Personalized approach for response prediction and treatment management for non-small cell lung cancer patients based on a liquid biopsy

• Published in: Journal of clinical oncology Vol. 40; no. 16_suppl; p. e21132
• Main Authors: Shaked, Yuval, Harel, Michal, Lahav, Coren, Yellini, Ben, Tepper, Ella, Wolf, Ido, Harkovsky, Tatiana, Leibowitz, Raya, Gottfried, Maya, Abu-Amana, Mahmud, Katzenelson, Rivka, Agbarya, Abed, Moskovitz, Mor, Lotem, Michal, Levy-Barda, Adva, Zer, Alona, Koch, Ina, Carbone, David Paul, Dicker, Adam P., Christopoulos, Petros
• Format: Journal Article
• Language: English
• Published: 01.06.2022
• ISSN: 0732-183X; 1527-7755
• DOI: 10.1200/JCO.2022.40.16_suppl.e21132

e21132 Background: To date, predicting response to immune checkpoint blockade (ICB) therapy in non-small cell lung cancer (NSCLC) patients is based on assessing PD-L1 levels in tumor biopsies. However, such assays are only moderately predictive. In addition, the assays require a tumor biopsy and do not aid in identifying patient-specific resistance mechanisms beyond PD-L1. To overcome these issues, we developed a novel computational approach for predicting response to ICB based on pre-treatment proteomic measurements in liquid biopsies. Methods: Plasma samples were collected from 184 NSCLC patients prior to treatment, along with comprehensive clinical data, as part of an ongoing multi-center clinical trial (PROPHETIC; NCT04056247). Overall response rate (ORR) was assessed 3- and 6-months following treatment initiation. A deep proteomic profiling of each plasma sample was performed, measuring the expression levels of approximately 7000 proteins. A novel proprietary machine learning approach was developed on a subset of samples (training set; n = 110) and then was tested on a blind independent validation set (n = 74). Results: A computational model was developed on the proteomic data by identifying patient-specific Resistance Associated Proteins (RAPs). Focusing on differentially expressed proteins between responders and non-responders, a protein was defined as a RAP in a given patient based on its expression level in the patient relative to the expression distribution of the RAP in responders and non-responders. The probability of response to ICB treatment was determined based on the patient’s RAP profile together with 4 clinical parameters. The RAP-based machine learning model successfully stratified between patients with prolonged and limited benefit with a hazard ratio (HR) of 4.5 (confidence interval 2.07-9.77; p-value < 0.0001) and 2.27 (confidence interval 1.7-4.03; p-value = 0.004) for overall survival and progression free survival, respectively. Each patient displayed a resistance map comprised of a unique combination of RAPs, suggesting a new approach for personalized medicine based on patient-specific pathway blockade. For example, a patient with KDR and IL-6 defined as RAPs may benefit from a clinical trial that targets any of these RAPS in combination with ICB. Last, an exploration into the biological functions of the identified RAPs revealed specific biological processes in each response group, including splicing, complement system, coagulation and signaling. Conclusions: We have developed a novel computational approach based on proteomic profiling of liquid biopsies for predicting response to ICB treatment in NSCLC patients. Our approach also sheds light on patient-specific resistance mechanisms, potentially enabling personalized treatment options and patient monitoring over time.