Abstract
Metastasis occurs frequently after resection of pancreatic cancer (PaC). In this study, we hypothesized that multi-parametric analysis of pre-metastatic liver biopsies would classify patients according to their metastatic risk, timing and organ site. Liver biopsies obtained during pancreatectomy from 49 patients with localized PaC and 19 control patients with non-cancerous pancreatic lesions were analyzed, combining metabolomic, tissue and single-cell transcriptomics and multiplex imaging approaches. Patients were followed prospectively (median 3 years) and classified into four recurrence groups; early (<6 months after resection) or late (>6 months after resection) liver metastasis (LiM); extrahepatic metastasis (EHM); and disease-free survivors (no evidence of disease (NED)). Overall, PaC livers exhibited signs of augmented inflammation compared to controls. Enrichment of neutrophil extracellular traps (NETs), Ki-67 upregulation and decreased liver creatine significantly distinguished those with future metastasis from NED. Patients with future LiM were characterized by scant T cell lobular infiltration, less steatosis and higher levels of citrullinated H3 compared to patients who developed EHM, who had overexpression of interferon target genes (MX1 and NR1D1) and an increase of CD11B+ natural killer (NK) cells. Upregulation of sortilin-1 and prominent NETs, together with the lack of T cells and a reduction in CD11B+ NK cells, differentiated patients with early-onset LiM from those with late-onset LiM. Liver profiles of NED closely resembled those of controls. Using the above parameters, a machine-learning-based model was developed that successfully predicted the metastatic outcome at the time of surgery with 78% accuracy. Therefore, multi-parametric profiling of liver biopsies at the time of PaC diagnosis may determine metastatic risk and organotropism and guide clinical stratification for optimal treatment selection.
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Data availability
Gene expression data have been deposited to the National Institutes of Health Gene Expression Omnibus repository and can be accessed as GSE245535 (bulk mRNA-seq) and GSE267209 (scRNA-seq). Cytoscape ClueGO with GO Immune System Process was used for gene clustering (https://apps.cytoscape.org/apps/cluego), and Metascape (https://metascape.org/gp/index.html#/main/step1) was used for Gene Ontology analysis. Gene set enrichement analysis was performed using the Hallmark gene sets from the MSigDB (https://www.gsea-msigdb.org/gsea/msigdb/human/genesets.jsp?collection=H). The LM22 dataset was used for deconvolution of bulk mRNA-seq by CIBERSORTx (matrix provided in Supplementary_Tables_1.xlsx). Metabolomics source data can be accessed in Supplementary Dataset 1. Clinical data in this study can be found in Extended Data Table 1 and Supplementary Tables 1, 8 and 11.
Code availability
Code used for image quantifications and generation of the prediction models is available at https://github.com/czambir/PC_pml_code. Code used for imaging mass cytometry analysis can be found at https://github.com/ElementoLab/imc, version 0.1.4.
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Acknowledgements
We thank the patients who participated in our study. We thank the research staff of the MSKCC HPB Service and the Rubenstein Center for Pancreatic Cancer Research; the MSKCC Integrated Genomics Core, the Flow Cytometry Core, the Microscopy and Molecular Cytology Core and the Institutional Core Grant (CCSG P30 CA008748-53); the Hospital for Special Surgery Flow Cytometry Core; the BIDMC Mass Spectrometry Facility, Harvard Medical School; the Epigenomics Core of Weill Cornell Medicine; the WCM Metabolomics Core Facility; and the Linköping University Core Facility and Region Östergötland, Departments of Surgery and Pathology. The authors gratefully acknowledge support from National Cancer Institute CA224175 (D.L.), CA210240 (D.L.), CA232093 (D.L.), CA163117 and CA207983 (D.L.), CA163120 (D.L.), CA169416 (D.L.), CA169538 (D.L.), CA218513 (D.L. and H.Z.) and AI144301 (D.L.); US Department of Defense W81XWH-13-1-0425 (D.L.), W81XWH-13-1-0427, W81XWH-13-1-0249 (D.L.) and W81XWH-14-1-0199 (D.L.); National Institutes of Health/WCM CTSC (NIH/NCATS (UL1TR00457) (H.Z.); and NIH/NCATS (UL1TR002384) (D.L. and H.Z.)). They also gratefully acknowledge support from the Hartwell