A method for predicting target drug efficiency in cancer based on the analysis of signaling pathway activation
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Artem Artemov1,2, Alexander Aliper2,3, Michael Korzinkin1, Ksenia Lezhnina1, Leslie Jellen4, Nikolay Zhukov2,3,5, Sergey Roumiantsev2,5, Nurshat Gaifullin6, Alex Zhavoronkov7, Nicolas Borisov3 and Anton Buzdin1,2,8
1 Pathway Pharmaceuticals, Wan Chai, Hong Kong, Hong Kong SAR
2 D. Rogachyov Federal Research Center of Pediatric Hematology, Oncology and Immunology, Moscow, Russia
3 First Oncology Research and Advisory Center, Moscow, Russia
4 Department of Genetics, Genomics, and Informatics, University of Tennessee Health Science Center, Memphis, TN, USA
5 Pirogov Russian National Research Medical University, Department of Oncology, Hematology and Radiotherapy, Moscow, Russia
6 Moscow State University, Faculty of Fundamental Medicine, Moscow, Russia
7 Insilico Medicine, Inc., ETC, Johns Hopkins University, Baltimore, MD, USA
8 Group for Genomic Regulation of Cell Signaling Systems, Shemyakin-Ovchinnikov Institute of Bioorganic Chemistry, Moscow, Russia
Anton Buzdin, email:
Keywords: cancer, response to target drug therapy, bioinformatic modeling, intracellular signaling pathway, personalized medicine
Received: January 17, 2015 Accepted: July 24, 2015 Published: August 07, 2015
A new generation of anticancer therapeutics called target drugs has quickly developed in the 21st century. These drugs are tailored to inhibit cancer cell growth, proliferation, and viability by specific interactions with one or a few target proteins. However, despite formally known molecular targets for every “target” drug, patient response to treatment remains largely individual and unpredictable. Choosing the most effective personalized treatment remains a major challenge in oncology and is still largely trial and error. Here we present a novel approach for predicting target drug efficacy based on the gene expression signature of the individual tumor sample(s). The enclosed bioinformatic algorithm detects activation of intracellular regulatory pathways in the tumor in comparison to the corresponding normal tissues. According to the nature of the molecular targets of a drug, it predicts whether the drug can prevent cancer growth and survival in each individual case by blocking the abnormally activated tumor-promoting pathways or by reinforcing internal tumor suppressor cascades. To validate the method, we compared the distribution of predicted drug efficacy scores for five drugs (Sorafenib, Bevacizumab, Cetuximab, Sorafenib, Imatinib, Sunitinib) and seven cancer types (Clear Cell Renal Cell Carcinoma, Colon cancer, Lung adenocarcinoma, non-Hodgkin Lymphoma, Thyroid cancer and Sarcoma) with the available clinical trials data for the respective cancer types and drugs. The percent of responders to a drug treatment correlated significantly (Pearson’s correlation 0.77 p = 0.023) with the percent of tumors showing high drug scores calculated with the current algorithm.
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