A comparative analysis of network mutation burdens across 21 tumor types augments discovery from cancer genomes

Horn, Heiko; Ms, Lawrence; Jx, Hu; Worstell, Elizabeth; Ilić, Nina; Shrestha, Yashaswi; Kim, Eun Jin; Kamburov, Atanas; Kashani, Alireza; Wc, Hahn; Boehm, Jesse S.; Getz, Gad; Lage, Kasper

doi:10.1101/025445

Cited by 6 publications

(4 citation statements)

References 46 publications

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“…PIK3CB A1048V and POT1 G76V were also rare alleles that were found only once in our cohort. PIK3CB was recently shown to be mutated in prostate cancer (52), and computational analysis using network mutation burden nominated PIK3CB to be a significantly mutated gene (53). Although further studies are required to elucidate the mechanisms by which PIK3CB A1048V and POT1 G76V contribute to malignant transformation, this study provides evidence that these alleles are indeed transforming alleles.…”

Section: Discussionmentioning

confidence: 99%

Systematic Functional Interrogation of Rare Cancer Variants Identifies Oncogenic Alleles

et al. 2016

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Cancer genome characterization efforts now provide an initial view of the somatic alterations in primary tumors. However, most point mutations occur at low frequency. and the function of these alleles remain undefined. We have developed a scalable systematic approach to interrogate the function of cancer-associated gene variants. We subjected 474 mutant alleles curated from 5,338 tumors to pooled in vivo tumor formation assays and gene expression profiling. We identified 12 transforming alleles including two in genes (PIK3CB, POT1) that have not been shown to be tumorigenic. One rare KRAS allele, D33E, displayed tumorigenicity and constitutive activation of known RAS effector pathways. By comparing gene expression changes induced upon expression of wild type and mutant alleles, we inferred the activity of specific alleles. Since alleles found to be mutated only once in 5,338 tumors rendered cells tumorigenic, these observations underscore the value of integrating genomic information with functional studies.

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Section: Discussionmentioning

confidence: 99%

Systematic Functional Interrogation of Rare Cancer Variants Identifies Oncogenic Alleles

et al. 2016

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“…The fact that NMB might reveal new insights into mutation profiles is an emerging idea supported by this study. Further support has been formalised with two recently published methods [ 57 , 58 ] which rely on NMB to achieve state-of-the-art performances for cancer gene discovery.…”

Section: Discussionmentioning

confidence: 99%

NetNorM: Capturing cancer-relevant information in somatic exome mutation data with gene networks for cancer stratification and prognosis

2017

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Genome-wide somatic mutation profiles of tumours can now be assessed efficiently and promise to move precision medicine forward. Statistical analysis of mutation profiles is however challenging due to the low frequency of most mutations, the varying mutation rates across tumours, and the presence of a majority of passenger events that hide the contribution of driver events. Here we propose a method, NetNorM, to represent whole-exome somatic mutation data in a form that enhances cancer-relevant information using a gene network as background knowledge. We evaluate its relevance for two tasks: survival prediction and unsupervised patient stratification. Using data from 8 cancer types from The Cancer Genome Atlas (TCGA), we show that it improves over the raw binary mutation data and network diffusion for these two tasks. In doing so, we also provide a thorough assessment of somatic mutations prognostic power which has been overlooked by previous studies because of the sparse and binary nature of mutations.

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“…We further assessed the accuracy of rDriver predictions by comparing them with cancer drivers known in the literature and with those predicted by other algorithms. First, we compared the sets of predicted driver genes against a set of 17 highly probable cancer genes that were listed in the cancer gene census (CGC) [ 25 , 26 ] and were mutated in our set ( S4 Dataset ). A gene is predicted as a driver gene if it contains one or more predicted driver mutations.…”

Section: Resultsmentioning

confidence: 99%

Cancer driver mutation prediction through Bayesian integration of multi-omic data

Wang

Chen

et al. 2018

PLoS ONE

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Identification of cancer driver mutations is critical for advancing cancer research and personalized medicine. Due to inter-tumor genetic heterogeneity, many driver mutations occur at low frequencies, which make it challenging to distinguish them from passenger mutations. Here, we show that a novel Bayesian hierarchical modeling approach, named rDriver can achieve enhanced prediction accuracy by identifying mutations that not only have high functional impact scores but also are associated with systemic variation in gene expression levels. In examining 3,080 tumor samples from 8 cancer types in The Cancer Genome Atlas, rDriver predicted 1,389 driver mutations. Compared with existing tools, rDriver identified more low frequency mutations associated with lineage specific functional properties, timing of occurrence and patient survival. Evaluation of rDriver predictions using engineered cell-line models resulted in a positive predictive value of 0.94 in PIK3CA genes. Our study highlights the importance of integrating multi-omic data in predicting cancer driver mutations and provides a statistically rigorous solution for cancer target discovery and development.

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A comparative analysis of network mutation burdens across 21 tumor types augments discovery from cancer genomes

Cited by 6 publications

References 46 publications

Systematic Functional Interrogation of Rare Cancer Variants Identifies Oncogenic Alleles

Systematic Functional Interrogation of Rare Cancer Variants Identifies Oncogenic Alleles

NetNorM: Capturing cancer-relevant information in somatic exome mutation data with gene networks for cancer stratification and prognosis

Cancer driver mutation prediction through Bayesian integration of multi-omic data

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