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The acquisition of a multidrug refractory state is a major cause of mortality in myeloma. Myeloma drugs that target the cereblon (CRBN) protein include widely used immunomodulatory drugs (IMiDs), and newer CRBN E3 ligase modulator drugs (CELMoDs), in clinical trials. CRBN genetic disruption causes resistance and poor outcomes with IMiDs. Here, we investigate alternative genomic associations of IMiD resistance, using large whole-genome sequencing patient datasets (n = 522 cases) at newly diagnosed, lenalidomide (LEN)-refractory and lenalidomide-then-pomalidomide (LEN-then-POM)-refractory timepoints. Selecting gene targets reproducibly identified by published CRISPR/shRNA IMiD resistance screens, we found little evidence of genetic disruption by mutation associated with IMiD resistance. However, we identified a chromosome region, 2q37, containing COP9 signalosome members COPS7B and COPS8, copy loss of which significantly enriches between newly diagnosed (incidence 5.5%), LEN-refractory (10.0%), and LEN-then-POM-refractory states (16.4%), and may adversely affect outcomes when clonal fraction is high. In a separate dataset (50 patients) with sequential samples taken throughout treatment, we identified acquisition of 2q37 loss in 16% cases with IMiD exposure, but none in cases without IMiD exposure. The COP9 signalosome is essential for maintenance of the CUL4-DDB1-CRBN E3 ubiquitin ligase. This region may represent a novel marker of IMiD resistance with clinical utility.

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.

Supplemental data tables and analysis

Expanded detail on MIB MS experiment

Structural reorganization and amelanotic intratumoral nests are evident in persistent melanoma on MEKi.

Kinome and transcriptome responses to combined BRAF and MEK inhibition in murine tumors, patient samples, and human cell lines

Graphical representation and model of results

mRNAseq pathway signature analysis reveals gene expression reprogramming on MEKi.

MEKi Experiment Overview

BRAFV600E and PTEN-loss are required for tumor growth, which is temporally controlled and reproducible at the cell level.

Sup Fig Legends

Achievement of reproducible spatial and temporal control of melanoma growth with

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

6584 Background: Analysis of real-world data is critical for clear understanding of treatment effectiveness. Here, we explore the clinical utility of next-generation sequencing (NGS) and the impact of mutational landscapes on targeted therapy and outcomes in almost 10,000 patients in real-world setting. Methods: We constructed a data analysis platform that integrated NGS results with machine and manually curated electronic medical records data from a single large healthcare system. We assessed eligibility for a targeted therapy based on the constellation of mutations detected by NGS, investigated whether a matched therapy was given and how this affected patient outcome. Results: The study comprised of 9848 patients with NGS results across 90 cancer types including lung (2208;22%), colorectal (1495;15%), multiple myeloma (1153;12%), breast (783;8%), and prostate (480;5%) and other remaining cancers (3,729; 38%) from 2011 to 2021. Overall, 95% of patients had a positive NGS result, of whom 6364 (65%) had a targetable mutation in 34 genes per any NCCN guidelines in any cancer type. Of these, 2930 (46%) were late-stage (3-4). 820 patients across 31 cancer types received any of 54 targeted therapies based on an NGS result; 605/820 (74%) were stage 3-4; overall 605/2930 (21%) of all tested stage 3-4 patients received targeted therapy. Overall stage 4 patients with a targetable mutation who were treated with a targeted therapy had better 5-year overall survival (OS) than patients who did not (HR = 0.71, 95% CI 0.57-0.89, p = 0.003). We further analyzed important biomarkers in specific cancers. Stage 4 lung cancer patients carrying EGFR, ALK, ROS1 or MET alteration and receiving targeted therapy had better 5-year OS (HR 0.56, 95% CI 0.40-0.80, p = 0.001). We observed that multiple myeloma patients carrying KRAS and NRAS mutation received off-label Trametinib, however it did not improve their OS (HR 1.28, 95% CI 0.76-2.15, p = 0.4). Moreover, there are 665 patients with tumor mutational burden (TMB) across 64 cancer types. TMB has been categorized into high (55, 8%), intermediate (177, 27%) and low (433, 65%). OS was significantly longer in TMB-low patients compared to TMB-high and TMB-intermediate patients across all cancers (HR 0.63, 95% CI 0.43-0.92, p = 0.016). There are 5051 patients with microsatellite instability status across 72 cancers categorized into high (828, 16%), and low (4223, 84%) with no significant survival difference between groups. Conclusions: We show that real-world data offers important insights into clinical practice and patient outcome, with respect to genomic alterations and associated targeted therapies. Moreover, we have built an analytics platform which allows users to ask relevant clinical questions around outcome of patients treated with targeted therapy by genomics biomarker and potential to identify off-label use and clinical response in any cancer type.

