Publications by authors named "Colin Collins"

149 Publications

ZRSR2 overexpression is a frequent and early event in castration-resistant prostate cancer development.

Prostate Cancer Prostatic Dis 2021 Feb 10. Epub 2021 Feb 10.

Vancouver Prostate Centre, Department of Urologic Sciences, University of British Columbia, Vancouver, BC, Canada.

Background: Androgen deprivation therapy (ADT) remains the leading systemic therapy for locally advanced and metastatic prostate cancers (PCa). While a majority of PCa patients initially respond to ADT, the durability of response is variable and most patients will eventually develop incurable castration-resistant prostate cancer (CRPC). Our research objective is to identify potential early driver genes responsible for CRPC development.

Methods: We have developed a unique panel of hormone-naïve PCa (HNPC) patient-derived xenograft (PDX) models at the Living Tumor Laboratory. The PDXs provide a unique platform for driver gene discovery as they allow for the analysis of differentially expressed genes via transcriptomic profiling at various time points after mouse host castration. In the present study, we focused on genes with expression changes shortly after castration but before CRPC has fully developed. These are likely to be potential early drivers of CRPC development. Such genes were further validated for their clinical relevance using data from PCa patient databases. ZRSR2 was identified as a top gene candidate and selected for further functional studies.

Results: ZRSR2 is significantly upregulated in our PDX models during the early phases of CRPC development after mouse host castration and remains consistently high in fully developed CRPC PDX models. Moreover, high ZRSR2 expression is also observed in clinical CRPC samples. Importantly, elevated ZRSR2 in PCa samples is correlated with poor patient treatment outcomes. ZRSR2 knockdown reduced PCa cell proliferation and delayed cell cycle progression at least partially through inhibition of the Cyclin D1 (CCND1) pathway.

Conclusion: Using our unique HNPC PDX models that develop into CRPC after host castration, we identified ZRSR2 as a potential early driver of CRPC development.
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http://dx.doi.org/10.1038/s41391-021-00322-7DOI Listing
February 2021

Multiomics characterization of low-grade serous ovarian carcinoma identifies potential biomarkers of MEK inhibitor sensitivity and therapeutic vulnerability.

Cancer Res 2021 Jan 13. Epub 2021 Jan 13.

Gynecologic Oncology, University of British Columbia

Low-grade serous ovarian carcinoma (LGSOC) is a rare tumor subtype with high case fatality rates in patients with metastatic disease. There is a pressing need to develop effective treatments using newly available preclinical models for therapeutic discovery and drug evaluation. Here we use multiomics integration of whole exome sequencing, RNA sequencing, and mass spectrometry-based proteomics on fourteen LGSOC cell lines to elucidate novel biomarkers and therapeutic vulnerabilities. Comparison of LGSOC cell line data to LGSOC tumor data enabled predictive biomarker identification of MEK inhibitor (MEKi) efficacy, with KRAS mutations found exclusively in MEKi-sensitive cell lines and NRAS mutations found mostly in MEKi-resistant cell lines. Distinct patterns of COSMIC mutational signatures were identified in MEKi-sensitive and MEKi-resistant cell lines. Deletions of CDKN2A/B and MTAP genes were more frequent in cell lines than tumor samples and possibly represent key driver events in the absence of KRAS/NRAS/BRAF mutations. These LGSOC cell lines were representative models of the molecular aberrations found in LGSOC tumors. For prediction of in vitro MEKi efficacy, proteomic data provided better discrimination than gene expression data. Condensin, MCM, and RFC protein complexes were identified as potential treatment targets in MEKi-resistant cell lines. This study suggests that CDKN2A/B or MTAP deficiency may be exploited using synthetically lethal treatment strategies, highlighting the importance of using proteomic data as a tool for molecular drug prediction. Multiomics approaches are crucial to improving our understanding of the molecular underpinnings of LGSOC and applying this information to develop new therapies.
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http://dx.doi.org/10.1158/0008-5472.CAN-20-2222DOI Listing
January 2021

Identification of a Transcriptomic Prognostic Signature by Machine Learning Using a Combination of Small Cohorts of Prostate Cancer.

Front Genet 2020 25;11:550894. Epub 2020 Nov 25.

Centre de Recherche du CHU de Québec - Université Laval, Québec, QC, Canada.

Determining which treatment to provide to men with prostate cancer (PCa) is a major challenge for clinicians. Currently, the clinical risk-stratification for PCa is based on clinico-pathological variables such as Gleason grade, stage and prostate specific antigen (PSA) levels. But transcriptomic data have the potential to enable the development of more precise approaches to predict evolution of the disease. However, high quality RNA sequencing (RNA-seq) datasets along with clinical data with long follow-up allowing discovery of biochemical recurrence (BCR) biomarkers are small and rare. In this study, we propose a machine learning approach that is robust to batch effect and enables the discovery of highly predictive signatures despite using small datasets. Gene expression data were extracted from three RNA-Seq datasets cumulating a total of 171 PCa patients. Data were re-analyzed using a unique pipeline to ensure uniformity. Using a machine learning approach, a total of 14 classifiers were tested with various parameters to identify the best model and gene signature to predict BCR. Using a random forest model, we have identified a signature composed of only three genes (JUN, HES4, PPDPF) predicting BCR with better accuracy [74.2%, balanced error rate (BER) = 27%] than the clinico-pathological variables (69.2%, BER = 32%) currently in use to predict PCa evolution. This score is in the range of the studies that predicted BCR in single-cohort with a higher number of patients. We showed that it is possible to merge and analyze different small and heterogeneous datasets altogether to obtain a better signature than if they were analyzed individually, thus reducing the need for very large cohorts. This study demonstrates the feasibility to regroup different small datasets in one larger to identify a predictive genomic signature that would benefit PCa patients.
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http://dx.doi.org/10.3389/fgene.2020.550894DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC7723980PMC
November 2020

Immune-focused multi-omics analysis of prostate cancer: leukocyte Ig-Like receptors are associated with disease progression.

Oncoimmunology 2020 12 1;9(1):1851950. Epub 2020 Dec 1.

Computational Biology Laboratory, CHU de Québec - Université Laval Research Center, Québec City, QC, Canada.

