Abstract 10P
Background
Tertiary lymphoid structures (TLS) are ectopic lymphoid formations which are composed predominantly of B cells, T cells and dendritic cells. The presence in the tumor compartment of mature TLS - characterized by mature follicles containing germinal centers - has been strongly associated with improved survival upon cancer immunotherapies for patients with solid tumors. For this reason, TLS could be used as a predictive factor biomarker to identify the patients more likely to benefit from immune checkpoint inhibitors. However, the pathological assessment of the TLS status remains time-consuming and usually requires additional analysis including CD3/CD20 staining. This study aims to show that artificial intelligence techniques applied to digital pathology could offer a fast and cheap mass patient screening solution to assess TLS from Hematoxylin/Eosin (HE) digital pathology slides used in clinical workflow.
Methods
We analyzed a pan-cancer cohort of 289 HE-stained Whole Slide Images (WSI) from Institut Bergonié (43% NSCLC, 12% sarcoma, 45% other solid tumors) on which TLS were manually contoured by expert pathologists. A Deep Learning (DL) model was trained on WSI to predict TLS status at the patient level (presence or absence of TLS). Models were evaluated using five-fold cross validation. The best performing architecture included two main components: a predicted score of TLS presence on small areas (112x112 micrometers) of the WSI followed by an aggregation at the patient level. To assess the transferability of the model, a validation cohort (PEMBROSARC) of 236 sarcoma WSI was used.
Results
The best performing model achieved a ROC AUC score of 0.917 (std of 0.036) in five-fold cross validation on the pan-cancer cohort (specificity of 68% for a sensitivity of 90%). This model transferred very well on the PEMBROSARC study, the first clinical trial implementing TLS status as an inclusion criteria (Italiano et al Nature Medicine 2022) with a ROC AUC score of 0.89 (specificity of 64% for a sensitivity of 90%).
Conclusions
Our study demonstrates the predictive power of DL applied to predict the patient's TLS status from HE images. This model could be implemented in pathology labs as an efficient pre-screening tool.
Legal entity responsible for the study
Institut Bergonie.
Funding
Owkin, Inc.
Disclosure
L. Gillet, J. Le Douget, B. Schmauch, C. Maussion: Financial Interests, Institutional, Full or part-time Employment: Owkin, Inc. All other authors have declared no conflicts of interest.