Veterinary and Comparative Biomedical Research

Veterinary and Comparative Biomedical Research

Detection of Clinically Relevant Mutated Genes in Canine Breast Cancer Using DNA Sequencing Data

Document Type : Original Article

Authors
Department of Clinical Sciences, Faculty of Veterinary Medicine, Shahid Bahonar University of Kerman, Kerman, Iran
Abstract
Breast cancer is one of the most important canine cancers and is considered an ideal animal model for its human counterpart. Compared to human breast cancer, the clinically relevant mutated genes are less well-known. In this study, we sought to find clinically relevant mutated genes using DNA sequencing data through an in-silico analysis.
Using a canine mammary tumor DNA sequencing dataset containing 183 samples, namely SRP159481, we first identified frequently mutated genes as those with various types of somatic mutations present in at least 5% of the studied population. Then, we evaluated the association between these frequently mutated genes and different clinicopathological features, including lymph node metastasis (LN+/LN-), estrogen receptor status (ER+/ER-), degree of malignancy (benign/malignant), and histopathological tumor type. We found 26 frequently mutated genes, which were reduced to 18 after removing the silent mutations. Among these genes, TP53 was found to be the most clinically relevant gene, because it had a significant relationship with all clinical features. The frequency of TP53 was significantly higher in malignant tumors compared to benign tumors, ER- tumors compared to ER+ tumors, and LN+ cases compared to LN- cases (p<0.05). In addition, the survival time in dogs with TP53 mutation was significantly shorter than in dogs without this mutation           (p<0.05). Moreover, the frequency of mutations in PIK3CA and SLC6A8 was significantly higher in benign cases than in malignant cases (p<0.05). We also validated our findings regarding TP53 using an external dataset.­
Keywords
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  1. Carvalho PT, Niza-Ribeiro J, Amorim I, Queiroga F, Severo M, Ribeiro AI, et al. Comparative epidemiological study of breast cancer in humans and canine mammary tumors: insights from Portugal. Front Vet Sci. 2023;10:1271097. https://doi.org/10.3389/fvets.2023.1271097 
  2. Zamani-Ahmadmahmudi M, Jajarmi M, Talebipour S. Molecular phenotyping of malignant canine mammary tumours: detection of high-risk group and its relationship with clinicomolecular characteristics. Vet Comp Oncol. 2023;21(1):73-81. https://doi.org/10.1111/vco.12863 
  3. Zamani-Ahmadmahmudi M, Nassiri SM, Rahbarghazi R. Serological proteome analysis of dogs with breast cancer unveils common serum biomarkers with human counterparts. Electrophoresis. 2014;35(6):901-10. https://doi.org/10.1002/elps.201300461 
  4. Gray M, Meehan J, Martínez-Pérez C, et al. Naturally-occurring canine mammary tumors as a translational model for human breast cancer. Front Oncol. 2020;10:617.
  5. Boggs RM, Wright ZM, Stickney MJ, et al. MicroRNA expression in canine mammary cancer. Mammalian Genome: Official Journal of the International. Mamm Genome. 2008;19(7-8):561-9. 
