Dynamic change of CD8⁺ T cell: immunotherapy fate tell?

In recent years, immune checkpoint blockade (ICB) has emerged as a new way to treat patients with non-small cell lung cancer (NSCLC). Two programmed cell death-1 (PD-1) inhibitors and one programmed death-ligand 1 (PD-L1) inhibitor are now approved for metastatic NSCLC when a treatable oncogenic driver mutation can be excluded and the tumor has an immunohistochemical tumor proportional score (TPS) of PD-L1 ≥50% (1). In addition, combinations of ICB in combination with chemotherapy and/or CTLA-4 inhibitors are approved for metastatic NSCLC regardless of PD-L1 expression (2). Because there have been no head-to-head clinical trials comparing ICB monotherapy with combination strategies, clinicians can use clinical and laboratory findings to decide whether to start monotherapy or combination therapy or to switch to chemo-immunotherapy after starting ICB alone. Early prognostic and predictive factors are of paramount importance for this important therapeutic decision. In addition to tumor mutational burden and performance status, the need to take oral glucocorticoids or an elevated C-reactive protein (CRP) level at baseline may also be criteria for the therapeutic decision (3). However, all these factors are not evaluated prospectively and independently.

Nowadays, scientific research focuses on the anti-tumor T-cell response, which is believed to be a necessary prerequisite for ICB-mediated restoration of anti-tumor T-cell activity (4,5). Indeed, a dynamic change in peripheral blood CD8⁺ T cells could be an early surrogate for ICB activity and detectable much earlier than radiological response according to RECIST criteria (6). In this approach, blood is drawn before initiation and during ICB treatment. The difference from baseline is expressed as Δ and describes an increase or decrease in CD8⁺ T-cell activity measured by flow cytometry or mRNA expression.

A recent article published in this journal entitled “Immune profile analysis of peripheral blood and tumors of lung cancer patients treated with immune checkpoint inhibitors” illustrates the research activities in this challenging field (7).

The aim of the research led by Ichiki and colleagues was to analyze the relationship between immune-related molecular expression in peripheral blood mononuclear cells (PBMCs) and lung cancer tissues and the effects of immune-checkpoint inhibitor (ICI) monotherapy. The study prospectively examined changes in the expression of immune-related molecules in PBMCs at baseline and after 4–8 weeks of ICB treatment, as well as major histocompatibility complex (MHC) class I, PD-L1, CD8, and CD103 expression in lung cancer tissue before ICI administration. The goal was to determine whether changes in immune-related molecular expression could be used as biomarkers to predict the efficacy of ICI therapy and inform treatment decisions.

In the study, researchers conducted univariate and multivariate analyses to identify factors associated with progression-free survival (PFS). One of the significant factors identified in these analyses was the change in CD103⁺ CD39⁺ CD8⁺ cells after dosing. The researchers found that this change correlated closely with clinical response to ICI therapy, with patients who had greater changes more likely to have a better outcome. In addition to the univariate analyses, two independent factors were identified in these analyses: the change in CD103⁺ CD39⁺ CD8⁺ cells after administration and the Brinkman index, a composite measure assessing immune system activity, taking into account T-cell counts and natural killer cell activity.

This work adds to the rapidly growing information that specific CD8⁺ T cells are expressed shortly after induction of ICB and can be measured in peripheral blood (4,7-15). While CD39⁺ CD103⁺ cells have already been detected on tumor-reactive T cells in tumor infiltrates and the presence of these T cells has been associated with response to ICB, other forms of CD8⁺ T cells in peripheral blood may show resistance to ICB therapy (10,13,14,16,17).

In particular, CD8⁺ PD1⁺ T cells are considered a hallmark of exhausted T cells (5). While Ichiki et al. did not find such a correlation with PFS in their work, there are other studies showing a dynamic change with a decrease in CD8⁺ PD1⁺ T cells after one or two ICB cycles that is predictive of a better outcome (7,10,13-15) (for an overview of recent data on Δ CD8⁺ T cell subtypes, see Table 1). Different ICB (PD-1 versus PD-L1 based immune-checkpoint inhibitors), a wide range of time points chosen for peripheral blood sampling during ICB (7 d to 12 weeks), and small case numbers might have contributed to conflicting results between different studies. Although CD8⁺ T cells are an essential prerequisite for the effect of ICB on tumor cells and dynamic change can be readily measured on peripheral blood cells, much more needs to be done to gain deeper insight into these complex mechanisms and establish robust predictive markers for early response.

Acknowledgments

Funding: This work was funded by W. Lutz Stiftung, Nuremberg, Germany.

Footnote

Provenance and Peer Review: This article was commissioned by the editorial office, Translational Lung Cancer Research. The article did not undergo external peer review.

Conflicts of Interest: The author has completed the ICMJE uniform disclosure form (available at https://tlcr.amegroups.com/article/view/10.21037/tlcr-23-139/coif). The author received honoraria for lectures or educational events from AstraZeneca, Boehringer, Novartis, MSD, BMS, Lilly, Pfizer and Roche. The author received support for attending meetings and/or travel from Boehringer, Roche and AstraZeneca. The author served on advisory board of Astra Zeneca, Boehringer, Novartis, MSD, Lilly Pharma, BMS, and Roche. The author received equipment, material, drugs, medical writing, gifts or other services from Boehringer for medical writing. The author’s patent application EP21183549.1 (method for predicting a clinical response towards an immune checkpoint inhibitor based on pretreatment therewith) was filed with regard to the results of this study. The author has no other conflicts of interests to declare.

