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Diagnostic and Prognostic DNA-Karyometry for Cancer Diagnostics

Authors

  • Alfred Böcking

    Institute of Cytopathology, University of Düsseldorf, Germany; Consultant at Institute of Pathology, City Hospital Düren, Germany

  • David Friedrich

    Definiens AG, Munich, Germany; Institute of Image Analysis and Computer Vision, RWTH Aachen University, Germany

  • Branko Palcic

    Cancer Imaging Department, BC Cancer Agency, Vancouver, Canada

  • Dietrich Meyer-Ebrech

    Institute of Image Analysis and Computer Vision, RWTH Aachen University, Germany

  • Jin Chen

    Department of Software Development, Motic, Xiamen, PR, China

DOI:

https://doi.org/10.30683/1929-2279.2020.09.05

Keywords:

DNA-karyometry, DNA-image-cytometry, nuclear classification, automation, DNA-grading, prostate cancer, early diagnosis, effusions, urinary cytology, cervical cytology, bronchial cytology, oral cytology.

Abstract

Diagnostic and prognostic DNA-karyometry represents an automated computerized microscopical procedure, designed to improve cancer diagnostics at three different aspects:

  1. Screening for cancer cells, e.g. in body cavity effusions, urines or mucosal smears
  2. Specifying the risk of dysplasias or borderline lesions to progress to manifest cancer, e.g. of oral, bronchial or cervical mucosa, or the ovary.
  3. Grading the malignancy of certain tumors, like prostate cancer.

It combines an automated diagnostic classification of Feulgen-stained nuclei with precise nuclear DNA-measurements. DNA-aneuploidy is used as a specific marker of malignancy and its degree for grading.

All types of cytological specimens can be used after (re-)staining specific for DNA according to Feulgen. Histological specimens are subjected to enzymatic cell separation before Feulgen-staining.

A video-slide scanner is used for automated scanning of microscopical slides. Diagnostic nuclear classifiers have tissue-specifically been trained by an expert-cytopathologist (A. B.), based on Random Forest Classifiers, applying 18 different morphometric features. They achieve an overall accuracy of 91.1% to differentiate 8 differents types of objects/nuclei. Nuclear DNA-measurements of diploid nuclei achieve a CV of <3%. DNA-stemline-aneuploidy, applied as a 100% specific marker for malignancy, is detected and quantified, using internationally accepted algorithms (ESACP 1995-2001). Suspicion of malignancy is raised in the absence of DNA-aneuploidy but presence of >1% morphometrically abnormal nuclei.

Time needed for loading, scanning and validation of results per slide is about 10 minutes. Results of digital diagnostic nuclear classification can be verified by a cytopathologist, using image galleries. Likewise automated diagnostic interpretation of nuclear DNA-distributions can be checked on the monitor, before a pathologists validated diagnoses are issued.

Screening-results are presented for body cavity effusions and urines. Evaluations of dysplasias are reported for oral, bronchial and cervical smears. Results of grading malignancy are shown for prostate cancers.

 

References

Goldblum JR, Lamps LW, McKenney J, Myers JL, Rosai and Ackermann´s surgical pathology. 11th ed. The C. V. Mosby Company, St. Louis, Toronto, London 2018.

Koss LG, Melamed M. Koss´diagnostic cytology and its histopathological basis. Lippincott, Williams & Wilkins, Philadelphia, Toronto 2006.

Kayser K, Borkenfeld S, Carvalho R, Dejounis A, Kayser G. How to analyze structure and function in tissue - based diagnosis? Diagnostic Pathology 2016; 4(1): 106-128.

Vuletic F, Zajec V, Vuletic LB, Seiwerth S. Intratumoral heterogeneity. Diagnostic Pathology 2018; 4(1): 257-271.

Kayser K, Borkenfeld S, Kayser G. Digital image content and context information in tissue based diagnosis. Diagnostic Pathology 2018; 4(1): 269-287.

Fritz A, et al. International classification of diseases for oncology. 3rd ed, World Health Organization, Geneva 2000.

