Clinical Bioinformatics: A New Emerging Science of Biomarker Development

Abstract

Cancer has become the leading cause of death in the last 50 years. Patients with early detection of cancer have better rate of the recovery and survival than patients with more advanced cancer. More than 90 % 5-year survival rate was found to associated with the detection of cancers at the stage one (Etzioni et al. 2003), which need cancer-specific and sensitive biomarkers to diagnose and monitor timely therapeutic interventions (Ullah and Aatif 2009). US Food and Drug Administration has approved a few biomarkers for early detection or screening of cancers, like prostate-specific antigen for prostate cancer, nuclear matrix protein 22 for bladder cancer, etc. Biomarkers can play roles before cancer diagnosis in risk assessment and screening, at diagnosis in classification, stage and grade, and after diagnosis in predicting response to therapy and toxicity related to treatment, selecting additional therapy and detecting recurrence (Fig. 9.1) (Ludwig and Weinstein 2005). Predictive biomarkers allow clinicians to assess clinical effects of chemotherapy and molecular targeted agents on response rate and survival time (Saijo 2012). The patients with poor responses have severe toxicities of chemotherapy or high prices of targeted drugs, if they did not have a reliable biomarker. Massive efforts have been carried out to identify such predictive biomarkers, of which some have been used in clinical trials. For example, somatic mutations in the tyrosine kinase domain of the epidermal growth factor receptor (EGFR) were shown to be a predictive marker for better efficacy of Gefitinib in patients with non small cell lung cancer (Lynch et al. 2004; Paez et al. 2004). Decisions in breast cancer are based on tumor size, node status, histological grade, age, estrogen receptor status, and EGF receptor 2 (HER2) status, of which two receptors have been considered as more important markers for the malignancy (Roukos 2010).