Abstract

Every year, cancer is responsible for millions of deaths worldwide

and, even though much progress has been achieved in medicine, there
are still many issues that must be addressed in order to improve
cancer therapy. For this reason, oncological research is putting a lot of
effort towards finding new and efficient therapies which can alleviate
critical side effects caused by conventional treatments. Different
technologies are currently under evaluation in clinical trials or have
been already introduced into clinical practice. While nanomedicine is
contributing to the development of biocompatible materials both for
diagnostic and therapeutic purposes, bioengineering of extracellular
vesicles and cells derived from patients has allowed designing ad
hoc systems and univocal targeting strategies. In this review, we will
provide an in-depth analysis of the most innovative advances in basic
and applied cancer research.

Keywords: cancer, nanomedicine, extracellular vesicles, targeted

therapy, immunotherapy, gene therapy, thermal ablation, radiomics,
pathomics
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Introduction

Cancer is one of the main causes of death worldwide, and in the past
decade, many research studies have focused on finding new therapies
to reduce the side effects caused by conventional therapies.

During cancer progression, tumours become highly heterogeneous,

creating a mixed population of cells characterised by different
molecular features and diverse responsivity to therapies. This
heterogeneity can be appreciated both at spatial and temporal levels
and is the key factor responsible for the development of resistant
phenotypes promoted by a selective pressure upon treatment
administration [1]. Usually, cancer is treated as a global and
homogeneous disease and tumours are considered as a whole
population of cells. Thus, a deep understanding of these complex
phenomena is of fundamental importance in order to design precise
and efficient therapies.

Nanomedicine offers a versatile platform of biocompatible and

biodegradable systems that are able to deliver conventional
chemotherapeutic drugs in vivo, increasing their bioavailability and
concentration around tumour tissues, and improving their release
profile [2]. Nanoparticles can be exploited for different applications,
ranging from diagnosis to therapy [2].

Recently, extracellular vesicles (EVs), responsible for cancer

development, microenvironment modification and required for
metastatic progression, have been widely investigated as efficient
drug delivery vehicles [3].

Natural antioxidants and many phytochemicals have been recently

introduced as anti-cancer adjuvant therapies due to their anti-
proliferative and pro-apoptotic properties [4, 5].

Targeted therapy is another branch of cancer therapy aiming at

targeting a specific site, such as tumour vasculature or intracellular
organelles, leaving the surroundings unaffected. This enormously
increases the specificity of the treatment, reducing its drawbacks [6].

Another promising opportunity relies on gene therapy and expression

of genes triggering apoptosis [7] and wild type tumour suppressors
[8], or the targeted silencing mediated by siRNAs, currently under
evaluation in many clinical trials worldwide [9].

Thermal ablation of tumours and magnetic hyperthermia are opening

new opportunities for precision medicine, making the treatment
localised in very narrow and precise areas. These methods could be a
potential substitute for more invasive practices, such as surgery
[10, 11].

Furthermore, new fields such as radiomics and pathomics are

contributing to the development of innovative approaches for
collecting big amounts of data and elaborate new therapeutic
strategies [12, 13] and predict accurate responses, clinical outcome
and cancer recurrence [14–16].

Taken all together, these strategies will be able to provide the best
personalised therapies for cancer patients, highlighting the
importance of combining multiple disciplines to get the best outcome.

In this review, we will provide a general overview of the most

advanced basic and applied cancer therapies, as well as newly
proposed methods that are currently under investigation at the
research stage that should overcome the limitation of conventional
therapies; different approaches to cancer diagnosis and therapy and
their current status in the clinical context will be discussed,
underlining their impact as innovative anti-cancer strategies.
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Nanomedicine

Nanoparticles are small systems (1–1,000 nm in size) with peculiar

physicochemical properties due to their size and high surface-to-
volume ratio [17]. Biocompatible nanoparticles are used in cancer
medicine to overcome some of the issues related to conventional
therapies, such as the low specificity and bioavailability of drugs or
contrast agents [2]. Therefore, encapsulation of the active agents in
nanoparticles will increase their solubility/biocompatibility, their
stability in bodily fluids and retention time in the tumour vasculature
[18–20]. Furthermore, nanoparticles can be engineered to be
extremely selective for a precise target [21, 22] (see the “Targeted
therapy and immunotherapy” section) and to release the drug in a
controlled way by responding to a specific stimulus [18, 23–25]. This
is the case of ThermoDox, a liposomal formulation that can release
doxorubicin as a response to an increment of temperature [26].

Inorganic nanoparticles are generally used as contrast agents for

diagnosis purposes. Among them, quantum dots are small light-
emitting semiconductor nanocrystals with peculiar electronic and
optical properties, which make them highly fluorescent, resistant to
photobleaching and sensitive for detection and imaging purposes [27].
Combined with active ingredients, they can be promising tools for
theranostic applications [27]. In a recent study, quantum dots coated
with poly(ethylene glycol) (PEG) were conjugated to anti-HER2
antibody and localised in specific tumour cells [28].

Superparamagnetic iron oxide nanoparticles (SPIONs) are usually

exploited as contrast agents in magnetic resonance imaging (MRI)
because they interact with magnetic fields [29, 30]. Five types of
SPIONs have been tested for MRI: ferumoxides (Feridex in the US,
Endorem in Europe), ferucarbotran (Resovist), ferucarbotran C
(Supravist, SHU 555 C), ferumoxtran-10 (Combidex) and NC100150
(Clariscan). Ferucarbotran is currently available in few countries,
while the others have been removed from the market [25]. SPIONs
have also been studied for cancer treatment by magnetic
hyperthermia (see the “Thermal ablation and magnetic hyperthermia”
section), and a formulation of iron oxide coated with aminosilane
called Nanotherm has been already approved for the treatment of
glioblastoma [31].

Gold nanoparticles have raised interest because of their optical and

electrical properties and low toxicity [32–34]. They are mainly used as
contrast agents for X-ray imaging, computed tomography [25],
photoacoustic imaging [35] and photodynamic therapy [36]. A
nanoshell made of a silica core and a gold shell coated with PEG was
approved by the Food and Drug Administration (FDA) in 2012 and
commercialised as AuroShell (Nanospectra) for the treatment of
breast cancer by photodynamic therapy [25].

Organic nanoparticles are mainly used as delivery systems for drugs.

Liposomes and micelles are both made of phospholipids, but they
differ in their morphology. Liposomes are spherical particles having at
least one lipid bilayer, resembling the structure of cell membranes.
They are mainly used to encapsulate hydrophilic drugs in their
aqueous core, but hydrophobic drugs can also be accommodated in
the bilayer or chemically attached to the particles [37]. Micelles,
instead, own a hydrophobic core that can encapsulate hydrophobic
drugs [38]. Doxil, doxorubicin-loaded PEGylated liposomes, were the
first nanoparticles approved by the FDA in 1995 to treat AIDS-
associated Kaposi’s sarcoma [39]. This formulation drastically reduces
doxorubicin side effects. Since then, other liposomal formulations
have been approved by the FDA for cancer therapy, such as Myocet
and DaunoXome [40–42]. Polymeric nanoparticles are made of
biocompatible or natural polymers, such as poly(lactide-co-glycolide),
poly(ε-caprolactone), chitosan, alginate and albumin [43]. Some
formulations have already been accepted by the FDA, such as
Abraxane (albumin-paclitaxel particles for the treatment of metastatic
breast cancer and pancreatic ductal adenocarcinoma) and Ontak (an
engineered protein combining interleukin-2 and diphtheria toxins for
the treatment of non-Hodgkin’s peripheral T-cell lymphomas).

As well as these systems, which have been either accepted or are

under clinical investigation, it is worth mentioning some new
nanoparticles currently undergoing testing at the research level,
which should improve treatment performance. For example, solid
lipid nanoparticles, made of lipids that are solid at body temperature
[44], and fabricated to load hydrophobic drugs [45] have been
demonstrated to give a higher drug stability and prolonged release
compared to other systems; however, the encapsulation efficiency is
often low because of their high crystallinity [46]. To overcome this
issue, one or more lipids, liquid at room temperature (like oleic acid,
for example), are included in the formulation [47]. Lipid nanoparticles
are good candidates for brain tumour therapy as they are able to cross
the blood–brain barrier (BBB) [48]. A recent work showed that lipid
nanoparticles loaded with SPIONs and temozolomide are efficient to
treat glioblastoma since they combine the effect of the conventional
chemotherapy and hyperthermia [49, 50]. Dendrimers are another
family of nanoparticles composed of polymers with a repetitive
branched structure and characterised by a globular morphology
[51, 52]. Their architecture can be easily controlled, making their
structure extremely versatile for many applications. For example,
some recent studies show that poly-L-lysine (PLL) dendrimers loaded
with doxorubicin induce anti-angiogenic responses in in vivo tumour
models [53]. Currently, there is only one clinical trial for a formulation
named ImDendrim based on a dendrimer and on a rhenium complex
coupled to an imidazolium ligand, for the treatment of inoperable liver
cancers that do not respond to conventional therapies [54].
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Extracellular vesicles for cancer diagnosis and therapy

EVs are classified in two categories based on their biogenesis.

Specifically, exosomes are small vesicles of around 30–150 nm
originated from endosomes in physiological and pathological
conditions and released by a fusion of multivesicular bodies (MVBs) to
the cell membrane [55, 56], while shed microvesicles (sMVs), with a
typical size of 50–1,300 nm, are present in almost any extracellular
bodily fluid and are responsible for the exchange of molecular
materials between cells [57, 58]. Exosomes are involved in cancer
development and spreading [3, 59, 60], in the bidirectional
communication between tumour cells and surrounding tissues, and in
the construction of the microenvironment needed for pre-metastatic
niche establishment and metastatic progression [61]. Hence,
circulating vesicles are clinically relevant in cancer diagnosis,
prognosis and follow up. Exosomes are actually recognised as valid
diagnostic tools, but they can also be isolated and exploited as anti-
cancer vaccines or nanosized drug carriers in cancer therapy [62].

Nowadays, one of the main issues in cancer diagnosis is the early

identification of biomarkers by non-invasive techniques. Obtaining a
significant amount of information, before and during tumour
treatment, should allow the monitoring of cancer progression and the
efficacy of therapeutic regimens. Liquid biopsies to detect circulating
tumour cells, RNAs, DNAs and exosomes have been used as indicators
for personalised medicine [63]. In recent years, exosomes detection
has been validated as a reliable tool for preclinical practice in different
cancer types [64], thanks to the identification of their content: double-
stranded DNA (dsDNA) [65, 66], messenger RNA (mRNA), micro RNA
(miRNA), long non-coding RNA (lncRNA) [67], proteins and lipids
[68]. DsDNA has been detected in exosomes isolated from plasma and
serum of different cancer cell types, and mutated genes involved in
tumorigenesis, such as mutated KRAS and TP53 [69, 70], have been
identified as disease predictors. Similarly, exosomal AR-V7 mRNA has
been used as a prognostic marker of resistance to hormonal therapy in
metastatic prostate cancer patients [71]. Gene expression profiling of
multiple RNAs from urinary exosomes has been adopted as an
efficient diagnostic tool [72]. LncRNAs isolated from serum exosomes
have been exploited for disease prognosis in colorectal cancer patients
[73], and multiple miRNAs allow one to distinguish between different
lung cancer subtypes [74]. GPC1-positive exosomes have been
employed to detect pancreatic cancer [75], while circulating exosomal
macrophage migration inhibitory factor (MIF) was able to predict
liver metastasis onset [76]. Finally, multiple lipids present in urinary
exosomes have been approved as prostate cancer indicators [77]. Due
to the high variability of patient classes and sample size, and in order
to obtain clinically significant results for a fast and effective diagnosis,
huge investments in exosome research will be required in the near
future.

Exosomes could also be exploited as natural, biocompatible and low

immunogenic nanocarriers for drug delivery in cancer therapy. They
can be passively loaded by mixing purified vesicles with small drugs
[78–82], or actively loaded by means of laboratory techniques, such as
electroporation and sonication [83, 84]. Superparamagnetic
nanoparticles conjugated to transferrin have been tested for the
isolation of exosomes expressing transferrin receptor from mice
blood. After incubation with doxorubicin, they have been used to
target liver cancer cells in response to external magnetic fields,
inhibiting cell growth both in vitro and in vivo [80]. Kim et al. [83]
engineered mouse macrophage-derived exosomes with aminoethyl
anisamide-PEG to target sigma receptor, overexpressed in lung cancer
cells and passively loaded them with paclitaxel. These systems acted
as targeting agents able to suppress metastatic growth in vivo.

Three clinical trials with loaded exosomes are currently ongoing for
the treatment of different tumours [85–87]: a phase I trial is
evaluating the ability of exosomes to deliver curcumin to normal and
colon cancer tissues [85]; a phase II trial is investigating the in
vivo performance of autologous tumour cell-derived microparticles
carrying methotrexate in lung cancer patients [86] and a clinical
inquiry is focusing on autologous erythrocyte-derived microparticles
loaded with methotrexate for gastric, colorectal and ovarian cancer
treatment [87].

Recently, new strategies to produce ad hoc exosomes have been

developed. Cells releasing exosomes have been genetically engineered
to overexpress specific macromolecules, or modified to release
exosomes with particular targeting molecules [88–90].

Exosomes derived from different cancer cells have already been

exploited as cancer vaccines. Autologous dendritic cell-derived
exosomes with improved immunostimulatory function have been
tested in a phase II clinical trial for the activation of CD8 + T cells [91]
in non-small cell lung cancer (NSCLC) patients, observing disease
stabilisation and a better overall survival [92]. In a phase I trial,
ascites-derived exosomes supplemented with granulocyte-
macrophage colony stimulating factor (GM-CSF) have been
administered to colorectal cancer patients, soliciting a tumour-specific
immune response [93].

