A transcriptomics approach to expand therapeutic options and optimize clinical trials in oncology

Author:

Lazar Vladimir1,Zhang Baolin2,Magidi Shai3,Le Tourneau Christophe4,Raymond Eric5,Ducreux Michel6,Bresson Catherine3,Raynaud Jacques3,Wunder Fanny3,Onn Amir7,Felip Enriqueta8,Tabernero Josep8,Batist Gerald9,Kurzrock Razelle3,Rubin Eitan10,Schilsky Richard L.3

Affiliation:

1. Worldwide Innovative Network (WIN) Association–WIN Consortium, 24 rue Albert Thuret, Villejuif 94550, France

2. Office of Biotechnology Products (OBP), Center for Drug Evaluation and Research (CDER), Food and Drug Administration (FDA), Silver Spring, MA, USA

3. Worldwide Innovative Network (WIN) Association–WIN Consortium, Villejuif, France

4. Department of Drug Development and Innovation (D3i), INSERM U900 Research Unit, Paris-Saclay University, Institut Curie, Paris, France

5. Oncology Department, Groupe Hospitalier Paris Saint Joseph, Paris, France

6. Department of Medical Oncology, Gustave Roussy, Université Paris-Saclay, Inserm U1279, Villejuif, France

7. Institute of Pulmonology, Sheba Medical Center, Tel-Hashomer, Israel

8. Oncology Department, Vall d’Hebron Hospital Campus and Institute of Oncology (VHIO), UVic-UCC, Barcelona, Spain

9. Department of Oncology, Segal Cancer Centre, Jewish General Hospital, McGill University, Montréal, Canada

10. Shraga Segal Department of Microbiology and Immunology, Faculty of Health Sciences Ben-Gurion University of the Negev, Beer-Sheeva, Israel

Abstract

Background: The current model of clinical drug development in oncology displays major limitations due to a high attrition rate in patient enrollment in early phase trials and a high failure rate of drugs in phase III studies. Objective: Integrating transcriptomics for selection of patients has the potential to achieve enhanced speed and efficacy of precision oncology trials for any targeted therapies or immunotherapies. Methods: Relative gene expression level in the metastasis and normal organ-matched tissues from the WINTHER database was used to estimate in silico the potential clinical benefit of specific treatments in a variety of metastatic solid tumors. Results: As example, high mRNA expression in tumor tissue compared to analogous normal tissue of c-MET and its ligand HGF correlated in silico with shorter overall survival (OS; p < 0.0001) and may constitute an independent prognostic marker for outcome of patients with metastatic solid tumors, suggesting a strategy to identify patients most likely to benefit from MET-targeted treatments. The prognostic value of gene expression of several immune therapy targets (PD-L1, CTLA4, TIM3, TIGIT, LAG3, TLR4) was investigated in non-small-cell lung cancers and colorectal cancers (CRCs) and may be useful to optimize the development of their inhibitors, and opening new avenues such as use of anti-TLR4 in treatment of patients with metastatic CRC. Conclusion: This in silico approach is expected to dramatically decrease the attrition of patient enrollment and to simultaneously increase the speed and detection of early signs of efficacy. The model may significantly contribute to lower toxicities. Altogether, our model aims to overcome the limits of current approaches.

Funder

FP7 Health

National Cancer Institute

novartis pharmaceuticals corporation

Israeli Science Foundation

instituto de salud carlos iii

Canadian Institutes for Health

canadian cancer society

Publisher

SAGE Publications

Subject

Oncology

Reference43 articles.

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