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Impact of KRAS mutational status on outcomes in patients with pancreatic cancer (PDAC).

2021· article· en· W3168980224 on OpenAlexaff
Lucy Xiaolu, Gun Ho Jang, Amy Zhang, Robert E. Denroche, Anna Dodd, Stephanie Ramotar, Shawn Hutchinson, Yifan Wang, Mustapha Tehfé, Ravi Ramjeesingh, James Biagi, Bernard Lam, Julie M. Wilson, Faiyaz Notta, Sandra E. Fischer, Robert C. Grant, George Zogopoulos, Steven Gallinger, Jennifer J. Knox, Grainne M. O’Kane

Bibliographic record

VenueJournal of Clinical Oncology · 2021
Typearticle
Languageen
FieldMedicine
TopicPancreatic and Hepatic Oncology Research
Canadian institutionsQueen's UniversityMcGill University Health CentreOntario Institute for Cancer ResearchNova Scotia Cancer CentrePrincess Margaret Cancer CentreCentre Hospitalier de l’Université de MontréalToronto General HospitalDalhousie UniversityUniversity Health Network
Fundersnot available
KeywordsKRASMedicineGemcitabineInternal medicineHazard ratioOverall survivalPancreatic cancerOncologyCancerCohortCDKN2ASTK11Proportional hazards modelCancer researchColorectal cancerConfidence interval

Abstract

fetched live from OpenAlex

4142 Background: KRAS mutations (m) (KRASm) are present in over 90% of pancreatic adenocarcinomas (PDAC) with a predominance of G12 substitutions. KRAS wildtype (WT) PDAC relies on alternate oncogenic drivers, and the prognostic impact of these remains unknown. We evaluated alterations in WT PDAC and explored the impact of specific KRASm and WT status on survival. Methods: WGS and RNAseq were performed on 570 patients (pts) ascertained through our translational research program from 2012-2021, of which 443 were included for overall survival (OS) analyses. This included 176 pts with resected and 267 pts with advanced PDAC enrolled on the COMPASS trial (NCT02750657). The latter cohort underwent biopsies prior to treatment with first line gemcitabine-nab-paclitaxel or mFOLFIRINOX as per physician choice. The Kaplan-Meier and Cox proportional hazards methods were used to estimate OS. Results: KRAS WT PDAC (n = 52) represented 9% of pts, and these cases trended to be younger than pts with KRASm (median age 61 vs 65 years p = 0.1). In resected cases, the most common alterations in WT PDAC (n = 23) included GNASm (n = 6) and BRAFm/fusions (n = 5). In advanced WT PDAC (n = 27), alterations in BRAF (n = 11) and ERBB2/3/4 (n = 6) were most prevalent. Oncogenic fusions (NTRK, NRG1, BRAF/RAF, ROS1, others) were identified in 9 pts. The BRAF in-frame deletion p.486_491del represented the most common single variant in WT PDAC, with organoid profiling revealing sensitivity to both 3rd generation BRAF inhibitors and MEK inhibition. In resected PDAC, multivariable analyses documented higher stage (p = 0.043), lack of adjuvant chemotherapy (p < 0.001), and the KRAS G12D variant (p = 0.004) as poor prognostic variables. In advanced disease, neither WT PDAC nor KRAS specific alleles had an impact on prognosis (median OS WT = 8.5 mths, G12D = 8.2, G12V = 10.0, G12R = 12.0, others = 9.2, p = 0.73); the basal-like RNA subtype conferred inferior OS (p < 0.001). A targeted therapeutic approach following first line chemotherapy was undertaken in 10% of pts with advanced PDAC: MMRd (n = 1), homologous recombination deficiency (HRD) (n = 19), KRASG12C (n = 1), CDK4/6 amplification (n = 3), ERBB family alterations (n = 2), BRAF variants (n = 2). OS in this group was superior (14.7 vs 8.8 mths, p = 0.04), mainly driven by HRD-PDAC where KRASm were present in 89%. Conclusions: In our dataset, KRAS G12D is associated with inferior OS in resected PDAC, however KRAS mutational status was not prognostic in advanced disease. This suggests that improved OS in the WT PDAC population can only be achieved if there is accelerated access to targeted drugs for pts.

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)0.001
metaresearch head score (Gemma)0.002
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: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.002
Threshold uncertainty score0.005

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.002
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0020.000

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 head0.119
GPT teacher head0.543
Teacher spread0.425 · 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".

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Citations2
Published2021
Admission routes1
Has abstractyes

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