In the last decade the study of cancer genomics has led to the remarkable revelation that cancer is a broad spectrum of diseases. In parallel, dramatic advances in our ability to design drugs against cancer has created an extensive toolbox of therapeutics. Use of these “targeted” drugs in the clinic has met with remarkable successes, but also disappointing failures. The answer to this mixed bag of results lies in the complexity of cancer. Since not all patient tumours are identical, some patients may respond to specific drugs, while others may not. The key therefore to enhancing efficacy of anti-cancer drugs is in matching the right patient to the right therapy. In other words, we need to know what tools to use to treat each individual patient's disease. Here we propose to develop qTAP, a transformative diagnostic platform that will provide a global view of an individual patient's tumour with the goal of identifying what types of drugs might be most effective at treating their cancer. In addition, we propose to develop novel classes of drugs that target understudied cancer-causing genes. By using qTAP we hope to inform what drugs and drug combinations will provide more effective cancer therapy and prevent relapse that is commonly observed in the clinic.