Background Grading schemes for breast cancer diagnosis are predominantly based on pathologists' qualitative assessment of altered nuclear structure from 2D brightfield microscopy images. However, cells are three-dimensional (3D) objects with features that are inherently 3D and thus poorly characterized in 2D. Our goal is to quantitatively characterize nuclear structure in 3D, assess its variation with malignancy, and investigate whether such variation correlates with standard nuclear grading criteria. Methodology We applied micro-optical computed tomographic imaging and automated 3D nuclear morphometry to quantify and compare morphological variations between human cell lines derived from normal, benign fibrocystic or malignant breast epithelium. To reproduce the appearance and contrast in clinical cytopathology images, we stained cells with hematoxylin and eosin and obtained 3D images of 150 individual stained cells of each cell type at sub-micron, isotropic resolution. Applying volumetric image analyses, we computed 42 3D morphological and textural descriptors of cellular and nuclear structure. Principal Findings We observed four distinct nuclear shape categories, the predominant being a mushroom cap shape. Cell and nuclear volumes increased from normal to fibrocystic to metastatic type, but there was little difference in the volume ratio of nucleus to cytoplasm (N/C ratio) between the lines. Abnormal cell nuclei had more nucleoli, markedly higher density and clumpier chromatin organization compared to normal. Nuclei of non-tumorigenic, fibrocystic cells exhibited larger textural variations than metastatic cell nuclei. At p<0.0025 by ANOVA and Kruskal-Wallis tests, 90% of our computed descriptors statistically differentiated control from abnormal cell populations, but only 69% of these features statistically differentiated the fibrocystic from the metastatic cell populations. Conclusions Our results provide a new perspective on nuclear structure variations associated with malignancy and point to the value of automated quantitative 3D nuclear morphometry as an objective tool to enable development of sensitive and specific nuclear grade classification in breast cancer diagnosis.
References
[1]
Anderson WF, Matsuno R (2006) Breast cancer heterogeneity: a mixture of at least two main types? Journal of the National Cancer Institute 98: 948–951.
[2]
Bertucci F, Birnbaum D (2008) Reasons for breast cancer heterogeneity. Journal of Biology 7:
[3]
Hsiao YH, Chou MC, Fowler C, Mason JT, Man Y (2010) Breast cancer heterogeneity: mechanisms, proofs, and implications. Journal of Cancer 1: 6–13.
[4]
Ferlay J, Héry C, Autier P, Sankaranarayanan R (2010) Global Burden of Breast Cancer. Breast Cancer Epidemiology 1–19.
[5]
Siegel R, Ward E, Brawley O, Jemal A (2011) Cancer statistics, 2011: The impact of eliminating socioeconomic and racial disparities on premature cancer deaths. CA: a cancer journal for clinicians: caac. 20121v20121 p.
[6]
Lim CN, Ho BCS, Bay BH, Yip G, Tan PH (2006) Nuclear morphometry in columnar cell lesions of the breast: is it useful? Journal of clinical pathology 59: 1283–1286.
[7]
Tan PH, Goh BB, Chiang G, Bay BH (2001) Correlation of nuclear morphometry with pathologic parameters in ductal carcinoma in situ of the breast. Modern Pathology 14: 937–941.
[8]
Pienta KJ, Coffey DS (1991) Correlation of nuclear morphometry with progression of breast cancer. Cancer 68: 2012–2016.
[9]
Mariuzzi G, Mariuzzi L, Mombello A, Santinelli A, Valli M, et al. (1996) Quantitative study of ductal breast cancer progression. A progression index (PI) for premalignant lesions and in situ carcinoma. Pathology, research and practice 192: 428–436.
[10]
Kronqvist P, Kuopio T, Collan Y (1998) Morphometric grading of invasive ductal breast cancer. I. Thresholds for nuclear grade. British journal of cancer 78: 800–805.
[11]
Hoque A, Lippman SM, Boiko IV, Atkinson EN, Sneige N, et al. (2001) Quantitative nuclear morphometry by image analysis for prediction of recurrence of ductal carcinoma in situ of the breast. Cancer Epidemiology Biomarkers & Prevention 10: 249–259.
