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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

Examples of drug candidates belonging to different pharmacological classes. (a) Kaplan-Meier curves of patients with DRS >0 and DRS <0 for etoposide. (b) Kaplan-Meier curves of patients with DRS >0 and DRS <0 for methotrexate. (c) Kaplan-Meier curves of patients with DRS >0 and DRS <0 for levonorgestrel. (d) Kaplan-Meier curves of patients with DRS >0 and DRS <0 for wortmannin. (e) Kaplan-Meier curves of patients with DRS >0 and DRS <0 for LY-294002. (f) Kaplan-Meier curves of patients with DRS >0 and DRS <0 for sirolimus.
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fig03: Examples of drug candidates belonging to different pharmacological classes. (a) Kaplan-Meier curves of patients with DRS >0 and DRS <0 for etoposide. (b) Kaplan-Meier curves of patients with DRS >0 and DRS <0 for methotrexate. (c) Kaplan-Meier curves of patients with DRS >0 and DRS <0 for levonorgestrel. (d) Kaplan-Meier curves of patients with DRS >0 and DRS <0 for wortmannin. (e) Kaplan-Meier curves of patients with DRS >0 and DRS <0 for LY-294002. (f) Kaplan-Meier curves of patients with DRS >0 and DRS <0 for sirolimus.

Mentions: Moreover, we identified chemotherapy drugs in our analysis including etoposide and methotrexate (Figure3a,b). These results suggest that our analysis was able to identify known anticancer agents, thus validating our results.25,26 Second, we identified lovastatin and levonorgestrel, both of which are currently undergoing clinical trials for the treatment of breast and ovarian cancer, respectively (ClinicalTrials.gov Identifiers: NCT00285857 (lovastatin), NCT00445887 (levonorgestrel)) (Supplementary Chart S1,Figure3c).27


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)

Examples of drug candidates belonging to different pharmacological classes. (a) Kaplan-Meier curves of patients with DRS >0 and DRS <0 for etoposide. (b) Kaplan-Meier curves of patients with DRS >0 and DRS <0 for methotrexate. (c) Kaplan-Meier curves of patients with DRS >0 and DRS <0 for levonorgestrel. (d) Kaplan-Meier curves of patients with DRS >0 and DRS <0 for wortmannin. (e) Kaplan-Meier curves of patients with DRS >0 and DRS <0 for LY-294002. (f) Kaplan-Meier curves of patients with DRS >0 and DRS <0 for sirolimus.
© Copyright Policy - open-access
Related In: Results  -  Collection

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

fig03: Examples of drug candidates belonging to different pharmacological classes. (a) Kaplan-Meier curves of patients with DRS >0 and DRS <0 for etoposide. (b) Kaplan-Meier curves of patients with DRS >0 and DRS <0 for methotrexate. (c) Kaplan-Meier curves of patients with DRS >0 and DRS <0 for levonorgestrel. (d) Kaplan-Meier curves of patients with DRS >0 and DRS <0 for wortmannin. (e) Kaplan-Meier curves of patients with DRS >0 and DRS <0 for LY-294002. (f) Kaplan-Meier curves of patients with DRS >0 and DRS <0 for sirolimus.
Mentions: Moreover, we identified chemotherapy drugs in our analysis including etoposide and methotrexate (Figure3a,b). These results suggest that our analysis was able to identify known anticancer agents, thus validating our results.25,26 Second, we identified lovastatin and levonorgestrel, both of which are currently undergoing clinical trials for the treatment of breast and ovarian cancer, respectively (ClinicalTrials.gov Identifiers: NCT00285857 (lovastatin), NCT00445887 (levonorgestrel)) (Supplementary Chart S1,Figure3c).27

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