Passenger Mutations in More Than 2,500 Cancer Genomes: Overall Molecular Functional Impact and Consequences

Kumar, Sushant; Warrell, Jonathan; Li, Shantao; McGillivray, Patrick D.; Meyerson, William; Salichos, Leonidas; Harmanci, Arif; Martínez-Fundichely, Alexander; Chan, Calvin Wing Yiu; Nielsen, Morten Muhlig; Lochovsky, Lucas; Zhang, Yan; Li, Xiaotong; Lou, Shaoke; Pedersen, Jakob Skou; Herrmann, Carl; Getz, Gad; Khurana, Ekta; Gerstein, Mark

doi:10.1016/j.cell.2020.01.032

Cited by 101 publications

(97 citation statements)

References 62 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…However, several recent reports have showed that passenger mutations may also have critical functional roles in driving cancer, with some authors describing them as mini drivers. They found that the aggregated impact of putative passenger mutations could provide significant predictive power to distinguish cancer from non-cancer phenotypes (50,51). The above content implied to us that in some types of cancers, such as UCEC, even most of these mutations belonged to the unknown class; more efforts are needed to determine the meanings of these mutations, which might be found to also have important functional roles in driving tumorigenesis.…”

Section: Discussionmentioning

confidence: 98%

Spectrum of Mesenchymal–Epithelial Transition Aberrations and Potential Clinical Implications: Insights From Integrative Pancancer Analysis

Zhou

et al. 2020

Front. Oncol.

View full text Add to dashboard Cite

Background: The receptor tyrosine kinase mesenchymal-epithelial transition factor (MET) is frequently altered in cancers and is a common therapeutic target for cancers with MET variants. However, abnormal MET alterations and their associations with patient outcome across different cancer types have not been studied simultaneously. In this study, we try to fill the vacancy in a comprehensive manner and capture the full MET alteration spectrum. Methods: A total of 10,967 tumor samples comprising 32 cancer types from The Cancer Genome Atlas (TCGA) datasets were analyzed for MET abnormal expression, mutations, and copy number variants (CNVs). Results: MET abnormal expression, alteration frequency, mutation site distribution, and functional impact varied across different cancer types. Lung adenocarcinoma (LUAD) has most targetable mutations located in the juxtamembrane domain, and both high expression and amplification of MET are significantly associated with poor prognosis. Kidney renal papillary cell carcinoma (KIRP) harbored the third highest alteration frequency of MET, which was dominated by mutations. While most mutations were in the Pkinase_Tyr domain, a few were targetable. Pancreatic adenocarcinoma (PAAD) harbors very few alterations, but increased MET expression is associated with poor outcomes. Esophageal carcinoma (ESCA), stomach adenocarcinoma (STAD), and ovarian serous cystadenocarcinoma (OV) had similar characteristics: a high frequency of MET CNVs but relatively few MET mutations, and high MET expression associated with poor prognosis. Conclusion: This study provided significant and comprehensive information regarding MET abnormal expression, alterations (mutations and CNVs), and their clinical associations among 32 cancer types and offered insights into the full MET alteration spectrum and its implications for prognosis and treatment.

show abstract

Section: Discussionmentioning

confidence: 98%

Spectrum of Mesenchymal–Epithelial Transition Aberrations and Potential Clinical Implications: Insights From Integrative Pancancer Analysis

Zhou

et al. 2020

Front. Oncol.

View full text Add to dashboard Cite

show abstract

“…Consistent with this finding, most high VAF synonymous variants are not observed to co-occur with any candidate 'driver' mutation. The large number of unobserved 'drivers' likely include mCAs 10,11,29,30 , large structural variants 31,32 , coding mutations in non-cancer-associated genes, mutations in regulatory regions [33][34][35][36] and, possibly, epigenetic alterations 37,38 . We hypothesize that a more comprehensive survey of these alterations will yield many further mutations that will be shown to be capable of driving clonal expansions in blood 16 .…”

