Immunotherapies targeting aspects of T cell functionality are efficacious in many solid tumors, but pancreatic ductal adenocarcinoma (PDAC) remains refractory to these treatments. Deeper understanding of the PDAC immune ecosystem is needed to identify additional therapeutic targets and predictive biomarkers for therapeutic response and resistance monitoring. To address these needs, we quantitatively evaluated leukocyte contexture in 135 human PDACs at single-cell resolution by profiling density and spatial distribution of myeloid and lymphoid cells within histopathologically defined regions of surgical resections from treatment-naive and presurgically (neoadjuvant)–treated patients and biopsy specimens from metastatic PDAC. Resultant data establish an immune atlas of PDAC heterogeneity, identify leukocyte features correlating with clinical outcomes, and, through an in silico study, provide guidance for use of PDAC tissue microarrays to optimally measure intratumoral immune heterogeneity. Atlas data have direct applicability as a reference for evaluating immune responses to investigational neoadjuvant PDAC therapeutics where pretherapy baseline specimens are not available.
We provide a phenotypic and spatial immune atlas of human PDAC identifying leukocyte composition at steady state and following standard neoadjuvant therapies. These data have broad utility as a resource that can inform on leukocyte responses to emerging therapies where baseline tissues were not acquired.
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Pancreatic ductal adenocarcinoma (PDAC) is a leading cause of cancer-related death in the United States, with a current 5-year survival rate of only 10% (1). Most patients present with late-stage metastatic disease, and even patients diagnosed at earlier stages who are eligible for potentially curative tumor resection have disease recurrence rates exceeding 80% (2). Standard-of-care cytotoxic therapies extend life expectancy only modestly (3, 4), and thus new therapeutic combinations are urgently needed.
Immunotherapies targeting immune checkpoint molecules have transformed clinical outcomes for patients with many types of solid tumors; however, these have yet to significantly affect outcomes for PDAC (5), except in a minority of patients harboring microsatellite instability–high tumors (6). Poor immunogenicity of PDAC and its highly immunosuppressive tumor immune microenvironment (TiME) represent considerable hurdles to immunotherapeutic efficacy (7). A more nuanced understanding of human PDAC immune contexture and immune heterogeneity is needed to inform rational drug combinations and to effectively stratify patients for immunotherapies to which they are most likely to respond.
Recent development of innovative multiplexed imaging strategies now enables in situ phenotyping and spatial characterization of multiple cell populations simultaneously (8–11), thereby facilitating advances in our understanding of the cellular composition of tumors. For example, recent studies in PDAC have revealed extensive T cell heterogeneity where spatial localization holds prognostic significance (11–14). Despite these technologic advances, many recent analyses have focused predominantly on features of adaptive immunity, particularly CD8+ T cells, without robust characterization of the full leukocyte milieu that also includes discrete subsets of B cells and myeloid cells, all of which harbor both pro- and antitumor functionality and likely influence patient outcome. Indeed, in a recent independent study, we profiled myeloid cells in more than 300 human surgically resected PDACs and found significant associations between myeloid densities, spatial localization, and patient survival (15), thus supporting the need for further investigations integrating spatial analysis of both adaptive and innate components of the PDAC TiME. It is also incompletely understood how human PDAC immune contexture evolves with disease progression from early-stage to late-stage metastatic disease or how various therapies affect the TiME. We previously reported that PDACs from patients who received neoadjuvant GVAX, a granulocyte-macrophage colony-stimulating factor tumor cell vaccine, clustered into low-myeloid inflamed and high-myeloid inflamed groups, in which enhanced myeloid inflammation was associated with features of T cell suppression and shorter survival (8). In a subsequent study, we evaluated immune correlates of previously treated metastatic PDAC in which patients received GVAX with cyclophosphamide (Cy) and CRS-207 (live, attenuated Listeria monocytogenes–expressing mesothelin), and we revealed that reduced myeloid to lymphoid ratios following therapy correlated with improved outcome (16). Together, these studies indicate that the PDAC TiME can be therapeutically affected. Going forward, a deeper understanding of steady-state PDAC leukocyte ecosystems will aid in identification of immune features that underlie response and/or resistance to standard cytotoxic therapies as well as established and investigational immune therapy regimens. In addition, this will enable advanced clinical trial design for precision medicine approaches, similar to what we have recently reported in metastatic breast cancer (17).
