1. Automatic discovery of image-based signatures for ipilimumab response prediction in malignant melanoma
- Author
-
Mariaelena Capone, Günter Schmidt, Gerardo Botti, Gabriele Madonna, Paolo A. Ascierto, Armin Meier, Katharina Nekolla, Nicolas Brieu, Nathalie Harder, Ralf Schönmeyer, and Carolina Vanegas
- Subjects
Male ,0301 basic medicine ,Skin Neoplasms ,Computer science ,lcsh:Medicine ,Cancer immunotherapy ,computer.software_genre ,Biomarkers, Pharmacological ,Machine Learning ,0302 clinical medicine ,Image Processing, Computer-Assisted ,Precision Medicine ,lcsh:Science ,Melanoma ,Aged, 80 and over ,Multidisciplinary ,Middle Aged ,Female ,Immunotherapy ,medicine.drug ,Adult ,Ipilimumab ,Image processing ,Predictive markers ,Machine learning ,Article ,Cross-validation ,03 medical and health sciences ,Lymphocytes, Tumor-Infiltrating ,medicine ,Humans ,Aged ,Retrospective Studies ,Tumor-infiltrating lymphocytes ,business.industry ,Deep learning ,lcsh:R ,Digital pathology ,Precision medicine ,medicine.disease ,030104 developmental biology ,lcsh:Q ,Artificial intelligence ,business ,computer ,030217 neurology & neurosurgery ,Companion diagnostic - Abstract
In the context of precision medicine with immunotherapies there is an increasing need for companion diagnostic tests to identify potential therapy responders and avoid treatment coming along with severe adverse events for non-responders. Here, we present a retrospective case study to discover image-based signatures for developing a potential companion diagnostic test for ipilimumab (IPI) in malignant melanoma. Signature discovery is based on digital pathology and fully automatic quantitative image analysis using virtual multiplexing as well as machine learning and deep learning on whole-slide images. We systematically correlated the patient outcome data with potentially relevant local image features using a Tissue Phenomics approach with a sound cross validation procedure for reliable performance evaluation. Besides uni-variate models we also studied combinations of signatures in several multi-variate models. The most robust and best performing model was a decision tree model based on relative densities of CD8+ tumor infiltrating lymphocytes in the intra-tumoral infiltration region. Our results are well in agreement with observations described in previously published studies regarding the predictive value of the immune contexture, and thus, provide predictive potential for future development of a companion diagnostic test.
- Published
- 2019