Prediction of patient’s TLS status from H&E whole slide images in pan-cancer cohort

Context

Tertiary lymphoid structures (TLSs) are formations at sites with persistent inflammatory stimulation, including tumors. These ectopic lymphoid organs mainly consist of chemo-attracting B cells, T cells, and supporting dendritic cells (DCs).

TLS presence in the tumor compartment is considered a novel biomarker to stratify the overall survival risk of untreated cancer patients and as a marker of efficient immunotherapies for patients with solid tumors.

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Methods

Based on a pan-cancer cohort of 289 WSI, Owkin has developed a deep learning method to predict the presence of TLS from routine digitized H&E WSI.

The model scores regions of 112x112μm² by their relevance for TLS status and aggregates scores to make the final prediction.

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Results

This model is able to predict TLS status at the patient level with an AUC of 0.91 in 5-fold cross validation on the pan-cancer cohort. Moreover the model is robust to the transfer on an external cohort of sarcoma patients and achieves an AUC = 0.89.

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Impact

• Select high-value subgroups of patients that are most likely to respond to the ICI being tested,  improving the statistical power of a trial.
• Better selection of trial participants will improve success rates across trial phases and ultimately faster regulatory approval and more precise marketing.