Many issues related to exosomes clinical translation remain open and

are mostly connected to the definition of preclinical procedures for
isolation, quantification, storage and standard protocols for drug
loading. It is becoming even more necessary to distinguish between
tumour and healthy blood cell-derived vesicles to characterise their
post-isolation half-life and to perform standard content analyses. For
these purposes, innovative approaches and technologies have been set
up, such as microarrays and specific monoclonal antibodies and RNA
markers amplification strategies [94].
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Natural antioxidants in cancer therapy

Every day, the human body undergoes several exogenous insults, such
as ultraviolet (UV) rays, air pollution and tobacco smoke, which result
in the production of reactive species, especially oxidants and free
radicals, responsible for the onset of many diseases, including cancer.
These molecules can also be produced as a consequence of clinical
administration of drugs, but they are also naturally created inside our
cells and tissues by mitochondria and peroxisomes, and from
macrophages metabolism, during normal physiological aerobic
processes.
Oxidative stress and radical oxygen species are able to damage DNA
(genetic alterations, DNA double strand breaks and chromosomal
aberrations [95, 96]) and other bio-macromolecules [97], such as
lipids (membrane peroxidation and necrosis [98]) and proteins
(significantly changing the regulation of transcription factors and, as a
consequence, of essential metabolic pathways [99]).

The protective mechanisms our body has developed against these

molecules are sometimes insufficient to counteract the huge damages
produced. Recently, in addition to research into the roles of the
physiological enzymes superoxide dismutase (SOD), catalase (CAT)
and glutathione peroxidase (GP), natural antioxidants such as
vitamins, polyphenols and plant-derived bioactive compounds are
being studied in order to introduce them as preventive agents and
potential therapeutic drugs [100, 101]. These molecules have anti-
inflammatory and anti-oxidant properties and are found in many
vegetables and spices [102]. Vitamins, alkaloids, flavonoids,
carotenoids, curcumin, berberine, quercetin and many other
compounds have been screened in vitro and tested in vivo, displaying
appreciable anti-proliferative and pro-apoptotic properties, and have
been introduced as complementary therapies for cancer [4, 5, 103].

Despite the advantages of using natural drugs, their translation into

clinical practice remains difficult due to their limited bioavailability
and/or toxicity. Curcumin, a polyphenolic compound extracted from
turmeric (Curcuma longa), is a traditional Southeast Asian remedy
with anti-inflammatory, anti-oxidant and chemopreventive and
therapeutic activities [104]. It has been shown to have cytotoxic
effects in different kinds of tumours, such as brain, lung, leukaemia,
pancreatic and hepatocellular carcinoma [105, 106], with no adverse
effects in normal cells at the effective therapeutic doses [107].
Curcumin can modulate a plethora of cellular mechanisms [108, 109];
however, its biological properties, and as a consequence, the
treatment duration and the efficient therapeutic doses, have not been
completely elucidated yet. This molecule is highly lipophilic, poorly
soluble in water and not very stable [110]. Different strategies and
specific carriers, such as liposomes and micelles [111, 112], have been
developed to improve its bioavailability. Currently, 24 clinical trials
involving curcumin are ongoing and 23 have been already completed
[113].

Berberine is an alkaloid compound extracted from different plants,

such as Berberis. Recently, it has been demonstrated to be effective
against different tumours and to act as a chemopreventive agent,
modulating many signalling pathways [114, 115]. Like curcumin, it is
poorly soluble in water; therefore, different nanotechnological
strategies have been developed to facilitate its delivery across cell
membranes [116–119]; six clinical trials are open and one has been
completed [120].

Quercetin, a polyphenolic flavonoid found in fruits and vegetable, has

been proven to be effective to treat several tumours, such as lung,
prostate, liver, colon and breast cancers [121–123], by binding cellular
receptors and interfering with many signalling pathways [124].
Interestingly, it has been shown to be effective also in combination
with chemotherapeutic agents [125]. Presently, seven clinical trials
are open and four have been completed [126].
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Targeted therapy and immunotherapy

One of the main problems of conventional cancer therapy is the low

specificity of chemotherapeutic drugs for cancer cells. In fact, most
drugs act both on healthy and diseased tissues, generating severe side
effects. Researchers are putting a lot of effort into finding a way to
target only the desired site. Nanoparticles have raised great interest
for their tendency to accumulate more in tumour tissues due to the
enhanced permeability and retention effect (EPR) [127]. This process,
called passive targeting, relies on the small size of nanoparticles and
the leaky vasculature and impaired lymphatic drainage of neoplastic
tissues [6]. Passive targeting, however, is difficult to control and can
induce multidrug resistance (MDR) [128]. Active targeting, on the
other hand, enhances the uptake by tumour cells by targeting specific
receptors that are overexpressed on them [129, 130]. Nanoparticles,
for example, can be functionalized with ligands that univocally bind
particular cells or subcellular sites [6]. Several kinds of ligands can be
used, such as small molecules, peptides, proteins, aptamers and
antibodies.

Folic acid and biotin are small molecules, whose receptors are
overexpressed in tumour tissues. Several nanocarriers have been
functionalized with folic acid to target ovarian and endometrial
cancers [131]: folic acid-conjugated polyethylene glycol-poly(lactic-
co-glycolic acid) nanoparticles delivering docetaxel increased drug
cellular uptake by human cervical carcinoma cells [132]. Small ligands
are cheap and can be linked to nanoparticles by simple conjugation
chemistry [133, 134].

Different kinds of small peptides and proteins are also effective in

active targeting. Angiopep-2 is a peptide that has raised great interest
in the treatment of brain cancer [135], because it binds to low-density
lipoprotein receptor-related protein-1 (LRP1) of endothelial cells in
the BBB, and it is also overexpressed in glioblastoma cancer cells
[136]. Bombesin peptide conjugated to poly(lactic-co-glycolic acid)
(PLGA) nanoparticles loaded with docetaxel was used to target the
gastrin-releasing peptide receptor, overexpressed on cell surface of
prostate, breast, ovarian, pancreatic and colorectal cancer cells
[137, 138]. Transferrin is a serum glycoprotein overexpressed on
many solid tumours, especially on glioblastoma multiforme cells
[139], and on epithelial cells of the BBB [6, 140]. Transferrin-
conjugated chitosan-PEG nanoparticles delivering paclitaxel exhibited
a higher cytotoxicity towards transferrin-overexpressing human non-
small cell lung cancer cells (NSCLCs) (HOP-62) [141].

Aptamers are small synthetic single-stranded RNA or DNA

oligonucleotides folded into specific shapes that make them capable of
binding specific targets [142]. Farokhzad et al. [143] reported that the
use of A10 RNA aptamer conjugated to docetaxel-loaded nanoparticles
significantly enhances in vitro cytotoxicity. The same aptamer has
been also used to prepare quantum dot-doxorubicin conjugates [144].

Antibodies are currently the most exploited ligands for active

targeting. These proteins have a typical ‘Y’ shape, where the two arms
are responsible for the selective interaction with the antigen [145].
Antibodies can be used as immunoconjugates, when conjugated to a
drug or nanoparticle, or naked. In the first case, their function is
mainly to target a specific antigen overexpressed on cancer cells.
Antibodies used for this purpose include those ones that bind to the
human epidermal growth factor receptor 2 (HER2), the epidermal
growth factor receptor (EGFR), the transferrin receptor (TfR) and the
prostate-specific membrane antigen (PSMA) [6]. Rapamycin-PLGA
nanoparticle conjugated to EGFR antibody exhibited higher cellular
uptake by human breast adenocarcinoma cells (MCF-7), with
enhanced apoptotic activity [146]. Loperamide-loaded human serum
albumin nanoparticles conjugated to antibodies that specifically bind
transferrin receptor successfully crossed the BBB and delivered the
drug to the desired site [147].

Naked antibodies or immunoconjugates can also be used in

immunotherapy, which is a cancer treatment that aims at stimulating
or restoring the immune system of the patient against cancer cells
[148]. Antibodies can act as markers for cancer cells to make them
more vulnerable to the immune system response (non-specific
immune stimulation), or as inhibitors for immune checkpoint proteins
on cancer cell surface, that can modulate the action of T-cells [148].
Several antibodies have been already tested and accepted by FDA for
immunotherapy, such as rituximab (1997, [149]), ibritumomab
tiuxetan (2002, [150]), trastuzumab emtansine (2013, [151]),
nivolumab (2014, [152]) and pembrolizumab (2014, [153]).

Immunotherapy can be achieved by another strategy called adoptive

cell transfer (ACT) and it consists of isolating T-lymphocytes (T-cells)
with the highest activity against cancer directly from the patient’s
blood, expanding them ex vivo, and reinfusing them again into the
patient [154]. Autologous T-cells can be genetically engineered in
vitro to express a chimaeric antigen receptor (CAR), which makes
them more specific against cancer cell antigens [148]. Different CARs
can be designed to be directed against a certain cancer antigen. The
genetic modification of T-cells can be achieved by different methods
such as viral transduction, non-viral methods like DNA-based
transposons, CRISPR/Cas9 or other plasmid DNA and mRNA transfer
techniques (i.e., electroporation, encapsulation in nanoparticles)
[155]. ACT protocols have been already adopted in clinical practice for
advanced or recurrent acute lymphoblastic leukaemia and for some
aggressive forms of non-Hodgkin’s lymphoma [148]. For example, it
has been shown that the treatment of end-stage patients affected by
acute lymphocytic leukaemia with CAR T-cells led to a full recovery in
up to 92% of patients [155]. Despite these very promising results,
much research is currently devoted to understanding the long-term
side effects of CAR T-cell therapies and their fate within tumours, and
to improving CAR T-cell expansion technologies.
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Gene therapy for cancer treatment

Gene therapy is intended as the introduction of a normal copy of a

defective gene in the genome in order to cure specific diseases [156].
The first application dates back to 1990 when a retroviral vector was
exploited to deliver the adenosine deaminase (ADA) gene to T-cells in
patients with severe combined immunodeficiency (SCID) [157].
Further research demonstrated that gene therapy could be applied in
many human rare and chronic disorders and, most importantly, in
cancer treatment. Approximately 2,900 gene therapy clinical trials are
currently ongoing, 66.6% of which are related to cancer [158].
Different strategies are under evaluation for cancer gene therapy: 1)
expression of pro-apoptotic [159, 160] and chemo-sensitising genes
[4]; 2) expression of wild type tumour suppressor genes [5]; 3)
expression of genes able to solicit specific antitumour immune
responses and 4) targeted silencing of oncogenes.

One approach relied on thymidine kinase (TK) gene delivery, followed

by administration of prodrug ganciclovir to activate its expression and
induce specific cytotoxicity [161]. This has been clinically translated
for the treatment of prostate cancer and glioma [162–164]. In recent
decades, different vectors carrying the p53 tumour suppressor gene
have been evaluated for clinical applications. ONYX-015 has been
tested in NSCLC patients and gave a high response rate when
administered alone or together with chemotherapy [165]. Gendicine, a
recombinant adenovirus carrying wild-type p53 in head and neck
squamous cell cancer had a similar success, inducing complete disease
regression when combined with radiotherapy [166].
Despite many achievements, there are still some challenges to face
when dealing with gene therapy, such as the selection of the right
conditions for optimal expression levels and the choice of the best
delivery system to univocally target cancer cells. Gene therapy also
presents some drawbacks linked to genome integration, limited
efficacy in specific subsets of patients and high chances of being
neutralised by the immune system. Therefore, particular interest has
been elicited by targeted gene silencing approaches.

RNA interference (RNAi) has been recently established as an efficient

technology both for basic research and medical translation. Small
interfering RNAs (siRNAs) consist of double-stranded RNAs [167] able
to produce targeted gene silencing. This process is intracellularly
mediated by the RNA-induced silencing complex (RISC), responsible
for cleaving the messenger RNA (mRNA), thus leading to interference
with protein synthesis [168]. This physiological mechanism has been
demonstrated in many eukaryotes, including animals. A few years
after RNAi discovery, the first clinical application for wet-age related
macular degeneration treatment entered phase I clinical trial [169].
Since cancer is triggered by precise molecular mechanisms, siRNAs
can be rationally designed to block desired targets responsible for cell
proliferation and metastatic invasion. This strategy relies on siRNA-
mediated gene silencing of anti-apoptotic proteins [170], transcription
factors (i.e., c-myc gene) [171, 172] or cancer mutated genes (i.e., K-
RAS) [173]. Most of the clinical trials currently ongoing are based on
local administration of siRNA oligonucleotides in a specific
tissue/organ or on systemic delivery throughout the entire body
[9, 174]. Using siRNA-based drugs has several advantages: 1) safety,
since they do not interact with the genome; 2) high efficacy, because
only small amounts can produce a dramatic gene downregulation; 3)
possibility of being designed for any specific target; 4) fewer side
effects when compared to conventional therapies and 5) low costs of
production [175, 176]. However, siRNAs are relatively unstable in
vivo and can be phagocytosed during blood circulation, excreted by
renal filtration, or undergo enzymatic degradation [177]. Occasionally,
they can induce off-target effects [178] or elicit innate immune
responses, followed by specific inflammation [179, 180]. Since naked
siRNAs are negatively charged hydrophilic molecules, they cannot
spontaneously cross cell membranes. Consequently, different delivery
strategies are currently under study, such as chemical modification,
encapsulation into lipid or polymeric carriers or conjugation with
organic molecules (polymers, peptides, lipids, antibodies, small
molecules [181], for efficient targeting [182, 183]). Chemical
modifications include the insertion of a phosphorothioate at 3’ end to
reduce exonuclease degradation [184], the introduction of 2’ O-methyl
group to obtain longer half-life in plasma [185] and the modification
by 2,4-dinitrophenol to favour membrane permeability [186].
Nevertheless, the degradation of modified siRNAs often elicits
cytotoxic effects; therefore, it is preferable to design ad
hoc nanocarriers.

Different cationic lipid nanoparticles, such as liposomes, micelles and

solid lipid nanoparticles [183], have been exploited for siRNA loading.
Cationic liposomes interact with negatively charged nucleic acids,
which can be easily transfected by simple electrostatic interactions
[187, 188]. They can be constituted by 1,2-dioleoyl-3-
trimethylammonium propane (DOTAP) and N-{1-(2,3-dioleoyloxy)
propyl]-N,N,N-trimethylammonium methyl sulphate (DOTMA) [189].
A theranostic agent consisting of an anticancer survivin siRNA
entrapped in PEGylated liposomes has been developed to achieve
simultaneous localisation inside tumour cells by means of entrapped
MR agents and fluorophores and reduction of proliferation in
vivo [190].