e13545 Background: The ASCO Quality Oncology Practice Initiative (QOPI) was established in 2006 to facilitate quality measurement and continuous improvement of cancer care.1 ASCO and QOPI leadership encourage a continuous learning environment to develop new tools and methodologies that improve quality and maximize outcomes and value in oncology practice.2 Parallel advances in tumor molecular profiling, bioinformatics, and EMR data curation are adding new insights to support the real-world research of longitudinal patient journeys. Methods: Referencing the QOPI Certification Track 2021 Measures Summary, we employed data visualization techniques to create a precision oncology dashboard for breast cancer. This evaluation included 409 breast cancer patients for whom genomic testing was performed and presented at the Avera molecular tumor board. Patients were then matched to appropriate biomarker-based targeted therapies. Results: The dashboard identified and prioritized areas for enhanced data capture to inform strategies for care-coordination, research, and delivery of specialized care. Conclusions: An interactive, visual platform may help facilitate the integration of QOPI reporting into CancerLinQ, thereby fostering a real-time, bidirectional system for quality reporting, clinical decision support, and rapid learning.3,4 The next stage will (1) expand the tumor list; and (2) apply NLP methods to assess accuracy, outcomes and temporal reasoning to create interactive graphs for patient referral patterns and surgery decision drivers. References: 1. Neuss MN, Desch CE, McNiff KK, et al: A process for measuring the quality of cancer care: The Quality Oncology Practice Initiative. J Clin Oncol. 2005 2. Blayney DW, McNiff K, Eisenberg PD, Gilmore T, Jacobsen PB, Jacobson JO, Kadlubek PJ, Neuss MN, Simone J. Development and future of the American Society of Clinical Oncology's Quality Oncology Practice Initiative. J Clin Oncol. 2014 3. Blayney DW: Enhancing quality through innovation: American Society of Clinical Oncology presidential address 2010. J Clin Oncol. 2010 4. Schilsky R, Hauser R, Mann J, et al: Lessons learned from the development of the CancerLinQ prototype: Clinical decision support. J Clin Oncol. 31, 2013[Table: see text]

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

Circulating miRNAs are known to play important roles in intercellular communication. However, the effects of exosomal miRNAs on cells are not fully understood.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.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.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.

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.

