Hsin-Ta Wu

Pan-cancer network analysis identifies combinations of rare somatic mutations across pathways and protein complexes

Cancers exhibit extensive mutational heterogeneity, and the resulting long-tail phenomenon complicates the discovery of genes and pathways that are significantly mutated in cancer. We perform a pan-cancer analysis of mutated networks in 3,281 samples from 12 cancer types from The Cancer Genome Atlas (TCGA) using HotNet2, a new algorithm to find mutated subnetworks that overcomes the limitations of existing single-gene, pathway and network approaches. We identify 16 significantly mutated subnetworks that comprise well-known cancer signaling pathways as well as subnetworks with less characterized roles in cancer, including cohesin, condensin and others. Many of these subnetworks exhibit co-occurring mutations across samples. These subnetworks contain dozens of genes with rare somatic mutations across multiple cancers; many of these genes have additional evidence supporting a role in cancer. By illuminating these rare combinations of mutations, pan-cancer network analyses provide a roadmap to investigate new diagnostic and therapeutic opportunities across cancer types.

Comprehensive Pan-Genomic Characterization of Adrenocortical Carcinoma

We describe a comprehensive genomic characterization of adrenocortical carcinoma (ACC). Using this dataset, we expand the catalogue of known ACC driver genes to include PRKAR1A, RPL22, TERF2, CCNE1, and NF1. Genome wide DNA copy-number analysis revealed frequent occurrence of massive DNA loss followed by whole-genome doubling (WGD), which was associated with aggressive clinical course, suggesting WGD is a hallmark of disease progression. Corroborating this hypothesis were increased TERT expression, decreased telomere length, and activation of cell-cycle programs. Integrated subtype analysis identified three ACC subtypes with distinct clinical outcome and molecular alterations which could be captured by a 68-CpG probe DNA-methylation signature, proposing a strategy for clinical stratification of patients based on molecular markers.

An integrative probabilistic model for identification of structural variation in sequencing data

Paired-end sequencing is a common approach for identifying structural variation (SV) in genomes. Discrepancies between the observed and expected alignments indicate potential SVs. Most SV detection algorithms use only one of the possible signals and ignore reads with multiple alignments. This results in reduced sensitivity to detect SVs, especially in repetitive regions. We introduce GASVPro, an algorithm combining both paired read and read depth signals into a probabilistic model that can analyze multiple alignments of reads. GASVPro outperforms existing methods with a 50 to 90% improvement in specificity on deletions and a 50% improvement on inversions. GASVPro is available at http://compbio.cs.brown.edu/software.

CoMEt: a statistical approach to identify combinations of mutually exclusive alterations in cancer

Cancer is a heterogeneous disease with different combinations of genetic alterations driving its development in different individuals. We introduce CoMEt, an algorithm to identify combinations of alterations that exhibit a pattern of mutual exclusivity across individuals, often observed for alterations in the same pathway. CoMEt includes an exact statistical test for mutual exclusivity and techniques to perform simultaneous analysis of multiple sets of mutually exclusive and subtype-specific alterations. We demonstrate that CoMEt outperforms existing approaches on simulated and real data. We apply CoMEt to five different cancer types, identifying both known cancer genes and pathways, and novel putative cancer genes.

Identification of Ovarian Cancer Metastatic miRNAs

Serous epithelial ovarian cancer (EOC) patients often succumb to aggressive metastatic disease, yet little is known about the behavior and genetics of ovarian cancer metastasis. Here, we aim to understand how omental metastases differ from primary tumors and how these differences may influence chemotherapy. We analyzed the miRNA expression profiles of primary EOC tumors and their respective omental metastases from 9 patients using miRNA Taqman qPCR arrays. We find 17 miRNAs with differential expression in omental lesions compared to primary tumors. miR-21, miR-150, and miR-146a have low expression in most primary tumors with significantly increased expression in omental lesions, with concomitant decreased expression of predicted mRNA targets based on mRNA expression. We find that miR-150 and miR-146a mediate spheroid size. Both miR-146a and miR-150 increase the number of residual surviving cells by 2–4 fold when challenged with lethal cisplatin concentrations. These observations suggest that at least two of the miRNAs, miR-146a and miR-150, up-regulated in omental lesions, stimulate survival and increase drug tolerance. Our observations suggest that cancer cells in omental tumors express key miRNAs differently than primary tumors, and that at least some of these microRNAs may be critical regulators of the emergence of drug resistant disease.

