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Record W2169034512 · doi:10.1186/1476-4598-13-135

Analyses of clinicopathological, molecular, and prognostic associations of KRAS codon 61 and codon 146 mutations in colorectal cancer: cohort study and literature review

2014· article· en· W2169034512 on OpenAlexfundno aff
Yu Imamura, Paul Lochhead, Mai Yamauchi, Aya Kuchiba, Zhi Rong Qian, Xiaoyun Liao, Reiko Nishihara, Seungyoun Jung, Kana Wu, Katsuhiko Nosho, Yaoyu E. Wang, Shouyong Peng, Adam J. Bass, Kevin M. Haigis, Jeffrey A. Meyerhardt, Andrew T. Chan, Charles S. Fuchs, Shuji Ogino

Bibliographic record

VenueMolecular Cancer · 2014
Typearticle
Languageen
FieldMedicine
TopicColorectal Cancer Treatments and Studies
Canadian institutionsnot available
FundersDamon Runyon Cancer Research FoundationGary Bennett Family FundNational Cancer InstituteNational Institutes of HealthHarvard UniversityEntertainment Industry Foundation
KeywordsKRASMicrosatellite instabilityColorectal cancerBiologyOncologyCancer researchInternal medicineHazard ratioCancerMutationGeneticsMedicineAlleleGeneMicrosatellite

Abstract

fetched live from OpenAlex

BACKGROUND: KRAS mutations in codons 12 and 13 are established predictive biomarkers for anti-EGFR therapy in colorectal cancer. Previous studies suggest that KRAS codon 61 and 146 mutations may also predict resistance to anti-EGFR therapy in colorectal cancer. However, clinicopathological, molecular, and prognostic features of colorectal carcinoma with KRAS codon 61 or 146 mutation remain unclear. METHODS: We utilized a molecular pathological epidemiology database of 1267 colon and rectal cancers in the Nurse's Health Study and the Health Professionals Follow-up Study. We examined KRAS mutations in codons 12, 13, 61 and 146 (assessed by pyrosequencing), in relation to clinicopathological features, and tumor molecular markers, including BRAF and PIK3CA mutations, CpG island methylator phenotype (CIMP), LINE-1 methylation, and microsatellite instability (MSI). Survival analyses were performed in 1067 BRAF-wild-type cancers to avoid confounding by BRAF mutation. Cox proportional hazards models were used to compute mortality hazard ratio, adjusting for potential confounders, including disease stage, PIK3CA mutation, CIMP, LINE-1 hypomethylation, and MSI. RESULTS: KRAS codon 61 mutations were detected in 19 cases (1.5%), and codon 146 mutations in 40 cases (3.2%). Overall KRAS mutation prevalence in colorectal cancers was 40% (=505/1267). Of interest, compared to KRAS-wild-type, overall, KRAS-mutated cancers more frequently exhibited cecal location (24% vs. 12% in KRAS-wild-type; P < 0.0001), CIMP-low (49% vs. 32% in KRAS-wild-type; P < 0.0001), and PIK3CA mutations (24% vs. 11% in KRAS-wild-type; P < 0.0001). These trends were evident irrespective of mutated codon, though statistical power was limited for codon 61 mutants. Neither KRAS codon 61 nor codon 146 mutation was significantly associated with clinical outcome or prognosis in univariate or multivariate analysis [colorectal cancer-specific mortality hazard ratio (HR) = 0.81, 95% confidence interval (CI) = 0.29-2.26 for codon 61 mutation; colorectal cancer-specific mortality HR = 0.86, 95% CI = 0.42-1.78 for codon 146 mutation]. CONCLUSIONS: Tumors with KRAS mutations in codons 61 and 146 account for an appreciable proportion (approximately 5%) of colorectal cancers, and their clinicopathological and molecular features appear generally similar to KRAS codon 12 or 13 mutated cancers. To further assess clinical utility of KRAS codon 61 and 146 testing, large-scale trials are warranted.

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 machine prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.

metaresearch head score (Codex)0.003
metaresearch head score (Gemma)0.007
Version: metacan-v3-hybrid-931329e0061cValidation 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: none
Teacher disagreement score0.011
Threshold uncertainty score0.016

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0030.007
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0020.002
Bibliometrics0.0110.014
Science and technology studies0.0000.000
Scholarly communication0.0010.001
Open science0.0010.000
Research integrity0.0010.000
Insufficient payload (model declined to judge)0.0010.000

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 head0.032
GPT teacher head0.387
Teacher spread0.355 · 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 source (direct Gemma or distilled Codex), 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

Citations147
Published2014
Admission routes1
Has abstractyes

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