TY - JOUR

T1 - An integrative approach for building personalized gene regulatory networks for precision medicine

AU - van der Wijst, Monique G. P.

AU - de Vries, Dylan H.

AU - Brugge, Harm

AU - Westra, Harm-Jan

AU - Franke, Lude

PY - 2018/12/19

Y1 - 2018/12/19

N2 - Only a small fraction of patients respond to the drug prescribed to treat their disease, which means that most are at risk of unnecessary exposure to side effects through ineffective drugs. This inter-individual variation in drug response is driven by differences in gene interactions caused by each patient's genetic background, environmental exposures, and the proportions of specific cell types involved in disease. These gene interactions can now be captured by building gene regulatory networks, by taking advantage of RNA velocity (the time derivative of the gene expression state), the ability to study hundreds of thousands of cells simultaneously, and the falling price of single-cell sequencing. Here, we propose an integrative approach that leverages these recent advances in single-cell data with the sensitivity of bulk data to enable the reconstruction of personalized, cell-type- and context-specific gene regulatory networks. We expect this approach will allow the prioritization of key driver genes for specific diseases and will provide knowledge that opens new avenues towards improved personalized healthcare.

AB - Only a small fraction of patients respond to the drug prescribed to treat their disease, which means that most are at risk of unnecessary exposure to side effects through ineffective drugs. This inter-individual variation in drug response is driven by differences in gene interactions caused by each patient's genetic background, environmental exposures, and the proportions of specific cell types involved in disease. These gene interactions can now be captured by building gene regulatory networks, by taking advantage of RNA velocity (the time derivative of the gene expression state), the ability to study hundreds of thousands of cells simultaneously, and the falling price of single-cell sequencing. Here, we propose an integrative approach that leverages these recent advances in single-cell data with the sensitivity of bulk data to enable the reconstruction of personalized, cell-type- and context-specific gene regulatory networks. We expect this approach will allow the prioritization of key driver genes for specific diseases and will provide knowledge that opens new avenues towards improved personalized healthcare.

KW - SINGLE-CELL

KW - RNA-SEQ

KW - EXPRESSION

KW - INFERENCE

KW - TIME

KW - VARIANTS

KW - IDENTIFICATION

KW - CIRCUITS

KW - TRANSCRIPTION

KW - ENCYCLOPEDIA

U2 - 10.1186/s13073-018-0608-4

DO - 10.1186/s13073-018-0608-4

M3 - Article

VL - 10

JO - Genome medicine

JF - Genome medicine

SN - 1756-994X

M1 - 96

ER -