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ORIGINAL RESEARCH article

Front. Genet.
Sec. Cancer Genetics and Oncogenomics
Volume 15 - 2024 | doi: 10.3389/fgene.2024.1497254
This article is part of the Research Topic Novel and Comprehensive Approaches for Profiling Biomarkers Underlying Cancer Immunotherapy View all articles

Identification and validation the predictive biomarkers based on riskadjusted control chart in Gemcitabine with or without Erlotinib for Pancreatic Cancer Therapy

Provisionally accepted
Aijun Zhao Aijun Zhao 1*Dongsheng Tu Dongsheng Tu 2Ye He Ye He 3*Liu Liu Liu Liu 1Bin Wu Bin Wu 4*Yixing Ren Yixing Ren 5*
  • 1 College of Mathematics and Physics and Geomathematics Key Laboratory of Sichuan Province, Chengdu University of Technology, Chengdu, China
  • 2 Canadian Cancer Trials Group, Kingston, Ontario, Canada
  • 3 Visual Computing and Virtual Reality Key Laboratory of Sichuan Province, Sichuan Normal University, chengdu, China
  • 4 North Sichuan Medical College, Nanchong, Sichuan Province, China
  • 5 Department of General Surgery, and Institute of Hepato-Biliary-Pancreas and Intestinal Disease, North Sichuan Medical College, Nanchong, Sichuan Province, China

The final, formatted version of the article will be published soon.

    Background: In a randomized clinical controlled trial (PA.3) conducted by the Canadian Cancer Trials Group, the effects of gemcitabine combined with the targeted drug erlotinib (GEM-E) versus gemcitabine alone (GEM) on patients with unresectable, locally advanced, or metastatic pancreatic cancer were studied. This trial statistically demonstrated that the GEM-E combination therapy moderately improves overall survival (OS) of patients. However, real-world analysis suggested that GEM-E for pancreatic cancer was not more effective than GEM. The heterogeneity in outcomes or treatment effect exist. Thus, we tried to find predictive biomarkers to identifying the heterogeneous patients. Methods: Of the 569 eligible patients, 480 patients had plasma samples. Univariate and multivariate Cox proportional hazards model were used to identify baseline characteristics related to OS, and a risk adjusted Exponentially Weighted Moving Average (EWMA) control chart based on a weighted score test from the Cox model was constructed to monitor patients' survival risk. Maximally selected rank statistics were constructed to identifying the predictive biomarkers, in addition, a risk adjusted control chart based on a weighted score test from the Cox model was constructed to validating the predictive biomarkers, discover the patients who sensitive to the GEM-E or GEM. Results: Three baseline characteristics (ECOG performance status, extent of disease, and pain intensity) were identified related to prognosis. A risk-adjusted EWMA control chart was constructed and showed that GEM-E did improve OS in a few patients. Three biomarkers (BMP2, CXCL6, and HER2) were identified as predictive biomarkers based on maximum selected rank test, and using the risk-adjusted EWMA control chart to validate the reality and discover some patients who are sensitive to the GEM-E therapy. Conclusion: In reality, GEM-E has not shown a significant advantage over GEM in the treatment of pancreatic cancer. However, we discovered some patients who are sensitive to the GEM-E therapy based on the predictive biomarkers, which suggest that the predictive biomarkers provide ideas for personalized medicine in pancreatic cancer.

    Keywords: Pancreatic Cancer, COX proportional hazards regression model, Score test, risk-adjusted control chart, predictive biomarkers

    Received: 16 Sep 2024; Accepted: 29 Nov 2024.

    Copyright: © 2024 Zhao, Tu, He, Liu, Wu and Ren. This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY). The use, distribution or reproduction in other forums is permitted, provided the original author(s) or licensor are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms.

    * Correspondence:
    Aijun Zhao, College of Mathematics and Physics and Geomathematics Key Laboratory of Sichuan Province, Chengdu University of Technology, Chengdu, China
    Ye He, Visual Computing and Virtual Reality Key Laboratory of Sichuan Province, Sichuan Normal University, chengdu, China
    Bin Wu, North Sichuan Medical College, Nanchong, Sichuan Province, China
    Yixing Ren, Department of General Surgery, and Institute of Hepato-Biliary-Pancreas and Intestinal Disease, North Sichuan Medical College, Nanchong, Sichuan Province, China

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