Ovarian cancer has poor survival outcomes particularly for advanced stage, metastatic disease. Metastasis is promoted by interactions of stromal cells, such as cancer-associated fibroblasts (CAFs) in the tumor microenvironment (TME), with tumor cells. CAFs play a key role in tumor progression by remodeling the TME and extracellular matrix (ECM) to result in a more permissive environment for tumor progression. It has been shown that fibroblasts, in particular myofibroblasts, utilize metabolism to support ECM remodeling. However, the intricate mechanisms by which CAFs support collagen production and tumor progression are poorly understood. In this study, we show that the fibrillar collagen receptor, Discoidin Domain Receptor 2 (DDR2), promotes collagen production in human and mouse omental CAFs through arginase activity. CAFs with high DDR2 or arginase promote tumor colonization in the omentum. In addition, DDR2-depleted CAFs had decreased ornithine levels leading to decreased collagen production and polyamine levels compared to WT control CAFs. Tumor cell invasion was decreased in the presence CAF conditioned media (CM) depleted of DDR2 or arginase-1, and this invasion defect was rescued in the presence of CM from DDR2-depleted CAFs that constitutively overexpressed arginase-1. Similarly, the addition of exogenous polyamines to CM from DDR2-depleted CAFs led to increased tumor cell invasion. We detected SNAI1 protein at the promoter region of the arginase-1 gene, and DDR2-depleted CAFs had decreased levels of SNAI1 protein at the arginase-1 promoter region. Furthermore, high stromal arginase-1 expression correlated with poor survival in ovarian cancer patients. These findings highlight how DDR2 regulates collagen production by CAFs in the tumor microenvironment by controlling the transcription of arginase-1, and CAFs are a major source of arginase activity and L-arginine metabolites in ovarian cancer models.

Objectives

To determine the association between timing of adjuvant therapy initiation and survival of early stage ovarian cancer patients.

Methods

Data were obtained from women who underwent primary surgical staging followed by adjuvant therapy from two Gynecologic Oncology Group trials (protocols # 95 and 157). Kaplan-Meier estimates and Cox proportional hazards model adjusted for covariates were used for analyses.

Results

Of 497 stage I-II epithelial ovarian cancer patients, the median time between surgery and initiation of adjuvant therapy was 23days (25th-75th%: 12-33days). The time interval from surgery to initiation of adjuvant therapy was categorized into three groups: <2weeks, 2-4weeks, and >4weeks. The corresponding 5-year recurrence-free survival rates were 72.8%, 73.9%, and 79.5% (p=0.62). The 5-year overall survival rates were 79.4%, 81.9%, and 82.8%, respectively (p=0.51; p=0.33 - global test). As compared to <2weeks, the hazard ratio for recurrence-free survival was 0.90 (95%CI=0.59-1.37) for 2-4weeks and 0.72 (95%CI=0.46-1.13) for >4weeks. Age, stage, grade, and cytology were important prognostic factors.

Conclusions

Timing of adjuvant therapy initiation was not associated with survival in early stage epithelial ovarian cancer patients.

PURPOSE: Despite therapeutic advances in the treatment of ovarian cancer (OC), 5-year survival remains low, and patients eventually die from recurrent, chemotherapy-resistant disease. The National Cancer Gynecologic Cancer Steering Committee identified the integration of scientifically defined subgroups as a top strategic priority in clinical trial planning. METHODS: A group of experts was convened to review the scientific literature in OC to identify validated predictive biomarkers that could inform patient selection and treatment stratification. Here, we report on these findings and their potential for use in future clinical trial design on the basis of hierarchal evidence grading. RESULTS: The biomarkers were classified on the basis of mechanistic targeting, including DNA repair and replication stress, immunotherapy and tumor microenvironment, oncogenic signaling, and angiogenesis. Currently, BRCA mutations and homologous recombination deficiency to predict poly (ADP-ribose) polymerase inhibitor response are supported in OC by the highest level of evidence. Additional biomarkers of response to agents targeting the pathways above have been identified but require prospective validation. CONCLUSION: Although a number of biomarkers of response to various agents in OC have been described in the literature, high-level evidence for the majority is lacking. This report highlights the unmet need for identification and validation of predictive biomarkers to guide therapy and future trial design in OC.

Angiogenesis is a hallmark of cancer that promotes tumor progression and metastasis. However, antiangiogenic agents have limited efficacy in cancer therapy due to the development of resistance. In clear cell renal cell carcinoma (ccRCC), AXL expression is associated with antiangiogenic resistance and poor survival. Here, we establish a role for GAS6/AXL signaling in promoting the angiogenic potential of ccRCC cells through the regulation of the plasminogen receptor S100A10. Genetic and therapeutic inhibition of AXL signaling in ccRCC tumor xenografts reduced tumor vessel density and growth under the renal capsule. GAS6/AXL signaling activated the expression of S100A10 through SRC to promote plasmin production, endothelial cell invasion, and angiogenesis. Importantly, treatment with the small molecule AXL inhibitor cabozantinib or an ultra-high affinity soluble AXL Fc fusion decoy receptor (sAXL) reduced the growth of a pazopanib-resistant ccRCC patient-derived xenograft. Moreover, the combination of sAXL synergized with pazopanib and axitinib to reduce ccRCC patient-derived xenograft growth and vessel density. These findings highlight a role for AXL/S100A10 signaling in mediating the angiogenic potential of ccRCC cells and support the combination of AXL inhibitors with antiangiogenic agents for advanced ccRCC. SIGNIFICANCE: These findings show that angiogenesis in renal cell carcinoma (RCC) is regulated through AXL/S100A10 signaling and support the combination of AXL inhibitors with antiangiogenic agents for the treatment of RCC.

