Automated Ki-67 labeling index assessment in prostate cancer using artificial intelligence and multiplex fluorescence immunohistochemistry.
Ki-67 labeling index
artificial intelligence
heterogeneity in prostate cancer
multiplex fluorescence immunohistochemistry
prostate cancer
Journal
The Journal of pathology
ISSN: 1096-9896
Titre abrégé: J Pathol
Pays: England
ID NLM: 0204634
Informations de publication
Date de publication:
05 2023
05 2023
Historique:
revised:
15
01
2023
received:
13
05
2022
accepted:
17
01
2023
medline:
6
4
2023
pubmed:
20
1
2023
entrez:
19
1
2023
Statut:
ppublish
Résumé
The Ki-67 labeling index (Ki-67 LI) is a strong prognostic marker in prostate cancer, although its analysis requires cumbersome manual quantification of Ki-67 immunostaining in 200-500 tumor cells. To enable automated Ki-67 LI assessment in routine clinical practice, a framework for automated Ki-67 LI quantification, which comprises three different artificial intelligence analysis steps and an algorithm for cell-distance analysis of multiplex fluorescence immunohistochemistry (mfIHC) staining, was developed and validated in a cohort of 12,475 prostate cancers. The prognostic impact of the Ki-67 LI was tested on a tissue microarray (TMA) containing one 0.6 mm sample per patient. A 'heterogeneity TMA' containing three to six samples from different tumor areas in each patient was used to model Ki-67 analysis of multiple different biopsies, and 30 prostate biopsies were analyzed to compare a 'classical' bright field-based Ki-67 analysis with the mfIHC-based framework. The Ki-67 LI provided strong and independent prognostic information in 11,845 analyzed prostate cancers (p < 0.001 each), and excellent agreement was found between the framework for automated Ki-67 LI assessment and the manual quantification in prostate biopsies from routine clinical practice (intraclass correlation coefficient: 0.94 [95% confidence interval: 0.87-0.97]). The analysis of the heterogeneity TMA revealed that the Ki-67 LI of the sample with the highest Gleason score (area under the curve [AUC]: 0.68) was as prognostic as the mean Ki-67 LI of all six foci (AUC: 0.71 [p = 0.24]). The combined analysis of the Ki-67 LI and Gleason score obtained on identical tissue spots showed that the Ki-67 LI added significant additional prognostic information in case of classical International Society of Urological Pathology grades (AUC: 0.82 [p = 0.002]) and quantitative Gleason score (AUC: 0.83 [p = 0.018]). The Ki-67 LI is a powerful prognostic parameter in prostate cancer that is now applicable in routine clinical practice. In the case of multiple cancer-positive biopsies, the sole automated analysis of the worst biopsy was sufficient. © 2023 The Authors. The Journal of Pathology published by John Wiley & Sons Ltd on behalf of The Pathological Society of Great Britain and Ireland.
Substances chimiques
Ki-67 Antigen
0
Types de publication
Journal Article
Research Support, Non-U.S. Gov't
Langues
eng
Sous-ensembles de citation
IM
Pagination
5-16Informations de copyright
© 2023 The Authors. The Journal of Pathology published by John Wiley & Sons Ltd on behalf of The Pathological Society of Great Britain and Ireland.
Références
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: 209-249.
Thompson IM Jr, Tangen CM. Prostate cancer--uncertainty and a way forward. N Engl J Med 2012; 367: 270-271.
Wilt TJ, Brawer MK, Jones KM, et al. Radical prostatectomy versus observation for localized prostate cancer. N Engl J Med 2012; 367: 203-213.
Egevad L, Ahmad AS, Algaba F, et al. Standardization of Gleason grading among 337 European pathologists. Histopathology 2013; 62: 247-256.
Ozkan TA, Eruyar AT, Cebeci OO, et al. Interobserver variability in Gleason histological grading of prostate cancer. Scand J Urol 2016; 50: 420-424.
