Josh Stuart

Assessing the clinical utility of cancer genomic and proteomic data across tumor types

Molecular profiling of tumors promises to advance the clinical management of cancer, but the benefits of integrating molecular data with traditional clinical variables have not been systematically studied. Here we retrospectively predict patient survival using diverse molecular data (somatic copy-number alteration, DNA methylation and mRNA, microRNA and protein expression) from 953 samples of four cancer types from The Cancer Genome Atlas project. We find that incorporating molecular data with clinical variables yields statistically significantly improved predictions (FDR < 0.05) for three cancers but those quantitative gains were limited (2.2–23.9%). Additional analyses revealed little predictive power across tumor types except for one case. In clinically relevant genes, we identified 10,281 somatic alterations across 12 cancer types in 2,928 of 3,277 patients (89.4%), many of which would not be revealed in single-tumor analyses. Our study provides a starting point and resources, including an open-access model evaluation platform, for building reliable prognostic and therapeutic strategies that incorporate molecular data.

Computational approaches to identify functional genetic variants in cancer genomes

The International Cancer Genome Consortium (ICGC) aims to catalog genomic abnormalities in tumors from 50 different cancer types. Genome sequencing reveals hundreds to thousands of somatic mutations in each tumor but only a minority of these drive tumor progression. We present the result of discussions within the ICGC on how to address the challenge of identifying mutations that contribute to oncogenesis, tumor maintenance or response to therapy, and recommend computational techniques to annotate somatic variants and predict their impact on cancer phenotype.

Enabling transparent and collaborative computational analysis of 12 tumor types within The Cancer Genome Atlas

The Cancer Genome Atlas Pan-Cancer Analysis Working Group collaborated on the Synapse software platform to share and evolve data, results and methodologies while performing integrative analysis of molecular profiling data from 12 tumor types. The group’s work serves as a pilot case study that provides (i) a template for future large collaborative studies; (ii) a system to support collaborative projects; and (iii) a public resource of highly curated data, results and automated systems for the evaluation of community-developed models.

Integrative Genomic Analysis of Cholangiocarcinoma Identifies Distinct IDH-Mutant Molecular Profiles

Summary Cholangiocarcinoma (CCA) is an aggressive malignancy of the bile ducts, with poor prognosis and limited treatment options. Here, we describe the integrated analysis of somatic mutations, RNA expression, copy number, and DNA methylation by The Cancer Genome Atlas of a set of predominantly intrahepatic CCA cases and propose a molecular classification scheme. We identified an IDH mutant-enriched subtype with distinct molecular features including low expression of chromatin modifiers, elevated expression of mitochondrial genes, and increased mitochondrial DNA copy number. Leveraging the multi-platform data, we observed that ARID1A exhibited DNA hypermethylation and decreased expression in the IDH mutant subtype. More broadly, we found that IDH mutations are associated with an expanded histological spectrum of liver tumors with molecular features that stratify with CCA. Our studies reveal insights into the molecular pathogenesis and heterogeneity of cholangiocarcinoma and provide classification information of potential therapeutic significance.

Comprehensive Characterization of Cancer Driver Genes and Mutations

Identifying molecular cancer drivers is critical for precision oncology. Multiple advanced algorithms to identify drivers now exist, but systematic attempts to combine and optimize them on large datasets are few. We report a PanCancer and PanSoftware analysis spanning 9,423 tumor exomes (comprising all 33 of The Cancer Genome Atlas projects) and using 26 computational tools to catalog driver genes and mutations. We identify 299 driver genes with implications regarding their anatomical sites and cancer/cell types. Sequence- and structure-based analyses identified >3,400 putative missense driver mutations supported by multiple lines of evidence. Experimental validation confirmed 60%-85% of predicted mutations as likely drivers. We found that >300 MSI tumors are associated with high PD-1/PD-L1, and 57% of tumors analyzed harbor putative clinically actionable events. Our study represents the most comprehensive discovery of cancer genes and mutations to date and will serve as a blueprint for future biological and clinical endeavors.

