Dataset Information

Predictive Value of Proteomic Markers for Advanced Rectal Cancer with Neoadjuvant Chemoradiotherapy

ABSTRACT: Thirteen paired pre- and post-nCRT sera from rectal cancer patients were analyzed by isobaric tags for relative and absolute quantitation (iTRAQ) method. Twenty-five proteins were selected for validation by parallel reaction monitoring (PRM) in ninety-one patients.

ORGANISM(S): Homo Sapiens

SUBMITTER: Jing Shen

PROVIDER: PXD032117 | iProX | Tue Mar 08 00:00:00 GMT 2022

REPOSITORIES: iProX

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Predictive value of proteomic markers for advanced rectal cancer with neoadjuvant chemoradiotherapy.

Wang Hanyang H Ji Dengbo D Tian Huifang H Gao Zhaoya Z Song Can C Jia Jinying J Cui Xinxin X Zhong Lijun L Shen Jing J Gu Jin J

BMC cancer 20220809 1

<h4>Background</h4>Preoperative neoadjuvant chemoradiation (nCRT) has been the standard treatment for locally advanced rectal cancer. Serum biomarkers to stratify patients with respect to prognosis and response to nCRT are needed due to the diverse response to the therapy.<h4>Methods</h4>Thirteen paired pre- and post-nCRT sera from rectal cancer patients were analyzed by isobaric tags for relative and absolute quantitation (iTRAQ) method. Twenty-five proteins were selected for validation by para ...[more]

PMID: 35945555

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Project description:Importance: Neoadjuvant chemoradiotherapy (CRT) is the standard of care for advanced rectal cancer. Yet, predicting response to CRT remains an unmet clinical challenge. Objective: To investigate the transcriptomic determinants to predict response to neoadjuvant CRT and survival in patients with advanced rectal cancer. Design: A single-center, retrospective, cohort study. Setting: A comprehensive cancer center. Participants: Pre-treatment biopsies from 298 patients with rectal cancer, who were later treated with neoadjuvant CRT between 2004 and 2020, were analyzed by RNA sequencing. Main measures and outcomes: Transcriptional subtyping was performed by consensus molecular subtype (CMS) classification. Immune cell infiltration was assessed using MCP-counter scores and single-sample gene set enrichment analysis (ssGSEA). Patients with surgical specimens of tumor regression grade (TRG) 3-4 or who were managed by the watch-and-wait approach for more than 3 years were defined as good responders. Results: Patients classified as CMS1 (6.4%) had a significantly higher rate of good responders; albeit survival was comparable among the four subtypes. Good responders exhibited an enrichment in various immune-related pathways (IFNg and a responses, allograft rejection, IL6 STAT3 signaling during acute phase response, and inflammation), as determined by ssGSEA. MCP-counter scores for cytotoxic lymphocytes were significantly higher for good responders than non-responders (p = 0.0003), and significantly higher for responders than non-responders in GSE109057 (p = 0.0137), GSE87211 (p = 0.0092), and GSE45404 (p = 0.0327) datasets. Cytotoxic lymphocyte MCP-counter score was thus considered an independent predictor of response to CRT, as determined in the multivariable Cox analysis (OR 3.810 [95% CI 1.820-7.970]; p = 0.0004). Multivariable Cox analysis, including post-operative pathological factors, revealed cytotoxic lymphocyte MCP-counter score to be independently associated with recurrence-free survival (HR 0.382 [95% CI 0.158-0.923]; p = 0.0325) and overall survival (HR 0.157 [95% CI 0.030-0.827]; p = 0.0290). ssGSEA showed significantly higher levels of four subpopulations of cytotoxic cells (effector memory CD8 T, natural killer T, natural killer, and activated CD8 T cells) among good responders than non-responders. Conclusions and Relevance: Cytotoxic lymphocyte score, as computed by RNA sequencing, could be a useful marker for predicting response to CRT and survival among patients with rectal cancer.

Dataset Information

Predictive Value of Proteomic Markers for Advanced Rectal Cancer with Neoadjuvant Chemoradiotherapy

Publications

Predictive value of proteomic markers for advanced rectal cancer with neoadjuvant chemoradiotherapy.

Similar Datasets

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