Prostate cancer (PCa) immunotherapy has shown limited efficacy so far, even in advanced-stage cancers. The success rate of PCa immunotherapy might be improved by approaches more adapted to the immunobiology of the disease. The objective of this study was to perform a multi-omics analysis to identify immune genes associated with PCa progression to better characterize PCa immunobiology and propose new immunotherapeutic targets. mRNA, miRNA, methylation, copy number aberration, and single nucleotide variant datasets from The Cancer Genome Atlas PRAD cohort were analyzed after filtering for genes associated with immunity. Sparse partial least squares-discriminant analyses were performed to identify features associated with biochemical recurrence (BCR) in each type of omics data. Selected features predicted BCR with a balanced error rate (BER) of 0.20 to 0.51 in single-omics and of 0.05 in multi-omics analyses. Amongst features associated with BCR were genes from the Immunoglobulin Ig-like Receptor (LILR) family which are immune checkpoints with immunotherapeutic potential. Using Multivariate INTegrative (MINT) analysis, the association of five genes with BCR was quantified in a combination of three RNA-seq datasets and confirmed with Kaplan-Meier analysis in both these and in an independent RNA-seq dataset. Finally, immunohistochemistry showed that a high number of LILRB1 positive cells within the tumors predicted long-term adverse outcomes. Thus, tumors characterized by abnormal expression of genes have an elevated risk of recurring after definitive local therapy. The immunotherapeutic potential of these regulators to stimulate the immune response against PCa should be evaluated in pre-clinical models.
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http://dx.doi.org/10.1080/2162402X.2020.1851950DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC7714461PMC
December 2020

GRB10 sustains AR activity by interacting with PP2A in prostate cancer cells.

Int J Cancer 2021 Jan 21;148(2):469-480. Epub 2020 Oct 21.

Vancouver Prostate Centre, Department of Urologic Sciences, Faculty of Medicine, University of British Columbia, Vancouver, British Columbia, Canada.

Prostate cancer (PCa) progression is driven by androgen receptor (AR) signaling. Unfortunately, androgen-deprivation therapy and the use of even more potent AR pathway inhibitors (ARPIs) cannot bring about a cure. ARPI resistance (ie, castration-resistant PCa, CRPC) will inevitably develop. Previously, we demonstrated that GRB10 is an AR transcriptionally repressed gene that functionally contributes to CRPC development and ARPI resistance. GRB10 expression is elevated prior to CRPC development in our patient-derived xenograft models and is significantly upregulated in clinical CRPC samples. Here, we analyzed transcriptomic data from GRB10 knockdown in PCa cells and found that AR signaling is downregulated. While the mRNA expression of AR target genes decreased upon GRB10 knockdown, AR expression was not affected at the mRNA or protein level. We further found that phosphorylation of AR serine 81 (S81), which is critical for AR transcriptional activity, is decreased by GRB10 knockdown and increased by its overexpression. Luciferase assay using GRB10-knockdown cells also indicate reduced AR activity. Immunoprecipitation coupled with mass spectrometry revealed an interaction between GRB10 and the PP2A complex, which is a known phosphatase of AR. Further validations and analyses showed that GRB10 binds to the PP2Ac catalytic subunit with its PH domain. Mechanistically, GRB10 knockdown increased PP2Ac protein stability, which in turn decreased AR S81 phosphorylation and reduced AR activity. Our findings indicate a reciprocal feedback between GRB10 and AR signaling, implying the importance of GRB10 in PCa progression.
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http://dx.doi.org/10.1002/ijc.33335DOI Listing
January 2021

Paternally Expressed Gene 10 (PEG10) Promotes Growth, Invasion, and Survival of Bladder Cancer.

Mol Cancer Ther 2020 10 26;19(10):2210-2220. Epub 2020 Aug 26.

The Vancouver Prostate Centre and Department of Urologic Sciences, University of British Columbia, Vancouver, British Columbia, Canada.

() has been associated with neuroendocrine muscle-invasive bladder cancer (MIBC), a subtype of the disease with the poorest survival. In this work, we further characterized the expression pattern of in The Cancer Genome Atlas database of 412 patients with MIBC, and found that, compared with other subtypes, mRNA level was enhanced in neuroendocrine-like MIBC and highly correlated with other neuroendocrine markers. PEG10 protein level also associated with neuroendocrine markers in a tissue microarray of 82 cases. In bladder cancer cell lines, PEG10 expression was induced in drug-resistant compared with parental cells, and knocking down of PEG10 resensitized cells to chemotherapy. Loss of PEG10 increased protein levels of cell-cycle regulators p21 and p27 and delayed G-S-phase transition, while overexpression of PEG10 enhanced cancer cell proliferation. PEG10 silencing also lowered levels of SLUG and SNAIL, leading to reduced invasion and migration. In an orthotopic bladder cancer model, systemic treatment with PEG10 antisense oligonucleotide delayed progression of T24 xenografts. In summary, elevated expression of in MIBC may contribute to the disease progression by promoting survival, proliferation, and metastasis. Targeting PEG10 is a novel potential therapeutic approach for a subset of bladder cancers.
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http://dx.doi.org/10.1158/1535-7163.MCT-19-1031DOI Listing
October 2020

Identification of conserved evolutionary trajectories in tumors.

Bioinformatics 2020 Jul;36(Supplement_1):i427-i435

Cancer Data Science Lab., National Cancer Institute, NIH, Bethesda, MD, USA.

Motivation: As multi-region, time-series and single-cell sequencing data become more widely available; it is becoming clear that certain tumors share evolutionary characteristics with others. In the last few years, several computational methods have been developed with the goal of inferring the subclonal composition and evolutionary history of tumors from tumor biopsy sequencing data. However, the phylogenetic trees that they report differ significantly between tumors (even those with similar characteristics).

Results: In this article, we present a novel combinatorial optimization method, CONETT, for detection of recurrent tumor evolution trajectories. Our method constructs a consensus tree of conserved evolutionary trajectories based on the information about temporal order of alteration events in a set of tumors. We apply our method to previously published datasets of 100 clear-cell renal cell carcinoma and 99 non-small-cell lung cancer patients and identify both conserved trajectories that were reported in the original studies, as well as new trajectories.

Availability And Implementation: CONETT is implemented in C++ and available at https://github.com/ehodzic/CONETT.

Supplementary Information: Supplementary data are available at Bioinformatics online.
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http://dx.doi.org/10.1093/bioinformatics/btaa453DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC7355238PMC
July 2020

AITL: Adversarial Inductive Transfer Learning with input and output space adaptation for pharmacogenomics.

Bioinformatics 2020 Jul;36(Supplement_1):i380-i388

School of Computing Science, Simon Fraser University, Burnaby, BC, Canada.

Motivation: The goal of pharmacogenomics is to predict drug response in patients using their single- or multi-omics data. A major challenge is that clinical data (i.e. patients) with drug response outcome is very limited, creating a need for transfer learning to bridge the gap between large pre-clinical pharmacogenomics datasets (e.g. cancer cell lines), as a source domain, and clinical datasets as a target domain. Two major discrepancies exist between pre-clinical and clinical datasets: (i) in the input space, the gene expression data due to difference in the basic biology, and (ii) in the output space, the different measures of the drug response. Therefore, training a computational model on cell lines and testing it on patients violates the i.i.d assumption that train and test data are from the same distribution.

