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Drug combination therapy is a main pillar of cancer therapy but the formation of an effective combinatorial standard of care (SOC) can take many years and its length of development is increasing with complexity of treatment. In this paper, we develop a path to extend the boundaries of complexity in combinatorial cancer treatments using text data mining (TDM). We first use TDM to characterize the current boundaries of cancer treatment complexity and find that the current complexity limit for clinical trials is 6 drugs per plan and for pre-clinical research is 10. We then present a TDM based assistive technology, cancer plan builder (CPB), which we make publicly available and allows experts to create literature-anchored high complexity combination treatment (HCCT) plans of significantly larger size. We develop metrics to evaluate HCCT plans and show that experts using CPB are able to create HCCT plans at much greater speed and quality, compared to experts without CPB. We hope that by releasing CPB we enable more researchers to engage with HCCT planning and demonstrate its clinical efficacy.

As knowledgebases become increasingly important for structuring vast amounts of scientific knowledge and making it accessible to researchers, their construction entails expensive multi-year projects involving teams of bio-curators, computer scientists, or both. This restricts the coverage of existing knowledgebases to a limited set of popular topics, leaving a long tail of more specialized interests uncovered.We present a methodology and a supporting tool to allow individual researchers or small teams, without background in bio-curation or computer science, to mine the scientific literature and construct ad-hoc, personalized, and literature-anchored knowledgebases, that are tailored around their specific research interests and support their scientific goals. The time investment involved in creating a knowledgebase ranges from a few hours to a few weeks, depending on the desired coverage and accuracy.We demonstrate the methodology by constructing knowledgebases for different purposes: a high-level overview of challenges and controversies in a field (the cancer frontiers knowledgebase); a mapping of main concepts and interactions in a field, to support lab-internal hypothesis generation (tissue engineering and regeneration, cancer surgery and radiotherapy knowledgebases); and a comprehensive and accurate knowledgebase designated as an online up-to-date resource for the wider research community (the cell specific drug delivery knowledgebase). In each case we show how the structured knowledgebase, coupled with effective visualizations, facilitates effective data exploration, hypothesis generation and meta-analysis.We implement the method as part of an open source web-based platform for knowledgebase construction, available publicly and freely at https://spike-kbc.apps.allenai.org.