Comparison of algorithms for the detection of cancer drivers at subgene resolution

Porta-Pardo, Eduard; Kamburov, Atanas; Tamborero, David; Pons, Tirso; Grases, Daniela; Valencia, Alfonso; López‐Bigas, Núria; Getz, Gad; Godzik, Adam

doi:10.1038/nmeth.4364

Cited by 75 publications

(70 citation statements)

References 60 publications

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“…Lastly, we reasoned that distinguishing cancer type-specificity of driver mutations within the same gene would be an even harder task to accomplish. It has been previously documented that lung adenocarcinoma (LUAD) missense mutations in EGFR appear predominantly in its kinase domain, while GBM missense mutations appear in its extracellular domain (Brennan et al, 2013;Ji et al, 2006;Paez et al, 2004;Porta-Pardo et al, 2017). We therefore scored TCGA missense mutations in the gene EGFR from LUAD patients and from GBM patients with CHASMplus, CanDrA, and CHASM ( Figure 2c).…”

Section: Chasmplus Predicts Cancer Type-specificity Of Driver Missensmentioning

confidence: 99%

“…Truncating or likely loss-of-function mutations are typical hallmarks of tumor suppressor genes (Vogelstein et al, 2013). However, the role of driver missense mutations may be undercharacterized in tumor suppressor genes, since these mutations are more diverse and occur over a larger region than in oncogenes (Porta-Pardo et al, 2017;Tokheim et al, 2016a). As a case in point, the tumor suppressor gene CASP8 contains many truncating variants, while all of the putative driver missense mutations identified by CHASMplus were considered rare ( Figure 3f).…”

Section: Chasmplus Identifies Both Common and Rare Cancer Driver Mutamentioning

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 Identifies Both Common and Rare Cancer Driver Mutamentioning

confidence: 99%

CHASMplus reveals the scope of somatic missense mutations driving human cancers

Tokheim

Karchin

2018

Preprint

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“…At the present time, which has seen rapid strides in the development of omics analysis technologies, such as genome analysis, it is expected that data‐driven studies based on analysis of clinical samples will help clarify the molecular mechanisms responsible for the occurrence, progression and treatment responsiveness of diseases, thereby leading to the development of new biomarkers for diagnosis and identification of drug discovery targets . In particular, analysis of pathological tissue samples collected from the exact site of a disease, such as cancer, will be indispensable for the realization of genomic medicine …”

mentioning

confidence: 99%

The Japanese Society of Pathology Guidelines on the handling of pathological tissue samples for genomic research: Standard operating procedures based on empirical analyses

Kanai

Nishihara

Miyagi

et al. 2018

Pathology International

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Genome research using appropriately collected pathological tissue samples is expected to yield breakthroughs in the development of biomarkers and identification of therapeutic targets for diseases such as cancers. In this connection, the Japanese Society of Pathology (JSP) has developed “The JSP Guidelines on the Handling of Pathological Tissue Samples for Genomic Research” based on an abundance of data from empirical analyses of tissue samples collected and stored under various conditions. Tissue samples should be collected from appropriate sites within surgically resected specimens, without disturbing the features on which pathological diagnosis is based, while avoiding bleeding or necrotic foci. They should be collected as soon as possible after resection: at the latest within about 3 h of storage at 4°C. Preferably, snap‐frozen samples should be stored in liquid nitrogen (about −180°C) until use. When intending to use genomic DNA extracted from formalin‐fixed paraffin‐embedded tissue, 10% neutral buffered formalin should be used. Insufficient fixation and overfixation must both be avoided. We hope that pathologists, clinicians, clinical laboratory technicians and biobank operators will come to master the handling of pathological tissue samples based on the standard operating procedures in these Guidelines to yield results that will assist in the realization of genomic medicine.

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“…In contrast to the numerous approaches/algorithms for identifying driver genes/mutations in protein-coding genes (e.g., MutSig2CV, HotSpot 3D, CLUMPS, PARADIGM, HotNet2, e-Driver and 20/20+) (92,93)), which take advantage of predictable consequences of mutations in affected proteins, the identification of drivers in non-protein-coding regions is limited to the analysis of mutation frequency/distribution in a region of interest. The challenge in identifying driver mutations in non-coding regions stems mostly from the lack of a simple code (such as the protein code) that would allow one to predict the function of mutations and to distinguish deleterious from benign or neutral mutations.…”

Section: Discussionmentioning

confidence: 99%

Somatic mutations in miRNA genes in lung cancer – potential functional consequences of non-coding sequence variants

Galka-Marciniak

Urbanek-Trzeciak

Nawrocka

et al. 2019

Preprint

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A growing body of evidence indicates that miRNAs may either drive or suppress oncogenesis. However, little is known about somatic mutations in miRNA genes. To determine the frequency and potential consequences of miRNA gene mutations, we analyzed whole exome sequencing datasets of ~500 lung adenocarcinoma (LUAD) and ~500 lung squamous cell carcinoma (LUSC) samples generated in the TCGA. Altogether, we identified >1000 mutations affecting ~500 different miRNA genes and showed that half of all cancers had at least one such mutation. Mutations occurred in most crucial parts of miRNA precursors, including mature miRNA and seed sequences. We showed that seed mutations strongly affected miRNA:target interactions, drastically changing the pool of predicted targets. Mutations may also affect miRNA biogenesis by changing the structure of miRNA precursors, DROSHA and DICER cleavage sites, and regulatory sequence/structure motifs. We identified 10 significantly overmutated hotspot miRNA genes, including the miR-379 gene in LUAD enriched in mutations in the mature miRNA and regulatory sequences. The occurrence of mutations in the hotspot miRNA genes was also shown experimentally. We present a comprehensive analysis of somatic mutations in miRNA genes and show that some of these genes are mutational hotspots, suggesting their potential role in cancer.3

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Comparison of algorithms for the detection of cancer drivers at subgene resolution

Cited by 75 publications

References 60 publications

CHASMplus reveals the scope of somatic missense mutations driving human cancers

CHASMplus reveals the scope of somatic missense mutations driving human cancers

The Japanese Society of Pathology Guidelines on the handling of pathological tissue samples for genomic research: Standard operating procedures based on empirical analyses

Somatic mutations in miRNA genes in lung cancer – potential functional consequences of non-coding sequence variants

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