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288 Kai Shi
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291Stromal modulation strategies
CHAPTER NINE
Spatial mapping of the tumor immune
microenvironment
Yi-Chien Wua, Joanna Pagaczb, Samantha C. Emerya, Stephen J. Kronb,
and Steve Seung-Young Lee
a
Department of Pharmaceutical Sciences, University of Illinois at Chicago, Chicago, IL, United States
b
Department of Molecular Genetics and Cell Biology, The University of Chicago, Chicago, IL, United States
a
Contents
1. Introduction 293
1.1 Short introduction to standard and emerging methods 295
2. Conventional tissue preparation and processing for cancer histology 295
2.1 FFPE tissue processing and H&E staining 296
3. Mapping proteins and transcripts in the TIME 299
3.1 Chromogenic and fluorescent immunodetection in FFPE 299
3.2 Multiplex immunodetection in FFPE 301
3.3 In situ hybridization to detect transcripts 305
3.4 High multiplexing with Digital Spatial Profiling 307
3.5 Multiplexing in FFPE tissue beyond fluorescence 308
3.6 Interrogating the TIME by immunodetection and ISH in 3-D 309
4. Spatial ’omics 310
4.1 Spatial transcriptomics 310
5. Conclusions and future prospects 320
Acknowledgments 321
References 321
1. Introduction
Despite tremendous advances in diagnosis and therapy, cancer remains a major
challenge to public health [1]. The recent and dramatic impact of immunotherapy represents a turning point in cancer treatment. Unlike conventional chemotherapy, immunotherapy leverages patients’ own innate and adaptive immunity to eradicate cancers by
treating them with immunomodulatory molecules (i.e., immune checkpoint inhibitors,
ICIs) or engineered immune cells (i.e., chimeric antigen receptor (CAR)-T cells) [2, 3].
Although several immunotherapies have been approved, challenges remain to enable
Engineering Technologies and Clinical Translation Copyright © 2022 Elsevier Inc.
All rights reserved.https://doi.org/10.1016/B978-0-323-90949-5.00009-7
293
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effective treatment for the majority of cancer patients. For the approved ICI therapies
targeting the programmed cell death-1 (PD-1)/programmed death-ligand 1 (PD-L1)
immune checkpoint pathway [4–6], only a fifth of patients will display a meaningful
response to therapy while a similar fraction will suffer major adverse effects.
A challenge is that the companion immunohistochemistry test for PD-L1 expression
in the tumor [7] is unable to predict response. One of the contributors to uncertainty
may be that this simple test is unable to account for other elements of heterogeneity
in the tumor immune microenvironment (TIME) [8–11]. Different immune cell types
form clusters and interact directly or indirectly with cancer cells in the TIME, which may
significantly impact cancer progression and treatment outcome. Along with cancer cells
and a diverse and dynamic immune infiltrate, the tumor microenvironment encompasses
a heterogeneous stroma including fibroblasts, vascular cells, and extracellular matrix
(ECM) [12, 13]. These other stromal components influence cancer and immune cells
and their interactions, providing additional complexity. As such, analysis of multiple
TIME parameters along with PD-L1 expression may be required to distinguish patients
who will respond or be resistant to ICI therapy [14, 15].
Based on the density and distribution of immune cells in the TIME, tumors are often
categorized into two broad classes, immune-inflamed tumor (hot) and immune-excluded
(cold) tumors [16]. Hot tumors are typically characterized by an inflammatory infiltrate
with a high proportion of cytotoxic T lymphocytes (CTLs) and are considered to be
more likely to respond to ICI [17–19]. Cold tumors lack abundant infiltrating CTLs
but T cells may cluster at the periphery in adjacent tissue or ECM. This distribution pattern is associated with resistance to ICI [20]. In addition to T lymphocytes, the TIME is
populated by a wide range of innate and adaptive immune cell types that can influence
ICI effects including other T cells, B cells, natural killer (NK) cells, macrophages, neutrophils, and myeloid-derived suppressor cells (MDSC) [21, 22]. Some can directly kill
cancer cells, or present cancer antigens to help indirectly drive cytolytic response. Other
immune cell types may support tumor growth and promote immune evasion. Were it
feasible to fully define the TIME, including immune cell subtypes, their locations, functions, and activation states, it may be possible to not only predict outcomes of current ICI
therapy but also enable strategies to enhance efficacy by TIME modulation.
