Abstract 5661: Identifying and intervening rare resistant subclones to BRAF/MEK inhibitors in metastatic melanoma
Bibliographic record
Abstract
Abstract Acquired drug resistance poses a significant challenge in the treatment of cancer. This study focuses on melanoma as a model system to understand and address pre-existing subclonal resistance. The use of BRAF and MEK inhibitors (BRAFi/MEKi) in patients with metastatic melanoma harboring BRAF activating mutations has resulted in remarkable responses in the majority of cases. However, most patients have disease relapse after one year, and there are limited subsequent treatment options. One possible cause of relapse is the existence of a subpopulation of cells resistant to BRAFi/MEKi prior to treatment, raising the possibility that commencing counter-resistance treatment earlier may delay or abolish resistance to BRAFi/MEKi. We aim to enhance the detection and treatment of low-frequency resistant mutations, addressing the current oversight by existing technologies. This research is crucial due to a lack of consensus on the clinical management of these mutations. By using quantitative blocker displacement amplification (qBDA) technology and integrating patient samples with cell line models, our project seeks to determine 1) the prevalence of low-frequency resistant subclones by measuring the Variant Allele Frequency (VAF) of known resistance mutations along the MAPK/PI3K pathways in FFPE biopsies from 149 metastatic melanoma patients and 2) determine the functional impact of different VAF levels on the outgrowth of resistant subclones. We assessed 14 loci in 6 known resistance genes: NRAS, KRAS, PIK3CA, AKT1, MAP2K1, and MAP2K2, as well as BRAF itself as a positive control. As expected, all 55 patients analyzed harbored BRAF activating mutations, while 17 patients had at least one resistant subclone above our preliminary cutoff of 60% VAF. To simulate the behavior of low-VAF resistance mutations, we conducted in vitro experiments by spiking in labeled BRAF/MEK inhibitor-resistant melanoma cells into their isogenic sensitive parental cell line. Our initial data demonstrated that, at least in this model system, a 10% VAF spike-in leads to the eventual outgrowth of resistant cells, but not in a 1% VAF spike-in, suggesting that either the density or the absolute number of the pre-existing resistant cells can dictate resistance evolution. In future experiments, we will assess multiple VAFs in an independent cell line as well as in vivo to identify the nature of its correlation with resistance outcomes and expand to additional models. In summary, our data suggest that a substantial proportion of metastatic melanoma patients with BRAF mutations have pre-existing resistant cell subclones, and that accurately assessing its VAF could provide clinically-relevant information. Ultimately, results from this work would be the basis for future personalized counter-resistance therapies, potentially extending treatment effectiveness for patients with pre-existing resistant mutations. Citation Format: Han Wang, Michael Nakazawa, Omid Veiseh, Lawrence Kwong, David Zhang. Identifying and intervening rare resistant subclones to BRAF/MEK inhibitors in metastatic 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 5661.
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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 | 0.001 | 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) | 0.002 | 0.000 |
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.
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".