Skip to content
2000
image of Exploiting Gene Expression Signatures in Breast Cancer Cell Lines to Unveil Novel Drug Candidates and Synergistic Combinations

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.

Loading

Article metrics loading...

/content/journals/cbio/10.2174/0115748936341887241122090418
2025-02-04
2025-07-17
Loading full text...

Full text loading...

References

  1. Babaei K. Khaksar R. Zeinali T. Hemmati H. Bandegi A. Samidoust P. Ashoobi M.T. Hashemian H. Delpasand K. Talebinasab F. Naebi H. Mirpour S.H. Keymoradzadeh A. Norollahi S.E. Epigenetic profiling of MUTYH, KLF6, WNT1 and KLF4 genes in carcinogenesis and tumorigenesis of colorectal cancer. Biomedicine (Taipei) 2019 9 4 22 10.1051/bmdcn/2019090422 31724937
    [Google Scholar]
  2. Massagué J. Obenauf A.C. Metastatic colonization by circulating tumour cells. Nature 2016 529 7586 298 306 10.1038/nature17038 26791720
    [Google Scholar]
  3. Chen M.C. Hsu S.L. Lin H. Yang T.Y. Retinoic acid and cancer treatment. Biomedicine (Taipei) 2014 4 4 22 10.7603/s40681‑014‑0022‑1 25520935
    [Google Scholar]
  4. Goodspeed A. Heiser L.M. Gray J.W. Costello J.C. Tumor-Derived Cell Lines as Molecular Models of Cancer Pharmacogenomics. Mol. Cancer Res. 2016 14 1 3 13 10.1158/1541‑7786.MCR‑15‑0189 26248648
    [Google Scholar]
  5. Mirabelli P. Coppola L. Salvatore M. Cancer Cell Lines Are Useful Model Systems for Medical Research. Cancers (Basel) 2019 11 8 1098 10.3390/cancers11081098 31374935
    [Google Scholar]
  6. Wang X. Sheu J.J.C. Lai M.T. Yin-Yi Chang C. Sheng X. Wei L. Gao Y. Wang X. Liu N. Xie W. Chen C.M. Ding W.Y. Sun L. RSF-1 overexpression determines cancer progression and drug resistance in cervical cancer. Biomedicine (Taipei) 2018 8 1 4 10.1051/bmdcn/2018080104 29480799
    [Google Scholar]
  7. Iorio F. Rittman T. Ge H. Menden M. Saez-Rodriguez J. Transcriptional data: a new gateway to drug repositioning? Drug Discov. Today 2013 18 7-8 350 357 10.1016/j.drudis.2012.07.014 22897878
    [Google Scholar]
  8. Taguchi Y. Drug candidate identification based on gene expression of treated cells using tensor decomposition-based unsupervised feature extraction for large-scale data. BMC Bioinformatics 2019 19 S13 Suppl. 13 388 10.1186/s12859‑018‑2395‑8 30717646
    [Google Scholar]
  9. Szalai B. Veres D.V. Application of perturbation gene expression profiles in drug discovery—From mechanism of action to quantitative modelling. Front. Syst. Biol. 2023 3 1126044 10.3389/fsysb.2023.1126044
    [Google Scholar]
  10. Lamb J. Crawford E.D. Peck D. Modell J.W. Blat I.C. Wrobel M.J. Lerner J. Brunet J.P. Subramanian A. Ross K.N. Reich M. Hieronymus H. Wei G. Armstrong S.A. Haggarty S.J. Clemons P.A. Wei R. Carr S.A. Lander E.S. Golub T.R. The Connectivity Map: using gene-expression signatures to connect small molecules, genes, and disease. Science 2006 313 5795 1929 1935 10.1126/science.1132939 17008526
    [Google Scholar]
  11. El Khili M.R. Memon S.A. Emad A. MARSY: a multitask deep-learning framework for prediction of drug combination synergy scores. Bioinformatics 2023 39 4 btad177 10.1093/bioinformatics/btad177 37021933
    [Google Scholar]
  12. Roth R.A. Kana O. Filipovic D. Ganey P.E. Pharmacokinetic and toxicodynamic concepts in idiosyncratic, drug-induced liver injury. Expert Opin. Drug Metab. Toxicol. 2022 18 7-8 469 481 10.1080/17425255.2022.2113379 36003040
    [Google Scholar]
