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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

Ciclosporin DTP. (a) Kaplan-Meier curves of patients with DRS >0 and DRS <0 for ciclosporin. (b) Empirical distribution of Pearson correlation coefficients (PCC) from comparing the ciclosporin DTP with all other DTPs. Red line indicates PCC from comparing ciclosporin DTP with thapsigargin DTP. (c) Scatterplot comparing ciclosporin and thapsigargin DTPs. Each point corresponds to a single gene in the DTP. (d) GO enrichment analysis of downregulated genes between treatment and control DTPs for ciclosporin.
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fig05: Ciclosporin DTP. (a) Kaplan-Meier curves of patients with DRS >0 and DRS <0 for ciclosporin. (b) Empirical distribution of Pearson correlation coefficients (PCC) from comparing the ciclosporin DTP with all other DTPs. Red line indicates PCC from comparing ciclosporin DTP with thapsigargin DTP. (c) Scatterplot comparing ciclosporin and thapsigargin DTPs. Each point corresponds to a single gene in the DTP. (d) GO enrichment analysis of downregulated genes between treatment and control DTPs for ciclosporin.

Mentions: In support of our hypothesis that survival information can reveal bioactive drug leads, we were able to identify a number of drugs that have been experimentally shown to exhibit anticancer activity. In particular, we identified several phosphatidylinositol-4,5-bisphosphate 3-kinase (PI3K) pathway inhibitors that are known to inhibit proliferation, reduce angiogenesis, and induce apoptosis in cancer cell lines.28–30 Identified PI3K/mTOR inhibitors include wortmannin, LY-294002, and sirolimus (Figure3d–f).28,31–47 In addition to known drugs, we identified drug candidates that have not yet been experimentally tested or lack rigorous testing. Among these compounds are ciclosporin, ascorbic acid, hydroflumethiazide, ticarcillin, and mycophenolic acid (Figures4, 5). To show that these candidates have therapeutic potential, we exemplify the case of ciclosporin (P = 4.69E-12, HR = 0.94, Figure5a), which has a DTP similar to that of thapsigargin, a drug shown to delay tumor growth in multiple cancer mouse xenograft models.48,49 Ciclosporin's DTP was significantly correlated with the DTPs of only a few drugs, as shown by the distribution of correlation coefficients (Figure5b).2 This suggests that the mechanism of action of ciclosporin, if it does possess anticancer activity, is inherently different from that of other top drugs identified in our analysis. Interestingly, ciclosporin was significantly correlated with thapsigargin in their GEPs with a Pearson's correlation coefficient of 0.6 (Figure5c). The fact that simple correlation analysis was able to detect a high similarity between the two DTPs suggests that ciclosporin may be related to thapsigargin in terms of drug effect. Additionally, we calculated the enrichment of Gene Ontology Biological Process terms in genes that were significantly downregulated between ciclosporin treatment and control groups (Figure5d, Supplementary Table S2). Interestingly, we found highly relevant cancer-associated pathways such as "steroid hormone receptor signaling pathway," "regulation of cell cycle," and "regulation of apoptosis" enriched in the downregulated gene set, suggesting that ciclosporin may have a bioactive effect on cancer growth. Overall, these results indicate that the drugs we identified that are not currently recognized as cancer therapeutics may in fact be strong candidates for further experimental testing.


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)

Ciclosporin DTP. (a) Kaplan-Meier curves of patients with DRS >0 and DRS <0 for ciclosporin. (b) Empirical distribution of Pearson correlation coefficients (PCC) from comparing the ciclosporin DTP with all other DTPs. Red line indicates PCC from comparing ciclosporin DTP with thapsigargin DTP. (c) Scatterplot comparing ciclosporin and thapsigargin DTPs. Each point corresponds to a single gene in the DTP. (d) GO enrichment analysis of downregulated genes between treatment and control DTPs for ciclosporin.
© Copyright Policy - open-access
Related In: Results  -  Collection

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getmorefigures.php?uid=PMC4544055&req=5

fig05: Ciclosporin DTP. (a) Kaplan-Meier curves of patients with DRS >0 and DRS <0 for ciclosporin. (b) Empirical distribution of Pearson correlation coefficients (PCC) from comparing the ciclosporin DTP with all other DTPs. Red line indicates PCC from comparing ciclosporin DTP with thapsigargin DTP. (c) Scatterplot comparing ciclosporin and thapsigargin DTPs. Each point corresponds to a single gene in the DTP. (d) GO enrichment analysis of downregulated genes between treatment and control DTPs for ciclosporin.
Mentions: In support of our hypothesis that survival information can reveal bioactive drug leads, we were able to identify a number of drugs that have been experimentally shown to exhibit anticancer activity. In particular, we identified several phosphatidylinositol-4,5-bisphosphate 3-kinase (PI3K) pathway inhibitors that are known to inhibit proliferation, reduce angiogenesis, and induce apoptosis in cancer cell lines.28–30 Identified PI3K/mTOR inhibitors include wortmannin, LY-294002, and sirolimus (Figure3d–f).28,31–47 In addition to known drugs, we identified drug candidates that have not yet been experimentally tested or lack rigorous testing. Among these compounds are ciclosporin, ascorbic acid, hydroflumethiazide, ticarcillin, and mycophenolic acid (Figures4, 5). To show that these candidates have therapeutic potential, we exemplify the case of ciclosporin (P = 4.69E-12, HR = 0.94, Figure5a), which has a DTP similar to that of thapsigargin, a drug shown to delay tumor growth in multiple cancer mouse xenograft models.48,49 Ciclosporin's DTP was significantly correlated with the DTPs of only a few drugs, as shown by the distribution of correlation coefficients (Figure5b).2 This suggests that the mechanism of action of ciclosporin, if it does possess anticancer activity, is inherently different from that of other top drugs identified in our analysis. Interestingly, ciclosporin was significantly correlated with thapsigargin in their GEPs with a Pearson's correlation coefficient of 0.6 (Figure5c). The fact that simple correlation analysis was able to detect a high similarity between the two DTPs suggests that ciclosporin may be related to thapsigargin in terms of drug effect. Additionally, we calculated the enrichment of Gene Ontology Biological Process terms in genes that were significantly downregulated between ciclosporin treatment and control groups (Figure5d, Supplementary Table S2). Interestingly, we found highly relevant cancer-associated pathways such as "steroid hormone receptor signaling pathway," "regulation of cell cycle," and "regulation of apoptosis" enriched in the downregulated gene set, suggesting that ciclosporin may have a bioactive effect on cancer growth. Overall, these results indicate that the drugs we identified that are not currently recognized as cancer therapeutics may in fact be strong candidates for further experimental testing.

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