About this Research Topic
Early detection of cancer can significantly improve mortality, thus, it is of utmost importance to continue research in this direction. Yet, owing to the genetic heterogeneity of cancer patients, different patients respond to cancer therapies differently. So, predicting the potential responders to certain types of cancer treatments like targeted therapy or immunotherapy is key to a more effective treatment strategy. The availability of large cohorts of tumour genomic and clinical data from platforms like The Cancer Genome Atlas (TCGA), International Cancer Genome Consortium (ICGC) and Therapeutically Applicable Research to Generate Effective Treatments (TARGET) has enabled computational researchers to mine these large-scale multi-omic cancer data to come up with an improved prediction of biomarkers and prioritize patients who are likely to respond to a certain type of therapy or drug treatment, based on their molecular profile.
This Research Topic aims to explore recent advances in computational techniques used to harness large-scale cancer genomic and clinical data for biomarker detection or precision therapeutic strategies. At the same time, our goal is keeping the focus on clinical relevance and attempting to bridge the gap between computational and clinical research. Computational approaches used for biomarker detection, therapeutic response prediction, drug repurposing, and identifying effective drug combination therapies will be discussed within this topic. Particularly, detection of cancer patients with aggressive vs non-aggressive phenotypes, or patients who may benefit from certain targeted therapies or immunotherapies, using molecular markers will be of interest. Please note, attempts to identify a candidate set of diagnostic or prognostic markers predicting clinical outcomes without validation in a clinical dataset will not be considered . Computational prediction studies must include some validation, protein or gene validation, for example, to stress their applicability in cancer detection and treatment.
We invite contributions of Original Research, Methods, Reviews and databases and/or software tools covering the following topics of interest:
1) Mining, analysis, and visualization of large cancer patient cohorts for exploration of molecular markers for patient's clinical outcomes;
2) Computational and statistical models for cancer multi-omic data analysis;
3) Data resources or software tools aimed for navigating biomarkers for cancer subtypes or therapeutic responses;
4) Application of machine-learning for cancer detection and precision medicine;
5) Methods and tools for drug repurposing and drug combination prediction.
This Research Topic aims to explore recent advances in computational techniques used to harness large-scale cancer genomic and clinical data for biomarker detection or precision therapeutic strategies. At the same time, our goal is keeping the focus on clinical relevance and attempting to bridge the gap between computational and clinical research. Computational approaches used for biomarker detection, therapeutic response prediction, drug repurposing, and identifying effective drug combination therapies will be discussed within this topic. Particularly, detection of cancer patients with aggressive vs non-aggressive phenotypes, or patients who may benefit from certain targeted therapies or immunotherapies, using molecular markers will be of interest. Please note, attempts to identify a candidate set of diagnostic or prognostic markers predicting clinical outcomes without validation in a clinical dataset will not be considered . Computational prediction studies must include some validation, protein or gene validation, for example, to stress their applicability in cancer detection and treatment.
We invite contributions of Original Research, Methods, Reviews and databases and/or software tools covering the following topics of interest:
1) Mining, analysis, and visualization of large cancer patient cohorts for exploration of molecular markers for patient's clinical outcomes;
2) Computational and statistical models for cancer multi-omic data analysis;
3) Data resources or software tools aimed for navigating biomarkers for cancer subtypes or therapeutic responses;
4) Application of machine-learning for cancer detection and precision medicine;
5) Methods and tools for drug repurposing and drug combination prediction.
Keywords: computational biology, precision medicine, bioinformatics, predictive modeling, cancer, genomics, omics, biomarker
Important Note: All contributions to this Research Topic must be within the scope of the section and journal to which they are submitted, as defined in their mission statements. Frontiers reserves the right to guide an out-of-scope manuscript to a more suitable section or journal at any stage of peer review.
