Abstract 5063: Exploring the mutational landscape of <i>KRAS</i> and <i>NRAS</i> in tumors with non-V600 <i>BRAF</i> mutations
Bibliographic record
Abstract
Abstract Background: BRAF, a key modulator of the Mitogen-Activated Protein Kinase (MAPK) pathway, is observed in 7% of all cancers. Therapeutic response to MAPK inhibition (MAPKi) often relies on molecular distinctions between members of varying classes: Class 1 (V600), Class 2 and 3 (non-V600) BRAF mutants. Preliminary data indicates that co-occurring RAS mutations in non-V600 BRAF mutant cancers are less responsive to MAPKi treatment. This emphasizes the need to investigate the characteristics of RAS co-mutations in non-V600 BRAF mutant tumors. Methods: Genomic data was obtained from a cohort of 183,292 patients provided by the AACR GENIE database (v14.1). Patient samples were clustered according to their BRAF mutation status and co-occurring K/NRAS mutations: WT BRAF (n=60,845) vs. non-V600 BRAF (n=1009). Samples were grouped based on cancer type: melanoma (n=3841), colorectal (n=15,434), and non-small cell lung cancer (n=14640) and were further categorized according to key biochemical features of RAS GTPase function and overall GTPase activity. Results: This dataset revealed a diverse array of allelic variants of K/NRAS between cancer types and their underlying BRAF mutation status (Table 1). Non-V600 BRAF mutant cancers showed an enrichment for KRAS mutations linked to amplified nucleotide exchange (37.9% vs. 13.4%; p<0.0001) and hydrolysis-impairing NRAS mutations (41.9% vs. 23%; p<0.0001), compared to WT BRAF cancers. Rare allelic variants including KRAS L19F, KRAS A146T, and NRAS G60E were seen in Class 2/3 BRAF mutants. Conclusion: This data suggests that non-V600 BRAF mutant tumors are characterized by a unique distribution of RAS mutations. More research into the difference in downstream effectors of RAS mutants overrepresented in non-V600 BRAF mutant tumors could provide important insights into how these tumors develop and resist targeted therapies. Table 1. Classification of KRAS and NRAS mutations in WT BRAF vs. non-V600 BRAF mutant tumors GTPase Function Cancer Type All cancers NSCLC Colorectal Melanoma BRAF mutation WT non-V600 p-value WT non-V600 p-value WT non-V600 p-value WT non-V600 p-value KRASmutation class Impaired hydrolysis n=20731 (79.8%) n=167 (51.9%) <0.0001 n=6068 (86.1%) n=60 (56.6%) <0.0001 n=4799 (68.2%) n=16 (29.1%) <0.0001 n=38 (48.1%) n=13 (50.0%) 0.4047 Nucleotide Exchange n=3475 (13.4%) n=122 (37.9%) n=530 (7.5%) n=36 (34.0%) n=1842 (26.2%) n=36 (65.5%) n=30 (38.0%) n=12 (46.2%) Hybrid n=1765 (6.8%) n=33 (10.2%) n=446 (6.3%) n=10 (9.4%) n=395 (5.6%) n=3 (5.5%) n=11 (13.9%) n=1 (3.8%) NRAS mutation class Impaired hydrolysis n = 974 (23.0%) n=78 (41.9%) <0.0001 n=29 (18.7%) n=9 (39.1%) 0.0196 n=188 (32.6%) n=17 (68.0%) <0.0001 n=86 (5.5%) n=22 (27.8%) <0.0001 Nucleotide Exchange n=459 (10.8%) n=22 (11.8%) n=5 (3.2%) n=2 (8.7%) n=48 (8.3%) n=4 (16.0%) n=93 (5.9%) n=10 (12.7%) Hybrid n=2811 (66.2%) n=86 (46.2%) n=121 (78.1%) n=12 (52.2%) n=341 (59.1%) n=4 (16.0%) n=1385 (88.6%) n=47 (59.5%) GTPase Activity BRAF mutation WT non-V600 p-value WT non-V600 p-value WT non-V600 p-value WT non-V600 p-value KRASmutation class Intermediate activity n=14408 (55.3%) n=142 (44.4%) 0.0001 n=5082 (72.3%) n=54 (50.5%) <0.0001 n=3540 (49.9%) n=17 (31.5%) 0.0089 n=46 (56.8%) n=13 (50.0%) 0.6516 High activity n=11630 (44.7%) n=178 (55.6%) n=1947 (27.7%) n=53 (49.5%) n=3552 (50.1%) n=37 (68.5%) n=35 (43.2%) n=13 (50.0%) NRAS mutation class Intermediate activity n=1364 (32.2%) n=101 (55.8%) <0.0001 n=36 (23.4%) n=12 (54.5%) 0.0041 n=239 (41.8%) n=20 (87.0%) <0.0001 n=190 (12.3%) n=34 (43.0%) <0.0001 High activity n=2868 (67.8%) n=80 (44.2%) n=118 (76.6%) n=10 (45.5%) n=333 (58.2%) n=3 (13.0%) n=1357 (87.7%) n=45 (57.0%) Citation Format: Chantel L. Mukonoweshuro, Emmanuelle Rousselle, April A. Rose. Exploring the mutational landscape of KRAS and NRAS in tumors with non-V600 BRAF mutations [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2024; Part 1 (Regular Abstracts); 2024 Apr 5-10; San Diego, CA. Philadelphia (PA): AACR; Cancer Res 2024;84(6_Suppl):Abstract nr 5063.
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| 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) | 0.003 | 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".