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.

Details

Title
Automatic discovery of image-based signatures for ipilimumab response prediction in malignant melanoma
Author
Harder, Nathalie 1   VIAFID ORCID Logo  ; Schönmeyer Ralf 1 ; Nekolla Katharina 1 ; Meier, Armin 1 ; Brieu Nicolas 1 ; Vanegas Carolina 1 ; Madonna Gabriele 2 ; Capone Mariaelena 3 ; Botti, Gerardo 3 ; Ascierto, Paolo A 3   VIAFID ORCID Logo  ; Schmidt, Günter 4 

 Definiens AG, Munich, Germany 
 Istituto Nazionale Tumori IRCCS Fondazione G. Pascale, Naples, Italy (GRID:grid.417893.0) (ISNI:0000 0001 0807 2568); University of Naples Federico II, Department of Translational Medical Sciences and Center for Basic and Clinical Immunology Research (CISI), Naples, Italy (GRID:grid.4691.a) (ISNI:0000 0001 0790 385X) 
 Istituto Nazionale Tumori IRCCS Fondazione G. Pascale, Naples, Italy (GRID:grid.417893.0) (ISNI:0000 0001 0807 2568) 
 Definiens AG, Munich, Germany (GRID:grid.417893.0) 
Publication year
2019
Publication date
2019
Publisher
Nature Publishing Group
e-ISSN
20452322
Source type
Scholarly Journal
Language of publication
English
ProQuest document ID
2225809295
Copyright
© The Author(s) 2019. This work is published under http://creativecommons.org/licenses/by/4.0/ (the “License”). Notwithstanding the ProQuest Terms and Conditions, you may use this content in accordance with the terms of the License.