Unsupervised detection of cancer driver mutations with parsimony-guided learning

Kumar, Runjun D.; Swamidass, S. Joshua; Bose, Ron

doi:10.1038/ng.3658

Cited by 50 publications

(72 citation statements)

References 45 publications

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“…CHASMplus provides a predictive model for each of 32 cancer types sequenced by TCGA. In contrast, most previous methods provide a single impact score for each missense mutation (Adzhubei et al, 2010;Carter et al, 2013;Gonzalez-Perez et al, 2012;Ioannidis et al, 2016;Jagadeesh et al, 2016;Kumar et al, 2016;Ng and Henikoff, 2001;Reva et al, 2011;Shihab et al, 2013), regardless of cancer type. However, two methods (CHASM (Carter et al, 2009) and…”

Section: Chasmplus Predicts Cancer Type-specificity Of Driver Missensmentioning

confidence: 99%

“…CanDrA (Mao et al, 2013)) do provide cancer type-specific prediction models, but this capability has not been validated. To illustrate the significant advance in cancer-specific prediction made by CHASMplus, we compared the cancer type-specificity of CHASMplus to CHASM and CanDrA, along with, for reference, two additional methods (ParsSNP (Kumar et al, 2016) and…”

Section: Chasmplus Predicts Cancer Type-specificity Of Driver Missensmentioning

confidence: 99%

“…We therefore compared predictions of breast cancer-specific CHASMplus, CHASM, and CanDrA models in known breast cancer driver genes to a previous large-scale validation of 698 missense mutations in MCF10A (breast epithelium) cells that measured cell viability (Ng et al, 2018) ( Figure 2a, Methods). We used the area under the Receiver Operating Characteristic curve (auROC) as a performance metric, similar to many prior studies of variant effect prediction (Adzhubei et al, 2010;Ioannidis et al, 2016;Kircher et al, 2014;Kumar et al, 2016;Mao et al, 2013). In general, auROC values range from 0.5 (random prediction performance) to 1.0 (perfect).…”

Section: Chasmplus Predicts Cancer Type-specificity Of Driver Missensmentioning

confidence: 99%

“…While many computational methods have been developed to predict whether a missense mutation is generally deleterious or pathogenic (Adzhubei et al, 2010;Carter et al, 2013;Ioannidis et al, 2016;Jagadeesh et al, 2016;Kircher et al, 2014;Ng and Henikoff, 2001), there has not previously been a method to score the oncogenic impact of a missense mutation specifically by cancer type. Although it is well known that missense mutations have different impacts in different cancer types, currently available computational methods (Adzhubei et al, 2010;Carter et al, 2009;Carter et al, 2013;Cohen et al, 2018;Gonzalez-Perez et al, 2012;Ioannidis et al, 2016;Jagadeesh et al, 2016;Kumar et al, 2016;Mao et al, 2013;Ng and Henikoff, 2001;Reva et al, 2011;Shihab et al, 2013) either do not take it into consideration or fail at the task of distinguishing the differences. A recent systematic study comparing 15 such methods concluded that none of them were yet sufficiently reliable to guide high-cost experimental or clinical followthrough (Martelotto et al, 2014).…”

Section: Introductionmentioning

confidence: 99%

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CHASMplus reveals the scope of somatic missense mutations driving human cancers

