Identification of structural features in chemicals associated with cancer drug response: a systematic data-driven analysis
Research output: Contribution to journal › Journal article › Research › peer-review
Standard
Identification of structural features in chemicals associated with cancer drug response : a systematic data-driven analysis. / Khan, Suleiman A; Virtanen, Seppo; Kallioniemi, Olli P; Wennerberg, Krister; Poso, Antti; Kaski, Samuel.
In: Bioinformatics (Online), Vol. 30, No. 17, 01.09.2014, p. i497-504.Research output: Contribution to journal › Journal article › Research › peer-review
Harvard
APA
Vancouver
Author
Bibtex
}
RIS
TY - JOUR
T1 - Identification of structural features in chemicals associated with cancer drug response
T2 - a systematic data-driven analysis
AU - Khan, Suleiman A
AU - Virtanen, Seppo
AU - Kallioniemi, Olli P
AU - Wennerberg, Krister
AU - Poso, Antti
AU - Kaski, Samuel
N1 - © The Author 2014. Published by Oxford University Press.
PY - 2014/9/1
Y1 - 2014/9/1
N2 - MOTIVATION: Analysis of relationships of drug structure to biological response is key to understanding off-target and unexpected drug effects, and for developing hypotheses on how to tailor drug therapies. New methods are required for integrated analyses of a large number of chemical features of drugs against the corresponding genome-wide responses of multiple cell models.RESULTS: In this article, we present the first comprehensive multi-set analysis on how the chemical structure of drugs impacts on genome-wide gene expression across several cancer cell lines [Connectivity Map (CMap) database]. The task is formulated as searching for drug response components across multiple cancers to reveal shared effects of drugs and the chemical features that may be responsible. The components can be computed with an extension of a recent approach called Group Factor Analysis. We identify 11 components that link the structural descriptors of drugs with specific gene expression responses observed in the three cell lines and identify structural groups that may be responsible for the responses. Our method quantitatively outperforms the limited earlier methods on CMap and identifies both the previously reported associations and several interesting novel findings, by taking into account multiple cell lines and advanced 3D structural descriptors. The novel observations include: previously unknown similarities in the effects induced by 15-delta prostaglandin J2 and HSP90 inhibitors, which are linked to the 3D descriptors of the drugs; and the induction by simvastatin of leukemia-specific response, resembling the effects of corticosteroids.AVAILABILITY AND IMPLEMENTATION: Source Code implementing the method is available at: http://research.ics.aalto.fi/mi/software/GFAsparse.SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.
AB - MOTIVATION: Analysis of relationships of drug structure to biological response is key to understanding off-target and unexpected drug effects, and for developing hypotheses on how to tailor drug therapies. New methods are required for integrated analyses of a large number of chemical features of drugs against the corresponding genome-wide responses of multiple cell models.RESULTS: In this article, we present the first comprehensive multi-set analysis on how the chemical structure of drugs impacts on genome-wide gene expression across several cancer cell lines [Connectivity Map (CMap) database]. The task is formulated as searching for drug response components across multiple cancers to reveal shared effects of drugs and the chemical features that may be responsible. The components can be computed with an extension of a recent approach called Group Factor Analysis. We identify 11 components that link the structural descriptors of drugs with specific gene expression responses observed in the three cell lines and identify structural groups that may be responsible for the responses. Our method quantitatively outperforms the limited earlier methods on CMap and identifies both the previously reported associations and several interesting novel findings, by taking into account multiple cell lines and advanced 3D structural descriptors. The novel observations include: previously unknown similarities in the effects induced by 15-delta prostaglandin J2 and HSP90 inhibitors, which are linked to the 3D descriptors of the drugs; and the induction by simvastatin of leukemia-specific response, resembling the effects of corticosteroids.AVAILABILITY AND IMPLEMENTATION: Source Code implementing the method is available at: http://research.ics.aalto.fi/mi/software/GFAsparse.SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.
KW - Antineoplastic Agents/chemistry
KW - Bayes Theorem
KW - Cell Line, Tumor
KW - Gene Expression/drug effects
KW - Humans
KW - Neoplasms/genetics
KW - Structure-Activity Relationship
U2 - 10.1093/bioinformatics/btu456
DO - 10.1093/bioinformatics/btu456
M3 - Journal article
C2 - 25161239
VL - 30
SP - i497-504
JO - Bioinformatics (Online)
JF - Bioinformatics (Online)
SN - 1367-4811
IS - 17
ER -
ID: 199429674