Cancer is complex and developing oncology therapeutics is a challenging endeavor. From 2011 to 2020, the overall likelihood of approval (LOA) for all cancer drugs that entered human trials was only 5.3%.[1] Immuno-oncology therapies, however, had an overall LOA of 12.4%, reflecting advances in our knowledge of cancer biology and approaches for leveraging the immune system to treat malignant tumors. In recent years, substantial scientific evidence has been accumulating on how the composition of the tumor microenvironment (TME) may impact therapeutic efficacy. These data underscore the importance of profiling, not just the tumor itself, but also the TME to guide immuno-oncology drug development and increase the likelihood of clinical trial success.

Importance of the tumor microenvironment
Understanding the interplay among malignant tumors, their microenvironment, and the immune system is critical for the translation and development of innovative cancer therapies. A growing body of research supports the pivotal role of the TME in multiple stages of solid tumor development, including local resistance, immune escape, and metastasis.[2]

The TME is metabolically complicated and varied, comprising hundreds of cellular elements that influence the probability of tumor progression and the of therapeutic response. By secreting cytokines, chemokines, and other substances, cancer cells can alter their TME and reprogram nearby cells, including those involved in both the innate and adaptive immune responses to the tumor. In the early stages of tumor development, the TME may be immune-enabling, but over time it becomes ‘polluted’ by the proliferation of immunosuppressive elements that promote tumor growth and metastasis.[3]

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The level of immune suppression observed in human cancers is a continuum but can be broken down into the following broad categories:[4]

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  • Immune-inflamed, or hot, with homing of CD8+ T-cells to the tumor, abundant tumor infiltration, and high tumor mutational burden (TMB). The efficacy of immune checkpoint inhibitors and cytotoxic immune cells is generally limited to hot or very warm tumors. Only an estimated 10 to 30 percent of all tumors are characterized as hot.

Cancers that are more likely to be hot include melanoma, head and neck, non-small cell lung cancer, liver, kidney, bladder, and high microsatellite instability (MSI) tumors.

  • Immune-excluded, or cold, with homing of CD8+ T-cells to the tumor but little to no infiltration into the TME. These tumors typically demonstrate suppressive T-cell checkpoints and immune-suppressive cells.
  • Immune-ignored, with neither T-cell homing nor infiltration. These tumors often have low pH and low oxygenation, making them hostile environments for potential therapeutics.
Table 1.0: Immune classification of tumor microenvironments.

Why location matters
The efficacy of cellular therapies and treatments that rely on immune cell activation appears to depend on:

  • The proximity of the activated cells to the target tumor cells
  • Effective tumor infiltration followed by activation of immune cells already within the tumor
  • Presence of certain cell types, such as regulatory T-cells (Tregs) or macrophages, in specific regions of the TME. For instance, in NSCLC, the presence of T-cell populations in the tumor compartment, but not the stromal compartment is associated with more favorable prognoses.[5]

Thus, the spatial orientation of cells in the TME is medically and therapeutically important, particularly in the development of new drugs to treat solid tumors.

Spatial omics and multiplexed imaging are now broadening and deepening our understanding of the cancer ecosystem. In addition to enabling the interrogation of complex, heterogeneous tissues, these technologies offer biological information while retaining tissue architecture and providing context for cell characterization without tissue dissociation bias.

Using multiplex immunofluorescence for spatial profiling
Multiplex immunofluorescence (IF) is a technology that allows for simultaneous single-cell resolution detection of multiple markers in a single formalin-fixed paraffin-embedded (FFPE) tissue section. Consequently, multiplex IF demonstrates higher sensitivity and a wider dynamic range than traditional chromogenic immunohistochemistry (IHC). Unlike immunohistochemistry, which is qualitative or semi-quantitative at best, multiplex is fully quantitative. Another advantage of this technology is the ability to generate high fidelity, high-content data from extremely small or scarce clinical samples.

Depending on the technology, multiplex IF can detect anywhere from two to dozens of markers for identifying and phenotyping cell populations.[6] Using software to track each cell and its associated data, it is possible to explore the spatial distribution and activation state of immune cells within a sample, which can help to distinguish between hot and cold tumors.[7] Technologies such as PhenoCycler (formerly known as CODEX), GeoMX, MACsima, which multiplex higher numbers of markers, are sometimes referred to as “high-plex” or “ultra-plexing”. These ultra-plexing technologies may be better suited for biomarker discovery or early phase trials since they provide higher content with less throughput and analysis is much more complicated.

Most multiplex IF technologies detect four to eight markers, with more robust assays, higher throughput, and easier analysis. These technologies are optimal for providing exploratory data to assess complex biomarkers and correlate them to the clinical outcome as a first step in validating clinical trial assays for patient enrollment or even developing companion diagnostics (CDx). Akoya Biosciences has developed the PhenoImager HT (formerly known as Vectra® Polaris™) Automated Quantitative Pathology System that integrates high-content, nine-color (8-plex) multispectral imaging with the ability to easily perform six-plex (7-color) whole-slide scanning. The system supports the quantification and analysis of tissue sections stained with either Akoya’s Opal™ detection kits or custom-developed panels using the Leica BOND RX. The resulting images can then be analyzed using Akoya’s InForm or Indica Lab’s HALO® software.

Figure 1.0: Sample multiplex IF workflow.

Applying spatial analysis to oncology R&D
Below are a few examples of how spatial profiling and analysis can be applied to provide a more complete picture of tumor biology and determining whether associations exist between cell distribution patterns and clinicopathologic information or outcomes.[8]

Infiltration analysis quantifies the number and depth of immune cell tumor invasion. A common application of this analysis is measuring the number and depth of effector cells infiltrating the tumor, which may help predict outcomes.

