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What Drives the Success of Adoptive T Cell Therapy?

We’re excited to highlight two recent publications in Cell Systems & Cell Reports describing the use of imaging data from the PhenoCycler platform to help define mechanisms responsible for the success of adoptive T cell therapy. Lead author of the papers, Dr. John Hickey (@johnhickey22), formerly in Dr. Garry Nolan’s lab at Stanford University when the studies took place, is now an Assistant Professor of Biomedical Engineering at Duke University. Included below is his commentary on the studies.

Integrating Multiplexed Imaging and Multiscale Modeling Identifies Tumor Phenotype Conversion as a Critical Component of Therapeutic T Cell Efficacy

While adoptive T cell therapy, a type of cancer immunotherapy which involves infusing tumor-specific cytotoxic T cells into patients, has shown decisive clinical results in hematological cancers, efficacy in solid tumors has been limited. Defining the biological mechanisms that dictate whether a patient will respond to this type of immunotherapy or not, is thus an essential step towards expanding the use and clinical impact of this approach.

Many questions remain about how adoptive T cell transfer leads to effective results. Among those cited by the authors include:

  • How does cancer cell phenotype influence T cell therapy efficacy?
  • Can T cells transform cancer phenotype, and is this related to T cell phenotype at the time of delivery?
  • How does the phenotype of the T cell product affect restructuring of the tumor tissue?

The authors of this paper reason that finding answers to these questions requires interrogation of the cancer-immune network to build models that capture complex interactions at molecular, cellular, and tissue scales. They first deconstructed the interaction pathways in cancer at these scales and then reconstructed and modeled these networks to enable predictions to be tested and hypotheses to be developed (Figure 1).

A combination of PhenoCycler-Fusion multiplexed tissue imaging and Vivarium multiscale modeling software was used to understand the interactions of T cell therapies with cancer at these scales.

John Hickey

John Hickey, PhD
Assistant Professor of Biomedical Engineering, Duke University

“This marriage of multiscale modeling and multiplexed imaging shares key data-driven features across scale, particularly the spatial positioning of distinct cells and molecules,” noted Dr. Hickey, Assistant Professor in the Department of Biomedical Engineering at Duke University. “Multiscale modeling enables the exploration of dynamic behaviors and the conduct of hypothetical experiments. For example, starting with a biopsy or tissue section, we can examine how different therapeutic approaches will play out.”

John Hickey May 2024 Figure 1

Figure 1. Deconstructing and reconstructing cancer-immune network interactions at multiple scales. Panel A shows interactions at the tissue, cellular, and molecular levels. Multiplexed imaging using the PhenoCycler platform enabled multiplexed molecular measurements of 50 or more proteins that could be quantified at a single-cell level and define cell types and states (B). Multicellular structures were identified and network interactions across these scales interpreted to deconstruct the complexity of a tissue. Multiscale modeling was then used to reconstruct complex biology across the different scales (C).

The findings described in the paper demonstrate the impact of T cell and tumor phenotype:

  • Tumor phenotype significantly influences T cell therapy efficacy, impacting tumor cell proliferation and killing
  • T cell phenotype influences both killing capacity and the ability to change tumor phenotype

The authors state that the model can serve as a starting point to investigate the effects of T cell therapies in solid tumors. This approach can be used to comprehensively investigate how alterations in T cell phenotype affect the inhibition of tumor cell proliferation, enhance tumor inflammatory state, initiate tumor killing, and sustain T cell longevity and efficacy – the combination of which may lead to better clinical outcomes.

T Cell-Mediated Curation and Restructuring of Tumor Tissue Coordinates an Effective Immune Response

In a paper published in 2023, a team led by Dr. Hickey used multiplexed tissue imaging from the PhenoCycler platform to characterize the unique cellular microenvironments created by antigen-specific T cells in a melanoma model of adoptive T cell therapy. The researchers also studied the dynamic changes in the tumor microenvironment of metastatic stage IV melanoma tissue samples from patients before and after the start of immune checkpoint inhibitor (ICI) therapy; both responders and non-responders to ICI therapy were included.

The study was designed to identify how T cells manipulate the local tissue microenvironment to achieve their therapeutic mission and to understand the extent to which T cell biology can be altered to overcome tumor immune evasion and promote positive therapeutic outcomes.

The authors developed a new spatial analysis technique to identify T cell-specific neighborhoods within tumors, revealing the changes necessary for an anti-tumor response. They also investigated the temporal dynamics of immune responses during tumor progression, correlating higher-order spatial biology with the success of therapy.

Multicellular neighborhood analysis revealed dynamic immune cell infiltration and inflamed tumor cell neighborhoods associated with CD8+ T cells (Figure 2). T cells were found along a continuum of neighborhoods that the authors describe as “reflecting the progressive steps coordinating the anti-tumor immune response.”

Therapeutic T cells can induce the formation of productive or unproductive tumor T cell neighborhoods, impacting the success of the immunotherapy treatment:

  • More effective anti-tumor immune responses are characterized by inflamed tumor-T cell neighborhoods, flanked by dense immune infiltration neighborhoods
  • Ineffective T cell therapies express anti-inflammatory cytokines, resulting in regulatory neighborhoods, spatially disrupting productive T cell-immune and -tumor interactions

“Determining how these spatial relationships and multicellular neighborhoods are altered based on T cell phenotype further reveals the molecular and cellular mechanisms driving therapeutic efficacy,” said Dr. Hickey. “In this study, we showed the formation of active, higher-order spatial biology that is correlated with the success of T cell-based immunotherapies. These findings highlight the need for transformative changes to how T cell therapies should be screened prior to transfer and suggests additional avenues for engineering of T cell therapies to prioritize cellular microenvironment manipulation to yield more productive anti-tumor immune responses.”

John Hickey May 2024 Figure 2 part 1
John Hickey May 2024 Figure 2 part 2

Figure 2. Study design (A) and representative multiplexed imaging overlays (6/58 markers shown) for responders and non-responders, pre and post therapy (B). Human cellular neighborhood (hCN) map from a patient tumor sample post checkpoint inhibitor treatment, imaged using PhenoCycler® multiplexed imaging (C). Magnified portion of the hCN map indicating a location where the Immune Infiltrate hCN (sky blue) is adjacent to the Productive T cell & Tumor hCN (blue) (D). The same area magnified with a seven-color overlay (out of 58 total markers) highlighting immune cell infiltration next to areas where CD8+ T cells are engaging with tumor cells with an inflamed phenotype (E).

Bringing Clarity to Complexity

Cancer immunotherapy such as adoptive T cell transfer has delivered durable responses and cures for some patients and holds great promise for improving clinical outcomes for many more – if the factors that determine their success can be more clearly defined.

The studies described above employed PhenoCycler imaging techniques and spatial analysis to further elucidate the complex interactions governing the response to adoptive T cell transfer. Revealing the dynamic interplay of therapeutic T cells, cancer cells, and the tumor microenvironment in greater detail across all scales, creates the foundation for strategies that drive a higher percentage of clinical success for this approach to treating cancer.

To learn more:

  • Hear Dr. John Hickey discuss this research in a live webinar on May 28 entitled “Spatiotemporal Dynamics of T Cell Therapy in Cancer” – Register to attend via SelectScience
  • See how the PhenoCycler-Fusion 2.0 solution is helping provide insights into complex biological systems and gain a better understanding of cellular microenvironments
  • Join us at an upcoming event near you or connect with us to discuss how Akoya’s spatial biology solutions can help you advance your research.

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