Disease Markers

Predicting and Understanding Cancer Response to Treatment


Status
Published

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

Articles

  • Special Issue
  • - Volume 2018
  • - Article ID 6159214
  • - Editorial

Predicting and Understanding Cancer Response to Treatment

Maurizio Callari | Paolo Gandellini | ... | Paul N. Span
  • Special Issue
  • - Volume 2018
  • - Article ID 9128128
  • - Clinical Study

Evaluation of Mediators Associated with the Inflammatory Response in Prostate Cancer Patients Undergoing Radiotherapy

Nice Bedini | Alessandro Cicchetti | ... | Riccardo Valdagni
  • Special Issue
  • - Volume 2018
  • - Article ID 2908609
  • - Review Article

Noninvasive Glioblastoma Testing: Multimodal Approach to Monitoring and Predicting Treatment Response

Maikel Verduin | Inge Compter | ... | Marc Vooijs
  • Special Issue
  • - Volume 2017
  • - Article ID 7687851
  • - Review Article

Metabolic Footprints and Molecular Subtypes in Breast Cancer

Vera Cappelletti | Egidio Iorio | ... | Maria Grazia Daidone
  • Special Issue
  • - Volume 2017
  • - Article ID 7849108
  • - Review Article

Predicting the Efficacy of HER2-Targeted Therapies: A Look at the Host

Martina Di Modica | Elda Tagliabue | Tiziana Triulzi
  • Special Issue
  • - Volume 2017
  • - Article ID 6870614
  • - Research Article

Are Fusion Transcripts in Relapsed/Metastatic Head and Neck Cancer Patients Predictive of Response to Anti-EGFR Therapies?

Paolo Bossi | Marco Siano | ... | Loris De Cecco
  • Special Issue
  • - Volume 2017
  • - Article ID 4934608
  • - Review Article

The Predictive Value of PITX2 DNA Methylation for High-Risk Breast Cancer Therapy: Current Guidelines, Medical Needs, and Challenges

Michaela Aubele | Manfred Schmitt | ... | Marion Kiechle
Disease Markers
 Journal metrics
Acceptance rate34%
Submission to final decision88 days
Acceptance to publication43 days
CiteScore3.500
Impact Factor2.738
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