Expression profiling of primary and metastatic ovarian tumors reveals differences indicative of aggressive disease.

The behavior and genetics of serous epithelial ovarian cancer (EOC) metastasis, the form of the disease lethal to patients, is poorly understood. The unique properties of metastases are critical to understand to improve treatments of the disease that remains in patients after debulking surgery. We sought to identify the genetic and phenotypic landscape of metastatic progression of EOC to understand how metastases compare to primary tumors. DNA copy number and mRNA expression differences between matched primary human tumors and omental metastases, collected at the same time during debulking surgery before chemotherapy, were measured using microarrays. qPCR and immunohistochemistry validated findings. Pathway analysis of mRNA expression revealed metastatic cancer cells are more proliferative and less apoptotic than primary tumors, perhaps explaining the aggressive nature of these lesions. Most cases had copy number aberrations (CNAs) that differed between primary and metastatic tumors, but we did not detect CNAs that are recurrent across cases. A six gene expression signature distinguishes primary from metastatic tumors and predicts overall survival in independent datasets. The genetic differences between primary and metastatic tumors, yet common expression changes, suggest that the major clone in metastases is not the same as in primary tumors, but the cancer cells adapt to the omentum similarly. Together, these data highlight how ovarian tumors develop into a distinct, more aggressive metastatic state that should be considered for therapy development.

Detecting independent and recurrent copy number aberrations using interval graphs

Motivation: Somatic copy number aberrations (SCNAs) are frequent in cancer genomes, but many of these are random, passenger events. A common strategy to distinguish functional aberrations from passengers is to identify those aberrations that are recurrent across multiple samples. However, the extensive variability in the length and position of SCNAs makes the problem of identifying recurrent aberrations notoriously difficult. Results: We introduce a combinatorial approach to the problem of identifying independent and recurrent SCNAs, focusing on the key challenging of separating the overlaps in aberrations across individuals into independent events. We derive independent and recurrent SCNAs as maximal cliques in an interval graph constructed from overlaps between aberrations. We efficiently enumerate all such cliques, and derive a dynamic programming algorithm to find an optimal selection of non-overlapping cliques, resulting in a very fast algorithm, which we call RAIG (Recurrent Aberrations from Interval Graphs). We show that RAIG outperforms other methods on simulated data and also performs well on data from three cancer types from The Cancer Genome Atlas (TCGA). In contrast to existing approaches that employ various heuristics to select independent aberrations, RAIG optimizes a well-defined objective function. We show that this allows RAIG to identify rare aberrations that are likely functional, but are obscured by overlaps with larger passenger aberrations. Availability: http://compbio.cs.brown.edu/software. Contact: braphael@brown.edu Supplementary information: Supplementary data are available at Bioinformatics online.

MAGI: visualization and collaborative annotation of genomic aberrations

1Max Planck Institute of Molecular Cell Biology and Genetics, Dresden, Germany. 2Janelia Research Campus, Howard Hughes Medical Institute, Ashburn, Virginia, USA. 3Department of Anatomy and Structural Biology, Albert Einstein College of Medicine, Bronx, New York, USA. 4Gruss Lipper Biophotonics Center, Albert Einstein College of Medicine, Bronx, New York, USA. e-mail: pietzsch@mpi-cbg.de or tomancak@mpi-cbg.de

Abstract 5324: Pan-cancer identification of mutated pathways and protein complexes