Introduction Identification of the causes of, and biomarkers for, drug resistance in myeloma is important for understanding treatment failures, and for future instigation of targeted therapeutics for myeloma. Using the largest set of whole genome sequencing (WGS) of advanced and drug resistant multiple myelomas to date, we reported that even heterozygous loss of the 3p region, which harbours immunomodulatory drug (IMiD) and CRBN E3 ligase modulator drug (CELMoD)-binding protein Cereblon (CRBN), undergoes strong therapeutic selection on lenalidomide (LEN) and/or pomalidomide (POM) treatment (Gooding et al 2021, PMC7893409). We hypothesized that copy loss of other genes required for IMiD activity may also have clinical relevance. Several groups have reported pharmacogenetic screens identifying genes essential for IMiD sensitivity in vitro, particularly genes required for the maintenance of the CUL4-DDB1-CRBN E3 Ubiquitin Ligase, such as members of the COP9 signalosome complex, function of which prevents CRBN protein degradation. However, loss of these genes has hitherto not been reported in myeloma. Methods and results We identified candidate genes whose loss may favor IMiD drug resistance from published pharmacogenetic screens (n=5), and shortlisted genes consistently identified as essential for LEN or POM function in ≥2 screens (n=23). In our WGS dataset of 455 patients (cohorts: newly diagnosed (ND) n = 198, LEN-refractory n = 203; and LEN-then-POM-refractory n = 54), the incidence of mutation of shortlisted LEN/POM-essential genes in drug-refractory cohorts was rare (10% copy loss at the LEN-then-POM-refractory state, plus incidence of copy loss that increased from ND to LEN-then-POM-refractory states by ≥1.5-fold. This delivered 3 copy loss regions for further investigation: a) 3p, which we had already reported; b) 17p, loss of which is known to be strongly selected in myeloma as the site of TP53; and c) 2q, previously unidentified as relevant in myeloma, but whose minimal common region contained two members of the COP9 signalosome (COPS7B, COPS8). Proportion of loss of this region increased between ND (5.5%), LEN-refractory (9.8%) and LEN-then-POM-refractory states (16.6%), p=0.009. Those patients who had lost a copy of these genes also demonstrated a significant reduction in COPS7B/COPS8 gene expression (p In a separate cohort of myeloma patients (n=24) with sequential sample WGS analysis before and after LEN and/or POM resistance acquisition, we traced acquisition of CNA-defined subclones. 5/24 (21%) patients had acquired either clonal or subclonal loss of the 2q region containing COPS7B and COPS8 at IMID resistance, which had been either absent or below limit of detection pre-IMiD exposure. No other CNA newly-emerged in such a high proportion during IMiD treatment. Relative decrease in even one COP9 signalosome gene has been shown to cause CRBN protein level to fall, and reduce LEN efficacy (Sievers et al 2018, PMC6148446). We are now analysing CRBN protein levels in sequential biopsies from these cases. Conclusion Copy number aberrations have not previously been shown to drive a therapy-specific clonal advantage in myeloma in the clinic. We have now identified a second novel CNA, 2q loss, which increases in incidence through LEN- and POM-refractory states to emerge as a marker of dominant clones in advanced, IMiD-resistant disease. Whether these CNAs will mark resistance to novel CELMoDs remains to be seen. The CRBN protein is key to the function of these drugs, and many novel proteolysis targeting chimeras (PROTACs) in development, but whether the kinetics of their CRBN binding are as sensitive to relative CRBN protein loss remains a key question. CNAs may be easily and cost-effectively detected in the clinic by targeted sequencing approaches, and may prove valuable in future therapeutic decision making. Disclosures Gooding: Bristol Myers Squibb: Research Funding. Ansari-Pour: Bristol Myers Squibb: Consultancy. Karagoz: h.: Research Funding. Ortiz Estevez: Bristol Myers Squibb: Current Employment, Current equity holder in publicly-traded company. Towfic: Bristol Myers Squibb: Current Employment, Current equity holder in publicly-traded company. Flynt: BMS: Current Employment, Current equity holder in publicly-traded company. Pierceall: BMS: Current Employment, Current equity holder in publicly-traded company. Yong: Sanofi: Honoraria, Research Funding; Janssen: Honoraria, Research Funding; Takeda: Honoraria; GSK: Honoraria; Amgen: Honoraria; BMS: Research Funding; Autolus: Research Funding. Vyas: Astellas: Consultancy, Honoraria; Takeda: Honoraria; Janssen: Honoraria; Novartis: Honoraria; Pfizer: Honoraria; Daiichi Sankyo: Honoraria; Bristol Myers Squibb: Consultancy, Honoraria, Research Funding; Gilead: Honoraria; Jazz: Honoraria; AbbVie: Consultancy, Honoraria. Thakurta: Bristol Myers Squibb: Current Employment, Current equity holder in publicly-traded company, Patents & Royalties.

