Francis Nguyen

Spatial genomic heterogeneity within localized, multifocal prostate cancer

Herein we provide a detailed molecular analysis of the spatial heterogeneity of clinically localized, multifocal prostate cancer to delineate new oncogenes or tumor suppressors. We initially determined the copy number aberration (CNA) profiles of 74 patients with index tumors of Gleason score 7. Of these, 5 patients were subjected to whole-genome sequencing using DNA quantities achievable in diagnostic biopsies, with detailed spatial sampling of 23 distinct tumor regions to assess intraprostatic heterogeneity in focal genomics. Multifocal tumors are highly heterogeneous for single-nucleotide variants (SNVs), CNAs and genomic rearrangements. We identified and validated a new recurrent amplification of MYCL, which is associated with TP53 deletion and unique profiles of DNA damage and transcriptional dysregulation. Moreover, we demonstrate divergent tumor evolution in multifocal cancer and, in some cases, tumors of independent clonal origin. These data represent the first systematic relation of intraprostatic genomic heterogeneity to predicted clinical outcome and inform the development of novel biomarkers that reflect individual prognosis.

Genomic hallmarks of localized, non-indolent prostate cancer

Prostate tumours are highly variable in their response to therapies, but clinically available prognostic factors can explain only a fraction of this heterogeneity. Here we analysed 200 whole-genome sequences and 277 additional whole-exome sequences from localized, non-indolent prostate tumours with similar clinical risk profiles, and carried out RNA and methylation analyses in a subset. These tumours had a paucity of clinically actionable single nucleotide variants, unlike those seen in metastatic disease. Rather, a significant proportion of tumours harboured recurrent non-coding aberrations, large-scale genomic rearrangements, and alterations in which an inversion repressed transcription within its boundaries. Local hypermutation events were frequent, and correlated with specific genomic profiles. Numerous molecular aberrations were prognostic for disease recurrence, including several DNA methylation events, and a signature comprised of these aberrations outperformed well-described prognostic biomarkers. We suggest that intensified treatment of genomically aggressive localized prostate cancer may improve cure rates.

The prognostic value of temporal in vitro and in vivo derived hypoxia gene-expression signatures in breast cancer

BACKGROUND AND PURPOSE Recent data suggest that in vitro and in vivo derived hypoxia gene-expression signatures have prognostic power in breast and possibly other cancers. However, both tumour hypoxia and the biological adaptation to this stress are highly dynamic. Assessment of time-dependent gene-expression changes in response to hypoxia may thus provide additional biological insights and assist in predicting the impact of hypoxia on patient prognosis. MATERIALS AND METHODS Transcriptome profiling was performed for three cell lines derived from diverse tumour-types after hypoxic exposure at eight time-points, which include a normoxic time-point. Time-dependent sets of co-regulated genes were identified from these data. Subsequently, gene ontology (GO) and pathway analyses were performed. The prognostic power of these novel signatures was assessed in parallel with previous in vitro and in vivo derived hypoxia signatures in a large breast cancer microarray meta-dataset (n=2312). RESULTS We identified seven recurrent temporal and two general hypoxia signatures. GO and pathway analyses revealed regulation of both common and unique underlying biological processes within these signatures. None of the new or previously published in vitro signatures consisting of hypoxia-induced genes were prognostic in the large breast cancer dataset. In contrast, signatures of repressed genes, as well as the in vivo derived signatures of hypoxia-induced genes showed clear prognostic power. CONCLUSIONS Only a subset of hypoxia-induced genes in vitro demonstrates prognostic value when evaluated in a large clinical dataset. Despite clear evidence of temporal patterns of gene-expression in vitro, the subset of prognostic hypoxia regulated genes cannot be identified based on temporal pattern alone. In vivo derived signatures appear to identify the prognostic hypoxia induced genes. The prognostic value of hypoxia-repressed genes is likely a surrogate for the known importance of proliferation in breast cancer outcome.

Expression Profiling during Mammary Epithelial Cell Three-Dimensional Morphogenesis Identifies PTPRO as a Novel Regulator of Morphogenesis and ErbB2-Mediated Transformation

ABSTRACT Identification of genes that are upregulated during mammary epithelial cell morphogenesis may reveal novel regulators of tumorigenesis. We have demonstrated that gene expression programs in mammary epithelial cells grown in monolayer cultures differ significantly from those in three-dimensional (3D) cultures. We identify a protein tyrosine phosphate, PTPRO, that was upregulated in mature MCF-10A mammary epithelial 3D structures but had low to undetectable levels in monolayer cultures. Downregulation of PTPRO by RNA interference inhibited proliferation arrest during morphogenesis. Low levels of PTPRO expression correlated with reduced survival for breast cancer patients, suggesting a tumor suppressor function. Furthermore, we showed that the receptor tyrosine kinase ErbB2/HER2 is a direct substrate of PTPRO and that loss of PTPRO increased ErbB2-induced cell proliferation and transformation, together with tyrosine phosphorylation of ErbB2. Moreover, in patients with ErbB2-positive breast tumors, low PTPRO expression correlated with poor clinical prognosis compared to ErbB2-positive patients with high levels of PTPRO. Thus, PTPRO is a novel regulator of ErbB2 signaling, a potential tumor suppressor, and a novel prognostic marker for patients with ErbB2-positive breast cancers. We have identified the protein tyrosine phosphatase PTPRO as a regulator of three-dimensional epithelial morphogenesis of mammary epithelial cells and as a regulator of ErbB2-mediated transformation. In addition, we demonstrated that ErbB2 is a direct substrate of PTPRO and that decreased expression of PTPRO predicts poor prognosis for ErbB2-positive breast cancer patients. Thus, our results identify PTPRO as a novel regulator of mammary epithelial transformation, a potential tumor suppressor, and a predictive biomarker for breast cancer.