Abstract 4165: Differential pathway activation associated with domain-specific PIK3CA mutations

A recent study of the mutation landscape of >3000 cancers across 12 major cancer types from the Cancer Genome Atlas (TCGA) program revealed PIK3CA as the second most commonly mutated gene, occurring at >10% frequency in 8 types of cancer. Limited preclinical evidence suggested that mutations affecting PIK3CA catalytic vs. non-catalytic domains can produce different phenotypic consequences; however, whether domain-specific PIK3CA mutations results in distinct pathway consequences across multiple cancer types remain unclear. Thus, we used the PARADIGM algorithm, which integrates gene expression and copy number data into a superimposed pathway structure, to infer the activities of ∼13K pathway features and compared the signaling consequences associated with different domain-specific PIK3CA mutations within the TCGA Pan-Cancer dataset. Restricting to tumors harboring missense mutations in the coding region of a single PIK3CA domain resulted in 447 unique cases. PIK3CA mutations are distributed across the domains as follows: adaptor binding domain (ABD) = 23, Ras-binding domain (RBD) = 1, C2 = 50, helical = 199, and kinase = 174. Interestingly, the distribution of PIK3CA mutations among the domains is significantly different across cancer types (chi-square test p 50% of breast cancer PIK3CA mutations, while mutations in the helical domain predominate in head-and-neck and lung squamous carcinomas. Employing logistic regression and adjusting for cancer type, we identified 711 pathway features associated with kinase domain mutations (p Citation Format: Christina Yau, Stephen Benz, Charles Vaske, Sam Ng, Josh Stuart, Christopher C. Benz. Differential pathway activation associated with domain-specific PIK3CA mutations. [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 4165. doi:10.1158/1538-7445.AM2014-4165

760PDNEUROENDOCRINE PROSTATE CANCER (NEPC) IN PATIENTS (PTS) WITH METASTATIC CASTRATION RESISTANT PROSTATE CANCER (MCRPC) RESISTANT TO ABIRATERONE (ABI) OR ENZALUTAMIDE (ENZ): PRELIMINARY RESULTS FROM THE SU2C/PCF/AACR WEST COAST PROSTATE CANCER DREAM TEAM (WCDT)

ABSTRACT Aim: The mechanisms of resistance to androgen signaling inhibitors such as Abi or Enz are poorly understood. A growing proportion of these pts are developing treatment resistant NEPC. Progressive mCRPC has historically been challenging to biopsy and characterize on a molecular basis because of its bone tropism. As part of the WCDT project, which aims to identify genetic pathways underlying primary and acquired resistance to Abi and Enz, RNA sequencing (RNAseq) was used to develop a NEPC expression signature in mCRPC biopsies. Methods: Following central radiologic review, eligible mCRPC pts underwent a metastasis (met) biopsy at one of 5 WCDT clinical sites, using a uniform biopsy protocol. Tissue was both frozen, and formalin fixed/paraffin embedded (FFPE). Frozen specimens underwent laser capture micro-dissection, RNA isolation, library preparation and RNAseq. Machine learning was used to derive NEPC markers from mCRPC adenocarcinoma and met NEPC RNAseq data. FFPE tissue was evaluated histologically, including NEPC markers. Results: 85 of 300 planned mCRPC pts have undergone a met biopsy. To date, 53 specimens have been evaluated histologically. Histologically identified NEPC was present in 17 (32%) of biopsies overall, 12% of bone biopsies, 64% of lymph nodes, and 27% of liver biopsies. To date RNAseq data are available on 20 pts, including 6 with NEPC. A106 gene signature for met NEPC was developed from this learning set. Conclusions: Genomic sequencing and expression analysis can be accomplished in small bone and soft tissue mCRPC biopsies. The development of NEPC is a common event in mCRPC resistant to ABi or Enz. The majority of liver metastases are not NEPC. A 106 gene signature was derived from this met NEPC learning set and has identified a number of genes that provide insight into the biology and potential treatment of NEPC. Disclosure: All authors have declared no conflicts of interest.

Pathway and network analysis of more than 2,500 whole cancer genomes

The catalog of cancer driver mutations in protein-coding genes has greatly expanded in the past decade. However, non-coding cancer driver mutations are less well-characterized and only a handful of recurrent non-coding mutations, most notably TERT promoter mutations, have been reported. Motivated by the success of pathway and network analyses in prioritizing rare mutations in protein-coding genes, we performed multi-faceted pathway and network analyses of non-coding mutations across 2,583 whole cancer genomes from 27 tumor types compiled by the ICGC/TCGA PCAWG project. While few non-coding genomic elements were recurrently mutated in this cohort, we identified 93 genes harboring non-coding mutations that cluster into several modules of interacting proteins. Among these are promoter mutations associated with reduced mRNA expression in TP53, TLE4, and TCF4. We found that biological processes had variable proportions of coding and non-coding mutations, with chromatin remodeling and proliferation pathways altered primarily by coding mutations, while developmental pathways, including Wnt and Notch, altered by both coding and non-coding mutations. RNA splicing was primarily targeted by non-coding mutations in this cohort, with samples containing non-coding mutations exhibiting similar gene expression signatures as coding mutations in well-known RNA splicing factors. These analyses contribute a new repertoire of possible cancer genes and mechanisms that are altered by non-coding mutations and offer insights into additional cancer vulnerabilities that can be investigated for potential therapeutic treatments.