Neutral liposomes based on 1,2-dioleoyl-sn-glycero-3-

phosphatidylcholine (DOPC) have shown high efficacy in mice models
of ovarian carcinoma and colorectal cancer [191, 192]. A phase I
clinical trial is currently recruiting patients for evaluating the safety of
siRNA-EphA2-DOPC when administered to patients with advanced
and recurrent cancer [193].

Stable nucleic acid lipid particles (SNALPs) have been evaluated in

non-human primates [194]. SiRNAs have been encapsulated in a
mixture of cationic lipids coated with a shell of polyethylene glycol
(PEG) [195]. SNALPs entered a phase I clinical trial in patients affected
by advanced solid tumours with liver involvement [196] and a phase
I/II trial for treating neuroendocrine tumours and adrenocortical
carcinoma patients refractory to standard therapy [197].
SiRNAs can be condensed in cationic polymers such as chitosan,
cyclodextrin and polyethylenimine (PEI). Chitosan is a natural
polysaccharide that, due to its cationic charge, has been exploited as
carrier for nucleic acids in vitro and in vivo [198]. Specifically, a
targeted siRNA has been delivered in mice xenografts of breast cancer
[199]. Cyclodextrin polymers coated with PEG, conjugated with
human transferrin and carrying a siRNA called CALAA-01, inhibit
tumour growth by reducing the expression of M2 subunit of
ribonucleotide reductase (R2), and have entered a phase I clinical trial
[200]. PEI is able to form small cationic nanoparticles containing
siRNAs and it has been exploited as antitumoural, upon loading with
HER-2 receptor-specific siRNA [201]. A phase II clinical trial is
presently starting to evaluate siG12D LODER directed to mutated
KRAS oncogene and encapsulated into a biodegradable polymeric
matrix for locally treating advanced pancreatic cancer patients in
combination with chemotherapy [202].

SiRNAs may be conjugated to peptides, antibodies and aptamers in

order to improve their stability during circulation and to enhance
cellular uptake [203]. A success is represented by siRNAs targeting
PSMA, overexpressed in this type of cancer [204].

The introduction of nanocarriers has largely improved siRNAs

stability, pharmacokinetics and biodistribution properties, and the
targeting specificity [205, 206]. Smart nanomaterials responsive to
external (i.e., magnetic field, ultrasounds) and tumour-specific stimuli
(i.e., acidic pH, redox conditions) are currently under the development
for controlled release and reduction of undesired negative effects
[207, 208]. Nanocarriers delivering siRNAs undergo a series of pH
variations from blood circulation to intracellular environment and, for
this reason, many pH responsive materials have been designed to
favour cargo release under specific pH conditions [209].
Poly(allylamine) phosphate nanocarriers, stable at physiological pH,
have been developed to release siRNAs in the cytoplasm after
disassembly at low endosomal pH [210].

Although there have been many successes, some questions remain

open and make the clinical translation of the siRNA-based approach
very challenging, such as the correct doses to be delivered to patients
and the many variabilities observed between individuals and different
stages of disease. Further research towards controlled release to
reach only specific targets, and the set-up of the best personalised
therapy for cancer patients will be necessary in the near future.
Go to:

Thermal ablation and magnetic hyperthermia

Thermal ablation of tumours includes a series of techniques that

exploit heat (hyperthermia) or cold (hypothermia) to destroy
neoplastic tissues [13]. It is known that cell necrosis occurs at
temperatures lower than -40°C or higher than 60°C. Long exposures
to temperatures between 41°C and 55°C are also effective for tumour
cell damage. Moreover, it has been shown that cancer cells are more
sensitive to high temperatures than healthy ones [211].

Hypothermic ablation is due to the formation of ice crystals upon

cooling, which destroy cell membranes and finally kill cells. Argon gas
is the preferred cooling agent because it can cool down the
surrounding tissues to -160°C. Also, gases at their critical point, such
as nitrogen, can be exploited since they have a higher heat capacity
than argon. However, the technology to control and direct them is not
well developed yet [10].

Hyperthermic ablation currently comprises radiofrequency (RF),

microwave and laser ablation [10].

RF ablation is the most used in clinics, because it is effective and safe

[212]. An alternated current of RF waves is applied to a target zone by
an insulated electrode tip, while a second electrode, needed to close
the circuit, is placed on the skin surface [10]. The interaction with the
current causes the oscillation of ions in the extracellular fluid, which,
in turns, produces heat. The more conductive the medium, the more
effective the process. For this reason, RF ablation works very well in
the liver and in other areas with a high content of water and ions,
whereas it has a poor effect in lungs [10]. Moreover, the efficiency of
the treatment decreases with the size of the lesion, giving the best
results for areas not larger than 3 cm2 [213, 214].
Microwave ablation is based on the electromagnetic interaction
between microwaves and the polar molecules in tissues, like water,
that causes their oscillation and the consequent increase in
temperature. Unlike the electrical current in RF ablation, microwaves
can propagate through any kind of tissue [215, 216], and this allows
high temperatures to be reached in a short amount of time, to have a
deeper penetration and to treat larger areas of tumours [217].

Laser therapy exploits the properties of laser beams of being very

narrow and extremely focused at a specific wavelength. This makes
the treatment very powerful and precise, thus a promising alternative
to conventional surgery [218]. The absorption of the light emitted by
the laser results in the heating and subsequent damage of the treated
area [219]. Depending on the specific application, different kinds of
lasers can be used. Neodymium:yttrium-aluminium-garnet (Nd:YAG)
lasers (wavelength of 1064 nm) and diode lasers (wavelength of 800–
900 nm) are used to treat internal organs, since they have a
penetration depth up to 10 cm [218]. Conversely, CO2 lasers (10,600
nm), with a penetration depth of 10 μm up to 1 mm maximum are
used for superficial treatments. Laser therapy is receiving a lot of
attention in research because of its advantages compared to other
ablation techniques, such as a higher efficacy, safety and precision,
and a shorter treatment session needed to achieve the same results
[220, 221]. Moreover, the fibres to transmit laser light are compatible
with MRI, allowing for a precise measure of the temperature and the
thermal dose [222]. However, there are still some limitations to
overcome, such as the need of a very skilled operator to place the fibre
in the correct position [218].

Finally, a new way to heat tumour tissues, currently under study, is

through magnetic hyperthermia. This technique exploits
superparamagnetic or ferromagnetic nanoparticles that can generate
heat after stimulation with an alternating magnetic field. The most
studied systems in nanomedicine are SPIONs [11]. The production of
heat, in this case, is due to the alignment of magnetic domains in the
particles when the magnetic field is applied, and the subsequent
relaxation processes (Brownian and/or Neel relaxations) during
which heat is released, when the magnetic field is removed and the
magnetisation of the particles reverts to zero [223]. Magnetic
hyperthermia can reach any area of the body and SPIONs can also act
as MRI contrast agents to follow their correct localisation before the
stimulation. The particles can be coated with biocompatible polymers
and/or lipid and functionalized with specific ligands to impart
targeting properties [224]. As already mentioned, until now, just a
formulation of 15-nm iron oxide nanoparticles coated with
aminosilane (Nanotherm) obtained approval for the treatment of
glioblastoma [31]. SPIONs have also been successfully encapsulated in
lipid nanocarriers together with a chemotherapeutic agent to combine
chemotherapy and hyperthermia [49, 50].
Go to:

Recent innovations in cancer therapy: Radiomics and

pathomics

Efficient cancer therapy currently relies on surgery and, in

approximately 50% of patients, on radiotherapy, that can be delivered
by using an external beam source or by inserting locally a radioactive
source (in this case, the approach is named brachytherapy), thus
obtaining focused irradiation. Currently, localisation of the beam is
facilitated by image-guided radiotherapy (IGRT), where images of the
patient are acquired during the treatment allowing the best amount of
radiation to be set. Thanks to the introduction of intensity-modulated
radiotherapy (IMRT), radiation fields of different intensities can be
created, helping to reduce doses received by healthy tissues and thus
limiting adverse side effects. Finally, by means of stereotactic ablative
radiotherapy (SABR), it has become feasible to convey an ablative
dose of radiation only to a small target volume, significantly reducing
undesired toxicity [225].

Unfortunately, radioresistance can arise during treatment, lowering

its efficacy. This has been linked to mitochondrial defects; thus,
targeting specific functions have proven to be helpful in restoring anti-
cancer effects [226]. A recent study has shown, for example, that
radioresistance in an oesophageal adenocarcinoma model is linked to
an abnormal structure and size of mitochondria, and the
measurement of the energy metabolism in patients has allowed
discrimination between treatment resistant and sensitive patients
[227]. Targeting mitochondria with small molecules acting as
radiosensitizers is being investigated for gastrointestinal cancer
therapy [228].

Cancer is a complex disease and its successful treatment requires huge

efforts in order to merge the plethora of information acquired during
diagnostic and therapeutic procedures. The ability to link the data
collected from medical images and molecular investigations has
allowed an overview to be obtained of the whole tridimensional
volume of the tumour by non-invasive imaging techniques. This
matches with the main aim of precision medicine, which is to
minimise therapy-related side effects, while optimising its efficacy to
achieve the best individualised therapy [229].

Radiomics and pathomics are two promising and innovative fields

based on accumulating quantitative image features from radiology
and pathology screenings as therapeutic and prognostic indicators of
disease outcome [12, 13, 230]. Many artificial intelligence
technologies, such as machine learning application, have been
introduced to manage and elaborate the massive amount of collected
datasets and to accurately predict the treatment efficacy, the clinical
outcome and the disease recurrence. Prediction of the treatment
response can help in finding an ad hoc adaptation for the best
prognosis and outcome. Nowadays, personalised medicine requires an
integrated interpretation of the results obtained by multiple
diagnostic approaches, and biomedical images are crucial to provide
real-time monitoring of disease progression, being strictly correlated
to cancer molecular characterisation.

Radiomics is intended as the high throughput quantification of tumour

properties obtained from the analysis of medical images [14, 15, 231].
Pathomics, on the other side, relies on generation and characterisation
of high-resolution tissue images [16, 232, 233]. Many studies are
focusing on the development of new techniques for image analysis in
order to extrapolate information by quantification and disease
characterisation [234, 235]. Flexible databases are required to
manage big volumes of data coming from gene expression, histology,
3D tissue reconstruction (MRI) and metabolic features (positron
emission tomography, PET) in order to identify disease phenotypes
[236, 237].

Currently, there is an urgent need to define univocal data acquisition

guidelines. Some initiatives to establish standardised procedures and
facilitate clinical translation have been already undertaken, such as
quantitative imaging network [238] or the German National Cohort
Consortium [239]. Precise description of the parameters required for
image acquisition and for the creation and use of computational and
statistical methods are necessary to set robust protocols for the
generation of models in radiation therapy. According to the US
National Library of Medicine, approximately 50 clinical trials involving
radiomics are currently recruiting patients, and a few have already
been completed [240].
Go to:

Conclusions and future perspectives

In recent years, research into cancer medicine has taken remarkable

steps towards more effective, precise and less invasive cancer
treatments (Figure 1). While nanomedicine, combined with targeted
therapy, helped improving the biodistribution of new or already
tested chemotherapeutic agents around the specific tissue to be
treated, other strategies, such as gene therapy, siRNAs delivery,
immunotherapy and antioxidant molecules, offer new possibilities to
cancer patients. On the other hand, thermal ablation and magnetic
hyperthermia are promising alternatives to tumour resection. Finally,
radiomics and pathomics approaches help the management of big data
sets from cancer patients to improve prognosis and outcome.
Figure 1.
Cancer therapy approaches: The image represents the most innovative strategies
to treat cancer, combining different disciplines to obtain the most efficient and
personalised therapy for patients.

At the moment, the most frequent entries concerning cancer therapies

in the database of clinical trials (www.clinicaltrials.gov) involve the
terms targeted therapy, immunotherapy and gene therapy,
highlighting that these are the most popular methodologies under
investigation, especially because, as already mentioned before, they
have been shown to be very promising and effective (Figure 2A).
However, Figure 2B shows that the clinical trials started in the past
decade on different therapies mentioned in this review (except for
liposomes-based therapies) have increased in number, showing how
the interest on these new approaches is quickly growing in order to
replace and/or improve conventional therapies. In particular,
radiomics, immunotherapy and exosomes are the entries whose
number has increased the most in the last 10 years.
Figure 2.
Cancer clinical trials. (A): Total number of clinical trials currently registered
on www.clinicaltrials.gov for each approach discussed in this review. (B): Number
of the clinical trials [in % respect with the total studies shown in (A)] started
during the years 2008–2010 (blue) and from 2017 until today (orange). Date
accessed: 01/08/19

The current scenario for cancer research is wide, offering many

possibilities for the constant improvement of treatment, considering
not only patient recovery but also caring for their well-being during
therapy. As summarised in Table 1, these new approaches offer many
advantages compared to conventional therapies. However, some
disadvantages still have to be overcome to improve their
performances. Much progress has been made, but many others are
likely to come in the near future, producing more and more ad
hoc personalised therapies.

Table 1.
Advantages and disadvantages of the main innovative cancer therapeutic
approaches.

Strategy Advantages Disadvantages

Nanoparticles • High stability and specificity • It depends on the particular
• Good biocompatibility and nanoparticle
bioavailability
EVs • Physiologically secreted • Lack of preclinical procedures
• Good molecular for isolation, quantification,
characterisation storage and drug loading
• High biocompatibility
• In vitro modifiable/loadable
Natural antioxidants • Easily available in large • Limited bioavailability
quantities • Possible toxicity
• Exploitation of their intrinsic
properties
Targeted therapy • High specificity • Lack of information regarding
• Reduction of adverse long-term side effects
reactions
Gene therapy • Expression of pro-apoptotic • Genome integration
and chemo-sensitising genes • Limited efficacy in specific
• Expression of wild type subsets of patients
tumour suppressor genes • High chances to be neutralised
• Expression of genes able to by immune system
solicit specific anti-tumour • Off-target effects and
immune responses inflammation (RNAi)
• Targeted silencing of • Need of ad hoc delivery systems
oncogenes and safety (RNAi) (RNAi)
• Set-up of doses and suitable
conditions for controlled release
(RNAi)
Thermal ablation • Precise treatment of the • High efficiency only for
Magnetic interested area localised areas
hyperthermia • Possibility to perform the • Low penetration power
treatment along with MRI
Strategy Advantages Disadvantages
imaging (magnetic • Need for a skilled operator to
hyperthermia) perform the treatment
Radiomics/pathomics • Creation of tumour whole • Definition of univocal data
tridimensional volume by non- acquisition guidelines
invasive imaging techniques • Standardisation of procedures to
• Therapeutic and prognostic facilitate clinical translation
indicators of disease outcome • Description of parameters and
computational/statistical methods
to set robust protocols for the
generation of models for therapy
Open in a separate window

Go to:

Conflicts of interest

The authors declare that they have no conflict of interest.

