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Harry Glickman

harry.glickman@mila.quebec
PhD - McGill University
Supervisor
Shirin A. Enger
Research Topics
AI and Healthcare
Causality
Genomics
Multimodal Learning

Publications

Pan-Cancer Gene Expression Predicts Per-Cancer Radiation Therapy Response
Harry Glickman
Shirin A. Enger
Background and purpose: Although radiotherapy response involves biological processes shared across cancers, most biomarker studies rely on s… (see more)mall, disease-specific sample sizes that limit the reliability of prognostic models. This study evaluated whether pan-cancer data can increase the information available for estimating genomic associations with progression after radiotherapy, based on Molecular Signatures Database hallmark gene sets applied to The Cancer Genome Atlas patients. Methods and materials: Cross-cancer predictive potential was evaluated in cancer types with at least 100 patients who received radiotherapy and had information on gene expression, progression, age, stage, and gender: breast invasive carcinoma (BRCA, n=553), cervical squamous cell carcinoma (CESC, n=183), head-neck squamous cell carcinoma (HNSC, n=320), low-grade glioma (LGG, n=316), lung adenocarcinoma (LUAD, n=104), skin cutaneous melanoma (SKCM, n=125), thyroid cancer (THCA, n=326), and uterine corpus endometrial carcinoma (UCEC, n=257). Gene set expression scores were calculated at the single-sample level. For each evaluation cancer type, scores from all other types were used to fit pan-cancer Cox proportional hazards models. Linear predictions from these models were evaluated in per-cancer prognostication via Kaplan Meier curves, Cox proportional hazards (CoxPH) estimates, including adjustment for age, stage, and gender, and optimism-corrected C-statistic and Nagelkerke pseudo-R 2 . Results: In Kaplan Meier analysis, pan-cancer scores predicted progression in all cancer types, with varying statistical significance. Univariable CoxPH models also demonstrated consistent association with progression, again with varying significance. Associations persisted when adjusted for clinical variables, and model performance metrics indicated the potential to improve per-cancer prognostication. Associations were particularly strong for BRCA, CESC, HNSC, LGG, and LUAD, with statistically insignificant results for SKCM, THCA, and UCEC. Conclusion: Pan-cancer gene expression data can predict per-cancer progression after radiotherapy, with variable strength across cancer types. These findings support the use of cross-cancer genomic information to address sample size limitations that affect radiotherapy biomarker development.
2026-07-26
Advances in Radiation Oncology (published)
doi.org