Risk stratification for the prognosis of patients with chemoresistant urothelial cancer treated with pembrolizumab
-
- Takashi Kobayashi
- Department of Urology Kyoto University Graduate School of Medicine Kyoto Japan
-
- Katsuhiro Ito
- Department of Urology Ijinkai Takeda General Hospital Kyoto Japan
-
- Takahiro Kojima
- Department of Urology University of Tsukuba Tsukuba Japan
-
- Minoru Kato
- Department of Urology Osaka City University Osaka Japan
-
- Souhei Kanda
- Department of Urology Akita University Akita Japan
-
- Shingo Hatakeyama
- Department of Urology Hirosaki University Hirosaki Japan
-
- Yoshiyuki Matsui
- Department of Urology National Cancer Center Hospital Tokyo Japan
-
- Yuto Matsushita
- Department of Urology Hamamatsu University School of Medicine Hamamatsu Japan
-
- Sei Naito
- Department of Urology Faculty of Medicine Yamagata University Yamagata Japan
-
- Masanobu Shiga
- Department of Urology University of Tsukuba Tsukuba Japan
-
- Makito Miyake
- Department of Urology Nara Medical University Kashihara Japan
-
- Yusuke Muro
- Department of Urology Shizuoka General Hospital Shizuoka Japan
-
- Shotaro Nakanishi
- Department of Urology University of the Ryukyus Nishihara Japan
-
- Yoichiro Kato
- Department of Urology Iwate Medical University Morioka Japan
-
- Tadamasa Shibuya
- Department of Urology Oita University Yufu Japan
-
- Tetsutaro Hayashi
- Department of Urology Hiroshima University Hiroshima Japan
-
- Hiroaki Yasumoto
- Department of Urology Shimane University Izumo Japan
-
- Takashi Yoshida
- Department of Urology Kansai Medical University Hirakata Japan
-
- Motohide Uemura
- Department of Urology Osaka University Suita Japan
-
- Rikiya Taoka
- Department of Urology Kagawa University Kita Japan
-
- Manabu Kamiyama
- Department of Urology University of Yamanashi Chuo Japan
-
- Osamu Ogawa
- Department of Urology Kyoto University Graduate School of Medicine Kyoto Japan
-
- Hiroshi Kitamura
- Department of Urology Toyama University Toyama Japan
-
- Hiroyuki Nishiyama
- Department of Urology University of Tsukuba Tsukuba Japan
説明
<jats:title>Abstract</jats:title><jats:p>The use of immune checkpoint inhibitors to treat urothelial carcinoma (UC) is increasing rapidly without clear guidance for validated risk stratification. This multicenter retrospective study collected clinicopathological information on 463 patients, and 11 predefined variables were analyzed to develop a multivariate model predicting overall survival (OS). The model was validated using an independent dataset of 292 patients. Patient characteristics and outcomes were well balanced between the discovery and validation cohorts, which had median OS times of 10.2 and 12.5 mo, respectively. The final validated multivariate model was defined by risk scores based on the hazard ratios (HRs) of independent prognostic factors including performance status, site of metastasis, hemoglobin levels, and the neutrophil‐to‐lymphocyte ratio. The median OS times (95% confidence intervals [CIs]) for the low‐, intermediate‐, and high‐risk groups (discovery cohort) were not yet reached (NYR) (NYR–19.1), 6.8 mo (5.8‐8.9), and 2.3 mo (1.2‐2.6), respectively. The HRs (95% CI) for OS in the low‐ and intermediate‐risk groups vs the high‐risk group were 0.07 (0.04‐0.11) and 0.23 (0.15‐0.37), respectively. The objective response rates for in the low‐, intermediate‐, and high‐risk groups were 48.3%, 28.8%, and 10.5%, respectively. These differential outcomes were well reproduced in the validation cohort and in patients who received pembrolizumab after perioperative or first‐line chemotherapy (N = 584). In conclusion, the present study developed and validated a simple prognostic model predicting the oncological outcomes of pembrolizumab‐treated patients with chemoresistant UC. The model provides useful information for external validation, patient counseling, and clinical trial design.</jats:p>
収録刊行物
-
- Cancer Science
-
Cancer Science 112 (2), 760-773, 2020-12-21
Wiley
- Tweet
詳細情報 詳細情報について
-
- CRID
- 1360009142847387776
-
- ISSN
- 13497006
- 13479032
-
- データソース種別
-
- Crossref
- KAKEN