Tokheim

Karchin

2018

Preprint

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Large-scale cancer sequencing studies of patient cohorts have statistically implicated many genes driving cancer growth and progression, and their identification has yielded substantial translational impact. However, a remaining challenge is to increase the resolution of driver prediction from the gene level to the mutation level, because mutation-level predictions are more closely aligned with the goal of precision cancer medicine. Here we present CHASMplus, a computational method, that is uniquely capable of identifying driver missense mutations, including those specific to a cancer type, as evidenced by significantly superior performance on diverse benchmarks. Applied to 8,657 tumor samples across 32 cancer types in The Cancer Genome Atlas, CHASMplus identifies over 4,000 unique driver missense mutations in 240 genes, supporting a prominent role for rare driver mutations. We show which TCGA cancer types are likely to yield discovery of new driver missense mutations by additional sequencing, which has important implications for public policy. SignificanceMissense mutations are the most frequent mutation type in cancers and the most difficult to interpret. While many computational methods have been developed to predict whether genes are cancer drivers or whether missense mutations are generally deleterious or pathogenic, there has not previously been a method to score the oncogenic impact of a missense mutation specifically by cancer type, limiting adoption of computational missense mutation predictors in the clinic.Cancer patients are routinely sequenced with targeted panels of cancer driver genes, but such genes contain a mixture of driver and passenger missense mutations which differ by cancer type.A patient's therapeutic response to drugs and optimal assignment to a clinical trial depends on both the specific mutation in the gene of interest and cancer type. We present a new machine learning method honed for each TCGA cancer type, and a resource for fast lookup of the cancerspecific driver propensity of every possible missense mutation in the human exome.

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Section: Chasmplus Predicts Cancer Type-specificity Of Driver Missensmentioning

confidence: 99%

Section: Chasmplus Predicts Cancer Type-specificity Of Driver Missensmentioning

confidence: 99%

Section: Chasmplus Predicts Cancer Type-specificity Of Driver Missensmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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CHASMplus reveals the scope of somatic missense mutations driving human cancers

Tokheim

Karchin

2018

Preprint

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“…The perturbation propagates across the molecule, 59 shifting the landscape (the ensemble) toward the stabilized state. 84 Clusters in PTEN tumor suppressor presented 2 established drivers and 46 rare mutations near the phosphatase catalytic motif. This can emerge from altered PTMs (eg, phosphorylation, ubiquitination, etc.)…”

Section: The Free Energy Landscape and Allosteric Mutationsmentioning

confidence: 99%

Autoinhibition can identify rare driver mutations and advise pharmacology

2019

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Identification of protein mutations that drive cancer is a major challenge. A primary reason is that driver mutations are principally identified by their high frequency even though they can also be rare. Driver mutations can locate at functional (binding or active) sites. We dub these orthosteric drivers. However, often they are allosteric drivers. Identification is particularly formidable for rare allosteric drivers. Autoinhibition, where a segment of the protein covers its functional site, is a common allosteric regulation mechanism. A modest shift in the equilibrium can switch the system from the autoinhibited to the active state. This can suggest why (i) mutations are likely to evolve to target it; (ii) inhibitors can straightforwardly relieve the autoinhibition but not vice versa; and why (iii) mutations that relieve the autoinhibition are likely to be drivers—even if they are rare. We explain in simple terms the linkage between allosteric driver mutations, release of autoinhibition, free energy landscapes, and targeted pharmacology in precision medicine. We review the literature and propose new concepts in identification of rare drivers in this framework.

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Understanding oncogenicity of cancer driver genes and mutations in the cancer genomics era

2020

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One of the key challenges of cancer biology is to catalogue and understand the somatic genomic alterations leading to cancer. Although alternative definitions and search methods have been developed to identify cancer driver genes and mutations, analyses of thousands of cancer genomes return a remarkably similar catalogue of around 300 genes that are mutated in at least one cancer type. Yet, many features of these genes and their role in cancer remain unclear, first and foremost when a somatic mutation is truly oncogenic. In this review, we first summarize some of the recent efforts in completing the catalogue of cancer driver genes. Then, we give an overview of different aspects that influence the oncogenicity of somatic mutations in the core cancer driver genes, including their interactions with the germline genome, other cancer driver mutations, the immune system, or their potential role in healthy tissues. In the coming years, this research holds promise to illuminate how, when, and why cancer driver genes and mutations are really drivers, and thereby move personalized cancer medicine and targeted therapies forward.

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Unsupervised detection of cancer driver mutations with parsimony-guided learning

Cited by 50 publications

References 45 publications

CHASMplus reveals the scope of somatic missense mutations driving human cancers

CHASMplus reveals the scope of somatic missense mutations driving human cancers

Autoinhibition can identify rare driver mutations and advise pharmacology

Understanding oncogenicity of cancer driver genes and mutations in the cancer genomics era

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