Given these considerations, along with a better molecular definition of cancer beyond
the few established markers, a more comprehensive characterization of the TIME to
develop new signatures of response and resistance will be critical to personalizing therapy
[23]. A challenge remains to develop clinically useful technologies that enable deep pro-
filing of immune cells, their activation states, and their distribution in the TIME. This
topic has been examined recently in several excellent reviews and method compilations
(e.g., [24–27]). Here, we provide a broad overview of the most practical strategies to
characterize cellular and molecular features of the TIME without ignoring spatial relationships and heterogeneity. We first discuss the technologies that remain key tools
for clinical pathology, hematoxylin and eosin (H&E) staining for morphology and
immunohistochemistry (IHC) for biomarker analysis, performed on thin sections cut
from formalin-fixed paraffin-embedded (FFPE) tissue blocks. Given that the complexity
of the TIME is incompatible with a one-biomarker-at-a-time approach such as IHC, we
discuss research tools for deeper profiling that appear poised to impact clinical practice,
including multiplexed detection of multiple proteins and/or transcript biomarkers by
fluorescence, mass cytometry, and other strategies to create TIME maps in two or three
dimensions. Looking to the future of diagnostics, we review emerging spatial "omic"
approaches that can interrogate thousands of analytes at a time to construct expression
profile maps at nearly cellular resolution. Ongoing studies applying both commercialized
technologies and others still under development to interrogate the TIME are already providing important insights about determinants of response and resistance to immunotherapy. With this in mind, the prospects for translation of spatial mapping of the TIME to
clinical practice will be discussed and highlighted.
1.1 Short introduction to standard and emerging methods
As improved research tools have gradually revealed the complexity of the TIME on the
cellular and molecular level, the need to find practical ways to analyze the spatial distribution of these characteristics has become paramount. Early microscopists observed that
even unstained tissue samples exhibit distinct morphological features, but it took the
development of reproducible chemical staining with hematoxylin and eosin (H&E) to
enable robust definition of characteristic cellular phenotypes within normal and malignant tissues, a foundation of modern pathology [28]. Efforts going back to the 1940s to
develop robust methods to track specific cells and molecules in tissue led to IHC [29] and
in situ hybridization (ISH) [30]. In recent decades, automated staining instruments have
reduced variability in staining [31], and digital imaging and data processing techniques
along with advances in machine learning are beginning to obviate the need for subjective
scoring [32, 33]. Developments in tissue clearing methods and software have empowered
scientists to progress beyond two-dimensional (2-D) maps to full three-dimensional
(3-D) rendering of the TIME [34]. Furthermore, the genomic, transcriptomic, and proteomic techniques introduced in the last 20 years and ongoing advances in single-cell
analysis and machine learning are revealing biomolecular expression signatures of immunotherapy response and resistance [27, 35]. This is driving remarkable recent progress in
mapping patterns of molecular expression in tumors that are poised to achieve omic-level
analysis at cellular resolution in the TIME.
295Spatial mapping of the tumor immune microenvironment
2. Conventional tissue preparation and processing for cancer
histology
Surgically excised tumors and biopsies are the primary sources for histopathological
studies of solid tumors. Careful sample preparation and processing are required to preserve morphology and biomarker integrity. Thin sections cut from blocks of FFPE tissue
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Fig. 1 Workflow of FFPE tissue processing and H&E staining. Chemically fixed tissues are subjected to
ethanol dehydration and xylene clearing prior to infiltration with heated paraffin wax. After solidification at room temperature, paraffin tissue blocks are sectioned using a microtome onto glass slides,
stained with H&E, and imaged by standard light microscopy.
using a microtome have long provided the key samples used to study cellular morphology
in cancer specimens (Figs. 1 and 2; [36]). Pathologists examine tissue architecture, cell
distribution, and cell shape regularity to determine pathological grading of cancers. In
short, the basic FFPE procedure involves tissue fixation, embedding, sectioning, H&E
staining, sample mounting, and microscope observation. To facilitate rapid and reliable
clinical diagnosis, the automated tissue processor was introduced in the 1940s, offering
faster and more reproducible embedding [37]. In this section, we provide an overview of
standard FFPE methodology, which remains the gold standard for tissue processing in
cancer diagnosis.
2.1 FFPE tissue processing and H&E staining
Fresh tissues are delicate and easily distorted during handling. Cells, organelles, and biomolecules are all rapidly degraded, starting within moments after tissue collection. Preservation often depends on applying chemical fixation promptly [38, 39]. Formaldehyde
has long been the fixative of choice due to its penetration, rapid action, and the ability to
maintain morphological and molecular integrity [40]. Formaldehyde reacts with proteins
and other cellular components by forming a dense matrix of polymers and intermolecular
and intramolecular crosslinks [41] that provide rigidity and a barrier to the diffusion of
proteases and nucleases. Once embedded in paraffin, the tissue remains stable almost
indefinitely, allowing archival storage at room temperature. Then, upon sectioning,
the tissue can be rehydrated, and the formaldehyde polymers, crosslinks, and adducts
can be (partially) chemically reversed, leaving the proteins and nucleic acids tethered
in place but accessible for detection.