Griffith DFR, et al. A study of Gleason score interpretation in different groups of UK pthologiasts: techniques for improving reproducibility. Histopathology 2006; 48: 655-662. https://doi.org/10.1111/j.1365-2559.2006.02394.x

Veloso S, et al. Interobserver agreement of Gleason score and modified Gleason score in needle biopsy and surgical specimen of prostate cancer. Clinical Urology 2007; 33(5): 639-651. https://doi.org/10.1590/S1677-55382007000500005

Cyll K, et al. Tumour heterogeneity poses a significant challenge to cancer biomarker research. British Journal of Cancer 2017; 117: 367-375. https://doi.org/10.1038/bjc.2017.171

Burchard M, et al. Interobserver reproducibility of Gleason grading: evaluation using prostate cancer tissue microarrays. Journal of Cancer Research and Clinical Oncology 2008; 134: 1071-1078. https://doi.org/10.1007/s00432-008-0388-0

Duesberg P, Li R, Fabarius A, Hehlmann R. The chromosomal basis of cancer. Cellular Oncology 2005; 27: 293-318.

Bloomfield M, Duesberg P. Karyotype alteration generates the neoplastic phenotype of SV40-infected human and rodent cells. Molecular Cytogenetics 2015; 8: 79-108. https://doi.org/10.1186/s13039-015-0183-y

Duesberg P, McCormack A. Immortality of cancers. A consequence of inherent karyotypic variations and selection for autonomy. Cell Cycle 2013; 12(5): 783-802. https://doi.org/10.4161/cc.23720

Heng H, et al. Cancer progression by non-clonal chromosome aberrations. Journal of Cellular Biochememistry 2006; 98: 1424-1435. https://doi.org/10.1002/jcb.20964

Böcking A. Comparability of tumor-cytogenetics and -DNA-cytometry. Molecular Cytogenetics 2015; 8: 28-30. https://doi.org/10.1186/s13039-015-0132-9

Onofre ASC, Pomjanski N, Buckstegge B, Böcking A. Immunocytochemical diagnosis of hepatocellular carcinoma and identification of carcinomas of unknown primary metastatic to the liver on fine-needle aspiration biopsies. Cancer Cytopathology 2007; 111: 259-268. https://doi.org/10.1002/cncr.22768

Bubendorf L, Grote HJ, Syriänen K. Molecular Techniques. In Bibbo M, Wilbur DC, Eds. Comprehensive Cytopathology. Philadelphia: Saunders Elsevier 2008; Vol. 3: pp. 1071-1088. https://doi.org/10.1016/B978-141604208-2.10036-3

Grote HJ, Schmiemann V, Geddert H, et al. Detection of RASSF1A aberrant promoter methylation in bronchial aspirates from patients with suspected lung cancer. Cancer Cytopathoogy 2006; 108: 129-134. https://doi.org/10.1002/cncr.21717

Kahn SL, Ronnett BM, Grant PE, Gustafson KS. Quantitative methylation-specific PCR for the detection of aberrant DNA-methylation in liquid Pap-tests. Cancer 2008; 114(1): 57-64. https://doi.org/10.1002/cncr.23258

Böcking A, et al. Diagnostic cytometry, In: Mehrothra R, Ed. Oral cytology. A concise guide. Springer New-York 2013; pp. 125-146.

Gerhauser C, et al. Molecular evolution of early-onset prostate cancer identifies molecular risk markers and clinical trajectories. Cancer Cell 2018; 34(6): 996-1011. https://doi.org/10.1016/j.ccell.2018.10.016

Friedrich D. Effective improvement of cancer diagnostics and prognostics by computer-assisted cell image analysis. PhD-thesis. Aachen, Germany. RWTH Aachen University 2015.

Böcking A, Chen J, Friedrich D, Meyer-Ebrecht D. Computer-unterstützte Erkennung von Krebszellen mittels DNA-Karyometrie. Trillium Krebsmedizin 2015; 24: 208-211.

