Review of applications of high-throughput sequencing in personalized medicine: barriers and facilitators of future progress in research and clinical application

Abstract

There has been an exponential growth in the performance and output of sequencing technologies (omics data) with full genome sequencing now producing gigabases of reads on a daily basis. These data may hold the promise of personalized medicine, leading to routinely available sequencing tests that can guide patient treatment decisions. In the era of high-throughput sequencing (HTS), computational considerations, data governance and clinical translation are the greatest rate-limiting steps. To ensure that the analysis, management and interpretation of such extensive omics data is exploited to its full potential, key factors, including sample sourcing, technology selection and computational expertise and resources, need to be considered, leading to an integrated set of high-performance tools and systems. This article provides an up-to-date overview of the evolution of HTS and the accompanying tools, infrastructure and data management approaches that are emerging in this space, which, if used within in a multidisciplinary context, may ultimately facilitate the development of personalized medicine.

Microfluidics as an Enabling Technology for Personalized Cancer Therapy

“…Already in the 1950's medical oncologists performed studies on the correlation between drug responses of cancer patients in vivo with that of corresponding tumor biopsies in tissue culture models . Over the years many more approaches to personalized medicine have been established, ranging from purely sequencing‐based methods to the use of patient‐derived xenograft mouse models that can be used as avatars to test different treatment options . However, so far only very few approaches have made it into routine clinical use.…”

Knowledge-guided analysis of "omics" data using the KnowEnG cloud platform

“…Whereas the common tools for gene expression clustering focus on the task of grouping genes, the KnowEnG Sample Clustering pipeline is geared toward finding groups of samples/conditions that have similar expression profiles. This distinction is crucial to its use of a knowledge network to guide the clustering, lends it a complementary strength, and is expected to be of increasing utility in the future as the practice of profiling tissue samples from individuals grows more popular [91]. The most common uses of sample clustering are in identifying subgroups in cancer patients, based on transcriptomic as well as other omics data sets, e.g., identifying breast cancer subgroups from copy number variations [92], colon cancer subgroups from gene expression data [93], refinement of breast cancer subtypes based on microRNA expression profiles [94], subtyping of different cancers from somatic mutation data [95], to name a few.…”

Mapping short reads, faithfully

“…High throughput DNA sequencing is now a well-established technology with countless applications in industry and medicine [1]. The Illumina short-read technology currently dominates the market of DNA sequencing over newer technologies that promise longer reads at the expense of higher error rates.…”