Purpose

To compare patient/tumor characteristics and outcomes of Asians to Caucasian patients with epithelial ovarian cancer.

Methods

Ancillary data were pooled and analyzed from ten prospective randomized front-line Gynecologic Oncology Group clinical trials from 1996 to 2011. Demographic, clinicopathologic features, disease-specific and all-cause survival were analyzed.

Results

Of 7914 patients, 7641 were Caucasian and 273 Asian. When compared to Caucasians, Asians were younger at trial enrollment, had a better performance status, earlier-stage cancers (17.2% vs. 8.1% with stage I; p < 0.001), and were more likely to be of clear cell (15.8% vs. 6.2%, p < 0.001) and mucinous (3.3% vs. 1.9%, p < 0.001) histology. Asians had an improved 5-year disease-specific survival of 54.1% compared to 46.1% for Caucasians, p = 0.001. In multivariate analysis, the Asian race remained a significant prognostic factor for all-cause survival (HR: 0.84; 95% CI: 0.72-0.99; p = 0.04). Other factors predictive of improved survival included younger age, better performance status, optimal cytoreduction, earlier stage, non-clear cell histology, and lower grade tumors.

Conclusion

Asians enrolled into phase III ovarian cancer clinical trials were younger, with better performance status, earlier-stage of disease, and have a greater number of clear cell and mucinous tumors. After adjusting for these prognostic factors, Asians have a better survival compared to Caucasians.

High-grade serous ovarian cancer (HGSC) is the most lethal histotype of ovarian cancer and the majority of cases present with metastasis and late-stage disease. Over the last few decades, the overall survival for patients has not significantly improved, and there are limited targeted treatment options. We aimed to better characterize the distinctions between primary and metastatic tumors based on short- or long-term survival. We characterized 39 matched primary and metastatic tumors by whole exome and RNA sequencing. Of these, 23 were short-term (ST) survivors (overall survival (OS) < 3.5 years) and 16 were long-term (LT) survivors (OS > 5 years). We compared somatic mutations, copy number alterations, mutational burden, differential gene expression, immune cell infiltration, and gene fusion predictions between the primary and metastatic tumors and between ST and LT survivor cohorts. There were few differences in RNA expression between paired primary and metastatic tumors, but significant differences between the transcriptomes of LT and ST survivors in both their primary and metastatic tumors. These findings will improve the understanding of the genetic variation in HGSC that exist between patients with different prognoses and better inform treatments by identifying new targets for drug development.

Development of candidate cancer treatments is a resource-intensive process, with the research community continuing to investigate options beyond static genomic characterization. Toward this goal, we have established the genomic landscapes of 536 patient-derived xenograft (PDX) models across 25 cancer types, together with mutation, copy number, fusion, transcriptomic profiles, and NCI-MATCH arms. Compared with human tumors, PDXs typically have higher purity and fit to investigate dynamic driver events and molecular properties via multiple time points from same case PDXs. Here, we report on dynamic genomic landscapes and pharmacogenomic associations, including associations between activating oncogenic events and drugs, correlations between whole-genome duplications and subclone events, and the potential PDX models for NCI-MATCH trials. Lastly, we provide a web portal having comprehensive pan-cancer PDX genomic profiles and source code to facilitate identification of more druggable events and further insights into PDXs' recapitulation of human tumors.

We created the PDX Network (PDXNet) portal (https://portal.pdxnetwork.org/) to centralize access to the National Cancer Institute-funded PDXNet consortium resources, to facilitate collaboration among researchers and to make these data easily available for research. The portal includes sections for resources, analysis results, metrics for PDXNet activities, data processing protocols and training materials for processing PDX data. Currently, the portal contains PDXNet model information and data resources from 334 new models across 33 cancer types. Tissue samples of these models were deposited in the NCI's Patient-Derived Model Repository (PDMR) for public access. These models have 2134 associated sequencing files from 873 samples across 308 patients, which are hosted on the Cancer Genomics Cloud powered by Seven Bridges and the NCI Cancer Data Service for long-term storage and access with dbGaP permissions. The portal includes results from freely available, robust, validated and standardized analysis workflows on PDXNet sequencing files and PDMR data (3857 samples from 629 patients across 85 disease types). The PDXNet portal is continuously updated with new data and is of significant utility to the cancer research community as it provides a centralized location for PDXNet resources, which support multi-agent treatment studies, determination of sensitivity and resistance mechanisms, and preclinical trials.