  13. Subramanian A. Narayan R. Corsello S.M. Peck D.D. Natoli T.E. Lu X. Gould J. Davis J.F. Tubelli A.A. Asiedu J.K. Lahr D.L. Hirschman J.E. Liu Z. Donahue M. Julian B. Khan M. Wadden D. Smith I.C. Lam D. Liberzon A. Toder C. Bagul M. Orzechowski M. Enache O.M. Piccioni F. Johnson S.A. Lyons N.J. Berger A.H. Shamji A.F. Brooks A.N. Vrcic A. Flynn C. Rosains J. Takeda D.Y. Hu R. Davison D. Lamb J. Ardlie K. Hogstrom L. Greenside P. Gray N.S. Clemons P.A. Silver S. Wu X. Zhao W.N. Read-Button W. Wu X. Haggarty S.J. Ronco L.V. Boehm J.S. Schreiber S.L. Doench J.G. Bittker J.A. Root D.E. Wong B. Golub T.R. A Next Generation Connectivity Map: L1000 Platform and the First 1,000,000 Profiles. Cell 2017 171 6 1437 1452.e17 10.1016/j.cell.2017.10.049 29195078
    [Google Scholar]
  14. Hanahan D. Weinberg R.A. The hallmarks of cancer: Perspectives for cancer medicine Oxford Academics 2016 10.1093/med/9780199656103.003.0001
    [Google Scholar]
  15. Welch D.R. Hurst D.R. Defining the Hallmarks of Metastasis. Cancer Res. 2019 79 12 3011 3027 10.1158/0008‑5472.CAN‑19‑0458 31053634
    [Google Scholar]
  16. Al-Bedeary S. Getta H. Al-Sharafi D. The hallmarks of cancer and their therapeutic targeting in current use and clinical trials. Iraqi Journal of Hematology 2020 9 1 1 10 10.4103/ijh.ijh_24_19
    [Google Scholar]
  17. Hainaut P. Plymoth A. Targeting the hallmarks of cancer. Curr. Opin. Oncol. 2013 25 1 50 51 10.1097/CCO.0b013e32835b651e 23150341
    [Google Scholar]
  18. Anthony J. Varalakshmi S. Sekar A.K. Devarajan N. Janakiraman B. Peramaiyan R. Glutaminase - A potential target for cancer treatment. Biomedicine (Taipei) 2024 14 2 29 37 10.37796/2211‑8039.1445 38939098
    [Google Scholar]
  19. Vasileiou M. Papageorgiou S. Nguyen N.P. Current Advancements and Future Perspectives of Immunotherapy in Breast Cancer Treatment. Immuno 2023 3 2 195 216 10.3390/immuno3020013
    [Google Scholar]
  20. Tehrani S.S. Zaboli E. Sadeghi F. Khafri S. Karimian A. Rafie M. Parsian H. MicroRNA-26a-5p as a potential predictive factor for determining the effectiveness of trastuzumab therapy in HER-2 positive breast cancer patients. Biomedicine (Taipei) 2021 11 2 30 39 35223402
    [Google Scholar]
  21. Mori R. Nagao Y. Efficacy of chemotherapy after hormone therapy for hormone receptor–positive metastatic breast cancer. SAGE Open Med. 2014 2 2050312114557376 10.1177/2050312114557376 26770749
    [Google Scholar]
  22. Lim S. Lee S. Han J. Park B.W. Kim S. Park S. Kim J.H. Choi H.J. Sohn J. Prolonged clinical benefit from the maintenance hormone therapy in patients with metastatic breast cancer. Breast 2013 22 6 1205 1209 10.1016/j.breast.2013.08.013 24135766
    [Google Scholar]
  23. Sánchez-Muñoz A. Pérez-Ruiz E. Jiménez B. Ribelles N. Márquez A. García-Ríos I. Alba Conejo E. Targeted therapy of metastatic breast cancer. Clin. Transl. Oncol. 2009 11 10 643 650 10.1007/s12094‑009‑0419‑6 19828406
    [Google Scholar]
  24. Balkrishna A. Mittal R. Malik R. Verma H. Mehra K.S. Chaturvedi H. Okeshwar O. Ishdev S. Kumar A. Arya V. Comparative analysis of Doxycycline and Ayurvedic herbs to target metastatic breast cancer: An in-silico approach. Biomedicine (Taipei) 2024 14 2 74 79 10.37796/2211‑8039.1448 38939099
    [Google Scholar]
  25. Spellman A. Tang S.C. Immunotherapy for breast cancer: past, present, and future. Cancer Metastasis Rev. 2016 35 4 525 546 10.1007/s10555‑016‑9654‑9 27913998
    [Google Scholar]
  26. Bustin S.A. Jellinger K.A. Advances in Molecular Medicine: Unravelling Disease Complexity and Pioneering Precision Healthcare. Int. J. Mol. Sci. 2023 24 18 14168 10.3390/ijms241814168 37762471
    [Google Scholar]
