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This document discusses the promise of drug repurposing for combination therapy in cancer, highlighting the potential of existing non-cancer drugs like metformin and HDAC inhibitors in synergistic effects with chemotherapeutic agents. It details methods for evaluating drug combinations through bioinformatics and gene expression analysis, focusing on promising pairings for breast cancer and acute myelogenous leukemia (AML). The findings suggest multiple effective treatment combinations and emphasize the importance of considering side effects while proposing new therapies.
Project Presentation
- 1. PROPRIETARY & CONFIDENTIAL Repurposing Drugs for Combination Therapy in Cancer BMI 217, Winter 2016 Jennifer Epler, Ivan Ip, Yooli Light, Tiffany Wang, Yiying Wang 1
- 2. +Introduction • Oncology drug development is lengthy and expensive • Drug repurposing promises reduced time and cost • Bioinformatics techniques can be used to predict efficacy of new drug combinations • Clinical trials are underway for new drug combinations that combine existing non-cancer drugs with existing chemotherapeutic agents
- 3. +Existing Non-Cancer Drugs • There is evidence that Metformin (diabetes) and HDAC inhibitors (Depakote, an anticonvulsant/mood stabilizer, and Zolinza, approved for some lymphomas) may have potent anti-tumor effects, particularly when combined with more established oncology drugs. • Metformin is an AMPK agonist, and also corrects insulin resistance. • HDAC inhibitors may enhance the DNA damaging effects of traditional chemotherapy. • Both drugs can alter cell growth signaling pathways which are often dysregulated in cancer. These pathways have already been the subject of numerous new drug development efforts. Valproic acid (Depakote) and Vorinostat (Zolinza) are HDAC inhibitors. Metformin is an AMPK agonist.
- 4. + Drug Combinations All “Partner B” drugs were curated from open studies at clinicaltrials.gov; all are currently being tested in cancer patients in combination with a “Partner A” drug. Connectivity Map contains data for all drugs in the indicated cell lines. For simplicity, create signatures for all pairings of Partner A drugs + Partner B drugs. For a more novel approach, integrate gene expression data with KEGG pathway data and see how the up- and down- regulated genes would be affected
- 5. +Collecting Data Step 1: Process normal and disease GEO datasets to identify significant up and down regulated genes by building a linear model Step 2: Process drug combinations to identify significant up and down regulated genes using Connectivity Map Step 3: Prepare a final gene list of potential targets that each drug combination pair can treat Step 4: Perform functional analysis of genes using MSigDB We chose to focus on three cells lines: breast, prostate and myeloid/heme based on Connectivity Map database Giant set of gene expression data from CCLE was manipulated and analyzed We found that SW900_LUNG and HUPT3_PANCREAS cell lines that clustered close to PC3 We applied the drug signatures from PC3 to these two cell lines Diseases of interest: breast cancer, prostate cancer, acute myelogenous leukemia (AML), pancreatic cancer and lung cancer
- 6. + Gene expression profile of drug A Gene expression profile of drug B Estimate expression change of drug A Estimate expression change of drug A Estimate the drug combination effect - Integrated expression data with KEGG pathway data - Examined sharing of 7 cell growth pathways known to be oncogenic Yang et al. Drug-Induced Genomic Residual Effect Model for Successful Prediction of Multidrug Effect. CPT Pharmacometrics. (2015) Cell Growth Pathway Approach
- 7. + 32 upstream and downstream KEGG pathways included in GP Yang et al. Drug-Induced Genomic Residual Effect Model for Successful Prediction of Multidrug Effect. CPT Pharmacometrics. (2015) Additional Growth Pathway Approach
- 8. + Results There was not enough data for the prostate cell line for us to evaluate new drug combinations The MsigDB also showed little correlation for the pancreatic and lung cell lines We shifted the bulk of our analysis to breast/AML cancers Total Transcripts per Disease Signature #Transcripts
- 9. + Breast Cancer: hundreds of genes up- & down-regulated. Metformin drug combinations showed significant genes overlapping in the CHARAFE_BREAST_CANCER_LUMINAL_VS_BASAL_UP and _DN, and the SMID_BREAST_CANCER_BASAL_DN gene sets. Depakote drug combinations showed significant genes overlapping in the MASSARWEH_TAMOXIFEN_RESISTANCE_UP gene set. Zolinza drug combinations showed significant genes overlapping in the ELVIDGE_HYPOXIA_UP gene set. #Transcripts
- 10. + Breast Cancer The two HDAC inhibitors showed similar results in MSigDB. Depakote drug combinations had significant amount of genes overlapping in: NAGASHIMA_NGR1_SIGNALING_UP gene set. Upregulation of Neuregulin-1 in breast cancer is significant, because tumors evade apoptosis by down regulating that gene. MASSARWEH_TAMOXIFEN_RESISTANCE_UP gene set, indicating beneficial second-line therapy for breast cancer patients Zolinza drug combinations had significant amount of genes overlapping in: ELVIDGE_HYPOXIA_UP gene set. As tumor adapt for growth in hypoxic conditions, upregulation of these genes may counter this survival advantage. Promising drug combinations include: Depakote + Letrozole, Azacitidine, Doxorubicin, and Irinotecan Zolina + Azacitidine Metformin + Prednisone, Simvastatin, and Doxorubicin
- 11. + AML: hundreds of genes up- & down- regulated. Several Metformin drug combinations showed significant genes overlapping in: PILON_KLF1_TARGETS_DN gene set. Depakote drug combinations had significant amount of genes overlapping in: HALLMARK_TNF-ALPHA_SIGNALING_VIA_NFKB gene set. Zolinza drug combinations had significant amount of genes overlapping in: HELLER_HDAC_SILENCED_BY_METHYLHYLATION_UP, HELLER_HDAC_TARGETS_UP, and KRIGE_RESPONSE_TO_TOSEDOSTAT gene sets. #Transcripts