To this end, we employed a chromogen-based multiplexed immunohistochemistry (mIHC) platform (8, 18) to build a comprehensive immune atlas of 135 clinically and genomically annotated human PDAC specimens from a multi-institutional patient population. Through mIHC profiling of lymphoid and myeloid leukocyte lineages across distinct histopathologic regions—including tumor, tumor adjacent stroma, tertiary lymphoid structures (TLS), and adjacent normal pancreas tissue—we report substantial inter- and intrapatient immune heterogeneity and identify leukocyte features associated with treatment status and clinical outcome. Collectively, this atlas will serve future studies by providing baseline steady-state characteristics of the PDAC TiME.
Evaluation of Human PDAC Immune Contexture
To reveal the immune landscape of human PDAC, we used a robust mIHC platform designed to capture multiple leukocyte lineages and their functional status (8, 18). As shown in Fig.1A, we evaluated primary PDAC from a multi-institutional population of treatment-naive patients (n = 104; cohorts 1 and 2) and patients who received physician's choice chemotherapy and/or radiotherapy prior to surgical resection (n = 13; cohort 1). In addition, we performed exploratory analysis of immune composition in primary PDACs versus distant PDAC metastases (n = 18; cohort 3) and examined healthy normal (HN) pancreas from organ transplant donors (n = 6; cohort 4) to compare neoplasia-associated leukocyte infiltration to steady-state healthy pancreatic tissue (Fig.1A; Supplementary Tables S1–S3).
Surgical resection specimens allowed evaluation of immune contexture from a “global” perspective in large tissue sections containing histopathologically distinct areas (Fig.1B). For all patient samples, we analyzed regions of interest (ROI) within pathologist-annotated areas containing invasive tumor (T; 100% of specimens). Where possible, we also evaluated tumor adjacent stroma (TAS; contained in 70% of specimens) and adjacent “normal” pancreas (AN; contained in 35% of specimens), each located outside of areas containing invasive carcinoma (Fig.1B). Dense lymphoid aggregates consistent with TLSs were present in 56% of specimens (located within and/or outside of tumor areas), and a small fraction of samples contained lymph node (LN) and/or preinvasive dysplasia (21% and 12% of specimens, respectively) that were also evaluated for leukocyte contexture (Fig.1B).
Three serial formalin-fixed, paraffin-embedded (FFPE) tissue sections per patient were subjected to mIHC staining with antibody panels designed to identify lymphoid and myeloid populations (Fig.1C; Supplementary Table S4), and digital images were quantitatively analyzed by image cytometry (ref. 8; Supplementary Fig. S1A–S1C). Within the lymphoid lineage, we identified CD3+CD8+ T cells, CD3+CD8− T helper cells (Th0, Th1, Th2, Th17), regulatory T cells (Treg), CD20+ B cells, and CD20− plasmablasts and plasma cells. T cells were further evaluated for expression of select immunoregulatory molecules (PD-1, ICOS/CD278, GzmB, EOMES) and proliferation (Ki-67). CD20+ B cells were categorized into memory and naive populations based on CD27 and IgD expression. Myeloid subsets were identified as CD66b+ granulocytes (neutrophils and eosinophils), tryptase+ mast cells, dendritic cells (DC), and CD68+ cells comprising both monocytes and macrophages. DC maturation status was assessed by expression of DC-LAMP, a lysosomal glycoprotein predominately expressed by mature DCs (19), whereas CD68+ cells were stratified by expression of the hemoglobin scavenger receptor CD163 to mark their alternative-activation status (20). Details on average tissue area and cell densities evaluated in each histopathologic region are provided in Supplementary Table S5.