Go to:

Funding declaration

This work was partially supported by the Fondazione CaRiPLo, grant

no. 2018-0156 (Nanotechnological countermeasures against Oxidative
stress in muscle cells Exposed to Microgravity—NOEMI) and by the
European Research Council (ERC) under the European Union’s
Horizon 2020 Research and Innovation Programme (grant agreement
N°709613, SLaMM).
Go to:

Authors’ contributions

Carlotta Pucci and Chiara Martinelli contributed equally to this work.

Go to:

References
1. Dagogo-Jack I, Shaw AT. Tumour heterogeneity and resistance to cancer
therapies. Nat Rev Clin Oncol. 2018;15(2):81–94.
doi: 10.1038/nrclinonc.2017.166. [PubMed] [CrossRef] [Google Scholar]
2. Martinelli C, Pucci C, Ciofani G. Nanostructured carriers as innovative tools for
cancer diagnosis and therapy. APL Bioeng. 2019;3(1):011502.
doi: 10.1063/1.5079943. [PMC free article] [PubMed] [CrossRef] [Google
Scholar]
3. Kumar B, Garcia M, Murakami JL, et al. Exosome-mediated microenvironment
dysregulation in leukemia. Biochim Biophys Acta. 2016;1863(3):464–470.
doi: 10.1016/j.bbamcr.2015.09.017. [PubMed] [CrossRef] [Google Scholar]
4. Chikara S, Nagaprashantha LD, Singhal J, et al. Oxidative stress and dietary
phytochemicals: role in cancer chemoprevention and treatment. Cancer
Lett. 2018;413:122–134. doi: 10.1016/j.canlet.2017.11.002. [PubMed]
[CrossRef] [Google Scholar]
5. Singh S, Sharma B, Kanwar SS, et al. Lead phytochemicals for anticancer drug
development. Front Plant Sci. 2016;7:1667. doi: 10.3389/fpls.2016.01667. [PMC
free article] [PubMed] [CrossRef] [Google Scholar]
6. Bazak R, Houri M, El Achy S, et al. Cancer active targeting by nanoparticles: a
comprehensive review of literature. J Cancer Res Clin Oncol. 2015;141(5):769–
784. doi: 10.1007/s00432-014-1767-3. [PMC free article] [PubMed]
[CrossRef] [Google Scholar]
7. Lebedeva IV, Su ZZ, Sarkar D, et al. Restoring apoptosis as a strategy for cancer
gene therapy: focus on p53 and mda-7. Semin Cancer Biol. 2003;13(2):169–178.
doi: 10.1016/S1044-579X(02)00134-7. [PubMed] [CrossRef] [Google Scholar]
8. Shanker M, Jin J, Branch CD, et al. Tumor suppressor gene-based nanotherapy:
from test tube to the clinic. J Drug Deliv. 2011. 465845. [PMC free
article] [PubMed] [CrossRef]
9. Vaishnaw AK, Gollob J, Gamba-Vitalo C, et al. A status report on RNAi
therapeutics. Silence. 2010;1(1):14. doi: 10.1186/1758-907X-1-14. [PMC free
article] [PubMed] [CrossRef] [Google Scholar]
10. Brace C. Thermal tumor ablation in clinical use. IEEE Pulse. 2011;2(5):28–38.
doi: 10.1109/MPUL.2011.942603. [PMC free article] [PubMed]
[CrossRef] [Google Scholar]
11. Hervault A, Thanh NTK. Magnetic nanoparticle-based therapeutic agents for
thermo-chemotherapy treatment of cancer. Nanoscale. 2014;6(20):11553–
11573. doi: 10.1039/C4NR03482A. [PubMed] [CrossRef] [Google Scholar]
12. Yu KH, Zhang C, Berry GJ, et al. Predicting non-small cell lung cancer
prognosis by fully automated microscopic pathology image features. Nat
Commun. 2016;7:12474. doi: 10.1038/ncomms12474. [PMC free
article] [PubMed] [CrossRef] [Google Scholar]
13. Aerts HJWL. The potential of radiomic-based phenotyping in precision
medicine a review. JAMA Oncol. 2016;2(12):1636–1642.
doi: 10.1001/jamaoncol.2016.2631. [PubMed] [CrossRef] [Google Scholar]
14. Grove O, Berglund AE, Schabath MB, et al. Quantitative computed
tomographic descriptors associate tumor shape complexity and intratumor
heterogeneity with prognosis in lung adenocarcinoma. PLoS
One. 2015;10(3):e0118261. doi: 10.1371/journal.pone.0118261. [PMC free
article] [PubMed] [CrossRef] [Google Scholar]
15. Aerts HJWL, Velazquez ER, Leijenaar RTH, et al. Decoding tumour phenotype
by noninvasive imaging using a quantitative radiomics approach. Nat
Commun. 2014;5:4006. doi: 10.1038/ncomms5006. [PMC free article] [PubMed]
[CrossRef] [Google Scholar]
16. Kong J, Cooper LAD, Wang F, et al. Machine-based morphologic analysis of
glioblastoma using whole-slide pathology images uncovers clinically relevant
molecular correlates. PLoS One. 2013;8(11):e81049.
doi: 10.1371/journal.pone.0081049. [PMC free article] [PubMed]
[CrossRef] [Google Scholar]
17. Tinkle S, Mcneil SE, Mühlebach S, et al. Nanomedicines: addressing the
scientific and regulatory gap. Ann N Y Acad Sci. 2014;1313:35–56.
doi: 10.1111/nyas.12403. [PubMed] [CrossRef] [Google Scholar]
18. Albanese A, Tang PS, Chan WCW. The effect of nanoparticle size, shape, and
surface chemistry on biological systems. Annu Rev Biomed Eng. 2012;14:1–16.
doi: 10.1146/annurev-bioeng-071811-150124. [PubMed] [CrossRef] [Google
Scholar]
19. Maeda H. Toward a full understanding of the EPR effect in primary and
metastatic tumors as well as issues related to its heterogeneity. Adv Drug Deliv
Rev. 2015;91:3–6. doi: 10.1016/j.addr.2015.01.002. [PubMed] [CrossRef] [Google
Scholar]
20. Gerlowski LE, Jain RK. Microvascular permeability of normal and neoplastic
tissues. Microvasc Res. 1986;31(3):288–305. doi: 10.1016/0026-2862(86)90018-
X. [PubMed] [CrossRef] [Google Scholar]
21. Shi J, Kantoff PW, Wooster R, et al. Cancer nanomedicine: progress, challenges
and opportunities. Nat Rev Cancer. 2017;17(1):20–37.
doi: 10.1038/nrc.2016.108. [PMC free article] [PubMed] [CrossRef] [Google
Scholar]
22. Shi J, Votruba AR, Farokhzad OC, et al. Nanotechnology in drug delivery and
tissue engineering: from discovery to applications. Nano Lett. 2010;10(9):3223–
3230. doi: 10.1021/nl102184c. [PMC free article] [PubMed] [CrossRef] [Google
Scholar]
23. Sinha R. Nanotechnology in cancer therapeutics: bioconjugated nanoparticles
for drug delivery. Mol Cancer Ther. 2006;5(8):1909–1917. doi: 10.1158/1535-
7163.MCT-06-0141. [PubMed] [CrossRef] [Google Scholar]
24. Bregoli L, Movia D, Gavigan-Imedio JD, et al. Nanomedicine applied to
translational oncology: a future perspective on cancer
treatment. Nanomed. 2016;12(1):81–103.
doi: 10.1016/j.nano.2015.08.006. [PubMed] [CrossRef] [Google Scholar]
25. Kim EM, Jeong HJ. Current status and future direction of nanomedicine: focus
on advanced biological and medical applications. Nucl Med Mol
Imaging. 2017;51(2):106–117. doi: 10.1007/s13139-016-0435-8. [PMC free
article] [PubMed] [CrossRef] [Google Scholar]
26. May JP, Li S-D. Hyperthermia-induced drug targeting. Expert Opin Drug
Deliv. 2013;10(4):511–527. doi: 10.1517/17425247.2013.758631. [PubMed]
[CrossRef] [Google Scholar]
27. Matea CT, Mocan T, Tabaran F, et al. Quantum dots in imaging, drug delivery
and sensor applications. Int J Nanomed. 2017;12:5421–5431.
doi: 10.2147/IJN.S138624. [PMC free article] [PubMed] [CrossRef] [Google
Scholar]
28. Gao J, Chen K, Miao Z, et al. Affibody-based nanoprobes for HER2-expressing
cell and tumor imaging. Biomaterials. 2011;32(8):2141–2148.
doi: 10.1016/j.biomaterials.2010.11.053. [PMC free article] [PubMed]
[CrossRef] [Google Scholar]
29. Leiner T, Gerretsen S, Botnar R, et al. Magnetic resonance imaging of
atherosclerosis. European Radiology. 2005;15(6):1087–1099.
doi: 10.1007/s00330-005-2646-8. [PubMed] [CrossRef] [Google Scholar]
30. Boyer C, Whittaker MR, Bulmus V, et al. The design and utility of polymer-
stabilized iron-oxide nanoparticles for nanomedicine applications. NPG Asia
Materials. 2010;2:23–30. doi: 10.1038/asiamat.2010.6. [CrossRef] [Google
Scholar]
31. Sanchez C, Belleville P, Popall M, et al. Applications of advanced hybrid
organic-inorganic nanomaterials: from laboratory to market. Chem Soc
Rev. 2011;40(2):696–753. doi: 10.1039/c0cs00136h. [PubMed]
[CrossRef] [Google Scholar]
32. Sun T, Zhang YS, Pang B, et al. Engineered nanoparticles for drug delivery in
cancer therapy. Angew Chem Int Ed Engl. 2014;53(46):12320–
12464. [PubMed] [Google Scholar]
33. Connor EE, Mwamuka J, Gole A, et al. Gold nanoparticles are taken up by
human cells but do not cause acute cytotoxicity. Small. 2005;1(3):325–327.
doi: 10.1002/smll.200400093. [PubMed] [CrossRef] [Google Scholar]
34. Abadeer NS, Murphy CJ. Recent progress in cancer thermal therapy using gold
nanoparticles. J Phys Chem C. 2016;120(9):4691–4716.
doi: 10.1021/acs.jpcc.5b11232. [CrossRef] [Google Scholar]
35. Zhong J, Wen L, Yang S, et al. Imaging-guided high-efficient photoacoustic
tumor therapy with targeting gold nanorods. Nanomed. 2015;11(6):1499–1509.
doi: 10.1016/j.nano.2015.04.002. [PubMed] [CrossRef] [Google Scholar]
36. Stuchinskaya T, Moreno M, Cook MJ, et al. Targeted photodynamic therapy of
breast cancer cells using antibody-phthalocyanine-gold nanoparticle
conjugates. Photochem Photobiol Sci. 2011;10(5):822–831.
doi: 10.1039/c1pp05014a. [PubMed] [CrossRef] [Google Scholar]
37. Gubernator J. Active methods of drug loading into liposomes: recent
strategies for stable drug entrapment and increased in vivo activity. Expert Opin
Drug Deliv. 2011;8(5):565–580. doi: 10.1517/17425247.2011.566552. [PubMed]
[CrossRef] [Google Scholar]
38. Narang AS, Delmarre D, Gao D. Stable drug encapsulation in micelles and
microemulsions. Int J Pharm. 2007;345(1–2):9–25.
doi: 10.1016/j.ijpharm.2007.08.057. [PubMed] [CrossRef] [Google Scholar]
39. Barenholz Y. Doxil—the first FDA-approved nano-drug: lessons learned. J
Control Release. 2012;160(2):117–134.
doi: 10.1016/j.jconrel.2012.03.020. [PubMed] [CrossRef] [Google Scholar]
40. Markman M. Pegylated liposomal doxorubicin in the treatment of cancers of
the breast and ovary. Expert Opin Pharmacother. 2006;7(11):1469–1474.
doi: 10.1517/14656566.7.11.1469. [PubMed] [CrossRef] [Google Scholar]
41. Hofheinz RD, Gnad-Vogt SU, Beyer U, et al. Liposomal encapsulated anti-
cancer drugs. Anticancer Drugs. 2005;16(7):691–707.
doi: 10.1097/01.cad.0000167902.53039.5a. [PubMed] [CrossRef] [Google
Scholar]
42. Malam Y, Loizidou M, Seifalian AM. Liposomes and nanoparticles: nanosized
vehicles for drug delivery in cancer. Trends Pharmacol Sci. 2009;30(11):592–599.
doi: 10.1016/j.tips.2009.08.004. [PubMed] [CrossRef] [Google Scholar]
43. Nasir A, Kausar A, Younus A. A review on preparation, properties and
applications of polymeric nanoparticle-based materials. Polym-Plast Technol
Eng. 2015;54(4):325–341.
doi: 10.1080/03602559.2014.958780. [CrossRef] [Google Scholar]
44. Waghmare AS, Grampurohit ND, Gadhave MV, et al. Solid lipid nanoparticles:
a promising drug delivery system. Int Res J Pharmacy. 2012;3(4):100–
107. [Google Scholar]
45. Liu J, Xiao Y, Allen C. Polymer-drug compatibility: a guide to the development
of delivery systems for the anticancer agent, ellipticine. J Pharm
Sci. 2004;93(1):132–143. doi: 10.1002/jps.10533. [PubMed] [CrossRef] [Google
Scholar]
46. Ganesan P, Narayanasamy D. Lipid nanoparticles: different preparation
techniques, characterization, hurdles, and strategies for the production of solid
lipid nanoparticles and nanostructured lipid carriers for oral drug
delivery. Sustainable Chemistry and Pharmacy. 2017;6:37–56.
doi: 10.1016/j.scp.2017.07.002. [CrossRef] [Google Scholar]
47. Müller RH, Radtke M, Wissing SA. Nanostructured lipid matrices for improved
microencapsulation of drugs. Int J Pharm. 2002;242(1-2):121–128.
doi: 10.1016/S0378-5173(02)00180-1. [PubMed] [CrossRef] [Google Scholar]
48. Kreuter J, Ramge P, Petrov V, et al. Direct evidence that polysorbate-80-
