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High-coverage whole-genome analysis of 1220 cancers reveals hundreds of genes deregulated by rearrangement-mediated cis-regulatory alterations
Yiqun Zhang, Fengju Chen, Nuno A. Fonseca, Yao He, Masashi Fujita, Hidewaki Nakagawa, Zemin Zhang, Alvis Brāzma, Philip Awadalla, Peter J. Bailey, Angela N. Brooks, Claudia Calabrese, Aurélien Chateigner, Isidro Cortés‐Ciriano, David Craft, Chad J. Creighton, Natalie R. Davidson, Deniz Demircioğlu, Milana Frenkel‐Morgenstern, Mary J. Goldman, Liliana Greger, Jonathan Göke, Katherine A. Hoadley, Matthew R. Huska, André Kahles, Ekta Khurana, Helena Kilpinen, Fabien C. Lamaze, Kjong-Van Lehmann, David K. Chang, Siliang Li, Xiaobo Li, Xinyue Li, Fenglin Liu, Xingmin Liu, Julia Markowski, Matthew Meyerson, Tannistha Nandi, Morten Muhlig Nielsen, Akinyemi I. Ojesina, B. F. Francis Ouellette, Peter J. Park, Jakob Skou Pedersen, Gunnar Rätsch, Yuichi Shiraishi, Cameron M. Soulette, Stefan G. Stark, Oliver Stegle, Patrick Tan, Bin Tean Teh, Lara Urban, Jian Wang, Sebastian M. 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Bibliographic record
VenueNature Communications · 2020
Typearticle
Languageen
FieldBiochemistry, Genetics and Molecular Biology
TopicGenomics and Chromatin Dynamics
Canadian institutionsSickKids FoundationBC Cancer AgencyUniversity of British ColumbiaProstate Cancer CanadaUniversité de MontréalHospital for Sick ChildrenLunenfeld-Tanenbaum Research InstituteMount Sinai HospitalPrincess Margaret Cancer CentreUniversity of CalgaryGenome CanadaCanada's Michael Smith Genome Sciences CentreVector InstituteSimon Fraser UniversityMcGill UniversityUniversity of OttawaMcGill University and Génome Québec Innovation CentreToronto General HospitalUniversity Health NetworkUniversity of TorontoInstitute of Cancer ResearchOntario Institute for Cancer Research
FundersNational Institute of Environmental Health SciencesNational Institute of General Medical SciencesU.S. Department of Health and Human ServicesNational Cancer InstituteNational Institutes of HealthCancer Prevention and Research Institute of Texas
KeywordsGenomeGeneBiologyComputational biologyGenetics
Abstract
The impact of somatic structural variants (SVs) on gene expression in cancer is largely unknown. Here, as part of the ICGC/TCGA Pan-Cancer Analysis of Whole Genomes (PCAWG) Consortium, which aggregated whole-genome sequencing data and RNA sequencing from a common set of 1220 cancer cases, we report hundreds of genes for which the presence within 100 kb of an SV breakpoint associates with altered expression. For the majority of these genes, expression increases rather than decreases with corresponding breakpoint events. Up-regulated cancer-associated genes impacted by this phenomenon include TERT, MDM2, CDK4, ERBB2, CD274, PDCD1LG2, and IGF2. TERT-associated breakpoints involve ~3% of cases, most frequently in liver biliary, melanoma, sarcoma, stomach, and kidney cancers. SVs associated with up-regulation of PD1 and PDL1 genes involve ~1% of non-amplified cases. For many genes, SVs are significantly associated with increased numbers or greater proximity of enhancer regulatory elements near the gene. DNA methylation near the promoter is often increased with nearby SV breakpoint, which may involve inactivation of repressor elements.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Bench or experimental · Consensus signal: Bench or experimental
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.546
Threshold uncertainty score0.646
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
Opus teacher head0.009
GPT teacher head0.245
Teacher spread0.236 · how far apart the two teachers sit on this one work
Validation status
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, not a consensus.
The models applied no category: nothing in the taxonomy fit this work.
Study designBench or experimental
Domainnot available
GenreEmpirical
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".
Quick stats
Citations76
Published2020
Admission routes1
Has abstractyes