Leveraging the new with the old: providing a framework for the integration of historic microarray studies with next generation sequencing

[摘要] Next Generation Sequencing (NGS) methods are rapidly providing remarkable advances in our ability to study the molecular profiles of human cancers. However, the scientific discovery offered by NGS also includes challenges concerning the interpretation of large and non-trivial experimental results. This task is potentially further complicated when a multitude of molecular profiling modalities are available, with the goal of a more integrative and comprehensive analysis of the cancer biology.Microarray transcriptome analyses have resulted in important advances in both the scientific and clinical domains of biomedicine. Importantly, as technology advances, it is critical to leverage what has been gained from historic approaches (e.g., microarrays) with new approaches (NGS). In this regard, necessity dictated a need to utilize and leverage the many years of historical microarray data with new NGS approaches. This is especially important since NGS approaches are now entering clinical medicine. For instance, NGS-based comprehensive analysis of certain cancers has already helped to uncover specific mutations that contribute to the malignant process, identify new therapeutic targets, and improve opportunities for choosing the best treatment for an individual patient.A suite of custom software tools have been developed to rapidly integrate, explore, discover and validate molecular profiling data from the NGS modalities of Whole Exome Sequencing (WES) and RNA-seq with each other, as well as with historical microarray and salient clinical datasets. Importantly, our approach is independent of any particular type of NGS suite(s) or cancer types. This novel bioinformatic framework is now assisting with the scientific and clinical management of patients with multiple myeloma.

[发布日期] 2014-10-21

[关键词] Multiple Myeloma;Next Generation Sequencing;Cancer Biology;Whole Exome Sequencing;Next Generation Sequencing Approach