NCAPH drives breast cancer progression and identifies a gene signature that predicts luminal a tumour recurrence

Abstract

Background: Luminal A tumours generally have a favourable prognosis but possess the highest 10-year recurrence risk among breast cancers. Additionally, a quarter of the recurrence cases occur within 5 years post-diagnosis. Identifying such patients is crucial as long-term relapsers could benefit from extended hormone therapy, while early relapsers might require more aggressive treatment.

Methods: We conducted a study to explore non-structural chromosome maintenance condensin I complex subunit H’s (NCAPH) role in luminal A breast cancer pathogenesis, both in vitro and in vivo, aiming to identify an intratumoural gene expression signature, with a focus on elevated NCAPH levels, as a potential marker for unfavourable progression. Our analysis included transgenic mouse models overexpressing NCAPH and a genetically diverse mouse cohort generated by backcrossing. A least absolute shrinkage and selection operator (LASSO) multivariate regression analysis was performed on transcripts associated with elevated intratumoural NCAPH levels.

Results: We found that NCAPH contributes to adverse luminal A breast cancer progression. The intratumoural gene expression signature associated with elevated NCAPH levels emerged as a potential risk identifier. Transgenic mice overexpressing NCAPH developed breast tumours with extended latency, and in Mouse Mammary Tumor Virus (MMTV)-NCAPH^ErbB2 double-transgenic mice, luminal tumours showed increased aggressiveness. High intratumoural Ncaph levels correlated with worse breast cancer outcome and subpar chemotherapy response. A 10-gene risk score, termed Gene Signature for Luminal A 10 (GSLA10), was derived from the LASSO analysis, correlating with adverse luminal A breast cancer progression.

Conclusions: The GSLA10 signature outperformed the Oncotype DX signature in discerning tumours with unfavourable outcomes, previously categorised as luminal A by Prediction Analysis of Microarray 50 (PAM50) across three independent human cohorts. This new signature holds promise for identifying luminal A tumour patients with adverse prognosis, aiding in the development of personalised treatment strategies to significantly improve patient outcomes.

Keywords

breast cancer / genetic signature / LASSO / luminal A subtype / NCAPH / prognosis / relapse-free survival

References

[1] 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.

[2] van ’t Veer LJ, Dai H, van de Vijver MJ, et al. Gene expression profiling predicts clinical outcome of breast cancer. Nature. 2002;415(6871):530-536.

[3] Buyse M, Loi S, van't Veer L, et al. Validation and clinical utility of a 70-gene prognostic signature for women with node-negative breast cancer. J Natl Cancer Inst. 2006;98(17):1183-1192.

[4] Cardoso F, Piccart-Gebhart M, Van't Veer L, Rutgers E, Consortium T. The MINDACT trial: the first prospective clinical validation of a genomic tool. Mol Oncol. 2007;1(3):246-251.

[5] Nielsen TO, Parker JS, Leung S, et al. A comparison of PAM50 intrinsic subtyping with immunohistochemistry and clinical prognostic factors in tamoxifen-treated estrogen receptor-positive breast cancer. Clin Cancer Res. 2010;16(21):5222-5232.

[6] Paik S, Shak S, Tang G, et al. A multigene assay to predict recurrence of tamoxifen-treated, node-negative breast cancer. N Engl J Med. 2004;351(27):2817-2826.

[7] Prat A, Ellis MJ, Perou CM. Practical implications of gene-expression-based assays for breast oncologists. Nat Rev Clin Oncol. 2011;9(1):48-57.

[8] Tang G, Shak S, Paik S, et al. Comparison of the prognostic and predictive utilities of the 21-gene recurrence score assay and adjuvant! for women with node-negative, ER-positive breast cancer: results from NSABP B-14 and NSABP B-20. Breast Cancer Res Treat. 2011;127(1):133-142.

[9] Parker JS, Mullins M, Cheang MC, et al. Supervised risk predictor of breast cancer based on intrinsic subtypes. J Clin Oncol. 2009;27(8):1160-1167.

