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A comprehensive bioinformatic study analyzes pretreatment plasma proteomic profiles to uncover the biology of ICI resistance. The work evaluates 388 resistance-associated proteins from the PROphet model, maps their putative tissue origins, and performs enrichment analyses to relate protein subsets to clinical benefit and lack of benefit in 272 patients.",{"@graph":69,"@context":126},[70,84,105],{"@type":71,"itemListElement":72},"BreadcrumbList",[73,77,79,82],{"item":74,"name":75,"@type":76,"position":8},"https://docshare.wps.com","Home","ListItem",{"item":78,"name":9,"@type":76,"position":14},"https://docshare.wps.com/document/",{"item":80,"name":35,"@type":76,"position":81},"https://docshare.wps.com/document/healthcare/",3,{"item":83,"name":65,"@type":76,"position":19},"https://docshare.wps.com/document/decoding-resistance-to-immune-checkpoint-inhibitors-in-non-small-cell-lung-cancer-a-comprehensive-analysis-of-plasma-proteomics-and-therapeutic-implications/373374/",{"url":83,"name":65,"@type":85,"image":86,"author":91,"headline":65,"publisher":94,"fileFormat":97,"inLanguage":63,"description":67,"dateModified":98,"datePublished":99,"encodingFormat":97,"isAccessibleForFree":100,"interactionStatistic":101},"DigitalDocument",{"url":87,"@type":88,"width":89,"height":90},"https://docshare.wps.com/thumbnails/decoding-resistance-to-immune-checkpoint-inhibitors-in-non-small-cell-lung-cancer-a-comprehensive-analysis-of-plasma-proteomics-and-therapeutic-implications/373374.png","ImageObject",300,407,{"name":92,"@type":93},"Alex Sinclair","Person",{"url":74,"name":95,"@type":96},"DocShare","Organization","application/pdf","2026-10-30","2026-09-23",true,{"@type":102,"interactionType":103,"userInteractionCount":52},"InteractionCounter",{"@type":104},"ViewAction",{"@type":106,"mainEntity":107},"FAQPage",[108,114,118,122],{"name":109,"@type":110,"acceptedAnswer":111},"What problem does the study address in NSCLC?","Question",{"text":112,"@type":113},"The study addresses resistance to immune checkpoint inhibitors in advanced nonsmall cell lung cancer, which remains a major clinical challenge.","Answer",{"name":115,"@type":110,"acceptedAnswer":116},"How was the resistance biology analyzed?",{"text":117,"@type":113},"Pretreatment plasma proteomic profiles were analyzed using 388 resistance-associated proteins (RAPs) from the PROphet computational model, with enrichment analyses and comparison between clinical benefit and no clinical benefit groups.",{"name":119,"@type":110,"acceptedAnswer":120},"What key findings were reported about RAP subsets?",{"text":121,"@type":113},"Five RAP subsets were identified that distinguish patterns between healthy subjects and NSCLC, and between clinical benefit versus no clinical benefit, offering mechanistic insights into treatment resistance.",{"name":123,"@type":110,"acceptedAnswer":124},"How do the RAPs relate to drug development?",{"text":125,"@type":113},"Drug and clinical trial databases indicated that 17.5% of the RAPs are drug targets, suggesting the RAP set as a valuable resource for developing therapies.","https://schema.org",{"og:url":83,"og:type":128,"og:title":65,"og:site_name":95,"og:description":67},"article",{"robots":130,"canonical":83},"index,follow",{"doc_id":132,"site_id":62},373374,1790545158,{"code":4,"msg":5,"data":135},{"doc_id":132,"user_id":136,"nickname":92,"user_avatar":137,"doc_module":4,"category_id":34,"category_name":35,"doc_title":65,"doc_description":67,"doc_content":138,"file_id":139,"file_url":140,"file_type":141,"file_size":142,"view_count":52,"is_deleted":4,"is_public":8,"is_downloadable":8,"audit_status":8,"page_count":143,"language":144,"language_code":63,"site_id":62,"html_lang":63,"table_of_contents":145,"faqs":146,"seo_title":147,"seo_description":67,"update_tm":148,"read_time":36},1099523882182,"https://ap-avatar.wpscdn.com/davatar_6f874abed73319feea01a86fa6f0fab8","Open access Original research  \nTo cite: Harel M, Dahan N, Lahav C, et al. Decoding resistance to immune checkpoint inhibitors in non-small cell lung cancer:  \na comprehensive analysis of plasma proteomics and therapeutic implications. Journal for ImmunoTherapy of Cancer 2025;13:e011427 . doi:10 . 