- Short report
- Open Access
Multiplexed tissue biomarker imaging
© Stack et al. 2016
- Received: 28 January 2016
- Accepted: 2 February 2016
- Published: 16 February 2016
- Tumor Infiltrate Lymphocyte
- Multispectral Imaging
- Spatial Point Pattern
- Tyramide Signal Amplification
- Immunoenzyme Method
Multiplexed Tissue Biomarker Imaging
Multiplexed tissue biomarker imaging can enable the comprehensive characterization and help to reveal the spatial relationships between stromal and immune cells within tumors. This method can label up to 6 biomarkers (plus 1 counterstain) in a single tissue section, where cells reside within a two dimensional relationship. Imaging of multiplexed samples uses morphological structures (e.g., tumor-specific cytokeratin expression) and cellular features (e.g., nuclear counterstaining) to identify all cells and their compartments (nucleus, membrane, and cytoplasm) within the tissue. The expression information for every biomarker in the multiplex is exported in table format in delimited .txt files. Subsequent analysis of the data generated by the multispectral imaging of multiplexed samples can include: phenotyping, positivity/negativity counts and H-scoring, density measurements, and spatial point pattern analyses. For example, this method allows for the assessment of FOXP3+ regulatory T cells (Tregs) in tumor tissues, which have been shown to be associated with adverse prognosis [4, 5]. In addition, multiplexed analysis of CD3, CD4, CD8, CD25, FOXP3, and Ki67 would yield a more informative assessment of Treg and cytotoxic T cell relationship in the context of CTLA-4 blockade. Moreover, the importance of the spatial distribution of immune cell infiltrates have been highlighted in a recent study, showing that the density of CD8+ T cell infiltrates in the melanoma invasive margin was the best full predictive parameter of clinical response to anti-PD-1 immunotherapy .
Another useful application would be the assessment of dendritic and myeloid cell types. For example, dendritic cells, known to upregulate expression of PD-L1 , can be subdivided into plasmacytoid and myeloid dendritic cells (pDC and mDC) . In this instance, multiplexed assessment of CD123, CD11c, CD1c and CD141 may aid in the assessment of dendritic cells within the context of anti-PD1 checkpoint blockade (pDC can be defined through CD11c (low) and CD123 co-expression, while mDC populations can be characterized by CD11c and CD1c or CD11c and CD141 co-expression). Myeloid derived suppressor cells (MDSC) can also be subdivided into granulocyte and monocyte lineages, defined by CD11b, CD15, and CD14. Interestingly, as tumor MDSCs mature, they give rise to tumor-associated macrophages (TAMs) . TAMs can be further subdivided into either ‘M1’ or ‘M2’ based on their differential responses to certain cytokines . In practice, M1 and M2 can be profiled through CD68 and CD163. Given the potential role for TAMs in immune suppression in classic Hodgkin’s lymphoma (cHL), PD-L1 upregulation in CD68+ TAMs was recently shown to occur only when these cells were in close proximity to CD30+ PD-L1+ Reed-Sternberg cells . These findings demonstrate a potential mechanism for checkpoint blockade, especially in light of the recent clinical findings of significant clinical benefit of PD-1 checkpoint blockade in advanced/refractory cHL .
Tumor-draining lymph nodes (TDLNs) have also been analyzed using this approach. In doing so, immune profile changes within TDLNs have been shown to correlate with clinical outcome [13–15]. Moreover, in a recent study, multispectral imaging of CD3, CD8, FOXP3, CD163, and PD-L1 was used to analyze the tumor microenvironment as a predictor of the successful generation and expansion of autologous tumor-reactive tumor infiltrating lymphocytes (TIL) in melanoma patients. Using this panel, it was shown that the CD8+:FOXP3+ ratio was a strong predictor of the successful generation of TIL. In addition, incorporating measures of CD163+ macrophages and the CD8+:PD-L1+ ratio increased the negative predictive value of this immunoprofile, which, if validated, could be used as a predictive biomarker to guide immunotherapy in melanoma patients .
