Abstract A035: Comparing the clinical and genomic landscapes of acral, mucosal, and cutaneous metastatic melanomas treated with immune checkpoint inhibitors
Bibliographic record
Abstract
Abstract Background: Melanoma is a melanocytic malignancy that is classified into different subtypes, including cutaneous (CM), acral (AM), and mucosal melanoma (MM). Immune checkpoint inhibitors (ICI) targeting PD-1 and/or CTLA-4 have emerged as the standard of care for advanced metastatic melanoma. We investigated responses to ICIs, genomic profiles, and molecular differences among these different subtypes of melanoma. Methods: We performed a multi-center retrospective cohort study including patients with metastatic melanoma who received anti-PD1 +/- CTLA4 inhibitor ICI for metastatic disease. We employed the AACR GENIE (v13.1) cancer database, DepMap portal, and Enrichr web tool with MSigDB Hallmark 2020 database to analyze the incidence and distribution of significant alterations, differentially expressed genes, and pathway enrichment within distinct melanoma subtypes. Results: We identified 337 patients with advanced melanoma who received anti-PD1 +/- anti-CTLA4 for metastatic disease. CM was the most frequent melanoma subtype (81%), followed by MM (12%), and AM (8%). Patients with AM had the shortest OS and PFS CM (OS 3.4 years, PFS of 1.1 years), AM median (OS 1.4 years, PFS 3.8 months), and MM (OS 1.7 years, PFS 6 months; OS, P= 0.028; PFS P=0.029). Patients with CM or MM experienced longer OS with anti-PD1 +/- CTLA4 vs. anti-PD1 monotherapy, but no survival advantage was observed in patients with AM. In the GENIE dataset, we identified 2015/374/177 samples with CM/MM/AM, respectively. AM and MM patients were significantly more likely to be Female, Black or Asian than CM patients. BRAF V600 mutations were most frequent in CM (40%), followed by MM (7%) and AM (14%). However, MM and AM had an increase in alterations in cell cycle regulator genes. The incidence of CDK4 and CCND1 amplification, respectively was highest in AM (17%, 16%), followed by MM (6%, 6%) and CM (2%, 3%). We compared the pathways represented by differentially altered genes in AM vs. CM: G2M-Checkpoint (Q<0.05) and E2F targets (Q<0.05) pathways were enriched in AM. We also compared RNAseq data from n=4) AM and n=20 CM cell lines, and found the same pathways enriched amongst genes that were differentially expressed between AM and CM: G2M-Checkpoint (Q<0.01) and E2F targets (Q<0.01) Conclusions: Our findings highlight comparatively poor outcomes with ICIs in Acral Melanoma. AM has lower rates of actionable BRAF mutations; as such, very few patients with metastatic AM have access to effective 1st or 2nd line therapies. Thus, there is an urgent need to explore alternative therapeutic targets. Our data implicate cell cycle inhibitors as potentially important components of novel treatment strategies that could augment immunotherapy efficacy for patients with metastatic AM. Citation Format: Sadaf Solati, April A. N. Rose, Anna Spreafico, Adrian Sacher, Denis Yahiaoui, Wilson H. Miller. Comparing the clinical and genomic landscapes of acral, mucosal, and cutaneous metastatic melanomas treated with immune checkpoint inhibitors [abstract]. In: Proceedings of the AACR Special Conference in Cancer Research: Tumor Immunology and Immunotherapy; 2023 Oct 1-4; Toronto, Ontario, Canada. Philadelphia (PA): AACR; Cancer Immunol Res 2023;11(12 Suppl):Abstract nr A035.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.002 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.002 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".