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Additional file 1: Suppl. Figure 1. Differential gene expression analysis of all patients showing increased PD-L1 expression. The log-fold changes between each state (PD-L1 positive or negative) are plotted against the p-value, displaying significant differences in expression between each state. Grey: Differential gene expression does not differ significantly between each state. Red: Top 10 most differentially expressed genes (p

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 +49 201 723 – 83035Fax +49 201 723 – 3880Email michael.wessolly@uk-essen.deBackground: 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

Background: 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 demon-strate 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 immu-notherapy failure. Especially when combined with PD-L1 status, this method can be used as a biomarker to identify patients not suitable for immunotherapy. CA - Wessolly

Fabian Mairinger,1 Agnes Bankfalvi,1 Kurt Werner Schmid,1 Elena Mairinger,2 Pawel Mach,3 Robert FH Walter,2 Sabrina Borchert,1 Sabine Kasimir-Bauer,3 Rainer Kimmig,3 Paul Buderath3 1Institute for Pathology, University Hospital Essen, Essen, Germany; 2Ruhrlandklinik, West German Lung Centre, University Hospital Essen, University of Duisburg-Essen, Essen, Germany; 3Department of Gynecology and Obstetrics, University Hospital Essen, Essen, GermanyCorrespondence: Paul BuderathDepartment of Gynecology and Obstetrics, University Hospital Essen, Hufelandstraße 55, Essen 45122, GermanyTel +49 201 723 2442Email paul.buderath@uk-essen.dePurpose: Response to platinum-based therapy is a major prognostic factor in high-grade serous ovarian cancer (HGSOC). While the exact mechanisms of platinum-resistance remain unclear, evidence is accumulating for a connection between the organism’s immune-response and response to platinum. However, predictive tools are missing. This study was performed to examine the putative role of the genetic tumor immune-microenvironment in mediating differential chemotherapy response in HGSOC patients.Patients and methods: Expression profiling of 770 immune-related genes was performed in tumor tissues from 23 HGSOC cases. Tumors were screened for prognostic and predictive biomarkers using the NanoString nCounter platform for digital gene expression analysis with the appurtenant PanCancer Immune Profiling panel. As validation cohort, gene expression data (RNA Seq) of 303 patients with epithelial ovarian carcinoma (EOC) were retrieved from the The Cancer Genome Atlas (TCGA) database. Different scoring-systems were computed for prediction of risk-of-resistance to cisplatin, disease-free survival (DFS) and overall survival (OS).Results: Validated on the TCGA-dataset, the developed scores identified 11 significantly differentially expressed genes (p

Figure S1. Expression of tested genes is independent of histomorphology of MPM. The boxplot shows no significant differences between gene expression patterns due to biphasic (B), epithelioid (E), or sarcomatoid (S) MPM. (PDF 59 kb)

Table S2. AURKA, RAD50, and DDB2 showed statistically significant dependencies on overall and progression-free survival. (PDF 33 kb)

Table S1. Normalized counts of genes measured by the Digital Analyzer. (PDF 343 kb)

supplemental_table_2 for Processing Escape Mechanisms Through Altered Proteasomal Cleavage of Epitopes Affect Immune Response in Pulmonary Neuroendocrine Tumors by Michael Wessolly, Robert F. H. Walter, Claudia Vollbrecht, Robert Werner, Sabrina Borchert, Jan Schmeller, Elena Mairinger, Thomas Herold, Anna Streubel, Daniel C. Christoph, Wilfried E. E. Eberhardt, Jens Kollmeier, Thomas Mairinger, Kurt W. Schmid, Jeremias Wohlschlaeger, Thomas Hager, and Fabian D. Mairinger in Technology in Cancer Research & Treatment