0000000000470655
AUTHOR
Victor Tkachev
Oncobox, gene expression-based second opinion system for predicting response to treatment in advanced solid tumors.
e13143 Background: Anticancer Targeted Drugs (ATDs) specifically target one or a few types of tumor-related molecules in a cell. More than two hundred of ATDs were approved worldwide. They have different mechanisms of action and are effective for different cohorts of patients. However, many individual cases remain poorly responsive and it is of great importance to identify predictive markers of ATD efficacy. Our aim was to develop a platform enabling smart selection of the most efficient ATD therapies. Methods: We generated a second-opinion platform for clinical oncologists termed Oncobox. It is based on the analysis of gene expression profile of a cancer sample in comparison with the corr…
Disparity between Inter-Patient Molecular Heterogeneity and Repertoires of Target Drugs Used for Different Types of Cancer in Clinical Oncology
Inter-patient molecular heterogeneity is the major declared driver of an expanding variety of anticancer drugs and personalizing their prescriptions. Here, we compared interpatient molecular heterogeneities of tumors and repertoires of drugs or their molecular targets currently in use in clinical oncology. We estimated molecular heterogeneity using genomic (whole exome sequencing) and transcriptomic (RNA sequencing) data for 4890 tumors taken from The Cancer Genome Atlas database. For thirteen major cancer types, we compared heterogeneities at the levels of mutations and gene expression with the repertoires of targeted therapeutics and their molecular targets accepted by the current guideli…
Clinical use of RNA sequencing and oncobox analytics to predict personalized targeted therapeutic efficacy.
e13676 Background: Analysis of mutation profiles in cancer patients does not provide clinical benefits in 80-90% of cases in the US (Marquart et al., 2018). Gene expression analysis potentially complements standard detection of clinically relevant mutations. Methods: 239 adult late-stage cancer patients. RNA gene expression sequencing completed on solid tumor samples using FFPE blocks. Patient mRNA profiles were analyzed using Oncobox bioinformatics, prioritizing target drugs according to their personalized predicted efficacy. Summary reports were provided to oncologists and resulting treatment selection and outcomes were assessed. Results: As of February 2020, feedback was received from p…
Algorithmically deduced FREM2 molecular pathway is a potent grade and survival biomarker of human gliomas
Gliomas are the most common malignant brain tumors with high mortality rates. Recently we showed that the FREM2 gene has a role in glioblastoma progression. Here we reconstructed the FREM2 molecular pathway using the human interactome model. We assessed the biomarker capacity of FREM2 expression and its pathway as the overall survival (OS) and progression-free survival (PFS) biomarkers. To this end, we used three literature and one experimental RNA sequencing datasets collectively covering 566 glioblastomas (GBM) and 1097 low-grade gliomas (LGG). The activation level of deduced FREM2 pathway showed strong biomarker characteristics and significantly outperformed the FREM2 expression level it…