Clinical features and impact of p53 status on sporadic mismatch repair deficiency and Lynch syndrome in uterine cancer.
Mayumi Kobayashi KatoErisa FujiiYuka AsamiYukihide MomozawaKengo HiranumaMasaaki KomatsuRyuji HamamotoTakahiro EbataKoji MatsumotoMitsuya IshikawaTakashi KohnoTomoyasu KatoHiroshi YoshidaKouya ShiraishiPublished in: Cancer science (2024)
The clinical features of sporadic mismatch repair deficiency (MMRd) and Lynch syndrome (LS) in Japanese patients with endometrial cancer (EC) were examined by evaluating the prevalence and prognostic factors of LS and sporadic MMRd in patients with EC. Targeted sequencing of five LS susceptibility genes (MLH1, MSH2, MSH6, PMS2, and EPCAM) was carried out in 443 patients with EC who were pathologically diagnosed with EC at the National Cancer Center Hospital between 2011 and 2018. Pathogenic variants in these genes were detected in 16 patients (3.7%). Immunohistochemistry for MMR proteins was undertaken in 337 of the 433 (77.9%) EC patients, and 91 patients (27.0%) showed absent expression of at least one MMR protein. The 13 cases of LS with MMR protein loss (93.8%) showed a favorable prognosis with a 5-year overall survival (OS) rate of 100%, although there was no statistically significant difference between this group and the sporadic MMRd group (p = 0.27). In the MMRd without LS group, the 5-year OS rate was significantly worse in seven patients with an aberrant p53 expression pattern than in those with p53 WT (53.6% vs. 93.9%, log-rank test; p = 0.0016). These results suggest that p53 abnormalities and pathogenic germline variants in MMR genes could be potential biomarkers for the molecular classification of EC with MMRd.
Keyphrases
- prognostic factors
- end stage renal disease
- newly diagnosed
- chronic kidney disease
- ejection fraction
- endometrial cancer
- late onset
- healthcare
- genome wide
- gene expression
- binding protein
- dna methylation
- oxidative stress
- machine learning
- transcription factor
- drug induced
- patient reported
- single molecule
- bioinformatics analysis