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Background: Despite toxic side effects and limited durable response, the current standard-of-care treatment for high grade serous ovarian cancer (HGSOC) remains platinum/taxane-based chemotherapy. Given that the overall prognosis has not improved drastically over the past several decades, there is a critical need to understand the underlying mechanisms that lead to tumour development and progression. Methods: We utilized an integrative proteogenomic analysis of HGSOC tumours applying a poor prognosis gene expression signature (PPS) as a conceptual framework to analyse orthogonal genomic and proteomic data from the TCGA (n = 488) and CPTAC (n = 169) studies. Genes identified through in silico analyses were assessed in vitro studies to demonstrate their impact on proliferation and cell cycle progression. Findings: These analyses identified DNA amplification and overexpression of the transcription factor ADNP (Activity Dependent Neuroprotector Homeobox) in poorly prognostic tumours. Validation studies confirmed the prognostic capacity of ADNP and suggested an oncogenic role for this protein given the association between ADNP expression and pro-proliferative signalling. In vitro studies confirmed ADNP as a novel and essential mediator of cell proliferation through dysregulation of cell cycle checkpoints. Interpretation: We identified ADNP as being amplified and overexpressed in poor prognosis HGSOC in silico analyses and demonstrated that ADNP is a novel and essential oncogene in HGSOC which mediates proliferation through dysregulation of cell cycle checkpoints in vitro. Funding: The National Cancer Institute of the National Institutes of Health, the V Foundation for Cancer Research and the New Jersey Commission for Cancer Research. Keywords: High-grade serous ovarian cancer, HGSOC, Proteogenomic, ADNP, Poor prognostic marker

Background Circulating miRNAs are known to play important roles in intercellular communication. However, the effects of exosomal miRNAs on cells are not fully understood. Methods To investigate the role of exosomal miR-1246 in ovarian cancer (OC) microenvironment, we performed RPPA as well as many other in vitro functional assays in ovarian cancer cells (sensitive; HeyA8, Skov3ip1, A2780 and chemoresistant; HeyA8-MDR, Skov3-TR, A2780-CP20). Therapeutic effect of miR-1246 inhibitor treatment was tested in OC animal model. We showed the effect of OC exosomal miR-1246 uptake on macrophages by co-culture experiments. Findings Substantial expression of oncogenic miR-1246 OC exosomes was found. We showed that Cav1 gene, which is the direct target of miR-1246, is involved in the process of exosomal transfer. A significantly worse overall prognosis were found for OC patients with high miR-1246 and low Cav1 expression based on TCGA data. miR-1246 expression were significantly higher in paclitaxel-resistant OC exosomes than in their sensitive counterparts. Overexpression of Cav1 and anti-miR-1246 treatment significantly sensitized OC cells to paclitaxel. We showed that Cav1 and multi drug resistance (MDR) gene is involved in the process of exosomal transfer. Our proteomic approach also revealed that miR-1246 inhibits Cav1 and acts through PDGFβ receptor at the recipient cells to inhibit cell proliferation. miR-1246 inhibitor treatment in combination with chemotherapy led to reduced tumor burden in vivo. Finally, we demonstrated that when OC cells are co-cultured with macrophages, they are capable of transferring their oncogenic miR-1246 to M2-type macrophages, but not M0-type macrophages. Interpretation Our results suggest that cancer exosomes may contribute to oncogenesis by manipulating neighboring infiltrating immune cells. This study provide a new mechanistic therapeutic approach to overcome chemoresistance and tumor progression through exosomal miR-1246 in OC patients.

KRAS is one of the most frequently mutated proto-oncogenes in pancreatic ductal adenocarcinoma (PDAC) and aberrantly activated in triple-negative breast cancer (TNBC). A profound role of microRNAs (miRNAs) in the pathogenesis of human cancer is being uncovered, including in cancer therapy. Using in silico prediction algorithms, we identified miR-873 as a potential regulator of KRAS, and we investigated its role in PDAC and TNBC. We found that reduced miR-873 expression is associated with shorter patient survival in both cancers. miR-873 expression is significantly repressed in PDAC and TNBC cell lines and inversely correlated with KRAS levels. We demonstrate that miR-873 directly bound to the 3′ UTR of KRAS mRNA and suppressed its expression. Notably, restoring miR-873 expression induced apoptosis; recapitulated the effects of KRAS inhibition on cell proliferation, colony formation, and invasion; and suppressed the activity of ERK and PI3K/AKT, while overexpression of KRAS rescued the effects mediated by miR-873. Moreover, in vivo delivery of miR-873 nanoparticles inhibited KRAS expression and tumor growth in PDAC and TNBC tumor models. In conclusion, we provide the first evidence that miR-873 acts as a tumor suppressor by targeting KRAS and that miR-873-based gene therapy may be a therapeutic strategy in PDAC and TNBC. Keywords: KRAS, oncogene, non-coding RNA, microRNA, ncRNA, miR-873, proliferation, invasion, gene regulation, tumorigenesis, gene silencing, therapy, nanoparticles, pancreatic cancer, liposomes, breast cancer, triple-negative breast cancer

