Hui-Zi Chen, Na Hyun Kim, Daisuke Nishizaki, Mary K Nesline, Jeffrey M Conroy, Paul DePietro, Sarabjot Pabla, Shumei Kato, Razelle Kurzrock
{"title":"PD-1 transcriptomic landscape across cancers and implications for immune checkpoint blockade outcome.","authors":"Hui-Zi Chen, Na Hyun Kim, Daisuke Nishizaki, Mary K Nesline, Jeffrey M Conroy, Paul DePietro, Sarabjot Pabla, Shumei Kato, Razelle Kurzrock","doi":"10.1038/s41525-025-00465-9","DOIUrl":null,"url":null,"abstract":"<p><p>Programmed cell death protein 1 (PD-1) is a critical immune checkpoint receptor and a target for cancer immune checkpoint inhibitors (ICI). We investigated PD-1 transcript expression across cancer types and its correlations to clinical outcomes. Using a reference population, PD-1 expression was calculated as percentiles in 489 of 514 patients (31 cancer types) with advanced/metastatic disease. PD-1 RNA expression varied across and within cancer types; pancreatic and liver/bile duct malignancies displayed the highest rates of high PD-1 (21.82% and 21.05%, respectively). Elevated CTLA-4, LAG-3, and TIGIT RNA expression were independently correlated with high PD-1. Although high PD-1 was not associated with outcome in immunotherapy-naïve patients (n = 272), in patients who received ICIs (n = 217), high PD-1 transcript expression was independently correlated with prolonged survival (hazard ratio 0.40; 95%CI, 0.18-0.92). This study identifies PD-1 as an important biomarker in predicting ICI outcomes, and advocates for comprehensive immunogenomic profiling in cancer management.</p>","PeriodicalId":19273,"journal":{"name":"NPJ Genomic Medicine","volume":"10 1","pages":"21"},"PeriodicalIF":4.7000,"publicationDate":"2025-03-11","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11897377/pdf/","citationCount":"0","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"NPJ Genomic Medicine","FirstCategoryId":"3","ListUrlMain":"https://doi.org/10.1038/s41525-025-00465-9","RegionNum":2,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q1","JCRName":"GENETICS & HEREDITY","Score":null,"Total":0}
引用次数: 0
Abstract
Programmed cell death protein 1 (PD-1) is a critical immune checkpoint receptor and a target for cancer immune checkpoint inhibitors (ICI). We investigated PD-1 transcript expression across cancer types and its correlations to clinical outcomes. Using a reference population, PD-1 expression was calculated as percentiles in 489 of 514 patients (31 cancer types) with advanced/metastatic disease. PD-1 RNA expression varied across and within cancer types; pancreatic and liver/bile duct malignancies displayed the highest rates of high PD-1 (21.82% and 21.05%, respectively). Elevated CTLA-4, LAG-3, and TIGIT RNA expression were independently correlated with high PD-1. Although high PD-1 was not associated with outcome in immunotherapy-naïve patients (n = 272), in patients who received ICIs (n = 217), high PD-1 transcript expression was independently correlated with prolonged survival (hazard ratio 0.40; 95%CI, 0.18-0.92). This study identifies PD-1 as an important biomarker in predicting ICI outcomes, and advocates for comprehensive immunogenomic profiling in cancer management.
NPJ Genomic MedicineBiochemistry, Genetics and Molecular Biology-Molecular Biology
CiteScore
9.40
自引率
1.90%
发文量
67
审稿时长
17 weeks
期刊介绍:
npj Genomic Medicine is an international, peer-reviewed journal dedicated to publishing the most important scientific advances in all aspects of genomics and its application in the practice of medicine.
The journal defines genomic medicine as "diagnosis, prognosis, prevention and/or treatment of disease and disorders of the mind and body, using approaches informed or enabled by knowledge of the genome and the molecules it encodes." Relevant and high-impact papers that encompass studies of individuals, families, or populations are considered for publication. An emphasis will include coupling detailed phenotype and genome sequencing information, both enabled by new technologies and informatics, to delineate the underlying aetiology of disease. Clinical recommendations and/or guidelines of how that data should be used in the clinical management of those patients in the study, and others, are also encouraged.