(A) Overall and disease-free Kaplan-Meier survival plots of PDAC patients segregated into high or low CCL5 gene expression levels within pancreatic tumors. Data are derived from CTPAC3 and TCGA cohorts and optimal cut-off values were calculated using the max-stat method for each respective cohort. (B) Schematic overview of CCL5-responsive immune cells and corresponding CCL5 receptor repertoire expression. (C) Correlation between overall and single cell gene signatures of CCL5-responsive immune cells with overall PDAC prognosis. Colour depicts positive (green), negative (red) or neutral (black) prognostic outcomes (*p<0.05, ** p<0.01, *** p<0.005). Data are derived from the Pathology Dataset of the Human Protein Atlas and based on human tissue micro arrays and correlated log-rank p-value for Kaplan Meier analysis

(A) Three different lineages of KPC pancreatic tumor cells (derived from KrasG12Dp53R172HPdx1-Cre mice) were obtained and stained for DAPI (blue; nucleus), E-cadherin (green; epithelial) and Vimentin (red; mesenchymal). Growth curve of orthotopically injected KPC-F cells (500 cells) into the pancreas of wildtype C57BL/6 over time in weeks. Tumor volume was measured using MRI. Representative MRI images over time are displayed, white arrow denotes tumor mass. (B) Timeline of maraviroc (CCR5i) treatment regimen. A total of 12 days post-orthotopic injection, tumor-bearing mice were treated daily with maraviroc (10 mg/kg via intraperitoneal injection) for 6 days and followed for up to 30 days after starting treatment. Frequencies of pancreatic tumor-infiltrating immune cells harvested at day 30 with or without maraviroc using spectral flow cytometry is shown. Data are represented as mean percentage positive cells of Live/CD45+cells±SD. For Tregs, the mean percentage positive cells of Live/CD45+ CD3+ cells± SD is shown. Significance was tested using the Welch and Brown-Forsythe ANOVA for parametric data or Kruskal-Wallis test for non-parametric data. Mock (n=6), IR (n=3), aPD1 (n=8), aPD1+IR (n=8), CCR5i (n=3), CCR5i+IR (n=8), aPD1+CCR5i (n=5), αPD1/CCR5i/IR (n=8).

(A) Timeline of triple treatment regimen (maraviroc, αPD1 and radiotherapy) following orthotopic injection of KPC-F cells. A total of 12 days post orthotopic injection of 500 KPC-F cells in the pancreas of wildtype C57BL/6 mice, mice were treated as follows: seven consecutive days of 10 mg/kg intraperitoneal injection of maraviroc and four alternating days of 10 mg/kg intraperitoneal injection of αPD1. Mice were followed for up to 30 days following the start of the treatment regimen. Tumor volumes were measured by MRI and growth curves of individual treatment groups are plotted with or without radiotherapy as measured by MRI. Average growth curves±SD are depicted in bold, individual mice are shaded (without IR; dashed, with IR; solid). Insert: expanded view of triple combination to show ‘responders’ display a significant benefit over RT alone. (B) Quantification of pancreatic tumors derived from (A) stained by IHC for p53. (C) Quantification of necrotic areas in pancreatic tumors derived from (A) based on H&E staining. (D) Quantification of infiltrating CD8 T cells in pancreatic tumors derived from (A) by flow cytometry. (E) Profiling of infiltrating immune cells in pancreatic tumors derived from (A) by flow cytometry as in Figure 2B. Single, live cells were included for analysis and are represented as frequencies of Live/CD45+ cells or total CD3+ for FoxP3+ Tregs. Significance was tested using the Welch and Brown-Forsythe ANOVA for parametric data # p<0.05, ##p<0.01 or Kruskal-Wallis test for non-parametric data #w p<0.01; pairwise comparisons (student t test) *p<0.05, **p<0.01, ***p<0.005, ****p<0.001.