Hedley DF, Friedlander M, Taylor TW. Application of flow cytometry to paraffin-embedded archival material for the study of aneuploidy and its clinical significance. Cytometry 1985; 6: 327-333. https://doi.org/10.1002/cyto.990060409

Feulgen R, Rossenbeck H. Mikroskopisch-chemischer Nachweis einer Nukleinsäure vom Typ der Thymonukleinsäure und die darauf beruhende elektive Färbung von Zellkernen in mikroskopischen Präparaten. Hoppe-Seyler´s Zeitschrift für Phyiologische Chemie 1924; 135: 203-248. https://doi.org/10.1515/bchm2.1924.135.5-6.203

Böcking A, Giroud F, Reith A. Consensus report of the ESACP task force on standardization of diagnostic DNA image cytometry. Analytical Cellular Pathoogy 1995; 8: 67-74.

Haroske G, Giroud F, Böcking A. DNA image cytometry. Part I. basic considerations and recommendations for preparation, measurement and interpretation. European Society for Analytical Cellular Pathology. Analytical Cellular Pathology 1998; 17: 189-200. https://doi.org/10.1155/1998/390837

Giroud F, Haroske G, Reith A, Böcking A. 1997 ESACP consensus report on diagnostic DNA image cytometry. Part II: specific reccommendations for quality assurance. European Society for Analytical Cellular Pathology. Analytical Cellular Pathology 1998; 17: 201-208. https://doi.org/10.1155/1998/237659

Haroske G, Baak JP, Danielsen H, et al. Fourth updated consensus report on diagnostic DNA image cytometry. Anayticall Cellular Pathology 2001; 23: 89-95. https://doi.org/10.1155/2001/657642

Böcking A, Friedrich D, Meyer-Ebrecht D, Zhu C. Feider A, Biesterfeld S. Automated detection of cancer cells in efusion specimens by DNA karyometry. Cancer Cytopathology 2019; 127: 18-25. https://doi.org/10.1002/cncy.22072

Bedrossian CWM. Malignant effusions. A multimodal approach to cytological diagnosis. Igaku-Shoin Medical Publishers, New York 1994.

Planz B, Synek C, Robben J, Böcking A, Marberger M. Diagnostic accuracy of DNA image cytometry with cells from voided urine in the detection of bladder cancer. Urology 56: 2000; 782-786. https://doi.org/10.1016/S0090-4295(00)00765-2

Remmerbach T, Weidenbach H, Pomjanski N, Knops C, Mathes S, Hemprich A, Böcking A. Cytologic and DNA-cytometric diagnosis of oral cancer. Analytical Cellular Pathology 2001; 22: 211-221. https://doi.org/10.1155/2001/807358

Schramm M, Wrobel C, Born I, Kazimirek M, Pomjanski N, William M, Kappes R, Gerharz CD, Biesterfeld S, Böcking A. Equivocal cytology in lung cancer diagnosis. Improvement of diagnostic accuracy using adjuvant multicolor FISH, DNA-image cytometry, and quantitative promoter hypermethylation analysis. Cancer Cytopathoogy 2011; 119: 177-192. https://doi.org/10.1002/cncy.20142

Grote HJ, Huy VQN, Leick AG, Böcking A. Identification of progressive cervical epithelial cell abnormalities using DNA image cytometry. Cancer Cytopathology 2004; 102: 373-379. https://doi.org/10.1002/cncr.20644

Böcking A. Prediction of non-progression in prostate cancer patients under active surveillancy by DNA-karyometry. SM Journal of Urology 2017; 3(1): 1030-1036. https://doi.org/10.36876/smju.1030

Böcking A, Tils M, Schramm M, Tils M, Biesterfeld S. DNA-cytometric grading of prostate cancer. Systematic review of the literature. Pathol Discovery 2014; 2: 1-20. https://doi.org/10.7243/2052-7896-2-7

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Published

2020-11-22 — Updated on 2021-05-26

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Issue

Vol. 9 No. 1 (2020)

Section

Articles

How to Cite

Diagnostic and Prognostic DNA-Karyometry for Cancer Diagnostics . (2021). Journal of Cancer Research Updates, 9(1), 25-36. https://doi.org/10.30683/1929-2279.2020.09.05 (Original work published 2020)

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