[12]
Mariuzzi L, Mombello A, Granchelli G, Rucco V, Tarocco E, et al. (2002) Quantitative study of breast cancer progression: different pathways for various in situ cancers. Modern Pathology 15: 18–25.
[13]
Ruiz A, Almenar S, Cerdá M, Hidalgo JJ, Puchades A, et al. (2002) Ductal carcinoma in situ of the breast: a comparative analysis of histology, nuclear area, ploidy, and neovascularization provides differentiation between low-and high-grade tumors. The Breast Journal 8: 139–144.
[14]
Petushi S, Garcia FU, Haber MM, Katsinis C, Tozeren A (2006) Large-scale computations on histology images reveal grade-differentiating parameters for breast cancer. BMC Medical Imaging 6: 14.
[15]
Chapman JAW, Miller NA, Lickley H, Lavina A, Qian J, et al. (2007) Ductal carcinoma in situ of the breast(DCIS) with heterogeneity of nuclear grade: prognostic effects of quantitative nuclear assessment. BMC cancer 7: 174–183.
[16]
Cui Y, Koop EA, Van Diest PJ, Kandel RA, Rohan TE (2007) Nuclear morphometric features in benign breast tissue and risk of subsequent breast cancer. Breast cancer research and treatment 104: 103–107.
[17]
Axelrod DE, Miller NA, Lickley HL, Qian J, Christens-Barry WA, et al. (2008) Effect of quantitative nuclear image features on recurrence of ductal carcinoma in situ (dcis) of the breast. Cancer informatics 6: 99–109.
[18]
Nyirenda N, Farkas DL, Ramanujan VK (2011) Preclinical evaluation of nuclear morphometry and tissue topology for breast carcinoma detection and margin assessment. Breast cancer research and treatment 126: 345–354.
[19]
Beli？n JAM, van Ginkel HAHM, Tekola P, Ploeger LS, Poulin NM, et al. (2002) Confocal DNA cytometry: a contour-based segmentation algorithm for automated three-dimensional image segmentation. Cytometry Part A 49: 12–21.
[20]
Liu S, Weaver DL, Taatjes D (1997) Three-dimensional reconstruction by confocal laser scanning microscopy in routine pathologic specimens of benign and malignant lesions of the human breast. Histochemistry and cell biology 107: 267–278.
[21]
Bussolati G, Marchiò C, Gaetano L, Lupo R, Sapino A (2008) Pleomorphism of the nuclear envelope in breast cancer: a new approach to an old problem. Journal of Cellular and Molecular Medicine 12: 209–218.
[22]
Fauver M, Seibel E, Rahn J, Meyer M, Patten F, et al. (2005) Three-dimensional imaging of single isolated cell nuclei using optical projection tomography. Optics Express 13: 4210–4223.
[23]
Meyer M, Fauver M, Rahn J, Neumann T, Patten F, et al. (2009) Automated cell analysis in 2D and 3D: A comparative study. Pattern Recognition 42: 141–146.
[24]
Herbert BS, Wright WE, Shay JW (2002) p16INK4a inactivation is not required to immortalize human mammary epithelial cells. Oncogene 21: 7897–7900.
[25]
Soule HD, Maloney TM, Wolman SR, Peterson WD, Brenz R, et al. (1990) Isolation and characterization of a spontaneously immortalized human breast epithelial cell line, MCF-10. Cancer research 50: 6075–6086.
[26]
Cailleau R, Young R, Olive M, Reeves W Jr (1974) Breast tumor cell lines from pleural effusions. Journal of the National Cancer Institute 53: 661–674.
[27]
J？nsson G, Staaf J, Olsson E, Heidenblad M, Vallon Christersson J, et al. (2007) High resolution genomic profiles of breast cancer cell lines assessed by tiling BAC array comparative genomic hybridization. Genes, Chromosomes and Cancer 46: 543–558.