Because multiplexed analyses on tissue are not common, there is risk that a lack of biological information could result from a multiplexed tissue biomarker imaging of 6 targets. Thus, the results may not correlate with clinical status or outcome. As a result, this approach could be rendered insufficient. While deeper fluorescent multiplexing has been demonstrated [2, 17], the multiplexed tissue biomarker imaging method is similar to both standard immunoenzyme assays and TSA multiplexing. Therefore, it has a more familiar protocol that could ease its adoption. More recently, enhanced multiplexing has been reported with antibodies labeled with metal isotopes and analyzed by mass cytometry-based approaches [18, 19]. However, although the morphology should be preserved with immunolabeling, there are concerns with using this approach that the preservation of the discrete microenvironment morphology may be compromised due to lack of resolution compared to that which is currently achieved with standard optical microscopy.
Tissue for this assay can either be fixed in formalin or flash-frozen. Fixation artifact from formalin-fixed paraffin embedded (FFPE) tissues, such as autofluorescence, can be abrogated via autofluorescence reduction using multispectral imaging. In this method, the spectral properties of autoflourescence can be isolated from all other relevant informative spectra. Optimization of the multiplexed staining is required to avoid spectral crosstalk that could complicate target unmixing, which is typically achieved through use of similar tissue types (for many immune markers, tonsil makes an excellent control tissue). Additional care must also be put into both image analysis, as well as the downstream data analysis, where large datasets can be obtained. This would include involving bioinformatic support, where use of advanced statistical scripting capacity would further interrogation of multiparametric data, such as that obtained from spatial point pattern assessments between varied cell phenotypes.
Multiplexed tissue biomarker imaging has undergone many years of development, while recent advances have been published within the last year [3, 11, 16, 20]. Multispectral tissue imaging has over 700 publications over the last ten years. The entire application – multiplexed staining currently up to 7 fluorescent dyes and multispectral imaging – is offering and will continue to offer novel, tissue-based contextual clues in cancer immunology.
Open AccessThis article is distributed under the terms of the Creative Commons Attribution 4.0 International License (http://creativecommons.org/licenses/by/4.0/), which permits unrestricted use, distribution, and reproduction in any medium, provided you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license, and indicate if changes were made. The Creative Commons Public Domain Dedication waiver (http://creativecommons.org/publicdomain/zero/1.0/) applies to the data made available in this article, unless otherwise stated.
- der Loos V. Chromogens in multiple immunohistochemical staining used for visual assessment and spectral imaging: the colorful future. J Histotechnol. 2010;33:31–40.View ArticleGoogle Scholar
- Gerdes MJ, Sevinsky CJ, Sood A, Adak S, Bello MO, Bordwell A, et al. Highly multiplexed single-cell analysis of formalinfixed, paraffin-embedded cancer tissue. PNAS. 2013;110:11982–7.View ArticlePubMedPubMed CentralGoogle Scholar
- 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.View ArticlePubMedGoogle Scholar
- Nishikawa H, Sakaguchi S. Regulatory T cells in cancer immunotherapy. Curr Opin Immunol. 2014;27:1–7.View ArticlePubMedGoogle Scholar
