Targeting neoadjuvant chemotherapy-induced metabolic reprogramming in pancreatic cancer promotes anti-tumor immunity and chemo-response

Abstract

The molecular dynamics of pancreatic ductal adenocarcinoma (PDAC) under chemotherapy remain incompletely understood. The widespread use of neoadjuvant chemotherapy (NAC) provides a unique opportunity to investigate PDAC samples post-chemotherapy. Leveraging a cohort from Fudan University Shanghai Cancer Center, encompassing PDAC samples with and without exposure to neoadjuvant albumin-bound paclitaxel and gemcitabine (AG), we have compiled data from single-cell and spatial transcriptomes, proteomes, bulk transcriptomes, and metabolomes, deepening our comprehension of the molecular changes in PDACs in response to chemotherapy. Metabolic flux analysis reveals that NAC induces a reprogramming of PDAC metabolic patterns and enhances immunogenicity. Notably, NAC leads to the downregulation of glycolysis and the upregulation of CD36. Tissue microarray analysis demonstrates that high CD36 expression is linked to poorer survival in patients receiving postoperative AG. Targeting CD36 synergistically improves the PDAC response to AG both in vitro and in vivo, including patient-derived preclinical models.

Keywords: immune microenvironment; neoadjuvant chemotherapy; pancreatic cancer; tumor metabolism.

Graphical abstract

Figure 1

NAC rebuilt a survival-benefitted transcriptome and proteome landscape in PDAC (A) Heatmap showing that more DEGs related to good prognosis were overexpressed in NAC samples. The hazard ratios (HR) were calculated by batched univariate Cox regression. Z score referred to relative gene expression level. (B) Kaplan-Meier curve indicating that NAC-upregulated genes were associated with a prolonged survival period and downregulated genes were associated with poor prognoses in E-MTAB-6134, The Cancer Genome Atlas (TCGA), and GSE71729 cohorts. The statistical significance of survival difference was detected by log rank test. (C) The genes that were downregulated in NAC samples were mostly associated with the proliferative phenotype in pancreatic cell lines (DepMap data). The statistical significance of percentages difference was detected by chi-square test. (D) Higher percentages of metabolic quiescent and less immune-cold and fibrotic PDAC subtypes were found in the NAC groups. The statistical significance of percentages difference was detected by chi-square test. RR, relative risk. (E) A complex heatmap showing the DEPs and the distribution of clinical parameters between NAC and UR groups. (F) A high proportion of DEPs were immunity or metabolism associated.

Figure 2

NAC reprogrammed the immune microenvironment in PDAC (A) UMAP plot revealed cell clusters in PDACs with or without NAC. NK, natural killer. (B) The distribution of marker genes among different cell clusters. Red dots refer to enriched gene expression. (C) The percentage of different cell clusters in PDAC samples with and without NAC. (D) UMAP plot revealing subclusters for NK and T cells. (E) More CD8⁺ T cells were enriched in PDACs with AG treatment, which was showed by chi-square test. (F) PDACs that received NAC showed higher TCR clonotype expansion. n, means the number of expanded clonotypes. (G) The Gini index for TCR clonotype was increased in PDACs with NAC (mean with standard deviation). (H) Correlation network for immune cells in either the NAC or UR group. The size of circles reflected the infiltration level for cells. (I) Comparison of immune cell constitution between the UR and NAC groups. (J) IHC analysis revealed increased CD8⁺ T cells in PDACs treated with NAC (n = 54). The upper panel shows the representative graph for positive staining of CD8⁺ T cells. (K) The spatial transcriptome showed that more CD8⁺ T cells were enriched in the tissue slices of PDAC samples with NAC. (L) mIF showed that an increase in GZMB+ CD8⁺ T cells was enriched in PDACs treated with NAC (n = 20) (mean with standard deviation). The Left) panel showed the representative graph for GZMB+ CD8⁺ T cells in PDACs. (M) More patients who had received NAC were predicted to be responders to immunotherapy, which analyzed using ImmuneCellAI algorithm. The statistical significance shown in this figure was detected using t test.

