Abstract 1267: Dual approach using unbiased proteomics and multiplexed immunofluorescence for the detection of markers predictive for immunotherapy in melanoma patients
Authors: Juncker-Jensen Anna , Beaton Nigel , Beeler Kristina , Treiber Tobias , Levesque Mitchell , Gomez Julia M. , Nunns Harry , Tan Xin-Xing , Vowinckel Jakob 
Published: June 2022

Background: Remarkable clinical success with immune checkpoint inhibitor (ICI) therapy has been achieved in recent years for the treatment of melanoma. However, despite the clinical advances of ICI therapy no durable responses are observed in 40-60% of melanoma patients, and current biomarkers such as tumor mutational burden (TMB) and PD-L1 expression do not clearly distinguish responders. Therefore, a growing focus of immuno-oncology (IO) research is focused on identifying novel biomarkers that are predictive for the response of treatment with ICIs. Due to the complexity of the interactions between cancer cells and the immune system, the identification of predictive biomarkers for patient response requires a combination of tools and efforts. Therefore, we have designed a multi-modality approach for the protein analysis of tumor tissue samples from late-stage melanoma patients treated with ICIs, consisting of an unbiased deep proteomic analysis followed by a multiplexed immunofluorescence (mIF) spatial tissue analysis.

Methods: Formalin-fixed paraffin-embedded (FFPE) tumor samples were firstly used for unbiased quantification of proteins using data-independent acquisition (DIA) LC-MS technology and Biognosys’ Spectronaut software. Baseline patient samples were classified as responders (n = 9) or non-responders (n = 15) based on the response at 3 months post ICI-treatment. Additionally, patient’s tumor mutational burden (TMB) was analyzed using whole exome sequencing (WES). Subsequently, the same patient samples will be analyzed by MultiOmyx™, a proprietary, immunofluorescence (IF) multiplexing assay that enables visualization and characterization of up to 60 biomarkers on a single FFPE section. A custom panel will be generated to verify key markers identified by proteomics analysis in a spatial context and after multiplexing images will be analyzed by applying the proprietary deep-learning based cell classification platform NeoLYTX.

Results: Analysis of the 24 FFPE samples from metastatic melanoma patients treated with ICI therapy stratified into responders and non-responders lead to the identification of 103 proteins that are significantly up- or down-regulated between the groups. Work is now ongoing to follow-up these findings by an mIF tissue analysis which will provide further details on the spatial relationship in the tumor microenvironment of these markers and pathways.

Conclusions: In this study we demonstrate the power of a dual proteomic and mIF profiling for a comprehensive characterization of melanoma patients and the discovery and detection of markers predictive for response to ICI-therapy.

Citation Format: Anna Juncker-Jensen, Nigel Beaton, Kristina Beeler, Tobias Treiber, Mitchell Levesque, Julia M. Gomez, Harry Nunns, Xin-Xing Tan, Jakob Vowinckel. Dual approach using unbiased proteomics and multiplexed immunofluorescence for the detection of markers predictive for immunotherapy in melanoma patients [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2022; 2022 Apr 8-13. Philadelphia (PA): AACR; Cancer Res 2022;82(12_Suppl):Abstract nr 1267.