Personalized anti-cancer drug treatment choice using RNA-seq and network
analysis.
D. Stelmashenko 1,2, Olga Kel-Margoulis 1, S. Apalko 3, A. Kel 1,2,4
1 geneXplain GmbH, Wolfenbüttel, Germany
2 biosoft.ru, Novosibirsk, Russia
3 City Hospital №40, St. Petersburg, Russia
4 Institute of Chemical Biology and Fundamental Medicine, Novosibisk, Russia

The development of omics technologies and emerging of targeted therapies have opened a newera in the medicine of 21st century - the era of personalized medicine based on identification of
the precise molecular mechanism of a certain pathology in a concrete patient. Personalized
medicine and targeted therapy play a very special role in cancer diseases as it has been widely
shown in multiple studies that the same types of cancer in two different patients can appear tobe very different in their origins and mechanisms and therefore responding differently to thesame treatment. Development of personalized drug target identification algorithm based on thepatient omics data analysis would solve this problem of optimal therapy selection and would
bring the cancer treatment strategy to a completely new level. Our work aims to introduce analgorithm for reconstruction of the molecular mechanism of a certain pathology and selection ofeffective therapies based on the personalized model of the disease. The drug target identification
algorithm introduced in this work can be applied for various types of omics data. For illustrating
the algorithm in the current study, we will show its application example on the basis of RNA-seq
data of colorectal cancer tumor sequencing. We analyzed RNA-seq data of the patients with thegood and pure response to standard therapy and revealed differentially expressed genes (DEGs)
in the tumor compared to normal surrounding tissue. We applied Upstream Analysis algorithm[1] for identification of key master-regulators responsible for pathologic gene regulation incancer cells of the studied cases. The analysis comprised of three main steps: 1) AI-basedalgorithm to scan promoters of DEGs using TRANSFAC database and to identify complexes oftranscription factors (TF), which regulate DEGs; 2) a graph search algorithm using TRANSPATHdatabase to identify common regulators of the TFs selected on the previous step as potential
drug targets; 3) effective treatments are then selected for the identified drug targets, on the basisof HumanPSD database, containing the information about the approved drugs, as well as thetherapies under development, and their targets. This algorithm is available at the Genome
Enhancer web site (ge.genexplain.com).
The performed analysis revealed that the studied patient cases had rather different mechanismof carcinogenesis that involve fairly different although partially overlapping sets of master
regulators in the molecular circuits that control activity of the gene expression in these tumors.
Based on the revealed master regulators our algorithm proposed highly tuned personalized
prospective drug treatment prognosis for the different patients with various cancer progression.
We have shown that the predicted by GE sensitivity to the anti VEGFA therapy highly correlatedwith observed response in the studied patients. The target-oriented approach towards treatmentprescription accelerates the off-label drug usage and enables effective treatment selection forclinically complicated cases with no obvious treatment options.
1. Kel A., et.al. Walking pathways with positive feedback loops reveal DNA methylation
biomarkers of colorectal cancer // BMC Bioinformatics. 2019. V. 20 (Suppl 4)