  6. Klopfleisch R, Lenze D, Hummel M, et al. Metastatic canine mammary carcinomas can be identified by a gene expression profile that partly overlaps with human breast cancer profiles. BMC Cancer. 2010;10(1):618. https://doi.org/10.1186/1471-2407-10-618 
  7. Rossi C, Cicalini I, Cufaro MC, et al. Breast cancer in the era of integrating "omics" approaches. Oncogenesis. 2022;11(1):17. https://doi.org/10.1038/s41389-022-00393-8 
  8. Parsons J, Francavilla C. ' Omics approaches to explore the breast cancer landscape. Front Cell Dev Biol. 2019;7:395. https://doi.org/10.3389/fcell.2019.00395 
  9. Judes G, Rifaï K, Daures M, et al. High-throughput «omics» technologies: new tools for the study of triple-negative breast cancer. Cancer Lett. 2016;382(1):77-85. https://doi.org/10.1016/j.canlet.2016.03.001 
  10. Leithner D, Horvat JV, Ochoa-Albiztegui RE, et al. Imaging and the completion of the omics paradigm in breast cancer. Radiologe. 2018;58(1):7-13. https://doi.org/10.1007/s00117-018-0409-1 
  11. Klopfleisch R, Klose P, Weise C, et al. Proteome of metastatic canine mammary carcinomas: similarities to and differences from human breast cancer. J Proteome Res. 2010;9(12):6380-91. https://doi.org/10.1021/pr100671c 
  12. Stucci LS, Internò V, Tucci M, et al. The ATM gene in breast cancer: its relevance in clinical practice. Genes (Basel). 2021;12(5):727. https://doi.org/10.3390/genes12050727 
  13. Barakeh DH, Aljelaify R, Bashawri Y, et al. Landscape of somatic mutations in breast cancer: new opportunities for targeted therapies in Saudi Arabian patients. Oncotarget. 2021;12(7):686-97. https://doi.org/10.18632/oncotarget.27909 
  14. Shahbandi A, Nguyen HD, Jackson JG. TP53 mutations and outcomes in breast cancer: reading beyond the headlines. Trends Cancer. 2020;6(2):98-110. https://doi.org/10.1016/j.trecan.2020.01.007 
  15. Xiao W, Zhang G, Chen B, et al. Characterization of frequently mutated cancer genes and tumor mutation burden in Chinese breast cancer. Front Oncol. 2021;11:618767. https://doi.org/10.3389/fonc.2021.618767 
  16. Lee CH, Kim WH, Lim JH, Kang MS, Kim DY, Kweon OK . Mutation and overexpression of p53 as a prognostic factor in canine mammary tumors. J Vet Sci. 2004;5(1):63-9. https://doi.org/10.4142/jvs.2004.5.1.63 
  17. Rivera P, Melin M, Biagi T, et al. Mammary tumor development in dogs is associated with BRCA1 and BRCA2. Cancer Res. 2009;69(22):8770-4. https://doi.org/10.1158/0008-5472.CAN-09-1725 
  18. Pereira B, Chin SF, Rueda OM, Vollan HKM, Provenzano E, Bardwell HA, et al. The somatic mutation profiles of 2,433 breast cancers refines their genomic and transcriptomic landscapes. Nat Commun. 2016;7:11479. https://doi.org/10.1038/ncomms11479 
  19. Nair SV, Madhulaxmi, Thomas G, et al. Next-generation sequencing in cancer. J Maxillofac Oral Surg. 2021;20(3):340-4. https://doi.org/10.1007/s12663-020-01462-4 
  20. Trivedi UH, Cézard T, Bridgett S, et al. Quality control of next-generation sequencing data without a reference. Front Genet. 2014;5:111. https://doi.org/10.3389/fgene.2014.00111 
  21. Bolger AM, Lohse M, Usadel B. Trimmomatic: a flexible trimmer for Illumina sequence data. Bioinformatics. 2014;30(15):2114-20. https://doi.org/10.1093/bioinformatics/btu170 
  22. Li H, Durbin R. Fast and accurate short read alignment with Burrows-Wheeler transform. Bioinformatics. 2009;25(14):1754-1760. https://doi.org/10.1093/bioinformatics/btp324 
  23. Li H, Handsaker B, Wysoker A, et al. The Sequence Alignment/Map format and SAMtools. Bioinformatics. 2009;25(16):2078-2079. https://doi.org/10.1093/bioinformatics/btp352 
  24. McKenna A, Hanna M, Banks E, et al. The Genome Analysis Toolkit: a MapReduce framework for analyzing next-generation DNA sequencing data. Genome Res. 2010;20(9):1297-1303. https://doi.org/10.1101/gr.107524.110 
  25. Cibulskis K, Lawrence MS, Carter SL, et al. Sensitive detection of somatic point mutations in impure and heterogeneous cancer samples. Nat Biotechnol. 2013;31(3):213-19. https://doi.org/10.1038/nbt.2514 
