Drugging cancer vulnerabilities toward innovative clinical trials

Guest Editors: Anna Spreafico¹, Lorenza Landi², Sabrina Strano³
_{1 - Princess Margaret Cancer Centre, University Health Network, Toronto, Canada
2 - IRCCS Regina Elena National Cancer Institute, Rome, Italy
3 - IRCCS Regina Elena National Cancer Institute, Rome, Italy}

Submission deadline: 30th June 2022. Submit your research here.

The study of cancer vulnerabilities remains a colossal challenge in oncology. Substantial advances have been made to identify the genetic makeup of tumor cells and related targets, using large-scale multi-omics approaches and innovative machine learning-based methods.

While non-clinical research is one of the drug discovery’s pillars, several “in vitro” screening methods and “in vivo” models are only suitable for “artificial” conditions, lack reproducibility and cannot be easily applicable in the clinical setting. The identification of cancer-specific dependencies and their translation into clinical utility is essential to implement precision medicine in daily practice.

Clinical trials represent essential resources for drug discovery and drive the “go versus no-go” decision in the development of novel anticancer treatments, laying the foundation for new ways to conquer cancer. Throughout the decades, the concept, design and execution of clinical trials have evolved. From conventional, histology pre-defined, and empirically based investigations, trials have shifted towards selective molecular and blood and/or tumor-based biomarker-driven approaches, that integrate circular pathways (bench-to-bedside-to-bench), cutting-edge technologies, and “smart” real-time reassessment study designs.

Keywords: Treatment, Clinical Trial, Microbiome, Radiomics, Artificial Intelligence, Metabolomics, Epigenomics, Proteomics, DNA/RNA Sequencing, Single Cell Sequencing

This special issue of the Journal of Experimental & Clinical Cancer Research, aims to recognize the pivotal role of experimental therapeutics and translational research, identify existing gaps through real-world, retrospective and prospective data, set priorities for the next generation of scientists, enhance global learning, and delineate a future framework to increase the efficiency of targeting and drugging cancer vulnerabilities in our lifetime.