Discovery and saturation analysis of cancer genes across 21 tumor types

By M. Lawrence, P. Stojanov, Craig H. Mermel, Levi A. Garraway, Todd R. Golub, Matthew Meyerson, Stacey Gabriel, Eric S. Lander, Gad Getz. Nature, 2014

2,932 citations recorded by the source corpus.

Although a few cancer genes are mutated in a high proportion of tumours of a given type (>20%), most are mutated at intermediate frequencies (2–20%). To explore the feasibility of creating a comprehensive catalogue of cancer genes, we analysed somatic point mutations in exome sequences from 4,742 human cancers and their matched normal-tissue samples across 21 cancer types. We found that large-scale genomic analysis can identify nearly all known cancer genes in these tumour types. Our analysis also identified 33 genes that were not previously known to be significantly mutated in cancer, including genes related to proliferation, apoptosis, genome stability, chromatin regulation, immune evasion, RNA processing and protein homeostasis. Down-sampling analysis indicates that larger sample sizes will reveal many more genes mutated at clinically important frequencies. We estimate that near-saturation may be achieved with 600–5,000 samples per tumour type, depending on background mutation frequency. The results may help to guide the next stage of cancer genomics.

Subfields: Cancer Research, Molecular Biology, Pathology and Forensic Medicine

View Paper: https://www.semanticscholar.org/paper/b06c1fd263ec875ec2a6236cb16833afd6e9c50d

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Citations in the corpus

High-Throughput Sequencing Technologies (2015, methodology): “These studies have described the mutational landscapes for over 20 cancer types, demonstrating that tumors can vary dramatically in both the type and quantity of mutations (Lawrence et al., 2014).”
Oncogenic Signaling Pathways in The Cancer Genome Atlas (2018, methodology): “We first assessed statistical recurrence using MutSigCV (Lawrence et al., 2014) for mutations and GISTIC 2.”
Comprehensive and Integrative Genomic Characterization of Hepatocellular Carcinoma (2017, neutral): “…small indel Calling - Indelocator https://www.broadinstitute.org/cancer/ cga/indelocator Broad Institute Mutation/Indel Annotation - Oncotator Ramos et al., 2015 PMID: 25703262 Mutation Significance Analysis - MutSigCV Lawrence et al., 2014 PMID: 24390350 RNA,DNaseq classifier - BioBloomTools(v1.”
Transcriptional Addiction in Cancer (2017, neutral): “A number of excellent reviews describe these alterations that affect cell signaling, transcription factors, enhancer elements, chromatin regulators and chromosome structure (Garraway and Lander, 2013; Kandoth et al., 2013; Lawrence et al., 2014; Stratton et al., 2009; Sur and Taipale, 2016; Vogelste”
Genomic Classification of Cutaneous Melanoma (2015, neutral): “…challenge of defining significance against a high background mutation rate, we used two algorithms to 1682 Cell 161, 1681–1696, June 18, 2015 ª2015 Elsevier Inc. define significantly mutated genes (SMGs): MutSig and InVEx (Hodis et al., 2012; Lawrence et al., 2014; Lawrence et al., 2013).”
Targeting p53 pathways: mechanisms, structures and advances in therapy (2023, background): “5 Most TP53 mutations are missense mutations located in the DBD. 3,4,203,205,380 p53 mutants mainly affect the thermostability of p53 protein, structural stability (structural mutants such as 175, 220, 245 and 249) or p53-DNA contact (DNA contact mutants such as 248 and 273).”
The long tail of oncogenic drivers in prostate cancer (2018, positive): “As predicted by prior power analyses 5 , the majority of these new SMGs occurred in less than 5% of the overall cohort and could only be discovered in cohorts with over 900 samples (Supplementary Table 5).”
Maftools: efficient and comprehensive analysis of somatic variants in cancer (2018, methodology): “To identify pathways involving gene sets ( n >2) mutated in an exclusive manner, we included Combinations of Mutually Exclusive Alterations (CoMEt) algorithm implemented in the cometExactTest R package (Leiserson et al. 2015).”
Biological and therapeutic impact of intratumor heterogeneity in cancer evolution. (2015, background): “A primary goal of cancer genomics research has been to provide a catalog of cancer genes and mutational processes that are operative during cancer evolution (Alexandrov et al., 2013; Lawrence et al., 2014).”