  27. Huang C.H. Drug repositioning for non-small cell lung cancer by using machine learning algorithms and topological graph theory. BMC Bioinform. 2016 17 S1 2 10.1186/s12859‑015‑0845‑0
    [Google Scholar]
  28. Huang C.H. Chang P.M.H. Lin Y.J. Wang C.H. Huang C.Y.F. Ng K.L. Drug repositioning discovery for early- and late-stage non-small-cell lung cancer. BioMed Res. Int. 2014 2014 1 13 10.1155/2014/193817 25210704
    [Google Scholar]
  29. Chen S.T. Huang C.H. Kok V.C. Huang C.Y.F. Ciou J.S. Tsai J.J.P. Kurubanjerdjit N. Ng K.L. 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. 2017 15 1 1650043 10.1142/S0219720016500438 28150521
    [Google Scholar]
  30. Huang C.H. Ciou J.S. Chen S.T. Kok V.C. Chung Y. Tsai J.J.P. Kurubanjerdjit N. Huang C.Y.F. Ng K.L. Identify potential drugs for cardiovascular diseases caused by stress-induced genes in vascular smooth muscle cells. PeerJ 2016 4 e2478 10.7717/peerj.2478 27703845
    [Google Scholar]
  31. Dai X. Cheng H. Bai Z. Li J. Breast Cancer Cell Line Classification and Its Relevance with Breast Tumor Subtyping. J. Cancer 2017 8 16 3131 3141 10.7150/jca.18457 29158785
    [Google Scholar]
  32. Hughes L. Malone C. Chumsri S. Burger A.M. McDonnell S. Characterisation of breast cancer cell lines and establishment of a novel isogenic subclone to study migration, invasion and tumourigenicity. Clin. Exp. Metastasis 2008 25 5 549 557 10.1007/s10585‑008‑9169‑z 18386134
    [Google Scholar]
  33. Zheng G. Ma Y. Zou Y. Yin A. Li W. Dong D. HCMDB: the human cancer metastasis database. Nucleic Acids Res. 2018 46 D1 D950 D955 10.1093/nar/gkx1008 29088455
    [Google Scholar]
  34. Liu Y. Li Z. Lu J. Zhao M. Qu H. CMGene: A literature-based database and knowledge resource for cancer metastasis genes. J. Genet. Genomics 2017 44 5 277 279 10.1016/j.jgg.2017.04.006 28527662
    [Google Scholar]
  35. Tran T.D. Pham D.T. Identification of anticancer drug target genes using an outside competitive dynamics model on cancer signaling networks. Sci. Rep. 2021 11 1 14095 10.1038/s41598‑021‑93336‑z 34238960
    [Google Scholar]
  36. Charrad M. Ghazzali N. Boiteau V. Niknafs A. NbClust : An R Package for Determining the Relevant Number of Clusters in a Data Set. J. Stat. Softw. 2014 61 6 1 36 10.18637/jss.v061.i06
    [Google Scholar]
  37. Neary B. Zhou J. Qiu P. Identifying gene expression patterns associated with drug-specific survival in cancer patients. Sci. Rep. 2021 11 1 5004 10.1038/s41598‑021‑84211‑y 33654134
    [Google Scholar]
  38. Malyutina A. Majumder M.M. Wang W. Pessia A. Heckman C.A. Tang J. Drug combination sensitivity scoring facilitates the discovery of synergistic and efficacious drug combinations in cancer. PLOS Comput. Biol. 2019 15 5 e1006752 10.1371/journal.pcbi.1006752 31107860
    [Google Scholar]
  39. Mäkelä P. Zhang S.M. Rudd S.G. Drug synergy scoring using minimal dose response matrices. BMC Res. Notes 2021 14 1 27 10.1186/s13104‑021‑05445‑7 33468238
    [Google Scholar]
  40. Guillen V.S. Ziegler Y. Gopinath C. Kumar S. Dey P. Plotner B.N. Dawson N.Z. Kim S.H. Katzenellenbogen J.A. Katzenellenbogen B.S. Effective combination treatments for breast cancer inhibition by FOXM1 inhibitors with other targeted cancer drugs. Breast Cancer Res. Treat. 2023 198 3 607 621 10.1007/s10549‑023‑06878‑3 36847915
    [Google Scholar]
  41. Crystal A.S. Shaw A.T. Sequist L.V. Friboulet L. Niederst M.J. Lockerman E.L. Frias R.L. Gainor J.F. Amzallag A. Greninger P. Lee D. Kalsy A. Gomez-Caraballo M. Elamine L. Howe E. Hur W. Lifshits E. Robinson H.E. Katayama R. Faber A.C. Awad M.M. Ramaswamy S. Mino-Kenudson M. Iafrate A.J. Benes C.H. Engelman J.A. Patient-derived models of acquired resistance can identify effective drug combinations for cancer. Science 2014 346 6216 1480 1486 10.1126/science.1254721 25394791