Schwab U, Stein H, Gerdes J, et al. Production of a monoclonal antibody specific for Hodgkin and Sternberg-Reed cells of Hodgkin's disease and a subset of normal lymphoid cells. Nature 1982; 299: 65-67.
Sun X, Kaufman PD. Ki-67: more than a proliferation marker. Chromosoma 2018; 127: 175-186.
Stattin P, Damber JE, Karlberg L, et al. Cell proliferation assessed by Ki-67 immunoreactivity on formalin fixed tissues is a predictive factor for survival in prostate cancer. J Urol 1997; 157: 219-222.
Tretiakova MS, Wei W, Boyer HD, et al. Prognostic value of Ki67 in localized prostate carcinoma: a multi-institutional study of >1000 prostatectomies. Prostate Cancer Prostatic Dis 2016; 19: 264-270.
Cowen D, Troncoso P, Khoo VS, et al. Ki-67 staining is an independent correlate of biochemical failure in prostate cancer treated with radiotherapy. Clin Cancer Res 2002; 8: 1148-1154.
Fisher G, Yang ZH, Kudahetti S, et al. Prognostic value of Ki-67 for prostate cancer death in a conservatively managed cohort. Br J Cancer 2013; 108: 271-277.
Tollefson MK, Karnes RJ, Kwon ED, et al. Prostate cancer Ki-67 (MIB-1) expression, perineural invasion, and gleason score as biopsy-based predictors of prostate cancer mortality: the Mayo model. Mayo Clin Proc 2014; 89: 308-318.
Berney DM, Gopalan A, Kudahetti S, et al. Ki-67 and outcome in clinically localised prostate cancer: analysis of conservatively treated prostate cancer patients from the Trans-Atlantic Prostate Group study. Br J Cancer 2009; 100: 888-893.
Khor LY, Bae K, Paulus R, et al. MDM2 and Ki-67 predict for distant metastasis and mortality in men treated with radiotherapy and androgen deprivation for prostate cancer: RTOG 92-02. J Clin Oncol 2009; 27: 3177-3184.
Fiore C, Bailey D, Conlon N, et al. Utility of multispectral imaging in automated quantitative scoring of immunohistochemistry. J Clin Pathol 2012; 65: 496-502.
Desmeules P, Hovington H, Nguilé-Makao M, et al. Comparison of digital image analysis and visual scoring of KI-67 in prostate cancer prognosis after prostatectomy. Diagn Pathol 2015; 10: 67.
Kreipe H, Harbeck N, Christgen M. Clinical validity and clinical utility of Ki67 in early breast cancer. Ther Adv Med Oncol 2022; 14: 17588359221122725.
Dowsett M, Nielsen TO, A'Hern R, et al. Assessment of Ki67 in breast cancer: recommendations from the International Ki67 in Breast Cancer working group. J Natl Cancer Inst 2011; 103: 1656-1664.
Egevad L, Delahunt B, Srigley JR, et al. International Society of Urological Pathology (ISUP) grading of prostate cancer - an ISUP consensus on contemporary grading. APMIS 2016; 124: 433-435.
Sauter G, Steurer S, Clauditz TS, et al. Clinical utility of quantitative Gleason grading in prostate biopsies and prostatectomy specimens. Eur Urol 2016; 69: 592-598.
Sauter G, Clauditz T, Steurer S, et al. Integrating tertiary Gleason 5 patterns into quantitative Gleason grading in prostate biopsies and prostatectomy specimens. Eur Urol 2018; 73: 674-683.
Steurer S, Riemann C, Büscheck F, et al. p63 expression in human tumors and normal tissues: a tissue microarray study on 10,200 tumors. Biomark Res 2021; 9: 7.
Python Software Foundation; 2021. Python Language Reference. [Accessed 09 January 2023]. Available from: http://www.python.org
R-Core-Team. R: A Language and Environment for Statistical Computing. R Foundation for Statistical Computing: Vienna. [Accessed 06 January 2023]. Available from: https://www.R-project.org/, 2021.
van der Maaten LJP. Accelerating t-SNE using tree-based algorithms. J Mach Learn Res 2014; 15: 3221-3245.