Figure 3

Single-cell and spatial transcriptome analyses revealed that metabolic enzyme alterations may contribute to immune infiltration in a treatment-associated background (A) UMAP analysis showed subclusters for ductal cells in PDACs with or without NAC treatment. The lower right panel presented the percentage of c10 ductal cluster was lower in PDAC samples treated with NAC. (B) Single-cell metabolism analysis showed differentially activated metabolic pathways between NAC and UR groups. (C) Transcriptome analysis at the single-cell level revealed differentially expressed glycolytic enzymes in pancreatic ductal cells. (D) The percentage of glycolytic ductal cells was lower in PDACs with NAC treatment. The statistical significance of percentage difference was detected by chi-square test. (E) Consensus clustering distinguished four types of ductal cells based on the single-cell level activity of metabolic pathways. (F) Heatmap showing the distribution of metabolic pathway activity in distinct ductal subtypes and treatment cohorts. (G) C4 ductal cells were more enriched in the NAC group, while C1 ductal cells were more enriched in the UR group. The statistical significance of percentage difference was detected by chi-square test. (H) Spatial transcriptome visualized the distribution of subtype-associated signatures in PDACs with or without NAC treatment. MA, malignant area; NA, normal pancreas area; SA, stroma area. (I) Most genes downregulated in treated PDACs were negatively correlated with the high level of cytotoxic signature and CD8⁺ T cell infiltration, and vice versa. (J) CellChat algorithm showed PDACs with or without NAC were featured with different communication patterns. (K) CellChat algorithm deregulated communication signaling from ductal cells to T cells in PDACs with or without NAC treatment.

Figure 4

Multiomics analysis supported that NAC decreased glycolysis but developed compensatory approaches (A) Transcriptome and proteome analysis revealed alterations in metabolic enzymes associated with AG treatment. The log fold-change value of transcriptome alteration is shown in yellow, while proteome alteration is shown in black. (B) Construct PDXO models from PDX mice with and without AG treatment. (C) A representative graph for PDXO in bright fields of microscope. (D) Metabolic flux experiments validated that PDXO separated from PDX mice treated with AG showed less glycolytic activity. The upper panel showsfocused isoforms of metabolites in glycolysis and TCA cycle. The heatmap reflects the relative abundance of isoforms shown in the ideographs. “m” referred to the number of C13 in the metabolite structure (n = 4). (E) Comparison of metabolic flux between the AG and control groups based on RNA-seq data (scFEA algorithm). (F) Targeted metabolomics analysis showed the differences of lactic acids, 3-PD, phosphoenolpyruvate, and alpha-ketoglutarate between PDAC samples with and without NAC, which implied NAC is associated with downregulated glycolytic activity in PDAC (n= 44) (mean with standard deviation). (G) Cell-Counting-Kit-8 (CCK-8) results showed oleic acid (50 μM) promoted the proliferation of panc-1 cells and could be blocked by targeting CD36, on the contrary (the upper panel), palmitic acid (50 μM) had no effect on the proliferation of panc-1 cells (the lower panel) (n = 5). (H and I) Oleic acid, as opposed to palmitic acid, was found to enhance the growth of PDOs according to the viability assay. The upper section of the figure displaysRepresentatives of PDOs cultured under the indicated conditions for 7 days. The lower section features a bar plot illustrating the relative viability of different groups, measured using the CellTiter-Glo 3D Cell Viability Assay (n = 5). (J) EdU assay showed oleic acid may fuel the drug resistance to AG but could be blocked by targeting CD36 (n = 4). The statistical significance shown in this figure was detected using t test.

