Abstract
To improve cancer precision medicine, prognostic and predictive biomarkers are critically needed to aid physicians in deciding treatment strategies in a personalized fashion. Due to the heterogeneous nature of cancer, most biomarkers are expected to be valid only in a subset of patients. Furthermore, there is no current approach to determine the applicability of biomarkers. In this study, we propose a framework to improve the clinical application of biomarkers. As part of this framework, we develop a clinical outcome prediction model (CPM) and a predictability prediction model (PPM) for each biomarker and use these models to calculate a prognostic score (P-score) and a confidence score (C-score) for each patient. Each biomarker’s P-score indicates its association with patient clinical outcomes, while each C-score reflects the biomarker applicability of the biomarker’s CPM to a patient and therefore the confidence of the clinical prediction. We assessed the effectiveness of this framework by applying it to three biomarkers, Oncotype DX, MammaPrint, and an E2F4 signature, which have been used for predicting patient response, pathologic complete response versus residual disease to neoadjuvant chemotherapy (a classification problem), and recurrence-free survival (a Cox regression problem) in breast cancer, respectively. In both applications, our analyses indicated patients with higher C scores were more likely to be correctly predicted by the biomarkers, indicating the effectiveness of our framework. This framework provides a useful approach to develop and apply biomarkers in the context of cancer precision medicine.
Highlights
In the era of precision medicine, biomarkers will be heavily implemented to improve diagnosis, prognosis, and treatment of human d iseases[1,2,3,4]
We develop a new framework to apply biomarkers that will jointly calculate a pair of scores for each patient: one indicates the clinical outcome predicted by the biomarker, while the other indicates the applicability of this biomarker to this patient
Depending on the type of clinical outcome variable, a clinical outcome prediction model (CPM) can be formularized as a classification problem (Classification-CPM) or a survival analysis (Cox regression-CPM)
Summary
In the era of precision medicine, biomarkers will be heavily implemented to improve diagnosis, prognosis, and treatment of human d iseases[1,2,3,4]. The Oncotype DX assay is one genomic test that has been widely used clinically to predict the recurrence risk of patients with estrogen-receptor-positive (ER+) breast cancer[15]. The American Society of Clinical Oncology (ASCO) and the National Comprehensive Cancer Network (NCCN) have recommended the application of the Oncotype DX assay to aid breast cancer clinical d ecisions[12,15] Another example of a successful genomic test. We use three multi-gene signatures, including Oncotype DX, MammaPrint, and an E2F4 s ignature[21,22,23], to demonstrate the potential of this framework to change the application of biomarkers We use these signatures to construct classification models to predict patient response to neoadjuvant (pCR vs RD, residual disease), and Cox regression models to predict patient recurrence-free survival in breast cancer. It may be extended to other human diseases
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