2513 - Development and Evaluation of a Pan-Cancer Transcriptomic Hypoxia Signature for Radiation Therapy Response Prediction across Tumor Types: A TCGA Multi-Omics Analysis
Presenter(s)
J. Lasington1, L. S. Mathew Lasington2, and S. Umamaheshwaran3; 1New York Medical College at St. Mary's and St. Clare's, Denville, NJ, 2Rutger's University, East Hanover, NJ, 3Personio, Manhattan, NY
Purpose/Objective(s): Tumor hypoxia drives radioresistance, yet no validated pan-cancer transcriptomic hypoxia biomarker exists for predicting RT response. We developed a hypoxia gene signature as a universal predictor of radiosensitivity using multi-omics data from TCGA across 33 cancer types, to determine whether a single hypoxia-based biomarker can identify patients likely to benefit from RT irrespective of histology.
Materials/Methods: We queried the TCGA pan-cancer cohort (n=11,301; 33 cancer types) via ISB-CGC BigQuery, extracting clinical data, RNA-seq expression (TPM), somatic mutations, and RT response. Of 3,117 RT-treated patients, 1,414 had documented response (1,027 sensitive, 387 resistant). A composite hypoxia score was constructed from 10 canonical hypoxia genes (HIF1A, EPAS1, VEGFA, SLC2A1, LDHA, CA9, LOX, ADM, PGK1, BNIP3). The feature set was restricted to 13 hypoxia-centric variables. Gradient Boosting with stratified 5-fold CV and class-balanced weighting was used. SHAP values assessed feature importance. Per-cancer AUC subgroup analysis was performed for tumor types with n=10.
Results: The pan-cancer model achieved AUC 0.726 (accuracy 77%). SHAP analysis identified LDHA as the most impactful feature, followed by CA9, SLC2A1, VEGFA, and ADM - all canonical HIF-downstream targets. Per-cancer analysis showed strongest performance in GBM (AUC 0.750, n=11), melanoma (0.708, n=41), breast (0.658, n=191), and sarcoma (0.656, n=59). The signature underperformed in HNSC (0.458, n=160) and LGG (0.482, n=165), suggesting alternative radioresistance mechanisms. Hypoxia gene co-expression confirmed coherent pathway activation (LDHA-PGK1 r=0.61).
Conclusion: A 10-gene hypoxia signature achieves robust pan-cancer RT response prediction (AUC 0.73) across 1,414 patients from 33 tumor types. The signature performed best in hypoxia-driven tumors (GBM, melanoma, sarcoma), with LDHA and CA9 as top predictors. Cancer types where the signature failed (HNSC, LGG) may require immune- or DNA-repair-based biomarkers, motivating multi-pathway radiogenomic models.
Pan-Cancer Hypoxia Model Performance, Cancer-Type Subgroup AUCs, and SHAP Feature Importance (N=1,414)
Acc - Accuracy Sens N - Number of sensitive patients Res N - Number of resistant patients Prec - Precision Recall - Recall SHAP Rank - Feature importance ranking by SHAP (SHapley Additive exPlanations) values LDHA - Lactate Dehydrogenase A CA9 - Carbonic Anhydrase 9 GBM - Glioblastoma Multiforme HNSC - Head and Neck Squamous Cell Carcinoma LGG - Low-Grade Glioma CV - Cross-Validation (stratified 5-fold) TCGA - The Cancer Genome Atlas HIF - Hypoxia-Inducible Factor| Category | Metric | AUC | Acc | Sens N | Res N | Total | Prec | Recall | SHAP Rank |
| Pan-Cancer | Overall | 0.726 | 77% | 1027 | 387 | 1414 | 0.75 | 0.77 | - |
| Top Cancers | GBM | 0.750 | - | - | - | 11 | - | - | Hypothesis |
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| Melanoma | 0.708 | - | - | - | 41 | - | - | Strong |
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| Breast | 0.658 | - | - | - | 191 | - | - | Moderate |
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| Sarcoma | 0.656 | - | - | - | 59 | - | - | Moderate |
| Weak | HNSC | 0.458 | - | - | - | 160 | - | - | Failed |
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| LGG | 0.482 | - | - | - | 165 | - | - | Failed |
| SHAP Top 3 | LDHA | - | - | - | - | - | - | - | 1st |
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| CA9 | - | - | - | - | - | - | - | 2nd |