Kendric Wang

Optimally discriminative subnetwork markers predict response to chemotherapy

Motivation: Molecular profiles of tumour samples have been widely and successfully used for classification problems. A number of algorithms have been proposed to predict classes of tumor samples based on expression profiles with relatively high performance. However, prediction of response to cancer treatment has proved to be more challenging and novel approaches with improved generalizability are still highly needed. Recent studies have clearly demonstrated the advantages of integrating protein–protein interaction (PPI) data with gene expression profiles for the development of subnetwork markers in classification problems. Results: We describe a novel network-based classification algorithm (OptDis) using color coding technique to identify optimally discriminative subnetwork markers. Focusing on PPI networks, we apply our algorithm to drug response studies: we evaluate our algorithm using published cohorts of breast cancer patients treated with combination chemotherapy. We show that our OptDis method improves over previously published subnetwork methods and provides better and more stable performance compared with other subnetwork and single gene methods. We also show that our subnetwork method produces predictive markers that are more reproducible across independent cohorts and offer valuable insight into biological processes underlying response to therapy. Availability: The implementation is available at: http://www.cs.sfu.ca/~pdao/personal/OptDis.html Contact: cenk@cs.sfu.ca; alapuk@prostatecentre.com; ccollins@prostatecentre.com

The Placental Gene PEG10 Promotes Progression of Neuroendocrine Prostate Cancer

More potent targeting of the androgen receptor (AR) in advanced prostate cancer is driving an increased incidence of neuroendocrine prostate cancer (NEPC), an aggressive and treatment-resistant AR-negative variant. Its molecular pathogenesis remains poorly understood but appears to require TP53 and RB1 aberration. We modeled the development of NEPC from conventional prostatic adenocarcinoma using a patient-derived xenograft and found that the placental gene PEG10 is de-repressed during the adaptive response to AR interference and subsequently highly upregulated in clinical NEPC. We found that the AR and the E2F/RB pathway dynamically regulate distinct post-transcriptional and post-translational isoforms of PEG10 at distinct stages of NEPC development. In vitro, PEG10 promoted cell-cycle progression from G0/G1 in the context of TP53 loss and regulated Snail expression via TGF-β signaling to promote invasion. Taken together, these findings show the mechanistic relevance of RB1 and TP53 loss in NEPC and suggest PEG10 as a NEPC-specific target.

Heterogeneity in the inter-tumor transcriptome of high risk prostate cancer

BackgroundGenomic analyses of hundreds of prostate tumors have defined a diverse landscape of mutations and genome rearrangements, but the transcriptomic effect of this complexity is less well understood, particularly at the individual tumor level. We selected a cohort of 25 high-risk prostate tumors, representing the lethal phenotype, and applied deep RNA-sequencing and matched whole genome sequencing, followed by detailed molecular characterization.ResultsTen tumors were exposed to neo-adjuvant hormone therapy and expressed marked evidence of therapy response in all except one extreme case, which demonstrated early resistance via apparent neuroendocrine transdifferentiation. We observe high inter-tumor heterogeneity, including unique sets of outlier transcripts in each tumor. Interestingly, outlier expression converged on druggable cellular pathways associated with cell cycle progression, translational control or immune regulation, suggesting distinct contemporary pathway affinity and a mechanism of tumor stratification. We characterize hundreds of novel fusion transcripts, including a high frequency of ETS fusions associated with complex genome rearrangements and the disruption of tumor suppressors. Remarkably, several tumors express unique but potentially-oncogenic non-ETS fusions, which may contribute to the phenotype of individual tumors, and have significance for disease progression. Finally, one ETS-negative tumor has a striking tandem duplication genotype which appears to be highly aggressive and present at low recurrence in ETS-negative prostate cancer, suggestive of a novel molecular subtype.ConclusionsThe multitude of rare genomic and transcriptomic events detected in a high-risk tumor cohort offer novel opportunities for personalized oncology and their convergence on key pathways and functions has broad implications for precision medicine.

