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• W2887631801 abstract "Technology advancements have enhanced our abilities to gain greater insight to the tumor environment. However, such emergent methods have brought new considerations in data storage, access and analysis. Modern large data projects and clinical trial materials could be explored to a greater degree if appropriate infrastructure could be built to support efforts. Such a scaffold would be an integrated and dynamic framework where novel hypotheses could be investigated in modern big data collections. Placing discovery back in the hands of the researcher through a reactive and supportive framework will enhance our understanding of cancer aetiology. The Cancer Integromics Research Application Framework (CIRAFm) was created to emulate current platforms that have a rigid analytical interface. We designed a robust architecture that supports a reactive user-friendly interface blended to several cross-platform coding technologies. It has been developed to accommodate an individualised framework where we can create an ‘app-store9 of key software to fit research questions. In efforts to encapsulate and accelerate differing data types it sits astride of two NoSQL based database management systems, minimising data redundancy. Initial requirements have begun to create algorithms to investigate alignment-free applications on next generation sequencing (NGS) data enhancing analysis of spatial and temporal heterogeneity in cancer. Key drivers can be explored by assisting software to build correlative marker associations using Darwinian approaches such as genetic algorithms. Information requirements from external platforms are assisted through a novel domain specific language (DSL) to enable a singular interface. The modularised architecture of the platform is enabled through an Angular framework supported by interactive and dynamic data visualisation software, D3.js. Data analytics can be explored through the ‘apps9 created, investigating new markers in large data and enhancing our understanding of tumor heterogeneity. Alignment-free phylogenetics of NGS data harnesses our capabilities to display fully sequence evolution in patient data. This highlights possible sub-types, markers of interest and maps to therapeutic compounds via the DSL to our externally developed drug discovery software, QUADrATiC. Future scientific endeavours further defining new tumor subtypes are paramount in efforts to help uncover treatment strategies. Unburdened by legacy pipelines and data, flexible and robust models of architecture provide an effective and efficient framework for research on our ever increasing search for precision medicine. Citation Format: Darragh G. McArt, Seedevi Senevirathne, Aideen Roddy, Jessica Black, Alan Gilmore, Suneil Jain, Philip Dunne, David Waugh. Integrative analytics: A framework for precision medicine [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2018; 2018 Apr 14-18; Chicago, IL. Philadelphia (PA): AACR; Cancer Res 2018;78(13 Suppl):Abstract nr 290." @default.
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• W2887631801 date "2018-07-01" @default.
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• W2887631801 title "Abstract 290: Integrative analytics: A framework for precision medicine" @default.
• W2887631801 doi "https://doi.org/10.1158/1538-7445.am2018-290" @default.
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