Targeted Therapy Database (TTD): a model to match patient's molecular profile with current knowledge on cancer biology.

<h4>Background</h4>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...

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Autores principales:	Simone Mocellin, Jeff Shrager, Richard Scolyer, Sandro Pasquali, Daunia Verdi, Francesco M Marincola, Marta Briarava, Randy Gobbel, Carlo Rossi, Donato Nitti
Formato:	article
Lenguaje:	EN
Publicado:	Public Library of Science (PLoS) 2010
Materias:	Medicine R Science Q
Acceso en línea:	https://doaj.org/article/11ba86b201f247b29f5a7d97c8a94420
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spelling	oai:doaj.org-article:11ba86b201f247b29f5a7d97c8a944202021-11-18T06:36:14ZTargeted Therapy Database (TTD): a model to match patient's molecular profile with current knowledge on cancer biology.1932-620310.1371/journal.pone.0011965https://doaj.org/article/11ba86b201f247b29f5a7d97c8a944202010-08-01T00:00:00Zhttps://www.ncbi.nlm.nih.gov/pmc/articles/pmid/20706624/?tool=EBIhttps://doaj.org/toc/1932-6203<h4>Background</h4>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.<h4>Objective</h4>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.<h4>Methods</h4>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.<h4>Results and conclusions</h4>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.Simone MocellinJeff ShragerRichard ScolyerSandro PasqualiDaunia VerdiFrancesco M MarincolaMarta BriaravaRandy GobbelCarlo RossiDonato NittiPublic Library of Science (PLoS)articleMedicineRScienceQENPLoS ONE, Vol 5, Iss 8, p e11965 (2010)
institution	DOAJ
collection	DOAJ
language	EN
topic	Medicine R Science Q
spellingShingle	Medicine R Science Q Simone Mocellin Jeff Shrager Richard Scolyer Sandro Pasquali Daunia Verdi Francesco M Marincola Marta Briarava Randy Gobbel Carlo Rossi Donato Nitti Targeted Therapy Database (TTD): a model to match patient's molecular profile with current knowledge on cancer biology.
description	<h4>Background</h4>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.<h4>Objective</h4>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.<h4>Methods</h4>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.<h4>Results and conclusions</h4>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.
format	article
author	Simone Mocellin Jeff Shrager Richard Scolyer Sandro Pasquali Daunia Verdi Francesco M Marincola Marta Briarava Randy Gobbel Carlo Rossi Donato Nitti
author_facet	Simone Mocellin Jeff Shrager Richard Scolyer Sandro Pasquali Daunia Verdi Francesco M Marincola Marta Briarava Randy Gobbel Carlo Rossi Donato Nitti
author_sort	Simone Mocellin
title	Targeted Therapy Database (TTD): a model to match patient's molecular profile with current knowledge on cancer biology.
title_short	Targeted Therapy Database (TTD): a model to match patient's molecular profile with current knowledge on cancer biology.
title_full	Targeted Therapy Database (TTD): a model to match patient's molecular profile with current knowledge on cancer biology.
title_fullStr	Targeted Therapy Database (TTD): a model to match patient's molecular profile with current knowledge on cancer biology.
title_full_unstemmed	Targeted Therapy Database (TTD): a model to match patient's molecular profile with current knowledge on cancer biology.
title_sort	targeted therapy database (ttd): a model to match patient's molecular profile with current knowledge on cancer biology.
publisher	Public Library of Science (PLoS)
publishDate	2010
url	https://doaj.org/article/11ba86b201f247b29f5a7d97c8a94420
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Targeted Therapy Database (TTD): a model to match patient's molecular profile with current knowledge on cancer biology.

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