(A) Spatial plots of individual cells identified using HALO software of scanned multiplex immunofluorescence murine orthotopic pancreatic tumor slices. Positive staining is identified as the marker of interest and DAPI+ (nucleus stain) signal. Responders and non-responders to treatment are based on loss of E-cadherin staining. (B) Correlations of total CD4 T (CD3+CD8-), CD8 T (CD3+CD8+) and NK cells (CD3-NK1.1+) plotted against %positive E-cadherin+ cells as derived from (A). (C) Correlation of %positive segregated NK cells plotted against %positive E-cadherin+ cells as derived from (A). NK cells were segregated based on expression of NKG2D; NKActive; NK1.1+NKG2D+; NKNKG2D-ve; NK1.1+NKG2D-. (D) Intra-tumoral immune cells of stratified pancreatic tumors based on low or high E-cadherin percentage (cut-off: 20%). Significance was tested for p<0.05 with a two-tailed student’s T-test. * censored non-responder. (E) Proportion of infiltrating trNK (Live/CD45+CD3-CD19-NK1.1+CD103+CD49a+), conventional NK cells (Live/CD45+CD3-CD19-NK1.1+CD103-CD49a-), CD103+ NK, and CD49a+ NK cells isolated from orthotopic pancreatic tumors of mice treated with the IR+IT regimen and controls, as a percentage of CD45+ cells. Significance was tested for p<0.05 with a student’s t-test. (F) Comparative surface expression of activation marker (CD69), activating receptors (NKp46, NKG2D) and exhaustion marker (TIM-3) on cNK cells and trNK cells isolated from orthotopic pancreatic tumours. Significance was tested for p<0.05 with a student’s t-test.

(A) UMAP of the CD8+ T and NK sub-clusters from Steele et al. (B) Dot plot showing the expression of exhaustion-related genes across CD8+ T and NK cell sub-clusters. (C) Dot plot showing highly expressed genes for each sub-cluster. (D) UMAP of the three NK subclusters (left), and violin plots comparing the expression of NK subtype-associated genes between the sub-clusters (right). (E) Dot plot showing the different gene expression programs across the three NK sub-clusters. (F) Heatmap showing the top 15 upregulated markers for each NK sub-cluster compared against total NK cluster.

(A) UMAP of the dendritic cell sub-clusters from the Steele dataset. (B) Violin plot showing the expression of XCL1, XCL2, and XCR1 across all cell types. (C) Circle plots showing interactions across all cell types (top left), signals coming from the tissue-resident NK cells (top right), the XCL1-XCR1 interaction (bottom left), and the XCL2-XCR1 interaction (bottom right). The width of edges represents the communication strength. (D) Heatmap showing the summary of secreted signalling communications of outgoing (left) and incoming (right) signals. The colour bar represents the communication strength, and the size of the bars represents the sum of signalling strength for each pathway or cell type. (E) Schematic overview of the trNK to cDC1 and cDC1 to CD8 T cell communication axis (top). Circle plots of all outgoing signals from cDC1 (bottom left) and the CXCL16-CXCR6 signalling (bottom right). (F) Correlation of HALO data on total NKNKG2D-ve NK cells (CD3-NK1.1+NKG2D-) with CD8 T cells (CD3+CD8+) from stained sections of treated KPC_F orthotopic tumors, R2 and p-values indicate positive correlation across all tumors (gray, n=15) or limited to mock, CCR5i+IR and IR/CCR5i/αPD1 combination (red, n =9). (G) Correlation of trNK signature with CD8A (left) or CD8B (right) in bulk RNA-seq from TCGA_PAAD and binned into quartiles based on extent of cDC1 involvement as assumed by cDC1 signature (Table supplement 1).

(A) Overall survival analysis correlating CD56 low/high expression with and without radiation therapy in the TCGA dataset. (B) Overall survival analysis correlating the deconvoluted NK C1 signature split into low and high. (C) Overall survival analysis correlating NK C1 low/high enrichment with and without radiation therapy in the TCGA dataset. P-values are adjusted using the Bonferroni method. (D) Boxplot correlating low and high CCL5 expression with NK C1 enrichment score in the TCGA (left) and CPTAC (right) datasets. (E) Overall (left) and disease-free survival (right) analysis of CCL5high patients segregated based on high/low enrichment of trNK (NK_C1) gene signature. (F) Overall survival of CCL5high segregated on CD56 (NCAM1) expression.

Correlation of trNK (NK_C1) gene signature in human cancer. The trNK cell gene signature is a positive prognostic factor across various malignancies using the TCGA_PAAD dataset. An enrichment score of the NK_C1 gene signature (see Table supplement 1) was first calculated per patient sample in the TCGA RNA-seq dataset using the Gene Set Variation Analysis (GSVA) method. A cut-off value was then determined using the maximally selected rank statistics (max-stat R package) method to divide patients into “high” and “low”.