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2000
Volume 20, Issue 8
  • ISSN: 1574-8936
  • E-ISSN: 2212-392X

Abstract

Aim

This study aimed to study breast cancer, the most common cancer affecting women worldwide, using one primary and two metastatic breast tumor cell lines to identify therapeutic drugs.

Background

Investigating the changes in gene expression triggered by drugs offers a robust method for uncovering potential new treatments. Through the analysis of the impacts of drugs on gene activity, scientists can unravel the molecular mechanisms within cells, comprehend the effects of drugs, identify chances for drug repositioning, and foresee patient outcomes to treatments.

Objective

Our approach has involved two main strategies: analyzing drug-perturbed gene expression profiles and leveraging drug-induced gene expression profiles. Firstly, we have assessed how drugs affect the expression of target genes in a dose-dependent manner, determining whether they inhibit or activate gene expression. This analysis could inform the identification of new potential drugs. Secondly, we have grouped drugs based on their expression profiles to explore potential synergistic effects.

Methods

Our methodology has involved quantifying gene profile changes relative to drug dosage, categorizing effects as up-regulating or down-regulating, and employing functional enrichment with cancer hallmark annotations to predict drugs with potential for cancer treatment. Additionally, we have determined the optimal number of drug groups with similar effects on gene expression and explored their mechanisms of action through cancer hallmark annotations.

Results

By analyzing dose-dependent gene expression, we have found that seven, three, and five drugs may induce similar sets of up-regulated and down-regulated genes in Hs-578-T, MCF7, and MDA-MB-231 cell lines, respectively. Clustering and functional enrichment analyses have suggested a shared molecular mechanism of action among these drug candidates.

Conclusion

We have thus categorized drugs with opposing gene expression profiles and proposed new drug candidates for breast cancer treatment based on cancer hallmark annotations. Moreover, our study has uncovered synergistic drug combinations, including those utilizing FDA-approved drugs, for primary and metastatic breast cancer cell lines.

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2025-02-04
2025-10-27
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References

