Christopher C Jackson, Jia Jenny Liu, Howard Y Liu, Steven G Williams, Asim Anees, Zainab Noor, Natasha Lucas, Dylan Xavier, Peter G Hains, Daniel Bucio-Noble, Adel T Aref, Sandro V Porceddu, Rahul Ladwa, Joseph Whitfield, Roger R Reddel, Qing Zhong, Benedict J Panizza, Phillip J Robinson
{"title":"A Proteomic Signature for Human Papillomavirus-Associated Oropharyngeal Squamous Cell Carcinoma Predicts Patients at High Risk of Recurrence.","authors":"Christopher C Jackson, Jia Jenny Liu, Howard Y Liu, Steven G Williams, Asim Anees, Zainab Noor, Natasha Lucas, Dylan Xavier, Peter G Hains, Daniel Bucio-Noble, Adel T Aref, Sandro V Porceddu, Rahul Ladwa, Joseph Whitfield, Roger R Reddel, Qing Zhong, Benedict J Panizza, Phillip J Robinson","doi":"10.1158/2767-9764.CRC-23-0460","DOIUrl":null,"url":null,"abstract":"<p><strong>Significance: </strong>HPV+OPSCC incidence is increasing, with heterogeneous treatment outcomes despite favorable prognosis. Current de-escalation strategies show inferior results, highlighting the need for precise risk stratification. Using data-independent acquisition mass spectrometry proteomics, we identified a 26-peptide signature that stratifies patients into risk categories, potentially enabling personalized treatment decisions and optimal patient selection for de-escalation trials.</p>","PeriodicalId":72516,"journal":{"name":"Cancer research communications","volume":" ","pages":"580-593"},"PeriodicalIF":2.0000,"publicationDate":"2025-04-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"Cancer research communications","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1158/2767-9764.CRC-23-0460","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q3","JCRName":"ONCOLOGY","Score":null,"Total":0}
引用次数: 0
Abstract
Significance: HPV+OPSCC incidence is increasing, with heterogeneous treatment outcomes despite favorable prognosis. Current de-escalation strategies show inferior results, highlighting the need for precise risk stratification. Using data-independent acquisition mass spectrometry proteomics, we identified a 26-peptide signature that stratifies patients into risk categories, potentially enabling personalized treatment decisions and optimal patient selection for de-escalation trials.