Bashashati A, Brinkman RR. A survey of flow cytometry data analysis methods. Adv Bioinformatics 2009; 2009: 584603.
Blessin NC, Li W, Mandelkow T, et al. Prognostic role of proliferating CD8+ cytotoxic Tcells in human cancers. Cell Oncol (Dordr) 2021; 44: 793-803.
Bankhead P, Loughrey MB, Fernández JA, et al. QuPath: open source software for digital pathology image analysis. Sci Rep 2017; 7: 16878. [Accessed 17 January 2022]. Available from: https://qupath.github.io.
Tippmann S. Programming tools: adventures with R. Nature 2015; 517: 109-110.
JMP® V. SAS Institute Inc. Cary, NC. [Accessed 28 May 2020]. Available from: https://www.jmp.com, 1989-2019.
Therneau TM, Grambsch PM. Modeling Survival Data: Extending the Cox Model. Springer: New York, 2000. ISBN: 0-387-98784-3. [Accessed 01 August 2022]. Available from: https://CRAN.R-project.org/package=survival.
Blanche P, Dartigues JF, Jacqmin-Gadda H. Estimating and comparing time-dependent areas under receiver operating characteristic curves for censored event times with competing risks. Stat Med 2013; 32: 5381-5397.
Aldaoud N, Hallak A, Abdo N, et al. Interobserver variability in the diagnosis of high-grade prostatic intraepithelial neoplasia in a Tertiary Hospital in Northern Jordan. Clin Pathol 2020; 13: 2632010X19898472.
Evans AJ, Henry PC, Van der Kwast TH, et al. Interobserver variability between expert urologic pathologists for extraprostatic extension and surgical margin status in radical prostatectomy specimens. Am J Surg Pathol 2008; 32: 1503-1512.
Nagpal K, Foote D, Liu Y, et al. Development and validation of a deep learning algorithm for improving Gleason scoring of prostate cancer. NPJ Digit Med 2019; 2: 48.
Nagpal K, Foote D, Tan F, et al. Development and validation of a deep learning algorithm for Gleason grading of prostate cancer from biopsy specimens. JAMA Oncol 2020; 6: 1372-1380.
Campanella G, Hanna MG, Geneslaw L, et al. Clinical-grade computational pathology using weakly supervised deep learning on whole slide images. Nat Med 2019; 25: 1301-1309.
Pollack A, DeSilvio M, Khor LY, et al. Ki-67 staining is a strong predictor of distant metastasis and mortality for men with prostate cancer treated with radiotherapy plus androgen deprivation: Radiation Therapy Oncology Group Trial 92-02. J Clin Oncol 2004; 22: 2133-2140.
Hammarsten P, Josefsson A, Thysell E, et al. Immunoreactivity for prostate specific antigen and Ki67 differentiates subgroups of prostate cancer related to outcome. Mod Pathol 2019; 32: 1310-1319.
Visakorpi T, Kylmälä T, Tainio H, et al. High cell proliferation activity determined by DNA flow cytometry predicts poor prognosis after relapse in prostate cancer. Eur J Cancer 1994; 30A: 129-130.
Visakorpi T. Proliferative activity determined by DNA flow cytometry and proliferating cell nuclear antigen (PCNA) immunohistochemistry as a prognostic factor in prostatic carcinoma. J Pathol 1992; 168: 7-13.
Cuzick J, Swanson GP, Fisher G, et al. Prognostic value of an RNA expression signature derived from cell cycle proliferation genes in patients with prostate cancer: a retrospective study. Lancet Oncol 2011; 12: 245-255.
Cuzick J, Berney DM, Fisher G, et al. Prognostic value of a cell cycle progression signature for prostate cancer death in a conservatively managed needle biopsy cohort. Br J Cancer 2012; 106: 1095-1099.
