Abstract 5847: Characterizing mechanisms of BRAF/MEK inhibitor resistance in BRAF V600 mutant colorectal cancer vs. melanoma
Bibliographic record
Abstract
Abstract Background: BRAF V600 mutations comprise ~80% of all oncogenic BRAF mutations in metastatic colorectal cancer (mCRC). While most cancers with BRAF V600 mutations can be effectively treated with combined BRAF and MEK inhibition, BRAF V600 mutant mCRC acquire resistance through EGFR-mediated signaling. The profound difference in response rates to BRAF/MEK inhibitors in BRAF V600 mutant mCRC (12%) vs. melanoma (63-68%) is not sufficiently explained by increased EGFR signaling in CRC. Therefore, we sought to characterize the differences that exist between BRAF V600 mutant CRC and melanomas that could explain the differential sensitivity to BRAF/MEK inhibition and identify more effective treatments for BRAF V600 mutant CRC. Methods: We assessed cell survival by clonogenic assay in the presence of small molecule inhibitors. In parallel, we mined TCGA RNAseq data to calculate the MAPK Pathway Activity Score (MPAS). RTK expression was assessed in TCGA data from patients with BRAF V600 mutant CRC vs. melanoma. We developed cells with acquired resistance to BRAF+MEK inhibitors (Encorafenib+Binimetinib; E+B) by culturing cells in increasing concentrations of drug over several months. RNA-sequencing and analysis was performed on parental and resistant cells treated with vehicle control or BRAF+MEK inhibitors. Results: When comparing the efficacy of BRAF+MEK inhibitors across BRAF V600 mutant cells, we found that BRAF V600 mutant CRC cell lines (n=2) were less sensitive than melanoma cell lines (n=2). MPAS scores were significantly lower in BRAF V600 mutant CRC (n=46) vs. melanoma (n=188) tumors. We interrogated protein array data from the DepMap and identified increased activation of several RTKs (EGFR, HER2, HER3) in BRAF V600 CRC (n=8) vs. melanoma (n=39) cell lines. Similarly, we established resistant HT29 (BRAF V600 CRC) cells and observed sustained MAPK pathway activity despite a lack of difference in EGFR activity in the presence of BRAF+MEK inhibitor treatment. Both HT29 parental and resistant cells showed re-establishment of MAPK signaling after 72 hours. Resistant SkMel28 (BRAF V600 melanoma) cells showed increased pERK levels compared to resistant HT29 cells. RNAseq revealed increased expression of multiple RTKs (PDGFRB, ERBB2, ERBB3) and ligands/growth factors for multiple RTKs (FGF3, PDGFA, HB-EGF) in resistant HT29 cells. Shp2 is a protein tyrosine phosphatase that regulates signaling of multiple RTKs to activate the MAPK pathway. Accordingly, we assessed the efficacy of an inhibitor of Shp2 (TNO155) overcoming MAPKi resistance in BRAF V600 CRC cells. We confirmed that E+B+TNO155 more effectively inhibited cell growth and MAPK pathway activity in BRAF V600 CRC cell lines (n=2) in vitro. Conclusion: Together, these data identify key, actionable drivers of MAPK inhibitor resistance in CRC and highlight Shp2 as a potential therapeutic target for BRAF V600 CRC. Citation Format: Jennifer Maxwell, Emmanuelle Rousselle, Lucy An, April Rose. Characterizing mechanisms of BRAF/MEK inhibitor resistance in BRAF V600 mutant colorectal cancer vs. melanoma [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 5847.
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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 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 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".