Background
The nonproliferating polyaneuploid cancer cell (PACC) state is associated with therapeutic resistance in cancer. A subset of cancer cells enters the PACC state by polyploidization and acts as cancer stem cells by undergoing depolyploidization and repopulating the tumor cell population after the therapeutic stress is relieved. Our
Conclusion
Our findings show that the number of PACCs and the number of cores positive for PACCs are statistically significant prognostic factors for metastasis-free survival, after adjusting for CAPRA-S, in a case-cohort of intermediate- or high-risk men who underwent radical prostatectomy. In addition, despite the small number of men with complete data to evaluate time to metastatic castration-resistant PCa (mCRPC), the total number of PACCs was a statistically significant predictor of mCRPC in univariate analysis and suggested a prognostic effect even after adjusting for CAPRA-S.
Methods
Men with National Comprehensive Cancer Network intermediate- or high-risk PCa who underwent radical prostatectomy l from 2007 to 2015 and who did not receive neoadjuvant treatment were included. From the cohort of 2159 patients, the analysis focused on a subcohort of 209 patients and 38 cases. Prostate tissue microarrays (TMAs) were prepared from formalin-fixed, paraffin-embedded blocks of the radical prostatectomy specimens. A total of 2807 tissue samples of matched normal/benign and cancer were arrayed in nine TMA blocks. The presence of PACCs and the number of PACCs on each core were noted.
Results
The total number of cells in the PACC state and the total number of cores with PACCs were significantly correlated with increasing Gleason score (p = 0.0004) and increasing Cancer of the Prostate Risk Assessment Postsurgical (CAPRA-S) (p = 0.004), but no other variables. In univariate proportional hazards models of metastasis-free survival, year of surgery, Gleason score (9-10 vs. 7-8), pathology stage, CAPRA-S, total PACCs, and cores positive for PACCs were all statistically significant. The multivariable models with PACCs that gave the best fit included CAPRA-S. Adding either total PACCs or cores positive for PACCs to CAPRA-S both significantly improved model fit compared to CAPRA-S alone.