[10] Ellis MJ, Suman VJ, Hoog J, et al. Randomized phase II neoadjuvant comparison between letrozole, anastrozole, and exemestane for postmenopausal women with estrogen receptor-rich stage 2 to 3 breast cancer: clinical and biomarker outcomes and predictive value of the baseline PAM50-based intrinsic subtype-ACOSOG Z1031. J Clin Oncol. 2011;29(17):2342-2349.

[11] Sestak I, Buus R, Cuzick J, et al. Comparison of the performance of 6 prognostic signatures for estrogen receptor-positive breast cancer: a secondary analysis of a randomized clinical trial. JAMA Oncol. 2018;4(4):545-553.

[12] Sestak I, Cuzick J. Markers for the identification of late breast cancer recurrence. Breast Cancer Res. 2015;17:10.

[13] Ignatov A, Eggemann H, Burger E, Ignatov T. Patterns of breast cancer relapse in accordance to biological subtype. J Cancer Res Clin Oncol. 2018;144(7):1347-1355.

[14] Pedersen RN, Esen BO, Mellemkjaer L, et al. The incidence of breast cancer recurrence 10-32 years after primary diagnosis. J Natl Cancer Inst. 2022;114(3):391-399.

[15] Natarajan L, Pu M, Parker BA, et al. Time-varying effects of prognostic factors associated with disease-free survival in breast cancer. Am J Epidemiol. 2009;169(12):1463-1470.

[16] Colleoni M, Sun Z, Price KN, et al. Annual hazard rates of recurrence for breast cancer during 24 years of follow-up: results from the International Breast Cancer Study Group Trials I to V. J Clin Oncol. 2016;34(9):927-935.

[17] Ogba N, Manning NG, Bliesner BS, et al. Luminal breast cancer metastases and tumor arousal from dormancy are promoted by direct actions of estradiol and progesterone on the malignant cells. Breast Cancer Res. 2014;16(6):489.

[18] Pu M, Messer K, Davies SR, et al. Research-based PAM50 signature and long-term breast cancer survival. Breast Cancer Res Treat. 2020;179(1):197-206.

[19] Ciriello G, Sinha R, Hoadley KA, et al. The molecular diversity of luminal A breast tumors. Breast Cancer Res Treat. 2013;141(3):409-420.

[20] Perez-Pena J, Alcaraz-Sanabria A, Nieto-Jimenez C, et al. Mitotic read-out genes confer poor outcome in luminal A breast cancer tumors. Oncotarget. 2017;8(13):21733-21740.

[21] Hirano T. Condensins: universal organizers of chromosomes with diverse functions. Genes Dev. 2012;26(15):1659-1678.

[22] Ono T, Losada A, Hirano M, Myers MP, Neuwald AF, Hirano T. Differential contributions of condensin I and condensin II to mitotic chromosome architecture in vertebrate cells. Cell. 2003;115(1):109-121.

[23] Hirota T, Gerlich D, Koch B, Ellenberg J, Peters JM. Distinct functions of condensin I and II in mitotic chromosome assembly. J Cell Sci. 2004;117(pt 26):6435-6445.

[24] Ono T, Fang Y, Spector DL, Hirano T. Spatial and temporal regulation of condensins I and II in mitotic chromosome assembly in human cells. Mol Biol Cell. 2004;15(7):3296-3308.

[25] Maeshima K, Laemmli UK. A two-step scaffolding model for mitotic chromosome assembly. Dev Cell. 2003;4(4):467-480.

[26] Gerlich D, Hirota T, Koch B, Peters JM, Ellenberg J. Condensin I stabilizes chromosomes mechanically through a dynamic interaction in live cells. Curr Biol. 2006;16(4):333-344.

[27] Neuwald AF, Hirano T. HEAT repeats associated with condensins, cohesins, and other complexes involved in chromosome-related functions. Genome Res. 2000;10(10):1445-1452.

[28] Hirano T. Condensins: organizing and segregating the genome. Curr Biol. 2005;15(7):R265-R275.