coated poly(butylcyanoacrylate) nanoparticles deliver drugs to the CNS via
specific mechanisms requiring prior binding of drug to the nanoparticles. Pharm
Res. 2003;20(3):409–416. doi: 10.1023/A:1022604120952. [PubMed]
[CrossRef] [Google Scholar]
49. Grillone A, Riva ER, Mondini A, et al. Active targeting of sorafenib:
preparation, characterization, and in vitro testing of drug-loaded magnetic solid
lipid nanoparticles. Adv Healthc Mater. 2015;4(11):1681–1690.
doi: 10.1002/adhm.201500235. [PubMed] [CrossRef] [Google Scholar]
50. Tapeinos C, Marino A, Battaglini M, et al. Stimuli-responsive lipid-based
magnetic nanovectors increase apoptosis in glioblastoma cells through synergic
intracellular hyperthermia and chemotherapy. Nanoscale. 2018;11(1):72–88.
doi: 10.1039/C8NR05520C. [PMC free article] [PubMed] [CrossRef] [Google
Scholar]
51. Gillies ER, Fréchet JMJ. Dendrimers and dendritic polymers in drug
delivery. Drug Discov Today. 2005;10(1):35–43. doi: 10.1016/S1359-
6446(04)03276-3. [PubMed] [CrossRef] [Google Scholar]
52. Kesharwani P, Jain K, Jain NK. Dendrimer as nanocarrier for drug
delivery. Prog Polym Sci. 2014;39(2):268–307.
doi: 10.1016/j.progpolymsci.2013.07.005. [CrossRef] [Google Scholar]
53. Al-jamal KT, Rubio N, Buddle J, et al. Cationic poly-l-lysine dendrimer
complexes doxorubicin and delays tumor growth in vitro and in vivo. ACS
Nano. 2013;7(3):1905–1917. doi: 10.1021/nn305860k. [PubMed]
[CrossRef] [Google Scholar]
54. ClinicalTrials.gov. US National Library of Medicine. [01/08/19]. [
https://clinicaltrials.gov/ct2/show/NCT03255343]
55. Colombo M, Raposo G, Théry C. Biogenesis, secretion, and intercellular
interactions of exosomes and other extracellular vesicles. Annu Rev Cell Dev
Biol. 2014;30:255–289. doi: 10.1146/annurev-cellbio-101512-122326. [PubMed]
[CrossRef] [Google Scholar]
56. Vlassov AV, Magdaleno S, Setterquist R, et al. Exosomes: current knowledge of
their composition, biological functions, and diagnostic and therapeutic
potentials. Biochim Biophys Acta. 2012;1820(7):940–948.
doi: 10.1016/j.bbagen.2012.03.017. [PubMed] [CrossRef] [Google Scholar]
57. Thery C. Exosomes: secreted vesicles and intercellular
communications. F1000 Biol Rep. 2011;3:15. doi: 10.3410/B3-15. [PMC free
article] [PubMed] [CrossRef] [Google Scholar]
58. Witwer KW, Buzas EI, Bemis LT, et al. Standardization of sample collection,
isolation and analysis methods in extracellular vesicle research: an ISEV position
paper. J Extracell Vesicles. 2013;2 doi: 10.3402/jev.v2i0.20360. [PMC free
article] [PubMed] [CrossRef] [Google Scholar]
59. Luga V, Wrana JL. Tumor-stroma interaction: revealing fibroblast-secreted
exosomes as potent regulators of Wnt-planar cell polarity signaling in cancer
metastasis. Cancer Res. 2013;73(23):6843–6847. doi: 10.1158/0008-5472.CAN-
13-1791. [PubMed] [CrossRef] [Google Scholar]
60. Suetsugu A, Honma K, Saji S, et al. Imaging exosome transfer from breast
cancer cells to stroma at metastatic sites in orthotopic nude-mouse models. Adv
Drug Deliv Rev. 2013;65(3):383–390. doi: 10.1016/j.addr.2012.08.007. [PubMed]
[CrossRef] [Google Scholar]
61. Raimondo S, Saieva L, Corrado C, et al. Chronic myeloid leukemia-derived
exosomes promote tumor growth through an autocrine mechanism. Cell Commun
Signal. 2015;13:8. doi: 10.1186/s12964-015-0086-x. [PMC free article] [PubMed]
[CrossRef] [Google Scholar]
62. Martinelli C. Molecular Oncology: Underlying Mechanisms and Translational
Advancements. 1st. Switzerland AG: Springer Nature, Springer; 2017. Exosomes:
new biomarkers for targeted cancer therapy; pp. 129–157. chapter 6. [Google
Scholar]
63. Siravegna G, Marsoni S, Siena S, et al. Integrating liquid biopsies into the
management of cancer. Nat Rev Clin Oncol. 2017;14(9):531–548.
doi: 10.1038/nrclinonc.2017.14. [PubMed] [CrossRef] [Google Scholar]
64. Kosaka N, Urabe F, Egawa S, et al. The small vesicular culprits: the
investigation of extracellular vesicles as new targets for cancer treatment. Clin
Transl Med. 2017;6(1):45. doi: 10.1186/s40169-017-0176-z. [PMC free
article] [PubMed] [CrossRef] [Google Scholar]
65. Thakur BK, Zhang H, Becker A, et al. Double-stranded DNA in exosomes: a
novel biomarker in cancer detection. Cell Res. 2014;24(6):766–769.
doi: 10.1038/cr.2014.44. [PMC free article] [PubMed] [CrossRef] [Google
Scholar]
66. Kahlert C, Melo SA, Protopopov A, et al. Identification of double stranded
genomic dna spanning all chromosomes with mutated KRAS and P53 DNA in the
serum exosomes of patients with pancreatic cancer. J Biol
Chem. 2014;289(7):3869–3875. doi: 10.1074/jbc.C113.532267. [PMC free
article] [PubMed] [CrossRef] [Google Scholar]
67. Valadi H, Ekström K, Bossios A, et al. Exosome-mediated transfer of mRNAs
and microRNAs is a novel mechanism of genetic exchange between cells. Nat Cell
Biol. 2007;9(6):654–659. doi: 10.1038/ncb1596. [PubMed] [CrossRef] [Google
Scholar]
68. Simpson RJ, Lim JWE, Moritz RL, et al. Exosomes: proteomic insights and
diagnostic potential. Expert Rev Proteomics. 2009;6(3):267–283.
doi: 10.1586/epr.09.17. [PubMed] [CrossRef] [Google Scholar]
69. Yang S, Che SPY, Kurywchak P, et al. Detection of mutant KRAS and TP53 DNA
in circulating exosomes from healthy individuals and patients with pancreatic
cancer. Cancer Biol Ther. 2017;18(3):158–165.
doi: 10.1080/15384047.2017.1281499. [PMC free article] [PubMed]
[CrossRef] [Google Scholar]
70. Allenson K, Castillo J, San Lucas FA, et al. High prevalence of mutant KRAS in
circulating exosome-derived DNA from early-stage pancreatic cancer
patients. Ann Oncol. 2017;28(4):741–747. [PMC free article] [PubMed] [Google
Scholar]
71. Del Re M, Biasco E, Crucitta S, et al. The detection of androgen receptor splice
variant 7 in plasma-derived exosomal rna strongly predicts resistance to
hormonal therapy in metastatic prostate cancer patients. Eur
Urol. 2017;71(4):680–687. doi: 10.1016/j.eururo.2016.08.012. [PubMed]
[CrossRef] [Google Scholar]
72. McKiernan J, Donovan MJ, O’Neill V, et al. A novel urine exosome gene
expression assay to predict high-grade prostate cancer at initial biopsy. JAMA
Oncol. 2016;2(7):882–889. doi: 10.1001/jamaoncol.2016.0097. [PubMed]
[CrossRef] [Google Scholar]
73. Liu T, Zhang X, Gao S, et al. Exosomal long noncoding RNA CRNDE-h as a novel
serum-based biomarker for diagnosis and prognosis of colorectal
cancer. Oncotarget. 2016;7(51):85551–85563.
doi: 10.18632/oncotarget.13465. [PMC free article] [PubMed] [CrossRef] [Google
Scholar]
74. Cazzoli R, Buttitta F, Di Nicola M, et al. MicroRNAs derived from circulating
exosomes as noninvasive biomarkers for screening and diagnosing lung cancer. J
Thorac Oncol. 2013;8(9):1156–1162.
doi: 10.1097/JTO.0b013e318299ac32. [PMC free article] [PubMed]
[CrossRef] [Google Scholar]
75. Melo SA, Luecke LB, Kahlert C, et al. Glypican-1 identifies cancer exosomes
and detects early pancreatic cancer. Nature. 2015;523(7559):177–182.
doi: 10.1038/nature14581. [PMC free article] [PubMed] [CrossRef] [Google
Scholar]
76. Costa-Silva B, Aiello NM, Ocean AJ, et al. Pancreatic cancer exosomes initiate
pre-metastatic niche formation in the liver. Nat Cell Biol. 2015;17(6):816–826.
doi: 10.1038/ncb3169. [PMC free article] [PubMed] [CrossRef] [Google Scholar]
77. Skotland T, Ekroos K, Kauhanen D, et al. Molecular lipid species in urinary
exosomes as potential prostate cancer biomarkers. Eur J Cancer. 2017;70:122–
132. doi: 10.1016/j.ejca.2016.10.011. [PubMed] [CrossRef] [Google Scholar]
78. Sun D, Zhuang X, Xiang X, et al. A novel nanoparticle drug delivery system: the
anti-inflammatory activity of curcumin is enhanced when encapsulated in
exosomes. Mol Ther. 2010;18(9):1606–1614. doi: 10.1038/mt.2010.105. [PMC
free article] [PubMed] [CrossRef] [Google Scholar]
79. Kim MS, Haney MJ, Zhao Y, et al. Development of exosome-encapsulated
paclitaxel to overcome MDR in cancer cells. Nanomed. 2016;12(3):655–664.
doi: 10.1016/j.nano.2015.10.012. [PMC free article] [PubMed] [CrossRef] [Google
Scholar]
80. Qi H, Liu C, Long L, et al. Blood exosomes endowed with magnetic and
targeting properties for cancer therapy. ACS Nano. 2016;10(3):3323–3333.
doi: 10.1021/acsnano.5b06939. [PubMed] [CrossRef] [Google Scholar]
81. Jang SC, Kim OY, Yoon CM, et al. Bioinspired exosome-mimetic nanovesicles
for targeted delivery of chemotherapeutics to malignant tumors. ACS
Nano. 2013;7(9):7698–7710. doi: 10.1021/nn402232g. [PubMed]
[CrossRef] [Google Scholar]
82. Yang T, Martin P, Fogarty B, et al. Exosome delivered anticancer drugs across
the blood-brain barrier for brain cancer therapy in Danio Rerio. Pharm
Res. 2015;32(6):2003–2014. doi: 10.1007/s11095-014-1593-y. [PMC free
article] [PubMed] [CrossRef] [Google Scholar]
83. Kim MS, Haney MJ, Zhao Y, et al. Engineering macrophage-derived exosomes
for targeted paclitaxel delivery to pulmonary metastases: in vitro and in vivo
evaluations. Nanomed. 2018;14(1):195–204.
doi: 10.1016/j.nano.2017.09.011. [PubMed] [CrossRef] [Google Scholar]
84. Hadla M, Palazzolo S, Corona G, et al. Exosomes increase the therapeutic index
of doxorubicin in breast and ovarian cancer mouse models. Nanomed
(Lond) 2016;11(18):2431–2441. doi: 10.2217/nnm-2016-0154. [PubMed]
[CrossRef] [Google Scholar]
85. ClinicalTrials.gov. US National Library of Medicine. [15/05/19]. [
https://clinicaltrials.gov/ct2/show/NCT01294072]
86. ClinicalTrials.gov. US National Library of Medicine. [15/05/19]. [
https://clinicaltrials.gov/ct2/show/NCT02657460]
87. ClinicalTrials.gov. US National Library of Medicine. [15/05/19]. [
https://clinicaltrials.gov/ct2/show/NCT03230708]
88. Mizrak A, Bolukbasi MF, Ozdener GB, et al. Genetically engineered
microvesicles carrying suicide mRNA/protein inhibit schwannoma tumor
growth. Mol Ther. 2013;21(1):101–108. doi: 10.1038/mt.2012.161. [PMC free
article] [PubMed] [CrossRef] [Google Scholar]
89. Ohno SI, Takanashi M, Sudo K, et al. Systemically injected exosomes targeted
to EGFR deliver antitumor microrna to breast cancer cells. Mol
Ther. 2013;21(1):185–191. doi: 10.1038/mt.2012.180. [PMC free
article] [PubMed] [CrossRef] [Google Scholar]
90. Cho JA, Yeo DJ, Son HY, et al. Exosomes: a new delivery system for tumor
antigens in cancer immunotherapy. Int J Cancer. 2005;114(4):613–622.
doi: 10.1002/ijc.20757. [PubMed] [CrossRef] [Google Scholar]
91. Lapierre V, Théry C, Virault-Rocroy P, et al. Updated technology to produce
highly immunogenic dendritic cell-derived exosomes of clinical grade. J
Immunother. 2010;34(1):65–75. doi: 10.1097/CJI.0b013e3181fe535b. [PubMed]
[CrossRef] [Google Scholar]
92. Besse B, Charrier M, Lapierre V, et al. Dendritic cell-derived exosomes as
maintenance immunotherapy after first line chemotherapy in
NSCLC. Oncoimmunology. 2016;5(4):e1071008.
doi: 10.1080/2162402X.2015.1071008. [PMC free article] [PubMed]
[CrossRef] [Google Scholar]
93. Dai S, Wei D, Wu Z, et al. Phase I clinical trial of autologous ascites-derived
exosomes combined with GM-CSF for colorectal cancer. Mol
Ther. 2008;16(4):782–790. doi: 10.1038/mt.2008.1. [PMC free article] [PubMed]
[CrossRef] [Google Scholar]
94. Boriachek K, Islam MN, Möller A, et al. Biological functions and current
advances in isolation and detection strategies for exosome
