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Record W2993552927 · doi:10.1186/s41199-019-0045-0

Genomic and human papillomavirus profiling of an oral cancer cohort identifies TP53 as a predictor of overall survival

2019· article· en· W2993552927 on OpenAlexafffund
Neil Mundi, Stephenie D. Prokopec, Farhad Ghasemi, Andrew Warner, Krupal Patel, Danielle MacNeil, Christopher J. Howlett, William Stecho, Paul Plantinga, Nicole Pinto, Kara M. Ruicci, Mohammed Imran Khan, Myung Woul Han, John Yoo, Kevin Fung, Axel Sahovaler, David A. Palma, Eric Winquist, Joe S. Mymryk, John W. Barrett, Paul C. Boutros, Anthony C. Nichols

Bibliographic record

VenueCancers of the Head & Neck · 2019
Typearticle
Languageen
FieldMedicine
TopicHead and Neck Cancer Studies
Canadian institutionsUniversity of TorontoOntario Institute for Cancer ResearchLondon Health Sciences CentreWestern University
FundersCanadian Institutes of Health Research
KeywordsHuman papillomavirusOncologyCohortProfiling (computer programming)Survival analysisInternal medicineOverall survivalMedicineBiologyComputational biologyComputer science

Abstract

fetched live from OpenAlex

Abtract Background The genomic landscape of head and neck cancer has been reported through The Cancer Genome Atlas project. We attempt to determine if high-risk human papillomavirus (HPV) or frequently mutated genes are correlated with survival in an oral cancer cohort. Methods Patient demographic data along with data from final pathology was collected. Tumor DNA was analyzed using a custom Illumina targeted sequencing panel. Five high-risk HPV types were tested by qPCR. Statistical analyses were used to identify associations between patient outcome and mutational status. Results High-risk HPV types were identified in 7% of cases; HPV status was not associated with survival. Mutations were identified in TP53, TERT promoter, & PIK3CA. Mutations in TP53 were significantly associated with poorer overall survival on multi-variate analysis ( p = 0.03). Conclusions Mutations in TP53 were associated with poor patient survival. Expanding our sample size may identify further predictors of outcome to direct customized cancer care.

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: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.201
Threshold uncertainty score0.997

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
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 head0.027
GPT teacher head0.325
Teacher spread0.299 · 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

Citations25
Published2019
Admission routes2
Has abstractyes

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