Enhanced anticancer activity of a combination of docetaxel and Aneustat (OMN54) in a patient-derived, advanced prostate cancer tissue xenograft model

The current first‐line treatment for advanced metastatic prostate cancer, i.e. docetaxel‐based therapy, is only marginally effective. The aim of the present study was to determine whether such therapy can be improved by combining docetaxel with Aneustat (OMN54), a multivalent botanical drug candidate shown to have anti‐prostate cancer activity in preliminary in vitro experiments, which is currently undergoing a Phase‐I Clinical Trial. Human metastatic, androgen‐independent C4‐2 prostate cancer cells and NOD‐SCID mice bearing PTEN‐deficient, metastatic and PSA‐secreting, patient‐derived subrenal capsule LTL‐313H prostate cancer tissue xenografts were treated with docetaxel and Aneustat, alone and in combination. In vitro, Aneustat markedly inhibited C4‐2 cell replication in a dose‐dependent manner. When Aneustat was combined with docetaxel, the growth inhibitions of the drugs were essentially additive. In vivo, however, the combination of docetaxel and Aneustat enhanced anti‐tumor activity synergistically and very markedly, without inducing major host toxicity. Complete growth inhibition and shrinkage of the xenografts could be obtained with the combined drugs as distinct from the drugs on their own. Analysis of the gene expression of the xenografts using microarray indicated that docetaxel + Aneustat led to expanded anticancer activity, in particular to targeting of cancer hallmarks that were not affected by the single drugs. Our findings, obtained with a highly clinically relevant prostate cancer model, suggest, for the first time, that docetaxel‐based therapy of advanced human prostate cancer may be improved by combining docetaxel with Aneustat.

HIT'nDRIVE: Multi-driver Gene Prioritization Based on Hitting Time

A key challenge in cancer genomics is the identification and prioritization of genomic aberrations that potentially act as drivers of cancer. HIT’nDRIVE is a combinatorial method to identify aberrant genes that can collectively influence possibly distant “outlier” genes based on the “random-walk facility location” (RWFL) problem on an interaction network. RWFL uses “multi-hitting time”, the expected minimum length of a random walk originating from any aberrant gene towards an outlier. HIT’nDRIVE aims to find the smallest set of aberrant genes from which one can reach outliers within desired multi-hitting time. It estimates multi-hitting time based on the independent hitting times and reduces the RWFL to a weighted multi-set cover problem, which it solves as an integer linear program (ILP). We apply HIT’nDRIVE to identify aberrant genes that potentially act as drivers in a cancer data set and make phenotype predictions using only the potential drivers, more accurately than alternative approaches. keywords: drivers, cancer, multi-hitting time, interaction networks, multi-set cover

Stromal Gene Expression is Predictive for Metastatic Primary Prostate Cancer

BACKGROUND Clinical grading systems using clinical features alongside nomograms lack precision in guiding treatment decisions in prostate cancer (PCa). There is a critical need for identification of biomarkers that can more accurately stratify patients with primary PCa. OBJECTIVE To identify a robust prognostic signature to better distinguish indolent from aggressive prostate cancer (PCa). DESIGN, SETTING, AND PARTICIPANTS To develop the signature, whole-genome and whole-transcriptome sequencing was conducted on five PCa patient-derived xenograft (PDX) models collected from independent foci of a single primary tumor and exhibiting variable metastatic phenotypes. Multiple independent clinical cohorts including an intermediate-risk cohort were used to validate the biomarkers. OUTCOME MEASUREMENTS AND STATISTICAL ANALYSIS The outcome measurement defining aggressive PCa was metastasis following radical prostatectomy. A generalized linear model with lasso regularization was used to build a 93-gene stroma-derived metastasis signature (SDMS). The SDMS association with metastasis was assessed using a Wilcoxon rank-sum test. Performance was evaluated using the area under the curve (AUC) for the receiver operating characteristic, and Kaplan-Meier curves. Univariable and multivariable regression models were used to compare the SDMS alongside clinicopathological variables and reported signatures. AUC was assessed to determine if SDMS is additive or synergistic to previously reported signatures. RESULTS AND LIMITATIONS A close association between stromal gene expression and metastatic phenotype was observed. Accordingly, the SDMS was modeled and validated in multiple independent clinical cohorts. Patients with higher SDMS scores were found to have worse prognosis. Furthermore, SDMS was an independent prognostic factor, can stratify risk in intermediate-risk PCa, and can improve the performance of other previously reported signatures. CONCLUSIONS Profiling of stromal gene expression led to development of an SDMS that was validated as independently prognostic for the metastatic potential of prostate tumors. PATIENT SUMMARY Our stroma-derived metastasis signature can predict the metastatic potential of early stage disease and will strengthen decisions regarding selection of active surveillance versus surgery and/or radiation therapy for prostate cancer patients. Furthermore, profiling of stroma cells should be more consistent than profiling of diverse cellular populations of heterogeneous tumors.

