RT Journal Article
SR Electronic
T1 Prediction Model for Gastric Cancer With DNA Mismatch Repair Deficiency
JF Anticancer Research
JO Anticancer Res
FD International Institute of Anticancer Research
SP 975
OP 982
DO 10.21873/anticanres.14851
VO 41
IS 2
A1 OKIHIDE SUZUKI
A1 TATSURO YAMAGUCHI
A1 MINORU FUKUCHI
A1 ERITO MOCHIKI
A1 TOMIO ARAI
A1 KIWAMU AKAGI
A1 HIDEYUKI ISHIDA
YR 2021
UL http://ar.iiarjournals.org/content/41/2/975.abstract
AB Background/Aim: DNA mismatch repair (MMR) deficiency has received increasing attention as a biomarker of anti-PD-1 treatments of solid tumors including gastric cancer (GC). However, efficient screening has not been established. Patients and Methods: A total of 513 patients were tested for the expression of MMR proteins by immunohistochemistry to identify MMR deficient GC. Development of a prediction model was attempted using the common clinicopathological features. Results: In total, 11% (57/513) of the patients showed loss of expression of either one or more MMR proteins (MMR protein deficiency; MMR-D). Multivariate analysis demonstrated that age (≥70 years), sex (female), tumor location (lower 1/3), depth invasion (low, T1/T2/T3), and absence of distant metastasis were significantly independent predictive factors of MMR-D GCs. The MMR-D GC probability estimated by the prediction model ranged from 0.4% to 62.2%, and the area under the curve of the receiver operating characteristics curve was 0.82 (95% confidence interval=0.75-0.87). Conclusion: Our prediction model can sufficiently and efficiently identify MMR-D GCs using clinical features.