nanovesicles. Small. 2018;14(6) doi: 10.1002/smll.201702153. 1702153 (1–21)
[PubMed] [CrossRef] [Google Scholar]
95. Cadet J, Douki T, Ravanat JL. Artifacts associated with the measurement of
oxidized DNA bases. Environ Health Perspect. 1997;105(10):1034–1039. [PMC
free article] [PubMed] [Google Scholar]
96. Floyd RA, Watson JJ, Wong PK, et al. Hydroxyl free radical adduct of
deoxyguanosine: sensitive detection and mechanisms of formation. Free Radic
Res Commun. 1986;1(3):163–172. doi: 10.3109/10715768609083148. [PubMed]
[CrossRef] [Google Scholar]
97. Gupta RK, Patel AK, Shah N, et al. Oxidative stress and cancer: an
overview. Asian Pac J Cancer Prev. 2014;15(11):4405–4409.
doi: 10.7314/APJCP.2014.15.11.4405. [PubMed] [CrossRef] [Google Scholar]
98. Gille G, Sigler K. Oxidative stress and living cells. Folia Microbiol
(Praha) 1995;40(2):131–152. doi: 10.1007/BF02815413. [PubMed]
[CrossRef] [Google Scholar]
99. Halliwell B. Oxidative stress and cancer: have we moved forward? Biochem
J. 2006;401(1):1–11. doi: 10.1042/BJ20061131. [PubMed] [CrossRef] [Google
Scholar]
100. Katz L, Baltz RH. Natural product discovery: past, present, and future. J Ind
Microbiol Biotechnol. 2016;43(2–3):155–176. doi: 10.1007/s10295-015-1723-
5. [PubMed] [CrossRef] [Google Scholar]
101. Bernardini S, Tiezzi A, Laghezza Masci V, et al. Natural products for human
health: an historical overview of the drug discovery approaches. Nat Prod
Res. 2018;32(16):1926–1950. doi: 10.1080/14786419.2017.1356838. [PubMed]
[CrossRef] [Google Scholar]
102. Iqbal J, Abbasi BA, Mahmood T, et al. Plant-derived anticancer agents: a
green anticancer approach. Asian Pac J Trop Biomed. 2017;7(12):1129–1150.
doi: 10.1016/j.apjtb.2017.10.016. [CrossRef] [Google Scholar]
103. González-Vallinas M, González-Castejón M, Rodríguez-Casado A, et al.
Dietary phytochemicals in cancer prevention and therapy: a complementary
approach with promising perspectives. Nutr Rev. 2013;71(9):585–599.
doi: 10.1111/nure.12051. [PubMed] [CrossRef] [Google Scholar]
104. Kocaadam B, Şanlier N. Curcumin, an active component of turmeric
(Curcuma longa), and its effects on health. Crit Rev Food Sci
Nutr. 2017;57(13):2889–2895.
doi: 10.1080/10408398.2015.1077195. [PubMed] [CrossRef] [Google Scholar]
105. Imran M, Ullah A, Saeed F, et al. Cucurmin, anticancer, & antitumor
perspectives: a comprehensive review. Crit Rev Food Sci Nutr. 2018;58(8):1271–
1293. doi: 10.1080/10408398.2016.1252711. [PubMed] [CrossRef] [Google
Scholar]
106. Perrone D, Ardito F, Giannatempo G, et al. Biological and therapeutic
activities, and anticancer properties of curcumin. Exp Ther
Med. 2015;10(5):1615–1623. doi: 10.3892/etm.2015.2749. [PMC free
article] [PubMed] [CrossRef] [Google Scholar]
107. Sordillo PP, Helson L. Curcumin and cancer stem cells: curcumin has
asymmetrical effects on cancer and normal stem cells. Anticancer
Res. 2015;35(2):599–614. [PubMed] [Google Scholar]
108. Kumar G, Mittal S, Sak K, et al. Molecular mechanisms underlying
chemopreventive potential of curcumin: current challenges and future
perspectives. Life Sci. 2016;148:312–328.
doi: 10.1016/j.lfs.2016.02.022. [PubMed] [CrossRef] [Google Scholar]
109. Kunnumakkara AB, Bordoloi D, Harsha C, et al. Curcumin mediates
anticancer effects by modulating multiple cell signaling pathways. Clin Sci
(Lond) 2017;131(15):1781–1799. doi: 10.1042/CS20160935. [PubMed]
[CrossRef] [Google Scholar]
110. Wang YJ, Pan MH, Cheng AL, et al. Stability of curcumin in buffer solutions
and characterization of its degradation products. J Pharm Biomed
Anal. 1997;15(12):1867–1876. doi: 10.1016/S0731-7085(96)02024-9. [PubMed]
[CrossRef] [Google Scholar]
111. Rahimi HR, Nedaeinia R, Sepehri Shamloo A, et al. Novel delivery system for
natural products: nano-curcumin formulations. Avicenna J
Phytomed. 2016;6(4):383–398. [PMC free article] [PubMed] [Google Scholar]
112. Liu W, Zhai Y, Heng X, et al. Oral bioavailability of curcumin: problems and
advancements. J Drug Target. 2016;24(8):694–702.
doi: 10.3109/1061186X.2016.1157883. [PubMed] [CrossRef] [Google Scholar]
113. ClinicalTrials.gov. US National Library of Medicine. [15/05/19].
[ https://clinicaltrials.gov/ct2/results?term=curcumin+cancer&Search=Apply&r
ecrs=b&recrs=a&recrs=f&recrs=d&recrs=e&age_v=&gndr=&type=&rslt=]
114. Farooqi AA, Qureshi MZ, Khalid S, et al. Regulation of cell signaling pathways
by berberine in different cancers: searching for missing pieces of an incomplete
jig-saw puzzle for an effective cancer therapy. Cancers (Basel) 2019;11(4)
doi: 10.3390/cancers11040478. pii: E478. [PMC free article] [PubMed]
[CrossRef] [Google Scholar]
115. Mohammadinejad R, Ahmadi Z, Tavakol S, et al. Berberine as a potential
autophagy modulator. J Cell Physiol. 2019. [PubMed] [CrossRef]
116. Bianchi S, Giovannini L. Inhibition of mTOR/S6K1/4E-BP1 signaling by
nutraceutical sirt1 modulators. Nutr Cancer. 2018;70(3):490–501.
doi: 10.1080/01635581.2018.1446093. [PubMed] [CrossRef] [Google Scholar]
117. Wang ZP, Wu JB, Chen T-S, et al. In vitro and in vivo antitumor efficacy of
berberine-nanostructured lipid carriers against H22 tumor. Progress in
Biomedical Optics and Imaging—Proceedings of
SPIE. 2015;9324 doi: 10.1117/12.2079107. id 93240Y 8 pp. [CrossRef] [Google
Scholar]
118. Kabary DM, Helmy MW, Abdelfattah E-ZA, et al. Inhalable multi-
compartmental phospholipid enveloped lipid core nanocomposites for localized
mTOR inhibitor/herbal combined therapy of lung carcinoma. Eur J Pharm
Biopharm. 2018;130:152–164. doi: 10.1016/j.ejpb.2018.06.027. [PubMed]
[CrossRef] [Google Scholar]
119. Shen R, Kim JJ, Yao M, et al. Development and evaluation of vitamin E D-α-
tocopheryl polyethylene glycol 1000 succinate-mixed polymeric phospholipid
micelles of berberine as an anticancer nanopharmaceutical. Int J
Nanomedicine. 2016;11:1687–1700. [PMC free article] [PubMed] [Google
Scholar]
120. ClinicalTrials.gov. US National Library of Medicine. [15/05/19].
[ https://clinicaltrials.gov/ct2/results?term=berberine+cancer&Search=Apply&r
ecrs=b&recrs=a&recrs=f&recrs=d&recrs=e&age_v=&gndr=&type=&rslt=]
121. Liu Y, Tang ZG, Lin Y, et al. Effects of quercetin on proliferation and
migration of human glioblastoma U251 cells. Biomed Pharmacother. 2017;92:33–
38. doi: 10.1016/j.biopha.2017.05.044. [PubMed] [CrossRef] [Google Scholar]
122. Murakami A, Ashida H, Terao J. Multitargeted cancer prevention by
quercetin. Cancer Lett. 2008;269(2):315–325.
doi: 10.1016/j.canlet.2008.03.046. [PubMed] [CrossRef] [Google Scholar]
123. Yang F, Song L, Wang H, et al. Quercetin in prostate cancer:
Chemotherapeutic and chemopreventive effects, mechanisms and clinical
application potential (review) Oncol Rep. 2015;33(6):2659–2668.
doi: 10.3892/or.2015.3886. [PubMed] [CrossRef] [Google Scholar]
124. Shih H, Pickwell GV, Quattrochi LC. Differential effects of flavonoid
compounds on tumor promoter-induced activation of the human CYP1A2
enhancer. Arch Biochem Biophys. 2000;373(1):287–294.
doi: 10.1006/abbi.1999.1550. [PubMed] [CrossRef] [Google Scholar]
125. Brito AF, Ribeiro M, Abrantes AM, et al. Quercetin in cancer treatment, alone
or in combination with conventional therapeutics? Curr Med
Chem. 2015;22(26):305–339.
doi: 10.2174/0929867322666150812145435. [PubMed] [CrossRef] [Google
Scholar]
126. ClinicalTrials.gov. US National Library of Medicine. [15/05/19].
[ https://clinicaltrials.gov/ct2/results?term=quercetin+cancer&Search=Apply&r
ecrs=b&recrs=a&recrs=f&recrs=d&recrs=e&age_v=&gndr=&type=&rslt=]
127. Alasvand N, Urbanska AM, Rahmati M, et al. Multifunctional Systems for
Combined Delivery, Biosensing and Diagnostics. 1st. Elsevier; 2017. Therapeutic
nanoparticles for targeted delivery of anticancer drugs; pp. 245–259. chapter 13.
[CrossRef] [Google Scholar]
128. Barua S, Mitragotri S. Challenges associated with penetration of
nanoparticles across cell and tissue barriers: A review of current status and
future prospects. Nano Today. 2014;9(2):223–243.
doi: 10.1016/j.nantod.2014.04.008. [PMC free article] [PubMed]
[CrossRef] [Google Scholar]
129. Xu S, Olenyuk BZ, Okamoto CT, et al. Targeting receptor-mediated
endocytotic pathways with nanoparticles: rationale and advances. Adv Drug Deliv
Rev. 2013;65(1):121–138. doi: 10.1016/j.addr.2012.09.041. [PMC free
article] [PubMed] [CrossRef] [Google Scholar]
130. Hymel D, Peterson BR. Synthetic cell surface receptors for delivery of
therapeutics and probes. Adv Drug Deliv Rev. 2012;64(9):797–810.
doi: 10.1016/j.addr.2012.02.007. [PMC free article] [PubMed] [CrossRef] [Google
Scholar]
131. Senol S, Ceyran AB, Aydin A, et al. Folate receptor α expression and
significance in endometrioid endometrium carcinoma and endometrial
hyperplasia. Int J Clin Exp Pathol. 2015;8(5):5633–5641. [PMC free
article] [PubMed] [Google Scholar]
132. Tao W, Zhang J, Zeng X, et al. Blended nanoparticle system based on miscible
structurally similar polymers: a safe, simple, targeted, and surprisingly high
efficiency vehicle for cancer therapy. Adv Healthc Mater. 2015;4(8):1203–1214.
doi: 10.1002/adhm.201400751. [PubMed] [CrossRef] [Google Scholar]
133. Byrne JD, Betancourt T, Brannon-Peppas L, et al. Active targeting schemes
for nanoparticle systems in cancer therapeutics. Advanced Drug Delivery
Rev. 2008;60(15):1615–1626. doi: 10.1016/j.addr.2008.08.005. [PubMed]
[CrossRef] [Google Scholar]
134. Yu B, Tai HC, Xue W, et al. Receptor-targeted nanocarriers for therapeutic
delivery to cancer. Mol Membr Biol. 2010;27(7):286–298.
doi: 10.3109/09687688.2010.521200. [PMC free article] [PubMed]
[CrossRef] [Google Scholar]
135. Demeule M, Currie JC, Bertrand Y, et al. Involvement of the low-density
lipoprotein receptor-related protein in the transcytosis of the brain delivery
vector Angiopep-2. J Neurochem. 2008;106(4):1534–1544. doi: 10.1111/j.1471-
4159.2008.05492.x. [PubMed] [CrossRef] [Google Scholar]
136. Huang S, Li J, Han L, et al. Dual targeting effect of Angiopep-2-modified, DNA-
loaded nanoparticles for glioma. Biomaterials. 2011;32(28):6832–6838.
doi: 10.1016/j.biomaterials.2011.05.064. [PubMed] [CrossRef] [Google Scholar]
137. Kulhari H, Pooja D, Shrivastava S, et al. Peptide conjugated polymeric
nanoparticles as a carrier for targeted delivery of docetaxel. Colloids Surf B
Biointerfaces. 2014;117:166–173. doi: 10.1016/j.colsurfb.2014.02.026. [PubMed]
[CrossRef] [Google Scholar]
138. Cornelio DB, Roesler R, Schwartsmann G. Gastrin-releasing peptide receptor
as a molecular target in experimental anticancer therapy. Ann
Oncol. 2007;18(9):1457–1466. doi: 10.1093/annonc/mdm058. [PubMed]
[CrossRef] [Google Scholar]
139. Recht L, Torres CO, Smith TW, et al. Transferrin receptor in normal and
neoplastic brain tissue: implications for brain-tumor immunotherapy. J
Neurosurg. 1990;72(6):941–945. doi: 10.3171/jns.1990.72.6.0941. [PubMed]
[CrossRef] [Google Scholar]
140. Daniels TR, Bernabeu E, Rodríguez JA, et al. The transferrin receptor and the
targeted delivery of therapeutic agents against cancer. Biochim Biophys
Acta. 2012;1820(3):291–317. doi: 10.1016/j.bbagen.2011.07.016. [PMC free
article] [PubMed] [CrossRef] [Google Scholar]
141. Muhamad N, Plengsuriyakarn T, Na-Bangchang K. Application of active
targeting nanoparticle delivery system for chemotherapeutic drugs and