  1. BabaeiK. KhaksarR. ZeinaliT. Epigenetic profiling of MUTYH, KLF6, WNT1 and KLF4 genes in carcinogenesis and tumorigenesis of colorectal cancer.Biomedicine (Taipei)2019942210.1051/bmdcn/2019090422 31724937
    [Google Scholar]
  2. MassaguéJ. ObenaufA.C. Metastatic colonization by circulating tumour cells.Nature2016529758629830610.1038/nature17038 26791720
    [Google Scholar]
  3. ChenM.C. HsuS.L. LinH. YangT.Y. Retinoic acid and cancer treatment.Biomedicine (Taipei)2014442210.7603/s40681‑014‑0022‑1 25520935
    [Google Scholar]
  4. GoodspeedA. HeiserL.M. GrayJ.W. CostelloJ.C. Tumor-derived cell lines as molecular models of cancer pharmacogenomics.Mol. Cancer Res.201614131310.1158/1541‑7786.MCR‑15‑0189 26248648
    [Google Scholar]
  5. MirabelliP. CoppolaL. SalvatoreM. Cancer cell lines are useful model systems for medical research.Cancers (Basel)2019118109810.3390/cancers11081098 31374935
    [Google Scholar]
  6. WangX. SheuJ.J.C. LaiM.T. RSF-1 overexpression determines cancer progression and drug resistance in cervical cancer.Biomedicine (Taipei)201881410.1051/bmdcn/2018080104 29480799
    [Google Scholar]
  7. IorioF. RittmanT. GeH. MendenM. Saez-RodriguezJ. Transcriptional data: A new gateway to drug repositioning?Drug Discov. Today2013187-835035710.1016/j.drudis.2012.07.014 22897878
    [Google Scholar]
  8. TaguchiY. Drug candidate identification based on gene expression of treated cells using tensor decomposition-based unsupervised feature extraction for large-scale data.BMC Bioinform.201919Suppl. 1338810.1186/s12859‑018‑2395‑8 30717646
    [Google Scholar]
  9. SzalaiB. VeresD.V. Application of perturbation gene expression profiles in drug discovery—From mechanism of action to quantitative modelling.Front. Syst. Biol.20233112604410.3389/fsysb.2023.1126044
    [Google Scholar]
  10. LambJ. CrawfordE.D. PeckD. The Connectivity Map: Using gene-expression signatures to connect small molecules, genes, and disease.Science200631357951929193510.1126/science.1132939 17008526
    [Google Scholar]
  11. El KhiliM.R. MemonS.A. EmadA. MARSY: A multitask deep-learning framework for prediction of drug combination synergy scores.Bioinformatics2023394btad17710.1093/bioinformatics/btad177 37021933
    [Google Scholar]
  12. RothR.A. KanaO. FilipovicD. GaneyP.E. Pharmacokinetic and toxicodynamic concepts in idiosyncratic, drug-induced liver injury.Expert Opin. Drug Metab. Toxicol.2022187-846948110.1080/17425255.2022.2113379 36003040
    [Google Scholar]
  13. SubramanianA. NarayanR. CorselloS.M. A Next Generation Connectivity Map: L1000 Platform and the First 1,000,000 Profiles.Cell2017171614371452.e1710.1016/j.cell.2017.10.049 29195078
    [Google Scholar]
  14. HanahanD. WeinbergR.A. The hallmarks of cancer: Perspectives for cancer medicine.Oxford Academics201610.1093/med/9780199656103.003.0001
    [Google Scholar]
  15. WelchD.R. HurstD.R. Defining the hallmarks of metastasis.Cancer Res.201979123011302710.1158/0008‑5472.CAN‑19‑0458 31053634
    [Google Scholar]
  16. Al-BedearyS. GettaH. Al-SharafiD. The hallmarks of cancer and their therapeutic targeting in current use and clinical trials.Iraqi Journal of Hematology20209111010.4103/ijh.ijh_24_19
    [Google Scholar]
  17. HainautP. PlymothA. Targeting the hallmarks of cancer.Curr. Opin. Oncol.2013251505110.1097/CCO.0b013e32835b651e 23150341
    [Google Scholar]
  18. AnthonyJ. VaralakshmiS. SekarA.K. DevarajanN. JanakiramanB. PeramaiyanR. Glutaminase - A potential target for cancer treatment.Biomedicine (Taipei)2024142293710.37796/2211‑8039.1445 38939098
    [Google Scholar]
  19. VasileiouM. PapageorgiouS. NguyenN.P. Current advancements and future perspectives of immunotherapy in breast cancer treatment.Immuno20233219521610.3390/immuno3020013
    [Google Scholar]
  20. TehraniS.S. ZaboliE. SadeghiF. MicroRNA-26a-5p as a potential predictive factor for determining the effectiveness of trastuzumab therapy in HER-2 positive breast cancer patients.Biomedicine (Taipei)20211123039 35223402
    [Google Scholar]
  21. MoriR. NagaoY. Efficacy of chemotherapy after hormone therapy for hormone receptor–Positive metastatic breast cancer.SAGE Open Med.20142205031211455737610.1177/2050312114557376 26770749
    [Google Scholar]
  22. LimS. LeeS. HanJ. Prolonged clinical benefit from the maintenance hormone therapy in patients with metastatic breast cancer.Breast20132261205120910.1016/j.breast.2013.08.013 24135766
    [Google Scholar]
  23. Sánchez-MuñozA. Pérez-RuizE. JiménezB. Targeted therapy of metastatic breast cancer.Clin. Transl. Oncol.2009111064365010.1007/s12094‑009‑0419‑6 19828406
    [Google Scholar]
