Related Experiment Video
Updated: Apr 30, 2026
Competing-Risk Nomogram for Predicting Cancer-Specific Survival in Multiple Primary Colorectal Cancer Patients after Surgery
Published on: September 27, 2024
Can a biomarker-based scoring system predict pathologic complete response after preoperative chemoradiotherapy for
Hyuk Hur1, Nam Kyu Kim, Byung Soh Min
11Department of Surgery, Severance Hospital, Yonsei University College of Medicine, Seoul, Korea 2Department of Radiation Oncology, Severance Hospital, Yonsei University College of Medicine, Seoul, Korea 3Department of Medical Oncology, Severance Hospital, Yonsei University College of Medicine, Seoul, Korea 4Department of Pathology, Severance Hospital, Yonsei University College of Medicine, Seoul, Korea.
Background:
Numerous molecular markers have been investigated as potential predictors of tumor responses to preoperative chemoradiotherapy (preCRT) for rectal cancer.
Objective:
To develop a system in which biomarkers are used to predict the likelihood of a pathologic complete response (pCR) to preCRT.
Design & Setting:
This is a retrospective analysis of tumor specimens collected prior to preCRT from 81 patients who underwent curative resection for primary rectal adenocarcinoma between June 2008 and February 2012.
Main Outcome Measures:
Using tissue microarrays and immunohistochemistry, expression levels of twelve candidate biomarkers (p53, p21, Bcl2, Bax, EGFR, Cox-2, MLH-1, MSH-2, Ku70, VEGF, TS, Ki-67) were evaluated in paraffin-embedded tumor samples collected before preCRT. The correlation between biomarker expression levels and the pathologic response to preCRT was assessed based on histopathological staging (pTNM) and tumor regression grade (TRG).
Results:
Expression levels of 4 biomarkers (p53, VEGF, p21, Ki67) correlated with pCR. Patients showing low expression of p53 and/or high expression of VEGF, p21, and Ki67 exhibited a significantly greater pCR rate. A scoring system devised so that one point was given for each biomarker whose expression level correlated with pCR (score range: 0-4) showed that 1 of 26 patients with scores of 0 to 1 achieved pCR, whereas 26 of 55 patients with scores of 2 to 4 achieved pCR (3.8% vs. 47.3%, p < 0.001). For prediction of pCR, the scoring system showed 96.3% sensitivity, 46.3% specificity, a 47.3% positive predictive value, and a 96.2% negative predictive value.
Limitations:
Immunohistochemistry has limitations related to reproducibility and the ability to provide quantitative information. In addition, this study lacks test and validation sets.
Conclusions:
Expression levels of 4 biomarkers correlated with pCR after preCRT for rectal cancer. A scoring system based on levels of biomarker expression showed good sensitivity and negative predictive value for pCR.
More Related Videos
07:13Comparison of Predictive Performance of Three Lymph Node Staging Systems in Colorectal Signet Ring Cell Carcinoma Based on Machine Learning Model
Published on: April 18, 2025
04:09Predicting Treatment Response to Image-Guided Therapies Using Machine Learning: An Example for Trans-Arterial Treatment of Hepatocellular Carcinoma
Published on: October 10, 2018