Figure 2.0: Sample immune cell infiltration analysis and histogram

Classifier analysis uses AI algorithms to classify different tissue regions or structures (e.g., tumor, stroma, necrosis), allowing them to be analyzed separately. This is valuable because each compartment may have distinct gene expression profiles and pathway activities.[9] For instance, an increase in the number of cytotoxic cells, as compared to Tregs, within the tumor region might correlate with response to immunotherapy. This type of analysis can also be used to classify different sub-regions with a tumor to compare their respective levels of immune infiltration.

Figure 3.0: Sample classifier analysis.

Proximity analysis measures interactions among cells by counting cells within a defined radius of every cell of a given phenotype, providing insight into how spatial interactions between cells encode clinical information. In NSCLC, for example, the proximity of macrophages to tumor cells is inversely correlated with prognosis.[10]

Neighboring cell analysis measures the distance between each cell of a phenotype and its closest neighbor of a different phenotype, enabling study of cell-cell interactions.

Both proximity and neighboring cell analysis can alternatively be visualized as a density heatmap (see Figure 5).

Figure 5.0: Sample nearest neighbor density heatmap showing the distance of every CD8+ cell to its nearest Pan-CK+ neighbor

Key takeaway
Immuno-oncology therapeutics have transformed the treatment landscape for certain cancers but may only demonstrate efficacy in a subset of patients and may not result in prolonged disease control.[11] There remains an unmet need for better, more comprehensive CDx, as shown by the still relatively low percentage of patients who respond to pembrolizumab (Keytruda®; Merck & Co) even with the use of a CDx for programmed death-ligand 1 (PD-L1). The approvals of FoundationOne®CDx for identifying patients with either TMB-or MSI-high solid tumors who may be appropriate for treatment with pembrolizumab demonstrate the predictors of response are multifactorial. [12][13]

Research shows that spatial characterization provides critical insight into tumor biology. Within the TME, it is not just the frequency or ratios of immune cells, but also their proximity to immunosuppressive elements that influence tumor progression, immuno-oncology treatment response, and recurrence. Cell-to-cell topography and the resultant likelihood of cell-to-cell interactions may also correlate with prognostic and clinical parameters.[14]

A greater understanding of the cellular architecture of normal and diseased tissues is essential for developing more effective cancer therapies and more accurately predicting disease progression. In combination with advanced computation methods and downstream analysis, multiplex IF helps researchers visualize, identify, quantify, and even localize clinically significant biomarkers. This knowledge fuels the capability to stratify tumors by cell type, biomarker profiles, and other features. Studying tumor and immune cells in their native spatial context provides valuable insight and create opportunities for identifying therapeutic targets and increasing the likelihood of clinical trial success. [15]

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Reference
[1] Biotechnology Innovation Organization (BIO), Informa Pharma Intelligence, Quantitative Life Sciences. Clinical Development Success Rates and Contributing Factors 2011-2020, February 2021. Online. Last accessed on April 12, 2022
[2] Chen F, et al. New horizons in tumor microenvironment biology: challenges and opportunities. BMC Med. 2015;13:45.
[3] Hinshaw DC, Shevde LA. The tumor microenvironment innately modulates cancer progression. Cancer Res. 2019;79(18):4557-4566.
[4] van der Woude LL, et al. Migrating into the tumor: a roadmap for T cells. Trends Cancer. 2017;3(11):797-808.
[5] Tuminello S, et al. Prognostic value of immune cells in the tumor microenvironment of early-stage lung cancer: A meta-analysis. Oncotarget. 2019;10:7142-7155.
[6] Parra ET, et al. Procedural requirements and recommendations for multiplex immunofluorescence tyramide signal amplification assays to support translational oncology studies. Cancers. 2020;12:255.
[7] Barua S, et al. A functional spatial analysis platform for discovery of immunological interactions predictive of low-grade to high-grade transition of pancreatic intraductal papillary mucinous neoplasms. Cancer Inform. 2018;17:1176935118782880.
[8] Parra ER. Methods to determine and analyze the cellular spatial distribution extracted from multiplex immunofluorescence data to understand the tumor microenvironment. Front Mol Biosci. 2021;8:668340.
[9] Berglund E, et al. Spatial maps of prostate cancer transcriptomes revealed an unexplored landscape of heterogeneity. Nat Commun. 2018:9(1):2419.
[10] Zheng X, et al. Spatial density and distribution of tumor-associated macrophages predict survival in non-small cell lung carcinoma. Cancer Res. 2020;80-4414-4425.
[11] Hofman P, et al. Multiplexed immunohistochemistry for molecular and immune profiling in lung cancer—just about ready for prime-time? Cancers (Basel). 2019;11(3):283.
[12] Foundation Medicine. FoundationOne®CDx Receives FDA Approval as the First Companion Diagnostic to Identify Advanced Cancer Patients with Solid Tumors that are Tumor Mutational Burden-High (TMB-H) and Appropriate for Immunotherapy Treatment with KEYTRUDA® (pembrolizumab), June 17, 2020.
[13] Business Wire. U.S. FDA Approves FoundationOne®CDx as a Companion Diagnostic for KEYTRUDA® (pembrolizumab) to Identify Patients with Microsatellite Instability-High (MSI-H) Solid Tumors, February 21, 2022.
[14] Feng Z et al. Multiparametric immune profile in HPV-oral squamous cell cancer. JCI Insight. 2017;2(14):e93652.
[15] Francisco-Cruz A, Parra ER, Tetzlaff MT, Wistuba II. Multiplex immunofluorescence assays. Methods Mol Biol. 2020;2055:467-495.

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