Sanofi Specialty Care Medical Oncology, 3-20-2 Nishi-Shinjuku, Shinjuku, Tokyo, 163-1488, Japan.
Sanofi Research and Development, 3-20-2 Nishi-Shinjuku, Shinjuku, Tokyo, 163-1488, Japan.
BMC Cancer. 2022 Apr 29;22(1):470. doi: 10.1186/s12885-022-09509-0.
We aimed to evaluate relationships between clinical outcomes and explanatory variables by network clustering analysis using data from a post marketing surveillance (PMS) study of castration-resistant prostate cancer (CRPC) patients.
The PMS was a prospective, multicenter, observational study of patients with metastatic, docetaxel-refractory CRPC treated with cabazitaxel in Japan after its launch in 2014. Graphical Markov (GM) model-based simulations and network clustering in 'R' package were conducted to identify correlations between clinical factors and outcomes. Factors shown to be associated with overall survival (OS) in the machine learning analysis were confirmed according to the clinical outcomes observed in the PMS.
Among the 660 patients analyzed, median patient age was 70.0 years, and median OS and time-to-treatment failure (TTF) were 319 and 116 days, respectively. In GM-based simulations, factors associated with OS were liver metastases, performance status (PS), TTF, and neutropenia (threshold 0.05), and liver metastases, PS, and TTF (threshold 0.01). Factors associated with TTF were OS and relative dose intensity (threshold 0.05), and OS (threshold 0.01). In network clustering in 'R' package, factors associated with OS were number of treatment cycles, discontinuation due to disease progression, and TTF (threshold 0.05), and liver and lung metastases, PS, discontinuation due to adverse events, and febrile neutropenia (threshold 0.01). Kaplan-Meier analysis of patient subgroups demonstrated that visceral metastases and poor PS at baseline were associated with worse OS, while neutropenia or febrile neutropenia and higher number of cabazitaxel cycles were associated with better OS.
Neutropenia may be a predictive factor for treatment efficacy in terms of survival. Poor PS and distant metastases to the liver and lungs were shown to be associated with worse outcomes, while factors related to treatment duration were shown to positively correlate with better OS.
我们旨在通过使用来自去势抵抗性前列腺癌（CRPC）患者上市后监测（PMS）研究的数据进行网络聚类分析来评估临床结果与解释变量之间的关系。
PMS 是一项针对转移性、多西他赛耐药 CRPC 患者的前瞻性、多中心、观察性研究，在 2014 年卡巴他赛上市后对这些患者进行了治疗。采用基于图形马尔可夫（GM）模型的模拟和“R”包中的网络聚类分析来识别临床因素与结局之间的相关性。在机器学习分析中与总生存期（OS）相关的因素根据 PMS 中观察到的临床结局进行确认。
在分析的 660 例患者中，中位患者年龄为 70.0 岁，中位 OS 和治疗失败时间（TTF）分别为 319 天和 116 天。在 GM 基础模拟中，与 OS 相关的因素为肝转移、表现状态（PS）、TTF 和中性粒细胞减少症（阈值 0.05），以及肝转移、PS 和 TTF（阈值 0.01）。与 TTF 相关的因素为 OS 和相对剂量强度（阈值 0.05）和 OS（阈值 0.01）。在“R”包中的网络聚类分析中，与 OS 相关的因素为治疗周期数、因疾病进展而停药和 TTF（阈值 0.05），以及肝和肺转移、PS、因不良事件而停药和发热性中性粒细胞减少症（阈值 0.01）。对患者亚组的 Kaplan-Meier 分析表明，基线时存在内脏转移和较差的 PS 与 OS 较差相关，而中性粒细胞减少症或发热性中性粒细胞减少症和更多的卡巴他赛周期与 OS 更好相关。
中性粒细胞减少症可能是生存方面治疗效果的预测因素。较差的 PS 和肝、肺远处转移与较差的结局相关，而与治疗持续时间相关的因素与更好的 OS 呈正相关。