ISOpureR: an R implementation of a computational purification algorithm of mixed tumour profiles

BackgroundTumour samples containing distinct sub-populations of cancer and normal cells present challenges in the development of reproducible biomarkers, as these biomarkers are based on bulk signals from mixed tumour profiles. ISOpure is the only mRNA computational purification method to date that does not require a paired tumour-normal sample, provides a personalized cancer profile for each patient, and has been tested on clinical data. Replacing mixed tumour profiles with ISOpure-preprocessed cancer profiles led to better prognostic gene signatures for lung and prostate cancer.ResultsTo simplify the integration of ISOpure into standard R-based bioinformatics analysis pipelines, the algorithm has been implemented as an R package. The ISOpureR package performs analogously to the original code in estimating the fraction of cancer cells and the patient cancer mRNA abundance profile from tumour samples in four cancer datasets.ConclusionsThe ISOpureR package estimates the fraction of cancer cells and personalized patient cancer mRNA abundance profile from a mixed tumour profile. This open-source R implementation enables integration into existing computational pipelines, as well as easy testing, modification and extension of the model.

Integrating RAS Status into Prognostic Signatures for Adenocarcinomas of the Lung

Purpose: While the dysregulation of specific pathways in cancer influences both treatment response and outcome, few current prognostic markers explicitly consider differential pathway activation. Here we explore this concept, focusing on K-Ras mutations in lung adenocarcinoma (present in 25%–35% of patients). Experimental Design: The effect of K-Ras mutation status on prognostic accuracy of existing signatures was evaluated in 404 patients. Genes associated with K-Ras mutation status were identified and used to create a RAS pathway activation classifier to provide a more accurate measure of RAS pathway status. Next, 8 million random signatures were evaluated to assess differences in prognosing patients with or without RAS activation. Finally, a prognostic signature was created to target patients with RAS pathway activation. Results: We first show that K-Ras status influences the accuracy of existing prognostic signatures, which are effective in K-Ras-wild-type patients but fail in patients with K-Ras mutations. Next, we show that it is fundamentally more difficult to predict the outcome of patients with RAS activation (RASmt) than that of those without (RASwt). More importantly, we demonstrate that different signatures are prognostic in RASwt and RASmt. Finally, to exploit this discovery, we create separate prognostic signatures for RASwt and RASmt patients and show that combining them significantly improves predictions of patient outcome. Conclusions: We present a nested model for integrated genomic and transcriptomic data. This model is general and is not limited to lung adenocarcinomas but can be expanded to other tumor types and oncogenes. Clin Cancer Res; 21(6); 1477–86. ©2015 AACR.

BPG: Seamless, Automated and Interactive Visualization of Scientific Data

We introduce BPG, an easy-to-use framework for generating publication-quality, highly-customizable plots in the R statistical environment. This open-source package includes novel methods of displaying high-dimensional datasets and facilitates generation of complex multi-panel figures, making it ideal for complex datasets. A web-based interactive tool allows online figure customization, from which R code can be downloaded for seamless integration with computational pipelines. BPG is available at http://labs.oicr.on.ca/boutros-lab/software/bpg

Network-Based Biomarkers Enable Cross-Disease Biomarker Discovery

Biomarkers lie at the heart of precision medicine, biodiversity monitoring, agricultural pathogen detection, amongst others. Surprisingly, while rapid genomic profiling is becoming ubiquitous, the development of biomarkers almost always involves the application of bespoke techniques that cannot be directly applied to other datasets. There is an urgent need for a systematic methodology to create biologically-interpretable molecular models that robustly predict key phenotypes. We therefore created SIMMS: an algorithm that fragments pathways into functional modules and uses these to predict phenotypes. We applied SIMMS to multiple data-types across four diseases, and in each it reproducibly identified subtypes, made superior predictions to the best bespoke approaches, and identified known and novel signaling nodes. To demonstrate its ability on a new dataset, we measured 33 genes/nodes of the PIK3CA pathway in 1,734 FFPE breast tumours and created a four-subnetwork prediction model. This model significantly out-performed existing clinically-used molecular tests in an independent 1,742-patient validation cohort. SIMMS is generic and can work with any molecular data or biological network, and is freely available at: https://cran.r-project.org/web/packages/SIMMS.

