A Novel Epitope Quality-Based Immune Escape Mechanism Reveals Patient’s Suitability for Immune Checkpoint Inhibition

Abstract

Michael Wessolly,1,2,* Susann Stephan-Falkenau,3,* Anna Streubel,3 Robert Werner,3 Sabrina Borchert,1,2 Sergej Griff,3 Elena Mairinger,1 Robert FH Walter,1,4 Torsten Bauer,5 Wilfried EE Eberhardt,4,6 Torsten G Blum,5 Kurt W Schmid,1 Jens Kollmeier,5 Thomas Mairinger,1,* Fabian D Mairinger1,2,* 1Institute of Pathology, University Hospital Essen, University of Duisburg-Essen, Essen, Germany; 2German Cancer Consortium (DKTK), Partner Site University Hospital Essen, Essen, Germany; 3Department of Tissue Diagnostics, Helios Klinikum Emil Von Behring, Berlin, Germany; 4Ruhrlandklinik, West German Lung Center, University Hospital Essen, University of Duisburg-Essen, Essen, Germany; 5Lungenklinik Heckeshorn, Helios Klinikum Emil Von Behring, Berlin, Germany; 6Department of Medical Oncology, West German Cancer Center, University, Hospital Essen, Essen, Germany*These authors contributed equally to this workCorrespondence: Michael Wessolly Research Group: Translational Cancer Research, Institute for PathologyUniversity Hospital Essen, Hufelandstraße 55, Essen D-45147, GermanyTel [phone] – 83035Fax [phone] – 3880Email [email protected]: Immune checkpoint inhibition, especially the blockade of PD-1 and PD-L1, has become one of the most thriving therapeutic approaches in modern oncology. Immune evasion caused by altered tumor epitope processing (so-called processing escapes) may be one way to explain immune checkpoint inhibition therapy failure. In the present study, we aim to demonstrate the effects of processing escapes on immunotherapy outcome in NSCLC patients.Patients and Methods: Whole exome sequencing data of 400 NSCLC patients (AdC and SCC) were extracted from the TCGA database. The ICB cohort was composed of primary tumor probes from 48 NSCLC patients treated with nivolumab. Mutations were identified by targeted amplicon-based sequencing including hotspots and whole exomes of 22 genes. The effect of mutations on proteasomal processing was evaluated by deep learning methods previously trained on 1260 known MHC-I ligands. Cox regression modelling was used to determine the influence on overall survival.Results: In the TCGA cohort, processing escapes were associated with decreased overall survival (p= 0.0140). In the ICB cohort, patients showing processing escapes in combination with high levels of PD-L1 (n=8/48) also showed significantly decreased overall survival, independently of mutational load or PD-L1 status.Conclusion: The concept of altered epitope processing may help to understand immunotherapy failure. Especially when combined with PD-L1 status, this method can be used as a biomarker to identify patients not suitable for immunotherapy.Keywords: massive parallel sequencing, NSCLC, immunotherapy, epitope, processing escape, deep learning

Keywords

• massive parallel sequencing
• nsclc
• immunotherapy
• epitope
• processing escape
• deep learning