Evaluating and comparing immunostaining and computational methods for spatial profiling of drug response in patient-derived explants

Abstract

Patient-derived explants (PDEs) represent the direct culture of fragments of freshly-resected tumour tissue under conditions that retain the original architecture of the tumour. PDEs have advantages over other preclinical cancer models as platforms for predicting patient-relevant drug responses in that they preserve the tumour microenvironment and tumour heterogeneity. At endpoint, PDEs may either be processed for generation of histological sections or homogenised and processed for ʻomic’ evaluation of biomarker expression. A significant advantage of spatial profiling is the ability to co-register drug responses with tumour pathology, tumour heterogeneity and changes in the tumour microenvironment. Spatial profiling of PDEs relies on the utilisation of robust immunostaining approaches for validated biomarkers and incorporation of appropriate image analysis methods to quantitatively and qualitatively monitor changes in biomarker expression in response to anti-cancer drugs. Automation of immunostaining and image analysis would provide a significant advantage for the drug discovery pipeline and therefore, here, we have sought to optimise digital pathology approaches. We compare three image analysis software platforms (QuPath, ImmunoRatio and VisioPharm) for evaluating Ki67 as a marker for proliferation, cleaved PARP (cPARP) as a marker for apoptosis and pan-cytokeratin (CK) as a marker for tumour areas and find that all three generate comparable data to the views of a histomorphometrist. We also show that Virtual Double Staining of sequential sections by immunohistochemistry results in imperfect section alignment such that CK-stained tumour areas are over-estimated. Finally, we demonstrate that multi-immunofluorescence combined with digital image analysis is a superior method for monitoring multiple biomarkers simultaneously in tumour and stromal areas in PDEs.

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Fig. 1: Immunostaining of NSCLC PDEs.
Fig. 2: Comparison of digital platforms.
Fig. 3: Comparison of digital platforms and histomorphometrist scoring.
Fig. 4: Determining the accuracy of virtual double staining.
Fig. 5: Comparison of mIF and IHC.

References

  1. 1.

    Powley IR, Patel M, Miles G, Pringle H, Howells L, Thomas A, et al. Patient-derived explants (PDEs) as a powerful preclinical platform for anti-cancer drug and biomarker discovery. Br J Cancer. 2020;122:735–44.

  2. 2.

    Karekla E, Liao WJ, Sharp B, Pugh J, Reid H, Quesne JL, et al. Ex vivo explant cultures of non-small cell lung carcinoma enable evaluation of primary tumor responses to anticancer therapy. Cancer Res. 2017;77:2029–39.

    CAS  Article  Google Scholar 

  3. 3.

    Collins A, Miles GJ, Wood J, MacFarlane M, Pritchard C, Moss E. Patient-derived explants, xenografts and organoids: 3-dimensional patient-relevant pre-clinical models in endometrial cancer. Gynecol Oncol. 2020;156:251–9.

    CAS  Article  Google Scholar 

  4. 4.

    Majumder B, Baraneedharan U, Thiyagarajan S, Radhakrishnan P, Narasimhan H, Dhandapani M, et al. Predicting clinical response to anticancer drugs using an ex vivo platform that captures tumour heterogeneity. Nat Commun. 2015;6:6169.

    CAS  Article  Google Scholar 

  5. 5.

    Furukawa T, Kubota T, Hoffman RM. Clinical applications of the histoculture drug response assay. Clin Cancer Res. 1995;1:305–11.

    CAS  PubMed  Google Scholar 

  6. 6.

    Pirnia F, Frese S, Gloor B, Hotz MA, Luethi A, Gugger M, et al. Ex vivo assessment of chemotherapy-induced apoptosis and associated molecular changes in patient tumor samples. Anticancer Res. 2006;26:1765–72.

    CAS  PubMed  Google Scholar 

  7. 7.

    Garcia-Chagollan M, Carranza-Torres IE, Carranza-Rosales P, Guzman-Delgado NE, Ramirez-Montoya H, Martinez-Silva MG, et al. Expression of NK cell surface receptors in breast cancer tissue as predictors of resistance to antineoplastic treatment. Technol Cancer Res Treat. 2018;17:1533033818764499.

    CAS  Article  Google Scholar 

  8. 8.

    Wei B, Wang J, Zhang X, Qian Z, Wu J, Sun Y, et al. Combination of histoculture drug response assay and qPCR as an effective method to screen biomarkers for personalized chemotherapy in esophageal cancer. Oncol Lett. 2017;14:6915–22.

    PubMed  PubMed Central  Google Scholar 

  9. 9.

    Stack EC, Wang C, Roman KA, Hoyt CC. Multiplexed immunohistochemistry, imaging, and quantitation: a review, with an assessment of Tyramide signal amplification, multispectral imaging and multiplex analysis. Methods. 2014;70:46–58.

    CAS  Article  Google Scholar 

  10. 10.

    Hofman P, Badoual C, Henderson F, Berland L, Hamila M, Long-Mira E, et al. Multiplexed immunohistochemistry for molecular and immune profiling in lung cancer-just about ready for prime-time? Cancers. 2019;11. https://doi.org/10.3390/cancers11030283.

  11. 11.

