학술논문
Novel metastatic burden‐stratified risk model in de novo metastatic hormone‐sensitive prostate cancer
Document Type
Report
Author
Shiota, Masaki; Terada, Naoki; Kitamura, Hiroshi; Kojima, Takahiro; Saito, Toshihiro; Yokomizo, Akira; Kohei, Naoki; Goto, Takayuki; Kawamura, Sadafumi; Hashimoto, Yasuhiro; Takahashi, Atsushi; Kimura, Takahiro; Tabata, Ken‐Ichi; Tomida, Ryotaro; Hashimoto, Kohei; Sakurai, Toshihiko; Shimazui, Toru; Sakamoto, Shinichi; Kamiyama, Manabu; Tanaka, Nobumichi; Mitsuzuka, Koji; Kato, Takuma; Narita, Shintaro; Yasumoto, Hiroaki; Teraoka, Shogo; Kato, Masashi; Osawa, Takahiro; Nagumo, Yoshiyuki; Matsumoto, Hiroaki; Enokida, Hideki; Sugiyama, Takayuki; Kuroiwa, Kentaro; Inoue, Takahiro; Sugimoto, Mikio; Mizowaki, Takashi; Kamoto, Toshiyuki; Nishiyama, Hiroyuki; Eto, Masatoshi
Source
Cancer Science. June 19, 2021, Vol. 112 Issue 9, p3616, 11 p.
Subject
Language
English
ISSN
1347-9032
Abstract
INTRODUCTION In 2015, upfront docetaxel chemotherapy with androgen‐deprivation therapy (ADT) (CHAARTED and STAMPEDE [arm C] trials) was revealed to provide survival benefits in patients with metastatic hormone‐sensitive prostate cancer (HSPC).[sup.1‐3] [...]
: The metastatic burden is a critical factor for decision‐making in the treatment of metastatic hormone‐sensitive prostate cancer (HSPC). This study aimed to develop and validate a novel risk model for survival in patients with de novo low‐ and high‐burden metastatic HSPC. The retrospective observational study included men with de novo metastatic prostate cancer who were treated with primary androgen‐deprivation therapy at 30 institutions across Japan between 2008 and 2017. We created a risk model for overall survival (OS) in the discovery cohort (n = 1449) stratified by the metastatic burden (low vs high) and validated its predictive ability in a separate cohort (n = 951). Based on multivariate analyses, lower hemoglobin levels, higher Gleason grades, and higher clinical T‐stage were associated with poor OS in low‐burden disease. Meanwhile, lower hemoglobin levels, higher Gleason grade group, liver metastasis, and higher extent of disease scores in bone were associated with poor OS in patients with high‐burden disease. In the discovery and validation cohorts, the risk model using the aforementioned parameters exhibited excellent discriminatory ability for progression‐free survival and OS. The predictive ability of this risk model was superior to that of previous risk models. Our novel metastatic burden‐stratified risk model exhibited excellent predictive ability for OS, and it is expected to have several clinical uses, such as precise prognostic estimation.
: The metastatic burden is a critical factor for decision‐making in the treatment of metastatic hormone‐sensitive prostate cancer (HSPC). This study aimed to develop and validate a novel risk model for survival in patients with de novo low‐ and high‐burden metastatic HSPC. The retrospective observational study included men with de novo metastatic prostate cancer who were treated with primary androgen‐deprivation therapy at 30 institutions across Japan between 2008 and 2017. We created a risk model for overall survival (OS) in the discovery cohort (n = 1449) stratified by the metastatic burden (low vs high) and validated its predictive ability in a separate cohort (n = 951). Based on multivariate analyses, lower hemoglobin levels, higher Gleason grades, and higher clinical T‐stage were associated with poor OS in low‐burden disease. Meanwhile, lower hemoglobin levels, higher Gleason grade group, liver metastasis, and higher extent of disease scores in bone were associated with poor OS in patients with high‐burden disease. In the discovery and validation cohorts, the risk model using the aforementioned parameters exhibited excellent discriminatory ability for progression‐free survival and OS. The predictive ability of this risk model was superior to that of previous risk models. Our novel metastatic burden‐stratified risk model exhibited excellent predictive ability for OS, and it is expected to have several clinical uses, such as precise prognostic estimation.