The transcriptomic profile of ovarian cancer grading

Ovarian carcinoma is the leading cause of gynecological malignancy, with the serous subtype being the most commonly presented subtype. Recent studies have demonstrated that grade does not yield significant prognostic information, independent of TNM staging. As such, several different grading systems have been proposed to reveal morphological characteristics of these tumors, however each yield different results. To help address this issue, we performed a rigorous computational analysis to better understand the molecular differences that fundamentally explain the different grades and grading systems. mRNA abundance levels were analyzed across 334 total patients and their association with each grade and grading system were assessed. Few molecular differences were observed between grade 2 and 3 tumors when using the International Federation of Gynecology and Obstetrics (FIGO) grading system, suggesting their molecular similarity. In contrast, grading by the Silverberg system reveals that grades 1–3 are molecularly equidistant from one another across a spectrum. Additionally, we have identified a few candidate genes with good prognostic information that could potentially be used for classifying cases with similar morphological appearances.

Abstract 2003: A molecular portrait of potentially curable prostate cancer.

Intermediate risk prostate cancer (CaP) with Gleason score (GS) of 7 show up to 100x variability in genetic instability. As CaP is multifocal and likely multiclonal, there is a need to characterize heterogeneity for patient stratification, which would increase the ability to act on genomic information by adding adjuvant therapies to offset systemic occult metastases that currently limit cure in ∼30% of patients. Individual genetic portraits could be used to improve cure on combined clinical-molecular staging criteria. We undertook a pilot study to assess the genetic heterogeneity of potentially curable GS=7 CaP. We selected 10 men with GS=7 CaP; 5 treated with external beam radiotherapy (frozen pre-treatment biopsies) and 5 treated with radical prostatectomy (RadP, frozen tumour). Additionally, DNA from 18 distinct formalin-fixed, paraffin-embedded (FFPE) foci from the 5 RadP were analysed. Each of these 28 foci were subjected to whole-genome sequencing (WGS) and OncoScan SNP arrays to yield comprehensive genetic profiles. mRNA expression was evaluated on frozen RadP by microarray. Germline DNA from whole-blood was also analysed. Following independent pathology reviews and manual macro-dissection of tumour areas of ≥70% cellularity, WGS (≥50x tumour, ≥30x germline) was performed on as little as 50 ng genomic DNA, and OncoScan arrays were performed using as little as 30ng DNA using either amplified or innate genomic DNA. Regions of CaP in FFPE RadP were recorded using a tissue map to identify independent malignant foci, and ERG immunostaining was performed to assist in the identification. In cases where ERG-positive and -negative foci were adjacent, ERG staining was repeated on an un-stained slide to confirm separate foci based on 3D multi-section analyses. ERG fusion status was also assessed in frozen samples by aCGH or IHC. Validation of SNVs via SNP array and deep-resequencing showed ∼99% accuracy. Tumour cellularity was estimated using Qpure and was >60% for all samples. Phylogenetic techniques were used to demonstrate clear multi-clonality in two tumours. Across all tumours, ∼50% of SNVs were specific to an individual tumour-region. Phylogenies were confirmed with both SNVs and CNAs, but CNAs generally exhibited greater concordance amongst different regions of the same tumour. Some previously observed recurrent mutations were previously identified as recurrent in CaP (e.g. SPOP), and the overall mutation rate for intermediate-risk CaP was only somewhat below that reported for castrate-resistant disease (11,230 somatic SNVs per tumour). Our studies support the concept that a complete characterization of inter- and intra-CaP heterogeneity is possible in fresh and archival tissues; the latter is important for correlations to clinical outcome. These approaches can then be streamlined for high-throughput analyses within personalized medicine laboratories leading to “point of care” molecular tests and individualization of therapy. Citation Format: Michael E. Fraser, Richard de Borja, Dominique Trudel, Nicholas J. Harding, Pablo H. Hennings-Yeomans, Alice Meng, Emilie R. Lalonde, Andrew Brown, Natalie S. Fox, Taryne Chong, Amin Zia, Michelle Sam, Jianxin Wang, Michelle A. Chan-Seng-Yue, Jeremy Johns, Lee Timms, Nicholas Buchner, Ada Wong, Fouad Yousif, Rob Denroche, Gaetano Zafarana, Maud HW Starmans, Hanbert Chen, Shaylan Govind, Francis Nguyen, Melania Pintilie, Neil Fleshner, Stanislav Volik, Lakshmi Muthuswamy, Colin C. Collins, Thomas J. Hudson, Lincoln D. Stein, Timothy Beck, John D. McPherson, Theodorus van der Kwast, Paul C. Boutros, Rob G. Bristow. A molecular portrait of potentially curable prostate cancer. [abstract]. In: Proceedings of the 104th Annual Meeting of the American Association for Cancer Research; 2013 Apr 6-10; Washington, DC. Philadelphia (PA): AACR; Cancer Res 2013;73(8 Suppl):Abstract nr 2003. doi:10.1158/1538-7445.AM2013-2003