  26. Wang K, Li M, Hakonarson H. ANNOVAR: functional annotation of genetic variants from high-throughput sequencing data. Nucleic Acids Res. 2010;38(16):e164. https://doi.org/10.1093/nar/gkq603 
  27. Koboldt DC, Zhang Q, Larson DE, et al. VarScan 2: somatic mutation and copy number alteration discovery in cancer by exome sequencing. Genome Res. 2012;22(3):568-576. https://doi.org/10.1101/gr.129684.111 
  28. Sherman BT, Hao M, Qiu J, et al. DAVID: a web server for functional enrichment analysis and functional annotation of gene lists (2021 update). Nucleic Acids Res. 2022;50(W1):W216-W221. https://doi.org/10.1093/nar/gkac194 
  29. Zamani-Ahmadmahmudi M. Relationship between microRNA genes incidence and cancer-associated genomic regions in canine tumors: a comprehensive bioinformatics study. Funct Integr Genomics. 2016; 16(2):143-52. https://doi.org/10.1007/s10142-016-0473-4 
  30. Lingjaerde OC, Baumbusch LO, Liestøl K, et al. CGH-Explorer: a program for analysis of array-CGH data. Bioinformatics. 2005;21(6):821-2. https://doi.org/10.1093/bioinformatics/bti113 
  31. Kastenhuber ER, Lowe SW. Putting p53 in context. Cell. 2017;170(6):1062-78.  https://doi.org/10.1016/j.cell.2017.08.028 
  32. Hafner A, Bulyk ML, Jambhekar A, et al. The multiple mechanisms that regulate p53 activity and cell fate. Nat Rev Mol Cell Biol. 2019;20(4):199-210. https://doi.org/10.1038/s41580-019-0110-x 
  33. The Cancer Genome Atlas Network. Comprehensive molecular portraits of human breast tumours. Nature. 2012;490(7418):61-70. https://doi.org/10.1038/nature11412 
  34. Iwaya K, Tsuda H, Hiraide H, et al. Nuclear p53 immunoreaction associated with poor prognosis of breast cancer. Jpn J Cancer Res. 1991;82(7):835-40. https://doi.org/10.1111/j.1349-7006.1991.tb02710.x 
  35. Yamashita H, Toyama T, Nishio M, et al. p53 protein accumulation predicts resistance to endocrine therapy and decreased post-relapse survival in metastatic breast cancer. Breast Cancer Res. 2006;8(4):R48. https://doi.org/10.1186/bcr1536 
  36. Shiao YH, Chen VW, Scheer WD, et al. Racial disparity in the association of p53 gene alterations with breast cancer survival. Cancer Res. 1995;55(7):1485-90. 
  37. Blaszyk H, Hartmann A, Cunningham JM, et al. A prospective trial of midwest breast cancer patients: a p53 gene mutation is the most important predictor of adverse outcome. Int J Cancer. 2000;89(1):32-8. https://doi.org/10.1002/(SICI)1097-0215(20000120)89:1%3C32::AID-IJC6%3E3.0.CO;2-G 
  38. Andersson J, Larsson L, Klaar S, et al. Worse survival for TP53 (p53)-mutated breast cancer patients receiving adjuvant CMF. Ann Oncol. 2005;16(5):743-748. https://doi.org/10.1093/annonc/mdi150 
  39. Bertheau P, Plassa F, Espié M, et al. Effect of mutated TP53 on response of advanced breast cancers to high-dose chemotherapy. Lancet. 2002;360(9336):852-4. https://doi.org/10.1016/S0140-6736(02)09969-5 
  40. Lehmann-Che J, André F, Desmedt C, et al. Cyclophosphamide dose intensification may circumvent anthracycline resistance of p53 mutant breast cancers. Oncologist. 2010;15(3):246-52. https://doi.org/10.1634/theoncologist.2009-0243 
  41. Zhang S, Wang C, Ma B, et al. Mutant p53 Drives Cancer Metastasis via RCP-Mediated Hsp90α Secretion. Cell Rep. 2020;32(1):107879. https://doi.org/10.1016/j.celrep.2020.107879 
  42. Horigome E, Fujieda M, Handa T, et al. Mutant TP53 modulates metastasis of triple negative breast cancer through adenosine A2b receptor signaling. Oncotarget. 2018;9(77):34554-66. https://doi.org/10.18632/oncotarget.26177 
  43. Meric-Bernstam F, Zheng X, Shariati M, et al. Survival Outcomes by TP53 Mutation Status in Metastatic Breast Cancer. JCO Precis Oncol. 2018;2:1-5. https://doi.org/10.1200/PO.17.00245 
  44. Langerød A, Zhao H, Borgan Ø, et al. TP53 mutation status and gene expression profiles are powerful prognostic markers of breast cancer. Breast Cancer Res. 2007;9(3):R30. https://doi.org/10.1186/bcr1675 
  45. Wakui S, Muto T, Yokoo K, et al. Prognostic status of p53 gene mutation in canine mammary carcinoma. Anticancer Res. 2001;21(1B):611-16.