    [Google Scholar]
  42. Dry J.R. Yang M. Saez-Rodriguez J. Looking beyond the cancer cell for effective drug combinations. Genome Med. 2016 8 1 125 10.1186/s13073‑016‑0379‑8 27887656
    [Google Scholar]
  43. Tong C.W.S. Wu W.K.K. Loong H.H.F. Cho W.C.S. To K.K.W. Drug combination approach to overcome resistance to EGFR tyrosine kinase inhibitors in lung cancer. Cancer Lett. 2017 405 100 110 10.1016/j.canlet.2017.07.023 28774798
    [Google Scholar]
  44. Allert C. Waclawiczek A. Zimmermann S.M.N. Göllner S. Heid D. Janssen M. Renders S. Rohde C. Bauer M. Bruckmann M. Zinz R. Pauli C. Besenbeck B. Wickenhauser C. Trumpp A. Krijgsveld J. Müller-Tidow C. Blank M.F. Protein tyrosine kinase 2b inhibition reverts niche-associated resistance to tyrosine kinase inhibitors in AML. Leukemia 2022 36 10 2418 2429 10.1038/s41375‑022‑01687‑x 36056084
    [Google Scholar]
  45. Anderson H.J. Galileo D.S. Small-molecule inhibitors of FGFR, integrins and FAK selectively decrease L1CAM-stimulated glioblastoma cell motility and proliferation. Cell Oncol. (Dordr.) 2016 39 3 229 242 10.1007/s13402‑016‑0267‑7 26883759
    [Google Scholar]
  46. Kanteti R. Mirzapoiazova T. Riehm J.J. Dhanasingh I. Mambetsariev B. Wang J. Kulkarni P. Kaushik G. Seshacharyulu P. Ponnusamy M.P. Kindler H.L. Nasser M.W. Batra S.K. Salgia R. Focal adhesion kinase a potential therapeutic target for pancreatic cancer and malignant pleural mesothelioma. Cancer Biol. Ther. 2018 19 4 316 327 10.1080/15384047.2017.1416937 29303405
    [Google Scholar]
  47. Cerbone A. Toaldo C. Minelli R. Ciamporcero E. Pizzimenti S. Pettazzoni P. Roma G. Dianzani M.U. Ullio C. Ferretti C. Dianzani C. Barrera G. Rosiglitazone and AS601245 decrease cell adhesion and migration through modulation of specific gene expression in human colon cancer cells. PLoS One 2012 7 6 e40149 10.1371/journal.pone.0040149 22761953
    [Google Scholar]
  48. Cui J. Wang Q. Wang J. Lv M. Zhu N. Li Y. Feng J. Shen B. Zhang J. Basal c-Jun NH2-terminal protein kinase activity is essential for survival and proliferation of T-cell acute lymphoblastic leukemia cells. Mol. Cancer Ther. 2009 8 12 3214 3222 10.1158/1535‑7163.MCT‑09‑0408 19996270
    [Google Scholar]
  49. Balboni A.L. Hutchinson J.A. DeCastro A.J. Cherukuri P. Liby K. Sporn M.B. Schwartz G.N. Wells W.A. Sempere L.F. Yu P.B. DiRenzo J. ΔNp63α-mediated activation of bone morphogenetic protein signaling governs stem cell activity and plasticity in normal and malignant mammary epithelial cells. Cancer Res. 2013 73 2 1020 1030 10.1158/0008‑5472.CAN‑12‑2862 23243027
    [Google Scholar]
  50. Chean J. Chen C. Shively J.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. 2017 359 1 62 75 10.1016/j.yexcr.2017.08.008 28800960
    [Google Scholar]
  51. Zhao C. Zhang Y. Zhang J. Li S. Liu M. Geng Y. Liu F. Chai Q. Meng H. Li M. Li J. Zheng Y. Zhang Y. Discovery of Novel Fedratinib-Based HDAC/JAK/BRD4 Triple Inhibitors with Remarkable Antitumor Activity against Triple Negative Breast Cancer. J. Med. Chem. 2023 66 20 14150 14174 10.1021/acs.jmedchem.3c01242 37796543
    [Google Scholar]
  52. Vijay G.V. Zhao N. Den Hollander P. Toneff M.J. Joseph R. Pietila M. Taube J.H. Sarkar T.R. Ramirez-Pena E. Werden S.J. Shariati M. Gao R. Sobieski M. Stephan C.C. Sphyris N. Miura N. Davies P. Chang J.T. Soundararajan R. Rosen J.M. Mani S.A. GSK3β regulates epithelial-mesenchymal transition and cancer stem cell properties in triple-negative breast cancer. Breast Cancer Res. 2019 21 1 37 10.1186/s13058‑019‑1125‑0 30845991
