In an effort to provide personalised treatment to patients, oncologists usually screen the tumours for the presence of specific genomic peculiarities — using immunohistochemistry, fluorescence in situ hybridisation (FISH), polymerase chain reaction (PCR), and next-generation sequencing (NGS). Although the tests for overexpression of certain genes are the standard of care in some cancers, RNA sequencing has not yet become as common; however, transcriptomics could help build a more comprehensive picture of gene expression. This knowledge is crucial, because DNA screening can only tell the likelihood of response to the treatment — but not the recurrence rate.

To address the issue, an international team of scientists from Russia, France, USA, Germany, Israel, Romania, Spain, and Canada has built a novel transcriptomics algorithm which can quite precisely predict the benefit of using targeted medications or immunotherapy — compared to genomic biomarkers. This paper contains contributions from Sechenov University, while the leading role belongs to the Worldwide Innovative Network (WIN) Association—WIN Consortium. The paper has been published in npj Precision Oncology.

The researchers describe the development of a prototype ‘digital display precision predictor’ (DDPP). This tool could be used to estimate the extent of the patients’ outcome with certain medications — in addition to the current genomic biomarker tests that can predict the response.

The algorithm has been used to analyse the data of patients who received cancer drug everolimus during the WINTHER clinical trial that was carried out earlier. The DDPP does not give a definite answer to the question whether or not the patients will respond to a treatment, but rather predicts the duration of progression-free survival.

The authors of the paper ranked individual genes in the order of their significance in terms of association with progression-free survival and built the predictor mechanism by adding the genes one by one.

Although the results are promising, the researchers conclude that it is only an initial demonstration of what the DDPP could do. A thorough validation will be performed in a large cohort of patients. Twenty people have already been recruited for this purpose, and their data are going to be published in near future.

The WIN Consortium was formed in 2010 with leadership from leading cancer centres worldwide, with the focus on personalised cancer treatment. The WIN Consortium is a non-profit, non-governmental organisation headquartered in France. Today, WIN includes 36 member organisations from 19 countries. Sechenov University is part of the WIN Consortium, represented by Prof Andrey Svistunov, First Vice-Rector of Sechenov University, and Prof Marina Sekacheva, Director of the Institute of Personalised Oncology at Sechenov University.

The research paper contains contributions from authors at Sechenov University; Worldwide Innovative Network (WIN) Association—WIN Consortium (Villejuif, France); Institut Curie (Paris, France); Pfizer Inc. (San Diego, CA, USA); Merck KGaA (Darmstadt, Germany); Sheba Medical Centre (Tel-Hashomer, Israel); ARC Foundation for Cancer Research (Villejuif, France); Iuliu Hatieganu University of Medicine and Pharmacy (Cluj-Napoca, Romania); Oncology Institute ‘Prof Dr Ion Chiricuta’ (Cluj-Napoca, Romania); Vall d’Hebron Hospital Campus and Institute of Oncology, IOB-Quiron, Universitat de Vic—Universitat Central de Catalunya (Barcelona, Spain); Ben-Gurion University of the Negev (Beer-Sheeva, Israel); Segal Cancer Centre, Jewish General Hospital, McGill University (Montreal, Canada); NCE Exactis Innovations (Montreal, Canada); Avera Cancer Centre (Sioux Falls, SD, USA); University of Texas M.D. Anderson Cancer Centre (Houston, TX, USA); Gustave Roussy (Villejuif, France); University Paris-Saclay (Orsay, France); Moores Cancer Centre at the University of California San Diego (San Diego, CA, USA); and American Society of Clinical Oncology—ASCO (Alexandria, VA, USA).

Read more: Lazar, V., Magidi, S., Girard, N. et al. Digital Display Precision Predictor: the prototype of a global biomarker model to guide treatments with targeted therapy and predict progression-free survival. npj Precis. Onc. 5, 33 (2021).