Results: We propose Adversarial Inductive Transfer Learning (AITL), a deep neural network method for addressing discrepancies in input and output space between the pre-clinical and clinical datasets. AITL takes gene expression of patients and cell lines as the input, employs adversarial domain adaptation and multi-task learning to address these discrepancies, and predicts the drug response as the output. To the best of our knowledge, AITL is the first adversarial inductive transfer learning method to address both input and output discrepancies. Experimental results indicate that AITL outperforms state-of-the-art pharmacogenomics and transfer learning baselines and may guide precision oncology more accurately.

Availability And Implementation: https://github.com/hosseinshn/AITL.

Supplementary Information: Supplementary data are available at Bioinformatics online.
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http://dx.doi.org/10.1093/bioinformatics/btaa442DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC7355265PMC
July 2020

Well-Differentiated Papillary Mesothelioma of the Peritoneum Is Genetically Distinct from Malignant Mesothelioma.

Cancers (Basel) 2020 Jun 13;12(6). Epub 2020 Jun 13.

Department of Pathology, Vancouver General Hospital, Vancouver, BC V5Z 1M9, Canada.

Well-differentiated papillary mesothelioma (WDPM) is an uncommon mesothelial proliferation that is most commonly encountered as an incidental finding in the peritoneal cavity. There is controversy in the literature about whether WDPM is a neoplasm or a reactive process and, if neoplastic, whether it is a variant or precursor of epithelial malignant mesothelioma or is a different entity. Using whole exome sequencing of five WDPMs of the peritoneum, we have identified distinct mutations in , , , , , , and shared by WDPM cases but not reported in malignant mesotheliomas. Furthermore, we show that WDPM is strongly enriched with C > A transversion substitution mutations, a pattern that is also not found in malignant mesotheliomas. The WDPMs lacked the alterations involving , , , , , , and that are frequently found in malignant mesotheliomas. We conclude that WDPMs are neoplasms that are genetically distinct from malignant mesotheliomas and, based on observed mutations, do not appear to be precursors of malignant mesotheliomas.
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http://dx.doi.org/10.3390/cancers12061568DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC7352777PMC
June 2020

Conditionally Reprogrammed Cells from Patient-Derived Xenograft to Model Neuroendocrine Prostate Cancer Development.

Cells 2020 06 4;9(6). Epub 2020 Jun 4.

Vancouver Prostate Centre, Department of Urologic Sciences, Faculty of Medicine, University of British Columbia, Vancouver, BC V6H 3Z6, Canada.

Neuroendocrine prostate cancer (NEPC) is a lethal subtype of prostate cancer. It develops mainly via NE transdifferentiation of prostate adenocarcinoma in response to androgen receptor (AR)-inhibition therapy. The study of NEPC development has been hampered by a lack of clinically relevant models. We previously established a unique and first-in-field patient-derived xenograft (PDX) model of adenocarcinoma (LTL331)-to-NEPC (LTL331R) transdifferentiation. In this study, we applied conditional reprogramming (CR) culture to establish a LTL331 PDX-derived cancer cell line named LTL331_CR_Cell. These cells retain the same genomic mutations as the LTL331 parental tumor. They can be continuously propagated in vitro and can be genetically manipulated. Androgen deprivation treatment on LTL331_CR_Cells had no effect on cell proliferation. Transcriptomic analyses comparing the LTL331_CR_Cell to its parental tumor revealed a profound downregulation of the androgen response pathway and an upregulation of stem and basal cell marker genes. The transcriptome of LTL331_CR_Cells partially resembles that of post-castrated LTL331 xenografts in mice. Notably, when grafted under the renal capsules of male NOD/SCID mice, LTL331_CR_Cells spontaneously gave rise to NEPC tumors. This is evidenced by the histological expression of the NE marker CD56 and the loss of adenocarcinoma markers such as PSA. Transcriptomic analyses of the newly developed NEPC tumors further demonstrate marked enrichment of NEPC signature genes and loss of AR signaling genes. This study provides a novel research tool derived from a unique PDX model. It allows for the investigation of mechanisms underlying NEPC development by enabling gene manipulations ex vivo and subsequent functional evaluations in vivo.
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http://dx.doi.org/10.3390/cells9061398DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC7349646PMC
June 2020

CircMiner: accurate and rapid detection of circular RNA through splice-aware pseudo-alignment scheme.

Bioinformatics 2020 06;36(12):3703-3711

Vancouver Prostate Centre, Vancouver, BC V6H3Z6, Canada.

Motivation: The ubiquitous abundance of circular RNAs (circRNAs) has been revealed by performing high-throughput sequencing in a variety of eukaryotes. circRNAs are related to some diseases, such as cancer in which they act as oncogenes or tumor-suppressors and, therefore, have the potential to be used as biomarkers or therapeutic targets. Accurate and rapid detection of circRNAs from short reads remains computationally challenging. This is due to the fact that identifying chimeric reads, which is essential for finding back-splice junctions, is a complex process. The sensitivity of discovery methods, to a high degree, relies on the underlying mapper that is used for finding chimeric reads. Furthermore, all the available circRNA discovery pipelines are resource intensive.

Results: We introduce CircMiner, a novel stand-alone circRNA detection method that rapidly identifies and filters out linear RNA sequencing reads and detects back-splice junctions. CircMiner employs a rapid pseudo-alignment technique to identify linear reads that originate from transcripts, genes or the genome. CircMiner further processes the remaining reads to identify the back-splice junctions and detect circRNAs with single-nucleotide resolution. We evaluated the efficacy of CircMiner using simulated datasets generated from known back-splice junctions and showed that CircMiner has superior accuracy and speed compared to the existing circRNA detection tools. Additionally, on two RNase R treated cell line datasets, CircMiner was able to detect most of consistent, high confidence circRNAs compared to untreated samples of the same cell line.

Availability And Implementation: CircMiner is implemented in C++ and is available online at https://github.com/vpc-ccg/circminer.

Supplementary Information: Supplementary data are available at Bioinformatics online.
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http://dx.doi.org/10.1093/bioinformatics/btaa232DOI Listing
June 2020

Transient Sox9 Expression Facilitates Resistance to Androgen-Targeted Therapy in Prostate Cancer.

Clin Cancer Res 2020 04 9;26(7):1678-1689. Epub 2020 Jan 9.

Vancouver Prostate Centre, Vancouver, British Columbia, Canada.

Purpose: Patients with metastatic prostate cancer are increasingly presenting with treatment-resistant, androgen receptor-negative/low (AR) tumors, with or without neuroendocrine characteristics, in processes attributed to tumor cell plasticity. This plasticity has been modeled by Rb1/p53 knockdown/knockout and is accompanied by overexpression of the pluripotency factor, Sox2. Here, we explore the role of the developmental transcription factor Sox9 in the process of prostate cancer therapy response and tumor progression.

