Abstract A037: Survival-based subtyping of lung cancer through integrated genomic and methylation signatures
Bibliographic record
Abstract
Abstract Background: Lung cancer prognosis varies significantly due to the interplay of clinical, genomic and epigenomic factors. We developed an integrative analytic pipeline to cluster lung cancer patients based on clinical characteristics, somatic mutations, and methylation profiles, linking these clusters to survival outcomes. Methods: We defined a cohort using clinical data from GuardantINFORM, a clinico-genomics database to identify 75,000 lines of therapy (LOT) records of advanced lung cancer patients with a Guardant360 on the Infinity platform resulting in genomic mutation data (3M variants) and targeted methylation data (top 1,000 variable regions) and used to predict clusters of progression on the following line of therapy . Features included clinical factors (LOT, therapy regimens), binary mutation indicators (top-100 genes), and 20 principal components (PCs) derived from methylation data. Ensemble clustering using K-means and agglomerative methods identified patient subgroups. Survival was analyzed via Kaplan–Meier methods, and a penalized Cox proportional hazards model determined feature-level hazard ratios. Results: Four robust clusters emerged with distinct survival profiles (p<0.0001). Cox modeling (concordance=0.72) highlighted several prognostic markers: KRAS mutations (HR=1.24, p=0.01), STK11 mutations (HR=1.49, p<0.005), KEAP1 mutations (HR=1.33, p=0.01), and methylation PC5 (HR=0.03, p=0.01). Biological interpretation of PC5 revealed enrichment of hypermethylation in key developmental genes such as PAX6, SOX9, OTX2, WT1, and TBX20. This suggests epigenetic suppression of differentiation-related transcription factors may contribute significantly to tumor aggressiveness. Conclusions: Integrating clinical, genetic, and methylation data via ensemble clustering effectively delineates lung cancer subgroups with clinically meaningful survival differences. Key mutations in KRAS, STK11, and KEAP1 and methylation-driven silencing of developmental transcription factors represent robust markers for patient risk stratification and potential therapeutic targeting Citation Format: Aaron Hardin, Sheila Solomon, Amar Das. Survival-based subtyping of lung cancer through integrated genomic and methylation signatures [abstract]. In: Proceedings of the AACR Special Conference in Cancer Research: Artificial Intelligence and Machine Learning; 2025 Jul 10-12; Montreal, QC, Canada. Philadelphia (PA): AACR; Clin Cancer Res 2025;31(13_Suppl):Abstract nr A037.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| 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".