- Miyara M, Yoshioka Y, Kitoh A, Shima T, Wing K, Niwa A, et al. Functional delineation and differentiation dynamics of human CD4+ T cells expressing the FoxP3 transcription factor. Immunity. 2009;30(6):899–911.View ArticlePubMedGoogle Scholar
- Tumeh PC, Harview CL, Yearley JH, Shintaku IP, Taylor EJ, Robert L, et al. PD-1 blockade induces responses by inhibiting adaptive immune resistance. Nature. 2014;515:568–71.View ArticlePubMedPubMed CentralGoogle Scholar
- Perrot I, Blanchard D, Freymond N, Isaac S, Guibert B, Pachéco Y, et al. Dendritic cells infiltrating human non-small cell lung cancer are blocked at immature stage. J Immunol. 2007;178(5):2763–9.View ArticlePubMedGoogle Scholar
- Boltjes A, van Wijk F. Human dendritic cell functional specialization in steady-state and inflammation. Front Immunol. 2014;5:1–31.View ArticleGoogle Scholar
- Delano MJ, Scumpia PO, Weinstein JS, Coco D, Nagaraj S, Kelly-Scumpia KM, et al. MyD88-dependent expansion of an immature GR-1(+)CD11b(+) population induces T cell suppression and Th2 polarization in sepsis. J Exp Med. 2007;204(6):1463–74.View ArticlePubMedPubMed CentralGoogle Scholar
- Ostuni R, Kratochvill F, Murray PJ, Natoli G. Macrophages and cancer: from mechanisms to therapeutic implications. Trends Immunol. 2015;36(4):229–39.View ArticlePubMedGoogle Scholar
- Carey C, Sun H, Stack EC, Peterson M, Hoyt C, Johnson K, et al. A novel method for the simultaneous assessment of PDL1 expression in the tumor cells and tumor associated macrophages of classical Hodgkin lymphoma. Mod Pathol. 2015;28(Suppl 2s):339.Google Scholar
- Ansell SM, Lesokhin AM, Borrello I, Halwani A, Scott EC, Gutierrez M, et al. PD-1 blockade with nivolumab in relapsed or refractory Hodgkin’s lymphoma. N Engl J Med. 2015;372(4):311–9.View ArticlePubMedGoogle Scholar
- Kohrt HE, Nouri N, Nowels K, Johnson D, Holmes S, Lee PP. Profile of immune cells in axillary lymph nodes predicts disease-free survival in breast cancer. PLoS Med. 2005;2(9), e284.View ArticlePubMedPubMed CentralGoogle Scholar
- Setiadi AF, Ray NC, Kohrt HE, Kapelner A, Carcamo-Cavazos V, Levic EB, et al. Quantitative, architectural analysis of immune cell subsets in tumor-draining lymph nodes from breast cancer patients and healthy lymph nodes. PLoS One. 2010;5(8), e12420.View ArticlePubMedPubMed CentralGoogle Scholar
- Chang AY, Bhattacharya N, Mu J, Setiadi AF, Carcamo-Cavazos V, Lee GH, et al. Spatial organization of dendritic cells within tumor draining lymph nodes impacts clinical outcome in breast cancer patients. J Transl Med. 2013;11:242.View ArticlePubMedPubMed CentralGoogle Scholar
- Feng Z, Puri S, Moudgil T, Wood W, Hoyt CC, Wang C, et al. Multispectral imaging of formalin-fixed tissue predicts ability to generate tumor-infiltrating lymphocytes from melanoma. JITC. 2015;3:47.PubMedPubMed CentralGoogle Scholar
- Schubert W. Systematic, spatial imaging of large multimolecular assemblies and the emerging principles of supramolecular order in biological systems. J Mol Recognit. 2014;27:3–18.View ArticlePubMedPubMed CentralGoogle Scholar
- Angelo M, Bendall SC, Finck R, Hale MB, Hitzman C, Borowsky AD, et al. Multiplexed ion beam imaging of human breast tumors. Nat Med. 2014;20:436–42.View ArticlePubMedPubMed CentralGoogle Scholar
- Giesen C, Wang HAO, Schapiro D, Zivanovic N, Jacobs A, Hattendorf B, et al. Highly multiplexed imaging of tumor tissues with subcellular resolution by mass cytometry. Nat Methods. 2014;11:417–22.View ArticlePubMedGoogle Scholar
- Sanmamed MF, Rodriguez I, Schalper KA, Oñate C, Azpilikueta A, Rodriguez-Ruiz ME, et al. Nivolumab and urelumab enhance antitumor activity of human T lymphocytes engrafted in Rag2−/−IL2Rγnull immunodeficient mice. Cancer Res. 2015;75(17):3466–78.View ArticlePubMedGoogle Scholar