Foundation (D.L.); the Thompson Family Foundation (D.L. and D.K); STARR Consortium I9-A9-056 (D.L. and H.Z.) and I8-A8-123 (D.L.); the Pediatric Oncology Experimental Therapeutics Investigator’s Consortium (D.L.); Alex’s Lemonade Stand Foundation (D.L.); the Breast Cancer Research Foundation (D.L.); the Feldstein Medical Foundation (D.L.); the Tortolani Foundation (D.L.); the Clinical & Translational Science Center (D.L. and H.Z.); the Mary Kay Ash Charitable Foundation (D.L. and I.M.); the Malcolm Hewitt Weiner Foundation; the Manning Foundation (D.L. and A.H.); the Daniel P. and Nancy C. Paduano Family Foundation; the James Paduano Foundation; the Sohn Foundation; the AHEPA Vth District Cancer Research Foundation; the Daedalus Fund Selma and Lawrence Ruben Science to Industry Bridge Award; Atossa Therapeutics; the Children’s Cancer and Blood Foundation (all to D.L.); a Swedish Cancer Society project grant (21 1824 Pj 01 H); a Swedish Research Society Starting Grant (2021-02356); the Swedish Society for Medical Research (grant no. S21-0079) (L.B); the Alan and Sandra Gerry Metastasis and Tumor Ecosystems Center of Memorial Sloan Kettering Cancer Center (C.P.Z.); the Conquer Cancer Foundation of the American Society of Clinical Oncology (J.M.H.); the National Institutes of Health (R01CA234614 and R01DK121072 to R.E.S.); the US Department of Defense (W81XWH-21-1-0978 to R.E.S.); and Paul G. Allen Family Foundation UWSC13448 (to R.E.S.).
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Contributions
L.B. designed and performed the study and experiments, analyzed and interpreted the data, wrote the manuscript and designed, performed and analyzed experiments for revisions. C.P.Z. procured clinical samples, designed and performed experiments, analyzed and interpreted the data, wrote the manuscript and analyzed data for revisions. J.M.H. designed the study, performed experiments, interpreted data and edited the manuscript. J.C., L.S. and J.K. contributed equally. J.C. designed the MLA model. L.S. performed experiments for revisions and edited the manuscript. J.K. performed imaging mass cytrometry analysis. K.E.J. performed scRNA-seq analysis. S.H. performed NET experiments. G.A., C.S., J.J., H.B. and O.B. performed pathological scorings. J.B. performed and analyzed experiments for revisions. H.R. performed imaging mass cytometry experiments for revisions. J.Z., J.S.J., R.S., Y.S., A.C. and N.N. procured clinical samples. H.S.K. provided input on the MLA model and the manuscript. M.C., E.v.B., P.L., W.B., Y.A., D.H., J.P.V.-B., M.F., C.G., L.F., G.L. and D.L.M. processed samples for experiments. P.V.R., C.J. and A.J. performed experiments for revisions. A.P.M., D.M.P., Y.B., B.C.-S., N.B., H.Z., I.M., A.H., D.K., I.S., A.S., R.S.-S., Y.Y., M.O., M.E., J.S.L., K.K., P.M.G., M.A.H., V.K.R., J.H.H., D.M.S., D.A.T., C.A.I.-D., J.B. and C.T.V. provided input on the manuscript. B.B. and P.S. provided a validation cohort for revision and input on the manuscript. E.M.O., R.P.D., V.P.B., M.I.D., T.P.K., P.J.A. and W.R.J. recruited patients and procured clinical samples. A.L.S. provided input on the study. O.E. provided bioinformatic expertise on imaging mass cytometry analysis. R.E.S., W.R.J. and D.L. jointly supervised this work.
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Competing interests
D.L. is on the scientific advisory board of Aufbau Holdings, Ltd. R.E.S. is on the scientific advisory board of Miromatrix, Inc. and Lime Therapeutics and is a speaker and consultant for Alnylam. The other authors declare no competing interests.
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Extended data
Extended Data Fig. 1 Gene expression patterns in the liver associated with future recurrence.
a, Immune cell gene clustering of the genes upregulated in EHM patients compared to NED (Cytoscape, ClueGO). b, Analysis of the timing of liver metastasis after resection of PaC demonstrated a pattern of an early peak of LiM, which occurred within 6 months of resection, followed by a second peak beyond 6 months.
Extended Data Fig. 2 Liver histology scoring.
a-b, Liver fibrosis, steatosis, and inflammation were scored by two blinded pathologists and compared between PaC and Non-PaC. No statistically significant differences were noted (Somer’s d test: portal inflammation, p = 0.361; lobular inflammation, p = 0.986; fibrosis, p = 0.695; steatosis, p = 0.442).