1136/ jitc-2024-011427  \n► Additional supplemental material is published online only. To view, please visit the journal online ([https://doi.org/10.1136/](https://doi.org/10.1136/)[ ](https://doi.org/10.1136/)[jitc-2024-011427](jitc-2024-011427)) .  \nAccepted 05 May 2025  \n© Author(s) (or their employer(s)) 2025. Re-use permitted under CC BY-NC. No commercial re-use. See rights and permissions. Published by BMJ Group.  \nFor numbered affiliations see end of article.  \nCorrespondence to  \nDr Michal Harel;  \n[michal@oncohost.com](michal@oncohost.com)  \nDecoding resistance to immune checkpoint inhibitors in non-small cell lung cancer: a comprehensive analysis of plasma proteomics and therapeutic implications  \nMichal Harel  ,1 Nili Dahan,1 Coren Lahav  ,1 Eyal Jacob,1 Yehonatan Elon,1 Igor Puzanov  ,2,3 Ronan J Kelly,4 Yuval Shaked  ,5 Raya Leibowitz,6 David P Carbone  ,7 David R Gandara  ,8 Adam P Dicker9  \nABSTRACT  \nBackground Immune checkpoint inhibitors (ICIs) have shown substantial benefit for patients with advanced nonsmall 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.  \nMethods The analysis was performed on 388  \n“resistance-associated proteins”(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.  \nResults 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.  \nConclusions The study provides insight into the underlying biology of ICI resistance in NSCLC and  \nWHAT IS ALREADY KNOWN ON THIS TOPIC  \n\n| ⇒ Cancer resistance to immune checkpoint inhibitor (ICI) therapy is a major clinical challenge. The plasma proteome, reflecting tumor-related and immune-related activity, serves as a valuable source of information for elucidating the mechanisms underlying ICI resistance.\u003Cbr>WHAT THIS STUDY ADDS |\n| --- |\n| ⇒ We performed a comprehensive bioinformatic analysis of pretreatment plasma proteomic profiles from 272 patients with metastatic non-small cell lung cancer, focusing on 388 “resistance-associated proteins”(RAPs) that form the basis of the P","cbCaih0Kej5yfZV7","https://ap.wps.com/l/cbCaih0Kej5yfZV7","pdf",4955082,16,"English","# Abstract\n## Background\n## Methods\n## Results\n## Conclusions\n# What is already known on this topic\n# What this study adds\n# How this study might affect research, practice or policy\n# Introduction","[{\"question\":\"What problem does the study address in NSCLC?\",\"answer\":\"The study addresses resistance to immune checkpoint inhibitors in advanced nonsmall cell lung cancer, which remains a major clinical challenge.\"},{\"question\":\"How was the resistance biology analyzed?\",\"answer\":\"Pretreatment plasma proteomic profiles were analyzed using 388 resistance-associated proteins (RAPs) from the PROphet computational model, with enrichment analyses and comparison between clinical benefit and no clinical benefit groups.\"},{\"question\":\"What key findings were reported about RAP subsets?\",\"answer\":\"Five RAP subsets were identified that distinguish patterns between healthy subjects and NSCLC, and between clinical benefit versus no clinical benefit, offering mechanistic insights into treatment resistance.\"},{\"question\":\"How do the RAPs relate to drug development?\",\"answer\":\"Drug and clinical trial databases indicated that 17.5% of the RAPs are drug targets, suggesting the RAP set as a valuable resource for developing therapies.\"}]","Decoding resistance to immune checkpoint inhibitors in non-small cell lung cancer - a comprehensive analysis of plasma proteomics and therapeutic implications | PDF",1790199422]