Quantitative comparison of immunohistochemical staining measured by digital image analysis versus pathologist visual scoring
Tóm tắt
Immunohistochemical (IHC) assays performed on formalin-fixed paraffin-embedded (FFPE) tissue sections traditionally have been semi-quantified by pathologist visual scoring of staining. IHC is useful for validating biomarkers discovered through genomics methods as large clinical repositories of FFPE specimens support the construction of tissue microarrays (TMAs) for high throughput studies. Due to the ubiquitous availability of IHC techniques in clinical laboratories, validated IHC biomarkers may be translated readily into clinical use. However, the method of pathologist semi-quantification is costly, inherently subjective, and produces ordinal rather than continuous variable data. Computer-aided analysis of digitized whole slide images may overcome these limitations. Using TMAs representing 215 ovarian serous carcinoma specimens stained for S100A1, we assessed the degree to which data obtained using computer-aided methods correlated with data obtained by pathologist visual scoring. To evaluate computer-aided image classification, IHC staining within pathologist annotated and software-classified areas of carcinoma were compared for each case. Two metrics for IHC staining were used: the percentage of carcinoma with S100A1 staining (%Pos), and the product of the staining intensity (optical density [OD] of staining) multiplied by the percentage of carcinoma with S100A1 staining (OD*%Pos). A comparison of the IHC staining data obtained from manual annotations and software-derived annotations showed strong agreement, indicating that software efficiently classifies carcinomatous areas within IHC slide images. Comparisons of IHC intensity data derived using pixel analysis software versus pathologist visual scoring demonstrated high Spearman correlations of 0.88 for %Pos (p < 0.0001) and 0.90 for OD*%Pos (p < 0.0001). This study demonstrated that computer-aided methods to classify image areas of interest (e.g., carcinomatous areas of tissue specimens) and quantify IHC staining intensity within those areas can produce highly similar data to visual evaluation by a pathologist.
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Schmechel SC, LeVasseur RJ, Yang KH, Koehler KM, Kussick SJ, Sabath DE: Identification of genes whose expression patterns differ in benign lymphoid tissue and follicular, mantle cell, and small lymphocytic lymphoma. Leukemia. 2004, 18: 841-855. 10.1038/sj.leu.2403293.
Tu IP, Schaner M, Diehn M, Sikic BI, Brown PO, Botstein D, Fero MJ: A method for detecting and correcting feature misidentification on expression microarrays. BMC Genomics. 2004, 5: 64-10.1186/1471-2164-5-64.
Kapur K, Jiang H, Xing Y, Wong WH: Cross-hybridization modeling on Affymetrix exon arrays. Bioinformatics. 2008, 24: 2887-2893. 10.1093/bioinformatics/btn571.
Norris AW, Kahn CR: Analysis of gene expression in pathophysiological states: balancing false discovery and false negative rates. Proc Natl Acad Sci U S A. 2006, 103: 649-653. 10.1073/pnas.0510115103.
Freedman AN, Seminara D, Gail MH, Hartge P, Colditz GA, Ballard-Barbash R, Pfeiffer RM: Cancer risk prediction models: a workshop on development, evaluation, and application. J Natl Cancer Inst. 2005, 97: 715-723. 10.1093/jnci/dji128.
McShane LM, Altman DG, Sauerbrei W, Taube SE, Gion M, Clark GM: Reporting recommendations for tumor marker prognostic studies (REMARK). J Natl Cancer Inst. 2005, 97: 1180-1184. 10.1093/jnci/dji237.
Cummings M, Iremonger J, Green CA, Shaaban AM, Speirs V: Gene expression of ERbeta isoforms in laser microdissected human breast cancers: implications for gene expression analyses. Cell Oncol. 2009, 31: 467-473.
Bouchie A: Coming soon: a global grid for cancer research. Nat Biotechnol. 2004, 22: 1071-1073. 10.1038/nbt0904-1071.
Alizadeh AA, Eisen MB, Davis RE, Ma C, Lossos IS, Rosenwald A, Boldrick JC, Sabet H, Tran T, Yu X, et al.: Distinct types of diffuse large B-cell lymphoma identified by gene expression profiling. Nature. 2000, 403: 503-511. 10.1038/35000501.
de Jong D, Xie W, Rosenwald A, Chhanabhai M, Gaulard P, Klapper W, Lee A, Sander B, Thorns C, Campo E, et al.: Immunohistochemical prognostic markers in diffuse large B-cell lymphoma: validation of tissue microarray as a prerequisite for broad clinical applications (a study from the Lunenburg Lymphoma Biomarker Consortium). J Clin Pathol. 2009, 62: 128-138.
