Genome-wide analysis of somatic noncoding mutation patterns in cancer

Dietlein, Felix; Wang, Alex B.; Fagre, Christian; Tang, Anran; Besselink, Nicolle; Cuppen, Edwin; Li, Chunliang; Sunyaev, Shamil; Neal, James T.; Allen, Eliezer M. Van

doi:10.1126/science.abg5601

Cited by 54 publications

(55 citation statements)

References 211 publications

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“…A typical example of such driver genes is TERT , the gene for the telomerase reverse transcriptase. The coding-region of this gene shows no significant sign of selection [ 3 ], whereas its promoter is a target for driver mutations in many types of cancer [ 19 , 20 ]. It is noteworthy in this respect that recent studies have shown that the enhancer region of one of the cancer genes identified here, TNFSF10 , is subject to somatic mutation in kidney cancer [ 20 ].…”

Section: Resultsmentioning

confidence: 99%

Use of Publication Dynamics to Distinguish Cancer Genes and Bystander Genes

Bányai

Trexler

Patthy

2022

Genes

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de Magalhães has shown recently that most human genes have several papers in PubMed mentioning cancer, leading the author to suggest that every gene is associated with cancer, a conclusion that contradicts the widely held view that cancer is driven by a limited number of cancer genes, whereas the majority of genes are just bystanders in carcinogenesis. We have analyzed PubMed to decide whether publication metrics supports the distinction of bystander genes and cancer genes. The dynamics of publications on known cancer genes followed a similar pattern: seminal discoveries triggered a burst of cancer-related publications that validated and expanded the discovery, resulting in a rise both in the number and proportion of cancer-related publications on that gene. The dynamics of publications on bystander genes was markedly different. Although there is a slow but continuous time-dependent rise in the proportion of papers mentioning cancer, this phenomenon just reflects the increasing publication bias that favors cancer research. Despite this bias, the proportion of cancer papers on bystander genes remains low. Here, we show that the distinctive publication dynamics of cancer genes and bystander genes may be used for the identification of cancer genes.

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Section: Resultsmentioning

confidence: 99%

Use of Publication Dynamics to Distinguish Cancer Genes and Bystander Genes

Bányai

Trexler

Patthy

2022

Genes

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“…These tests are based on determining an increased mutational frequency in DNA regions of interest (e.g., cis-regulatory elements (CREs)) compared to a background mutational frequency [10][11][12][13][14][15][16][17][18] . Methods have employed a range of different parameters to estimate the background mutational frequency in CREs, including cancer-specific mutational signatures, sequence conservation, functional annotations, mutational frequencies in neighboring regions or other "similar" genomic regions, replication timing, and expression levels 9,19 . Despite these varied approaches to estimate mutational burden and the increasing number of sequenced tumor samples, studies have only identified ~100 driver NCVs.…”

Section: Introductionmentioning

confidence: 99%

“…A more recent analysis of 3,949 tumors from PCAWG and the Hartwig Medical Foundation identified driver NCVs in the promoters and enhancers of 52 genes 19 . Additional driver NCVs have been identified in the super-enhancers of BLC6, BCL2, CXCR4 in diffuse large B-cell lymphomas 23 .…”

Section: Introductionmentioning

confidence: 99%

“…Further, a global analysis of 2,568 cancer whole genome samples from the Pan-Cancer Analysis of Whole Genomes (PCAWG) identified driver NCVs in the promoters of TERT, HES1 and seven additional genes 9 . A more recent analysis of 3,949 tumors from PCAWG and the Hartwig Medical Foundation identified driver NCVs in the promoters and enhancers of 52 genes 19 . Additional driver NCVs have been identified in the super-enhancers of BLC6, BCL2, CXCR4 in diffuse large B-cell lymphomas 23 .…”

Section: Introductionmentioning

confidence: 99%

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Widespread perturbation of ETS factor binding sites in cancer

Pro

Hook

Bray

et al. 2022

Preprint

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Although >90% of somatic mutations reside in non-coding regions, few have been reported as cancer drivers. To predict driver non-coding variants (NCVs), we present a novel transcription factor (TF)-aware burden test (TFA-BT) based on a model of coherent TF function in promoters. We applied our TFA-BT to NCVs from the Pan-Cancer Analysis of Whole Genomes cohort and predicted 2,555 driver NCVs in the promoters of 813 genes across 20 cancer-types. These genes are enriched in cancer-related gene ontologies, essential genes, and genes associated with cancer prognosis. We found that 765 candidate driver NCVs alter transcriptional activity, 510 lead to differential binding of TF-cofactor regulatory complexes, and that they primarily impact the binding of ETS factors. Finally, we show that different NCVs within a promoter often affect transcriptional activity through shared mechanisms. Our integrated computational and experimental approach shows that cancer NCVs are widespread and that ETS factors are commonly disrupted.

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“…A number of low-penetrance risk variants have been identified over years but the effect sizes of such individual single-nucleotide polymorphisms (SNPs) are quite small 6 . Besides, given the fact that only 2% mutations are related to the protein-coding genome and non-coding regions may harbour driver genes 7 , to detect or elucidate cancer prognosis we clearly require more downstream information.…”

Section: Introductionmentioning

confidence: 99%

ioSearch: a tool for searching disease-associated interacting omics; application on breast cancer data

Das

Srivastava

2022

Preprint

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Biomarkers identification is difficult for cancer and other polygenic traits because such complicated diseases occur due to an intricate interplay of various genetic materials. Although high-throughput data from recent technologies provide access to a tremendous amount of information still there is a huge gap in harnessing knowledge from the generated multi-omics data. It is evident from the availability of subject-specific multi-omics data from large consortiums that there is a growing need for appropriate tools to analyze such data. Traditional single-omics association tests more often identify strong signals but fail to explore the between-omics relationship and find moderately weak signals due to multiple testing burdens. Multi-omics data integration intuitively provides a clear advantage in understanding the genetic architecture of disease a little better by imparting complementary information. But the construction of such methods is challenging because of the diversity in the nature of multiple omics and the sample size which is much less than the number of omics variables. It is important to consider factors such as data diversity and prior biological knowledge to make meaningful and better predictions. Dimension reduction techniques such as feature selection are used to circumvent the sample size issue in general but treating all the omics variables similarly might be an oversimplification of the complex biological interactions. The lack of appropriate approaches for biomarker identification from complex multi-omics data led us to develop this method. ioSearch is a tool for integrating two omics assays with continuous measurements. Based on a two-step model, ioSearch explores the inter-relationship of the omics in a principal regression framework and selects features using sparse principal component analysis to provide easily interpretable inference in terms of p-values. Also, it uses prior biological information to reduce multiple testing burdens. Extensive simulation results show that our method is statistically powerful with a controlled type I error rate. Application of ioSearch to two publicly available breast cancer datasets identified relevant genes and proteins in important pathways.

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Genome-wide analysis of somatic noncoding mutation patterns in cancer

Cited by 54 publications

References 211 publications

Use of Publication Dynamics to Distinguish Cancer Genes and Bystander Genes

Use of Publication Dynamics to Distinguish Cancer Genes and Bystander Genes

Widespread perturbation of ETS factor binding sites in cancer

ioSearch: a tool for searching disease-associated interacting omics; application on breast cancer data

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