{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,10,14]],"date-time":"2026-10-14T02:08:43Z","timestamp":1791943723743,"version":"4.3.4"},"reference-count":58,"publisher":"BMJ","issue":"5","license":[{"start":{"date-parts":[[2025,5,21]],"date-time":"2025-05-21T00:00:00Z","timestamp":1747785600000},"content-version":"unspecified","delay-in-days":20,"URL":"http:\/\/creativecommons.org\/licenses\/by-nc\/4.0\/"}],"funder":[{"name":"Oncohost","award":["n\/a"],"award-info":[{"award-number":["n\/a"]}]}],"content-domain":{"domain":["bmj.com"],"crossmark-restriction":true},"short-container-title":["J Immunother Cancer"],"accepted":{"date-parts":[[2025,5,5]]},"published-print":{"date-parts":[[2025,5]]},"abstract":"<jats:sec>\n                    <jats:title>Background<\/jats:title>\n                    <jats:p>Immune checkpoint inhibitors (ICIs) have shown substantial benefit for patients with advanced non-small cell lung cancer (NSCLC). However, resistance to ICIs remains a major clinical challenge. Here, we perform a comprehensive bioinformatic analysis of plasma proteomic profiles to explore the underlying biology of treatment resistance in NSCLC.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Methods<\/jats:title>\n                    <jats:p>The analysis was performed on 388 \u201cresistance-associated proteins\u201d (RAPs) that were previously described as pretreatment plasma proteomic predictors within the PROphet computational model designed to predict ICI clinical benefit in NSCLC. Putative tissue origins of the RAPs were explored using publicly available datasets. Enrichment analyses were performed to investigate RAP-related biological processes. Plasma proteomic data from 50 healthy subjects and 272 patients with NSCLC were compared, where patients were classified as displaying clinical benefit (CB; n=76) or no CB (NCB; n=196). Therapeutic agents targeting RAPs were identified in drug and clinical trial databases.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Results<\/jats:title>\n                    <jats:p>The RAP set was significantly enriched with proteins associated with lung cancer, liver tissue, cell proliferation, extracellular matrix, invasion, and metastasis. Comparison of RAP expression in healthy subjects and patients with NSCLC revealed five distinct RAP subsets that provide mechanistic insights. The RAP subset displaying a pattern of high expression in the healthy population relative to the NSCLC population included multiple proteins associated with antitumor activities, while the subset displaying a pattern of highest expression in the NCB population included proteins associated with various hallmarks of treatment resistance. Analysis of patient-specific RAP profiles revealed inter-patient diversity of potential resistance mechanisms, suggesting that RAPs may aid in developing personalized therapeutic strategies. Furthermore, examination of drug and clinical trial databases revealed that 17.5% of the RAPs are drug targets, highlighting the RAP set as a valuable resource for drug development.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Conclusions<\/jats:title>\n                    <jats:p>The study provides insight into the underlying biology of ICI resistance in NSCLC and highlights the potential clinical value of RAP profiles for developing personalized therapies.<\/jats:p>\n                  <\/jats:sec>","DOI":"10.1136\/jitc-2024-011427","type":"journal-article","created":{"date-parts":[[2025,5,22]],"date-time":"2025-05-22T04:35:34Z","timestamp":1747888534000},"page":"e011427","update-policy":"https:\/\/doi.org\/10.1136\/crossmarkpolicy","source":"Crossref","is-referenced-by-count":5,"title":["Decoding resistance to immune checkpoint inhibitors in non-small cell lung cancer: a comprehensive analysis of plasma proteomics and 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