PanDrugs: a novel method to prioritize anticancer drug treatments according to individual genomic data

Abstract

BackgroundLarge-sequencing cancer genome projects have shown that tumors have thousands of molecular alterations and their frequency is highly heterogeneous. In such scenarios, physicians and oncologists routinely face lists of cancer genomic alterations where only a minority of them are relevant biomarkers to drive clinical decision-making. For this reason, the medical community agrees on the urgent need of methodologies to establish the relevance of tumor alterations, assisting in genomic profile interpretation, and, more importantly, to prioritize those that could be clinically actionable for cancer therapy.ResultsWe present PanDrugs, a new computational methodology to guide the selection of personalized treatments in cancer patients using the variant lists provided by genome-wide sequencing analyses. PanDrugs offers the largest database of drug-target associations available from well-known targeted therapies to preclinical drugs. Scoring data-driven gene cancer relevance and drug feasibility PanDrugs interprets genomic alterations and provides a prioritized evidence-based list of anticancer therapies. Our tool represents the first drug prescription strategy applying a rational based on pathway context, multi-gene markers impact and information provided by functional experiments. Our approach has been systematically applied to TCGA patients and successfully validated in a cancer case study with a xenograft mouse model demonstrating its utility.ConclusionsPanDrugs is a feasible method to identify potentially druggable molecular alterations and prioritize drugs to facilitate the interpretation of genomic landscape and clinical decision-making in cancer patients. Our approach expands the search of druggable genomic alterations from the concept of cancer driver genes to the druggable pathway context extending anticancer therapeutic options beyond already known cancer genes. The methodology is public and easily integratable with custom pipelines through its programmatic API or its docker image. The PanDrugs webtool is freely accessible at http://www.pandrugs.org.Electronic supplementary materialThe online version of this article (10.1186/s13073-018-0546-1) contains supplementary material, which is available to authorized users.

Personalized Cancer Therapy Prioritization Based on Driver Alteration Co-occurrence Patterns

“…The development of tools for personalized treatment prioritization based on genomic profiles is an active field of research. Recently Al-Shahrour and colleagues presented PanDrugs [80], an in silico drug prescription tool that uses genomic information, pathway context and pharmacological evidence to prioritize the drug therapies that are most suitable for individual tumor profiles. PanDrugs goes beyond the single-gene biomarker by taking into account the collective gene impact and pathway context of the oncogenic alterations identified in a given patient.…”

“…Due to the lack of systematic reporting of treatment history of the patients enrolled in genomic studies [23], it is difficult to match response to a drug with individual molecular profiles from clinical data. This practically impairs the systematic assessment of the prediction accuracy in patients for computational frameworks like TCT4U, PanDrugs [80], PANOPLY [82], iCAGES [83], or other in silico drug prescription tools such as the Cancer Genome Interpreter [14] or OncoKB [13]. Experimental validation of computational approaches is time-intensive and very expensive.…”

Atrial structural remodeling gene variants in patients with atrial fibrillation

“…For example, SNP on chromosome 4q25 associated with AF modulates response to antiarrhythmic therapy [56]. This work opens research directions to establish personalised therapies according to individual genomic data as in cancer patients [57].…”

Morphologic and Genomic Heterogeneity in the Evolution and Progression of Breast Cancer

“…UC San Diego Moores Cancer Centre uses the Foundation One panel and has matched 45% of BC patients to a 'personalised' therapy [145,146], however it should be noted that most of these matches were ERBB2 amplifications to HER2 therapies and the applicability of this panel outside of ERBB2 in breast cancer is uncertain. Increasing numbers of tools are emerging to facilitate the matching of alterations and therapies, including for example, PanDrugs [147], while the MD Anderson program [148] is feeding back 'sequence-drug' matching data into the public arena through their Precision Cancer Therapy interface.…”