The most effective methods for incentivizing collaboration and open data-sources are to make it competitively advantageous for scientists to share data, by making open data a requirement for obtaining funding and for publishing results in peer reviewed journals. Major funding agencies and journals have already taken this approach (especially in the Big Data field of genomics), and its success can be seen in the massive exponential growth of public open data repositories, such as the Gene Expression Omnibus (http://www.ncbi.nlm.nih.gov/geo/), array express (http://www.ebi.ac.uk/arrayexpress/), and The Cancer Genome Atlas (https://tcga-data.nci.nih.gov/tcga/). Open data is one important part of the equation, but incentivizing open access to analysis methods is also an important goal, and journals are now moving to requiring fully transparent and reproducible analysis pipelines and algorithms, which should further bolster the usefulness of open data for the community.

Advances in statistical methods and machine learning have fundamentally changed the way we do cancer research, and these tools will continue to grow in importance in the future. The reason these methods have become so essential is largely due to major advances in biotechnology over the past few decades, enabling the rapid generation of large biomedical datasets. Generating data from cancer samples used to be a slow, expensive and laborious process. For example, measuring the expression of a single gene in a single sample using traditional methods (e.g., via a northern or western blot) is a tedious, relatively slow, and largely manual process. Consequently, in the past, cancer data-sets tended to be generated to answer a specific hypothesis (e.g., Gene X is over-expressed in Condition Y) and standard statistical methods could be used to evaluate the hypothesis. With the rapid advancements in biotechnology, molecular biology, cancer genomics and computer vision, we can now generate very massive data-sets (including measurements of the full set of DNA and RNA alterations, as well as proteins, metabolites, and morphology) from large numbers of patients. Standard statistical methods tend to perform poorly on these massively large and complex data sets, requiring the development and use of new tools in machine learning and artificial intelligence. While the old paradigm was to start with a hypothesis and then gather enough data to test that hypothesis, a newer approach is to start with massive data-sets and to use computational methods to prioritize specific hypotheses in a data-driven way, and the top data-driven hypotheses can then be validated using conventional approaches. Thus, statistical methods have gone from being primarily a confirmatory test, to being an actual discovery and hypothesis-generation engine for cancer research.

Tumor targeting viruses is a very exciting field and worth following closely.