Molecular pathway activation – New type of biomarkers for tumor morphology and personalized selection of target drugs. (December 2018)

• Record Type: Journal Article
• Authors: Buzdin, Anton; Sorokin, Maxim; Garazha, Andrew; Sekacheva, Marina; Kim, Ella; Zhukov, Nikolay; Wang, Ye; Li, Xinmin; Kar, Souvik; Hartmann, Christian; Samii, Amir; Giese, Alf; Borisov, Nicolas
• Is Part Of: Seminars in cancer biology. Volume 53(2018)
• Page Start: 110
• Page End: 124
• Publication Date: 2018-12
• Subjects: IMP intracellular molecular pathway -- PAS pathway activation strength, calculated using mRNA or protein expression data -- NGS next generation sequencing; Systems biology -- Bioinformatics -- Intracellular molecular pathways -- Gene expression -- Transcriptomics -- Proteomics -- Epigenetics -- Micro RNA -- miR -- Cancer -- Biomarkers -- Machine learning -- Big data analytics -- Anticancer target drugs -- Response to cancer therapy; Cancer -- Periodicals; Neoplasms -- Periodicals; Review Literature; Cancer -- Périodiques; Electronic journals; 616.994
• DOI: 10.1016/j.semcancer.2018.06.003
• Languages: English
• ISSNs: 1044-579X

Abstract

Abstract: Anticancer target drugs (ATDs) specifically bind and inhibit molecular targets that play important roles in cancer development and progression, being deeply implicated in intracellular signaling pathways. To date, hundreds of different ATDs were approved for clinical use in the different countries. Compared to previous chemotherapy treatments, ATDs often demonstrate reduced side effects and increased efficiency, but also have higher costs. However, the efficiency of ATDs for the advanced stage tumors is still insufficient. Different ATDs have different mechanisms of action and are effective in different cohorts of patients. Personalized approaches are therefore needed to select the best ATD candidates for the individual patients. In this review, we focus on a new generation of biomarkers – molecular pathway activation – and on their applications for predicting individual tumor response to ATDs. The success in high throughput gene expression profiling and emergence of novel bioinformatic tools reinforced quick development of pathway related field of molecular biomedicine. The ability to quantitatively measure degree of a pathway activation using gene expression data has revolutionized this field and made the corresponding analysis quick, robust and inexpensive. This success was further enhanced by using machine learning algorithms for selection of the best biomarkers. We review here the current progress in translating these studies to clinical oncology and patient-oriented adjustment of cancer therapy.