Section: Discussionmentioning

confidence: 99%

Synonymous mutations reveal genome-wide driver mutation rates in healthy tissues

Poon

et al. 2020

Preprint

View full text Add to dashboard Cite

Genetic alterations that drive clonal expansions in ostensibly healthy tissues have implications for cancer risk. However, the total rate at which clonal expansions occur in healthy tissues remains unknown. Synonymous passenger mutations that hitchhike to high variant allele frequency due to a linked driver mutation can be used to estimate the total rate of positive selection across the genome. Because these synonymous hitchhikers are influenced by all mutations under selection, regardless of type or location, they can be used to estimate how many driver mutations are missed by narrow gene-focused sequencing panels. Here we analyse the variant allele frequency spectrum of synonymous passenger mutations to estimate the total rate at which mutations driving clonal expansions occur in healthy tissues. By applying our framework to data from physiologically healthy blood, we find that a large fraction of mutations driving clonal expansions occur outside of canonical cancer driver genes. In contrast, analysis of data from healthy oesophagus reveals little evidence for many driver mutations outside of those in NOTCH1 and TP53. Our framework, which generalizes to other tissues, sheds light on the fraction of drivers mutations that remain undiscovered and has implications for cancer risk prediction.

show abstract

“…composer, era, genre). As a biological example, we show a mutational spectrum observed in a type of Kidney cancer, as adapted from [6]. As a musical example, we show the motif spectrum of three popular songs for comparison, where chord transitions have been normalized by the tonic chord.…”

Section: Extracting Musical Featuresmentioning

confidence: 99%

“…Particularly, in cancer genomics, the underlying processes that cause these mutations (e.g. tobacco, UV, aging, etc) can be directly linked with specific mutational signatures, and specific cancer types (6)(7)(8).…”

Section: Introductionmentioning

confidence: 99%

Latent Evolutionary Signatures: A General Framework for Analyzing Music and Cultural Evolution

Warrell

Salichos

Gerstein

2020

Preprint

Self Cite

View full text Add to dashboard Cite

Cultural processes of change bear many resemblances to biological evolution. Identifying underlying units of evolution, however, has remained elusive in non-biological domains, especially in music, where music evolution is often considered to be a loose metaphor. Here we introduce a general framework to jointly identify underlying units and their associated evolutionary processes using a latent modeling approach. Musical styles and principles of organization in dimensions such as harmony, melody and rhythm can be modeled as partly following an evolutionary process. Furthermore, we propose that such processes can be identified by extracting latent evolutionary signatures from musical corpora, analogous to the analysis of mutational signatures in evolutionary genomics, particularly in cancer. These latent signatures provide a generative code for each song, which allows us to identify broad trends and associations between songs and genres. To provide a test case, we analyze songs from the McGill Billboard dataset, in order to find popular chord transitions (k-mers), associate them with music genres and identify latent evolutionary signatures related to these transitions. First, we use a generalized singular value decomposition to identify associations between songs, motifs and genres, and then we use a deep generative model based on a Variational Autoencoder (VAE) framework to extract a latent code for each song. We tie these latent representations together across the dataset by incorporating an energy-based prior, which encourages songs close in evolutionary space to share similar codes. Using this framework, we are able to identify broad trends and genre-specific features of the dataset. Further, our evolutionary model outperforms non-evolutionary models in tasks such as period and genre prediction. To our knowledge, ours is the first computational approach to identify and quantify patterns of music evolution de novo.

show abstract

Passenger Mutations in More Than 2,500 Cancer Genomes: Overall Molecular Functional Impact and Consequences

Cited by 101 publications

References 62 publications

Spectrum of Mesenchymal–Epithelial Transition Aberrations and Potential Clinical Implications: Insights From Integrative Pancancer Analysis

Spectrum of Mesenchymal–Epithelial Transition Aberrations and Potential Clinical Implications: Insights From Integrative Pancancer Analysis

Synonymous mutations reveal genome-wide driver mutation rates in healthy tissues

Latent Evolutionary Signatures: A General Framework for Analyzing Music and Cultural Evolution

Contact Info

Product

Resources

About