The field of cancer research is now generating vast amounts of data from a variety of high throughput techniques and these have helped to define cancers based on their genetic foundations. As this knowledge on the processes and underlying genetics of cancer improve, these should be factored back into the research and analyses conducted by other researchers. Managing this volume of data, often conflicting, is becoming increasingly challenging for researchers. This work demonstrates an innovative application of argumentation theory within cancer research by providing a framework to accommodate missing data, address critical questions and generate hypotheses. The prototype system has been validated to demonstrate it identifies the same interesting interactions and molecules as researchers, even when certain key data was deliberately withheld from the system.