traditional/herbal medicines in cancer therapy: a systematic review. Int J
Nanomedicine. 2018;13:3921–3935. doi: 10.2147/IJN.S165210. [PMC free
article] [PubMed] [CrossRef] [Google Scholar]
142. Ni X, Castanares M, Mukherjee A, et al. Nucleic acid aptamers: clinical
applications and promising new horizons. Curr Med Chem. 2011;18(27):4206–
4214. doi: 10.2174/092986711797189600. [PMC free article] [PubMed]
[CrossRef] [Google Scholar]
143. Farokhzad OC, Cheng J, Teply BA, et al. Targeted nanoparticle-aptamer
bioconjugates for cancer chemotherapy in vivo. Proc Natl Acad Sci
USA. 2006;103(16):6315–20. doi: 10.1073/pnas.0601755103. [PMC free
article] [PubMed] [CrossRef] [Google Scholar]
144. Bagalkot V, Zhang L, Levy-Nissenbaum E, et al. Quantum dot-aptamer
conjugates for synchronous cancer imaging, therapy, and sensing of drug
delivery based on Bi-fluorescence resonance energy transfer. Nano
Lett. 2007;7(10):3065–3070. doi: 10.1021/nl071546n. [PubMed]
[CrossRef] [Google Scholar]
145. Sharkey RM, Goldenberg DM. Targeted therapy of cancer: new prospects for
antibodies and immunoconjugates. CA Cancer J Clin. 2009;56(4):226–243.
doi: 10.3322/canjclin.56.4.226. [PubMed] [CrossRef] [Google Scholar]
146. Acharya S, Dilnawaz F, Sahoo SK. Targeted epidermal growth factor receptor
nanoparticle bioconjugates for breast cancer
therapy. Biomaterials. 2009;30(29):5737–5750.
doi: 10.1016/j.biomaterials.2009.07.008. [PubMed] [CrossRef] [Google Scholar]
147. Ulbrich K, Hekmatara T, Herbert E, et al. Transferrin- and transferrin-
receptor-antibody-modified nanoparticles enable drug delivery across the blood-
brain barrier (BBB) Eur J Pharm Biopharm. 2009;71(2):251–256.
doi: 10.1016/j.ejpb.2008.08.021. [PubMed] [CrossRef] [Google Scholar]
148. McCune JS. Rapid advances in immunotherapy to treat cancer. Clin
Pharmacol Ther. 2018;103(4):540–544. doi: 10.1002/cpt.985. [PubMed]
[CrossRef] [Google Scholar]
149. U.S. Food & Drug Administration. [15/05/19].
[ https://www.accessdata.fda.gov/scripts/cder/daf/index.cfm?event=overview.
process&varApplNo=103705]
150. U.S. Food & Drug Administration. [15/05/19].
[ https://www.accessdata.fda.gov/drugsatfda_docs/nda/2002/125019_0000_Ze
valinTOC.cfm]
151. U.S. Food & Drug Administration. [15/05/19].
[ https://www.accessdata.fda.gov/scripts/cder/daf/index.cfm?event=overview.
process&varApplNo=125427]
152. U.S. Food and Drug Administration. [15/05/19].
[ https://www.accessdata.fda.gov/drugsatfda_docs/label/2017/125554s055lbl.
pdf]
153. U.S. Food & Drug Administration. [15/05/19].
[ https://www.accessdata.fda.gov/drugsatfda_docs/label/2018/125514s034lbl.
pdf]
154. Rosenberg SA, Restifo NP, Yang JC, et al. Adoptive cell transfer: a clinical
path to effective cancer immunotherapy. Nat Rev Cancer. 2008;8(4):299–308.
doi: 10.1038/nrc2355. [PMC free article] [PubMed] [CrossRef] [Google Scholar]
155. Miliotou AN, Papadopoulou LC. CAR t-cell therapy: a new era in cancer
immunotherapy. Curr Pharm Biotechnol. 2018;19(1):5–18.
doi: 10.2174/1389201019666180418095526. [PubMed] [CrossRef] [Google
Scholar]
156. Friedmann T. A brief history of gene therapy. Nat Genet. 1992;2(2):93–98.
doi: 10.1038/ng1092-93. [PubMed] [CrossRef] [Google Scholar]
157. Rosenberg SA, Aebersold P, Cornetta K, et al. Gene transfer into humans–
immunotherapy of patients with advanced melanoma, using tumor-infiltrating
lymphocytes modified by retroviral gene transduction. N Engl J
Med. 1990;323(9):570–578. doi: 10.1056/NEJM199008303230904. [PubMed]
[CrossRef] [Google Scholar]
158. Ginn SL, Amaya AK, Alexander IE, et al. Gene therapy clinical trials
worldwide to 2017: an update. J Gene Med. 2018;20(5):e3015.
doi: 10.1002/jgm.3015. [PubMed] [CrossRef] [Google Scholar]
159. Roth JA, Nguyen D, Lawrence DD, et al. Retrovirus-mediated wild-type p53
gene transfer to tumors of patients with lung cancer. Nat Med. 1996;2(9):985–
991. doi: 10.1038/nm0996-985. [PubMed] [CrossRef] [Google Scholar]
160. Griffith TST, Stokes B, Kucaba TA, et al. TRAIL gene therapy: from preclinical
development to clinical application. Curr Gene Ther. 2009;9(1):9–19.
doi: 10.2174/156652309787354612. [PMC free article] [PubMed]
[CrossRef] [Google Scholar]
161. Freeman SM, Abboud C, Freeman SM, et al. The “bystander effect”: tumor
regression when a fraction of the tumor mass is genetically modified. Cancer
Res. 1993;53(21):5274–5283. [PubMed] [Google Scholar]
162. Nasu Y, Saika T, Ebara S, et al. Suicide gene therapy with adenoviral delivery
of HSV-tK gene for patients with local recurrence of prostate cancer after
hormonal therapy. Mol Ther. 2007;15(4):834–840.
doi: 10.1038/sj.mt.6300096. [PubMed] [CrossRef] [Google Scholar]
163. Natsume A, Yoshida J. Gene therapy for high-grade glioma: current
approaches and future directions. Cell Adh Migr. 2008;2(3):186–191.
doi: 10.4161/cam.2.3.6278. [PMC free article] [PubMed] [CrossRef] [Google
Scholar]
164. Westphal M, Ylä-Herttuala S, Martin J, et al. Adenovirus-mediated gene
therapy with sitimagene ceradenovec followed by intravenous ganciclovir for
patients with operable high-grade glioma (ASPECT): a randomised, open-label,
phase 3 trial. Lancet Oncol. 2013;14(9):823–833. doi: 10.1016/S1470-
2045(13)70274-2. [PubMed] [CrossRef] [Google Scholar]
165. Ahrendt SA, Hu Y, Buta M, et al. p53 mutations and survival in stage I non-
small-cell lung cancer: Results of a prospective study. J Natl Cancer
Inst. 2003;95(13):961–970. doi: 10.1093/jnci/95.13.961. [PubMed]
[CrossRef] [Google Scholar]
166. Raty J, Pikkarainen J, Wirth T, et al. Gene therapy: the first approved gene-
based medicines, molecular mechanisms and clinical indications. Curr Mol
Pharmacol. 2010;1(1):13–23. doi: 10.2174/1874467210801010013. [PubMed]
[CrossRef] [Google Scholar]
167. Elbashir SM, Harborth J, Lendeckel W, et al. Duplexes of 21-nucleotide RNAs
mediate RNA interference in cultured mammalian
cells. Nature. 2001;411(6836):494–498. doi: 10.1038/35078107. [PubMed]
[CrossRef] [Google Scholar]
168. Weiss B, Davidkova G, Zhou LW. Antisense RNA gene therapy for studying
and modulating biological processes. Cell Mol Life Sci. 1999;55(3):334–358.
doi: 10.1007/s000180050296. [PubMed] [CrossRef] [Google Scholar]
169. Whelan J. First clinical data on RNAi. Drug Discov Today. 2005;10(15):1014–
1015. doi: 10.1016/S1359-6446(05)03547-6. [PubMed] [CrossRef] [Google
Scholar]
170. Jia LT, Chen SY, Yang AG. Cancer gene therapy targeting cellular apoptosis
machinery. Cancer Treat Rev. 2012;38(7):868–876.
doi: 10.1016/j.ctrv.2012.06.008. [PubMed] [CrossRef] [Google Scholar]
171. Putney SD, Brown J, Cucco C, et al. Enhanced anti-tumor effects with
microencapsulated c-myc antisense oligonucleotide. Antisense Nucleic Acid Drug
Dev. 1999;9(5):451–458. doi: 10.1089/oli.1.1999.9.451. [PubMed]
[CrossRef] [Google Scholar]
172. Vita M, Henriksson M. The Myc oncoprotein as a therapeutic target for
human cancer. Semin Cancer Biol. 2006;16(4):318–330.
doi: 10.1016/j.semcancer.2006.07.015. [PubMed] [CrossRef] [Google Scholar]
173. Fleming JB. Molecular consequences of silencing mutant k-ras in pancreatic
cancer cells: justification for k-ras-directed therapy. Mol Cancer
Res. 2005;3(7):413–423. doi: 10.1158/1541-7786.MCR-04-0206. [PubMed]
[CrossRef] [Google Scholar]
174. Scanlon K. Anti-genes: sirna, ribozymes and antisense. Curr Pharm
Biotechnol. 2005;5(5):415–420. doi: 10.2174/1389201043376689. [PubMed]
[CrossRef] [Google Scholar]
175. Fire AZ. Gene silencing by double-stranded RNA. Cell Death
Differ. 2007;14(12):1998. doi: 10.1038/sj.cdd.4402253. 2012. [PubMed]
[CrossRef] [Google Scholar]
176. Bora RS, Gupta D, Mukkur TKS, et al. RNA interference therapeutics for
cancer: challenges and opportunities (review) Mol Med Rep. 2012;6(1):9–
15. [PubMed] [Google Scholar]
177. Van De Water FM, Boerman OC, Wouterse AC, et al. Intravenously
administered short interfering RNA accumulates in the kidney and selectively
suppresses gene function in renal proximal tubules. Drug Metab
Dispos. 2006;34(8):1393–1397. doi: 10.1124/dmd.106.009555. [PubMed]
[CrossRef] [Google Scholar]
178. Jackson AL, Linsley PS. Recognizing and avoiding siRNA off-target effects for
target identification and therapeutic application. Nat Rev Drug
Discov. 2010;9(1):57–67. doi: 10.1038/nrd3010. [PubMed] [CrossRef] [Google
Scholar]
179. Hornung V, Guenthner-Biller M, Bourquin C, et al. Sequence-specific potent
induction of IFN-α by short interfering RNA in plasmacytoid dendritic cells
through TLR7. Nat Med. 2005;11(3):263–270. doi: 10.1038/nm1191. [PubMed]
[CrossRef] [Google Scholar]
180. Judge AD, Sood V, Shaw JR, et al. Sequence-dependent stimulation of the
mammalian innate immune response by synthetic siRNA. Nat
Biotechnol. 2005;23(4):457–462. doi: 10.1038/nbt1081. [PubMed]
[CrossRef] [Google Scholar]
181. Martina MS, Nicolas V, Wilhelm C, et al. The in vitro kinetics of the
interactions between PEG-ylated magnetic-fluid-loaded liposomes and
macrophages. Biomaterials. 2007;28(28):4143–4153.
doi: 10.1016/j.biomaterials.2007.05.025. [PubMed] [CrossRef] [Google Scholar]
182. Whitehead KA, Langer R, Anderson DG. Knocking down barriers: advances
in siRNA delivery. Nat Rev Drug Discov. 2009;8(2):129–138.
doi: 10.1038/nrd2742. [PMC free article] [PubMed] [CrossRef] [Google Scholar]
183. Xu C fei, Wang J. Delivery systems for siRNA drug development in cancer
therapy. Asian J Pharm Sci. 2015;10(1):1–12.
doi: 10.1016/j.ajps.2014.08.011. [CrossRef] [Google Scholar]
184. Czauderna F, Fechtner M, Dames S, et al. Structural variations and stabilising
modifications of synthetic siRNAs in mammalian cells. Nucleic Acids
Res. 2003;31(11):2705–2716. doi: 10.1093/nar/gkg393. [PMC free
article] [PubMed] [CrossRef] [Google Scholar]
185. Liao H, Wang JH. Biomembrane-permeable and ribonuclease-resistant
siRNA with enhanced activity. Oligonucleotides. 2005;15(3):196–205.
doi: 10.1089/oli.2005.15.196. [PubMed] [CrossRef] [Google Scholar]
186. Hall AHS, Wan J, Shaughnessy EE, et al. RNA interference using
boranophosphate siRNAs: structure-activity relationships. Nucleic Acids
Res. 2004;32(20):5991–6000. doi: 10.1093/nar/gkh936. [PMC free
article] [PubMed] [CrossRef] [Google Scholar]
187. Elouahabi A, Ruysschaert JM. Formation and intracellular trafficking of
lipoplexes and polyplexes. Mol Ther. 2005;11(3):336–347.
doi: 10.1016/j.ymthe.2004.12.006. [PubMed] [CrossRef] [Google Scholar]
188. Sarisozen C, Salzano G, Torchilin VP. Recent advances in siRNA
delivery. Biomol Concepts. 2015;6(5–6):321–341. doi: 10.1515/bmc-2015-
0019. [PubMed] [CrossRef] [Google Scholar]
189. Kim HS, Song IH, Kim JC, et al. In vitro and in vivo gene-transferring
characteristics of novel cationic lipids, DMKD (O,O’-dimyristyl-N-lysyl aspartate)
and DMKE (O,O’-dimyristyl-N-lysyl glutamate) J Control
Release. 2006;115(2):234–241. doi: 10.1016/j.jconrel.2006.08.003. [PubMed]
[CrossRef] [Google Scholar]
190. Kenny GD, Kamaly N, Kalber TL, et al. Novel multifunctional nanoparticle
mediates siRNA tumour delivery, visualisation and therapeutic tumour reduction
in vivo. J Control Release. 2011;149(2):111–116.
doi: 10.1016/j.jconrel.2010.09.020. [PubMed] [CrossRef] [Google Scholar]