[29] Hirano T. Condensin-based chromosome organization from bacteria to vertebrates. Cell. 2016;164(5):847-857.

[30] Weyburne E, Bosco G. Cancer-associated mutations in the condensin II subunit CAPH2 cause genomic instability through telomere dysfunction and anaphase chromosome bridges. J Cell Physiol. 2021;236(5):3579-3598.

[31] Tibshirani R. The lasso method for variable selection in the Cox model. Stat Med. 1997;16(4):385-395.

[32] Schindelin J, Arganda-Carreras I, Frise E, et al. Fiji: an open-source platform for biological-image analysis. Nat Methods. 2012;9(7):676-682.

[33] Nguyen D, Quantifying chromogen intensity in immunohistochemistry via reciprocal intensity. CancerInCytes. 2013.

[34] Guy CT, Webster MA, Schaller M, Parsons TJ, Cardiff RD, Muller WJ. Expression of the neu protooncogene in the mammary epithelium of transgenic mice induces metastatic disease. Proc Natl Acad Sci U S A. 1992;89(22):10578-10582.

[35] Castellanos-Martin A, Castillo-Lluva S, Saez-Freire Mdel M, et al. Unraveling heterogeneous susceptibility and the evolution of breast cancer using a systems biology approach. Genome Biol. 2015;16:40.

[36] Ashburner M, Ball CA, Blake JA, et al. Gene ontology: tool for the unification of biology. The Gene Ontology Consortium. Nat Genet. 2000;25(1):25-29.

[37] Yu G, Wang LG, Han Y, He QY. clusterProfiler: an R package for comparing biological themes among gene clusters. Omics. 2012;16(5):284-287.

[38] Therneau T. A Package for Survival Analysis in R. R package version 3.4-0. 2022.

[39] Therneau T, Grambsch P. Modeling Survival Data: Extending the Cox Model. Springer; 2000.

[40] Kassambara A, Kosinski M, Biecek P, Fabian S. Package ‘survminer’. Drawing Survival Curves using ‘ggplot2’ (R package version 03 1). 2017.

[41] Ringner M, Fredlund E, Hakkinen J, Borg A, Staaf J. GOBO: gene expression-based outcome for breast cancer online. PLoS One. 2011;6(3):e17911.

[42] Hosmer DW, Lemeshow S, Sturdivant RX. Applied Logistic Regression. John Wiley & Sons; 2013.

[43] Harrell FE. Regression Modeling Strategies: With Applications to Linear Models, Logistic Regression, and Survival Analysis. Springer; 2001.

[44] Robin X, Turck N, Hainard A, et al. pROC: an open-source package for R and S+ to analyze and compare ROC curves. BMC Bioinf. 2011;12(1):1-8.

[45] Curtis C, Shah SP, Chin SF, et al. The genomic and transcriptomic architecture of 2,000 breast tumours reveals novel subgroups. Nature. 2012;486(7403):346-352.

[46] Cancer Genome Atlas Network. Comprehensive molecular portraits of human breast tumours. Nature. 2012;490(7418):61-70.

[47] Lawrence I, Lin K. A concordance correlation coefficient to evaluate reproducibility. Biometrics. 1989;45(1):255-268.

[48] Longato E, Vettoretti M, Di Camillo B. A practical perspective on the concordance index for the evaluation and selection of prognostic time-to-event models. J Biomed Inform. 2020;108:103496.

[49] Castillo S, Blanco-Gómez A, Pérez-Losada J. Papel de la Kleisina NCAPH en el desarrollo y evolución del cáncer de mama HER2 positivo. XXXIX Congress of the Spanish Society of Biochemistry and Molecular Biology (SEBBM). 2016.

[50] García-Sancha N, Corchado-Cobos R, Martínez-López A, et al. Analyzing the role of non-SMC condensin I complex subunit H in breast cancer development and evolution. Symposium on Cancer Genomics and Epitranscriptomics: From the Bench to the Clinic. 2022.