Prostate cancer is the most common non-skin related cancer affecting 1 in 7 men in the United States. Treatment of patients with prostate cancer still remains a difficult decision-making process that requires physicians to balance clinical benefits, life expectancy, comorbidities, and treatment-related side effects. Gleason score (a sum of the primary and secondary Gleason patterns) solely based on morphological prostate glandular architecture has shown as one of the best predictors of prostate cancer outcome. Significant progress has been made on molecular subtyping prostate cancer delineated through the increasing use of gene sequencing. Prostate cancer patients with Gleason score of 7 show heterogeneity in recurrence and survival outcomes. Therefore, we propose to assess the correlation between histopathology images and genomic data with disease recurrence in prostate tumors with a Gleason 7 score to identify prognostic markers. In the study, we identify image biomarkers within tissue WSIs by modeling the spatial relationship from automatically created patches as a sequence within WSI by adopting a recurrence network model, namely long short-term memory (LSTM). Our preliminary results demonstrate that integrating image biomarkers from CNN with LSTM and genomic pathway scores, is more strongly correlated with patients recurrence of disease compared to standard clinical markers and engineered image texture features. The study further demonstrates that prostate cancer patients with Gleason score of 4+3 have a higher risk of disease progression and recurrence compared to prostate cancer patients with Gleason score of 3+4.

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.

A complex framework of interacting partners including genetic, proteomic, and metabolic networks that cooperate to mediate specific functional phenotypes drives human biological processes. Recent technological and analytical advances in “omic” sciences allow the identification and elucidation of reprogramming biological functions in response to perturbations in cells and tissues. To understand such a complex system, biological networks are generated to reduce the complexity into relatively simple models, and the integration of these molecular networks from different perspectives is implemented for a holistic interpretation of the entire system. Ultimately, network-based methods will effectively facilitate the development and improvement of precision medicine by directing therapies based on the underlying biology of a given patient’s disease. The goal of precision medicine is to identify novel therapeutic strategies that can be optimized for each disease type or each patient based on the underlying genetic, environmental, and lifestyle factors. Pharmaco-omics analyses based on an integration of pharmacology and various “omics” data types can be employed to develop effective treatment strategies using particular drugs and doses that are tailored to each individual. In the current review, we first present the core elements of network-based systems biology in the context of pharmaco-omics followed by integration of multi-omics data using various biological networks. Next, we provide an opening into precise medicine and drug targeting based on network approaches. Lastly, we review the current significant efforts as well as the accomplishments and limitations in precise drug targeting with the utility of network-based guided drug discovery methods for effective treatment of breast cancer.

Circulating miRNAs are known to play important roles in intercellular communication.However, the effects of exosomal miRNAs on cells are not fully understood. In this study, substantial expression of oncogenic miR-1246 in sensitive as well as chemoresistant (paclitaxel and multidrug) ovarian cancer (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 PDGFI² 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. 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 chemosensitization and tumor progression through exosomal miR-1246 in OC patients. Funding: This study was funded by the NIH Common Fund (UH3 TR000943), through the Office of Strategic Coordination/Office of the NIH Director and MD Anderson’s Cancer Center Support Grant (CCSG) (CA016672) to G. Lopez-Berestein, A.K. Sood, G.A. Calin,, the American Cancer Society Research Professor Award to A.K. Sood, and The Center for RNA Interference and Non-Coding RNA to G.A. Calin, A.K. Sood, and G. Lopez-Berestein and CA166228 from the National Cancer Institute of the NIH to MLG. Declaration of Interest: We have no conflicts of interest to disclose. Ethical Approval: All animal work was approved by the Institutional Animal Use and Care Committee of MD Anderson.

