Ioannis S. Pateras

Background: Immune checkpoint blockade (ICB) targeting the PD-1/PD-L1 axis has improved outcomes in non-small cell lung cancer (NSCLC), although reliable biomarkers for predicting benefit are still limited. Methods: In this exploratory study, we conducted a longitudinal analysis of the peripheral T-cell receptor (TCR) β repertoire in 28 patients with unresectable stage IIIb non-small cell lung cancer (NSCLC) who received anti-PD-L1 immunotherapy following chemoradiotherapy. Blood samples were collected at baseline and three months after treatment initiation. Results: At the cohort level, global TCR repertoire features such as diversity and richness did not change significantly over time. However, when looking at individual patients, more specific patterns became evident. Patients could be broadly separated based on changes in clonotype richness, with reductions generally accompanied by lower diversity and decreased convergent TCR frequency. We also observed differences in TRBV gene usage in relation to clinical outcome, with higher TRBV20-1 and lower TRBV28 frequencies tending to associate with improved survival and delayed disease progression. Interestingly, the disappearance of dominant clonotypes from the peripheral blood during treatment was linked to longer progression-free survival (PFS). In addition, patients with higher baseline blood plasma tumor mutational burden (bTMB) showed greater clonotype richness and were more likely to exhibit this clonotype loss. The combination of high bTMB and clonotype disappearance identified a subgroup of patients with particularly favorable outcomes. Conclusions: Overall, these results suggest that early responses to PD-L1 blockade may be reflected less in global TCR repertoire shifts, including clonality and diversity measures, and more in subtle changes in clonotype composition and dynamics, since the frequencies of certain TRBV genes and the disappearance of dominant clonotypes following ICB were associated with clinical outcomes integrating TCR profiling with bTMB and could therefore help refine patient stratification and improve the understanding of immune responses in NSCLC. Nevertheless, due to the small number of recruited patients, our study is exploratory and hypothesis-generating, and further validation in larger patient cohorts is warranted.

Background: The spatial distribution and functional heterogeneity of tumor-infiltrating lymphocytes (TILs) significantly impact patient outcomes in non-small cell lung cancer (NSCLC). While T cell factor 1 (TCF1) expressing TILs have emerged as key players in sustaining anti-tumor immunity, their subset characterization, localization, and clinical significance within the tumor microenvironment remain poorly defined. Method: We performed multiplex immunohistochemistry and immunofluorescence to characterize TCF1 ⁺ immune cell subsets, in 102 NSCLC tumors, separately analyzing the tumor center (TC) and invasive front (IF). We integrated this data with publicly available single-cell RNA-sequencing datasets and clinical outcome analyses. Results: CD4 ⁺ T cells and CD79α ⁺ B cells, dominate the TCF1 ⁺ landscape, while CD8 ⁺ T cells constitute a minority of TCF1 ⁺ immune cells, particularly in the TC. We demonstrated the presence of tumor-infiltrating IgG ⁺ /IgA ⁺ plasma cells co-expressing TCF1. PD1 ⁺ TCF1 ^- cells were more frequent than PD1 ⁺ TCF1 ⁺ cells both in the TC and IF, reflecting that terminally differentiated exhausted TILs predominate within the tumor microenvironment. Survival analyses revealed significantly different prognostic impact of TILs including TCF1-expressing cells based on topography. Multivariate analysis showed that increased CD8 ⁺ TCF1 ⁺ cells (HR: 2.5; p=0.039) and increased TCF1 expression by cancer cells (HR: 2,7; p=0.041) in the TC and CD4 ⁺ TCF1 ⁺ cells (HR: 0.4; p=0.043) in the IF emerged as negative and positive independent prognostic markers for Disease-free survival (DFS), respectively. Integrating PD-L1 expression with TILs, PD-L1 immunopositivity was correlated with increased CD8 ⁺ and PD1 ⁺ TCF1 ^- cell infiltration and was associated with favorable DFS especially in the TC. Conclusions: Our findings support a more refined framework for TCF1 ⁺ TIL assessment and TCF1 expression across cellular populations in the tumor microenvironment, with implications for prognostication in operable NSCLC.

