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Published on: September 27, 2024
Combination of growth pattern and tumor regression identifies a high-risk group in neoadjuvant treated rectal cancer
Jonas Jessberger1, Katharina Erlenbach-Wünsch2, Rebecca Posselt1
1Department of Radiation Oncology, University Hospital of Erlangen, Friedrich-Alexander University of Erlangen-Nürnberg, Erlangen, Germany.
Objective:
To analyze the prognostic effect of E-cadherin expression, the growth pattern of the tumor and the regression grade in a rectal cancer cohort treated with neoadjuvant radiochemotherapy (RCT).
Methods:
A total of 223 patients with rectal cancer treated with neoadjuvant RCT followed by surgery were included. Altogether 88 biopsies prior to RCT and 213 tumor resections in an average of 55 days post-RCT were investigated. Protein expression of E-cadherin and tumor growth pattern (solid glandular vs single-cell pattern) was assessed by staining tissue microarrays. The regression grade at the invasion front was determined according to the Dworak scale.
Results:
There was a significant decrease of E-cadherin expression (P = 0.002) and a significant increased single-cell growth (P < 0.001) at the invasion front in tumor samples after RCT compared with primary biopsies of the tumor. A low E-cadherin expression in the biopsy was related to a longer metastasis-free survival (P = 0.033) and tumor-specific survival (P = 0.030). Post-RCT single-cell growth at the tumor invasion front was a prognostic factor for longer tumor-specific survival (P = 0.021). A combination of growth pattern and the Dworak regression grade was an independent prognostic parameter for tumor-specific survival (P = 0.015).
Conclusions:
Loss of E-cadherin protein expression in the pretreatment biopsy of rectal cancer is associated with fewer metastases and improved survival. Furthermore, the growth pattern in the post-RCT resection specimen has a prognostic value for survival. A combination of growth pattern and tumor regression score (the RegPat score) showed the highest discriminatory power to identify high-risk patients.
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