Dear Colleagues,

Bioinformatics applications in cancer have rapidly evolved over the past several years. Ever since its initial implementation, next generation sequencing has altered our understanding of cancer biology, and the approaches to analyze more and more complex datasets have also become increasingly complex.  Routine bioinformatics pipelines now range from those that rapidly detect and predict functional impact of molecular alterations to those that quantify changes to the tumor microenvironment. For example, several tools that analyze tumor-immune interactions have been successfully developed to assess tumor infiltrating lymphocyte content, microsatellite instability, total mutational burden and neoantigen presentation. Further complexity of integrated omics-based analysis is also now coupled with the emergence of modern machine learning and network-based approaches to analyze large datasets in the context of publicly available resources, such as the cancer genome atlas.

While much of the focus has so far been on annotating molecular alterations as well as infiltrating cell types or cell states in ideal sequencing conditions, alternative and application-specific approaches are now emerging that improve on a wide variety of established analysis techniques. These include techniques that range from improved quantification of copy number and gene expression from formalin fixed tissues as well as applications that require high sensitivity such as the quantification of tumor mutations from liquid biopsies (circulating cell free DNA).  Further novel applications attempt to improve the ability to analyze the distribution and molecular impact of complicated genetic features such as repetitive or transposable endogenous elements (e.g., LINE-1) as well as exogenous genetic elements (e.g., human papilloma virus).

As we develop a better understanding of the limitations of these new informatics approaches, we can ultimately hope to apply these techniques to existing datasets and build well-annotated databases of easily accessible information that can be leveraged in multi-variable analysis pipelines. Similar to the success of SIGdb and cBioPortal, this should help yield new diagnostic and prognostic/predictive biomarkers for standard interventional modalities as well as emerging areas like immuno-oncology, and areas of unmet clinical need. This Special Issue will highlight the current state of the art in bioinformatics applications in cancer biology, and infer future prospects for improving informatics applications through artificial intelligence and machine learning approaches.

Dr. J. Chad Brenner
Guest Editor

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• Bioinformatics
• Machine learning
• Artificial Intelligence
• Network Analysis
• Single cell sequencing
• Circulating tumor DNA (ctDNA)
• Neoantigen Prediction
• Precision medicine
• Computational Immunology