  24. BalkrishnaA. MittalR. MalikR. Comparative analysis of Doxycycline and Ayurvedic herbs to target metastatic breast cancer: An in-silico approach.Biomedicine (Taipei)2024142747910.37796/2211‑8039.1448 38939099
    [Google Scholar]
  25. SpellmanA. TangS.C. Immunotherapy for breast cancer: Past, present, and future.Cancer Metastasis Rev.201635452554610.1007/s10555‑016‑9654‑9 27913998
    [Google Scholar]
  26. BustinS.A. JellingerK.A. Advances in molecular medicine: unravelling disease complexity and pioneering precision healthcare.Int. J. Mol. Sci.202324181416810.3390/ijms241814168 37762471
    [Google Scholar]
  27. HuangC.H. ChangP.M.H. HsuC.W. Drug repositioning for non-small cell lung cancer by using machine learning algorithms and topological graph theory.BMC Bioinformatics201617S1210.1186/s12859‑015‑0845‑0
    [Google Scholar]
  28. HuangC.H. ChangP.M.H. LinY.J. WangC.H. HuangC.Y.F. NgK.L. Drug repositioning discovery for early- and late-stage non-small-cell lung cancer.BioMed Res. Int.2014201411310.1155/2014/193817 25210704
    [Google Scholar]
  29. ChenS.T. HuangC.H. KokV.C. Drug repurposing and therapeutic anti-microRNA predictions for inhibition of oxidized low-density lipoprotein-induced vascular smooth muscle cell-associated diseases.J. Bioinform. Comput. Biol.2017151165004310.1142/S0219720016500438 28150521
    [Google Scholar]
  30. HuangC.H. CiouJ.S. ChenS.T. Identify potential drugs for cardiovascular diseases caused by stress-induced genes in vascular smooth muscle cells.PeerJ20164e247810.7717/peerj.2478 27703845
    [Google Scholar]
  31. DaiX. ChengH. BaiZ. LiJ. Breast cancer cell line classification and its relevance with breast tumor subtyping.J. Cancer20178163131314110.7150/jca.18457 29158785
    [Google Scholar]
  32. HughesL. MaloneC. ChumsriS. BurgerA.M. McDonnellS. Characterisation of breast cancer cell lines and establishment of a novel isogenic subclone to study migration, invasion and tumourigenicity.Clin. Exp. Metastasis200825554955710.1007/s10585‑008‑9169‑z 18386134
    [Google Scholar]
  33. ZhengG. MaY. ZouY. YinA. LiW. DongD. HCMDB: The human cancer metastasis database.Nucleic Acids Res.201846D1D950D95510.1093/nar/gkx1008 29088455
    [Google Scholar]
  34. LiuY. LiZ. LuJ. ZhaoM. QuH. CMGene: A literature-based database and knowledge resource for cancer metastasis genes.J. Genet. Genomics201744527727910.1016/j.jgg.2017.04.006 28527662
    [Google Scholar]
  35. TranT.D. PhamD.T. Identification of anticancer drug target genes using an outside competitive dynamics model on cancer signaling networks.Sci. Rep.20211111409510.1038/s41598‑021‑93336‑z 34238960
    [Google Scholar]
  36. CharradM. GhazzaliN. BoiteauV. NiknafsA. NbClust: An R package for determining the relevant number of clusters in a data set.J. Stat. Softw.201461613610.18637/jss.v061.i06
    [Google Scholar]
  37. NearyB. ZhouJ. QiuP. Identifying gene expression patterns associated with drug-specific survival in cancer patients.Sci. Rep.2021111500410.1038/s41598‑021‑84211‑y 33654134
    [Google Scholar]
  38. MalyutinaA. MajumderM.M. WangW. PessiaA. HeckmanC.A. TangJ. Drug combination sensitivity scoring facilitates the discovery of synergistic and efficacious drug combinations in cancer.PLOS Comput. Biol.2019155e100675210.1371/journal.pcbi.1006752 31107860
    [Google Scholar]
  39. MäkeläP. ZhangS.M. RuddS.G. Drug synergy scoring using minimal dose response matrices.BMC Res. Notes20211412710.1186/s13104‑021‑05445‑7 33468238
    [Google Scholar]
  40. GuillenV.S. ZieglerY. GopinathC. Effective combination treatments for breast cancer inhibition by FOXM1 inhibitors with other targeted cancer drugs.Breast Cancer Res. Treat.2023198360762110.1007/s10549‑023‑06878‑3 36847915
    [Google Scholar]
  41. CrystalA.S. ShawA.T. SequistL.V. Patient-derived models of acquired resistance can identify effective drug combinations for cancer.Science201434662161480148610.1126/science.1254721 25394791
    [Google Scholar]
  42. DryJ.R. YangM. Saez-RodriguezJ. Looking beyond the cancer cell for effective drug combinations.Genome Med.20168112510.1186/s13073‑016‑0379‑8 27887656
    [Google Scholar]
  43. TongC.W.S. WuW.K.K. LoongH.H.F. ChoW.C.S. ToK.K.W. Drug combination approach to overcome resistance to EGFR tyrosine kinase inhibitors in lung cancer.Cancer Lett.201740510011010.1016/j.canlet.2017.07.023 28774798
    [Google Scholar]
  44. AllertC. WaclawiczekA. ZimmermannS.M.N. Protein tyrosine kinase 2b inhibition reverts niche-associated resistance to tyrosine kinase inhibitors in AML.Leukemia202236102418242910.1038/s41375‑022‑01687‑x 36056084
    [Google Scholar]
  45. AndersonH.J. GalileoD.S. Small-molecule inhibitors of FGFR, integrins and FAK selectively decrease L1CAM-stimulated glioblastoma cell motility and proliferation.Cell Oncol. (Dordr.)201639322924210.1007/s13402‑016‑0267‑7 26883759