A Meta-Analysis Approach for Characterizing Pan-Cancer Mechanisms of Drug Sensitivity in Cell Lines

Understanding the heterogeneous drug response of cancer patients is essential to precision oncology. Pioneering genomic analyses of individual cancer subtypes have begun to identify key determinants of resistance, including up-regulation of multi-drug resistance (MDR) genes and mutational alterations of drug targets. However, these alterations are sufficient to explain only a minority of the population, and additional mechanisms of drug resistance or sensitivity are required to explain the remaining spectrum of patient responses to ultimately achieve the goal of precision oncology. We hypothesized that a pan-cancer analysis of in vitro drug sensitivities across numerous cancer lineages will improve the detection of statistical associations and yield more robust and, importantly, recurrent determinants of response. In this study, we developed a statistical framework based on the meta-analysis of expression profiles to identify pan-cancer markers and mechanisms of drug response. Using the Cancer Cell Line Encyclopaedia (CCLE), a large panel of several hundred cancer cell lines from numerous distinct lineages, we characterized both known and novel mechanisms of response to cytotoxic drugs including inhibitors of Topoisomerase 1 (TOP1; Topotecan, Irinotecan) and targeted therapies including inhibitors of histone deacetylases (HDAC; Panobinostat) and MAP/ERK kinases (MEK; PD-0325901, AZD6244). Notably, our analysis implicated reduced replication and transcriptional rates, as well as deficiency in DNA damage repair genes in resistance to TOP1 inhibitors. The constitutive activation of several signaling pathways including the interferon/STAT-1 pathway was implicated in resistance to the pan-HDAC inhibitor. Finally, a number of dysregulations upstream of MEK were identified as compensatory mechanisms of resistance to the MEK inhibitors. In comparison to alternative pan-cancer analysis strategies, our approach can better elucidate relevant drug response mechanisms. Moreover, the compendium of putative markers and mechanisms identified through our analysis can serve as a foundation for future studies into these drugs.

Abstract 2001: GATA2: Potential role as a prostate cancer metastasis-driving gene

Proceedings: AACR Annual Meeting 2014; April 5-9, 2014; San Diego, CA Metastasis is thought to result from changes in the expression of specific metastasis-driving genes leading to a cascade of activated downstream genes that set the metastatic process in motion. The present study was aimed at identifying such metastasis-driving genes in prostate cancer for potential therapy and identification of primary prostate cancers that are likely to metastasize. To this end, a differential gene expression profile was established of metastatic LTL-313H and non-metastatic LTL-313B prostate cancer tissue xenografts, derived from one patients specimen using sub-renal capsule grafting technology. The profile was then subjected to integrative analysis using the Ingenuity Upstream Regulator Analysis tool. Six candidate master regulatory genes were identified, including GATA2, a gene encoding a pioneer factor in prostate cancer. Elevated GATA2 expression in clinical metastatic prostate cancer tissues was found to correlate with poor patient prognosis. Furthermore, GATA2 gene silencing in human prostate cancer LNCaP cells led to a marked reduction in cell migration, tissue invasion, focal adhesion disassembly and to a drastic change in cell transcriptome. Furthermore, 582 genes were identified that are (i) differentially expressed after GATA2 gene silencing in LNCaP cells and (ii) whose changes in gene expression significantly correlated with changes in GATA2 expressions in a MSKCC prostate cancer patient cohort. Taken together, the data suggest that GATA2 could represent a prostate cancer metastasis-driving gene and that the expression patterns of GATA2 and its associated-genes could serve as signatures (biomarkers) for poor prognosis in prostate cancer. Citation Format: Yan Ting Chiang, Kendric Wang, Francesco Crea, Colin Collins, Peter Gout, Yuzhuo Wang. GATA2: Potential role as a prostate cancer metastasis-driving gene. [abstract]. In: Proceedings of the 105th Annual Meeting of the American Association for Cancer Research; 2014 Apr 5-9; San Diego, CA. Philadelphia (PA): AACR; Cancer Res 2014;74(19 Suppl):Abstract nr 2001. doi:10.1158/1538-7445.AM2014-2001