Retrospective Analysis of Transcriptomic Differences between Triple-Negative Breast Cancer (TNBC) and non-TNBC

Year 2024, Volume: 83 Issue: 1, 19 - 27, 30.05.2024

Çağlar Berkel

https://doi.org/10.26650/EurJBiol.2024.1362117

Abstract

Objective: Triple-negative breast cancer (TNBC), which has no expression of estrogen receptor, progesterone receptor and HER2, is an aggressive subgroup. Molecular differences between TNBC and non-TNBC should be better understood to develop tailored treatment strategies.
Materials and Methods: The expression of the most frequently mutated genes, and of genes for which copy number variation events are observed in the highest percentage of breast cancer patients, was compared between TNBC and non-TNBC samples, in R programming environment, using TCGA-BRCA transcriptomics dataset.
Results: 70% of the most frequently mutated genes in breast cancer (CDH1, GATA3, MLL3 (KMT2C), MAP3K1, PTEN, NCOR1, FAT3, MAP2K4, NF1, ARID1A, LRP1B, RUNX1, MLL2 (KMT2D) and TBX3) was found to have decreased expression in TNBC compared to non-TNBC. The expression of 40% of the genes with the highest frequency of copy number gain events in breast cancer (SLC45A3, PTPRC, ELF3, FCGR2B, AKT3, FH, TPM3 and SETDB1) was increased in TNBC compared with non-TNBC. The half of the genes with the highest percentage of copy number loss events in breast cancer (CBFA2T3, CDH1, ZFHX3, CDH11, MAP2K4, GAS7, PER1, RABEP1, NCOR1 and PCM1) was observed to have decreased expression in TNBC compared to non-TNBC. Lastly, the expression of BRCA2, but not of BRCA1, was found to be higher in TNBC than in non-TNBC.
Conclusion: This study provides further evidence in support of previous research, which show the presence of a large number of molecular differences between TNBC and non-TNBC, pointing to the need for more tailored treatment strategies for patients with TNBC.

Keywords

Breast cancer, Estrogen receptor, Progesterone receptor, HER2, Copy number variation, Transcriptomics, Triplenegative breast cancer