[51] Riis ML, Zhao X, Kaveh F, et al. Gene expression profile analysis of t1 and t2 breast cancer reveals different activation pathways. ISRN Oncol. 2013;2013:924971.

[52] Pawitan Y, Bjohle J, Amler L, et al. Gene expression profiling spares early breast cancer patients from adjuvant therapy: derived and validated in two population-based cohorts. Breast Cancer Res. 2005;7(6):R953-R964.

[53] Wang Y, Klijn JG, Zhang Y, et al. Gene-expression profiles to predict distant metastasis of lymph-node-negative primary breast cancer. Lancet. 2005;365(9460):671-679.

[54] Ivshina AV, George J, Senko O, et al. Genetic reclassification of histologic grade delineates new clinical subtypes of breast cancer. Cancer Res. 2006;66(21):10292-10301.

[55] van de Vijver MJ, He YD, van't Veer LJ, et al. A gene-expression signature as a predictor of survival in breast cancer. N Engl J Med. 2002;347(25):1999-2009.

[56] Chandrashekar DS, Karthikeyan SK, Korla PK, et al. UALCAN: an update to the integrated cancer data analysis platform. Neoplasia. 2022;25:18-27.

[57] Otten AD, Sanders MM, McKnight GS. The MMTV LTR promoter is induced by progesterone and dihydrotestosterone but not by estrogen. Mol Endocrinol. 1988;2(2):143-147.

[58] Jezequel P, Gouraud W, Ben Azzouz F, et al. bc-GenExMiner 4.5: new mining module computes breast cancer differential gene expression analyses. Database. 2021;2021:baab007.

[59] Lanczky A, Gyorffy B. Web-based survival analysis tool tailored for medical research (KMplot): development and implementation. J Med Internet Res. 2021;23(7):e27633.

[60] Bastien RR, Rodriguez-Lescure A, Ebbert MT, et al. PAM50 breast cancer subtyping by RT-qPCR and concordance with standard clinical molecular markers. BMC Med Genet. 2012;5:44.

[61] Vasconcelos I, Hussainzada A, Berger S, et al. The St. Gallen surrogate classification for breast cancer subtypes successfully predicts tumor presenting features, nodal involvement, recurrence patterns and disease free survival. Breast. 2016;29:181-185.

[62] Cortazar AR, Torrano V, Martín-Martín N, et al. CANCERTOOL: a visualization and representation interface to exploit cancer datasets. Cancer Res. 2018;78(21):6320-6328.

[63] Saez-Freire MDM, Blanco-Gomez A, Castillo-Lluva S, et al. The biological age linked to oxidative stress modifies breast cancer aggressiveness. Free Radical Biol Med. 2018;120:133-146.

[64] Schadt EE, Monks SA, Drake TA, et al. Genetics of gene expression surveyed in maize, mouse and man. Nature. 2003;422(6929):297-302.

[65] Sanchez-Vega F, Mina M, Armenia J, et al. Oncogenic signaling pathways in The Cancer Genome Atlas. Cell. 2018;173(2):321-337.e10.

[66] Balmain A. Cancer as a complex genetic trait: tumor susceptibility in humans and mouse models. Cell. 2002;108(2):145-152.

[67] Mao JH, Perez-Losada J, Wu D, et al. Fbxw7/Cdc4 is a p53-dependent, haploinsufficient tumour suppressor gene. Nature. 2004;432(7018):775-779.

[68] Auchincloss H, Winn HJ. Clarence Cook Little (1888-1971): the genetic basis of transplant immunology. Am J Transpl. 2004;4(2):155-159.

[69] Quigley DA, To MD, Perez-Losada J, et al. Genetic architecture of mouse skin inflammation and tumour susceptibility. Nature. 2009;458(7237):505-508.

[70] Twelves C, Jove M, Gombos A, Awada A. Cytotoxic chemotherapy: still the mainstay of clinical practice for all subtypes metastatic breast cancer. Crit Rev Oncol Hematol. 2016;100:74-87.