The identification of protein–protein interactions (PPIs) and their networks is vitally important to systemically define and understand the roles of proteins in biological systems. In spite of development of numerous experimental systems to detect PPIs and diverse research on assessment of the quality of the obtained data, a consensus – highly reliable, almost complete – interactome of Saccharomyces cerevisiae is not presented yet. In this work, we proposed an unsupervised statistical approach to create a high-confidence yeast PPI network. For this, we assembled databases of interacting protein pairs for yeast and obtained an extremely large PPI dataset which comprises of 135 154 non-redundant interactions between 6191 yeast proteins. A scoring scheme considering eight heterogeneous biological features resulted with a broad score distribution and a highly reliable network consisting of 29 046 physical interactions with scores higher than the threshold value of 0.85, for which sensitivity, specificity and coverage were 86%, 68%, and 72%, respectively. We evaluated our method by comparing it with other scoring schemes and showed that reducing the noise inherent in experimental PPIs via our scoring scheme further increased the accuracy. Current study is expected to increase the efficiency of the methodologies in biological research which make use of protein interaction networks.

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.

High-grade serous ovarian cancer (HGSOC) is the most commonly diagnosed and most lethal subtype of gynecologic cancer. The poor prognosis associated with this disease stems from two major problems: diagnosis at later stages when ovarian cancer cells have metastasized to distant organs and intrinsic or developed resistance to primary platinum-taxane based therapies. Therefore, understanding the driver mechanisms of HGSOC is required to discover more effective therapeutic agents and identify potential drug targets. In this study, we applied an integrative multi-omics approach based on the use of a poor-prognostic gene expression signature as a conceptual framework to analyze orthogonal genomic and proteomic data from human ovarian tumors derived from the TCGA (n=489) and CPTAC (n=169) projects, including RNA expression, DNA copy number alterations, protein and phosphoprotein expression. In combination with data from a genome-wide RNA-mediated interference screen in human ovarian cancer cell lines, we also identified essential genetic drivers of poor prognosis. We identified genes in amplified regions that were positively correlated with poor-prognostic signature and showed an increased amplification frequency (q < 0.05). Then, we applied the poor-prognostic gene expression signatures to a panel of 29 ovarian cancer cell lines that had mRNA expression data and were also part of an RNAi proliferation screen in which a genome-wide shRNA library (~9,000 genes) had been used to identify essential genes. We used a negative Spearman rank correlation to identify essential genes in context of poor-prognosis signature. To prioritize these candidate genes, we next compared the results of these analyses and identified 128 genes that were uniquely essential for cell viability in ovarian cancer cell lines and that were amplified in the context of the poor-prognosis signature. In order to uncover underlying mechanisms in HGSOC, we examined relationships between the poor-prognostic gene expression signature score and proteins and phosphoprotein expression. We used a Spearman correlation comparing poor-prognostic signature score to protein expression level. The proteomic data obtained from different platforms that are analyses of reverse phase protein array (RPPA) (n=412) data including 141 proteins and 31 phosphoproteins and mass-spectrometry (MS)-based (n=169) data including 3330 proteins (n=169) and 2533 (n=69) phosphoproteins. Significant proteins derived from TCGA data enriched with biologic processes including cell cycle control (p=6.8E-11), cell proliferation and differentiation (P=1.9E-7) and inhibition of apoptosis (P=1.4E-5) and signaling pathways including ERBB signaling (P=3.0E-10), mTOR signaling (P=5.9E-9) and insulin signaling (P=2.9E-7) pathways. Significant proteins derived from CPTAC data played role in splicesome (P=2.4E-24), chromatin packaging and remodeling (P=3.7E-4), and cell cycle (P=1.2E-3). Next, we integrated all these analyses and determined that amplified essential genes including ADNP (Activity-Dependent Neuroprotective Protein), CEP250 (Centrosomal Nek2-Associated Protein 1), and MED1 (Mediator Complex Subunit 1) are also involved in significant phosphorylated proteins (P In conclusion, these analyses suggest that ADNP and CEP250 amplification are novel modulators of poor prognosis in ovarian cancer, and that these alterations are not only potential drivers of oncogenesis but also their associated pathways may represent novel therapeutic targets in HGSOC. Citation Format: Kubra Karagoz, Christen Khella, Michael L. Gatza. Amplification of ADNP and CEP250 promotes poor prognosis in high-grade serous ovarian cancer. [abstract]. In: Proceedings of the AACR Conference: Addressing Critical Questions in Ovarian Cancer Research and Treatment; Oct 1-4, 2017; Pittsburgh, PA. Philadelphia (PA): AACR; Clin Cancer Res 2018;24(15_Suppl):Abstract nr B45.