Figure 5

CD36 was systematically upregulated in tumor cells and resident and circulating immune cells (A) Immunohistochemical staining indicated that CD36 expression is increased in PDACs treated with NAC compared with UR samples (n= 54) (mean with standard deviation). Representative graphs are shown on the left. (B) Metabolic flux experiments showed that PDXOs derived from PDXs treated with AG had increased capability to uptake oleic acid (n = 3). (C) The percentages of CD36⁺CD8⁺ T cells and CD36⁺GZMB⁺CD8⁺ T cells were significantly upregulated in PDACs treated with NAC (n = 10) (mean ± SD). Representative graphs are shown on the left. (D) Representative graph by mIF showed co-localization of CD36 and TLS in PDAC. (E) Heatmap showing the correlation between CD36 expression and infiltration of immune cells, which indicated that CD36 expression was highly correlated with CD8⁺ T cell abundance only in PDACs treated with NAC. (F) Flow cytometry for PBMCs from PDAC patients showed CD36 was upregulated in circulating CD8⁺ T cells from patients treated with NAC. Left panel displays t-Distributed stochastic neighbor embedding (TSNE) analysis for labeled cell clusters (mean with standard deviation). Right panel displays the higher percentage of both CD36⁺ CD8⁺ T cells and CD36⁺ CD45⁺ immune cells in samples with NAC. (G) Flow cytometry showed that oleic acid decreased the percentage of IFN-γ⁺ CD8⁺ T cells and could be rescued by CD36 blockage (n = 3). (H) T cells treated with lysates from different PDAC samples manifested distinct tumor-killing ability, which is showed by LDH-releasing experiments (n= 5). (I) T cells treated with lysates from CD36-low NAC samples showed significantly higher IFN-γ secretion compared with UR samples. (J) Caspase3/7 detection indicated that targeting CD36 synergistically enhanced the killing effect of AG based on a PDAC organoid/PBMC coculture system. Left panel shows the representative graph of caspase3/7-positive organoids at the time points 0 and 48 h. Right panel shows the percentage of apoptotic organoids in each group, which was evaluated through flow cytometry. (K) mIF showed CD36 blockage enhanced the killing effect of AG, which was showed by detecting Ki67⁺ organoids via mIF technology. (L) Successful construction of ovalbumin (OVA+) murine KPC organoids. (M) The OVA+organoid/OT-1-cell coculture system validated the synergistic effect of CD36 blockade on AG-mediated tumor killing (n= 3). Left panel shows the representative graph of caspase3/7-positive organoids. Right panel shows the percentage of apoptotic organoids in each group, which was evaluated through flow cytometry. The statistical significance shown in this figure was detected using t test.

Figure 6

Targeting CD36 synergistically promoted AG-mediated killing of PDAC in preclinical models (A) Visual presentation of subcutaneous xenograft murine PDAC tumor models (C57 mice) for each group. (B) Measurement of tumor volumes showed CD36 blockage synergistically promoted AG-mediated killing of PDAC in subcutaneous xenograft murine PDAC tumor models. (C) Measurement of tumor weights showed CD36 blockage synergistically promoted AG-mediated killing of PDAC in subcutaneous xenograft murine PDAC tumor models (n = 5). (D) Representative IHC staining showed Ki67 expression in subcutaneous xenografts treated with different regimens. (E) t-Distributed stochastic neighbor embedding (TSNE) analyses showed the clustering for CD36⁺ CD8⁺ T cells and GZMB⁺ CD8⁺ T cells. (F) Flow cytometry revealed that more CD8⁺ T cells infiltrated PDAC with NAC, while the percentage of CD36⁺ CD8⁺ T cells also increased (n = 5) (mean with standard deviation). (G) ELISA results showed the combination of AG and CD36 blockade significantly improved IFN-γ and tumor necrosis factor α (TNF-α) levels intratumorally (n = 5). (H) Representative image of orthotopic murine models of PDAC. (I) Kaplan-Meier curve revealed the combination of CD36 blockade and AG significantly prolonged the survival interval of mice that received orthotopic PDAC cell transplantation (n = 10). Circle or square referred to a happened event (death or censored). Censored event means the mice is still alive at the time point that we ended follow-up. (J) CD36 blockade synergistically with AG regimens optimally narrowed the PDAC tumor size in a humanized PDX model (n = 10). (K) Representative IHC staining image of CD36-high and -low PDAC. (L) Kaplan-Meier curve showed increased CD36 expression predicted worse prognosis of PDAC patients with adjuvant AG chemotherapy. The statistical significance shown in this figure was detected using t test.