Targeted therapy database (TTD): A model to match patient's molecular profile with current knowledge on cancer biology

Simone Mocellin, Jeff Shrager, Richard Scolyer, Sandro Pasquali, Daunia Verdi, Francesco M. Marincola, Marta Briarava, Randy Gobbel, Carlo Rossi, Donato Nitti

Research output: Contribution to journalArticlepeer-review


Background: The efficacy of current anticancer treatments is far from satisfactory and many patients still die of their disease.A general agreement exists on the urgency of developing molecularly targeted therapies, although their implementation inthe clinical setting is in its infancy. In fact, despite the wealth of preclinical studies addressing these issues, the difficulty oftesting each targeted therapy hypothesis in the clinical arena represents an intrinsic obstacle. As a consequence, we arewitnessing a paradoxical situation where most hypotheses about the molecular and cellular biology of cancer remainclinically untested and therefore do not translate into a therapeutic benefit for patients.Objective: To present a computational method aimed to comprehensively exploit the scientific knowledge in order tofoster the development of personalized cancer treatment by matching the patient's molecular profile with the availableevidence on targeted therapy.Methods: To this aim we focused on melanoma, an increasingly diagnosed malignancy for which the need for noveltherapeutic approaches is paradigmatic since no effective treatment is available in the advanced setting. Relevant data weremanually extracted from peer-reviewed full-text original articles describing any type of anti-melanoma targeted therapytested in any type of experimental or clinical model. To this purpose, Medline, Embase, Cancerlit and the Cochranedatabases were searched.Results and Conclusions: We created a manually annotated database (Targeted Therapy Database, TTD) where the relevantdata are gathered in a formal representation that can be computationally analyzed. Dedicated algorithms were set up forthe identification of the prevalent therapeutic hypotheses based on the available evidence and for ranking treatmentsbased on the molecular profile of individual patients. In this essay we describe the principles and computational algorithmsof an original method developed to fully exploit the available knowledge on cancer biology with the ultimate goal offruitfully driving both preclinical and clinical research on anticancer targeted therapy. In the light of its theoretical nature,the prediction performance of this model must be validated before it can be implemented in the clinical setting.

Original languageEnglish
Article numbere11965
JournalPLoS One
Issue number8
Publication statusPublished - 2010

ASJC Scopus subject areas

  • Agricultural and Biological Sciences(all)
  • Biochemistry, Genetics and Molecular Biology(all)
  • Medicine(all)


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