References

• Harding C, Pompei F, Burmistrov D, Welch HG, Abebe R, Wilson R. Breast cancer screening, incidence, and mortality across US counties. JAMA Intern Med. 2015;175(9):1483-1489. google scholar
• Sung H, Ferlay J, Siegel RL et al. Global cancer statistics 2020: GLOBOCAN estimates of incidence and mortality worldwide for 36 cancers in 185 countries. CA Cancer J Clin. 2021;71(3):209-249. google scholar
• Kim M, Park J, Bouhaddou M, et al. A protein interaction land-scape of breast cancer. Science. 2021;374(6563):eabf3066. doi: 10.1126/science.abf3066 google scholar
• Bianchini G, Balko JM, Mayer IA, Sanders ME, Gianni L. Triple-negative breast cancer: Challenges and opportunities of a hetero-geneous disease. Nat Rev Clin Oncol. 2016;13(11):674-690. google scholar
• Bianchini G, De Angelis C, Licata L, Gianni L. Treatment land-scape of triple-negative breast cancer-expanded options, evolving needs. Nat Rev Clin Oncol. 2022;19(2):91-113. google scholar
• Fallahpour S, Navaneelan T, De P, Borgo A. Breast cancer sur-vival by molecular subtype: A population-based analysis of cancer registry data. CMAJ Open. 2017;5:E734-E739. google scholar
• Dent R, Trudeau M, Pritchard KI, et al. Triple-negative breast cancer: Clinical features and patterns of recurrence. Clin Cancer Res. 2007;13:4429-4434. google scholar
• Tan AR, Swain SM. Therapeutic strategies for triple-negative breast cancer. CancerJ. 2008;14:343-351. google scholar
• Kaplan HG, Malmgren JA, Atwood M. T1N0 triple negative breast cancer: Risk of recurrence and adjuvant chemotherapy. Breast J. 2009;15:454-460. google scholar
• Won KA, Spruck C. Triple negative breast cancer ther-apy: Current and future perspectives (Review). Int J Oncol. 2020;57:1245-1261. google scholar
• Almansour NM. Triple-negative breast cancer: A brief review about epidemiology, risk factors, signaling pathways, treat-ment and role of artificial intelligence. Front Mol Biosci. 2022;9:836417. doi:10.3389/fmolb.2022.836417. google scholar
• Berkel C, Kucuk B, Usta M, Yılmaz E, Cacan E. The effect of olaparib and bortezomib combination treatment on ovarian cancer cell lines. Eur J Biol. 2020;79(2):115-123. google scholar
• Ciriello G, Gatza ML, Beck AH, et al. Comprehensive molecular portraits of ınvasive lobular breast cancer. Cell. 2015;163(2):506-519. google scholar
• Jensen MA, Ferretti V, Grossman RL, Staudt LM. The NCI Ge-nomic Data Commons as an engine for precision medicine. Blood. 2017;130(4):453-459. google scholar
• Zhang Z, Hernandez K, Savage J, et al. Uniform genomic data analysis in the NCI Genomic Data Commons. Nat Commun. 2021;12(1):1226. doi: 10.1038/s41467-021-21254-9. google scholar
• Berger AC, Korkut A, Kanchi RS, et al. A Comprehensive pan-cancer molecular study of gynecologic and breast cancers. Cancer Cell. 20189;33(4):690-705.e9. doi: 10.1016/j.ccell.2018.03.014. google scholar
• Cancer Genome Atlas Network. Comprehensive molecular por-traits of human breast tumours. Nature. 2012;490(7418):61-70. google scholar
• Rahman M, Jackson LK, Johnson WE, Li DY, Bild AH, Pic-colo SR. Alternative preprocessing of RNA-Sequencing data in The Cancer Genome Atlas leads to improved analysis results. Bioinformatics. 2015;31(22):3666-3672. google scholar
• Weinstein JN, Collisson EA, Mills GB, et al. Cancer Genome Atlas Research Network; The Cancer Genome Atlas Pan-Cancer analysis project. Nat Genet. 2013;45(10):1113-1120. google scholar
• Wilks C, Cline MS, Weiler E, et al. The Cancer Genomics Hub (CGHub): Overcoming cancer through the power of torrential data. Database. 2014:1-10. google scholar
• Arora S. GSE62944: GEO accession data GSE62944 as a SummarizedExperiment. R package version 1.28.1;2023. http://bioconductor.org/packages/release/bioc/html/GSE62944.html google scholar