Experimental Design: Unique prostate cancer cell models that capture AR stem cell-like intermediates were analyzed for features of plasticity and the functional role of Sox9. Human prostate cancer xenografts and tissue microarrays were evaluated for temporal alterations in Sox9 expression. The role of NF-κB pathway activity in Sox9 overexpression was explored.

Results: Prostate cancer stem cell-like intermediates have reduced Rb1 and p53 protein expression and overexpress Sox2 as well as Sox9. Sox9 was required for spheroid growth, and overexpression increased invasiveness and neural features of prostate cancer cells. Sox9 was transiently upregulated in castration-induced progression of prostate cancer xenografts and was specifically overexpressed in neoadjuvant hormone therapy (NHT)-treated patient tumors. High Sox9 expression in NHT-treated patients predicts biochemical recurrence. Finally, we link Sox9 induction to NF-κB dimer activation in prostate cancer cells.

Conclusions: Developmentally reprogrammed prostate cancer cell models recapitulate features of clinically advanced prostate tumors, including downregulated Rb1/p53 and overexpression of Sox2 with Sox9. Sox9 is a marker of a transitional state that identifies prostate cancer cells under the stress of therapeutic assault and facilitates progression to therapy resistance. Its expression may index the relative activity of the NF-κB pathway.
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http://dx.doi.org/10.1158/1078-0432.CCR-19-0098DOI Listing
April 2020

MOLI: multi-omics late integration with deep neural networks for drug response prediction.

Bioinformatics 2019 07;35(14):i501-i509

School of Computing Science, Simon Fraser University, Burnaby, BC, Canada.

Motivation: Historically, gene expression has been shown to be the most informative data for drug response prediction. Recent evidence suggests that integrating additional omics can improve the prediction accuracy which raises the question of how to integrate the additional omics. Regardless of the integration strategy, clinical utility and translatability are crucial. Thus, we reasoned a multi-omics approach combined with clinical datasets would improve drug response prediction and clinical relevance.

Results: We propose MOLI, a multi-omics late integration method based on deep neural networks. MOLI takes somatic mutation, copy number aberration and gene expression data as input, and integrates them for drug response prediction. MOLI uses type-specific encoding sub-networks to learn features for each omics type, concatenates them into one representation and optimizes this representation via a combined cost function consisting of a triplet loss and a binary cross-entropy loss. The former makes the representations of responder samples more similar to each other and different from the non-responders, and the latter makes this representation predictive of the response values. We validate MOLI on in vitro and in vivo datasets for five chemotherapy agents and two targeted therapeutics. Compared to state-of-the-art single-omics and early integration multi-omics methods, MOLI achieves higher prediction accuracy in external validations. Moreover, a significant improvement in MOLI's performance is observed for targeted drugs when training on a pan-drug input, i.e. using all the drugs with the same target compared to training only on drug-specific inputs. MOLI's high predictive power suggests it may have utility in precision oncology.

Availability And Implementation: https://github.com/hosseinshn/MOLI.

Supplementary Information: Supplementary data are available at Bioinformatics online.
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http://dx.doi.org/10.1093/bioinformatics/btz318DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC6612815PMC
July 2019

Metagenomic analysis reveals a rich bacterial content in high-risk prostate tumors from African men.

Prostate 2019 11 27;79(15):1731-1738. Epub 2019 Aug 27.

Laboratory for Human Comparative and Prostate Cancer Genomics, Garvan Institute of Medical Research, The Kinghorn Cancer Centre, Darlinghurst, New South Wales, Australia.

Background: Inflammation is a hallmark of prostate cancer (PCa), yet no pathogenic agent has been identified. Men from Africa are at increased risk for both aggressive prostate disease and infection. We hypothesize that pathogenic microbes may be contributing, at least in part, to high-risk PCa presentation within Africa and in turn the observed ethnic disparity.

Methods: Here we reveal through metagenomic analysis of host-derived whole-genome sequencing data, the microbial content within prostate tumor tissue from 22 men. What is unique about this study is that patients were separated by ethnicity, African vs European, and environments, Africa vs Australia.

Results: We identified 23 common bacterial genera between the African, Australian, and Chinese prostate tumor samples, while nonbacterial microbes were notably absent. While the most abundant genera across all samples included: Escherichia, Propionibacterium, and Pseudomonas, the core prostate tumor microbiota was enriched for Proteobacteria. We observed a significant increase in the richness of the bacterial communities within the African vs Australian samples (t = 4.6-5.5; P = .0004-.001), largely driven by eight predominant genera. Considering core human gut microbiota, African prostate tissue samples appear enriched for Escherichia and Acidovorax, with an abundance of Eubacterium associated with host tumor hypermutation.

Conclusions: Our study provides suggestive evidence for the presence of a core, bacteria-rich, prostate microbiome. While unable to exclude for fecal contamination, the observed increased bacterial content and richness within the African vs non-African samples, together with elevated tumor mutational burden, suggests the possibility that bacterially-driven oncogenic transformation within the prostate microenvironment may be contributing to aggressive disease presentation in Africa.
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http://dx.doi.org/10.1002/pros.23897DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC6790596PMC
November 2019

The evolution of long noncoding RNA acceptance in prostate cancer initiation, progression, and its clinical utility in disease management.

Eur Urol 2019 11 22;76(5):546-559. Epub 2019 Aug 22.

Vancouver Prostate Centre, Vancouver, BC, Canada; Department of Urologic Sciences, University of British Columbia, Vancouver, BC, Canada. Electronic address:

Context: It is increasingly evident that non-protein-coding regions of the genome can give rise to transcripts that form functional layers of the cancer genome. One of most abundant classes in these regions is long noncoding RNAs (lncRNAs). They have gained increasing attention in prostate cancer (PCa) and paved the way for a greater understanding of these cryptic regulators in cancer.

Objective: To review current research exploring the functional biology of lncRNAs in PCa over the past three decades.

Evidence Acquisition: A systematic review was performed using PubMed to search for reports with terms "long noncoding RNA", "prostate", and "cancer" over the past 30 yr (1988-2018).

Evidence Synthesis: We comprehensively surveyed the literature collected and summarise experiments leading to the characterisation of lncRNAs in PCa. A historical timeline of lncRNA identification is described, where each lncRNA is categorised mechanistically and within the primary areas of carcinogenesis: tumour risk and initiation, tumour promotion, tumour suppression, and tumour treatment resistance. We describe select lncRNAs that exemplify these areas. We also review whether these lncRNAs have a clinical utility in PCa diagnosis, prognosis, and prediction, and as therapeutic targets.

Conclusions: The biology of lncRNA is multifaceted, demonstrating a complex array of molecular and cellular functions. These studies reveal that lncRNAs are involved in every stage of PCa. Their clinical utility for diagnosis, prognosis, and prediction of PCa is well supported, but further evaluation for their therapeutic candidacy is needed. We provide a detailed resource and view inside the lncRNA landscape for other cancer biologists, oncologists, and clinicians.

