Division of Cancer Medicine, Department of Investigational Cancer Therapeutics, The University of Texas MD Anderson Cancer Center, 1515 Holcombe Blvd, Houston, TX, 77030, USA.
Early Development Biostatistics, Novartis Pharma AG, Basel, Switzerland.
Breast Cancer Res. 2024 Mar 4;26(1):36. doi: 10.1186/s13058-024-01773-1.
Hyperglycemia is an on-target effect of PI3Kα inhibitors. Early identification and intervention of treatment-induced hyperglycemia is important for improving management of patients receiving a PI3Kα inhibitor like alpelisib. Here, we characterize incidence of grade 3/4 alpelisib-related hyperglycemia, along with time to event, management, and outcomes using a machine learning model.
Data for the risk model were pooled from patients receiving alpelisib ± fulvestrant in the open-label, phase 1 X2101 trial and the randomized, double-blind, phase 3 SOLAR-1 trial. The pooled population (n = 505) included patients with advanced solid tumors (X2101, n = 221) or HR+/HER2- advanced breast cancer (SOLAR-1, n = 284). External validation was performed using BYLieve trial patient data (n = 340). Hyperglycemia incidence and management were analyzed for SOLAR-1.
A random forest model identified 5 baseline characteristics most associated with risk of developing grade 3/4 hyperglycemia (fasting plasma glucose, body mass index, HbA, monocytes, age). This model was used to derive a score to classify patients as high or low risk for developing grade 3/4 hyperglycemia. Applying the model to patients treated with alpelisib and fulvestrant in SOLAR-1 showed higher incidence of hyperglycemia (all grade and grade 3/4), increased use of antihyperglycemic medications, and more discontinuations due to hyperglycemia (16.7% vs. 2.6% of discontinuations) in the high- versus low-risk group. Among patients in SOLAR-1 (alpelisib + fulvestrant arm) with PIK3CA mutations, median progression-free survival was similar between the high- and low-risk groups (11.0 vs. 10.9 months). For external validation, the model was applied to the BYLieve trial, for which successful classification into high- and low-risk groups with shorter time to grade 3/4 hyperglycemia in the high-risk group was observed.
A risk model using 5 clinically relevant baseline characteristics was able to identify patients at higher or lower probability for developing alpelisib-induced hyperglycemia. Early identification of patients who may be at higher risk for hyperglycemia may improve management (including monitoring and early intervention) and potentially lead to improved outcomes.
ClinicalTrials.gov: NCT01219699 (registration date: October 13, 2010; retrospectively registered), ClinicalTrials.gov: NCT02437318 (registration date: May 7, 2015); ClinicalTrials.gov: NCT03056755 (registration date: February 17, 2017).
高血糖是 PI3Kα 抑制剂的一种靶向作用。早期识别和干预治疗引起的高血糖对于改善接受 PI3Kα 抑制剂（如阿培利司）治疗的患者的管理非常重要。在这里，我们使用机器学习模型来描述 3/4 级阿培利司相关高血糖的发生率、时间事件、管理和结局。
风险模型的数据来自接受阿培利司联合氟维司群治疗的开放标签、1 期 X2101 试验和随机、双盲、3 期 SOLAR-1 试验的患者。汇总人群（n=505）包括晚期实体瘤（X2101，n=221）或 HR+/HER2-晚期乳腺癌（SOLAR-1，n=284）患者。使用 BYLieve 试验患者数据（n=340）进行外部验证。分析 SOLAR-1 中高血糖的发生率和管理。
随机森林模型确定了 5 个与发生 3/4 级高血糖风险最相关的基线特征（空腹血糖、体重指数、HbA、单核细胞、年龄）。该模型用于得出一个评分，以将患者分为发生 3/4 级高血糖的高风险或低风险组。将该模型应用于 SOLAR-1 中接受阿培利司和氟维司群治疗的患者，显示出更高的高血糖发生率（所有等级和 3/4 级）、更频繁使用抗高血糖药物以及更多因高血糖而停药（高风险组与低风险组分别为 16.7%和 2.6%的停药）。在 SOLAR-1（阿培利司+氟维司群组）中存在 PIK3CA 突变的患者中，高风险组和低风险组的中位无进展生存期相似（11.0 个月与 10.9 个月）。对于外部验证，该模型应用于 BYLieve 试验，观察到高风险组在发生 3/4 级高血糖的时间方面成功分为高风险和低风险组。
使用 5 个临床相关基线特征的风险模型能够识别出发生阿培利司诱导的高血糖可能性更高或更低的患者。早期识别可能存在更高血糖风险的患者，可能有助于改善管理（包括监测和早期干预）并有可能改善结局。
ClinicalTrials.gov：NCT01219699（登记日期：2010 年 10 月 13 日；回顾性登记），ClinicalTrials.gov：NCT02437318（登记日期：2015 年 5 月 7 日），ClinicalTrials.gov：NCT03056755（登记日期：2017 年 2 月 17 日）。
