In this groundbreaking study supervised by Antoine Italiano from the Bergonié Comprehensive Cancer Center, we utilized advanced spatial transcriptomics to explore the determinants of response to PD1 inhibition in TLS-positive sarcomas. This research identified specific immune cell subsets – regulatory T cells (Tregs) and plasma cells – associated with therapeutic response.

(A) Estimation by SpatialDecon deconvolution analysis of GeoMx data of the percentage of Tregs in the TLS of anti-PD1 responders (R – n=20 AOIs) and non-responders (NR – n=21 AOIs). Adjusted p-value was calculated using Wilcoxon test with BH correction. (B) Volcano plot representation of the gene differentially expressed between anti-PD1 responders (red) and non-responders (blue) in their tumor areas. Dashed lines for FDR <10% and Fold Change >1.5 are depicted. (C) Estimation by SpatialDecon deconvolution analysis of GeoMx data of the percentage of plasma cells in the stroma of anti-PD1 responders (R – n=16 AOIs) and non-responders (NR – n=13 AOIs). Adjusted p-value was calculated using Wilcoxon test with BH correction.

Explicyte, in collaboration with the Institut Bergonié Comprehensive Cancer Center, investigated the role of IDO1 expression in the outcome of non-small cell lung cancer (NSCLC) patients treated with immunotherapy. The study highlighted that the highest IDO1 expression was found within inflamed tumors, which was associated with a favorable response to immune checkpoint blockade (ICB).

Immuno-HistoFluorescence (IHF) Tumor analysis

A Kaplan–Meier curves representing the PFS of patients classified according to their level of PanCK+/IDO1+ B. Proportion of patients who experienced a PFS ≥ 12 months (R) according to their tumor expression of IDO1. C. Representative illustrations of CD8+ T cell infiltration D. Boxplot representation of CD8+/PD1+ percentage in the stroma and tumor of IDO1 High and Low patients. E. Representative illustrations of the nearest neighbor interaction between PanCK+ and CD8+ T cells F. Boxplot representation of mean distance between PanCK+ and CD8+ T cells in IDO1 High and Low patients. P values were calculated by Wilcoxon assay.

Explicyte, in collaboration with the Gustave Roussy and Institut Bergonié Comprehensive Cancer Centers, analyzed the proteome of plasma samples, collected before treatment onset, from two independent prospective cohorts of cancer patients treated with immune checkpoint inhibitors (discovery cohort n = 95, validation cohort n = 292). The study identifies LIF as a robust biomarker associated with resistance to immunotherapy.

Unbiased proteomic analysis identified that baseline serum levels of LIF are associated with poor clinical outcome in cancer patients treated with immune-checkpoint blockers

(A) Display of the logrank P-values for progression-free survival (PFS) (y axis) and of the delta median PFS (x axis) associated with each plasmatic marker. Median value of each plasmatic marker was used to categorize patients with high or low status. Each dot represents one marker. (B) Kaplan-Meier curves of PFS (left) and overall survival (right) according to baseline plasmatic LIF levels. (C) Quantification of baseline plasmatic LIF in patients with non durable benefit (NDB, n = 72) and durable clinical benefit (DCB, n = 23) patients. P value was calculated using Wilcoxon Rank sum test. (D) Proportion of patients who experienced DCB or NCB according to their baseline plasmatic level of LIF classified as high (above median value) and low (below median value).