Patient Summary: In this study, we review current knowledge of the non-protein-coding genome in prostate cancer (PCa). We conclude that many of these regions are functional and a source of accurate biomarkers in PCa. With a strong research foundation, they hold promise as future therapeutic targets, yet clinical trials are necessary to determine their intrinsic value to PCa disease management.
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http://dx.doi.org/10.1016/j.eururo.2019.07.040DOI Listing
November 2019

Characterization of transcriptomic signature of primary prostate cancer analogous to prostatic small cell neuroendocrine carcinoma.

Int J Cancer 2019 12 10;145(12):3453-3461. Epub 2019 Jun 10.

Department of Pathology & Laboratory Medicine, Arnie Charbonneau Cancer Institute, Cumming School of Medicine, University of Calgary, Calgary, AB, Canada.

Prostatic small cell neuroendocrine carcinoma (SC/NE) is well studied in metastatic castration-resistant prostate cancer; however, it is not well characterized in the primary setting. Herein, we used gene expression profiling of SC/NE prostate cancer (PCa) to develop a 212 gene signature to identify treatment-naïve primary prostatic tumors that are molecularly analogous to SC/NE (SC/NE-like PCa). The 212 gene signature was tested in several cohorts confirming similar molecular profile between prostatic SC/NE and small cell lung carcinoma. The signature was then translated into a genomic score (SCGScore) using modularized logistic regression modeling and validated in four independent cohorts achieving an average AUC >0.95. The signature was evaluated in more than 25,000 primary adenocarcinomas to characterize the biology, prognosis and potential therapeutic response of predicted SC/NE-like tumors. Assessing SCGScore in a prospective cohort of 17,967 RP and 6,697 biopsy treatment-naïve primary tumors from the Decipher Genomic Resource Information Database registry, approximately 1% of the patients were found to have a SC/NE-like transcriptional profile, whereas 0.5 and 3% of GG1 and GG5 patients respectively showed to be SC/NE-like. More than 80% of these patients are genomically high-risk based on Decipher score. Interrogating in vitro drug sensitivity analyses, SC/NE-like prostatic tumors showed higher response to PARP and HDAC inhibitors.
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http://dx.doi.org/10.1002/ijc.32430DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC6852174PMC
December 2019

Combinatorial Detection of Conserved Alteration Patterns for Identifying Cancer Subnetworks.

Gigascience 2019 04;8(4)

Laboratory for Advanced Genome Analysis, Vancouver Prostate Centre, 2660 Oak St, Vancouver, BC, V6H 3Z6, Canada.

Background: Advances in large-scale tumor sequencing have led to an understanding that there are combinations of genomic and transcriptomic alterations specific to tumor types, shared across many patients. Unfortunately, computational identification of functionally meaningful and recurrent alteration patterns within gene/protein interaction networks has proven to be challenging.

Findings: We introduce a novel combinatorial method, cd-CAP (combinatorial detection of conserved alteration patterns), for simultaneous detection of connected subnetworks of an interaction network where genes exhibit conserved alteration patterns across tumor samples. Our method differentiates distinct alteration types associated with each gene (rather than relying on binary information of a gene being altered or not) and simultaneously detects multiple alteration profile conserved subnetworks.

Conclusions: In a number of The Cancer Genome Atlas datasets, cd-CAP identified large biologically significant subnetworks with conserved alteration patterns, shared across many tumor samples.
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http://dx.doi.org/10.1093/gigascience/giz024DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC6458499PMC
April 2019

RNA Splicing of the BHC80 Gene Contributes to Neuroendocrine Prostate Cancer Progression.

Eur Urol 2019 08 23;76(2):157-166. Epub 2019 Mar 23.

Vancouver Prostate Centre, Department of Urologic Sciences, University of British Columbia, Vancouver, BC, Canada. Electronic address:

Background: Prostate adenocarcinoma (AdPC) progression to treatment-induced neuroendocrine prostate cancer (t-NEPC) is associated with poor patient survival. While AdPC and t-NEPC share similar genomes, they possess distinct transcriptomes, suggesting that RNA splicing and epigenetic mechanisms may regulate t-NEPC development.

Objective: To characterize the role of alternative RNA splicing of the histone demethylase BHC80 during t-NEPC progression.

Design, Setting, And Participants: The expression of BHC80 splice variants (BHC80-1 and BHC80-2) were compared between AdPC and t-NEPC patient tumors. Regulatory mechanisms of RNA splicing of the BHC80 gene were studied, and the signal pathways mediated by BHC80 splice variants were investigated in t-NEPC cell and xenograft models.

Results: Global transcriptome analyses identified that the BHC80-2 variant is highly expressed in t-NEPC. Compared with the known histone demethylation activities of the BHC80 gene, we discovered a novel nonepigenetic action of BHC80-2, whereby BHC80-2 is localized in the cytoplasm to trigger the MyD88-p38-TTP pathway, which results in increased RNA stability of multiple tumor-promoting cytokines. While BHC80-2 does not induce neuroendocrine differentiation of cancer cells, it stimulates cell proliferation and tumor progression independent of androgen receptor signaling. Blockade of BHC80-2-regulated MyD88 signaling suppresses growth of several t-NEPC cell spheroid and xenograft models.

Conclusions: Gain of function of BHC80-2 through alternative RNA splicing activates immune responses of cancer cells to promote t-NEPC development.

Patient Summary: The main obstacle to develop effective therapies for patients with t-NEPC is the lack of understanding on how t-NEPC is developed. Our study not only identifies a previously unknown BHC80-2-MyD88 signaling pathway that plays an important role during t-NEPC development, but also provides a proof of principle that targeting this signal pathway may offer an avenue to treat t-NEPC.
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http://dx.doi.org/10.1016/j.eururo.2019.03.011DOI Listing
August 2019

The Proteogenomic Landscape of Curable Prostate Cancer.

Cancer Cell 2019 03;35(3):414-427.e6

Department of Medical Biophysics, University of Toronto, Toronto, ON M5G 1L7, Canada; Ontario Institute for Cancer Research, Toronto, ON M5G 0A3, Canada; Department of Pharmacology and Toxicology, University of Toronto, Toronto, ON M5S 1A8, Canada; Department of Human Genetics, University of California, 12-109 CHS, 10833 Le Conte Avenue, Los Angeles, CA 90095, USA; Department of Urology, University of California, Los Angeles, CA 90024, USA; Jonsson Comprehensive Cancer Centre, University of California, Los Angeles, CA 90024, USA; Institute for Precision Health, University of California, Los Angeles, CA 90024, USA. Electronic address:

DNA sequencing has identified recurrent mutations that drive the aggressiveness of prostate cancers. Surprisingly, the influence of genomic, epigenomic, and transcriptomic dysregulation on the tumor proteome remains poorly understood. We profiled the genomes, epigenomes, transcriptomes, and proteomes of 76 localized, intermediate-risk prostate cancers. We discovered that the genomic subtypes of prostate cancer converge on five proteomic subtypes, with distinct clinical trajectories. ETS fusions, the most common alteration in prostate tumors, affect different genes and pathways in the proteome and transcriptome. Globally, mRNA abundance changes explain only ∼10% of protein abundance variability. As a result, prognostic biomarkers combining genomic or epigenomic features with proteomic ones significantly outperform biomarkers comprised of a single data type.
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http://dx.doi.org/10.1016/j.ccell.2019.02.005DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC6511374PMC
March 2019

BAP1 haploinsufficiency predicts a distinct immunogenic class of malignant peritoneal mesothelioma.

