PubMed]
164. Goossens, N.; Nakagawa, S.; Sun, X.; Hoshida, Y. Cancer Biomarker Discovery and Validation. Transl. Cancer Res. 2015, 4, 256–269.
[CrossRef]
165. Gion, M.; Trevisiol, C.; Fabricio, A.S.C. State of the Art and Trends of Circulating Cancer Biomarkers. Int. J. Biol. Markers 2020, 35,
12–15. [CrossRef]
166. Hayes, D.F. Biomarker Validation and Testing. Mol. Oncol. 2015, 9, 960–966. [CrossRef] [PubMed]
167. Ou, F.S.; Michiels, S.; Shyr, Y.; Adjei, A.A.; Oberg, A.L. Biomarker Discovery and Validation: Statistical Considerations. J. Thorac.
Oncol. 2021, 16, 537–545. [CrossRef] [PubMed]
168. Simon, R. Clinical Trials for Predictive Medicine: New Challenges and Paradigms. Clin. Trials 2010, 7, 516–524. [CrossRef]
[PubMed]
169. Su, K.; Yu, Q.; Shen, R.; Sun, S.-Y.; Moreno, C.S.; Li, X.; Qin, Z.S. Pan-Cancer Analysis of Pathway-Based Gene Expression Pattern
at the Individual Level Reveals Biomarkers of Clinical Prognosis. Cell Rep. Methods 2021, 1, 100050. [CrossRef]
Biomolecules 2022, 12, 1021 39 of 39

170. FDA-NIH Biomarker Working Group. BEST (Biomarkers, EndpointS, and Other Tools) Resource; Food and Drug Administration:
Silver Spring, MD, USA, 2016.
171. O’Connor, J.P.B.; Aboagye, E.O.; Adams, J.E.; Aerts, H.J.W.L.; Barrington, S.F.; Beer, A.J.; Boellaard, R.; Bohndiek, S.E.; Brady, M.;
Brown, G.; et al. Imaging Biomarker Roadmap for Cancer Studies. Nat. Rev. Clin. Oncol. 2017, 14, 169–186. [CrossRef]
172. Saah, A.J.; Hoover, D.R. “Sensitivity” and “Specificity” Reconsidered: The Meaning of These Terms in Analytical and Diagnostic
Settings. Ann. Intern. Med. 1997, 126, 91–94. [CrossRef]
173. Hayes, D.F. Defining Clinical Utility of Tumor Biomarker Tests: A Clinician’s Viewpoint. J. Clin. Oncol. 2021, 39, 238–248.
[CrossRef]
174. Henry, N.L.; Hayes, D.F. Cancer Biomarkers. Mol. Oncol. 2012, 6, 140–146. [CrossRef]
175. Ray, P.; Manach, Y.; Riou, B.; Houle, T.T. Statistical Evaluation of a Biomarker. Anesthesiology 2010, 112, 1023–1040. [CrossRef]
176. Brower, V. Biomarkers: Portents of Malignancy. Nature 2011, 471, S19–S20. [CrossRef]
177. Parker, J.L.; Kuzulugil, S.S.; Pereverzev, K.; Mac, S.; Lopes, G.; Shah, Z.; Weerasinghe, A.; Rubinger, D.; Falconi, A.; Bener, A.; et al.
Does Biomarker Use in Oncology Improve Clinical Trial Failure Risk? A Large-Scale Analysis. Cancer Med. 2021, 10, 1955–1963.
[CrossRef] [PubMed]
178. Hayes, D.F. Precision Medicine and Testing for Tumor Biomarkers-Are All Tests Born Equal? JAMA Oncol. 2018, 4, 773–774.
[CrossRef] [PubMed]
179. Poste, G. Bring on the Biomarkers. Nature 2011, 469, 156–157. [CrossRef] [PubMed]