Integrative analysis for identifying joint modular patterns of gene-expression and drug-response data

Bioinformatics
Jinyu Chen, Shihua Zhang

Abstract

The underlying relationship between genomic factors and the response of diverse cancer drugs still remains unclear. A number of studies showed that the heterogeneous responses to anticancer treatments of patients were partly associated with their specific changes in gene expression and somatic alterations. The emerging large-scale pharmacogenomic data provide us valuable opportunities to improve existing therapies or to guide early-phase clinical trials of compounds under development. However, how to identify the underlying combinatorial patterns among pharmacogenomics data are still a challenging issue. In this study, we adopted a sparse network-regularized partial least square (SNPLS) method to identify joint modular patterns using large-scale pairwise gene-expression and drug-response data. We incorporated a molecular network to the (sparse) partial least square model to improve the module accuracy via a network-based penalty. We first demonstrated the effectiveness of SNPLS using a set of simulation data and compared it with two typical methods. Further, we applied it to gene expression profiles for 13 321 genes and pharmacological profiles for 98 anticancer drugs across 641 cancer cell lines consisting of diverse types of ...Continue Reading

References

Mar 17, 2000·Science·J Drews
Feb 19, 2002·Journal of Human Hypertension·T Stanton, J L Reid
Sep 5, 2002·Nature Reviews. Drug Discovery·Andrew L Hopkins, Colin R Groom
Oct 3, 2002·Proceedings of the National Academy of Sciences of the United States of America·P T Ravi RajagopalanGordon G Hammes
Feb 18, 2003·The American Journal of the Medical Sciences·Syed U BokhariWilliam C Duckworth
Nov 13, 2003·Seminars in Oncology·Lee F AllenMark B Meyer
Mar 3, 2004·Nature Reviews. Cancer·P Andrew FutrealMichael R Stratton
Apr 6, 2005·Trends in Pharmacological Sciences·Péter CsermelySándor Pongor
Dec 31, 2005·Nucleic Acids Research·David S WishartJennifer Woolsey
Jun 15, 2006·Briefings in Bioinformatics·Anne-Laure Boulesteix, Korbinian Strimmer
Jul 15, 2006·Nature Biotechnology·Gaia V PaoliniAndrew L Hopkins
Sep 23, 2006·Nature Reviews. Cancer·Robert H Shoemaker
Nov 25, 2006·Hematology·Miguel A Sanz
Dec 1, 2007·Nucleic Acids Research·David S WishartMurtaza Hassanali
Oct 22, 2008·Nature Chemical Biology·Andrew L Hopkins
Dec 4, 2008·Statistical Applications in Genetics and Molecular Biology·Kim-Anh Lê CaoPhilippe Besse
Jan 10, 2009·Nature Protocols·Da Wei HuangRichard A Lempicki
Feb 19, 2009·Bioinformatics·Shuangge Ma, Michael R Kosorok
Dec 24, 2009·Nature Reviews. Cancer·Jay S Desgrosellier, David A Cheresh
Jan 29, 2010·Journal of the Royal Statistical Society. Series B, Statistical Methodology·Hyonho Chun, Sündüz Keleş
Aug 7, 2010·Database : the Journal of Biological Databases and Curation·Marilyn SafranDoron Lancet
Nov 6, 2010·Seminars in Cutaneous Medicine and Surgery·Igor Puzanov, Keith T Flaherty
Nov 10, 2010·Nucleic Acids Research·Craig KnoxDavid S Wishart
Nov 13, 2010·Nucleic Acids Research·Ethan G CeramiChris Sander
Aug 25, 2011·Trends in Pharmacological Sciences·Nadia M PenrodJason H Moore
Jan 6, 2012·PLoS Computational Biology·Xing-Ming ZhaoPeer Bork
Jan 31, 2012·Cancer Research·J Chad BrennerArul M Chinnaiyan
Apr 25, 2012·Radiation Oncology·Katsutoshi MiuraMasato Hareyama
Jul 7, 2012·PloS One·Jin-Jian LuYi-Tao Wang
Aug 7, 2012·Bioinformatics·Wenyuan LiXianghong Jasmine Zhou
Aug 11, 2012·Nucleic Acids Research·Shihua ZhangXianghong Jasmine Zhou
Oct 2, 2012·The New England Journal of Medicine·Keith T FlahertyJeffrey Weber
Nov 20, 2012·Genetic Epidemiology·Jin LiuShuangge Ma
Jan 1, 2013·Expert Review of Clinical Pharmacology·A Srinivas Reddy, Shuxing Zhang
Jan 24, 2013·Drug Discovery Today·José L Medina-FrancoRichard A Houghten
Aug 24, 2013·Molecular Cancer Therapeutics·Hae-June LeeSam S Yoon

❮ Previous
Next ❯

Citations

Jan 10, 2017·Scientific Reports·Zachary StanfieldMehmet Koyutürk
May 15, 2018·Expert Review of Proteomics·Hassan DihaziOliver Valerius
Nov 18, 2018·Briefings in Bioinformatics·Qinjie ChuChu-Yu Ye
Sep 21, 2019·IET Systems Biology·Yunda HaoLimin Li
Jun 28, 2020·Briefings in Bioinformatics·Jie HuangHongmin Cai
Oct 1, 2019·Briefings in Bioinformatics·Pingjian DingChee-Keong Kwoh
Jun 14, 2019·Pharmacogenomics·Claire-Cécile BarrotNicolas Picard
Sep 10, 2019·Frontiers in Genetics·Qianqian ShiChuanchao Zhang
Aug 14, 2020·Scientific Reports·Sayantan MitraMohammed Hasanuzzaman
May 14, 2019·Frontiers in Genetics·Hui TangLuonan Chen
Jun 19, 2018·Frontiers in Genetics·Jinyu Chen, Shihua Zhang
Jul 4, 2017·Frontiers in Genetics·Sijia HuangLana X Garmire
Mar 9, 2021·Technology and Health Care : Official Journal of the European Society for Engineering and Medicine·Zhiming ZhouYong Liang
Aug 19, 2021·Journal of Molecular Cell Biology·Cheng Hu, Weiping Jia

❮ Previous
Next ❯

Related Concepts

Related Feeds

Cancer Genomics (Keystone)

Cancer genomics approaches employ high-throughput technologies to identify the complete catalog of somatic alterations that characterize the genome, transcriptome and epigenome of cohorts of tumor samples. Discover the latest research using such technologies in this feed.