Genome Med 2019 02 18;11(1). Epub 2019 Feb 18.

Vancouver Prostate Centre, 2660 Oak St, Vancouver, BC, V6H 3Z6, Canada.

Background: Malignant peritoneal mesothelioma (PeM) is a rare and fatal cancer that originates from the peritoneal lining of the abdomen. Standard treatment of PeM is limited to cytoreductive surgery and/or chemotherapy, and no effective targeted therapies for PeM exist. Some immune checkpoint inhibitor studies of mesothelioma have found positivity to be associated with a worse prognosis.

Methods: To search for novel therapeutic targets for PeM, we performed a comprehensive integrative multi-omics analysis of the genome, transcriptome, and proteome of 19 treatment-naïve PeM, and in particular, we examined BAP1 mutation and copy number status and its relationship to immune checkpoint inhibitor activation.

Results: We found that PeM could be divided into tumors with an inflammatory tumor microenvironment and those without and that this distinction correlated with haploinsufficiency of BAP1. To further investigate the role of BAP1, we used our recently developed cancer driver gene prioritization algorithm, HIT'nDRIVE, and observed that PeM with BAP1 haploinsufficiency form a distinct molecular subtype characterized by distinct gene expression patterns of chromatin remodeling, DNA repair pathways, and immune checkpoint receptor activation. We demonstrate that this subtype is correlated with an inflammatory tumor microenvironment and thus is a candidate for immune checkpoint blockade therapies.

Conclusions: Our findings reveal BAP1 to be a potential, easily trackable prognostic and predictive biomarker for PeM immunotherapy that refines PeM disease classification. BAP1 stratification may improve drug response rates in ongoing phases I and II clinical trials exploring the use of immune checkpoint blockade therapies in PeM in which BAP1 status is not considered. This integrated molecular characterization provides a comprehensive foundation for improved management of a subset of PeM patients.
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http://dx.doi.org/10.1186/s13073-019-0620-3DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC6378747PMC
February 2019

Metagenomic and metatranscriptomic analysis of human prostate microbiota from patients with prostate cancer.

BMC Genomics 2019 Feb 18;20(1):146. Epub 2019 Feb 18.

Vancouver Prostate Centre, Vancouver, Canada.

Background: Prostate cancer (PCa) is the most common malignant neoplasm among men in many countries. Since most precancerous and cancerous tissues show signs of inflammation, chronic bacterial prostatitis has been hypothesized to be a possible etiology. However, establishing a causal relationship between microbial inflammation and PCa requires a comprehensive analysis of the prostate microbiome. The aim of this study was to characterize the microbiome in prostate tissue of PCa patients and investigate its association with tumour clinical characteristics as well as host expression profiles.

Results: The metagenome and metatranscriptome of tumour and the adjacent benign tissues were assessed in 65 Chinese radical prostatectomy specimens. Escherichia, Propionibacterium, Acinetobacter and Pseudomonas were abundant in both metagenome and metatranscriptome, thus constituting the core of the prostate microbiome. The biodiversity of the microbiomes could not be differentiated between the matched tumour/benign specimens or between the tumour specimens of low and high Gleason Scores. The expression profile of ten Pseudomonas genes was strongly correlated with that of eight host small RNA genes; three of the RNA genes may negatively associate with metastasis. Few viruses could be identified from the prostate microbiomes.

Conclusions: This is the first study of the human prostate microbiome employing an integrated metagenomics and metatranscriptomics approach. In this Chinese cohort, both metagenome and metatranscriptome analyses showed a non-sterile microenvironment in the prostate of PCa patients, but we did not find links between the microbiome and local progression of PCa. However, the correlated expression of Pseudomonas genes and human small RNA genes may provide tantalizing preliminary evidence that Pseudomonas infection may impede metastasis.
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http://dx.doi.org/10.1186/s12864-019-5457-zDOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC6379980PMC
February 2019

The long noncoding RNA HORAS5 mediates castration-resistant prostate cancer survival by activating the androgen receptor transcriptional program.

Mol Oncol 2019 05 5;13(5):1121-1136. Epub 2019 Mar 5.

British Columbia Cancer Research Centre, Vancouver, Canada.

Prostate cancer (PCa) is driven by the androgen receptor (AR)-signaling axis. Hormonal therapy often mitigates PCa progression, but a notable number of cases progress to castration-resistant PCa (CRPC). CRPC retains AR activity and is incurable. Long noncoding RNA (lncRNA) represent an uncharted region of the transcriptome. Several lncRNA have been recently described to mediate oncogenic functions, suggesting that these molecules can be potential therapeutic targets. Here, we identified CRPC-associated lncRNA by analyzing patient-derived xenografts (PDXs) and clinical data. Subsequently, we characterized one of the CRPC-promoting lncRNA, HORAS5, in vitro and in vivo. We demonstrated that HORAS5 is a stable, cytoplasmic lncRNA that promotes CRPC proliferation and survival by maintaining AR activity under androgen-depleted conditions. Most strikingly, knockdown of HORAS5 causes a significant reduction in the expression of AR itself and oncogenic AR targets such as KIAA0101. Elevated expression of HORAS5 is also associated with worse clinical outcomes in patients. Our results from HORAS5 inhibition in in vivo models further confirm that HORAS5 is a viable therapeutic target for CRPC. Thus, we posit that HORAS5 is a novel, targetable mediator of CRPC through its essential role in the maintenance of oncogenic AR activity. Overall, this study adds to our mechanistic understanding of how lncRNA function in cancer progression.
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http://dx.doi.org/10.1002/1878-0261.12471DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC6487714PMC
May 2019

Structural variation and fusion detection using targeted sequencing data from circulating cell free DNA.

Nucleic Acids Res 2019 04;47(7):e38

Department of Urologic Sciences, Faculty of Medicine, University of British Columbia, Vancouver, British Columbia V52 1M9, Canada.