Discovery and characterization of coding and non-coding driver mutations in more than 2,500 whole cancer genomes

Discovery of cancer drivers has traditionally focused on the identification of protein-coding genes. Here we present a comprehensive analysis of putative cancer driver mutations in both protein-coding and non-coding genomic regions across >2,500 whole cancer genomes from the Pan-Cancer Analysis of Whole Genomes (PCAWG) Consortium. We developed a statistically rigorous strategy for combining significance levels from multiple driver discovery methods and demonstrate that the integrated results overcome limitations of individual methods. We combined this strategy with careful filtering and applied it to protein-coding genes, promoters, untranslated regions (UTRs), distal enhancers and non-coding RNAs. These analyses redefine the landscape of non-coding driver mutations in cancer genomes, confirming a few previously reported elements and raising doubts about others, while identifying novel candidate elements across 27 cancer types. Novel recurrent events were found in the promoters or 5’UTRs of TP53, RFTN1, RNF34, and MTG2, in the 3’UTRs of NFKBIZ and TOB1, and in the non-coding RNA RMRP. We provide evidence that the previously reported non-coding RNAs NEAT1 and MALAT1 may be subject to a localized mutational process. Perhaps the most striking finding is the relative paucity of point mutations driving cancer in non-coding genes and regulatory elements. Though we have limited power to discover infrequent non-coding drivers in individual cohorts, combined analysis of promoters of known cancer genes show little excess of mutations beyond TERT.

Abstract 2466: Identifying confidently measured genes in single pediatric cancer patient samples using RNA sequencing

In the UC Santa Cruz Treehouse Childhood Cancer Initiative (treehousegenomics.soe.ucsc.edu), we are exploring the utility of using RNA-Seq analysis of tumor samples from children to identify potential novel therapeutic options for each individual. Within a single RNA-Seq data set, the gene expression measurements are not equally accurate. The identification of activated, druggable pathways requires accurate gene-level expression measurements. We receive samples from a variety of clinical and research settings, and the quantity and complexity of the available input material and the depth of sequencing differ. These factors inspired us to develop a tool that will allow us to identify accurate measurements in most RNA-Seq samples we receive. First, we characterized the relationship between depth of sequencing and the accuracy of the gene expression measurement. We analyzed subsets of reads in samples with more than 50 million Uniquely Mapped, Exonic, Non-duplicate (UMEND) reads. UMEND reads typically constitute over 80% of the reads in a high quality experiment with sufficient starting material. We compared gene expression across the subsets of reads to calculate how many UMEND reads are required to produce consistent measurements. We found that, on average, genes expressed at 1-5 TPM in our data require 30 million reads to be accurately measured. For this calculation, we define accuracy as the condition in which 75% of genes are measured to within 25% of the true value. Secondly, we use these known relationships to identify genes that have been accurately measured in our tumor RNA-Seq samples. For a sample with 15 million UMEND reads, we find that genes expressed above 5 TPM can be accurately measured and are retained. In the first twelve samples analyzed, samples with more than 10 million UMEND reads retained at least 46% of the genes expressed above zero. We exclude as references those samples with fewer than 10 million UMEND reads due to the marked gene loss after thresholding for this group. Using accurately measured genes allows us to more confidently assess similarity to other samples, identify enriched pathways, and confirm the expression of drug targets and related molecules under consideration. For example, we reconsidered the CDK4 inhibitor Palbociclib in one patient because the expression of RB1, downstream effector required for Palbociclib-mediated tumor cell death, was under our accuracy threshold. Accuracy thresholds can also be used in experiment planning. Accuracy thresholding allows us to better assess the value of an RNA-Seq data set and, if necessary, identify the subset of genes whose expression can be confidently considered in a clinical setting. Our experience points to the importance of careful quality control in this process. Citation Format: Holly Beale, Du Linh Lam, John Vivian, Yulia Newton, Avanthi Tayi Shah, Isabel Bjork, Ted Goldstein, Angela N. Brooks, Josh Stuart, Sofie Salama, E. Alejandro Sweet-Cordero, David Haussler1, Olena Morozova. Identifying confidently measured genes in single pediatric cancer patient samples using RNA sequencing [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2017; 2017 Apr 1-5; Washington, DC. Philadelphia (PA): AACR; Cancer Res 2017;77(13 Suppl):Abstract nr 2466. doi:10.1158/1538-7445.AM2017-2466