Choi WW, Weisenburger DD, Greiner TC, Piris MA, Banham AH, Delabie J, Braziel RM, Geng H, Iqbal J, Lenz G, et al.: A new immunostain algorithm classifies diffuse large B-cell lymphoma into molecular subtypes with high accuracy. Clin Cancer Res. 2009, 15: 5494-5502. 10.1158/1078-0432.CCR-09-0113.
McCarty KS, Szabo E, Flowers JL, Cox EB, Leight GS, Miller L, Konrath J, Soper JT, Budwit DA, Creasman WT, et al.: Use of a monoclonal anti-estrogen receptor antibody in the immunohistochemical evaluation of human tumors. Cancer Res. 1986, 46: 4244s-4248s.
Camp RL, Neumeister V, Rimm DL: A decade of tissue microarrays: progress in the discovery and validation of cancer biomarkers. J Clin Oncol. 2008, 26: 5630-5637. 10.1200/JCO.2008.17.3567.
Rimm DL, Camp RL, Charette LA, Costa J, Olsen DA, Reiss M: Tissue microarray: a new technology for amplification of tissue resources. Cancer J. 2001, 7: 24-31.
Camp RL, Charette LA, Rimm DL: Validation of tissue microarray technology in breast carcinoma. Lab Invest. 2000, 80: 1943-1949. 10.1038/labinvest.3780204.
Griffin MC, Robinson RA, Trask DK: Validation of tissue microarrays using p53 immunohistochemical studies of squamous cell carcinoma of the larynx. Mod Pathol. 2003, 16: 1181-1188. 10.1097/01.MP.0000097284.40421.D6.
Weaver DL, Krag DN, Manna EA, Ashikaga T, Harlow SP, Bauer KD: Comparison of pathologist-detected and automated computer-assisted image analysis detected sentinel lymph node micrometastases in breast cancer. Mod Pathol. 2003, 16: 1159-1163. 10.1097/01.MP.0000092952.21794.AD.
Borlot VF, Biasoli I, Schaffel R, Azambuja D, Milito C, Luiz RR, Scheliga A, Spector N, Morais JC: Evaluation of intra- and interobserver agreement and its clinical significance for scoring bcl-2 immunohistochemical expression in diffuse large B-cell lymphoma. Pathol Int. 2008, 58: 596-600. 10.1111/j.1440-1827.2008.02276.x.
Jaraj SJ, Camparo P, Boyle H, Germain F, Nilsson B, Petersson F, Egevad L: Intra- and interobserver reproducibility of interpretation of immunohistochemical stains of prostate cancer. Virchows Arch. 2009, 455: 375-381. 10.1007/s00428-009-0833-8.
Gavrielides MA, Gallas BD, Lenz P, Badano A, Hewitt SM: Observer variability in the interpretation of HER2/neu immunohistochemical expression with unaided and computer-aided digital microscopy. Arch Pathol Lab Med. 2011, 135: 233-242.
Yagi Y, Gilbertson JR: A relationship between slide quality and image quality in whole slide imaging (WSI). Diagn Pathol. 2008, 3 (Suppl 1): S12-10.1186/1746-1596-3-S1-S12.
Rimm DL, Giltnane JM, Moeder C, Harigopal M, Chung GG, Camp RL, Burtness B: Bimodal population or pathologist artifact?. J Clin Oncol. 2007, 25: 2487-2488. 10.1200/JCO.2006.07.7537.
DeRycke MS, Andersen JD, Harrington KM, Pambuccian SE, Kalloger SE, Boylan KL, Argenta PA, Skubitz AP: S100A1 expression in ovarian and endometrial endometrioid carcinomas is a prognostic indicator of relapse-free survival. Am J Clin Pathol. 2009, 132: 846-856. 10.1309/AJCPTK87EMMIKPFS.
Ruifrok AC, Johnston DA: Quantification of histochemical staining by color deconvolution. Anal Quant Cytol Histol. 2001, 23: 291-299.
Krajewska M, Smith LH, Rong J, Huang X, Hyer ML, Zeps N, Iacopetta B, Linke SP, Olson AH, Reed JC, Krajewski S: Image analysis algorithms for immunohistochemical assessment of cell death events and fibrosis in tissue sections. J Histochem Cytochem. 2009, 57: 649-663. 10.1369/jhc.2009.952812.