Ethical Statement: The author is accountable for all aspects of the work in ensuring that questions related to the accuracy or integrity of any part of the work are appropriately investigated and resolved.

Open Access Statement: This is an Open Access article distributed in accordance with the Creative Commons Attribution-NonCommercial-NoDerivs 4.0 International License (CC BY-NC-ND 4.0), which permits the non-commercial replication and distribution of the article with the strict proviso that no changes or edits are made and the original work is properly cited (including links to both the formal publication through the relevant DOI and the license). See: https://creativecommons.org/licenses/by-nc-nd/4.0/.

References

1. Reck M, Remon J, Hellmann MD. First-Line Immunotherapy for Non-Small-Cell Lung Cancer. J Clin Oncol 2022;40:586-97. [Crossref] [PubMed]
2. De Giglio A, Di Federico A, Deiana C, et al. Advanced non-small-cell lung cancer: how to manage non-oncogene disease. Drugs Context 2022;2022: [Crossref] [PubMed]
3. Brueckl WM, Ficker JH, Zeitler G. Clinically relevant prognostic and predictive markers for immune-checkpoint-inhibitor (ICI) therapy in non-small cell lung cancer (NSCLC). BMC Cancer 2020;20:1185. [Crossref] [PubMed]
4. Fehlings M, Jhunjhunwala S, Kowanetz M, et al. Late-differentiated effector neoantigen-specific CD8+ T cells are enriched in peripheral blood of non-small cell lung carcinoma patients responding to atezolizumab treatment. J Immunother Cancer 2019;7:249. [Crossref] [PubMed]
5. Tumeh PC, Harview CL, Yearley JH, et al. PD-1 blockade induces responses by inhibiting adaptive immune resistance. Nature 2014;515:568-71. [Crossref] [PubMed]
6. Lesterhuis WJ, Bosco A, Millward MJ, et al. Dynamic versus static biomarkers in cancer immune checkpoint blockade: unravelling complexity. Nat Rev Drug Discov 2017;16:264-72. [Crossref] [PubMed]
7. Ichiki Y, Fukuyama T, Ueno M, et al. Immune profile analysis of peripheral blood and tumors of lung cancer patients treated with immune checkpoint inhibitors. Transl Lung Cancer Res 2022;11:2192-207. [Crossref] [PubMed]
8. Brueckl NF, Wirtz RM, Reich FPM, et al. Predictive value of mRNA expression and dynamic changes from immune related biomarkers in liquid biopsies before and after start of pembrolizumab in stage IV non-small cell lung cancer (NSCLC). Transl Lung Cancer Res 2021;10:4106-19. [Crossref] [PubMed]
9. Kamphorst AO, Pillai RN, Yang S, et al. Proliferation of PD-1+ CD8 T cells in peripheral blood after PD-1-targeted therapy in lung cancer patients. Proc Natl Acad Sci U S A 2017;114:4993-8. [Crossref] [PubMed]
10. Kim CG, Hong MH, Kim KH, et al. Dynamic changes in circulating PD-1(+)CD8(+) T lymphocytes for predicting treatment response to PD-1 blockade in patients with non-small-cell lung cancer. Eur J Cancer 2021;143:113-26. [Crossref] [PubMed]
11. Kim KH, Cho J, Ku BM, et al. The First-week Proliferative Response of Peripheral Blood PD-1(+)CD8(+) T Cells Predicts the Response to Anti-PD-1 Therapy in Solid Tumors. Clin Cancer Res 2019;25:2144-54. [Crossref] [PubMed]
12. Maniar R, Wang PH, Washburn RS, et al. Self-Renewing CD8+ T-cell Abundance in Blood Associates with Response to Immunotherapy. Cancer Immunol Res 2023;11:164-70. [Crossref] [PubMed]
13. Ottonello S, Genova C, Cossu I, et al. Association Between Response to Nivolumab Treatment and Peripheral Blood Lymphocyte Subsets in Patients With Non-small Cell Lung Cancer. Front Immunol 2020;11:125. [Crossref] [PubMed]
14. Sheng J, Wang H, Liu X, et al. Deep Sequencing of T-Cell Receptors for Monitoring Peripheral CD8(+) T Cells in Chinese Advanced Non-Small-Cell Lung Cancer Patients Treated With the Anti-PD-L1 Antibody. Front Mol Biosci 2021;8:679130. [Crossref] [PubMed]
15. Yan Y, Wang X, Liu C, et al. Association of lymphocyte subsets with efficacy and prognosis of immune checkpoint inhibitor therapy in advanced non-small cell lung carcinoma: a retrospective study. BMC Pulm Med 2022;22:166. [Crossref] [PubMed]
16. Djenidi F, Adam J, Goubar A, et al. CD8+CD103+ tumor-infiltrating lymphocytes are tumor-specific tissue-resident memory T cells and a prognostic factor for survival in lung cancer patients. J Immunol 2015;194:3475-86. [Crossref] [PubMed]
17. Duhen T, Duhen R, Montler R, et al. Co-expression of CD39 and CD103 identifies tumor-reactive CD8 T cells in human solid tumors. Nat Commun 2018;9:2724. [Crossref] [PubMed]