Treatment-Naive PDACs Differentially Cluster Based on Global Immune Phenotype
To evaluate immune complexity and interpatient heterogeneity of treatment-naive PDACs, we performed unsupervised hierarchical clustering based on cell densities of CD45+ leukocyte subpopulations, pan-cytokeratin (PanCK+) epithelial cells, and α smooth muscle actin (αSMA+) mesenchymal support cells (Fig.2A). We combined cell density data from all histopathologic regions (Fig.1B), as we hypothesized that “global” quantitation, independent of histopathologic annotation, would yield data analogous to that obtained from tumor biopsy specimens or tissue microarray (TMA) cores, in which histopathology of tissue available for analysis is often less prospectively defined. PDACs clustered into three groups based on leukocyte profiles: myeloid enriched, lymphoid enriched, and hypo-inflamed (Fig.2A). Principal component analysis revealed two clusters were driven by relative densities of myeloid versus lymphoid cells, whereas the hypo-inflamed cluster was not driven by either lineage and contained the lowest abundance of overall leukocyte infiltrate (Fig.2B and C). Notably, densities of total lymphoid cells and lymphoid subpopulations did not significantly differ between the myeloid-enriched and lymphoid-enriched clusters, but lymphoid to myeloid ratio was elevated in the lymphoid-enriched group (Fig.2C; Supplementary Fig. S2A). In contrast, total myeloid cells, as well as select myeloid subpopulations—particularly neutrophils/eosinophils and monocytes/macrophages—were significantly elevated in the myeloid-enriched cluster as compared with others (Fig.2C; Supplementary Fig. S2A). We observed that these clusters arose independently of total area of tissue analyzed (Supplementary Fig. S2B), although there was some variability in the histopathologic regions represented in each cluster (Supplementary Fig. S2C). We next evaluated whether clusters were associated with patient survival and observed that patients in the lymphoid-enriched cluster had a modestly improved median overall survival (OS) compared with patients in other clusters (24.3 months lymphoid enriched, 20.7 months hypo-inflamed, 19.3 months myeloid enriched); however, these global immune phenotypes were not significantly associated with outcome (Fig.2D).
Spatial proximity of immune cells to invasive carcinoma holds prognostic significance for many cancer types (10, 21, 22). To investigate leukocyte distribution, we subclassified individual ROIs from all histopathologic regions into intratumoral, border, spanning border–distal, or distal spatial categories based on ROI proximity to nearest annotated area of invasive carcinoma (Fig.2E; Supplementary Table S6). We then evaluated densities of total CD45+ leukocytes, PanCK+ epithelial cells, and αSMA+ cells in each spatial category and created corresponding spatial maps (Fig.2F). Of the cell types evaluated, PanCK+ cells were the densest population in intratumoral and spanning distal–border regions, whereas CD45+ leukocytes were densest in border and distal regions (Fig.2F). We used t-distributed stochastic neighbor embedding (t-SNE) to visualize relationships between ROI cell density, ROI location, and ROI histopathology type (Fig.2G). t-SNEs revealed a considerable admixture of individual ROIs based on location and histopathology type, although TLS ROIs formed a distinct cluster (Fig.2G; Supplementary Fig. S2D).
Total Leukocyte Density Varies by Histopathologic Region
To further evaluate leukocyte composition and interpatient heterogeneity in distinct histopathologic regions, we quantified leukocyte density in each histopathologic region within treatment-naive PDACs (cohorts 1 and 2) and HN pancreas (cohort 4). LNs and preinvasive dysplasia were excluded from analyses given their low relative abundance in the data set. Notably, leukocyte density was significantly higher in AN regions of PDAC compared with HN pancreas (Fig.3A), although increased density in AN was not driven by an influx of a particular leukocyte subset (Fig.3B). Thus, ostensibly “normal” pancreas tissue adjacent to invasive PDAC is not spared from inflammatory assault within the diseased organ.
Evaluation of leukocyte density in other regions of PDAC revealed that TAS and T exhibited considerable interpatient heterogeneity, although average leukocyte density was highly similar between the two cohorts (Supplementary Fig. S3A and S3B). Comparison of all region types revealed that T, TAS, and TLS each contained significantly elevated leukocyte density compared with AN. TAS regions compared with T regions contained a significantly higher leukocyte infiltrate, whereas TLSs contained the highest overall leukocyte density of any histopathology type evaluated (Fig.3C and D; Supplementary Fig. S3C).
