| Autor: |
Michael E Scheurer, Renke Zhou, Mark R Gilbert, Melissa L Bondy, Erik P Sulman, Ying Yuan, Yanhong Liu, Elizabeth Vera, Merideth M Wendland, Emad F Youssef, Volker W Stieber, Ritsuko R Komaki, John C Flickinger, Lawrence C Kenyon, H Ian Robins, Grant K Hunter, Ian R Crocker, Samuel T Chao, Stephanie L Pugh, Terri S Armstrong |
| Rok vydání: |
2022 |
| Předmět: |
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| Zdroj: |
Neuro-Oncology Advances. 4 |
| ISSN: |
2632-2498 |
| Popis: |
Background We sought to identify clinical and genetic predictors of temozolomide-related myelotoxicity among patients receiving therapy for glioblastoma. Methods Patients (n = 591) receiving therapy on NRG Oncology/RTOG 0825 were included in the analysis. Cases were patients with severe myelotoxicity (grade 3 and higher leukopenia, neutropenia, and/or thrombocytopenia); controls were patients without such toxicity. A risk-prediction model was built and cross-validated by logistic regression using only clinical variables and extended using polymorphisms associated with myelotoxicity. Results 23% of patients developed myelotoxicity (n = 134). This toxicity was first reported during the concurrent phase of therapy for 56 patients; 30 stopped treatment due to toxicity. Among those who continued therapy (n = 26), 11 experienced myelotoxicity again. The final multivariable clinical factor model included treatment arm, gender, and anticonvulsant status and had low prediction accuracy (area under the curve [AUC] = 0.672). The final extended risk prediction model including four polymorphisms in MGMT had better prediction (AUC = 0.827). Receiving combination chemotherapy (OR, 1.82; 95% CI, 1.02–3.27) and being female (OR, 4.45; 95% CI, 2.45–8.08) significantly increased myelotoxicity risk. For each additional minor allele in the polymorphisms, the risk increased by 64% (OR, 1.64; 95% CI, 1.43–1.89). Conclusions Myelotoxicity during concurrent chemoradiation with temozolomide is an uncommon but serious event, often leading to treatment cessation. Successful prediction of toxicity may lead to more cost-effective individualized monitoring of at-risk subjects. The addition of genetic factors greatly enhanced our ability to predict toxicity among a group of similarly treated glioblastoma patients. |
| Databáze: |
OpenAIRE |
| Externí odkaz: |
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