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Poissonnier, A.

Publications and source records attributed to Poissonnier, A..

2 recordsLinked to original sources

CD38hiCD19dim cells in lymph nodes predict favorable prognosis in patients with stage III melanoma receiving adjuvant PD-1-blockade

BackgroundAdjuvant immunotherapy has significantly improved survival for patients with stage III cutaneous melanoma, yet a fraction of patients will not benefit from immune checkpoint inhibitors (ICI). The tumor microenvironment plays a pivotal role in generating durable responses to ICI. By analyzing the cellular composition of tumor-associated subsets, key immune components essential for promoting an anti-tumor environment can be pinpointed. This will allow for both patient stratification and identification of biomarkers associated with improved patient outcome. MethodRegional lymph nodes were obtained from patients with stage III melanoma at surgery (n=29). Patients eligible for anti-PD-1 therapy (PD-1; pembrolizumab or nivolumab) received adjuvant treatment for up to one year. CyTOF was used to determine cellular composition in pre-treatment surgical specimens. Bulk gene expression data generated by NanoString from patients receiving surgery without adjuvant therapy (n=125) was implemented for evaluating trends observed in the CyTOF dataset. ResultsAlthough no significant differences were observed across major hierarchical immune cell types between patients who developed distant metastasis after surgery and those that did not, an increased proportion of CD103+PD-1+CD8+ (TRM) T cells and plasmablast-like CD38hiCD19dim cells were associated with improved prognosis in the CyTOF cohort. In the untreated cohort, a subset of patients defined as "Ultra-cold" (< 2.5 % tumor-infiltrating lymphocyte (TIL) scored by a pathologist) had significantly worse outcome than those with higher TIL infiltration. This Ultra-cold TIL group was associated with reduced B cell score, but not CD8+ T cell score, as well as reduced expression of activation genes like CD38. ConclusionIn this study, CD103+PD-1+CD8+ (TRM) T cells and plasmablast-like CD38hiCD19dim cell populations were found to be strongly associated with prolonged distant metastasis-free survival in regional lymph nodes from patients with stage III melanoma treated with PD-1. This suggests an association between progression and infiltration of these cell types at baseline and highlights the potential of using immune cell subsets as prognostic biomarkers. What is already known on this topic - Patients being diagnosed with stage III melanoma will receive immune checkpoint inhibitors but will often be cured by surgery alone. Selection of which patients might benefit from treatment is still unresolved. Accurate biomarkers would aid in treatment stratification, to avoid overtreatment, and unnecessary toxicities. What this study adds - This study highlights how baseline CD103+PD-1+CD8+ (TRM) T cells and plasmablast-like CD38hiCD19dim cell populations in the regional lymph node, is strongly associated with improved outcome in patients receiving anti-PD-1 therapy. How this study might affect research, practice or policy - The strong association between baseline plasmablast-like cell infiltration in RLN and prolonged distant metastasis free- survival, highlights this cell type as a potential treatment stratification criterion to identify patients with good prognosis.

cancer biology↗

Differential cell signaling testing for cell-cell communication inference from single-cell data by dominoSignal

Algorithms for ligand-receptor network inference have emerged as commonly used tools to estimate cell-cell communication from reference single-cell data. Many studies employ these algorithms to compare signaling between conditions and lack methods to statistically identify signals that are significantly different. We previously developed the cell communication inference algorithm Domino, which considers ligand and receptor gene expression in association with downstream transcription factor activity scoring. We developed the dominoSignal software to innovate upon Domino and extend its functionality to test statistically differential cellular signaling. This new functionality includes compilation of active signals as linkages from multiple subjects in a single-cell data set and testing condition-dependent signaling linkage. The software is applicable for analysis of single-cell data sets with multiple subjects as biological replicates as well as with bootstrapped replicates from data sets with few or pooled subjects. We use simulation studies to benchmark the number of subjects in compared groups and cells within an annotated cell type sufficient to accurately identify differential linkages. We demonstrate the application of the Differential Cell Signaling Test (DCST) in the dominoSignal software to investigate consequences of cancer cell phenotypes and immunotherapy on cell-cell communication in tumor microenvironments. These applications in cancer studies demonstrate the ability of differential cell signaling analysis to infer changes to cell communication networks from therapeutic or experimental perturbations, which is broadly applicable across biological systems.

bioinformatics↗