    Stalhammar G, Fuentes Martinez N, Lippert M, Tobin NP, Molholm I, Kis L, et al. Digital image analysis outperforms manual biomarker assessment in breast cancer. Mod Pathol. 2016;29:318–29.

    Article  Google Scholar 

  12. 12.

    Tuominen VJ, Ruotoistenmaki S, Viitanen A, Jumppanen M, Isola J. ImmunoRatio: a publicly available web application for quantitative image analysis of estrogen receptor (ER), progesterone receptor (PR), and Ki-67. Breast Cancer Res. 2010;12:R56.

    Article  Google Scholar 

  13. 13.

    Bankhead P, Loughrey MB, Fernandez JA, Dombrowski Y, McArt DG, Dunne PD, et al. QuPath: open source software for digital pathology image analysis. Sci Rep. 2017;7:16878.

    Article  Google Scholar 

  14. 14.

    Tang LH, Gonen M, Hedvat C, Modlin IM, Klimstra DS. Objective quantification of the Ki67 proliferative index in neuroendocrine tumors of the gastroenteropancreatic system: a comparison of digital image analysis with manual methods. Am J Surg Pathol. 2012;36:1761–70.

    Article  Google Scholar 

  15. 15.

    Bland JM, Altman DG. Measuring agreement in method comparison studies. Stat Methods Med Res. 1999;8:135–60.

    CAS  Article  Google Scholar 

  16. 16.

    Passing H, Bablok W. A new biometrical procedure for testing the equality of measurements from two different analytical methods. Application of linear regression procedures for method comparison studies in clinical chemistry, Part I. J Clin Chem Clin Biochem. 1983;21:709–20.

  17. 17.

    Tsujikawa T, Kumar S, Borkar RN, Azimi V, Thibault G, Chang YH, et al. Quantitative multiplex immunohistochemistry reveals myeloid-inflamed tumor-immune complexity associated with poor prognosis. Cell Rep. 2017;19:203–17.

    CAS  Article  Google Scholar 

  18. 18.

    Remark R, Merghoub T, Grabe N, Litjens G, Damotte D, Wolchok JD, et al. In-depth tissue profiling using multiplexed immunohistochemical consecutive staining on single slide. Sci Immunol. 2016;1:aaf6925.

    Article  Google Scholar 

  19. 19.

    Laenkholm AV, Grabau D, Moller Talman ML, Balslev E, Bak Jylling AM, Tabor TP, et al. An inter-observer Ki67 reproducibility study applying two different assessment methods: on behalf of the Danish Scientific Committee of Pathology, Danish breast cancer cooperative group (DBCG). Acta Oncol. 2018;57:83–9.

    Article  Google Scholar 

  20. 20.

    Roge R, Riber-Hansen R, Nielsen S, Vyberg M. Proliferation assessment in breast carcinomas using digital image analysis based on virtual Ki67/cytokeratin double staining. Breast Cancer Res Treat. 2016;158:11–9.

    Article  Google Scholar 

  21. 21.

    Blobel GA, Moll R, Franke WW, Vogt-Moykopf I. Cytokeratins in normal lung and lung carcinomas. Virchows Archiv B. 1984;45:407–29.

    CAS  Article  Google Scholar 

  22. 22.

    Baschong W, Suetterlin R, Laeng RH. Control of autofluorescence of archival formaldehyde-fixed, paraffin-embedded tissue in confocal laser scanning microscopy (CLSM). J Histochem Cytochem. 2001;49:1565–72.

    CAS  Article  Google Scholar 

  23. 23.

    Pivetta E, Spessotto P. Multispectral imaging technology: Visualize, analyze, phenotyping, and quantify immune cells in situ. Int J Biol Markers. 2020;35 1_Suppl:26–30.

    Article  Google Scholar 

  24. 24.

    Lin JR, Fallahi-Sichani M, Chen JY, Sorger PK. Cyclic Immunofluorescence (CycIF), a highly multiplexed method for single-cell imaging. Curr Protoc Chem Biol. 2016;8:251–64.

    Article  Google Scholar 

  25. 25.

    Robertson D, Savage K, Reis-Filho JS, Isacke CM. Multiple immunofluorescence labelling of formalin-fixed paraffin-embedded (FFPE) tissue. BMC Cell Biol. 2008;9:13.

    Article  Google Scholar 

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Acknowledgements

We thank Kees Straatman and the Leicester Core Biotechnology Services Imaging Facility for support with image analysis. We also thank Angie Gilles, Linda Potter and Janine Morton of the Leicester Core Biotechnology Services Histology Facility. We also thank the MRC Toxicology Unit Histopathology Facility for support with digital scanning and image analysis. This research was supported and funded by the Explant Consortium comprising four partners: The University of Leicester, The MRC Toxicology Unit, Cancer Research UK Therapeutic Discovery Laboratories and LifeArc. Additional support was provided by the CRUK-NIHR Leicester Experimental Cancer Medicine Centre (C10604/A25151). Funding for GM was provided by Breast Cancer Now’s Catalyst Programme (2017NOVPCC1066), which is supported by funding from Pfizer.

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Correspondence to Gareth J. Miles.

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Miles, G.J., Powley, I., Mohammed, S. et al. Evaluating and comparing immunostaining and computational methods for spatial profiling of drug response in patient-derived explants. Lab Invest (2020). https://doi.org/10.1038/s41374-020-00511-3

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