Intratumoral density of leukocyte subpopulations has been associated with patient prognosis in PDAC and other malignancies (8, 11, 13, 23–25), but it is unclear if total leukocyte density—as opposed to density or effector status of individual subpopulations—predicts outcome. When we examined matched pairs of TAS and T within the same patient, we found significant correlation in leukocyte density between the two sites (Fig.3E and F). Among leukocyte subpopulations, densities of CD8+ T cells, Th0 T cells, and Tregs were most significantly associated with total leukocyte density in both TAS and T regions (Supplementary Fig. S3D). However, regional leukocyte density in TAS or T was not independently predictive of disease-free survival (DFS), OS, or other evaluated clinical characteristics, including tumor stage and grade (Supplementary Fig. S3E; Supplementary Tables S7 and S8), indicating that relative frequencies of leukocyte subpopulations as opposed to overall CD45+ leukocyte density may represent a critical metric.
Leukocyte Subpopulations Are Differentially Enriched between PDACs and across Tissue Regions
To investigate leukocyte identity within treatment-naive PDACs, we first determined the degree of regional skewing present within myeloid and lymphoid infiltrates. AN possessed the lowest myeloid and lymphoid density of all regions evaluated. TLSs exhibited significantly increased lymphoid density compared with all other regions and increased myeloid density compared with T, likely reflecting TLS compactness and paucity of embedded epithelial cells. Myeloid density was similar across TAS and T regions, whereas lymphocytes were significantly enriched in TAS compared with T (Fig.4A).
Deeper auditing revealed that discrete leukocyte subpopulations were present within each histopathologic region to varying degrees (Fig.4B and C; Supplementary Fig. S4A). Of lymphoid subsets, CD8+ T cell, Th0 and Th2 T helper cell, Treg, and B cell densities were significantly higher in TAS compared with T regions (Fig.4B, left). Of myeloid populations, mast cells were significantly enriched in TAS compared with T (Fig.4B, left). TLSs were primarily composed of B cells, CD8+ T cells, and T helper cells, although DC-LAMP− DCs and mature DC-LAMP+ DCs were also present (Fig.4B, right), the latter of which are known to be critical within TLSs for intra- or peritumoral antigen presentation and T cell priming (26). Further phenotyping of B cells and T helper cells revealed that CD27− IgD+ naive B cells were most dense in AN, whereas expanded memory B cells (particularly CD27− IgD−) had the highest density in TAS, T, and TLS (Supplementary Fig. S4B). Tregs were least abundant in TLSs compared with other regions, and Th2 cells were significantly enriched in TLSs compared with other regions (Supplementary Fig. S4C).
Leukocyte milieu was independent of tumor histologic grade (Supplementary Fig. S4D). To determine if genomic alterations affected PDAC leukocyte contexture, we next evaluated intratumoral leukocyte composition based on molecular status of the four main driver genes in PDAC (KRAS, CDK2NA, TP53, SMAD4; ref. 27) in a subset of treatment-naive samples. Leukocyte densities were generally consistent across tumors with distinct molecular profiles, and although sample sizes were small within molecular subgroups, we observed modest increases in neutrophil/eosinophil density in some PDACs, particularly those harboring TP53 alterations (Supplementary Fig. S4E).
To determine whether histologically discrete regions of treatment-naive PDACs clustered into prognostically relevant groups based on immune profile, we again performed unsupervised hierarchical clustering of patients, now considering leukocyte densities of either T regions or TAS regions only. Three clusters emerged in both T and TAS analyses (myeloid enriched, lymphoid enriched, and a mixed cluster with enrichment of both lymphocytes and myeloid cell types), although we did not observe associations between clusters and OS (Fig.4D; Supplementary Fig. S4F; Supplementary Tables S9 and S10).
High Endothelial Venule Presence within TLSs Is Associated with TLS Cell Density but Not Clinical Outcome
TLSs represent potential sites of T cell entry, priming, and clonal expansion within solid tumors and have been associated with improved survival and response to immunotherapy in several tumor contexts, including PDAC (28–32). To determine if TLS abundance represented a significant variable associated with survival in treatment-naive PDACs, we quantified the number of total TLSs present per single FFPE sample for each patient. Sixty-one treatment-naive PDACs in cohorts 1 and 2 contained TLSs, ranging from 1 to 46 (Supplementary Fig. S4G and S4H), where TLS quantity was modestly correlated with size of resection specimen (Supplementary Fig. S4I) but not associated with patient OS (Supplementary Fig. S4J).
