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Case study: TLS status

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

Pan Cancer
Prognostic biomarkers
Histology Data

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.


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.


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.

  • 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.
“Today, we are working with Owkin to develop a deep learning model to identify TLS from routine histology slides. This work has the potential to better select patients who are more likely to benefit from immune checkpoint inhibitors.”
Antoine Italiano MD, PhD
Institut Bergonié, Gustave Roussy