Characterizing <i>KRAS</i> allele variants within biliary tract cancers.
Bibliographic record
Abstract
4088 Background: Biliary tract cancers (BTC) are aggressive malignancies with a poor 5-year survival rate and growing incidence globally. KRAS mutations (mut) in BTC are associated with a poor prognosis; however, PD-L1 inhibition with Durvalumab may lead to an improved survival with KRAS mut (TOPAZ-1 trial). It is important to understand the genomic landscape and immunophenotype of KRAS mut in BTC given the advent of immunotherapeutics and small molecular inhibitors targeting KRAS mut. Methods: A retrospective pooled analysis was performed from the following patient databases: Princess Margaret Cancer Centre, MD Anderson Cancer Center, Foundation Medicine, along with the publicly accessible cBioPortal for Cancer Genomics that includes the American Association for Cancer Research Project Genie cancer registry of real-world data assembled between 19 leading international cancer centers. Any overlapping cases were excluded. Patients included had a diagnosis of a BTC and completed molecular testing from January 2017 to December 2022. Cohort demographics, KRAS allelic variants, concurrent genetic aberrations, and immune biomarkers (PD-L1, TMB, MSI and gLOH) were summarized. Log-rank, Wilcoxon, and Kaplan-Meier tests were conducted for survival analysis. Results: 5,813 BTC patients were included, and 1000 patients (17.2%) had a KRAS mutation. The prevalence of KRAS mut was higher in extra-hepatic cholangiocarcinoma (EH-CCA) (36.1%) and perihilar (PH)-CCA (28.6%) than in intra-hepatic (IH)-CCA (11.82%) and gallbladder cancer (GBC) (7.6 %). The most common KRAS allelic variant was G12D, and the most common co-mutation was TP53, except in PH-CCA, which was G12V and SMAD4, respectively. In this cohort, race was primarily White (73%). The most prevalent variant in North America was G12D, while G12V and Q61H were more prevalent with genomic African American and genomic East Asian descent, respectively. For patients with KRAS mut, GBC had the most PD-L1 high positivity (17%) compared to IH-CCA (7%) and EH-CCA (3%), along with the most MSI-H phenotypes and the highest mean and median TMB compared to other sites, especially in G12V and Q61H variant patients. Genomic loss of heterozygosity was low among all groups. In the survival analysis, patients with the G12V allele subtype had the lowest OS at 17.8 months, followed by Q61H (22.8 months) and G12D (25.1 months) (p = 0.022). Survival analysis with KRAS co-mut ( TP53, SMAD4, CDK2NA, or additional KRAS mut) was not significant (p = 0.7). In a co-variance analysis of KRAS variants and tumour site, there was no difference in IH-CCA and GBC but lower OS in Q61H variants in PH-CCA and G12V variants in EH-CCA (p = 0.0081). Conclusions: This large series adds to the growing body of comprehensive genomic and immune landscape data of KRAS mut in BTC and will be of value in planning specific therapies in this heterogeneous group. Immune profiling studies are ongoing to further describe the immunophenotype of this subset.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot 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.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
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.
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 source (direct Gemma or distilled Codex), not a consensus.
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".