Targeted Therapy Database (TTD): a model to match patient's molecular profile with current knowledge on cancer biology.
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 in the clinical setting is in its infancy. In fact, despite the wealth of preclinical studies addressing these issues, the difficulty of testing each targeted therapy hypothesis in the clinical arena represents an intrinsic obstacle. As a consequence, we are witnessing a paradoxical situation where most hypotheses about the molecular and cellular biology of cancer remain clinically 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 to foster the development of personalized cancer treatment by matching the patient's molecular profile with the available evidence on targeted therapy.


METHODS
To this aim we focused on melanoma, an increasingly diagnosed malignancy for which the need for novel therapeutic approaches is paradigmatic since no effective treatment is available in the advanced setting. Relevant data were manually extracted from peer-reviewed full-text original articles describing any type of anti-melanoma targeted therapy tested in any type of experimental or clinical model. To this purpose, Medline, Embase, Cancerlit and the Cochrane databases were searched.


CONCLUSIONS
We created a manually annotated database (Targeted Therapy Database, TTD) where the relevant data are gathered in a formal representation that can be computationally analyzed. Dedicated algorithms were set up for the identification of the prevalent therapeutic hypotheses based on the available evidence and for ranking treatments based on the molecular profile of individual patients. In this essay we describe the principles and computational algorithms of an original method developed to fully exploit the available knowledge on cancer biology with the ultimate goal of fruitfully 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.
Mocellin
Simone
S
Clinica Chirurgica Generale 2, Department of Oncological and Surgical Sciences, University of Padova, Padova, Italy. simone.mocellin@unipd.it
Shrager
Jeff
J
Scolyer
Richard
R
Pasquali
Sandro
S
Verdi
Daunia
D
Marincola
Francesco M
FM
Briarava
Marta
M
Gobbel
Randy
R
Rossi
Carlo
C
Nitti
Donato
D
D019295:Computational Biology; D003198:Computer Simulation; D003625:Data Collection; D019008:Drug Resistance, Neoplasm; D019317:Evidence-Based Medicine; D006801:Humans; D008545:Melanoma / Q000188:drug therapy / Q000378:metabolism; D008954:Models, Biological; D009369:Neoplasms / Q000188:drug therapy* / Q000378:metabolism*; D057285:Precision Medicine