• Huber W, Carey VJ, Gentleman R, et al. Orchestrating high-throughput genomic analysis with bioconductor. Nat Methods. 2015;12(2):115-121. google scholar
• Gentleman RC, Carey VJ, Bates DM, et al. Bioconductor: Open software development for computational biology and bioinfor-matics. Genome Biol. 2004;5(10):R80. doi:10.1186/gb-2004-5-10-r80 google scholar
• R Core Team. R: A language and environment for statistical com-puting. R Foundation for Statistical Computing; 2022. Vienna, Austria. URL https://www.R-project.org/ google scholar
• Wickham H, Averick M, Bryan J, et al. Welcome to the tidyverse. J Open Source Softw. 2019;4(43):1686. doi:10.21105/joss.01686 google scholar
• Wickham H. stringr: Simple, consistent wrappers for common string operations. R package version 1.5.0;2022. https://CRAN.R-project.org/package=stringr google scholar
• Wickham H. ggplot2: Elegant Graphics for Data Analysis. Springer-Verlag New York, 2016. google scholar
• Wickham H, Bryan J. readxl: Read Excel Files. R package version 1.4.2;2023. https://CRAN.R-project.org/package=readxl google scholar
• Morgan M, Shepherd L. ExperimentHub: Client to access Exper-imentHub resources. R package version 2.4.0;2022. google scholar
• Morgan M, Obenchain V, Hester J, Pages H. SummarizedExperiment: SummarizedExperi- ment container. R package version 1.26.1;2022. https://bioconductor.org/packages/SummarizedExperiment google scholar
• Kassambara A. ggpubr: ’ggplot2’ Based Publication Ready Plots. R package version 0.6.0;2023. https://CRAN.R-project.org/package=ggpubr google scholar
• Allaire J, Xie Y, Dervieux C, et al. rmarkdown: Dy-namic Documents for R. R package version 2.21;2023. https://github.com/rstudio/rmarkdown google scholar
• Xie Y. knitr: A General-Purpose Package for Dynamic Report Generation in R. R package version 1.42;2023. google scholar
• Berkel C, Cacan E. In silico analysis of DYNLL1 expression in ovarian cancer chemoresistance. Cell Biol Int. 2020;44(8):1598-1605. google scholar
• Berkel C, Cacan E. Transcriptomic analysis reveals tumor stage-or grade-dependent expression of miRNAs in serous ovarian can-cer. Hum Cell. 2021;34(3):862-877. google scholar
• Li X, Yang J, Peng L, et al. Triple-negative breast cancer has worse overall survival and cause-specific survival than non-triple-negative breast cancer. Breast Cancer Res Treat. 2017;161(2):279-287. google scholar
• Bai X, Ni J, Beretov J, Graham P, Li Y. Triple-negative breast cancer therapeutic resistance: Where is the Achilles’ heel? Cancer Lett. 2021;497:100-111. google scholar
• Bai F, Zhang LH, Liu X, et al. GATA3 functions downstream of BRCA1 to suppress EMT in breast cancer. Theranostics. 2021;11(17):8218-8233. google scholar
• Yu W, Huang W, Yang Y, et al. GATA3 recruits UTX for gene transcriptional activation to suppress metastasis of breast cancer. Cell Death Dis. 2019;10(11):832. doi:10.1038/s41419-019-2062-7 google scholar
• Li S, Shen Y, Wang M, et al. Loss of PTEN expression in breast cancer: Association with clinicopathological characteristics and prognosis. Oncotarget. 2017;8(19):32043-32054. google scholar
• Hong D, Fritz AJ, Gordon JA, et al. RUNX1-dependent mech-anisms in biological control and dysregulation in cancer. J Cell Physiol. 2019;234(6):8597-8609. google scholar
• Chaudhary S, Appadurai MI, Maurya SK, et al. MUC16 pro-motes triple-negative breast cancer lung metastasis by modu-lating RNA-binding protein ELAVL1/HUR. Breast Cancer Res. 2023;25(1):25. doi:10.1186/s13058-023-01630-7 google scholar
• Ryu TY, Kim K, Kim SK, et al. SETDB1 regulates SMAD7 ex-pression for breast cancer metastasis. BMB Rep. 2019;52(2):139-144. google scholar