Extended Data Fig. 3 Liver immune cell characterization.
a, Liver biopsies stained by immunofluorescence (IF) for CD68, quantified using ImageJ and compared between PaC (n = 33) and Non-PaC (n = 10; Mann-Whitney U-test; p = 0.356). b, Liver biopsies were stained by immunohistochemistry (IHC) for the macrophage activation marker IBA-1. The percentage of stained area was quantified with ImageJ and compared between PaC (n = 45) and Non-PaC (n = 8; Mann-Whitney U-test; p = 0.687). c Liver biopsies were co-stained by IF for CD11B, CD68, and IBA-1 to assess for overlap of these markers (n = 3). d-f, Liver biopsies were co-stained by IF for CD3 and CD8 as in Fig. 2e,f. d, CD3 + CD8 + T cells were quantified using ImageJ and compared between PaC (n = 42) and Non-PaC (n = 13; Mann-Whitney U-test; p = 0.565). e, The intensity of CD8 staining and the degree of CD8 + T cell lobular infiltration in PaC livers (n = 42) were assessed by a blinded pathologist and compared to non-PaC livers (n = 12; Somers’ d; p = 0.112 and p = 0.648, respectively). f, CD3 + CD8− lymphocytes were quantified using ImageJ and compared between PaC (n = 42) and Non-PaC (n = 13; Mann-Whitney U-test; p = 0.070). Mean ± SEM are shown in bar graphs.
Extended Data Fig. 4 Single cell RNA sequencing (scRNAseq) of liver immune cells.
a, Hepatic NPCs (>95% CD45+) were isolated from 3 non-PaC and 5 PaC patients and subjected to scRNAseq. A total of 33,311 cells were sequenced, with 48,294 mean reads per cell and 1,000 median genes per cell detected. The sequencing saturation was >78% for all samples. a, tSNE plot combining all samples showing clustering into 10 major cell clusters. b, Distribution of gene expression of conventional immune cell markers further defining the different cell types. c, Heatmap of top 5 genes assigning the main cell types. d, Co-expression of CD11B/ITGAM, CD68, and IBA-1/AIF1 was assessed at the gene level, revealing CD11B expression predominantly by the CD14+ monocyte subset of the myeloid cluster and by the NK cell subset of the lymphoid cluster, showing little co-expression with CD68 (top tSNE plot). IBA-1 was expressed by all 3 subsets of the myeloid cluster, and most CD68-expressing cells (bottom tSNE plot).
Extended Data Fig. 5 scRNAseq of liver immune cells showing altered NK cell subsets.
a, GO pathway analysis (Metascape) of the upregulated genes (upper panel) and downregulated genes (lower panel; cutoff p < 0.1, after adjustment for multiple comparisons). b, Immune cell gene clustering (Cytoscape, ClueGO) of genes upregulated in CD11B + NK cells in PaC vs non-PaC (cutoff p < 0.1, after adjustment for multiple comparisons). c–e, Sub-analysis of the lymphoid cluster (corresponding to cluster 5 of Extended Data Fig. 5a) to explore subsets of CD3-expressing lymphocytes demonstrated 7 sub-clusters (MAIT, mucosa-associated invariant T cells). c. Key defining genes are shown in d and in Fig. 3i. e, The relative proportion of these subclusters was compared between PaC and Non-PaC (multiple t-tests with correction for multiple comparisons, shown if p < 0.25). f, Cibersort-based deconvolution of the bulk liver mRNA sequencing data using the LM22 immune cell reference gene set for activated NK cells (PaC, n = 31; Non-PaC, n = 12; Mann-Whitney U-test, p = 0.053, Cibersort). g Cibersort-based deconvolution of the bulk liver mRNA sequencing data using the T/NKT immune cell gene set derived in Extended Data Fig. 4 (PaC, n = 30; Non-PaC, n = 12; Mann-Whitney U-test, p = 0.042). Mean ± SEM are shown in bar graphs.
Extended Data Fig. 6 Imaging mass cytometry for characterization of CD3+ cell subsets.
Imaging mass cytometry (IMC) was performed on a tissue microarray including 2-3 cores per patient from 5 Non-PaC and 30 PaC patients. a, Representative image from a patient with LiM>6 demonstrating the staining pattern and the spatial distribution. For calculation of lobular cell densities, portal areas (enclosed in dotted line here) were segmented and subtracted from the total cell count for each patient. b, Subsets of CD3+ cells in the entire liver section, or in the lobular areas only, were compared between PaC and non-PaC: CD3+, p = 0.048 (total) and p = 0.981 (lobular; Mann-Whitney U-test); CD4+, p = 0.048 (total) and p = 0.742 (lobular; t-test); CD8+, p = 0.170 (total) and p = 0.715 (lobular; Mann-Whitney U-test); NKT/γδΤ (TCRγδ+ and/or NKG2A+), p = 0.477 (total) and p = 0.604 (lobular; Mann-Whitney U-test); Treg (FOXP3+), p = 0.727 (total) and p = 0.448 (lobular; Mann-Whitney U-test). Mean ± SEM are shown in bar graphs. Only p < 0.25 are shown on the graphs.
