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Certain Immune Cell Types Linked With ICI Response in Patients With NSCLC

Certain immune cell types are associated with immune checkpoint inhibitor (ICI) response in patients with non–small cell lung cancer (NSCLC).

The proportion of certain blood immune cell types, predominately T cells and neutrophils, was linked with response to immune checkpoint inhibitors (ICIs) in patients with non–small cell lung cancer (NSCLC), according to a review in Journal for ImmunoTherapy of Cancer.

At present, there are no cell subpopulations that can singularly predict the outcome of ICI-treated patients with NSCLC and replace programmed death-ligand 1 (PD-L1) expression testing as the standard option. A combination approach that accounts for the levels of several immune cell types would give a more comprehensive snapshot of the immunological processes involved in the response to ICIs, the review's authors noted

Lung Cancer X-Ray

Lung Cancer X-Ray

The goal of their report was to describe new techniques that will ease the discovery of more immune cell subpopulations that have potential to become predictive biomarkers and reflect on the remaining challenges to bring this type of analysis to routine clinical care in the near future.

Providers currently rely on biomarkers like PD-L1 expression, T-cell signatures, and tumor mutational burden (TMB), which can be good at predicting whether patients will respond well to programmed cell death protein 1 (PD-1) inhibitor or ICI treatments. These and other tissue-based biomarkers, however, require an invasive procedure that is not always possible to perform. Additionally, longitudinal follow-up of treatment response is burdensome for most patients.

Aside from the invasive nature in acquiring the tumor tissue, other disadvantages consist of the inability to capture the complete spatial heterogeneity of the tumor and longitudinal treatment follow-up difficulties. In addition, 60% to 80% of patients with NSCLC do not benefit from ICI treatment, and serious toxicities still happen in 10% to 30% of the cases, which emphasize the need for biomarkers that predict who is more likely to respond.

Some systemic factors are associated with therapeutic outcome under ICI:

  • Natural killer (NK) cells express several immune checkpoint molecules, including LAG-3, CTLA-4, and PD-1 (although this has been disputed), suggesting that NK cells might be a possible ICI target
  • In T cells, cytotoxic CD8+ T lymphocytes (CTLs) are the main effectors during the antitumor immune response; central and effector memory CTL subsets are activated much faster when they re-encounter a specific tumor neoantigen, which can play an important role
  • Myeloid-derived suppressor cells (MDSCs) can directly suppress antitumoral T-cell function through specific metabolic mechanisms, so several myeloid cell subsets, such as neutrophils, have been evaluated as predictors of ICI therapeutic efficacy
  • Neoantigen-specific T cells have been found in peripheral blood of patients who have melanoma, and patients with cancer who respond best to anti–PD-L1 therapy are shown to have a clonotypic expansion of circulating T cell clones that are also present in the tumor, suggesting a replenishment of newly primed T cells from outside the tumor
  • Peripheral immune cell types in blood and secondary lymphoid organs, like NK cells, dendritic cells, and B cells, rise in numbers after ICI treatment, indicating that systemic immune events complement the ICI-induced antitumor response.

The challenge lies in identifying the systemic immune events that are most strongly and consistently connected to therapeutic outcome and packaging this knowledge into a biomarker that is fully validated and technologically easy to implement at scale.

Recent findings support the hypothesis that the frequency of tumor specific peripheral CD8+ T cells can be a sufficient predictive biomarker for ICI response, which has been explored in several studies involving patients with NSCLC. It was recently demonstrated that T helper cells are vital in initiating and sustaining the anti-PD1-induced systemic response.

“Chronic inflammation is one of the hallmarks of cancer, and the associated immunosuppressive climate is largely mediated by myeloid cell infiltration in the TME [tumor microenvironment]. Under proper stimuli, however, these cells can elicit powerful antitumoral effects,” said the researchers.

Additionally, some studies have found significant differences in the neutrophil-to-lymphocyte ratio (NLR) between responders and nonresponders several weeks after the initiation of the treatment with anti–PD-1 antibodies, which suggests that the NLR might be a possible biomarker for monitoring ICI response. The NLR has also been linked with therapeutic success following ICI treatment in several cancers; this parameter has the advantage of mirroring the tug of war between MDSCs and effector T cells in an easily implementable way.

If this finding and others are validated in larger cohorts, they may make up a compound biomarker that can be identified and standardized into an easy flow-cytometrical panel within reach of most clinical hematology labs.

Some limitations are present in this review. A substantial number of studies featured included small patient cohorts, and some lacked a validation cohort. Additionally, most of the cohorts comprise only patients treated with ICIs as monotherapy in the first or second line. Further, patients treated with ICIs and chemotherapy, considered the dominant first-line treatment in advanced NSCLC, were not included in these reports.

"Further research and methodological improvements are needed to determine whether the interrogation of immune cell proportions in the blood of the patients can be translated into an accurate predictive biomarker that can outcompete the currently available ones,” said the study authors.

Reference

Rubio AM, Everaert C, Van Damme E, De Preter E, Vermaelen K. Circulating immune cell dynamics as outcome predictors for immunotherapy in non-small cell lung cancer. J Immunother Cancer. Published online August 3, 2023. doi:10.1136/jitc-2023-007023

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