A risk analysis of alpelisib-induced hyperglycemia in patients with advanced solid tumors and breast cancer
Abstract Background 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-...
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| Format: | Article |
| Language: | English |
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BMC
2024-03-01
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| Series: | Breast Cancer Research |
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| Online Access: | https://doi.org/10.1186/s13058-024-01773-1 |
| _version_ | 1797272668504850432 |
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| author | Jordi Rodón David Demanse Hope S. Rugo Howard A. Burris Rafael Simó Azeez Farooki Melissa F. Wellons Fabrice André Huilin Hu Dragica Vuina Cornelia Quadt Dejan Juric |
| author_facet | Jordi Rodón David Demanse Hope S. Rugo Howard A. Burris Rafael Simó Azeez Farooki Melissa F. Wellons Fabrice André Huilin Hu Dragica Vuina Cornelia Quadt Dejan Juric |
| author_sort | Jordi Rodón |
| collection | DOAJ |
| description | Abstract Background 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. Methods 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. Results A random forest model identified 5 baseline characteristics most associated with risk of developing grade 3/4 hyperglycemia (fasting plasma glucose, body mass index, HbA1c, 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. Conclusions 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. Registration: 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). |
| first_indexed | 2024-03-07T14:33:41Z |
| format | Article |
| id | doaj.art-211162ffb10842579ce7426078c215cf |
| institution | Directory Open Access Journal |
| issn | 1465-542X |
| language | English |
| last_indexed | 2024-03-07T14:33:41Z |
| publishDate | 2024-03-01 |
| publisher | BMC |
| record_format | Article |
| series | Breast Cancer Research |
| spelling | doaj.art-211162ffb10842579ce7426078c215cf2024-03-05T20:46:35ZengBMCBreast Cancer Research1465-542X2024-03-0126111010.1186/s13058-024-01773-1A risk analysis of alpelisib-induced hyperglycemia in patients with advanced solid tumors and breast cancerJordi Rodón0David Demanse1Hope S. Rugo2Howard A. Burris3Rafael Simó4Azeez Farooki5Melissa F. Wellons6Fabrice André7Huilin Hu8Dragica Vuina9Cornelia Quadt10Dejan Juric11Division of Cancer Medicine, Department of Investigational Cancer Therapeutics, The University of Texas MD Anderson Cancer CenterEarly Development Biostatistics, Novartis Pharma AGDivision of Hematology and Oncology, Department of Medicine, University of California San Francisco Helen Diller Family Comprehensive Cancer CenterDepartment of Oncology, Sarah Cannon Research Institute, Tennessee Oncology Professional Limited Liability CorporationDiabetes and Metabolism Research Unit, Vall d’Hebron Research InstituteEndocrinology Service, Department of Medicine, Memorial Sloan Kettering Cancer CenterVanderbilt University Medicine CenterDepartment of Medical Oncology, INSERM U981, Gustave Roussy, Université Paris-SudNovartis Pharmaceuticals CorporationNovartis Pharma AGTranslational Clinical Oncology, Novartis Pharma AGDepartment of Medicine, Massachusetts General Hospital Cancer CenterAbstract Background 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. Methods 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. Results A random forest model identified 5 baseline characteristics most associated with risk of developing grade 3/4 hyperglycemia (fasting plasma glucose, body mass index, HbA1c, 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. Conclusions 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. Registration: 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).https://doi.org/10.1186/s13058-024-01773-1AlpelisibHyperglycemiaMachine learningSOLAR-1BYLieveHR+/HER2− advanced breast cancer |
| spellingShingle | Jordi Rodón David Demanse Hope S. Rugo Howard A. Burris Rafael Simó Azeez Farooki Melissa F. Wellons Fabrice André Huilin Hu Dragica Vuina Cornelia Quadt Dejan Juric A risk analysis of alpelisib-induced hyperglycemia in patients with advanced solid tumors and breast cancer Breast Cancer Research Alpelisib Hyperglycemia Machine learning SOLAR-1 BYLieve HR+/HER2− advanced breast cancer |
| title | A risk analysis of alpelisib-induced hyperglycemia in patients with advanced solid tumors and breast cancer |
| title_full | A risk analysis of alpelisib-induced hyperglycemia in patients with advanced solid tumors and breast cancer |
| title_fullStr | A risk analysis of alpelisib-induced hyperglycemia in patients with advanced solid tumors and breast cancer |
| title_full_unstemmed | A risk analysis of alpelisib-induced hyperglycemia in patients with advanced solid tumors and breast cancer |
| title_short | A risk analysis of alpelisib-induced hyperglycemia in patients with advanced solid tumors and breast cancer |
| title_sort | risk analysis of alpelisib induced hyperglycemia in patients with advanced solid tumors and breast cancer |
| topic | Alpelisib Hyperglycemia Machine learning SOLAR-1 BYLieve HR+/HER2− advanced breast cancer |
| url | https://doi.org/10.1186/s13058-024-01773-1 |
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