Because the incidence of breast cancer increases decades after ionizing radiation exposure, aging has been implicated in the evolution of the tumor microenvironment and tumor progression. Here, we investigated radiation-induced carcinogenesis using a model in which the mammary glands of 10-month-old BALB/c mice were transplanted with Trp53-null mammary tissue 3 days after exposure to low doses of sparsely ionizing γ-radiation or densely ionizing particle radiation. Mammary transplants in aged, irradiated hosts gave rise to significantly more tumors that grew more rapidly than those in sham-irradiated mice, with the most pronounced effects seen in mice irradiated with densely ionizing particle radiation. Tumor transcriptomes identified a characteristic immune signature of these aggressive cancers. Consistent with this, fast-growing tumors exhibited an immunosuppressive tumor microenvironment with few infiltrating lymphocytes, abundant immunosuppressive myeloid cells, and high COX-2 and TGFβ. Only irradiated hosts gave rise to tumors lacking cytotoxic CD8+ lymphocytes (defined here as immune desert), which also occurred in younger irradiated hosts. These data suggest that host irradiation may promote immunosuppression. To test this, young chimera mice were fed chow containing a honeybee-derived compound with anti-inflammatory and immunomodulatory properties, caffeic acid phenethyl ester (CAPE). CAPE prevented the detrimental effects of host irradiation on tumor growth rate, immune signature, and immunosuppression. These data indicated that low-dose radiation, particularly densely ionizing exposure of aged mice, promoted more aggressive cancers by suppressing antitumor immunity. Dietary intervention with a nontoxic immunomodulatory agent could prevent systemic effects of radiation that fuel carcinogenesis, supporting the potential of this strategy for cancer prevention.

Targeted therapeutics that are initially effective in cancer patients nearly invariably engender resistance at some stage, an inherent challenge in the use of any molecular-targeted drug in cancer settings. In this study, we evaluated resistance mechanisms arising in metastatic melanoma to MAPK pathway kinase inhibitors as a strategy to identify candidate strategies to limit risks of resistance. To investigate longitudinal responses, we developed an intravital serial imaging approach that can directly visualize drug response in an inducible RAF-driven, autochthonous murine model of melanoma incorporating a fluorescent reporter allele (tdTomatoLSL). Using this system, we visualized formation and progression of tumors in situ, starting from the single-cell level longitudinally over time. Reliable reporting of the status of primary murine tumors treated with the selective MEK1/2 inhibitor (MEKi) trametinib illustrated a time-course of initial drug response and persistence, followed by the development of drug resistance. We found that tumor cells adjacent to bundled collagen had a preferential persistence in response to MEKi. Unbiased transcriptional and kinome reprogramming analyses from selected treatment time points suggested increased c-Kit and PI3K/AKT pathway activation in resistant tumors, along with enhanced expression of epithelial genes and epithelial-mesenchymal transition downregulation signatures with development of MEKi resistance. Similar trends were observed following simultaneous treatment with BRAF and MEK inhibitors aligned to standard-of-care combination therapy, suggesting these reprogramming events were not specific to MEKi alone. Overall, our results illuminate the integration of tumor–stroma dynamics with tissue plasticity in melanoma progression and provide new insights into the basis for drug response, persistence, and resistance. Significance: A longitudinal study tracks the course of MEKi treatment in an autochthonous imageable murine model of melanoma from initial response to therapeutic resistance, offering new insights into the basis for drug response, persistence, and resistance. Cancer Res; 78(2); 542–57. ©2017 AACR.

Triple-negative breast cancer (TNBC), which is largely synonymous with the basal-like molecular subtype, is the 5th leading cause of cancer deaths for women in the United States. The overall prognosis for TNBC patients remains poor given that few treatment options exist; including targeted therapies (not FDA approved), and multi-agent chemotherapy as standard-of-care treatment. TNBC like other complex diseases is governed by the perturbations of the complex interaction networks thereby elucidating the underlying molecular mechanisms of this disease in the context of network principles, which have the potential to identify targets for drug development. Here, we present an integrated "omics" approach based on the use of transcriptome and interactome data to identify dynamic/active protein-protein interaction networks (PPINs) in TNBC patients. We have identified three highly connected modules, EED, DHX9, and AURKA, which are extremely activated in TNBC tumors compared to both normal tissues and other breast cancer subtypes. Based on the functional analyses, we propose that these modules are potential drivers of proliferation and, as such, should be considered candidate molecular targets for drug development or drug repositioning in TNBC. Consistent with this argument, we repurposed steroids, anti-inflammatory agents, anti-infective agents, cardiovascular agents for patients with basal-like breast cancer. Finally, we have performed essential metabolite analysis on personalized genome-scale metabolic models and found that metabolites such as sphingosine-1-phosphate and cholesterol-sulfate have utmost importance in TNBC tumor growth.