Bland JM, Altman DG: Statistical methods for assessing agreement between two methods of clinical measurement. Lancet. 1986, 1: 307-310.
Joshi AS, Sharangpani GM, Porter K, Keyhani S, Morrison C, Basu AS, Gholap GA, Gholap AS, Barsky SH: Semi-automated imaging system to quantitate Her-2/neu membrane receptor immunoreactivity in human breast cancer. Cytometry A. 2007, 71: 273-285.
Skaland I, Ovestad I, Janssen EA, Klos J, Kjellevold KH, Helliesen T, Baak JP: Comparing subjective and digital image analysis HER2/neu expression scores with conventional and modified FISH scores in breast cancer. J Clin Pathol. 2008, 61: 68-71.
Masmoudi H, Hewitt SM, Petrick N, Myers KJ, Gavrielides MA: Automated quantitative assessment of HER-2/neu immunohistochemical expression in breast cancer. IEEE Trans Med Imaging. 2009, 28: 916-925.
Turashvili G, Leung S, Turbin D, Montgomery K, Gilks B, West R, Carrier M, Huntsman D, Aparicio S: Inter-observer reproducibility of HER2 immunohistochemical assessment and concordance with fluorescent in situ hybridization (FISH): pathologist assessment compared to quantitative image analysis. BMC Cancer. 2009, 9: 165-10.1186/1471-2407-9-165.
Laurinaviciene A, Dasevicius D, Ostapenko V, Jarmalaite S, Lazutka J, Laurinavicius A: Membrane connectivity estimated by digital image analysis of HER2 immunohistochemistry is concordant with visual scoring and fluorescence in situ hybridization results: algorithm evaluation on breast cancer tissue microarrays. Diagn Pathol. 2011, 6: 87-10.1186/1746-1596-6-87.
Brugmann A, Eld M, Lelkaitis G, Nielsen S, Grunkin M, Hansen JD, Foged NT, Vyberg M: Digital image analysis of membrane connectivity is a robust measure of HER2 immunostains. Breast Cancer Res Treat. 2011, 132: 41-49.
Atkinson R, Mollerup J, Laenkholm AV, Verardo M, Hawes D, Commins D, Engvad B, Correa A, Ehlers CC, Nielsen KV: Effects of the change in cutoff values for human epidermal growth factor receptor 2 status by immunohistochemistry and fluorescence in situ hybridization: a study comparing conventional brightfield microscopy, image analysis-assisted microscopy, and interobserver variation. Arch Pathol Lab Med. 2011, 135: 1010-1016. 10.5858/2010-0462-OAR.
Turbin DA, Leung S, Cheang MC, Kennecke HA, Montgomery KD, McKinney S, Treaba DO, Boyd N, Goldstein LC, Badve S, et al.: Automated quantitative analysis of estrogen receptor expression in breast carcinoma does not differ from expert pathologist scoring: a tissue microarray study of 3,484 cases. Breast Cancer Res Treat. 2008, 110: 417-426. 10.1007/s10549-007-9736-z.
Gokhale S, Rosen D, Sneige N, Diaz LK, Resetkova E, Sahin A, Liu J, Albarracin CT: Assessment of two automated imaging systems in evaluating estrogen receptor status in breast carcinoma. Appl Immunohistochem Mol Morphol. 2007, 15: 451-455. 10.1097/PAI.0b013e31802ee998.
Faratian D, Kay C, Robson T, Campbell FM, Grant M, Rea D, Bartlett JM: Automated image analysis for high-throughput quantitative detection of ER and PR expression levels in large-scale clinical studies: the TEAM Trial Experience. Histopathology. 2009, 55: 587-593. 10.1111/j.1365-2559.2009.03419.x.
Krecsak L, Micsik T, Kiszler G, Krenacs T, Szabo D, Jonas V, Csaszar G, Czuni L, Gurzo P, Ficsor L, Molnar B: Technical note on the validation of a semi-automated image analysis software application for estrogen and progesterone receptor detection in breast cancer. Diagn Pathol. 2011, 6: 6-10.1186/1746-1596-6-6.