- 12. + AML The two HDAC inhibitors did not show similar results in MSigDB, this is not surprising since Zolinza has already been approved for a different heme indication As an HDAC inhibitor, Depakote only covers 8 HDAC’s in Classes I and IIa whereas Zolinza can effectively target all 11 HDAC. Metformin drug combinations affecting the PILON_KLF1_TARGETS_DN gene set will alter KLF activities, likely restoring normal cell cycle progression . Depakote drug combinations affecting the HALLMARK_TNF- ALPHA_SIGNALING_VIA_NFKB gene set may improve overall survival. High TNFα levels are associated with poor prognosis in AML.. Zolinza drug combinations affecting the KRIGE_RESPONSE_TO_TOSEDOSTAT gene sets , indicate beneficial second-line therapy for leukemia patients. Promising drug combinations include: Depakote + Prednisone or Gefitinib Zolinza + Prednisone, Hydralazine, or Letrozole Metformin + Aspirin, Simvastatin, or Letrozole
- 13. + Tripled Drug Combinations In addition to our double drug combinations, we proposed another approach which was to triple our drug combinations Results from MSigDb showed similar results to the double drug combination but there was an increase in the number of overlapping genes for both breast and AML cancers Most promising triple therapies: Breast Metformin + either HDACi + Prednisone Metformin + either HDACi + Doxorubicin AML Metformin + either HDACi + Hydralazine Metformin + either HDACi + Letrozole Metformin Valproic acid (Depakote) Aspirin Metformin Vorinostat (Zolinza) Aspirin Hydralazine Hydralazine Prednisone Prednisone Dexamethasone Dexamethasone Simvastatin Simvastatin Letrozole Letrozole Azacitidine Azacitidine Doxorubicin Doxorubicin Irinotecan Irinotecan Paclitaxel Paclitaxel Gefitinib Gefitinib Sirolimus Sirolimus
- 14. + KEGG Pathway Approach Integrating the gene expression data with the KEGG pathway data produced four data sets that showed similar trends to simple drug addition Primary focus was placed on cell growth pathways, which is important in cancer studies 0 100 200 300 400 500 600 700 acetylsalicylicacid hydralazine prednisone dexamethasone simvastan letrozole azacidine doxorubicin irinotecan paclitaxel gefinib sirolimus acetylsalicylicacid hydralazine prednisone dexamethasone simvastan letrozole azacidine doxorubicin irinotecan paclitaxel gefinib sirolimus acetylsalicylicacid hydralazine prednisone dexamethasone simvastan letrozole azacidine doxorubicin irinotecan paclitaxel gefinib sirolimus me ormin valproic acid (depakote) Vorinostat #ofgenes Drug Combina ons Breast/MCF7 - Cell Growth Path Drug - / Disease + Drug + / Disease -
- 15. + KEGG Pathway Approach This approach allowed us to examine up- and down-stream signaling factors that would be affected by our initial up- and down- regulated genes Genes that were regulated in same direction were considered to have an inductive effect on the cell growth pathway From MSigDB, we saw similar gene sets up and down regulated, we also saw significant amount of genes overlapping in: KEGG_MAPK_SIGNALING_PATWAY and KEGG_CELL_CYCLE Promising drug combinations Breast: Metformin + Azacitadine, Depakote + Azacitidine, Vorinostat + Azacitadine AML: Metformin + Gefitinib, Depakote + Gefitinib, Vorinostat + Hydralazine
- 16. + Side Effects While we have proposed many promising drug combinations, we must also consider the side effects of these drug combinations Examination of the combined side effects of some promising drug combinations using drug- drug interaction side effects database TWOSIDES TWOSIDES database: only synergistic interactions of drug pairs are reported. Exclude drug pair with one drug of the pair that is likely responsible for the adverse event If no synergistic interactions are associated, recapitulations of known effects of each drug is considered Tatonetti et.al.,Data-Driven Prediction of Drug Effects and Interactions. Sci Transl Med (2012). OFFSIDES Database 1332 drugs 10,097 side effects adverse event report in AERS from FDA TWOSIDES Database 59,220 drug pairs 868,221 significant associations 1301 side effects List of Promising drug combinations Drug pairs with only synergistic interactions Canada’s MedEffect FDA drug labels Only significant associations (p<0.05) Prediction and evaluation of side effects of the target drug combinations No significant synergistic interactions
- 17. + Side Effects Side effects counts associated with promising drug pairs List of serious side effects associated with the drug pairs
- 18. + Conclusions Using a bioinformatics approach, we have proposed several promising drug combinations that might have a therapeutic effect for breast or AML cancer. Our approach builds upon earlier studies, which cast a wider net on drug repurposing, but looked only at monotherapies. Our approach simulated combination therapies, which are likely to have longer-lived efficacy in clinical practice. We were also careful to preserve tissue specificity, increasing the likelihood of predicting successful combinations. With more comprehensive, publically available data sets, bioinformatic approaches will have greater power to predict new drug combinations. Confirming our results, clinicaltrails.gov currently shows these drug combinations in trials: Metformin + Aspirin for prostate cancer Metformin + Irinotecan for brain tumors Metformin + Letrozole for endometrial cancer Depakote + Prednisone for B-cell Lymphoma Depakote + Azacitidine for AML Vorinostat + Dexamethasone for mantle cell lymphoma Vorinostat + Gefitinib for Non-Small-Cell Lung Carcinoma