T cell metabolic adaptation: crucial checkpoint for antitumor immunity

Introduction

Tumor micro-environment is characterized by a consistent reduction in oxygen and blood-borne nutrients that significantly affect the metabolism of distinct cell subsets. Immune cells populating malignant lesions need to activate alternative pathways to overcome tumor-prolonged nutrient deprivation. In particular, the metabolic switch occurring in transforming tissues dramatically impacts on tumor-infiltrating T cell biology.

Immunotherapies that block inhibitory checkpoint receptors on T cells have transformed the clinical care of cancer patients. However, for a significant number of patients, these therapies have proven ineffective.

Tailoring immune responses by manipulating cellular metabolic pathways provide new options for cancer immunotherapy.

This case study describes in the context of the tissue microenvironment and more specifically according to tumor infiltrating lymphocytes (TILs), the metabolic patterns of a stage II colorectal cancer sample and discuss how immunomodulatory strategies could synergize with immunotherapy to circumvent its current limitations with the use of innovative imaging capabilities and data analysis workflows.

T cell exhaustion in tumor microenvironment

The case study combined standard microscopy Imaging and Quantitative Mass Spectrometry Imaging (QMSI) from stage II colorectal cancer tissue sample in order to map up to 5,000 metabolites and small molecules and track metabolic phenotypes as well analysis the tissue structure and TILs (Tumor Infiltrated Lymphocytes). In order to decipher the complexity of the highly multiplex Mass Spectrometry imaging data, we used image segmentation with Uniform Manifold Approximation and Projection (UMAP) for Dimension Reduction and analysed the content of each sub-structure of the image/tissue as reported in a previous White paper #8.

Once the clusters are transposed and colorized on the tissue section, the density of Tumor Infiltrated Lymphocytes (TILs) is assessed in each metabolite cluster by measuring the area occupied by mononuclear cells over the stromal area on hematoxylin and eosin (H-E)-stained sections. Tumor content is also assessed in each defined metabolite cluster.

Tumor metabolism reprogramming regulates tumor microenvironment T cell infiltration

The four (4) metabolite patterns identified in our study clustered with tumor content indicating a tumor specific metabolic reprogramming. In these highly tumor content environments, lymphocyte infiltration is also modulated indicating that the metabolic signatures shape the inclusion/exclusion of T cells in the tumor microenvironment.

Metabolic interplay between tumor cells and immune cells

The competitive uptake of glucose under TME conditions is responsible for the damage to T cell function and the ensuing low pH of the TME has been shown to be beneficial for the selection of more aggressive tumor cells and suppress tumor immunity to promote tumor progression. Using Metlin database, peaks in the high resolution MSI datasets have been annotated and significant upregulation of lactic acid and glucose were found in clusters with impaired immune cell response. This demonstrates that the Metabolic interplay between tumor cells and immune cells can contribute to the exhaustion of TILs and therefore immunosuppression .

Conclusion

In this case study, a QMSI data exploration study was made on a stage II colorectal cancer tissue in order to reveal the role of metabolic signatures in anti-tumor immunity.  Based on the founded metabolic clusters, predictive metabolic signatures can be identified that monitor the status of the TME and therefore the infiltration levels of immune cells. This can be a powerful tool for the prediction of response to immunotherapies or to the exploration of novel therapeutic strategies.

Nowadays, current pathological staging present limitations in prognosis and response to therapies. Patients could have totally different final outcomes although they might possess similar clinical and pathological types. The development of metabolic signatures which could comprehensively combine the metabolic profile with the clinical parameters could improve the accuracy of the prediction. In conclusion, the metabolic signature could indicate the status of the tumor microenvironment to provide more clues in the exploration of immunomodulatory strategies.

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