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Cyclic Multiplexed-Immunofluorescence (cmIF), a Highly Multiplexed Method for Single-Cell Analysis

  • Jennifer Eng
  • Guillaume Thibault
  • Shiuh-Wen Luoh
  • Joe W. Gray
  • Young Hwan Chang
  • Koei ChinEmail author
Protocol
First Online:
Part of the Methods in Molecular Biology book series (MIMB, volume 2055)

Abstract

Immunotherapy harnesses the power of the adaptive immune system and has revolutionized the field of oncotherapy, as novel therapeutic strategies have been introduced into clinical use. The development of immune checkpoint inhibitors has led to durable control of disease in a subset of advanced cancer patients, such as those with melanoma and non-small cell lung cancer. However, predicting patient responses to therapy remains a major challenge, due to the remarkable genomic, epigenetic, and microenvironmental heterogeneity present in each tumor. Breast cancer (BC) is the most common cancer in women, where hormone receptor-positive (HR+; estrogen receptor and/or progesterone receptor) BC comprises the majority (>50%) and has better prognosis, while a minority (<20%) are triple negative BC (TNBC), which has an aggressive phenotype. There is a clinical need to identify predictors of late recurrence in HR+ BC and predictors of immunotherapy outcomes in advanced TNBC. Tumor-infiltrating lymphocytes (TILs) have recently been shown to predict late recurrence in HR+, counter to the findings that TILs confer good prognosis in TNBC and human epidermal growth factor receptor 2 positive (HER2+) subtypes. Furthermore, the spatial arrangement of TILs also appears to have prognostic value, with dense clusters of immune cells predicting poor prognosis in HR+ and good prognosis in TNBC. Whether TIL clusters in different breast cancer subtypes represent the same or different landscapes of TILs is unknown and may have treatment implications for a significant portion of breast cancer patients. Current histopathological staining technology is not sufficient for characterizing the ensembles of TILs and their spatial patterns, in addition to tumor and microenvironmental heterogeneity. However, recent advances in cyclic immunofluorescence enable differentiation of the subsets based on TILs, tumor heterogeneity, and microenvironment composition between good and poor responders. A computational framework for understanding the importance of the spatial relationships between TILs and tumor cells in cancer tissues, which will allow for quantitative interpretation of cyclic immunostaining, is also under development. This chapter will explore the workflow for a newly developed cyclic multiplexed-immunofluorescence (cmIF) assay, which has been optimized for formalin-fixed. paraffin-embedded tissues and developed to process digital images for quantitative single-cell based spatial analysis of tumor heterogeneity and microenvironment, including immune cell composition.

Key words

Immunotherapy Breast cancer Tumor infiltrating lymphocytes (TILs) Cyclic multiplexed-immunofluorescence (cmIF) Cyclic immunofluorescence Cyclic IF 
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Notes

Acknowledgments

The authors thank Ting Zheng and Lydia Grace Campbell for technical assistance and Yuki Chin for proofreading. The development of cmIF was supported by the funding from Prospect Creek Foundation, Susan G. Komen Foundation, OHSU Foundation, OHSU Center for Spatial Systems Biomedicine (OCSSB), and J.W.G acknowledges the support from NIH/NCI U54 CA209988, NIH/NCI U2C CA233280, and NCI SBIR 1R44CA224994-01. Y.H.C acknowledges the support from Biomedical Innovation Program (Oregon Clinical & Translational Research Institute). The contents do not represent the view of the US Department of Veterans Affairs or the US government.

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© Springer Science+Business Media, LLC, part of Springer Nature 2020

Authors and Affiliations

  • Jennifer Eng
    • 1
  • Guillaume Thibault
    • 1
  • Shiuh-Wen Luoh
    • 2
    • 3
  • Joe W. Gray
    • 1
    • 2
  • Young Hwan Chang
    • 1
    • 2
  • Koei Chin
    • 1
    • 2
    Email author
  1. 1.Department of Biomedical Engineering and OHSU Center for Spatial Systems BiomedicineOregon Health and Science UniversityPortlandUSA
  2. 2.Knight Cancer InstituteOregon Health and Science UniversityPortlandUSA
  3. 3.Veterans Administration Portland Health Care SystemPortlandUSA

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