[71] Cardoso F, Kyriakides S, Ohno S, et al. Early breast cancer: ESMO Clinical Practice Guidelines for diagnosis, treatment and follow-updagger. Ann Oncol. 2019;30(8):1194-1220.

[72] Diessner J, Wischnewsky M, Blettner M, et al. Do patients with luminal A breast cancer profit from adjuvant systemic therapy? A retrospective multicenter study. PLoS One. 2016;11(12):e0168730.

[73] Li Y, Ma L. Efficacy of chemotherapy for lymph node-positive luminal A subtype breast cancer patients: an updated meta-analysis. World J Surg Oncol. 2020;18(1):316.

[74] Sparano JA, Gray RJ, Makower DF, et al. Adjuvant chemotherapy guided by a 21-gene expression assay in breast cancer. N Engl J Med. 2018;379(2):111-121.

[75] Paik S, Tang G, Shak S, et al. Gene expression and benefit of chemotherapy in women with node-negative, estrogen receptor-positive breast cancer. J Clin Oncol. 2006;24(23):3726-3734.

[76] Paik S, Tang G, Shak S, et al. Gene expression and benefit of chemotherapy in women with node-negative, estrogen receptor-positive breast cancer. J Clin Oncol. 2023;41(20):3565-3575.

[77] Pan H, Gray R, Braybrooke J, et al. 20-Year risks of breast-cancer recurrence after stopping endocrine therapy at 5 years. N Engl J Med. 2017;377(19):1836-1846.

[78] Davies C, Pan H, Godwin J, et al. Long-term effects of continuing adjuvant tamoxifen to 10 years versus stopping at 5 years after diagnosis of oestrogen receptor-positive breast cancer: ATLAS, a randomised trial. Lancet. 2013;381(9869):805-816.

[79] Goss PE, Ingle JN, Pritchard KI, et al. Extending aromatase-inhibitor adjuvant therapy to 10 years. N Engl J Med. 2016;375(3):209-219.

[80] Li W, Hu Y, Oh S, et al. Condensin I and II complexes license full estrogen receptor alpha-dependent enhancer activation. Mol Cell. 2015;59(2):188-202.

[81] Zhan SJ, Liu B, Linghu H. Identifying genes as potential prognostic indicators in patients with serous ovarian cancer resistant to carboplatin using integrated bioinformatics analysis. Oncol Rep. 2018;39(6):2653-2663.

[82] Qi Y, Mo K, Zhang T. A transcription factor that promotes proliferation, migration, invasion, and epithelial-mesenchymal transition of ovarian cancer cells and its possible mechanisms. Biomed Eng Online. 2021;20(1):83.

[83] Qiu X, Gao Z, Shao J, Li H. NCAPH is upregulated in endometrial cancer and associated with poor clinicopathologic characteristics. Ann Hum Genet. 2020;84(6):437-446.

[84] Cui F, Hu J, Xu Z, Tan J, Tang H. Overexpression of NCAPH is upregulated and predicts a poor prognosis in prostate cancer. Oncol Lett. 2019;17(6):5768-5776.

[85] Arai T, Kojima S, Yamada Y, et al. Micro-ribonucleic acid expression signature of metastatic castration-resistant prostate cancer: regulation of NCAPH by antitumor miR-199a/b-3p. Int J Urol. 2019;26(4):506-520.

[86] Wang M, Qiao X, Cooper T, et al. HPV E7-mediated NCAPH ectopic expression regulates the carcinogenesis of cervical carcinoma via PI3K/AKT/SGK pathway. Cell Death Dis. 2020;11(12):1049.

[87] Xiong YC, Wang J, Cheng Y, Zhang XY, Ye XQ. Overexpression of MYBL2 promotes proliferation and migration of non-small-cell lung cancer via upregulating NCAPH. Mol Cell Biochem. 2020;468(1-2):185-193.

[88] Shimomura H, Sasahira T, Nakashima C, Kurihara-Shimomura M, Kirita T. Non-SMC condensin I complex subunit H (NCAPH) is associated with lymphangiogenesis and drug resistance in oral squamous cell carcinoma. J Clin Med. 2019;9(1):72.

