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Record W3043743612 · doi:10.1038/s41467-020-17386-z

Landscape of somatic single nucleotide variants and indels in colorectal cancer and impact on survival

2020· article· en· W3043743612 on OpenAlexafffund
Syed Hassan Ejaz Zaidi, Tabitha A. Harrison, Amanda I. Phipps, Robert S. Steinfelder, Quang M. Trinh, Conghui Qu, Barbara L. Banbury, Peter Georgeson, Catherine S. Grasso, Marios Giannakis, Jeremy Adams, Elizabeth Alwers, Efrat L. Amitay, Richard Barfield, Sonja I. Berndt, Ivan Borozan, Hermann Brenner, Stefanie Brezina, Daniel D. Buchanan, Yin Cao, Andrew T. Chan, Jenny Chang‐Claude, Charles M. Connolly, David A. Drew, Alton B. Farris, Jane C. Figueiredo, Amy J. French, Charles S. Fuchs, Levi A. Garraway, Steve Gruber, Mark A. Guinter, Stanley R. Hamilton, Sophia Harlid, Lawrence E. Heisler, Akihisa Hidaka, John L. Hopper, Wen‐Yi Huang, Jeroen R. Huyghe, Mark A. Jenkins, Paul M. Krzyzanowski, Mathieu Lemire, Yi Lin, Xuemei Luo, Elaine R. Mardis, John D. McPherson, Jessica K. Miller, Vı́ctor Moreno, Xinmeng Jasmine Mu, Reiko Nishihara, Nickolas Papadopoulos, Danielle Pasternack, Michael J. Quist, Adilya Rafikova, Emma Reid, Eve Shinbrot, Brian H. Shirts, Lincoln Stein, Cherie Teney, Lee E. Timms, Caroline Y. Um, Bethany Van Guelpen, Megan Van Tassel, Xiaolong Wang, David A. Wheeler, Christina K. Yung, Li Hsu, Shuji Ogino, Andrea Gsur, Polly A. Newcomb, Steven Gallinger, Michael Hoffmeister, Peter T. Campbell, Stephen N. Thibodeau, Wei Sun, Thomas J. Hudson, Ulrike Peters

Bibliographic record

VenueNature Communications · 2020
Typearticle
Languageen
FieldBiochemistry, Genetics and Molecular Biology
TopicCancer Genomics and Diagnostics
Canadian institutionsLunenfeld-Tanenbaum Research InstituteUniversity Health NetworkMount Sinai HospitalUniversity of TorontoOntario Institute for Cancer Research
FundersOntario Ministry of Research and InnovationCanadian Institutes of Health ResearchCenters for Disease Control and PreventionNational Cancer InstituteU.S. Department of Health and Human ServicesNational Institutes of HealthFred Hutchinson Cancer Research Center
KeywordsIndelMUTYHColorectal cancerBiologySomatic cellGeneGeneticsMutationDNA mismatch repairCancerCancer researchGermline mutationSingle-nucleotide polymorphismGenotype

Abstract

fetched live from OpenAlex

Colorectal cancer (CRC) is a biologically heterogeneous disease. To characterize its mutational profile, we conduct targeted sequencing of 205 genes for 2,105 CRC cases with survival data. Our data shows several findings in addition to enhancing the existing knowledge of CRC. We identify PRKCI, SPZ1, MUTYH, MAP2K4, FETUB, and TGFBR2 as additional genes significantly mutated in CRC. We find that among hypermutated tumors, an increased mutation burden is associated with improved CRC-specific survival (HR = 0.42, 95% CI: 0.21-0.82). Mutations in TP53 are associated with poorer CRC-specific survival, which is most pronounced in cases carrying TP53 mutations with predicted 0% transcriptional activity (HR = 1.53, 95% CI: 1.21-1.94). Furthermore, we observe differences in mutational frequency of several genes and pathways by tumor location, stage, and sex. Overall, this large study provides deep insights into somatic mutations in CRC, and their potential relationships with survival and tumor features.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

How this classification was reachedexpand

Full frame distilled prediction

Teacher imitation

Not 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: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.587
Threshold uncertainty score0.244

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

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 head0.016
GPT teacher head0.290
Teacher spread0.274 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one teacher head, not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designObservational
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

Citations94
Published2020
Admission routes2
Has abstractyes

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