Motivation: Cancer is a complex disease that involves rapidly evolving cells, often forming multiple distinct clones. In order to effectively understand progression of a patient-specific tumor, one needs to comprehensively sample tumor DNA at multiple time points, ideally obtained through inexpensive and minimally invasive techniques. Current sequencing technologies make the 'liquid biopsy' possible, which involves sampling a patient's blood or urine and sequencing the circulating cell free DNA (cfDNA). A certain percentage of this DNA originates from the tumor, known as circulating tumor DNA (ctDNA). The ratio of ctDNA may be extremely low in the sample, and the ctDNA may originate from multiple tumors or clones. These factors present unique challenges for applying existing tools and workflows to the analysis of ctDNA, especially in the detection of structural variations which rely on sufficient read coverage to be detectable.

Results: Here we introduce SViCT , a structural variation (SV) detection tool designed to handle the challenges associated with cfDNA analysis. SViCT can detect breakpoints and sequences of various structural variations including deletions, insertions, inversions, duplications and translocations. SViCT extracts discordant read pairs, one-end anchors and soft-clipped/split reads, assembles them into contigs, and re-maps contig intervals to a reference genome using an efficient k-mer indexing approach. The intervals are then joined using a combination of graph and greedy algorithms to identify specific structural variant signatures. We assessed the performance of SViCT and compared it to state-of-the-art tools using simulated cfDNA datasets with properties matching those of real cfDNA samples. The positive predictive value and sensitivity of our tool was superior to all the tested tools and reasonable performance was maintained down to the lowest dilution of 0.01% tumor DNA in simulated datasets. Additionally, SViCT was able to detect all known SVs in two real cfDNA reference datasets (at 0.6-5% ctDNA) and predict a novel structural variant in a prostate cancer cohort.

Availability: SViCT is available at https://github.com/vpc-ccg/svict. Contact:faraz.hach@ubc.ca.
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http://dx.doi.org/10.1093/nar/gkz067DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC6468241PMC
April 2019

Widespread and Functional RNA Circularization in Localized Prostate Cancer.

Cell 2019 02;176(4):831-843.e22

Department of Medical Biophysics, University of Toronto, Toronto, Ontario, Canada; Princess Margaret Cancer Center, University Health Network, Toronto, Ontario, Canada. Electronic address:

The cancer transcriptome is remarkably complex, including low-abundance transcripts, many not polyadenylated. To fully characterize the transcriptome of localized prostate cancer, we performed ultra-deep total RNA-seq on 144 tumors with rich clinical annotation. This revealed a linear transcriptomic subtype associated with the aggressive intraductal carcinoma sub-histology and a fusion profile that differentiates localized from metastatic disease. Analysis of back-splicing events showed widespread RNA circularization, with the average tumor expressing 7,232 circular RNAs (circRNAs). The degree of circRNA production was correlated to disease progression in multiple patient cohorts. Loss-of-function screening identified 11.3% of highly abundant circRNAs as essential for cell proliferation; for ∼90% of these, their parental linear transcripts were not essential. Individual circRNAs can have distinct functions, with circCSNK1G3 promoting cell growth by interacting with miR-181. These data advocate for adoption of ultra-deep RNA-seq without poly-A selection to interrogate both linear and circular transcriptomes.
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http://dx.doi.org/10.1016/j.cell.2019.01.025DOI Listing
February 2019

Markers of MEK inhibitor resistance in low-grade serous ovarian cancer: EGFR is a potential therapeutic target.

Cancer Cell Int 2019 8;19:10. Epub 2019 Jan 8.

1Obstetrics and Gynecology, University of British Columbia, Vancouver, BC Canada.

Background: Although low-grade serous ovarian cancer (LGSC) is rare, case-fatality rates are high as most patients present with advanced disease and current cytotoxic therapies are not overly effective. Recognizing that these cancers may be driven by MAPK pathway activation, MEK inhibitors (MEKi) are being tested in clinical trials. LGSC respond to MEKi only in a subgroup of patients, so predictive biomarkers and better therapies will be needed.

Methods: We evaluated a number of patient-derived LGSC cell lines, previously classified according to their MEKi sensitivity. Two cell lines were genomically compared against their matching tumors samples. MEKi-sensitive and MEKi-resistant lines were compared using whole exome sequencing and reverse phase protein array. Two treatment combinations targeting MEKi resistance markers were also evaluated using cell proliferation, cell viability, cell signaling, and drug synergism assays.

Results: Low-grade serous ovarian cancer cell lines recapitulated the genomic aberrations from their matching tumor samples. We identified three potential predictive biomarkers that distinguish MEKi sensitive and resistant lines: mutation status, and EGFR and PKC-alpha protein expression. The biomarkers were validated in three newly developed LGSC cell lines. Sub-lethal combination of MEK and EGFR inhibition showed drug synergy and caused complete cell death in two of four MEKi-resistant cell lines tested.

Conclusions: mutations and the protein expression of EGFR and PKC-alpha should be evaluated as predictive biomarkers in patients with LGSC treated with MEKi. Combination therapy using a MEKi with EGFR inhibition may represent a promising new therapy for patients with MEKi-resistant LGSC.
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http://dx.doi.org/10.1186/s12935-019-0725-1DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC6325847PMC
January 2019

Modeling human prostate cancer progression in vitro.

Carcinogenesis 2019 07;40(7):893-902

Department of Urology, University of Wisconsin-Madison, Madison, WI, USA.

Detailed mechanisms involved in prostate cancer (CaP) development and progression are not well understood. Current experimental models used to study CaP are not well suited to address this issue. Previously, we have described the hormonal progression of non-tumorigenic human prostate epithelial cells (BPH1) into malignant cells via tissue recombination. Here, we describe a method to derive human cell lines from distinct stages of CaP that parallel cellular, genetic and epigenetic changes found in patients with cancers. This BPH1-derived Cancer Progression (BCaP) model represents different stages of cancer. Using diverse analytical strategies, we show that the BCaP model reproduces molecular characteristics of CaP in human patients. Furthermore, we demonstrate that BCaP cells have altered gene expression of shared pathways with human and transgenic mouse CaP data, as well as, increasing genomic instability with TMPRSS2-ERG fusion in advanced tumor cells. Together, these cell lines represent a unique model of human CaP progression providing a novel tool that will allow the discovery and experimental validation of mechanisms regulating human CaP development and progression. This BPH1-derived Cancer Progression (BCaP) model represents different stages of cancer. The BCaP model reproduces molecular characteristics of prostate cancer. The cells have altered gene expression with TMPRSS2-ERG fusion representing a unique model for prostate cancer progression.
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http://dx.doi.org/10.1093/carcin/bgy185DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC6642366PMC
July 2019

Alignment-free clustering of UMI tagged DNA molecules.

Bioinformatics 2019 06;35(11):1829-1836

Vancouver Prostate Centre, Vancouver BC, Canada.