High endothelial venules (HEV) within TLSs, identified by MECA-79 expression on lymphatic endothelial cells, facilitate trafficking of leukocytes and are associated with enhanced antitumoral immune responses (33). We observed that PDACs containing multiple TLSs often exhibited heterogeneity in HEV presence. We detected MECA-79+ HEVs in 557 individual TLSs (66% of total; Supplementary Fig. S4K) and observed that HEVs were typically located within larger (by area) TLSs containing higher densities of CD3+ T cells and CD20+ B cells, compared with TLSs lacking HEVs (Supplementary Fig. S4L). Higher overall T cell density in TLSs with HEVs was driven by increased density of T helper cells, particularly Th0 and Th2 cells (Supplementary Fig. S4M). T- and B cell functional state did not significantly differ based on HEV status (Supplementary Fig. S4N–S4P). We next examined whether HEV presence in TLSs was associated with clinical features and revealed that HEV status was not associated with tumor histologic grade (Supplementary Fig. S4Q). Analysis of all treatment-naive PDACs containing TLSs (n = 61) revealed that proportion of HEV-containing TLSs in a given specimen was not associated with patient survival (Supplementary Fig. S4R). Further, assessment of the longest-term and shortest-term treatment-naive survivors (first- and fourth-quartile OS) who had TLSs (n = 16 and 19, respectively) revealed that proportion of TLSs containing HEVs was not correlated with exceptionally good or poor outcome (Supplementary Fig. S4S).
PDAC Surgical Resections Exhibit Extensive Intrapatient Immune Heterogeneity
We endeavored to understand degree of intrapatient heterogeneity in the current study by utilizing several approaches. First, we observed heterogeneity in leukocyte composition across individual ROIs from individual PDACs in T, TAS, and TLS regions (Supplementary Fig. S5A and S5B). Next, we measured concordance of leukocyte population densities between two T ROIs from single patients and observed moderate correlations for many cell types (Supplementary Fig. S5C). Because studies investigating tumor immune contexture commonly utilize smaller TMAs for uniform, high-throughput analyses of large patient cohorts, we sought to determine how leukocyte composition in 1.0-mm-diameter virtual TMA (vTMA) cores from T regions compared with composition within the multiple larger T ROIs evaluated per patient in the surgical resections studied herein. We first evaluated a random subset of 22 resection specimens from cohorts 1 and 2 to determine the proportion of total pathologist-annotated invasive tumor on each slide that was captured in T ROIs analyzed by mIHC. Within these specimens, total tumor area ranged from 20 to 209 mm2 (average 94 mm2), and area within T ROIs was 12 to 31 mm2 (average 23 mm2; Supplementary Fig. S5D). Five of these specimens representing a range of tumor sizes (Supplementary Fig. S5D, highlighted in red) and total leukocyte densities (Supplementary Fig. S5E) were selected for subsequent vTMA analysis. For each specimen, previously established T ROIs were further subdivided into 19 to 28 vTMA cores (Fig.5A). A variable abundance of CD3+ T cells, CD20+ B cells, CD68+ monocyte/macrophages, and other CD45+ leukocytes was documented across the vTMA cores, revealing that leukocyte composition of individual vTMA cores within an mIHC ROI varied compared with the composition of the full mIHC ROI (Fig.5B; Supplementary Fig. S5F), thereby identifying the spatial scale of leukocyte heterogeneity within PDAC.
