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Semin Cancer Biol. 2018 Dec;53:110-124. doi: 10.1016/j.semcancer.2018.06.003. Epub 2018 Jun 20.

Molecular pathway activation - New type of biomarkers for tumor morphology and personalized selection of target drugs.

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I.M. Sechenov First Moscow State Medical University, Moscow, Russia; Omicsway Corp., Walnut, CA, USA; Shemyakin-Ovchinnikov Institute of Bioorganic Chemistry, Moscow, Russia. Electronic address:
I.M. Sechenov First Moscow State Medical University, Moscow, Russia.
Omicsway Corp., Walnut, CA, USA.
Johannes Gutenberg University Mainz, Mainz, Germany.
D. Rogachev Federal Research Center of Pediatric Hematology, Oncology and Immunology, Moscow, 117198, Russia.
Qingdao Central Hospital, 127 Siliu Road South, Qingdao, Shandong, 266042, China. Electronic address:
Department of Pathology and Laboratory Medicine, University of California Los Angeles, CA, 90095, USA. Electronic address:
International Neuroscience Institute, Hannover, Germany.
Institute of Neuropathology at Hannover Medical School, Hannover, Germany.


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.


Anticancer target drugs; Big data analytics; Bioinformatics; Biomarkers; Cancer; Epigenetics; Gene expression; Intracellular molecular pathways; Machine learning; Micro RNA; Proteomics; Response to cancer therapy; Systems biology; Transcriptomics; miR

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