Innovative methods for the identification of predictive biomarker signatures in oncology: Application to bevacizumab
| العنوان: | Innovative methods for the identification of predictive biomarker signatures in oncology: Application to bevacizumab |
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| المؤلفون: | Paul Delmar, Lu Tian, Cornelia Irl |
| المصدر: | Contemporary Clinical Trials Communications Contemporary Clinical Trials Communications, Vol 5, Iss C, Pp 107-115 (2017) |
| بيانات النشر: | Elsevier, 2017. |
| سنة النشر: | 2017 |
| مصطلحات موضوعية: | Oncology, medicine.medical_specialty, RCT, randomized clinical trial, Bevacizumab, Placebo, 01 natural sciences, Article, law.invention, 010104 statistics & probability, 03 medical and health sciences, 0302 clinical medicine, Breast cancer, Randomized controlled trial, law, Internal medicine, Machine learning, medicine, MRST, in mean restricted survival time, HER2, human epidermal growth factor receptor 2, 0101 mathematics, Pharmacology, lcsh:R5-920, business.industry, Hazard ratio, General Medicine, medicine.disease, Metastatic breast cancer, Personalized medicine, VEGF, vascular endothelial growth factor, Confidence interval, 3. Good health, FGF, fibroblast growth factor, FLT, fms-like tyrosine kinase, 030220 oncology & carcinogenesis, Multiple comparisons problem, Population study, Subgroup analysis, Multivariate statistical classification, business, lcsh:Medicine (General), medicine.drug |
| الوصف: | Current methods for subgroup analyses of data collected from randomized clinical trials (RCTs) may lead to false-positives from multiple testing, lack power to detect moderate but clinically meaningful differences, or be too simplistic in characterizing patients who may benefit from treatment. Herein, we present a general procedure based on a set of newly developed statistical methods for the identification and evaluation of complex multivariate predictors of treatment effect. Furthermore, we implemented this procedure to identify a subgroup of patients who may receive the largest benefit from bevacizumab treatment using a panel of 10 biomarkers measured at baseline in patients enrolled on two RCTs investigating bevacizumab in metastatic breast cancer. Data were collected from patients with human epidermal growth factor receptor 2 (HER2)-negative (AVADO) and HER2-positive (AVEREL) metastatic breast cancer. We first developed a classification rule based on an estimated individual scoring system, using data from the AVADO study only. The classification rule takes into consideration a panel of biomarkers, including vascular endothelial growth factor (VEGF)-A. We then classified the patients in the independent AVEREL study into patient groups according to “promising” or “not-promising” treatment benefit based on this rule and conducted a statistical analysis within these subgroups to compute point estimates, confidence intervals, and p-values for treatment effect and its interaction. In the group with promising treatment benefit in the AVEREL study, the estimated hazard ratio of bevacizumab versus placebo for progression-free survival was 0.687 (95% confidence interval [CI]: 0.462–1.024, p = 0.065), while in the not-promising group the hazard ratio (HR) was 1.152 (95% CI: 0.526–2.524, p = 0.723). Using the median level of VEGF-A from the AVEREL study to divide the study population, then the HR becomes 0.711 (95% CI: 0.435–1.163, p = 0.174) in the promising group and 0.828 (95% CI: 0.496–1.380, p = 0.468) in the not-promising group. Similar results were obtained with the median VEGF-A levels from the AVADO study (“promising” group: HR = 0.709, 95%CI: 0.444–1.133, p = 0.151; “not-promising” group: HR = 0.851, 95% CI: 0.497–1.458, p = 0.556). Our analysis shows it is feasible to employ statistical methods for empirically constructing and validating a scoring system based on a panel of biomarkers. This scoring system can be used to estimate the treatment effect for individual patients and identify a subgroup of patients who may benefit from treatment. The proposed procedure can provide a general framework to organize many statistical methods (existing or to be developed) into a coherent set of analyses for the development of personalized medicines and has the potential of broad applications. |
| اللغة: | English |
| تدمد: | 2451-8654 |
| الوصول الحر: | https://explore.openaire.eu/search/publication?articleId=doi_dedup___::abf31ea1146afb07efdf89e7a5e517caTest http://europepmc.org/articles/PMC5936698Test |
| حقوق: | OPEN |
| رقم الانضمام: | edsair.doi.dedup.....abf31ea1146afb07efdf89e7a5e517ca |
| قاعدة البيانات: | OpenAIRE |
| تدمد: | 24518654 |
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