American Association for Cancer Research
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Figure S1 from Weakly Supervised Deep Learning Predicts Immunotherapy Response in Solid Tumors Based on PD-L1 Expression

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journal contribution
posted on 2024-01-11, 14:20 authored by Marta Ligero, Garazi Serna, Omar S.M. El Nahhas, Irene Sansano, Siarhei Mauchanski, Cristina Viaplana, Julien Calderaro, Rodrigo A. Toledo, Rodrigo Dienstmann, Rami S. Vanguri, Jennifer L. Sauter, Francisco Sanchez-Vega, Sohrab P. Shah, Santiago Ramón y Cajal, Elena Garralda, Paolo Nuciforo, Raquel Perez-Lopez, Jakob Nikolas Kather

Performance overview of the model for predicting PD-L1 status and response to immunotherapy when trained on pan-cancer-VHIO cohort and validated in NSCLC-MSK. Area under the receiver operating characteristic (ROC) curves for the model to predict PD-L1 status (TPS≥1%) in the training (pan-cancer-VHIO cohort) (A) and in the test cohort (NSCLC-MSK cohort) (B) for the 5-folds cross-validation. All trained models were deployed in the test cohort. Kaplan-Meier curves for the predicted PD-L1 status (high/low) differentiates patients with longer Progression Free Survival to immunotherapy from patients with shorter survival in both pan-cancer-VHIO (C) and NSCLC-MSK cohort (D).


Bundesministerium für Gesundheit (BMG)

Deutsche Krebshilfe (German Cancer Aid)

Bundesministerium für Bildung und Forschung (BMBF)

'la Caixa' Foundation ('la Caixa')

CRIS Cancer Foundation (CRIS Foundation)

MEC | Instituto de Salud Carlos III (ISCIII)

NIHR | National Institute for Health and Care Research Applied Research Collaboration Oxford and Thames Valley (ARC OTV)

Fundación Fero (Fundació Fero)

Prostate Cancer Foundation (PCF)




Programmed death-ligand 1 (PD-L1) IHC is the most commonly used biomarker for immunotherapy response. However, quantification of PD-L1 status in pathology slides is challenging. Neither manual quantification nor a computer-based mimicking of manual readouts is perfectly reproducible, and the predictive performance of both approaches regarding immunotherapy response is limited. In this study, we developed a deep learning (DL) method to predict PD-L1 status directly from raw IHC image data, without explicit intermediary steps such as cell detection or pigment quantification. We trained the weakly supervised model on PD-L1–stained slides from the non–small cell lung cancer (NSCLC)-Memorial Sloan Kettering (MSK) cohort (N = 233) and validated it on the pan-cancer-Vall d'Hebron Institute of Oncology (VHIO) cohort (N = 108). We also investigated the performance of the model to predict response to immune checkpoint inhibitors (ICI) in terms of progression-free survival. In the pan-cancer-VHIO cohort, the performance was compared with tumor proportion score (TPS) and combined positive score (CPS). The DL model showed good performance in predicting PD-L1 expression (TPS ≥ 1%) in both NSCLC-MSK and pan-cancer-VHIO cohort (AUC 0.88 ± 0.06 and 0.80 ± 0.03, respectively). The predicted PD-L1 status showed an improved association with response to ICIs [HR: 1.5 (95% confidence interval: 1–2.3), P = 0.049] compared with TPS [HR: 1.4 (0.96–2.2), P = 0.082] and CPS [HR: 1.2 (0.79–1.9), P = 0.386]. Notably, our explainability analysis showed that the model does not just look at the amount of brown pigment in the IHC slides, but also considers morphologic factors such as lymphocyte conglomerates. Overall, end-to-end weakly supervised DL shows potential for improving patient stratification for cancer immunotherapy by analyzing PD-L1 IHC, holistically integrating morphology and PD-L1 staining intensity. The weakly supervised DL model to predict PD-L1 status from raw IHC data, integrating tumor staining intensity and morphology, enables enhanced patient stratification in cancer immunotherapy compared with traditional pathologist assessment.