Predicting and Understanding Cancer Response to Treatment
1University of Cambridge, Cambridge, UK
2Innsbruck Medical University, Innsbruck, Austria
3Fondazione IRCCS Istituto Nazionale dei Tumori, Milan, Italy
4Radboud University, Nijmegen, Netherlands
Predicting and Understanding Cancer Response to Treatment
Description
One of currently unmet needs in oncology is an adequate understanding of mechanisms driving response or resistance to cancer therapy. An interconnected issue is our limited ability to predict which patients will or will not respond to a specific treatment. As a consequence, a large proportion of patients is either overtreated or receives ineffective treatments. Moreover, in those responding to treatment, a major challenge is represented by the development of acquired resistance.
The advent of cancer genomics has given an important contribution to identifying novel cancer vulnerabilities and highlighting the role of tumour heterogeneity and evolution in drug response. The systematic use of “omics” technologies is being instrumental in searching for novel biomarkers on a genome scale and through the integration of multidimensional molecular data.
In parallel, the concept of “liquid biopsy” is emerging as a promising strategy to noninvasively monitor the disease and evaluate predictive biomarkers.
In this special issue we aim to publish high-quality research articles and reviews reporting advances in the field. The goal is to cover several aspects related with cancer response to treatment, with primary attention to the identification and development of tissue and circulating biomarkers, possibly using a combination of experimental and computational approaches.
Potential topics include but are not limited to the following:
- Predictive biomarkers in preclinical and clinical settings
- Noninvasive predictive biomarkers (plasma/serum miRNA, ctDNA, CTCs, and exosomes)
- Identification of new pharmacogenomics associations
- Computational approaches to identify predictive markers
- Predictive biomarker assay validity and quality assessment
- Markers linked with mechanisms of resistance to chemotherapy, radiotherapy, immunotherapy, and targeted treatments