Motivation: Next-Generation Sequencing has led to the availability of massive genomic datasets whose processing raises many challenges, including the handling of sequencing errors. This is especially pertinent in cancer genomics, e.g. for detecting low allele frequency variations from circulating tumor DNA. Barcode tagging of DNA molecules with unique molecular identifiers (UMI) attempts to mitigate sequencing errors; UMI tagged molecules are polymerase chain reaction (PCR) amplified, and the PCR copies of UMI tagged molecules are sequenced independently. However, the PCR and sequencing steps can generate errors in the sequenced reads that can be located in the barcode and/or the DNA sequence. Analyzing UMI tagged sequencing data requires an initial clustering step, with the aim of grouping reads sequenced from PCR duplicates of the same UMI tagged molecule into a single cluster, and the size of the current datasets requires this clustering process to be resource-efficient.

Results: We introduce Calib, a computational tool that clusters paired-end reads from UMI tagged sequencing experiments generated by substitution-error-dominant sequencing platforms such as Illumina. Calib clusters are defined as connected components of a graph whose edges are defined in terms of both barcode similarity and read sequence similarity. The graph is constructed efficiently using locality sensitive hashing and MinHashing techniques. Calib's default clustering parameters are optimized empirically, for different UMI and read lengths, using a simulation module that is packaged with Calib. Compared to other tools, Calib has the best accuracy on simulated data, while maintaining reasonable runtime and memory footprint. On a real dataset, Calib runs with far less resources than alignment-based methods, and its clusters reduce the number of tentative false positive in downstream variation calling.

Availability And Implementation: Calib is implemented in C++ and its simulation module is implemented in Python. Calib is available at https://github.com/vpc-ccg/calib.

Supplementary Information: Supplementary data are available at Bioinformatics online.
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http://dx.doi.org/10.1093/bioinformatics/bty888DOI Listing
June 2019

Proteogenomic Characterization of Patient-Derived Xenografts Highlights the Role of REST in Neuroendocrine Differentiation of Castration-Resistant Prostate Cancer.

Clin Cancer Res 2019 01 1;25(2):595-608. Epub 2018 Oct 1.

Department of Drug Design and Pharmacology, Faculty of Health and Medical Sciences, University of Copenhagen, Copenhagen, Denmark.

Purpose: An increasing number of castration-resistant prostate cancer (CRPC) tumors exhibit neuroendocrine (NE) features. NE prostate cancer (NEPC) has poor prognosis, and its development is poorly understood. We applied mass spectrometry-based proteomics to a unique set of 17 prostate cancer patient-derived xenografts (PDX) to characterize the effects of castration , and the proteome differences between NEPC and prostate adenocarcinomas. Genome-wide profiling of REST-occupied regions in prostate cancer cells was correlated to the expression changes to investigate the role of the transcriptional repressor REST in castration-induced NEPC differentiation.

Results: An average of 4,881 proteins were identified and quantified from each PDX. Proteins related to neurogenesis, cell-cycle regulation, and DNA repair were found upregulated and elevated in NEPC, while the reduced levels of proteins involved in mitochondrial functions suggested a prevalent glycolytic metabolism of NEPC tumors. Integration of the REST chromatin bound regions with expression changes indicated a direct role of REST in regulating neuronal gene expression in prostate cancer cells. Mechanistically, depletion of REST led to cell-cycle arrest in G, which could be rescued by p53 knockdown. Finally, the expression of the REST-regulated gene secretagogin (SCGN) correlated with an increased risk of suffering disease relapse after radical prostatectomy.

Conclusions: This study presents the first deep characterization of the proteome of NEPC and suggests that concomitant inhibition of REST and the p53 pathway would promote NEPC. We also identify SCGN as a novel prognostic marker in prostate cancer.
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http://dx.doi.org/10.1158/1078-0432.CCR-18-0729DOI Listing
January 2019

Pre-clinical Models for Malignant Mesothelioma Research: From Chemical-Induced to Patient-Derived Cancer Xenografts.

Front Genet 2018 4;9:232. Epub 2018 Jul 4.

Department of Urologic Sciences, Vancouver Prostate Centre, University of British Columbia, Vancouver, BC, Canada.

Malignant mesothelioma (MM) is a rare disease often associated with environmental exposure to asbestos and other erionite fibers. MM has a long latency period prior to manifestation and a poor prognosis. The survival post-diagnosis is often less than a year. Although use of asbestos has been banned in the United States and many European countries, asbestos is still being used and extracted in many developing countries. Occupational exposure to asbestos, mining, and migration are reasons that we expect to continue to see growing incidence of mesothelioma in the coming decades. Despite improvements in survival achieved with multimodal therapies and cytoreductive surgeries, less morbid, more effective interventions are needed. Thus, identifying prognostic and predictive biomarkers for MM, and developing novel agents for targeted therapy, are key unmet needs in mesothelioma research and treatment. In this review, we discuss the evolution of pre-clinical model systems developed to study MM and emphasize the remarkable capability of patient-derived xenograft (PDX) MM models in expediting the pre-clinical development of novel therapeutic approaches. PDX disease model systems retain major characteristics of original malignancies with high fidelity, including molecular, histopathological and functional heterogeneities, and as such play major roles in translational research, drug development, and precision medicine.
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http://dx.doi.org/10.3389/fgene.2018.00232DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC6040159PMC
July 2018

Targeting MCT4 to reduce lactic acid secretion and glycolysis for treatment of neuroendocrine prostate cancer.

Cancer Med 2018 Jun 14. Epub 2018 Jun 14.

The Vancouver Prostate Centre, Vancouver General Hospital, The University of British Columbia, Vancouver, BC, Canada.

Development of neuroendocrine prostate cancer (NEPC) is emerging as a major problem in clinical management of advanced prostate cancer (PCa). As increasingly potent androgen receptor (AR)-targeting antiandrogens are more widely used, PCa transdifferentiation into AR-independent NEPC as a mechanism of treatment resistance becomes more common and precarious, since NEPC is a lethal PCa subtype urgently requiring effective therapy. Reprogrammed glucose metabolism of cancers, that is elevated aerobic glycolysis involving increased lactic acid production/secretion, plays a key role in multiple cancer-promoting processes and has been implicated in therapeutics development. Here, we examined NEPC glucose metabolism using our unique panel of patient-derived xenograft PCa models and patient tumors. By calculating metabolic pathway scores using gene expression data, we found that elevated glycolysis coupled to increased lactic acid production/secretion is an important metabolic feature of NEPC. Specific inhibition of expression of MCT4 (a plasma membrane lactic acid transporter) by antisense oligonucleotides led to reduced lactic acid secretion as well as reduced glucose metabolism and NEPC cell proliferation. Taken together, our results indicate that elevated glycolysis coupled to excessive MCT4-mediated lactic acid secretion is clinically relevant and functionally important to NEPC. Inhibition of MCT4 expression appears to be a promising therapeutic strategy for NEPC.
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http://dx.doi.org/10.1002/cam4.1587DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC6051138PMC
June 2018