    [Google Scholar]
  53. Ji X. Meng X. He Q. Xiang X. Shi Y. Zhu X. 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. 2023 24 1 757 10.3390/ijms24010757 36614199
    [Google Scholar]
  54. van Oorschot B. Granata G. Di Franco S. Cate R. Rodermond H.M. Todaro M. Medema J.P. Franken N.A.P. Targeting DNA double strand break repair with hyperthermia and DNA-PKcs inhibition to enhance the effect of radiation treatment. Oncotarget 2016 7 40 65504 65513 10.18632/oncotarget.11798 27602767
    [Google Scholar]
  55. Choi C. Cho W.K. Park S. Shin S.W. Park W. Kim H. Choi D.H. Checkpoint Kinase 1 (CHK1) Inhibition Enhances the Sensitivity of Triple-Negative Breast Cancer Cells to Proton Irradiation via Rad51 Downregulation. Int. J. Mol. Sci. 2020 21 8 2691 10.3390/ijms21082691 32294924
    [Google Scholar]
  56. Frasor J. Weaver A. Pradhan M. Dai Y. Miller L.D. Lin C.Y. Stanculescu A. Positive cross-talk between estrogen receptor and NF-kappaB in breast cancer. Cancer Res. 2009 69 23 8918 8925 10.1158/0008‑5472.CAN‑09‑2608 19920189
    [Google Scholar]
  57. Frasor J. NFκB affects estrogen receptor expression and activity in breast cancer through multiple mechanisms. Mol. Cell Endocrinol. 2015 418 Pt 3 235 239 10.1016/j.mce.2014.09.013
    [Google Scholar]
  58. Smart E. Semina S.E. Frasor J. Update on the Role of NFκB in Promoting Aggressive Phenotypes of Estrogen Receptor–Positive Breast Cancer. Endocrinology 2020 161 10 bqaa152 10.1210/endocr/bqaa152 32887995
    [Google Scholar]
  59. Zagidullin B. Aldahdooh J. Zheng S. Wang W. Wang Y. Saad J. Malyutina A. Jafari M. Tanoli Z. Pessia A. Tang J. DrugComb: an integrative cancer drug combination data portal. Nucleic Acids Res. 2019 47 W1 W43 W51 10.1093/nar/gkz337 31066443
    [Google Scholar]
  60. Kaklamani V.G. Richardson A.L. Arteaga C.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. Oncologist 2019 24 3 305 312 10.1634/theoncologist.2018‑0314 30651399
    [Google Scholar]
  61. Baselga J. Im S.A. Iwata H. Cortés J. De Laurentiis M. Jiang Z. Arteaga C.L. Jonat W. Clemons M. Ito Y. Awada A. Chia S. Jagiełło-Gruszfeld A. Pistilli B. Tseng L.M. Hurvitz S. Masuda N. Takahashi M. Vuylsteke P. Hachemi S. Dharan B. Di Tomaso E. Urban P. Massacesi C. Campone M. 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. 2017 18 7 904 916 10.1016/S1470‑2045(17)30376‑5 28576675
    [Google Scholar]
  62. Di Leo A. Johnston S. Lee K.S. Ciruelos E. Lønning P.E. Janni W. O’Regan R. Mouret-Reynier M.A. Kalev D. Egle D. Csőszi T. Bordonaro R. Decker T. Tjan-Heijnen V.C.G. Blau S. Schirone A. Weber D. El-Hashimy M. Dharan B. Sellami D. Bachelot T. 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. 2018 19 1 87 100 10.1016/S1470‑2045(17)30688‑5 29223745
    [Google Scholar]
/content/journals/cbio/10.2174/0115748936341887241122090418
Loading
/content/journals/cbio/10.2174/0115748936341887241122090418
Loading

Data & Media loading...

Supplements

Supplementary material is available on the publisher's website along with the published article.


  • Article Type:
    Research Article
Keywords: Tumor metastasis ; hallmarks of cancer ; breast tumor ; drugs ; drug repositioning ; cell lines
This is a required field
Please enter a valid email address
Approval was a Success
Invalid data
An Error Occurred
Approval was partially successful, following selected items could not be processed due to error
Please enter a valid_number test