Bolton KL, Garcia-Closas M, Pfeiffer RM, Duggan MA, Howat WJ, Hewitt SM, Yang XR, Cornelison R, Anzick SL, Meltzer P, et al.: Assessment of automated image analysis of breast cancer tissue microarrays for epidemiologic studies. Cancer Epidemiol Biomarkers Prev. 2010, 19: 992-999. 10.1158/1055-9965.EPI-09-1023.
Alexander BM, Wang XZ, Niemierko A, Weaver DT, Mak RH, Roof KS, Fidias P, Wain J, Choi NC: DNA Repair Biomarkers Predict Response to Neoadjuvant Chemoradiotherapy in Esophageal Cancer. Int J Radiat Oncol Biol Phys. 2011, in press
Messersmith W, Oppenheimer D, Peralba J, Sebastiani V, Amador M, Jimeno A, Embuscado E, Hidalgo M, Iacobuzio-Donahue C: Assessment of Epidermal Growth Factor Receptor (EGFR) signaling in paired colorectal cancer and normal colon tissue samples using computer-aided immunohistochemical analysis. Cancer Biol Ther. 2005, 4: 1381-1386. 10.4161/cbt.4.12.2287.
Muirhead D, Aoun P, Powell M, Juncker F, Mollerup J: Pathology economic model tool: a novel approach to workflow and budget cost analysis in an anatomic pathology laboratory. Arch Pathol Lab Med. 2010, 134: 1164-1169.
Ong CW, Kim LG, Kong HH, Low LY, Wang TT, Supriya S, Kathiresan M, Soong R, Salto-Tellez M: Computer-assisted pathological immunohistochemistry scoring is more time-effective than conventional scoring, but provides no analytical advantage. Histopathology. 2010, 56: 523-529. 10.1111/j.1365-2559.2010.03496.x.
Skaland I, Ovestad I, Janssen EA, Klos J, Kjellevold KH, Helliesen T, Baak JP: Digital image analysis improves the quality of subjective HER-2 expression scoring in breast cancer. Appl Immunohistochem Mol Morphol. 2008, 16: 185-190. 10.1097/PAI.0b013e318059c20c.
Bloom K, Harrington D: Enhanced accuracy and reliability of HER-2/neu immunohistochemical scoring using digital microscopy. Am J Clin Pathol. 2004, 121: 620-630. 10.1309/Y73U8X72B68TMGH5.
Harigopal M, Barlow WE, Tedeschi G, Porter PL, Yeh IT, Haskell C, Livingston R, Hortobagyi GN, Sledge G, Shapiro C, et al.: Multiplexed assessment of the Southwest Oncology Group-directed Intergroup Breast Cancer Trial S9313 by AQUA shows that both high and low levels of HER2 are associated with poor outcome. Am J Pathol. 2010, 176: 1639-1647. 10.2353/ajpath.2010.090711.
Camp RL, Dolled-Filhart M, King BL, Rimm DL: Quantitative analysis of breast cancer tissue microarrays shows that both high and normal levels of HER2 expression are associated with poor outcome. Cancer Res. 2003, 63: 1445-1448.
Camp RL, Chung GG, Rimm DL: Automated subcellular localization and quantification of protein expression in tissue microarrays. Nat Med. 2002, 8: 1323-1327. 10.1038/nm791.
Glass G, Papin JA, Mandell JW: SIMPLE: a sequential immunoperoxidase labeling and erasing method. J Histochem Cytochem. 2009, 57: 899-905. 10.1369/jhc.2009.953612.
Olin MR, Andersen BM, Zellmer DM, Grogan PT, Popescu FE, Xiong Z, Forster CL, Seiler C, SantaCruz KS, Chen W, et al.: Superior efficacy of tumor cell vaccines grown in physiologic oxygen. Clin Cancer Res. 2010, 16: 4800-4808. 10.1158/1078-0432.CCR-10-1572.
Dandrea MR, Reiser PA, Gumula NA, Hertzog BM, Andrade-Gordon P: Application of triple immunohistochemistry to characterize amyloid plaque-associated inflammation in brains with Alzheimer’s disease. Biotech Histochem. 2001, 76: 97-106.
Mucci LA, Pawitan Y, Demichelis F, Fall K, Stark JR, Adami HO, Andersson SO, Andren O, Eisenstein A, Holmberg L, et al.: Testing a multigene signature of prostate cancer death in the Swedish Watchful Waiting Cohort. Cancer Epidemiol Biomarkers Prev. 2008, 17: 1682-1688. 10.1158/1055-9965.EPI-08-0044.