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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

Output of IDEA. (a) Distribution of hazard ratios and P-values for all drugs. Each point corresponds to a drug, with red points corresponding to drugs with P < 1E-5 and blue points corresponding to drugs with P > 1E-5. Drugs with HR <1 indicates that survival is correlated with increased similarity of the DTP with breast cancer GEPs. Drugs with HR >1 indicates that survival is anticorrelated with increased similarity of the DTP with breast cancer GEPs. (b) Kaplan-Meier plot of patients with DRS >0 and DRS <0 for alpha estradiol. Patients with high DRS exhibit significantly more favorable prognosis than patients with low DRS (P = 3E-20, Logrank test). (c) Kaplan-Meier plot of patients with DRS >0 and DRS <0 for scoulerine. Patients with low DRS exhibit significantly more favorable prognosis than patients with high DRS (P = 5E-13, Logrank test).
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fig02: Output of IDEA. (a) Distribution of hazard ratios and P-values for all drugs. Each point corresponds to a drug, with red points corresponding to drugs with P < 1E-5 and blue points corresponding to drugs with P > 1E-5. Drugs with HR <1 indicates that survival is correlated with increased similarity of the DTP with breast cancer GEPs. Drugs with HR >1 indicates that survival is anticorrelated with increased similarity of the DTP with breast cancer GEPs. (b) Kaplan-Meier plot of patients with DRS >0 and DRS <0 for alpha estradiol. Patients with high DRS exhibit significantly more favorable prognosis than patients with low DRS (P = 3E-20, Logrank test). (c) Kaplan-Meier plot of patients with DRS >0 and DRS <0 for scoulerine. Patients with low DRS exhibit significantly more favorable prognosis than patients with high DRS (P = 5E-13, Logrank test).

Mentions: By hypothesizing that drugs with DRS profiles that significantly correlate with patient survival are pharmacologically active, we were able to identify several candidates using the METABRIC dataset from Curtis et al. (Supplementary Table S1).16 These candidates belonged to a variety of pharmacological classes including known antineoplastic agents, antioxidants, hormone therapeutics, and immunosuppressants (Supplementary Chart S1). To note, the CMap dataset contains data corresponding to several replicate treatment experiments with varying concentrations for each drug.2 Thus, we chose the replicate that yielded the most significant P-value from the survival analysis to represent the drug. Replicates where higher drug concentrations were used typically yielded more significant results. Figure2 shows the P-value and hazard ratio (HR) distribution (Cox proportional hazards model) of all drugs after selecting the most significant replicate. In total, there were 169 drugs (out of 1,215 drugs) that yielded P < 1E-05 with 110 of these having HR <1 and 59 having HR >1, when all samples were included in the analysis (Supplementary Table S1). We reasoned that drugs whose DRS profiles yield P < 1E-5 from the model will exhibit a pharmacologic effect (therapeutic or toxic). For example, alpha-estradiol was predicted to have an effect with P = 1.1E-20, Wald test, HR = 0.91, along with scoulerine with P = 1.3E-15, Wald test, HR = 1.1 (Supplementary Table S1). Figure2b,c shows Kaplan-Meier plots for these two drugs, respectively, where patients are stratified by DRS. Patients with high alpha-estradiol DRS exhibited improved survival, whereas patients with low scoulerine DRS exhibited better prognosis.


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)

Output of IDEA. (a) Distribution of hazard ratios and P-values for all drugs. Each point corresponds to a drug, with red points corresponding to drugs with P < 1E-5 and blue points corresponding to drugs with P > 1E-5. Drugs with HR <1 indicates that survival is correlated with increased similarity of the DTP with breast cancer GEPs. Drugs with HR >1 indicates that survival is anticorrelated with increased similarity of the DTP with breast cancer GEPs. (b) Kaplan-Meier plot of patients with DRS >0 and DRS <0 for alpha estradiol. Patients with high DRS exhibit significantly more favorable prognosis than patients with low DRS (P = 3E-20, Logrank test). (c) Kaplan-Meier plot of patients with DRS >0 and DRS <0 for scoulerine. Patients with low DRS exhibit significantly more favorable prognosis than patients with high DRS (P = 5E-13, Logrank test).
© Copyright Policy - open-access
Related In: Results  -  Collection

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

fig02: Output of IDEA. (a) Distribution of hazard ratios and P-values for all drugs. Each point corresponds to a drug, with red points corresponding to drugs with P < 1E-5 and blue points corresponding to drugs with P > 1E-5. Drugs with HR <1 indicates that survival is correlated with increased similarity of the DTP with breast cancer GEPs. Drugs with HR >1 indicates that survival is anticorrelated with increased similarity of the DTP with breast cancer GEPs. (b) Kaplan-Meier plot of patients with DRS >0 and DRS <0 for alpha estradiol. Patients with high DRS exhibit significantly more favorable prognosis than patients with low DRS (P = 3E-20, Logrank test). (c) Kaplan-Meier plot of patients with DRS >0 and DRS <0 for scoulerine. Patients with low DRS exhibit significantly more favorable prognosis than patients with high DRS (P = 5E-13, Logrank test).
Mentions: By hypothesizing that drugs with DRS profiles that significantly correlate with patient survival are pharmacologically active, we were able to identify several candidates using the METABRIC dataset from Curtis et al. (Supplementary Table S1).16 These candidates belonged to a variety of pharmacological classes including known antineoplastic agents, antioxidants, hormone therapeutics, and immunosuppressants (Supplementary Chart S1). To note, the CMap dataset contains data corresponding to several replicate treatment experiments with varying concentrations for each drug.2 Thus, we chose the replicate that yielded the most significant P-value from the survival analysis to represent the drug. Replicates where higher drug concentrations were used typically yielded more significant results. Figure2 shows the P-value and hazard ratio (HR) distribution (Cox proportional hazards model) of all drugs after selecting the most significant replicate. In total, there were 169 drugs (out of 1,215 drugs) that yielded P < 1E-05 with 110 of these having HR <1 and 59 having HR >1, when all samples were included in the analysis (Supplementary Table S1). We reasoned that drugs whose DRS profiles yield P < 1E-5 from the model will exhibit a pharmacologic effect (therapeutic or toxic). For example, alpha-estradiol was predicted to have an effect with P = 1.1E-20, Wald test, HR = 0.91, along with scoulerine with P = 1.3E-15, Wald test, HR = 1.1 (Supplementary Table S1). Figure2b,c shows Kaplan-Meier plots for these two drugs, respectively, where patients are stratified by DRS. Patients with high alpha-estradiol DRS exhibited improved survival, whereas patients with low scoulerine DRS exhibited better prognosis.

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