Peripheral Immune Cell Numbers and C-Reactive Protein in Parkinson’s Disease: Results from a Population-Based Study

Background: The immune system is known to be involved in Parkinson’s disease (PD) pathogenesis, but the temporal relationship between peripheral immune responses and PD remains unknown. Objective: We determined the association between peripheral immune cell numbers, C-Reactive protein (CRP), and prevalent as well as incident PD. Methods: This study was embedded in the population-based setting of the Rotterdam Study. We repeatedly measured peripheral immune cell numbers (differential leukocyte count and platelet count, granulocyte-to-lymphocyte ratio [GLR], platelet-to-lymphocyte ratio [PLR], and adapted systemic immune-inflammation index [adapted SII]) and CRP between 1990 and 2016. Participants were continuously followed-up for PD until 2018. We estimated the association of the markers with prevalent and incident PD using logistic regression models and joint models, respectively. Models were adjusted for age, sex, smoking, body mass index, and medication use. Odds ratios (OR) and hazard ratios (HR) are shown per doubling of the marker. Results: A total of 12,642 participants were included in this study. The mean age (standard deviation) was 65.1 (9.8) years and 57.5%were women. Participants with a higher lymphocyte count were less likely to have prevalent PD (adjusted OR: 0.34, 95%CI 0.17–0.68). Participants with a higher GLR, PLR, and adapted SII were more likely to have prevalent PD, but these effects were explained by the lymphocyte count. The peripheral immune cell numbers and CRP were not significantly associated with the risk of incident PD. Conclusion: We found participants with a higher lymphocyte count to be less likely to have prevalent PD, but we did not find an association between peripheral immune cell numbers nor CRP and the risk of incident PD.

Circulating immune cell phenotype dynamics reflect the strength of tumor–immune cell interactions in patients during immunotherapy

The extent to which immune cell phenotypes in the peripheral blood reflect within-tumor immune activity prior to and early in cancer therapy is unclear. To address this question, we studied the population dynamics of tumor and immune cells, and immune phenotypic changes, using clinical tumor and immune cell measurements and single-cell genomic analyses. These samples were serially obtained from a cohort of advanced gastrointestinal cancer patients enrolled in a trial with chemotherapy and immunotherapy. Using an ecological population model, fitted to clinical tumor burden and immune cell abundance data from each patient, we find evidence of a strong tumor-circulating immune cell interaction in responder patients but not in those patients that progress on treatment. Upon initiation of therapy, immune cell abundance increased rapidly in responsive patients, and once the peak level is reached tumor burden decreases, similar to models of predator–prey interactions; these dynamic patterns were absent in nonresponder patients. To interrogate phenotype dynamics of circulating immune cells, we performed single-cell RNA sequencing at serial time points during treatment. These data show that peripheral immune cell phenotypes were linked to the increased strength of patients’ tumor–immune cell interaction, including increased cytotoxic differentiation and strong activation of interferon signaling in peripheral T cells in responder patients. Joint modeling of clinical and genomic data highlights the interactions between tumor and immune cell populations and reveals how variation in patient responsiveness can be explained by differences in peripheral immune cell signaling and differentiation soon after the initiation of immunotherapy.

Circulating immune cell phenotype dynamics reflect the strength of tumor-immune cell interactions in patients during immunotherapy

The extent that immune cell phenotypes in the peripheral blood reflect within-tumor immune activity prior to and early in cancer therapy is unclear. To address this question, we studied the population dynamics of tumor and immune cells, and immune phenotypic changes, using clinical tumor and immune cell measurements and single cell genomic analyses. These samples were serially obtained from a cohort of advanced gastrointestinal cancer patients enrolled on a trial with chemotherapy and immunotherapy. Using an ecological population model, fitted to clinical tumor burden and immune cell abundance data from each patient, we find evidence of a strong tumor-circulating immune cell interaction in responder patients, but not those patients that progress on treatment. Upon initiation of therapy, immune cell abundance increased rapidly in responsive patients, and once the peak level is reached, tumor burden decreases, similar to models of predator-prey interactions; these dynamic patterns were absent in non-responder patients. To interrogate phenotype dynamics of circulating immune cells, we performed single cell RNA sequencing at serial time points during treatment. These data show that peripheral immune cell phenotypes were linked to the increased strength of patients’ tumor-immune cell interaction, including increased cytotoxic differentiation and strong activation of interferon signaling in peripheral T-cells in responder patients. Joint modeling of clinical and genomic data highlights the interactions between tumor and immune cell populations and reveals how variation in patient responsiveness can be explained by differences in peripheral immune cell signaling and differentiation soon after the initiation of immunotherapy.One sentence summaryPeripheral immune cell differentiation and signaling, upon initiation of immunotherapy, reflects tumor attacking ability and patient response.Significance statementThe evolution of peripheral immune cell abundance and signaling over time, as well as how these immune cells interact with the tumor, may impact a cancer patient’s response to therapy. By developing an ecological population model, we provide evidence of a dynamic predator-prey like relationship between circulating immune cell abundance and tumor size in patients that respond to immunotherapy. This relationship is not found either in patients that are non-responsive to immunotherapy or during chemotherapy. Single cell RNA-sequencing (scRNAseq) of serial peripheral blood samples from patients show that the strength of tumor-immune cell interactions is reflected in T-cells interferon activation and differentiation early in treatment. Thus, circulating immune cell dynamics reflect a tumor’s response to immunotherapy.