To capture intratumoral heterogeneity, it is standard practice to sample individual tumors using multiple TMA cores. Thus, we next investigated the effect of vTMA core number on the precision of leukocyte density estimates. To do this, we subsampled combinations of 1 to 18 vTMA cores from annotated T ROIs from each of five patient specimens 100 times each to generate a distribution of cellular abundance for CD3+ T cell, CD20+ B cell, and CD68+ monocytes/macrophages with “N” number of vTMA cores (Fig.5C; Supplementary Fig. S5G). As the number of vTMA cores increased, average cell abundance converged toward the average of total vTMA cores per patient (Fig.5C; Supplementary Fig. S5G). To estimate how the number of vTMA cores reflected the average leukocyte abundance within the larger sample, we calculated the percentage of vTMA subsamples that fell within 20% of the reference mean (average of all vTMA cores per specimen). To reach 75% probability of sampling within 20% of the reference mean, 10 or more vTMA cores were needed for the three cell populations evaluated (CD3+ T cells: 10–15 cores; CD20+ B cells: >13 cores; CD68+ monocytes/macrophages: 11–14 cores; Fig.5D and E). Although we evaluated only a small subset of patients and several T region ROIs, as opposed to the entire tumor area on a slide, this finding is consistent with analyses of similarly sized TMA cores from different solid tumor types that concluded multiple TMA cores provide enhanced representation of immune contexture compared with a larger tissue area or whole slide image (34, 35).
Long-Term and Short-Term Survivors Differ in Ratio of T Cells to CD68+ Cells
Because unsupervised clustering of treatment-naive PDACs by global and intratumoral leukocyte profiles did not reveal associations between immune phenotype and OS (Fig.2D; Fig.4D), we hypothesized that evaluating the subset of patients with exceptionally poor versus exceptionally favorable OS might more clearly reveal distinct immune landscapes associated with clinical outcome. We thus identified patients as long-term and short-term survivors by stratifying treatment-naive cases into quartiles and evaluating the first quartile (short-term survivors; median OS of 9 months) and fourth quartile (long-term survivors; median OS of 58 months) for leukocyte complexity (Fig.6A). Leukocyte subpopulation densities were similar in long-term and short-term survivors, with the exception of CD163− monocyte/macrophage density, which was significantly lower in PDACs from long-term survivors (Fig.6B; Supplementary Fig. S6A). Importantly, the ratio of CD8+ T cells to CD68+ monocytes/macrophages was significantly greater in T regions and modestly increased in TAS of long-term survivors (Fig.6C; Supplementary Fig. S6B).
When measured as a frequency of total CD3+CD8− T cells, Th1 cells were significantly enriched in TAS of long-term survivors but not within intratumoral regions (Supplementary Fig. S6C). B cell subsets were similar in both long-term and short-term survivors (Supplementary Fig. S6D). Interrogation of T cell functional status revealed no differences in cells singly positive for biomarkers indicative of activation (PD-1, ICOS), cytotoxicity (GzmB), or proliferation (Ki-67) in long-term versus short-term survivors (Supplementary Fig. S6E). That said, T cells exist in tissues within a continuum of differentiation/activation/exhaustion states, and thus we also evaluated CD8+ T cells for combined expression of PD-1 and EOMES to further inform on T cell functionality (36). PD-1 expression indicates T cell activation in response to antigen, whereas EOMES is expressed by effector cells and is required for conventional memory differentiation. Combined assessment of PD-1 and EOMES expression revealed no differences in intratumoral regions but an increased PD-1−EOMES− fraction in TAS of long-term survivors, likely representing a heterogeneous pool of naive and effector cells (Fig.6D).
Presurgical Cytotoxic Therapy Does Not Relieve Indicators of Intratumoral T Cell Dysfunction
Only 15% of patients with PDAC are eligible for surgical resection at time of diagnosis (37), and treatment with chemotherapy and/or radiotherapy is one strategy for downstaging locally advanced or borderline resectable PDACs to resectable tumors. It is not yet fully understood how cytotoxic therapies affect the PDAC TiME. We thus investigated how presurgical treatment with chemo- and/or radiotherapy may affect leukocyte composition, spatial/regional distributions, and T cell functional status. Unsupervised hierarchical clustering of presurgically treated PDACs revealed lymphoid-enriched, myeloid-enriched, and mixed tumors (Fig.7A), similar to results from treatment-naive PDACs (Fig.4D). In contrast to treatment-naive patients, survival analysis of presurgically treated patients based on tumor clusters revealed a significant survival advantage for patients with lymphoid-enriched versus myeloid-enriched PDACs, although sample size was modest (Fig.7B). Moreover, presurgically treated patients with highest intratumoral CD8+ T cell density, or high CD8+ T cell to CD68+ cell ratio, trended toward enhanced OS (Supplementary Fig. S7A), analogous to our previous findings in patients with PDAC treated with neoadjuvant GVAX (8) or GVAX/Cy/CRS-207 (16).