Sacrificing preservation and considering speed, tissue can be immersed in a cryopres-
ervation medium [e.g., optimal cutting temperature (OCT)], frozen, and promptly sectioned in a cryostat [42]. The sections are mounted onto glass slides for frozen storage or
immediate staining [43]. An advantage of cryopreservation is that proteins and nucleic
297Spatial mapping of the tumor immune microenvironment
Fig. 2 H&E histological analysis with automated segmentation and feature detection applied to ovarian cancer tumor tissue. Purple, hematoxylin stained nuclei; pink, eosin-stained cytoplasm. Green
pseudocolor automatically identified cancer cell regions; red, stromal regions; blue, TILs. (A) Three classes of cells can be identified at high magnification based on the morphology of cell nuclei. (B) Two
tumor sections with H&E staining were imaged at lower magnification (left panels) and subjected
to automated analysis (right panels) based on morphology shown in (A). (Images reproduced with per-
mission from Lan C, et al. Quantitative histology analysis of the ovarian tumour microenvironment. Sci Rep
2015;5:16317.)
acids may not be as chemically altered as in the FFPE workflow, but as soon as the section
thaws, the lack of chemical fixation allows degradation to commence. To preserve tissue
integrity, an ice-cold diluted solution of fixative (e.g., 10% formalin) can be briefly
applied to frozen tissues on slides just prior to H&E staining.
Embedding tissues in paraffin wax blocks helps to turn a fragile specimen into a stable
form, facilitating microtome sectioning to obtain thin but mechanically stable tissue sections (Fig. 1). A challenge for FFPE tissue processing is that paraffin wax is immiscible
298 Yi-Chien Wu et al.
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with the water that makes up much of living tissues. Prior to embedding, water is
removed by immersing the fixed tissue specimens in a series of increasingly concentrated
ethanol solutions. Then, after replacing ethanol with xylene as an intermediate solvent,
the specimen is infiltrated with heated paraffin wax and cast in a mold to form a block.
When returned to room temperature, paraffin blocks are readily sectioned at 2- to 10-μm
thickness by a microtome. The orientation of a specimen in the block determines the
plane of sectioning and impacts subsequent data interpretation.
In general, slide-mounted FFPE tis sue sections are next deparaffinized with xylene
and rehydrated through descending et hanol concentrations into the water prior to
being chemically stained with H&E [44]. Hematoxylin is a basic dye t hat binds nucleic
acids to yield a blue stain, while eosin is an acidic dye to counterstain extracellular
matrix and cytoplasm in pink. H&E staining reveals both broad tissue architecture
at low power and cell distribution and morphology at higher ma gnifica tion (Fig. 2).
Pathologists can infer the cancer type and stage by comparing and recognizing the
distinct morphologies of cancer cells and examining their distribution within the
tissue. Overall, detection of malignancy by H&E staining, whether by a pathologist
or a well-trained alternative [45, 46], is remarkably robust. Indeed, there is active discussion of whether automated systems are ready to replace diagnosis by pathologists
(e.g., [47]).
With respect to characterizing the TIME, H&E staining offers limited but still valuable information. Tumor-infiltrating lymphocytes (TILs) have a characteristic nuclear
shape and density and little surrounding cytoplasm (Fig. 2), allowing them to be recognized and enumerated by pathologists or automated systems, thereby distinguishing
cold and hot tumors [48–50]. However, myeloid cells such as macrophages are more
heterogeneous and thus challenging to be distinguished from tumor cells or nonimmune stromal cells.
While H&E staining can be highly reproducible, certain preanalytical variables in
tissue handling, processing, sectioning, and staining may lead to artifacts that alter histological interpretation or impact biomarker analysis [51]. These variables can be managed by careful processing and use of standardized protocols across laboratories. Yet,
even using ideal conditions, a major shortcoming of H&E staining is the limited information content of two-color staining distinguishing only cell nuclei and cytoplasm.
While machine learning strategies to link morphology to prognostic gene expression
signatures appear promising [52], they are unlikely to replace the need for specific
detection of molecular biomarkers in tissue. IHC and ISH offer complementary tools
to detect specific proteins and nucleotide sequences in tissues, facilitating the spatial
mapping of specific molecular features of the TIME. As yet underutilized, it was
recently shown that H&E stained sections can be destained and then specific antigens
detected by IHC [53], which may be a powerful tool to obtain images to train machine
learning for diagnosis.
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