Aiming at immune fitness in cancer: Longitudinal functional profiling to predict clinical outcome

Abstract

Abstract Cancer patients experience immune dysfunction due to both cancer and its treatment. Immune dysfunction leads to impaired responses to infections and vaccinations and may compromise immune-based anti-cancer therapies (Dai et al. 2020, Figueiredo et al. 2021, Cortes et al. 2022, Gagelmann et al. 2022). In previous work on cancer patients treated with SARS-CoV-2 vaccines, we identified phenotypic states of T cells prior to vaccination that are associated with humoral and cellular immune responses (Kazerani et al., ASH 2024). A limitation of this prior work was that T cells were characterized by immunophenotype and TCR repertoire, which does not provide direct functional assessment. To address this gap, we performed a longitudinal analysis of T-cell function in cancer patients using the activation-induced marker (AIM) assay to test T-cell function and correlated this to various clinical and oncologic outcomes. Our goal in this study is to define immune fitness in cancer patients as a prognostic and predictive biomarker. As part of the NCI SeroNet study, we have collected clinical data and blood samples from over 1,500 patients with cancer receiving primarily immune-based anti-cancer treatments and have reported their SARS-CoV-2 cellular and humoral responses (Figueiredo et al. 2021, 2024). Here, we performed a high-dimensional spectral flow cytometry-based AIM assay on PBMC samples collected from patients at multiple time points. Cells were stimulated with SARS-CoV-2 spike, nucleocapsid peptide megapools (both kindly provided by Daniela Weiskopf's team), control peptide pools (CMV, Flu, EBV, etc.), and DMSO (negative control) for 18-20 hrs (Antunes et al. 2023). Using a 26-marker immunophenotyping panel, unsupervised clustering identified 31 immune cell clusters, including 9 distinct T-cell subsets. Each AIM marker (CD69, 4-1BB, OX40, and CD40L) was DMSO subtracted, and AIM+ cells were defined using a Boolean strategy (Lemieux et al. 2024) with cells expressing 2 markers being considered AIM+, and we reported both relative and absolute numbers of AIM+ cells. In total, we analyzed 2563 samples from 815 patients for 338 million cell events. We integrated AIM data with prior TCR sequencing and serologic measurements of spike-specific antibodies. Active treatment was defined as any therapy being initiated or concluded within 6 months of vaccination (or within 3 months for immune checkpoint inhibitors). The distribution of malignancy types significantly differed between AIM responders and non-responders. Notably, patients with hematologic malignancies exhibited significantly higher frequencies of both AIM+ CD4+ (P < 0.001) and CD8+ T cells (P < 0.001), as well as a trend toward higher absolute counts of AIM+ CD4+ T cells, compared to patients with solid tumors. Longitudinal monitoring further confirmed sustained and superior T-cell activity in the hematologic malignancy cohort. Given the inferior longitudinal humoral response in patients with hematologic malignancies (Figueiredo et al. 2021), our data support the notion that virus-specific T cells may compensate lack of B-cell immunity in patients with lymphoma or myeloma receiving B-cell depleting therapies (Liebers et al., Enßle et al., Blood 2022). Conversely, patients with solid tumors demonstrated impaired T-cell activity despite showing comparable humoral responses to healthy individuals. Furthermore, in 41% of AIM+ patients, no Spike-specific TCRs were detected, highlighting the limitation of TCR sequencing in capturing unique or non-public T-cell clones. In contrast, 64% of AIM– patients had detectable Spike-specific TCRs, implying potential T-cell dysfunction. Many of these patients had solid tumors, and 50% of them also showed no response to control peptide stimulation, indicating a broader state of T-cell unresponsiveness. Finally, survival analysis revealed that patients who were AIM+ in response to control peptide stimulation exhibited better survival than AIM– patients (P = 0.04), suggesting the clinical significance of T-cell immune competence. Our large-scale functional profiling provides valuable insights into T-cell fitness in cancer patients. Ongoing work aims to expand the dataset and characterize T-cell subsets and immune populations influencing T-cell function. These findings underscore the clinical relevance of T-cell functionality as a surrogate for immune fitness to inform personalized immunotherapeutic strategies.