Prostate cancer is the most common nonskin-related cancer, affecting one in seven men in the United States. Gleason score, a sum of the primary and secondary Gleason patterns, is one of the best predictors of prostate cancer outcomes. Recently, significant progress has been made in molecular subtyping prostate cancer through the use of genomic sequencing. It has been established that prostate cancer patients presented with a Gleason score 7 show heterogeneity in both disease recurrence and survival. We built a unified system using publicly available whole-slide images and genomic data of histopathology specimens through deep neural networks to identify a set of computational biomarkers. Using a survival model, the experimental results on the public prostate dataset showed that the computational biomarkers extracted by our approach had hazard ratio as 5.73 and C-index as 0.74, which were higher than standard clinical prognostic factors and other engineered image texture features. Collectively, the results of this study highlight the important role of neural network analysis of prostate cancer and the potential of such approaches in other precision medicine applications.

Low selenium levels have been linked to a higher incidence of cancer and other diseases, including Keshan, Chagas, and Kashin-Beck, and insulin resistance. Additionally, muscle and cardiovascular disorders, immune dysfunction, cancer, neurological disorders, and endocrine function have been associated with mutations in genes encoding for selenoproteins. Selenium biology is complex, and a systems biology approach to study global metabolomics, genomics, and/or proteomics may provide important clues to examining selenium-responsive markers in circulation. In the current investigation, we applied a global proteomics approach on plasma samples collected from a previously conducted, double-blinded placebo controlled clinical study, where men were supplemented with selenized-yeast (Se-Yeast; 300 μg/day, 3.8 μmol/day) or placebo-yeast for 48 weeks. Proteomic analysis was performed by iTRAQ on 8 plasma samples from each arm at baseline and 48 weeks. A total of 161 plasma proteins were identified in both arms. Twenty-two proteins were significantly altered following Se-Yeast supplementation and thirteen proteins were significantly changed after placebo-yeast supplementation in healthy men. The differentially expressed proteins were involved in complement and coagulation pathways, immune functions, lipid metabolism, and insulin resistance. Reconstruction and analysis of protein-protein interaction network around selected proteins revealed several hub proteins. One of the interactions suggested by our analysis, PHLD-APOA4, which is involved in insulin resistance, was subsequently validated by Western blot analysis. Our systems approach illustrates a viable platform for investigating responsive proteomic profile in 'before and after' condition following Se-Yeast supplementation. The nature of proteins identified suggests that selenium may play an important role in complement and coagulation pathways, and insulin resistance.

The biological function of a protein is usually determined by its physical interaction with other proteins. Protein-protein interactions (PPIs) are identified through various experimental methods and are stored in curated databases. The noisiness of the existing PPI data is evident, and it is essential that a more reliable data is generated. Furthermore, the selection of a set of PPIs at different confidence levels might be necessary for many studies. Although different methodologies were introduced to evaluate the confidence scores for binary interactions, a highly reliable, almost complete PPI network of Homo sapiens is not proposed yet. The quality and coverage of human protein interactome need to be improved to be used in various disciplines, especially in biomedicine. In the present work, we propose an unsupervised statistical approach to assign confidence scores to PPIs of H. sapiens. To achieve this goal PPI data from six different databases were collected and a total of 295,288 non-redundant interactions between 15,950 proteins were acquired. The present scoring system included the context information that was assigned to PPIs derived from eight biological attributes. A high confidence network, which included 147,923 binary interactions between 13,213 proteins, had scores greater than the cutoff value of 0.80, for which sensitivity, specificity, and coverage were 94.5%, 80.9%, and 82.8%, respectively. We compared the present scoring method with others for evaluation. Reducing the noise inherent in experimental PPIs via our scoring scheme increased the accuracy significantly. As it was demonstrated through the assessment of process and cancer subnetworks, this study allows researchers to construct and analyze context-specific networks via valid PPI sets and one can easily achieve subnetworks around proteins of interest at a specified confidence level.

Esophageal squamous cell carcinoma (ESCC), which is the most common subtype of esophageal cancers, is the sixth leading cause of cancer death worldwide with a five-year survival rate of 19%. Identification of efficient biomarkers for early detection and better understanding of the molecular mechanisms of ESCC may offer reduced mortality. However, proper biomarkers for clinical diagnosis and prognosis have not been defined yet. In the presented study, we employed a systematic and integrative 'omics' strategy to reconstruct networks of transcriptional regulation and protein-protein interaction to identify novel biomarkers, potential molecular targets, and mechanisms of transcriptional control in ESCC. Towards this end, we revealed 30 down-regulated and 21 up-regulated genes as ESCC specific biomarkers since these were differentially expressed between 91 ESCC tumor samples compared to normal tissues in five different datasets. We report the association of ACPP, C2orf54, DYNLT3, ENDOU, FMO2, and KANK1 (down-regulated genes) and COL10A1, FNDC3B, HOMER3, MARCKSL1, and RFC4 (up-regulated genes) to ESCC for the first time. Further, the ESCC driven molecular pathways were also constructed to elucidate the molecular mechanism of the disease; specifically several metabolic pathways were down-regulated while the signaling pathways were up-regulated. Additionally, reporter metabolites for ESCC were analyzed and metabolic dysfunction was ascertained in arachidonic acid metabolism and steroid hormone biosynthesis pathways. The multi-omics network strategy presented here may enable discovery of novel biomarkers and targets for personalized medicine in ESCC patients.