The study is part of a larger initiative called the Canadian Prostate Cancer Genome Network (CPC-GENE) that has brought together a team of multidisciplinary researchers from across Canada to crack the genetic code of prostate cancer. CPC-GENE researchers are identifying mutations in the DNA sequences of prostate cancer to develop better ways of detecting tumours, determining tumour aggressiveness and identifying the best treatment needed to personalize prostate cancer medicine for individual patients. CPC-GENE is funded by the Movember Foundation with an investment of $15 million, the largest donation ever made by the Foundation to a single research project, PCC and OICR, which has contributed $5 million to the Network.

Here are a few of the CPC-GENE projects the Boutros lab is involved with:

"Spatio-genomic heterogeneity within localized, multi-focal prostate cancer”

Paul C Boutros, et al.
Nature Genetics (in press) (PMID: 26005866)

Prostate cancer is clinically heterogeneous; even within patients with localized cancer, disease progression is heterogeneous with some patients surviving for decades while others rapidly fail treatment leading to non-curable metastatic disease. We mined the copy-number profile of 74 intermediate-risk prostate cancer patients and identified a recurrent amplification of a new putative oncogene in prostate cancer, MYCL, which is associated with TP53 deletion and unique profiles of DNA damage and transcriptional dysregulation. Another complexity in prostate cancer is that the majority of men with prostate cancer have multiple regions of cancer within their prostate. We thus performed whole-genome sequencing of 23 cancerous regions from the prostates of 5 patients. We found extensive intra-prostatic heterogeneity at the levels of SNVs, CNAs and genomic rearrangements, including one patient who had two completely independent prostate cancers. Moreover, we find intra-prostatic heterogeneity for clinically-actionable and prognostic mutations. These data represent the first systematic relation of intra-prostatic genomic heterogeneity to predicted clinical outcome and inform the development of novel biomarkers that reflect individual prognosis.

Figure: Visualization of the mutation pattern over several foci of a Gleason score 7 prostate tumor (CPCG0103). Multiple slices were taken for pathological examination, with both hematoxylin and eosin (H&E) and ERG staining shown for each. Note that no positional information is available for the bottom sample. Tissue from each region was subjected to whole-genome sequencing, and common SNVs and CNAs are displayed in the central panel. Right, a subset of selected targetable mutations.

“Tumour genomic and microenvironmental heterogeneity as integrated predictors for prostate cancer recurrence: a retrospective study"

Emilie Lalonde, et al.
Lancet Oncology 15(13):1521-1532 (PMID: 25456371)

Two major problems remain in managing localized prostate cancer: one third of patients are failing primary treatment and simultaneously men with indolent disease are burdened with unnecessary treatment. This study addresses this issue by identifying genomic markers of poor prognosis. Using copy-number profiles in 397 patients, we examined genomic variables associated the heterogeneous outcome of prostate cancer patients with multi-gene approaches including measures of genomic instability, genomic subtypes, and a 100-loci gene signature. Additionally, we find that tumour hypoxia is independent of individual genomic features including genes, genomic instability and genomic clusters. Furthermore, we identified a synergistic relationship between genomic instability and tumour hypoxia on patient outcome, identifying a major role integrative indices in better stratifying patients with different clinical outcome. Patients exhibiting these aggressive features on biopsy are candidates for treatment intensification or de-intensification trials.

DREAM Somatic Mutation Calling

The ICGC-TCGA DREAM Somatic Mutation Calling Challenge (herein, The Challenge) is an international effort to improve standard methods for identifying cancer-associated mutations and rearrangements in whole-genome sequencing (WGS) data. Leaders of the International Cancer Genome Consortium (ICGC) and The Cancer Genome Atlas (TCGA) cancer genomics projects are joining with Sage Bionetworks and IBM-DREAM to initiate this innovative open crowd-sourced Challenge.

The Boutros lab is involved in hosting three such somatic mutation calling challenges: SMC DNA, SMC RNA, and SMC Het.

The first, SMC DNA, aims to identify the most accurate mutation detection algorithms using NGS data, and establish the state-of-the-art. Initial results of the in silico SNV benchmarking can be found here:

  • Boutros, P.C. et al. Global optimization of somatic variant identification in cancer genomes with a global community challenge. Nat. Genet. 46, 318–319 (2014)
  • Ewing, A.D. et al. Combining tumor genome simulation with crowdsourcing to benchmark somatic single-nucleotide-variant detection. Nat Methods. (2015)

SMC RNA aims to identify and establish the most accurate, state-of-the-art RNA mutation and rearrangement detection algorithms using RNA-seq data. For more information regarding the SMC RNA:!Synapse:syn2813589/wiki/

Finally, the Challenge – Tumour Heterogeneity and Evaluation (SMC Het) aims to identify the best subclonal reconstruction algorithms and to identify the conditions that affect their performance. For more information regarding SMC Het:!Synapse:syn2813581/wiki/


As high-throughput sequencing continues to increase in speed and throughput, routine clinical and industrial application draws closer. These 'production' settings will require enhanced quality monitoring and quality control to optimize output and reduce costs. We developed SeqControl, a framework for predicting sequencing quality and coverage using a set of 15 metrics describing overall coverage, coverage distribution, basewise coverage and basewise quality. Using whole-genome sequences of 27 prostate cancers and 26 normal references, we derived multivariate models that predict sequencing quality and depth. SeqControl robustly predicted how much sequencing was required to reach a given coverage depth (area under the curve (AUC) = 0.993), accurately classified clinically relevant formalin-fixed, paraffin-embedded samples, and made predictions from as little as one-eighth of a sequencing lane (AUC = 0.967). These techniques can be immediately incorporated into existing sequencing pipelines to monitor data quality in real time.SeqControl is available at