Large-scale cancer sequencing efforts such as The Cancer Genome Atlas (TCGA) and others have shown that tumors exhibit extensive mutational heterogeneity with relatively few genes mutated at significant frequency and many genes mutated in only a small number of individuals. This long tail phenomenon complicates the identification of driver mutations by their observed frequency. The long tail is explained in part by the fact that driver mutations target genes in signaling and regulatory pathways, and these pathways may be perturbed by different mutations in different tumors. We developed two complementary algorithms, HotNet2 and Dendrix++, to analyze combinations of mutations in known or novel pathways. HotNet2 uses prior knowledge of pathways and protein complexes represented in a genome-scale protein-protein interaction network, and identifies significantly mutated subnetworks using a heat diffusion model. HotNet2 simultaneously assesses both the significance of mutations in individual proteins and the local topology of protein interactions. Dendrix++ identifies combinations of mutations de novo, without prior knowledge of pathways or protein interactions, by finding sets of mutations that are mutually exclusive across the tumor cohort. There are numerous examples of mutually exclusive mutations between interacting proteins; e.g. BRAF and KRAS in colorectal cancer. Dendrix++ generalizes this idea to find larger groups of mutually exclusive mutations. We applied HotNet2 and Dendrix++ to whole-exome sequencing and copy number aberration data from 3299 samples of twelve tumor types from TCGA Pan-Cancer project. Both algorithms identified gene sets that overlap well-known cancer pathways (e.g. TP53, MAPK, and RAS signaling pathways), as well as genes and complexes with less characterized roles in cancer (e.g. the cohesin and condensin complexes). HotNet2 subnetworks also contained novel candidate cancer genes that were rarely mutated in this cohort and thus not reported by single-gene tests of significance, including KDM5A, SHPRH, and ARID4A, each of which interacts with well-known cancer genes. Many of these gene sets have biological functions often perturbed in cancer, such as chromatin modification (e.g. the SWI/SNF and BAP1 complexes) and DNA damage repair (e.g. SHPRH). These results demonstrate the ability of HotNet2 and Dendrix++ to identify novel combinations of mutations in thousands of tumors, prioritizing genes and mutations in the long tail for further experimental studies. Citation Format: Mark D. Leiserson, Fabio Vandin, Hsin-Ta Wu, Jason R. Dobson, Benjamin R. Raphael. Pan-cancer identification of mutated pathways and protein complexes. [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 5324. doi:10.1158/1538-7445.AM2014-5324

Abstract 1936: CoMEt: A statistical approach to identify combinations of mutually exclusive alterations in cancer

Identifying driver mutations in cancer genomes is a significant challenge due to the mutational heterogeneity of tumors: different combinations of somatic mutations drive different tumors, even those of the same cancer type. This mutational heterogeneity arises because driver mutations target genes in signaling and regulatory pathways, each of which can be perturbed in numerous ways. We introduce CoMEt, an algorithm to identify combinations of candidate driver mutations de novo, without any prior biological knowledge (e.g. pathways or protein interactions). CoMEt searches for combinations of mutations that exhibit mutual exclusivity, a pattern expected for mutations in pathways. CoMEt uses an exact statistical test for mutual exclusivity that is less biased toward high frequency alterations than previous approaches and more sensitive in detecting combinations of lower frequency alterations. We compute the exact test using a novel tail enumeration procedure and also derive a binomial approximation. CoMEt simultaneously identifies collections of one or more combinations of mutually exclusive alterations, consistent with the observation of multiple hallmarks of cancer, and also performs simultaneous analysis of subtype-specific mutations. Finally, CoMEt uses an MCMC algorithm to sample from collections in proportion to their significance, summarizing the distribution in a marginal probability graph. We show that CoMEt outperforms other mutual exclusivity approaches on simulated and real data. We apply CoMEt to hundreds of samples from four different TCGA cancer types: gastric cancer (STAD), glioblastoma (GBM) and acute myeloid leukemia (AML), and breast cancer (BRCA). We identify multiple mutually exclusive sets within each cancer type. These include the RTK/RAS pathway in gastric cancer, the Rb and p53 signaling pathways in glioblastoma, and a collection containing multiple kinases, including FLT3, as well as the RAS genes in AML. In addition, we analyze subtype-specific mutations using CoMEt by encoding the subtype of each sample and computing exclusivity between and within subtypes. We apply this approach using four gene expression subtypes of breast cancer and identify several pathways that are enriched for mutations in specific subtypes including the PI(3)K/AKT signaling pathway in the Luminal A subtype. Many of these overlap known pathways, but others reveal novel putative cancer genes. These findings provide testable hypotheses for experimental validation. Citation Format: Hsin-Ta Wu, Mark D.M. Leiserson, Fabio Vandin, Benjamin J. Raphael. CoMEt: A statistical approach to identify combinations of mutually exclusive alterations in cancer. [abstract]. In: Proceedings of the 106th Annual Meeting of the American Association for Cancer Research; 2015 Apr 18-22; Philadelphia, PA. Philadelphia (PA): AACR; Cancer Res 2015;75(15 Suppl):Abstract nr 1936. doi:10.1158/1538-7445.AM2015-1936