Memorial Sloan Kettering Cancer Center, New York, NY.
Duke University Medical Center, Durham, NC.
JCO Clin Cancer Inform. 2024 Sep;8:e2400031. doi: 10.1200/CCI.24.00031.
The magnitude of cardiorespiratory fitness (CRF) impairment during anticancer treatment and CRF response to aerobic exercise training (AT) are highly variable. The aim of this ancillary analysis was to leverage machine learning approaches to identify patients at high risk of impaired CRF and poor CRF response to AT.
We evaluated heterogeneity in CRF among 64 women with metastatic breast cancer randomly assigned to 12 weeks of highly structured AT (n = 33) or control (n = 31). Unsupervised hierarchical cluster analyses were used to identify representative variables from multidimensional prerandomization (baseline) data, and to categorize patients into mutually exclusive subgroups (ie, phenogroups). Logistic and linear regression evaluated the association between phenogroups and impaired CRF (ie, ≤16 mL O·kg·min) and CRF response.
Baseline CRF ranged from 10.2 to 38.8 mL O·kg·min; CRF response ranged from -15.7 to 4.1 mL O·kg·min. Of the n = 120 candidate baseline variables, n = 32 representative variables were identified. Patients were categorized into two phenogroups. Compared with phenogroup 1 (n = 27), phenogroup 2 (n = 37) contained a higher number of patients with none or >three lines of previous anticancer therapy for metastatic disease and had lower resting left ventricular systolic and diastolic function, cardiac output reserve, hematocrit, lymphocyte count, patient-reported outcomes, and CRF ( < .05) at baseline. Among patients allocated to AT (phenogroup 1, n = 12; 44%; phenogroup 2, n = 21; 57%), CRF response (-1.94 ± 3.80 mL O·kg·min 0.70 ± 2.22 mL O·kg·min) was blunted in phenogroup 2 compared with phenogroup 1.
Phenotypic clustering identified two subgroups with unique baseline characteristics and CRF outcomes. The identification of CRF phenogroups could help improve cardiovascular risk stratification and guide investigation of targeted exercise interventions among patients with cancer.
在癌症治疗过程中心肺适能（CRF）的损害程度以及 CRF 对有氧运动训练（AT）的反应具有高度可变性。本辅助分析的目的是利用机器学习方法来识别 CRF 受损风险高和 AT 后 CRF 反应差的患者。
我们评估了 64 名转移性乳腺癌女性在随机分配至 12 周高强度结构化 AT（n = 33）或对照组（n = 31）期间的 CRF 异质性。使用无监督层次聚类分析从多维预随机化（基线）数据中识别代表性变量，并将患者分类为互斥亚组（即表型组）。逻辑回归和线性回归评估了表型组与 CRF 受损（即，≤16 mL O·kg·min）和 CRF 反应之间的关联。
基线 CRF 范围为 10.2 至 38.8 mL O·kg·min；CRF 反应范围为-15.7 至 4.1 mL O·kg·min。在 n = 120 个候选基线变量中，确定了 n = 32 个代表性变量。患者被分为两个表型组。与表型组 1（n = 27）相比，表型组 2（n = 37）包含更多的转移性疾病既往接受无或＞三线抗癌治疗的患者，且基线时左心室收缩和舒张功能、心输出量储备、红细胞压积、淋巴细胞计数、患者报告结局和 CRF 较低（<0.05）。在接受 AT 分配的患者中（表型组 1，n = 12；44%；表型组 2，n = 21；57%），与表型组 1 相比，表型组 2 的 CRF 反应（-1.94 ± 3.80 mL O·kg·min 0.70 ± 2.22 mL O·kg·min）减弱。
表型聚类确定了两个具有独特基线特征和 CRF 结局的亚组。CRF 表型组的识别可以帮助改善心血管风险分层，并指导癌症患者的靶向运动干预研究。
