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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

Drug candidates for ER+ and ER– patients. (a) P-value and hazard ratio distribution for all drugs for ER+ patients. (b) P-value and hazard ratio distribution for all drugs for ER– patients. Red points indicate drugs with P < 1E-5 and blue points indicate drugs with P > 1E-5. (c) Kaplan-Meier curves of patients with and for wortmannin for ER+ patients. (d) Kaplan-Meier curves of patients with and for wortmannin for ER– patients. (e) Kaplan-Meier curves of patients with and for repaglinide for ER+ patients. (f) Kaplan-Meier curves of patients with and for wortmannin for ER– patients. For ER– patients, stratifying patients at yields disproportionate sample sizes; therefore, the median DRS was used instead.
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fig06: Drug candidates for ER+ and ER– patients. (a) P-value and hazard ratio distribution for all drugs for ER+ patients. (b) P-value and hazard ratio distribution for all drugs for ER– patients. Red points indicate drugs with P < 1E-5 and blue points indicate drugs with P > 1E-5. (c) Kaplan-Meier curves of patients with and for wortmannin for ER+ patients. (d) Kaplan-Meier curves of patients with and for wortmannin for ER– patients. (e) Kaplan-Meier curves of patients with and for repaglinide for ER+ patients. (f) Kaplan-Meier curves of patients with and for wortmannin for ER– patients. For ER– patients, stratifying patients at yields disproportionate sample sizes; therefore, the median DRS was used instead.

Mentions: In the clinic, immunohistochemical categorization of breast cancer is an essential component of determining patient prognosis and designing treatment regimens. Thus, the effectiveness of drug treatment may vary according to ER, p53, and/or HER2 expression. As such, we predicted drug candidates for patients belonging to each of the six breast cancer histological subtypes to increase the resolution of our analysis and to approach drug discovery from a precision medicine standpoint. We found several significant drug candidates for ER+ (P < 1E-05, Wald Test), ER– (P < 0.05, Wald Test), p53+ (P < 1E-04, Wald Test), and HER2– (P < 1E-04, Wald Test) breast cancer subtypes and no significant candidates for p53– and HER2+ subtypes (Supplementary Tables S3–S8). Furthermore, to demonstrate that drug efficacy varies across breast cancer subtypes, we compared drugs between ER+ (n = 1,518) and ER– (n = 474) breast cancer groups. Figure6a,b shows the global P-value and hazard ratio distribution of each drug for ER+ and ER– tumors, respectively. Comparing the top 100 drug candidates from each subtype, we found that only 10 drugs were common between the two groups. This suggests that ER status is an important predictive biomarker for drug efficacy. For instance, wortmannin yielded P = 8.42E-12 (Wald test) and HR = 0.94 in ER+ samples but was not significant in ER– samples (Figure6c,d). Conversely, repaglinide was significant in ER– samples (P = 0.02, Wald test) but not significant in ER+ samples (Figure6e,f). These results suggest that our analysis is sensitive to differences in prognostic features exhibited by different tumor groups. Indeed, being able to identify drugs for a specific subset of patients is powerful in that it allows for the development of tailored treatments based on optimal drug efficacy.


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)

Drug candidates for ER+ and ER– patients. (a) P-value and hazard ratio distribution for all drugs for ER+ patients. (b) P-value and hazard ratio distribution for all drugs for ER– patients. Red points indicate drugs with P < 1E-5 and blue points indicate drugs with P > 1E-5. (c) Kaplan-Meier curves of patients with and for wortmannin for ER+ patients. (d) Kaplan-Meier curves of patients with and for wortmannin for ER– patients. (e) Kaplan-Meier curves of patients with and for repaglinide for ER+ patients. (f) Kaplan-Meier curves of patients with and for wortmannin for ER– patients. For ER– patients, stratifying patients at yields disproportionate sample sizes; therefore, the median DRS was used instead.
© Copyright Policy - open-access
Related In: Results  -  Collection

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

fig06: Drug candidates for ER+ and ER– patients. (a) P-value and hazard ratio distribution for all drugs for ER+ patients. (b) P-value and hazard ratio distribution for all drugs for ER– patients. Red points indicate drugs with P < 1E-5 and blue points indicate drugs with P > 1E-5. (c) Kaplan-Meier curves of patients with and for wortmannin for ER+ patients. (d) Kaplan-Meier curves of patients with and for wortmannin for ER– patients. (e) Kaplan-Meier curves of patients with and for repaglinide for ER+ patients. (f) Kaplan-Meier curves of patients with and for wortmannin for ER– patients. For ER– patients, stratifying patients at yields disproportionate sample sizes; therefore, the median DRS was used instead.
Mentions: In the clinic, immunohistochemical categorization of breast cancer is an essential component of determining patient prognosis and designing treatment regimens. Thus, the effectiveness of drug treatment may vary according to ER, p53, and/or HER2 expression. As such, we predicted drug candidates for patients belonging to each of the six breast cancer histological subtypes to increase the resolution of our analysis and to approach drug discovery from a precision medicine standpoint. We found several significant drug candidates for ER+ (P < 1E-05, Wald Test), ER– (P < 0.05, Wald Test), p53+ (P < 1E-04, Wald Test), and HER2– (P < 1E-04, Wald Test) breast cancer subtypes and no significant candidates for p53– and HER2+ subtypes (Supplementary Tables S3–S8). Furthermore, to demonstrate that drug efficacy varies across breast cancer subtypes, we compared drugs between ER+ (n = 1,518) and ER– (n = 474) breast cancer groups. Figure6a,b shows the global P-value and hazard ratio distribution of each drug for ER+ and ER– tumors, respectively. Comparing the top 100 drug candidates from each subtype, we found that only 10 drugs were common between the two groups. This suggests that ER status is an important predictive biomarker for drug efficacy. For instance, wortmannin yielded P = 8.42E-12 (Wald test) and HR = 0.94 in ER+ samples but was not significant in ER– samples (Figure6c,d). Conversely, repaglinide was significant in ER– samples (P = 0.02, Wald test) but not significant in ER+ samples (Figure6e,f). These results suggest that our analysis is sensitive to differences in prognostic features exhibited by different tumor groups. Indeed, being able to identify drugs for a specific subset of patients is powerful in that it allows for the development of tailored treatments based on optimal drug efficacy.

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