Targeted Therapy Database (TTD): A Model to Match Patient's Molecular Profile with Current Knowledge on Cancer Biology

Abstract

BackgroundThe 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.ObjectiveTo 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.MethodsTo 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.Results and ConclusionsWe 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.

Cancer Resistance to Type II Topoisomerase Inhibitors

“…In this regard, highthroughput technologies should provide us with precious information at the genome [77] and proteome [78] level, and ultimately define a molecular profile associated (with high accuracy) to drug resistance or sensitivity on an individual patient basis: some results in this direction have been already published for a variety of chemotherapeutics (including TOPO2 poisons [79][80][81][82]), but no validation on the clinical setting is yet available. A different approach has been proposed that implies the collection of the already available evidence (which includes thousands of scientific articles published in hundreds of Journals) and its synthesis through dedicated computational algorithms [83]: this investigational method, whose reliability still needs to be proven on the clinical ground, would allow to match the patient/tumor molecular profile with the available evidence and ultimately calculate the probability of response to a given drug.…”

Adding Individual Patient Case Data to The Melanoma Targeted Therapy Advisor

“…The Personal Targeted Therapy Analyzer adds individualized case data into the Targeted Therapy Database and Analyzer (TTD/TTA) [11], thus significantly extending their utility by enabling them to be kept up-to-date much more rapidly than is possible if one waited for cases to appear in the literature (many of which will never make it there). This approach can be applied to diseases other than melanoma.…”

“…The profile and treatment content is used to create a report ranking potential treatments and tests through the TTA algorithm [11]. The report includes relevant literature and other statistical information that may be useful to a physician in choosing further tests and possible treatments.…”

“…The Data Layer (PTTA database, or PTTD) extends the TTD [11] to include patient profiling and treatment outcomes (Table 1, Figure 1 (points 1 and 5), see [11] for more details). In Table 1, Molecule represents the gene or gene product under investigation.…”

Complex Regulatory Network of MicroRNAs, Transcription Factors, Gene Alterations in Adrenocortical Cancer

“…org/geneweb/index.html) and NCBI SNP database that can be accessed online (http://www.ncbi.nlm.nih.gov/snp/) as well as pertinent literatures. We collected adrenocortical cancer-related genes from Targeted Therapy Database (Mocellin et al, 2010) and pertinent literatures including these genes that affect tumor growth, migration, radial therapy and clinical outcome of human adrenocortical cancer. Additionally, we extracted popular TFs by P-match method (Chekmenev et al, 2005).…”