• Wu M, Fan B, Guo Q, et al. Knockdown of SETDB1 inhibits breast cancer progression by miR-381-3p-related regulation. Biol Res. 2018;51(1):39. doi: 10.1186/s40659-018-0189-0 google scholar
• Liu Z, Liu J, Ebrahimi B, et al. SETDB1 interactions with PELP1 contributes to breast cancer endocrine therapy resistance. Breast Cancer Res. 2022;24(1):26. doi: 10.1186/s13058-022-01520-4 google scholar
• Swetzig WM, Wang J, Das GM. Estrogen receptor alpha (ERa/ESR1) mediates the p53-independent overexpression of MDM4/MDMX and MDM2 in human breast cancer. Oncotarget. 2016;7(13):16049-16069. google scholar
• Zhang Z, Zhang J, Li J, et al. miR-320/ELF3 axis inhibits the progression of breast cancer via the PI3K/AKT pathway. Oncol Lett. 2020;19(4):3239-3248. google scholar
• Kochetkova M, McKenzie OL, Bais AJ, et al. CBFA2T3 (MTG16) is a putative breast tumor suppressor gene from the breast cancer loss of heterozygosity region at 16q24.3. Cancer Res. 2002;62(16):4599-4604. google scholar
• Dong G, MaG,WuR, et al. ZFHX3 promotes the proliferation and tumor growth of ER-positive breast cancer cells likely by enhanc-ing stem-like features and MYC and TBX3 transcription. Cancers (Basel). 2020;12(11):3415. doi: 10.3390/cancers12113415 google scholar
• Chang JW, Kuo WH, Lin CM, et al. Wild-type p53 upregu-lates an early onset breast cancer-associated gene GAS7 to sup-press metastasis via GAS7-CYFIP1-mediated signaling pathway. Oncogene. 2018;37(30):4137-4150. google scholar
• Hsu CH, Ma HP, Ong JR, et al. Cancer-associated exosomal CBFB facilitates the aggressive phenotype, evasion of oxidative stress, and preferential predisposition to bone prometastatic factor of breast cancer progression. Dis Markers. 2022;2022:8446629. doi: 10.1155/2022/8446629 google scholar
• Zhang Z, Yamashita H, Toyama T, et al. NCOR1 mRNA is an independent prognostic factor for breast cancer. Cancer Lett. 2006;237(1):123-129. google scholar
• Mavaddat N, Barrowdale D, Andrulis IL, et al. Consortium of Investigators of Modifiers of BRCA1/2. Pathology of breast and ovarian cancers among BRCA1 and BRCA2 mutation car-riers: results from the Consortium of Investigators of Modi-fiers of BRCA1/2 (CIMBA). Cancer Epidemiol Biomarkers Prev. 2012;21(1):134-147. google scholar
• De Talhouet S, Peron J, Vuilleumier A, et al. Clini-cal outcome of breast cancer in carriers of BRCA1 and BRCA2 mutations according to molecular subtypes. Sci Rep. 202027;10(1):7073. doi:10.1038/s41598-020-63759-1 Erratum in: Sci Rep. 2020;10(1):19248. google scholar
• Lakhani SR, Reis-Filho JS, Fulford L, et al. Prediction of BRCA1 status in patients with breast cancer using estrogen receptor and basal phenotype. Clin Cancer Res. 2005;11:5175-5180. google scholar
• Armes JE, Trute L, White D, et al. Distinct molecular pathogeneses of early-onset breast cancers in BRCA1 and BRCA2 mutation carriers: a population-based study. Cancer Res. 1999;59:2011-2017. google scholar
• Palacios J, Honrado E, Osorio A, et al. Phenotypic characteriza-tion of BRCA1 and BRCA2 tumors based in a tissue microarray study with 37 immunohistochemical markers. Breast Cancer Res Treat. 2005;90:5-14. google scholar
• Lakhani SR, Van De Vijver MJ, Jacquemier J, et al. The pathology of familial breast cancer: predictive value of immunohistochemi-cal markers estrogen receptor, progesterone receptor, HER-2, and p53 in patients with mutations in BRCA1 and BRCA2. J Clin Oncol. 2002;20:2310-2318. google scholar
• Lehmann BD, Bauer JA, Chen X, et al. Identification of human triple-negative breast cancer subtypes and preclinical models for selection of targeted therapies. J Clin Invest. 2011;121(7):2750-2767. google scholar
• Sporikova Z, Koudelakova V, Trojanec R, Hajduch M. Genetic markers in triple-negative breast cancer. Clin Breast Cancer. 2018;18(5):e841-e850. doi: 10.1016/j.clbc.2018.07.023 google scholar