Type 2 diabetes mellitus is a major global public health burden. A complex metabolic disease, type 2 diabetes affects multiple different tissues, demanding a "systems medicine" approach to biomarker and novel diagnostic discovery, not to mention data integration across omics-es. In the present study, transcriptomics data from different tissues including beta-cells, pancreatic islets, arterial tissue, peripheral blood mononuclear cells, liver, and skeletal muscle of 228 samples were integrated with protein-protein interaction data and genome scale metabolic models to unravel the molecular and tissue-specific biomarker signatures of type 2 diabetes mellitus. Classifying differentially expressed genes, reconstruction and topological analysis of active protein-protein interaction subnetworks indicated that genomic reprogramming depends on the type of tissue, whereas there are common signatures at different levels. Among all tissue and cell types, Mannosidase Alpha Class 1A Member 2 was the common signature at genome level, and activation-ppara reaction, which stimulates a nuclear receptor protein, was found out as the mutual reporter at metabolic level. Moreover, miR-335 and miR-16-5p came into prominence in regulation of transcription at different tissues. On the other hand, distinct signatures were observed for different tissues at the metabolome level. Various coenzyme-A derivatives were significantly enriched metabolites in pancreatic islets, whereas skeletal muscle was enriched for cholesterol, malate, L-carnitine, and several amino acids. Results have showed utmost importance concerning relations between T2D and cancer, blood coagulation, neurodegenerative diseases, and specific metabolic and signaling pathways.

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.

Triple negative breast cancer (TNBC) represents approximately 15% of breast cancers and is characterized by lack of expression of both estrogen receptor (ER) and progesterone receptor (PR), together with absence of human epidermal growth factor 2 (HER2). TNBC has attracted considerable attention due to its aggressiveness such as large tumor size, high proliferation rate, and metastasis. The absence of clinically efficient molecular targets is of great concern in treatment of patients with TNBC. In light of the complexity of TNBC, we applied a systematic and integrative transcriptomics and interactomics approach utilizing transcriptional regulatory and protein-protein interaction networks to discover putative transcriptional control mechanisms of TNBC. To this end, we identified TNBC-driven molecular pathways such as the Janus kinase-signal transducers, and activators of transcription (JAK-STAT) and tumor necrosis factor (TNF) signaling pathways. The multi-omics molecular target and biomarker discovery approach presented here can offer ways forward on novel diagnostics and potentially help to design personalized therapeutics for TNBC in the future.

Ceramide functioning in sphingolipid metabolism is known to inhibit Akt/ protein kinase B (PKB) which is one of the core proteins of insulin signaling pathway. However, the mechanism of crosstalk between sphingolipid signaling pathway and insulin signaling pathway has not been determined completely. In this study, we have investigated the proteins which play important roles in the association of sphingolipid and insulin signaling pathways. Our results show that in Ypk1 deletion mutant, the glucose uptake is decreased whereas the amounts of complex sphingolipids are increased. Glucose uptake is relatively higher in Ypk2 and Pkh1 deletion mutants compared to Ypk1 deletion mutant and glucose uptake is highest in wild type yeast strain. The amounts of complex sphingolipids are found to decrease in Ypk2 and Pkh1 deletion mutants of yeast.