[89] Yin L, Jiang LP, Shen QS, et al. NCAPH plays important roles in human colon cancer. Cell Death Dis. 2017;8(3):e2680.

[90] Li B, Xiao Q, Shan L, Song Y. NCAPH promotes cell proliferation and inhibits cell apoptosis of bladder cancer cells through MEK/ERK signaling pathway. Cell Cycle. 2022;21(4):427-438.

[91] Wang Y, Li JQ, Yang ZL, et al. NCAPH regulates gastric cancer progression through DNA damage response. Neoplasma. 2022;69(2):283-291.

[92] Sun C, Huang S, Wang H, et al. Non-SMC condensin I complex subunit H enhances proliferation, migration, and invasion of hepatocellular carcinoma. Mol Carcinog. 2019;58(12):2266-2275.

[93] Ma Q, Xu Y, Liao H, et al. Identification and validation of key genes associated with non-small-cell lung cancer. J Cell Physiol. 2019;234(12):22742-22752.

[94] Zhou W, Hu J, Zhao J. Non-SMC condensin I complex subunit H (NCAPH), a regulator of cell cycle, predicts poor prognosis in lung adenocarcinoma patients: a study mainly based on TCGA and GEO database. Transl Cancer Res. 2020;9(12):7572-7587.

[95] Li Z, Sang M, Tian Z, et al. Identification of key biomarkers and potential molecular mechanisms in lung cancer by bioinformatics analysis. Oncol Lett. 2019;18(5):4429-4440.

[96] Ryu B, Kim DS, Deluca AM, Alani RM. Comprehensive expression profiling of tumor cell lines identifies molecular signatures of melanoma progression. PLoS One. 2007;2(7):e594.

[97] Lee SH, Ryu HS, Jang MJ, et al. Glandular tissue component and breast cancer risk in mammographically dense breasts at screening breast US. Radiology. 2021;301(1):57-65.

[98] Schmitt CA. Senescence, apoptosis and therapy—cutting the lifelines of cancer. Nat Rev Cancer. 2003;3(4):286-295.

[99] Liu X-L, Ding J, Meng L-H. Oncogene-induced senescence: a double edged sword in cancer. Acta Pharmacol Sin. 2018;39(10):1553-1558.

[100] Hills SA, Diffley JF. DNA replication and oncogene-induced replicative stress. Curr Biol. 2014;24(10):R435-R444.

[101] Mao J-H, Wu D, Perez-Losada J, et al. Crosstalk between Aurora-A and p53: frequent deletion or downregulation of Aurora-A in tumors from p53 null mice. Cancer Cell. 2007;11(2):161-173.

[102] Lowe SW, Cepero E, Evan G. Intrinsic tumour suppression. Nature. 2004;432(7015):307-315.

[103] Murphy DJ, Junttila MR, Pouyet L, et al. Distinct thresholds govern Myc's biological output in vivo. Cancer Cell. 2008;14(6):447-457.

[104] Larsson L-G, editor. Oncogene-and tumor suppressor gene-mediated suppression of cellular senescence. Seminars in Cancer Biology. Elsevier; 2011.

[105] Nassar A, Hoskin TL, Stallings-Mann ML, et al. Ki-67 expression in sclerosing adenosis and adjacent normal breast terminal ductal lobular units: a nested case-control study from the Mayo Benign Breast Disease Cohort. Breast Cancer Res Treat. 2015;151(1):89-97.

[106] Huh SJ, Oh H, Peterson MA, et al. The proliferative activity of mammary epithelial cells in normal tissue predicts breast cancer risk in premenopausal women. Cancer Res. 2016;76(7):1926-1934.

[107] Oh H, Eliassen AH, Wang M, et al. Expression of estrogen receptor, progesterone receptor, and Ki67 in normal breast tissue in relation to subsequent risk of breast cancer. NPJ Breast Cancer. 2016;2:16032.

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