191. Halder J, Kamat AA, Landen CN, et al. Focal adhesion kinase targeting using
in vivo short interfering RNA delivery in neutral liposomes for ovarian carcinoma
therapy. Clin Cancer Res. 2006;12(16):4916–4924. doi: 10.1158/1078-0432.CCR-
06-0021. [PMC free article] [PubMed] [CrossRef] [Google Scholar]
192. Gray MJ, Van Buren G, Dallas NA, et al. Therapeutic targeting of neuropilin-2
on colorectal carcinoma cells implanted in the murine liver. J Natl Cancer
Inst. 2008;100(2):109–120. doi: 10.1093/jnci/djm279. [PubMed]
[CrossRef] [Google Scholar]
193. ClinicalTrials.gov. US National Library of Medicine. [15/05/19]. [
https://clinicaltrials.gov/ct2/show/NCT01591356]
194. Soutschek J, Akinc A, Bramlage B, et al. Therapeutic silencing of an
endogenous gene by systemic administration of modified
siRNAs. Nature. 2004;432(7014):173–178. doi: 10.1038/nature03121. [PubMed]
[CrossRef] [Google Scholar]
195. Rossi JJ. RNAi therapeutics: SNALPing siRNAs in vivo. Gene
Ther. 2006;13(7):583–584. doi: 10.1038/sj.gt.3302661. [PubMed]
[CrossRef] [Google Scholar]
196. ClinicalTrials.gov. US National Library of Medicine. [15/05/19]. [
https://clinicaltrials.gov/ct2/show/NCT00882180]
197. ClinicalTrials.gov. US National Library of Medicine. [15/05/19]. [
https://clinicaltrials.gov/ct2/show/NCT01262235]
198. Mao S, Sun W, Kissel T. Chitosan-based formulations for delivery of DNA and
siRNA. Adv Drug Deliv Rev. 2010;62(1):12–27.
doi: 10.1016/j.addr.2009.08.004. [PubMed] [CrossRef] [Google Scholar]
199. Pillé J-YY, Li H, Blot E, et al. Intravenous delivery of anti-RhoA small
interfering RNA loaded in nanoparticles of chitosan in mice: safety and efficacy in
xenografted aggressive breast cancer. Hum Gene Ther. 2006;17(10):1019–1026.
doi: 10.1089/hum.2006.17.1019. [PubMed] [CrossRef] [Google Scholar]
200. ClinicalTrials.gov. US National Library of Medicine. [15/05/19]. [
https://clinicaltrials.gov/ct2/show/NCT00689065]
201. Urban-Klein B, Werth S, Abuharbeid S, et al. RNAi-mediated gene-targeting
through systemic application of polyethylenimine (PEI)-complexed siRNA in
vivo. Gene Ther. 2005;12(5):461–466. doi: 10.1038/sj.gt.3302425. [PubMed]
[CrossRef] [Google Scholar]
202. ClinicalTrials.gov. US National Library of Medicine. [15/05/19]. [
https://clinicaltrials.gov/ct2/show/NCT01676259]
203. Jeong JH, Mok H, Oh YK, et al. SiRNA conjugate delivery systems. Bioconjug
Chem. 2009;20(1):5–14. doi: 10.1021/bc800278e. [PubMed] [CrossRef] [Google
Scholar]
204. McNamara JO, Andrechek ER, Wang Y, et al. Cell type-specific delivery of
siRNAs with aptamer-siRNA chimeras. Nat Biotechnol. 2006;24(8):1005–1015.
doi: 10.1038/nbt1223. [PubMed] [CrossRef] [Google Scholar]
205. Gallas A, Alexander C, Davies MC, et al. Chemistry and formulations for
siRNA therapeutics. Chem Soc Rev. 2013;42(20):7983–7997.
doi: 10.1039/c3cs35520a. [PubMed] [CrossRef] [Google Scholar]
206. Williford J-MM, Wu J, Ren Y, et al. Recent advances in nanoparticle-mediated
sirna delivery. Annu Rev Biomed Eng. 2014;16:347–370. doi: 10.1146/annurev-
bioeng-071813-105119. [PubMed] [CrossRef] [Google Scholar]
207. Zhou J, Wu Y, Wang C, et al. pH-sensitive nanomicelles for high-efficiency
sirna delivery in vitro and in vivo: an insight into the design of polycations with
robust cytosolic release. Nano Lett. 2016;16(11):6916–6923.
doi: 10.1021/acs.nanolett.6b02915. [PubMed] [CrossRef] [Google Scholar]
208. Sun CY, Shen S, Xu CF, et al. Tumor acidity-sensitive polymeric vector for
active targeted sirna delivery. J Am Chem Soc. 2015;137(48):15217–15224.
doi: 10.1021/jacs.5b09602. [PubMed] [CrossRef] [Google Scholar]
209. Truong NP, Gu W, Prasadam I, et al. An influenza virus-inspired polymer
system for the timed release of siRNA. Nat Commun. 2013;4:1902.
doi: 10.1038/ncomms2905. [PubMed] [CrossRef] [Google Scholar]
210. Andreozzi P, Diamanti E, Py-Daniel KR, et al. Exploring the pH sensitivity of
poly(allylamine) phosphate supramolecular nanocarriers for intracellular siRNA
delivery. ACS Appl Mater Interfaces. 2017;9(44):38242–38254.
doi: 10.1021/acsami.7b11132. [PubMed] [CrossRef] [Google Scholar]
211. van der Zee J. Heating the patient: a promising approach? Ann
Oncol. 2002;13(8):1173–1184. doi: 10.1093/annonc/mdf280. [PubMed]
[CrossRef] [Google Scholar]
212. Bruix J, Sherman M. Management of hepatocellular
carcinoma. Hepatol. 2005;42(5):1208–1236. doi: 10.1002/hep.20933. [PubMed]
[CrossRef] [Google Scholar]
213. Lencioni R, Crocetti L. Image-guided thermal ablation of hepatocellular
carcinoma. Crit Rev Oncol Hematol. 2008;66(3):200–207.
doi: 10.1016/j.critrevonc.2008.01.003. [PubMed] [CrossRef] [Google Scholar]
214. Kudo M. Local ablation therapy for hepatocellular carcinoma: current status
and future perspectives. J Gastroenterol. 2004;39(3):205–214.
doi: 10.1007/s00535-003-1280-y. [PubMed] [CrossRef] [Google Scholar]
215. Brace CL. Radiofrequency and microwave ablation of the liver, lung, kidney,
and bone: what are the differences? Curr Probl Diagn Radiol. 2009;38(3):135–
143. doi: 10.1067/j.cpradiol.2007.10.001. [PMC free article] [PubMed]
[CrossRef] [Google Scholar]
216. Schramm W, Yang D, Haemmerich D. Contribution of direct heating, thermal
conduction and perfusion during radiofrequency and microwave ablation. Open
Biomed Eng J. 2007;1:47–52. doi: 10.2174/1874120700701010047. [PMC free
article] [PubMed] [CrossRef] [Google Scholar]
217. Sartori S, Tombesi P, Di Vece F. Radiofrequency, microwave, and laser
ablation of liver tumors: time to move toward a tailored ablation
technique? Hepatoma Res. 2015;1:52–57. doi: 10.4103/2394-
5079.155697. [CrossRef] [Google Scholar]
218. Schena E, Saccomandi P, Fong Y. Laser ablation for cancer: past, present and
future. J Funct Biomater. 2017;8(2) doi: 10.3390/jfb8020019. pii: E19. [PMC free
article] [PubMed] [CrossRef] [Google Scholar]
219. Izzo F. Other thermal ablation techniques: microwave and interstitial laser
ablation of liver tumors. Ann Surg Oncol. 2003;10(5):491–497.
doi: 10.1245/ASO.2003.07.016. [PubMed] [CrossRef] [Google Scholar]
220. Giorgio A, Tarantino L, De Stefano G, et al. Interstitial laser photocoagulation
under ultrasound guidance of liver tumors: Results in 104 treated patients. Eur J
Ultrasound. 2000;11(3):181–188. doi: 10.1016/S0929-8266(00)00086-
0. [PubMed] [CrossRef] [Google Scholar]
221. Francica G, Iodice G, Delle Cave M, et al. Factors predicting complete
necrosis rate after ultrasound-guided percutaneous laser thermoablation of
small hepatocellular carcinoma tumors in cirrhotic patients: A multivariate
analysis. Acta Radio. 2007;48(5):514–519.
doi: 10.1080/02841850701199942. [PubMed] [CrossRef] [Google Scholar]
222. Puls R, Langner S, Rosenberg C, et al. Laser ablation of liver metastases from
colorectal cancer with MR thermometry: 5-year survival. J Vasc Interv
Radiol. 2009;20(2):225–234. doi: 10.1016/j.jvir.2008.10.018. [PubMed]
[CrossRef] [Google Scholar]
223. Dennis CL, Ivkov R. Physics of heat generation using magnetic nanoparticles
for hyperthermia. Int J Hyperthermia. 2013;29(8):715–729.
doi: 10.3109/02656736.2013.836758. [PubMed] [CrossRef] [Google Scholar]
224. Giustini AJ, Petryk AA, Cassim SM, et al. Magnetic nanoparticle hyperthermia
in cancer treatment. Nano Life. 2010;1(1n02)
doi: 10.1142/S1793984410000067. [PMC free article] [PubMed]
[CrossRef] [Google Scholar]
225. Society of Radiographers. A guide to modern radiotherapy. 2013. ISBN: 1-
871101-94-8.
226. Gogvadze V, Orrenius S, Zhivotovsky B. Mitochondria in cancer cells: what is
so special about them? Trends Cell Biol. 2008;18(4):165–173.
doi: 10.1016/j.tcb.2008.01.006. [PubMed] [CrossRef] [Google Scholar]
227. Lynam-Lennon N, Maher SG, Maguire A, et al. Altered mitochondrial function
and energy metabolism is associated with a radioresistant phenotype in
oesophageal adenocarcinoma. PLoS One. 2014;9(6):e100738.
doi: 10.1371/journal.pone.0100738. [PMC free article] [PubMed]
[CrossRef] [Google Scholar]
228. Liao X, Chaudhary P, Qiu G, et al. The role of propranolol as a radiosensitizer
in gastric cancer treatment. Drug Des Devel Ther. 2018;12:639–645.
doi: 10.2147/DDDT.S160865. [PMC free article] [PubMed] [CrossRef] [Google
Scholar]
229. Collins FS, Varmus H. A new initiative on precision medicine. N Engl J
Med. 2015;372(9):793–795. doi: 10.1056/NEJMp1500523. [PMC free
article] [PubMed] [CrossRef] [Google Scholar]
230. Gutman DA, Cooper LAD, Hwang SN, et al. MR imaging predictors of
molecular profile and survival: multi-institutional study of the tcga glioblastoma
data set. Radiol. 2013;267(2):560–569. doi: 10.1148/radiol.13120118. [PMC free
article] [PubMed] [CrossRef] [Google Scholar]
231. Lambin P, Rios-Velazquez E, Leijenaar R, et al. Radiomics: extracting more
information from medical images using advanced feature analysis. Eur J
Cancer. 2012;48(4):441–446. doi: 10.1016/j.ejca.2011.11.036. [PMC free
article] [PubMed] [CrossRef] [Google Scholar]
232. Fuchs TJ, Buhmann JM. Computational pathology: challenges and promises
for tissue analysis. Comput Med Imaging and Graph. 2011;35(7):515–530.
doi: 10.1016/j.compmedimag.2011.02.006. [PubMed] [CrossRef] [Google
Scholar]
233. Foran DJ, Yang L, Chen W, et al. ImageMiner: A software system for
comparative analysis of tissue microarrays using content-based image retrieval,
high-performance computing, and grid technology. J Am Med Informa
Assoc. 2011;18(4):403–415. doi: 10.1136/amiajnl-2011-000170. [PMC free
article] [PubMed] [CrossRef] [Google Scholar]
234. Clarke LP, Croft BS, Nordstrom R, et al. Quantitative imaging for evaluation
of response to cancer therapy. Transl Oncol. 2014;2(4):195–197.
doi: 10.1593/tlo.09217. [PMC free article] [PubMed] [CrossRef] [Google Scholar]
235. Cooper LAD, Kong J, Gutman DA, et al. Integrated morphologic analysis for
the identification and characterization of disease subtypes. J Am Med Inform
Assoc. 2012;19(2):317–323. doi: 10.1136/amiajnl-2011-000700. [PMC free
article] [PubMed] [CrossRef] [Google Scholar]
236. Romo-Bucheli D, Janowczyk A, Gilmore H, et al. Automated tubule nuclei
quantification and correlation with oncotype dx risk categories in ER+ breast
cancer whole slide images. Sci Rep. 2016;6:32706. doi: 10.1038/srep32706. [PMC
free article] [PubMed] [CrossRef] [Google Scholar]
237. Lewis JS, Ali S, Luo J, et al. A quantitative histomorphometric classifier
(QuHbIC) identifies aggressive versus indolent p16-positive oropharyngeal
squamous cell carcinoma. Am J Surg Pathol. 2014;38(1):128–137.
doi: 10.1097/PAS.0000000000000086. [PMC free article] [PubMed]
[CrossRef] [Google Scholar]
238. Kalpathy-Cramer J, Freymann JB, Kirby JS, et al. Quantitative imaging
network: data sharing and competitive algorithmvalidation leveraging the cancer
imaging archive. Transl Oncol. 2014;7(1):147–152. doi: 10.1593/tlo.13862. [PMC
free article] [PubMed] [CrossRef] [Google Scholar]
239. Schlett CL, Hendel T, Weckbach S, et al. Population-based imaging and
radiomics: rationale and perspective of the german national cohort MRI
study. RoFo. 2016;188(7):652–661. doi: 10.1055/s-0042-104510. [PubMed]
[CrossRef] [Google Scholar]
240. ClinicalTrials.gov. US National Library of Medicine. [15/05/19].
[ https://clinicaltrials.gov/ct2/results?cond=&term=radiomics&cntry=&state=&
city=&dist=]