Top 30 markers of clusters in CAF subset of single cell data

Lists of genes used to calculate signature scores for cytolytic activity, T cell activation and M1 vs M2 macrophage polarization in single cell data analysis

Orthotopic transplantation of gal 4–KO cell lines identifies differences in CD4+ and CD8+ cell infiltration and function. A, Schematic of the orthotopic transplantation experiments using gal 4–KO and R26 control cell lines transplanted into immune competent C57BL6/J mice. B, Representative IHC images of gal 4 in R26 control and gal 4–KO tumors (top scale bar, 50 μm; bottom, scale bar, 20 μm). A total of 10 tumors were stained, 5 of each condition. C–E, Representative IHC images (left) and quantification (right) of CD4+ cells (P = 0.0032; C), CD8+ cells (P = <0.0001; D), and FOXP3+ cells (ns., no significant difference; E), in orthotopically transplanted tumors with gal 4 (R26 controls) and no gal 4 expression (Gal 4–KO); scale bar, 100 μm. Significance calculated using the Mann–Whitney test. Error bars show mean ± SEM. F, Representative contour plots illustrating gating strategies of intratumoral CD8+ T cells (left). Scatter plot with bar graph showing the fraction of CD8+ cells out of the total amount of CD45+ CD3+ cells in R26 control and gal 4–KO tumors (right; P = 0.0001). Significance calculated using the Mann–Whitney test. Error bars show mean±SEM. G Representative contour plots of intratumoral CD45+, CD3+, CD8+ cells (CD8+ T cells gated as in Fig. 3 F) expressing the exhaustion markers PD-1 (x-axis) and Tim-3 (y-axis) in R26 control and gal 4–KO tumors (left). Scatter plot with bar graph s (right) showing quantification of CD8+ T cells exhaustion (PD1+ and Tim-3 cells in Q2) in R26 control and gal 4–KO tumors (P = 0.046). Significance calculated using the Mann–Whitney test. Error bars show mean with SEM. **, P < 0.01; ***, P < 0.001.

Top 30 markers of clusters in Immune subset of single cell data

Galectin 4 is a cancer cell–derived marker of disease progression and prognosis in pancreatic cancer, with an association to reduced immune activity. A, Representative ISH images of cytokeratin 18 (Krt18, blue, top image) and fibroblast activation protein alpha (Fap, blue, the bottom image), together with gal 4 (Lgals4, in red), in mouse model of PDAC tumor tissue. 4 tumors analyzed in total; scale bar, 10 μm. B, Violin plot of single-cell RNA-seq data from Peng et al. (40) showing LGALS4 expression in cell types from human PDAC tumors. C, Western blot and quantification (semiquantitative) of gal 4 protein in normal murine ductal organoid (N, two different cell lines), PanIN organoid (P, two different cell lines), and tumor organoid (T, three different cell lines) cell lines. Vinculin was used as loading control. D, Kaplan–Meier survival curve adapted from the TCGA data set (26), with patients grouped into high (n = 26) and low (n = 49) gal 4 mRNA expression (groups defined in Materials and Methods) and relationship to survival in patients with PDAC with moderately differentiated tumors. *, Significance (P = 0.0254) calculated using the Mantel–Cox test. E, Z-score comparison of high (n = 26) and low (n = 49) gal 4-expressing tumors within the group of moderately differentiated PDAC tumors in the TCGA data set. Genes associated with cytolytic effect of immune cells analyzed in the left graph (P = 0.036), and CD8 activation scores analyzed in the right graph (P = 0.0253). Genes used for CD8 activation and cytolytic effect are listed in Supplementary Table S2. *, Indicates significance calculated using an unpaired t test. Error bar indicates mean ± SEM.

Raw read count, normalized expression values (TPM) and sample metadata for bulk RNA sequencing data

Metadata and single cell annotation for all 7677 cells in single cell dataset