[28]
Nandakumar V, Kelbauskas L, Johnson R, Meldrum D (2011) Quantitative characterization of preneoplastic progression using single cell computed tomography and three dimensional karyometry. Cytometry Part A 79A: 25–34.
[29]
Kak A, Slaney M (2001) Principles of Computerized Tomographic Imaging. Philadelphia: Society for Industrial and Applied Mathematics.
[30]
Doudkine A, MacAulay C, Poulin N, Palcic B (1995) Nuclear texture measurements in image cytometry. Pathologica 87: 286–299.
[31]
Haralick RM, Shanmugam K, Dinstein IH (1973) Textural features for image classification. IEEE Transactions on systems, man and cybernetics 3: 610–621.
[32]
Zink D, Fischer A, Nickerson J (2004) Nuclear Structure in Cancer Cells. Nature Reviews Cancer 4: 677–687.
[33]
Geyer PK, Vitalini MW, Wallrath LL (2011) Nuclear organization: taking a position on gene expression. Current opinion in cell biology.
[34]
Marella NV, Bhattacharya S, Mukherjee L, Xu J, Berezney R (2009) Cell type specific chromosome territory organization in the interphase nucleus of normal and cancer cells. Journal of cellular physiology 221: 130–138.
[35]
Bacus J, Boone C, Bacus J, Follen M, Kelloff G, et al. (1999) Image morphometric nuclear grading of intraepithelial neoplastic lesions with applications to cancer chemoprevention trials. Cancer Epidemiology Biomarkers & Prevention 8: 1087.
[36]
da Silva V, Prolla J, Sharma P, Sampliner R, Thompson D, et al. (2001) Karyometry in Barrett's esophagus. Analytical and quantitative cytology and histology/the International Academy of Cytology [and] American Society of Cytology 23: 40–46.
[37]
Kemp RA, Reinders DM, Turic B (2007) Detection of lung cancer by automated sputum cytometry. Journal of Thoracic Oncology 2: 993–1000.
[38]
Veltri R, Partin A, Miller M (2005) Quantitative Nuclear Grade. Cancer Chemoprevention 97–108.
[39]
Cohet N, Stewart KM, Mudhasani R, Asirvatham AJ, Mallappa C, et al. (2010) SWI/SNF chromatin remodeling enzyme ATPases promote cell proliferation in normal mammary epithelial cells. Journal of cellular physiology 223: 667–678.
[40]
Silva JM, Marran K, Parker JS, Silva J, Golding M, et al. (2008) Profiling essential genes in human mammary cells by multiplex RNAi screening. Science 319: 617.
[41]
Spencer VA, Xu R, Bissell MJ (2007) Extracellular matrix, nuclear and chromatin structure, and gene expression in normal tissues and malignant tumors: a work in progress. Advances in cancer research 97: 275–294.
[42]
Lee GY, Kenny PA, Lee EH, Bissell MJ (2007) Three-dimensional culture models of normal and malignant breast epithelial cells. Nature methods 4: 359–365.
[43]
Ma XJ, Salunga R, Tuggle JT, Gaudet J, Enright E, et al. (2003) Gene expression profiles of human breast cancer progression. Proceedings of the National Academy of Sciences of the United States of America 100: 5974.
[44]
Sotiriou C, Pusztai L (2009) Gene-expression signatures in breast cancer. New England Journal of Medicine 360: 790.
[45]
Neve RM, Chin K, Fridlyand J, Yeh J, Baehner FL, et al. (2006) A collection of breast cancer cell lines for the study of functionally distinct cancer subtypes. Cancer cell 10: 515–527.
[46]
Luftner D, Possinger K (2002) Nuclear matrix proteins as biomarkers for breast cancer. Expert Review of Molecular Diagnostics 2: 23–31.
[47]
Knowles DW, Sudar D, Bator-Kelly C, Bissell MJ, Lelièvre SA (2006) Automated local bright feature image analysis of nuclear protein distribution identifies changes in tissue phenotype. Proceedings of the National Academy of Sciences of the United States of America 103: 4445.