Cooperberg MR, Simko JP, Cowan JE, et al. Validation of a cell-cycle progression gene panel to improve risk stratification in a contemporary prostatectomy cohort. J Clin Oncol 2013; 31: 1428-1434.
Rubicz R, Zhao S, Wright JL, et al. Gene expression panel predicts metastatic-lethal prostate cancer outcomes in men diagnosed with clinically localized prostate cancer. Mol Oncol 2017; 11: 140-150.
Erho N, Crisan A, Vergara IA, et al. Discovery and validation of a prostate cancer genomic classifier that predicts early metastasis following radical prostatectomy. PLoS One 2013; 8: e66855.
Klein EA, Cooperberg MR, Magi-Galluzzi C, et al. A 17-gene assay to predict prostate cancer aggressiveness in the context of Gleason grade heterogeneity, tumor multifocality, and biopsy undersampling. Eur Urol 2014; 66: 550-560.
Mortensen MM, Høyer S, Lynnerup AS, et al. Expression profiling of prostate cancer tissue delineates genes associated with recurrence after prostatectomy. Sci Rep 2015; 5: 16018.
Jang MH, Kim HJ, Chung YR, et al. A comparison of Ki-67 counting methods in luminal breast cancer: the average method vs. the hot spot method. PLoS One 2017; 12: e0172031.
Mesko S, Kupelian P, Demanes DJ, et al. Quantifying the ki-67 heterogeneity profile in prostate cancer. Prostate Cancer 2013; 2013: 717080.
Rubin MA, Dunn R, Strawderman M, et al. Tissue microarray sampling strategy for prostate cancer biomarker analysis. Am J Surg Pathol 2002; 26: 312-319.
Singh SS, Qaqish B, Johnson JL, et al. Sampling strategy for prostate tissue microarrays for Ki-67 and androgen receptor biomarkers. Anal Quant Cytol Histol 2004; 26: 194-200.
Morlacco A, Cheville JC, Rangel LJ, et al. Adverse disease features in Gleason score 3 + 4 “favorable intermediate-risk” prostate cancer: implications for active surveillance. Eur Urol 2017; 72: 442-447.
Knipper S, Sadat-Khonsari M, Boehm K, et al. Impact of adherence to multidisciplinary recommendations for adjuvant treatment in radical prostatectomy patients with high risk of recurrence. Clin Genitourin Cancer 2020; 18: e112-e121.
Blom S, Paavolainen L, Bychkov D, et al. Systems pathology by multiplexed immunohistochemistry and whole-slide digital image analysis. Sci Rep 2017; 7: 15580.
Lin JR, Izar B, Wang S, et al. Highly multiplexed immunofluorescence imaging of human tissues and tumors using t-CyCIF and conventional optical microscopes. Elife 2018; 7: e31657.
Goltsev Y, Samusik N, Kennedy-Darling J, et al. Deep profiling of mouse splenic architecture with CODEX multiplexed imaging. Cell 2018; 174: 968-81.e15.
Ahyai SA, Graefen M, Steuber T, et al. Contemporary prostate cancer prevalence among T1c biopsy-referred men with a prostate-specific antigen level < or = 4.0 ng per milliliter. Eur Urol 2008; 53: 750-757.
Epstein JI, Allsbrook WC Jr, Amin MB, et al. The 2005 International Society of Urological Pathology (ISUP) Consensus Conference on Gleason Grading of Prostatic Carcinoma. Am J Surg Pathol 2005; 29: 1228-1242.
Kononen J, Bubendorf L, Kallionimeni A, et al. Tissue microarrays for high-throughput molecular profiling of tumor specimens. Nat Med 1998; 4: 844-847.
Mirlacher M, Simon R. Recipient block TMA technique. Methods Mol Biol 2010; 664: 37-44.
Wise AM, Stamey TA, McNeal JE, et al. Morphologic and clinical significance of multifocal prostate cancers in radical prostatectomy specimens. Urology 2002; 60: 264-269.