Extended Data Fig. 7 Liver immune cells among recurrence groups.
a, Liver biopsies obtained at the time of resection from patients with NED, or distant recurrence (EHM, LiM>6, or LiM<6) were manually scored by a blinded pathologist for lobular inflammation (Kruskal-Wallis test). b-h, Liver biopsies were stained by IHC (b, d) or IF (c, e-h) for different immune cell markers, quantified using ImageJ and compared between the defined PaC recurrence pattern groups (ANOVA and pair-wise t-tests with multiple comparison correction by FDR; only p-values < 0.25 are shown). Mean ± SEM are shown in bar graphs. b, CD45+ cells (n = 22; ANOVA p = 0.161). c, CD11B+ cells (n = 37; ANOVA p = 0.504). d, IBA1+ cells (n = 38; ANOVA p = 0.185). e, CD68+ cells (n = 29; ANOVA p = 0.544). f, CD3+ cells (n = 36; ANOVA p = 0.335). g, CD3+CD8+ cells (n = 36; ANOVA p = 0.289). h, CD3+CD8− cells (n = 36; ANOVA p = 0.420).
Extended Data Fig. 8 Metabolic dysregulations in the pre-metastatic liver.
a, Liver steatosis, graded at the time of resection, was examined separately among patients with LiM (LiM<6 and LiM>6), and patients without LiM (either distant EHM or isolated local recurrence) or disease-free during follow-up (NED). Patients who developed LiM had significantly less steatosis compared to those who developed recurrence at other sites (either distant EHM or isolated local recurrence) which correlated with the severity of metastatic pattern (LiM<6 being the worst and isolated local recurrence being the best prognostic group, based on overall survival outcomes [not shown]; Somer’s d test; p = 0.034). b, Kaplan-Meier curve of time to LiM in patients with (n = 24) or without (n = 19) evidence of liver steatosis (Log-rank test). c, Top 25 metabolites correlated with creatine in the pre-metastatic liver and d, expression of metabolites in the arginine/proline pathway (PaC, n = 24; Non-PaC, n = 9; t-test with correction for multiple comparisons). e, Comparison of serum creatinine levels among patients who underwent liver metabolomic analysis showed no difference (PaC, n = 24; Non-PaC, n = 9; Mean ± SEM; t-test, p = 0.680). f, Top 15 metabolites separating the defined recurrence groups (EHM, n = 5; LiM<6, n = 5; LiM>6, n = 5; NED, n = 7), including creatine and g, comparison of creatine levels in all analyzed samples (ANOVA; p < 0.001, FDR = 0.229). Box plots represent Median±IQR, with whiskers at 95th percentiles.
Supplementary information
Supplementary Information
Supplementary Fig. 1 and Supplementary Tables 1–12.
Supplementary Dataset 1
(a) Metabolomics source data. (b) GSEA comparisons: PaCvsNonPaC.INTERFERON_ALPHA, PaCvsNonPaC.ALLOGRAFT_REJECT, distMETvsNED.E2F_TARGETS, distMETvsNED.INTERFERON_ALPHA, distMETvsNED.KRAS_SIGNALING_DN, distMETvsNED.PI3K_AKT_MTOR_SIGN, distMETvsNED.SPERMATOGENESIS, LiM>6vsNED.E2F_TARGETS, LiM>6vsNED.G2M_CHECKPOINT, LiM>6vsNED.MITOTIC_SPINDLE, LiM>6vsNED.MYC_TARGETS, LiM>6vsNED.P53_PATHWAY, LiM>6vsNED.PI3K_AKT_MTOR_SIGN, NEDvsLiM>6.BILE_ACID_METABOLISM. (c) LM22 CIBERSORT gene matrix, used for CIBERSORT analysis.
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Bojmar, L., Zambirinis, C.P., Hernandez, J.M. et al. Multi-parametric atlas of the pre-metastatic liver for prediction of metastatic outcome in early-stage pancreatic cancer. Nat Med (2024). https://doi.org/10.1038/s41591-024-03075-7
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DOI: https://doi.org/10.1038/s41591-024-03075-7
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