    [Google Scholar]
  46. KantetiR. MirzapoiazovaT. RiehmJ.J. Focal adhesion kinase a potential therapeutic target for pancreatic cancer and malignant pleural mesothelioma.Cancer Biol. Ther.201819431632710.1080/15384047.2017.1416937 29303405
    [Google Scholar]
  47. CerboneA. ToaldoC. MinelliR. Rosiglitazone and AS601245 decrease cell adhesion and migration through modulation of specific gene expression in human colon cancer cells.PLoS One201276e4014910.1371/journal.pone.0040149 22761953
    [Google Scholar]
  48. CuiJ. WangQ. WangJ. Basal c-Jun NH2-terminal protein kinase activity is essential for survival and proliferation of T-cell acute lymphoblastic leukemia cells.Mol. Cancer Ther.20098123214322210.1158/1535‑7163.MCT‑09‑0408 19996270
    [Google Scholar]
  49. BalboniA.L. HutchinsonJ.A. DeCastroA.J. ΔNp63α-mediated activation of bone morphogenetic protein signaling governs stem cell activity and plasticity in normal and malignant mammary epithelial cells.Cancer Res.20137321020103010.1158/0008‑5472.CAN‑12‑2862 23243027
    [Google Scholar]
  50. CheanJ. ChenC. ShivelyJ.E. ETS transcription factor ELF5 induces lumen formation in a 3D model of mammary morphogenesis and its expression is inhibited by Jak2 inhibitor TG101348.Exp. Cell Res.20173591627510.1016/j.yexcr.2017.08.008 28800960
    [Google Scholar]
  51. ZhaoC. ZhangY. ZhangJ. Discovery of novel fedratinib-based HDAC/JAK/BRD4 triple inhibitors with remarkable antitumor activity against triple negative breast cancer.J. Med. Chem.20236620141501417410.1021/acs.jmedchem.3c01242 37796543
    [Google Scholar]
  52. VijayG.V. ZhaoN. Den HollanderP. GSK3β regulates epithelial-mesenchymal transition and cancer stem cell properties in triple-negative breast cancer.Breast Cancer Res.20192113710.1186/s13058‑019‑1125‑0 30845991
    [Google Scholar]
  53. JiX. MengX. HeQ. XiangX. ShiY. ZhuX. Foretinib is effective against triple-negative breast cancer cells MDA-MB-231 in vitro and in vivo by down-regulating p-MET/HGF signaling.Int. J. Mol. Sci.202324175710.3390/ijms24010757 36614199
    [Google Scholar]
  54. van OorschotB. GranataG. Di FrancoS. Targeting DNA double strand break repair with hyperthermia and DNA-PKcs inhibition to enhance the effect of radiation treatment.Oncotarget2016740655046551310.18632/oncotarget.11798 27602767
    [Google Scholar]
  55. ChoiC. ChoW.K. ParkS. Checkpoint kinase 1 (CHK1) inhibition enhances the sensitivity of triple-negative breast cancer cells to proton irradiation via Rad51 downregulation.Int. J. Mol. Sci.2020218269110.3390/ijms21082691 32294924
    [Google Scholar]
  56. FrasorJ. WeaverA. PradhanM. Positive cross-talk between estrogen receptor and NF-kappaB in breast cancer.Cancer Res.200969238918892510.1158/0008‑5472.CAN‑09‑2608 19920189
    [Google Scholar]
  57. FrasorJ. El-ShennawyL. StenderJ.D. NFκB affects estrogen receptor expression and activity in breast cancer through multiple mechanisms.Mol. Cell. Endocrinol.2015418Pt 323523910.1016/j.mce.2014.09.013
    [Google Scholar]
  58. SmartE. SeminaS.E. FrasorJ. Update on the role of NFκB in promoting aggressive phenotypes of estrogen receptor–positive breast cancer.Endocrinology202016110bqaa15210.1210/endocr/bqaa152 32887995
    [Google Scholar]
  59. ZagidullinB. AldahdoohJ. ZhengS. DrugComb: An integrative cancer drug combination data portal.Nucleic Acids Res.201947W1W43-5110.1093/nar/gkz337 31066443
    [Google Scholar]
  60. KaklamaniV.G. RichardsonA.L. ArteagaC.L. Exploring biomarkers of phosphoinositide 3-kinase pathway activation in the treatment of hormone receptor positive, human epidermal growth receptor 2 negative advanced breast cancer.Oncologist201924330531210.1634/theoncologist.2018‑0314 30651399
    [Google Scholar]
  61. BaselgaJ. ImS.A. IwataH. Buparlisib plus fulvestrant versus placebo plus fulvestrant in postmenopausal, hormone receptor-positive, HER2-negative, advanced breast cancer (BELLE-2): A randomised, double-blind, placebo-controlled, phase 3 trial.Lancet Oncol.201718790491610.1016/S1470‑2045(17)30376‑5 28576675
    [Google Scholar]
  62. Di LeoA. JohnstonS. LeeK.S. Buparlisib plus fulvestrant in postmenopausal women with hormone-receptor-positive, HER2-negative, advanced breast cancer progressing on or after mTOR inhibition (BELLE-3): A randomised, double-blind, placebo-controlled, phase 3 trial.Lancet Oncol.20181918710010.1016/S1470‑2045(17)30688‑5 29223745
    [Google Scholar]
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  • Article Type:
    Research Article
Keyword(s): breast tumor; cell lines; drug repositioning; drugs; hallmarks of cancer; Tumor metastasis
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