April 23, 2024

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How to get a large number of cytotoxic T cells into tumors?

How to get a large number of cytotoxic T cells into tumors?

How to get a large number of cytotoxic T cells into tumors?

As the main force of anti-tumor immune response, the actions of CD8+ T cells are closely monitored. However, from a temporal perspective, most basic research only focuses on specific time points, without continuously tracking the dynamic changes of CD8+ T cells in the tumor microenvironment. What mysteries could be revealed if this were achieved?

Recently, in the journal Nature Cancer, a team from the Weizmann Institute of Science in Israel published a study that successfully constructed a model to continuously track the differentiation and proliferation dynamics of tumor-specific CD8+ T cells at the single-cell level. They then evaluated the role of PD-1 inhibitors in treatment and factors influencing treatment efficacy.

The researchers found that the co-stimulation of type 1 conventional dendritic cells (cDC1) is a crucial step for a large number of tumor-specific CD8+ T cells to enter the tumor microenvironment. Combining PD-1 inhibitors with 4-1BB agonists can positively regulate the “flow” of CD8+ T cells, effectively activating the immune response.

How to get a large number of cytotoxic T cells into tumors?

To achieve the goal of continuous tracking of tumor-specific CD8+ T cells, the researchers went to great lengths in constructing their model: they used the OT-1 mouse model (which can specifically recognize ovalbumin/OVA antigen) and the corresponding melanoma cell line (B16-OVA). Ten days after implanting melanoma cells, the researchers injected equal amounts of OT-1 specific CD8+ T cells and non-specific “bystander” CD8+ T cells into the mice.

From day 1 to day 45 after the initial model construction, the researchers sampled the tumor microenvironment, tumor-draining lymph nodes (tdLN), and spleens to perform single-cell RNA sequencing on OT-1 specific CD8+ T cells, “bystander” CD8+ T cells, and endogenous T cells. They also used the same tracking strategy to evaluate a colon cancer mouse model as a control. The researchers collected over 120,000 characteristics of CD8+ T cells in different states for further analysis.

Based on the expression of key cytokines suggesting cell functional states, the researchers first classified the CD8+ T cells. The prominent categories included naive-like (characterized by high expression of Sell, Klf2, and Tcf7), memory-like (characterized by high expression of Tcf7 and Ccr7), and effector CD8+ T cells, which could also be further subdivided into inflammatory effector, early effector, and cytotoxic effector types, among others. However, regardless of the type of CD8+ T cell, they all underwent dynamic changes.

Taking the tumor-specific CD8+ T cells (i.e., OT-1 specific) analyzed in the study as an example, they did not outnumber the “bystander” CD8+ T cells in the tdLN until the third day after implantation in the mice, and only became the majority in the tumor microenvironment by the sixth day. Functionally, tumor-specific CD8+ T cells underwent changes in six stages:

  1. Early effector stage: characterized by rapid and abundant secretion of cytokines after entering the mice.
  2. Three days after implantation in mice, the early effector characteristics of CD8+ T cells gradually decreased, and they began to enrich in activated states and cytotoxic-functional abnormality features. 3-4. The activity of CD8+ T cells further decreased, gradually transitioning to precursor naive-like and memory-like states.
  3. Corresponding to 6-15 days after implantation in mice, the frequency of cytotoxic effector and functional abnormality of CD8+ T cells increased, along with a significant increase in immune checkpoint expression, among other characteristics.
  4. Corresponding to 4 weeks after implantation in mice, the implanted CD8+ T cells entered a late functional abnormality state, similar to the endogenous CD8+ T cells in the mice.

However, the changes that tumor-specific CD8+ T cells undergo in tdLN and spleen are significantly different from those in the tumor microenvironment, especially in tdLN, where the proliferation rate of tumor-specific CD8+ T cells significantly accelerates three days after implantation in mice. The researchers speculate that this rapid proliferation of CD8+ T cells from tdLN is a decisive factor for their subsequent large influx into the tumor microenvironment; the cDC1s cells mentioned at the beginning play a crucial regulatory role in this step.

Next, the researchers analyzed the impact of PD-1 inhibitor treatment on the above dynamic changes: with the significant inhibition of tumor growth by PD-1 inhibitors, tumor-specific CD8+ T cells could occupy a numerical advantage in the tumor microenvironment earlier (more than 90% by the 5th day after implantation), and were more likely to exhibit early effector and cytotoxic effector phenotypes. There were also significant changes in the expression of regulatory genes, such as Tnfrsf9, which encodes 4-1BB (corresponding to the early effector type).

The only significant change that occurred in the tdLN was the more vigorous proliferation of CD8+ T cells. Taken together, the effect of PD-1 inhibitors on the dynamic changes of tumor-specific CD8+ T cells mainly involves causing more CD8+ T cells to migrate from tdLN to the tumor microenvironment and promoting their differentiation into effector phenotypes. The value of using 4-1BB agonists in combination lies in influencing the second step, which is to convert more CD8+ T cells into early effector and cytotoxic effector types.

Finally, the researchers analyzed the gene characteristics of CD8+ T cells in human breast cancer patients and found immature, cytotoxic, and functionally abnormal CD8+ T cell states and dynamic changes highly similar to those in the mouse experiments. For breast cancer patients receiving PD-1 inhibitor treatment, the expression of co-stimulatory genes (CD27, TNFRSF9), effector genes (GZMH), and immune checkpoint LAG3 can serve as key markers for predicting treatment response.

In the paper, the researchers expressed their hope for more similar studies in the future, expanding the scope of tracking dynamic changes to key immune cell subgroups such as CD4+ T cells, NK cells, and dendritic cells. Only then can the complex tumor microenvironment and factors influencing immunotherapy be truly clarified, and clinical studies based on this dynamic change framework can be designed to achieve personalized and precise immunotherapy, instead of relying on a single drug or a single approach to solve the problem.

How to get a large number of cytotoxic T cells into tumors?

(source:internet, reference only)

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