  46. Lee CH, Kweon OK. Mutations of p53 tumor suppressor gene in spontaneous canine mammary tumors. J Vet Sci. 2002;3(4):321-5.
  47. Dumont AG, Dumont SN, Trent JC. The favorable impact of PIK3CA mutations on survival: an analysis of 2587 patients with breast cancer. Chin J Cancer. 2012;31(7):327-34. https://doi.org/10.5732/cjc.012.10032 
  48. Pang B, Cheng S, Sun SP, et al. Prognostic role of PIK3CA mutations and their association with hormone receptor expression in breast cancer: a meta-analysis. Sci Rep. 2014;4:6255. https://doi.org/10.1038/srep06255 
  49. Kalinsky K, Jacks LM, Heguy A, et al. PIK3CA mutation associates with improved outcome in breast cancer. Clin Cancer Res. 2009;15(16):5049-59. https://doi.org/10.1158/1078-0432.ccr-09-0632 
  50. Maruyama N, Miyoshi Y, Taguchi T, et al. Clinicopathologic analysis of breast cancers with PIK3CA mutations in Japanese women. Clin Cancer Res. 2007;13(2 Pt 1):408-14. https://doi.org/10.1158/1078-0432.CCR-09-0632 
  51. Li SY, Rong M, Grieu F, et al. PIK3CA mutations in breast cancer are associated with poor outcome. Breast Cancer Res Treat. 2006;96(1):91-5. https://doi.org/10.1007/s10549-005-9048-0 
  52. Lerma E, Catasus L, Gallardo A, et al. Exon 20 PIK3CA mutations decreases survival in aggressive (HER-2 positive) breast carcinomas. Virchows Arch. 2008;453(2):133-9. https://doi.org/10.1007/s00428-008-0643-4 
  53. Sobhani N, Roviello G, Corona SP, et al. The prognostic value of PI3K mutational status in breast cancer: A meta-analysis. J Cell Biochem. 2018;119(6):4287-92. https://doi.org/10.1002/jcb.26687 
  54. Fan H, Li C, Xiang Q, et al. PIK3CA mutations and their response to neoadjuvant treatment in early breast cancer: A systematic review and meta-analysis. Thorac Cancer. 2018;9(5):571-9. https://doi.org/10.1111/1759-7714.12618 
  55. Reinhardt K, Stückrath K, Hartung C, et al. PIK3CA-mutations in breast cancer. Breast Cancer Res Treat. 2022;196(3):483-93. https://doi.org/10.1007/s10549-022-06637-w 
  56. Michelucci A, Di Cristofano C, Lami A, et al. PIK3CA in breast carcinoma: a mutational analysis of sporadic and hereditary cases. Diagn Mol Pathol. 2009;18(4):200-5. https://doi.org/10.1097/pdm.0b013e31818e5fa4 
  57. Boyault S, Drouet Y, Navarro C, et al. Mutational characterization of individual breast tumors: TP53 and PI3K pathway genes are frequently and distinctively mutated in different subtypes. Breast Cancer Res Treat. 2012;132(1):29-39. https://doi.org/10.1007/s10549-011-1518-y 
  58. Li H, Prever L, Hirsch E, et al. Targeting PI3K/AKT/mTOR Signaling Pathway in Breast Cancer. Cancers (Basel). 2021;13(14):3517. https://doi.org/10.3390/cancers13143517 
  59. Lefebvre C, Bachelot T, Filleron T, et al. Mutational Profile of Metastatic Breast Cancers: A Retrospective Analysis. PLoS Med. 2016;13(12):e1002201. https://doi.org/10.1371/journal.pmed.1002201