Abstract 7395: Histology-based prognosis prediction using deep learning outperforms and is independent of the MGMT methylation status in patients with glioblastoma

Abstract

Abstract Introduction: MGMT gene promoter methylation status is today the only biomarker of survival for GBM patients and predicts favorable response to temozolomide (TMZ). Here we propose a deep learning-based model using histology data (H&E slides) to predict the prognosis of patients with Glioblastoma (GBM) treated by standard of care (SOC). Methods: We included TCGA GBM patients and selected only patients treated after 2005 (Stupp protocol start); compliant to WHO 2021 classification; treated by surgery and adjuvant therapy; with a follow-up superior to median overall survival (OS) (14.5 months). Short and long survivor labels were defined based on median OS cut-off. A deep learning pipeline was trained to predict these labels from the H&E slides (baseline surgery samples). First, the slides were automatically segmented into tissue and background, divided into a regular grid of tiles (112 × 112µm), and tiles were vectorized using a vision transformer-based feature extractor trained on H&E slides of 5,500 patients with 16 cancer subtypes (GBM excluded). In the second step, ABMIL deep learning model was trained to predict the survival category using the bag of tile features available from step 1 for each patient. The cohort was divided into five folds with even numbers of short and long survivors. For each fold, 50 ABMIL models were trained on the remaining folds and ensembled to predict the category of the patients in the fold. Training hyperparameters were tuned on a separated cohort excluding GBM patients. Results: After filtering, we analyzed n = 192 TCGA GBM patients including 154 patients with a known MGMT promoter methylation status. Our histology-based model had an accuracy of 0.661 and a ROC AUC of 0.706. It stratified the population into short and long survivors with an Hazard Ratio (HR) of 0.48 (95% CI 0.34-0.69). Our model outperformed the MGMT methylation status that had an HR of 0.57 (95% CI 0.40-0.83). Moreover, our model was independent of MGMT methylation status and the combination of both was able to further improve prognosis prediction: we applied our model independently to the 91 patients with an unmethylated MGMT status (predicted: 43 low, 48 high) and to the 63 patients with a methylated MGMT status (predicted: 30 low, 33 high), and obtained HR of 0.42 (95% CI 0.27-0.67) and 0.49 (95% CI 0.28-0.86) respectively. Conclusion: Having selected patients treated by the latest SOC and classified as GBM by the latest WHO criteria, we propose a model predicting survival based on histological specimens available in routine care, which outperformed the current unique biomarker MGMT methylation status. The combination of this status and our model improves prognosis predictions compared to each strategy alone. Citation Format: Lucas Fidon, Celine Thiriez, Alexandre Grimaldi, Omar Darwiche Domingues, Charles Maussion, Caroline Hoffmann. Histology-based prognosis prediction using deep learning outperforms and is independent of the MGMT methylation status in patients with glioblastoma [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2024; Part 1 (Regular Abstracts); 2024 Apr 5-10; San Diego, CA. Philadelphia (PA): AACR; Cancer Res 2024;84(6_Suppl):Abstract nr 7395.