Abstract 2107: Insights into inherited genetic variations and genetic ancestry of patients with high-risk melanoma
Bibliographic record
Abstract
Abstract Introduction. For melanoma patients (pts) with resectable regional or distant metastases, there is paucity of data related to inherited genetic variations and genetic ancestry in the North American patient population. Methods. We conducted genome-wide genotyping on samples from 744 consenting pts enrolled in E1609 adjuvant trial that tested ipilimumab vs interferon-α in high-risk melanoma including sites across the U. S. and Canada. We used Illumina Infinium Global Screening Array v.3.0. After imputation, pruning, and incorporating genotypes from 1KGP3 as reference, genetic ancestry was estimated using admixture v1.3.0 in the unsupervised mode. Population structure was visualized through a UMAP dimensionality reduction (R package umap_0.2.9.0). Genetic ancestry proportions were visualized using python package PONG v1.5. Results. In UMAP reduction, most (728) cases clustered with the 1KGP3 European (EUR) reference, with small subsets (12 and 14, respectively) clustering with the admixed American (AMR) and East Asian (EAS) references. Considering potential ancestral origins for the study pts, we assumed between 4 to 8 ancestral populations in order to capture a wider range of ancestral contributions. Unsupervised admixture inference on the combined genetic data from our cohort and the 1000 Genomes reference identified ancestral axes clearly along continental geographical lines. K=5 was deemed most optimal in broadly capturing the potential complexity of genetic contributions in U.S. context. The ancestry populations identified were Africa (AFR), AMR, EAS, EUR, South Asia (SAS). For most (734) samples, the estimated EUR proportion was >50%, while 4 had >50% EAS ancestry, 4 >50% AMR ancestry, and 2 >40% EUR and AMR ancestry. Most (725) participants self-reported their race as White, 4 as Asian, and 1 as multi-race; 14 missing information on race. For the 725 White participants, 13 identified as Hispanic and 24 missing information on ethnicity; and for these participants, there was a high degree of EUR ancestry (>95% in 22), with only 2 having between 4-6% combined AMR and AFR contributions. The 4 Asian participants had a high degree of EAS ancestry, and the multi-race participant had a high degree of EUR ancestry with minor degrees of AMR and EAS ancestry. Participants with missing information on self-reported race had either a high degree of EUR ancestry or various combinations of significant AMR, EUR, and AFR contributions. The 17 participants reporting Hispanic ethnicity had a significant AMR ancestral contribution. The vast majority of participants not reporting (699) or missing (28) information on Hispanic ethnicity did not have AMR ancestry. Conclusions. Our analysis in the context of a U.S. Intergroup phase 3 trial revealed important insights into genetic ancestry of patients with high-risk melanoma. Ongoing analyses are investigating associations with survival outcomes and the risk of irAEs. Citation Format: Ahmad A. Tarhini, Zhihua Chen, Sandra J. Lee, F. Stephen Hodi, Islam Eljilany, Tingyi Li, Howard Streicher, Vernon K. Sondak, Xuefeng Wang, Peter A. Kanetsky, John M. Kirkwood. Insights into inherited genetic variations and genetic ancestry of patients with high-risk melanoma [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2025; Part 1 (Regular Abstracts); 2025 Apr 25-30; Chicago, IL. Philadelphia (PA): AACR; Cancer Res 2025;85(8_Suppl_1):Abstract nr 2107.
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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 | 0.003 |
| 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) | 0.006 | 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".