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IDEA: Integrated Drug Expression Analysis-Integration of Gene Expression and Clinical Data for the Identification of Therapeutic Candidates.

Ung MH, Varn FS, Cheng C - CPT Pharmacometrics Syst Pharmacol (2015)

Bottom Line: Cancer drug discovery is an involved process spanning efforts from several fields of study and typically requires years of research and development.However, the advent of high-throughput genomic technologies has allowed for the use of in silico, genomics-based methods to screen drug libraries and accelerate drug discovery.We were able to predict drug candidates that have been known and those that have not been known in the literature to exhibit anticancer effects.

View Article: PubMed Central - PubMed

Affiliation: Department of Genetics, Geisel School of Medicine at Dartmouth Hanover, New Hampshire, USA.

ABSTRACT
Cancer drug discovery is an involved process spanning efforts from several fields of study and typically requires years of research and development. However, the advent of high-throughput genomic technologies has allowed for the use of in silico, genomics-based methods to screen drug libraries and accelerate drug discovery. Here we present a novel approach to computationally identify drug candidates for the treatment of breast cancer. In particular, we developed a Drug Regulatory Score similarity metric to evaluate gene expression profile similarity, in the context of drug treatment, and incorporated time-to-event patient survival information to develop an integrated analysis pipeline: Integrated Drug Expression Analysis (IDEA). We were able to predict drug candidates that have been known and those that have not been known in the literature to exhibit anticancer effects. Overall, our method enables quick preclinical screening of drug candidates for breast cancer and other diseases by using the most important indicator of drug efficacy: survival.

No MeSH data available.


Related in: MedlinePlus

Overview of IDEA. Treatment and control DTPs derived from MCF-7 cell lines were downloaded from CMap and combined to construct a DTP. DTPs were then compared against GEPs (GEP) of each patient from the breast cancer dataset of interest to generate a DRS. This resulted in a DRS profile for each drug. Each DRS profile was then used as the covariate(s) in Cox proportional hazards models. All drugs with DRS profiles that were significantly associated with patient survival were considered potential therapeutic candidates.
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fig01: Overview of IDEA. Treatment and control DTPs derived from MCF-7 cell lines were downloaded from CMap and combined to construct a DTP. DTPs were then compared against GEPs (GEP) of each patient from the breast cancer dataset of interest to generate a DRS. This resulted in a DRS profile for each drug. Each DRS profile was then used as the covariate(s) in Cox proportional hazards models. All drugs with DRS profiles that were significantly associated with patient survival were considered potential therapeutic candidates.

Mentions: Our computational pipeline begins by first calculating a DRS between a DTP and a tumor GEP for each drug-tumor pair; a high DRS indicates that the baseline GEP of a tumor sample closely reflects the DTP that is induced by a drug, and vice versa for a low DRS (Figure1). A Cox proportional hazards model was then fitted to the DRS profile (DRS across all patient tumors) of each drug to evaluate its association with disease-specific patient survival (Figure1).18 If the DRS profile of a drug was significantly correlated with patient survival, we considered the drug to be a potential therapeutic candidate. Moreover, to underscore the application of our methodology to precision medicine, we modified our models to identify candidates in several molecular subtypes of breast cancer.


IDEA: Integrated Drug Expression Analysis-Integration of Gene Expression and Clinical Data for the Identification of Therapeutic Candidates.

Ung MH, Varn FS, Cheng C - CPT Pharmacometrics Syst Pharmacol (2015)

Overview of IDEA. Treatment and control DTPs derived from MCF-7 cell lines were downloaded from CMap and combined to construct a DTP. DTPs were then compared against GEPs (GEP) of each patient from the breast cancer dataset of interest to generate a DRS. This resulted in a DRS profile for each drug. Each DRS profile was then used as the covariate(s) in Cox proportional hazards models. All drugs with DRS profiles that were significantly associated with patient survival were considered potential therapeutic candidates.
© Copyright Policy - open-access
Related In: Results  -  Collection

License
Show All Figures
getmorefigures.php?uid=PMC4544055&req=5

fig01: Overview of IDEA. Treatment and control DTPs derived from MCF-7 cell lines were downloaded from CMap and combined to construct a DTP. DTPs were then compared against GEPs (GEP) of each patient from the breast cancer dataset of interest to generate a DRS. This resulted in a DRS profile for each drug. Each DRS profile was then used as the covariate(s) in Cox proportional hazards models. All drugs with DRS profiles that were significantly associated with patient survival were considered potential therapeutic candidates.
Mentions: Our computational pipeline begins by first calculating a DRS between a DTP and a tumor GEP for each drug-tumor pair; a high DRS indicates that the baseline GEP of a tumor sample closely reflects the DTP that is induced by a drug, and vice versa for a low DRS (Figure1). A Cox proportional hazards model was then fitted to the DRS profile (DRS across all patient tumors) of each drug to evaluate its association with disease-specific patient survival (Figure1).18 If the DRS profile of a drug was significantly correlated with patient survival, we considered the drug to be a potential therapeutic candidate. Moreover, to underscore the application of our methodology to precision medicine, we modified our models to identify candidates in several molecular subtypes of breast cancer.

Bottom Line: Cancer drug discovery is an involved process spanning efforts from several fields of study and typically requires years of research and development.However, the advent of high-throughput genomic technologies has allowed for the use of in silico, genomics-based methods to screen drug libraries and accelerate drug discovery.We were able to predict drug candidates that have been known and those that have not been known in the literature to exhibit anticancer effects.

View Article: PubMed Central - PubMed

Affiliation: Department of Genetics, Geisel School of Medicine at Dartmouth Hanover, New Hampshire, USA.

ABSTRACT
Cancer drug discovery is an involved process spanning efforts from several fields of study and typically requires years of research and development. However, the advent of high-throughput genomic technologies has allowed for the use of in silico, genomics-based methods to screen drug libraries and accelerate drug discovery. Here we present a novel approach to computationally identify drug candidates for the treatment of breast cancer. In particular, we developed a Drug Regulatory Score similarity metric to evaluate gene expression profile similarity, in the context of drug treatment, and incorporated time-to-event patient survival information to develop an integrated analysis pipeline: Integrated Drug Expression Analysis (IDEA). We were able to predict drug candidates that have been known and those that have not been known in the literature to exhibit anticancer effects. Overall, our method enables quick preclinical screening of drug candidates for breast cancer and other diseases by using the most important indicator of drug efficacy: survival.

No MeSH data available.


Related in: MedlinePlus