Oncobox
EFFECTIVE THERAPY SELECTION FOR EACH PATIENT

Oncobox is developed to choose drug therapy for difficult and late-stage cancer cases.
The platform analyzes the molecular characteristics of a patient's tumor and calculates an individual efficacy rating for over 160 cancer drugs.
Oncobox technology is protected by international patents, and its effectiveness has been proven in prospective and retrospective clinical studies.
Learn more in the brochure

Major steps
A tumor sample (FFPE) is sent to the lab. DNA and RNA are extracted from it, and then a molecular profile analysis is performed using NGS sequencing.
Oncobox predicts the effectiveness of more than 160 targeted and immunotherapy drugs for the treatment of a specific tumor and computes their personalized rating.
Oncobox Dx is always effective and allows you to choose the right drugs for each case. Finally, the doctor prescribes the optimal treatment based on the report.
Clinical utility of Oncobox

The effectiveness of Oncobox technology has been proven in retrospective and prospective clinical studies in which hundreds of patients with advanced cancer received targeted cancer drugs following personalized Oncobox ratings.

The clinical study results demonstrate a twofold decrease in the incidence of disease progression, a significant increase in response to therapy (up to 76%), and an increase in progression-free survival. In 2020, details of the research were presented at the American Society of Clinical Oncology (ASCO) convention, as well as at the WIN International Personalized Oncology Consortium conference.

Sample Report

Oncobox Dx includes analysis of genomic (DNA) and transcriptomic (RNA) data obtained from a tumor sample. The final report contains a rating of the effectiveness of targeted and immunotherapeutic drugs for a particular patient. Additionally, the report includes baseline data on molecular abnormalities and the results of relevant clinical trials.

This approach provides high clinical value and allows the clinician to build an evidence-based roadmap for further treatment.

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Team

Our team includes world-renowned scientists, molecular biologists, bioinformaticians, and medical doctors, who have been investigating molecular mechanisms of cancer for years and have been published in many peer-reviewed scientific journals, such as Nature and the Journal of Clinical Oncology. Our mission is to develop solutions that help doctors find the most effective treatment for late-stage cancer patients, who often face limited options.

andrew_garazha

Andrew Garazha

Chief Executive Officer
anton_buzdin

Anton Buzdin, PhD

Chief Scientific Officer

Max Sorokin, PhD

Head of Bioinformatics

Victor Tkachev

Chief Technology Officer

Alex Poltorak, Prof., PhD

Molecular Immunology Advisor. Tufts University

Ilya Muchnik, Prof., PhD

Biostatistics Algorithms Advisor. Rutgers University
News

Machine learning application for finding bortezomib success gene expression biomarkers in multiple myeloma

Multiple myeloma (MM) is a highly heterogenous hematological cancer that is especially frequent in elderly. There are currently few dozen chemotherapy MM treatment regimens, many of which include bortezomib, a proteasome-targeted therapeutic. Oncobox team for the first time successfully applied ML methods to find biomarker gene signature predicting tumor response on bortezomib-containing treatments. Interestingly, biomarker gene sets for the specific types of combination therapies contained 5 redundant genes FGFR3, MAF, IGHA2, IGHV1-69, and GRB14, of which four were directly connected with MM-linked genomic rearrangements in the previous studies. The paper is in press: https://www.frontiersin.org/articles/10.3389/fonc.2021.652063/abstract
May 2021

First of its kind gene signature for minimum-invasive diagnosis of endometriosis

Approximately 200 million women face with the problem of endometriosis, benign endometrial neoplasm. Diagnostics of endometriosis is problematic because it is highly invasive laparoscopic procedure with relatively low sensitivity and specificity. Oncobox researchers took part in identification of a robust 5-gene RNA signature effectively predicting endometriosis based on the uterine endometrium probes. The paper is available online: https://pubmed.ncbi.nlm.nih.gov/33839309/
April 2021

Oncobox CSO elected as the Chair of EORTC subgroup

We congratulate Oncobox Chief Scientific Officer professor Anton Buzdin who was elected as the chair of Biostatistics and Bioinformatics subgroup of Pathobiology group of European Organization for Research and Treatment of Cancer (EORTC) https://www.eortc.org
March 2021
All news
Oncobox research

More than

90

publications

More than

2500

patients

7

patents
and licenses

The latest and most important scientific articles of our team are available here. Full list of publications is available through Pubmed.

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