To gain additional insight into how therapy potentially alters PDAC leukocyte composition, we evaluated leukocyte density and spatial distribution in presurgically treated samples compared with unmatched treatment-naive samples. Although unmatched samples and small sample size precluded our ability to directly determine causal relationships between therapy and immune contexture, we observed several notable differences based on treatment status. Total leukocyte density was significantly reduced in TAS and TLSs of presurgically treated samples compared with treatment-naive samples, although similar leukocyte composition was observed within histopathologic regions (Fig.7C). To determine if presurgical therapy may have affected T cell functional profiles, we evaluated CD3+CD8+ and CD3+CD8− T helper cells for expression of activation and effector biomarkers (Supplementary Fig. S7B and S7C). No differences in GzmB or ICOS were identified in T cells from treatment-naive versus presurgically treated PDACs, although increased frequency of PD-1+ CD8+ T cells and T helper cells was evident in TAS regions of presurgically treated PDACs. T helper cell proliferation was also significantly elevated in TAS and T regions of presurgically treated versus treatment-naive PDACs (Supplementary Fig. S7B and S7C), but this was not attributable to increases in any specific T helper subpopulation (Supplementary Fig. S7D). In addition, we evaluated CD8+ T cells for coexpression of PD-1 and EOMES; PD-1−EOMES− CD8+ T cells represented the majority of CD8+ T cells in AN, TAS, and T regions in both groups (Fig.7D). Within TLSs, a significant decrease in PD-1−EOMES− cells and an increase in PD-1−EOMES+ cells were observed in treated specimens compared with treatment-naive specimens, indicating potential T cell skewing toward a late effector/memory phenotype following therapy; however, this change was not accompanied by changes in proliferation (Fig.7D).
To investigate whether therapy influences spatial distributions of leukocytes, we again subclassified individual ROIs into border, spanning border–distal or distal spatial categories based on proximity to nearest area of invasive carcinoma, as in Fig.2. Because high leukocyte density in TLSs substantially skewed spatial maps to reflect high B- and T cell densities, we performed spatial analysis with (Supplementary Fig. S7E) and without (Fig.7E) TLS data to better appreciate their contribution to the PDAC TiME. Spatial maps omitting TLSs revealed that densities of many leukocyte subpopulations were relatively constant across spatial categories independent of therapy. Spatial dynamics were most pronounced in neutrophils/eosinophils, DC-LAMP− DCs, and CD8+ T cells. CD8+ T cells were present at their highest density outside of tumor regions in treated and naive tumors, whereas neutrophils/eosinophils were the most abundant population within intratumoral areas in both groups (Fig.7E). In contrast to treatment-naive specimens, the second most abundant intratumoral leukocyte population in presurgically treated PDACs was CD163+ monocytes/macrophages, perhaps correlating with their protumoral and T cell suppressive phenotype potentially elicited in response to cytotoxic therapy and consistent with other reports revealing increased protumoral macrophage recruitment following neoadjuvant chemotherapy (38, 39). Inclusion of TLS regions to these analyses revealed that CD20+ B cell density shifted to represent the most abundant leukocyte subtype in several spatial categories, particularly in border regions, where TLSs comprised approximately 50% of analyzed ROIs in both groups (Supplementary Fig. S7E). Collectively, spatial mapping revealed differences in treatment-naive and treated PDACs—namely, presurgically treated tumors exhibited higher relative density of putatively immunosuppressive monocytes/macrophages than treatment-naive tumors.
In addition to potential impact of therapy on the PDAC TiME, we hypothesized that leukocyte composition might also evolve with disease progression from primary tumor to distant metastases. We thus applied the mIHC antibody panels to a small independent cohort (Fig.1A, cohort 3) of primary PDACs and PDAC metastases from unmatched patients. Unsupervised clustering of primary and metastatic tumors revealed that most distant metastases derived from multiple anatomic sites clustered with other metastases as opposed to clustering with primary PDACs (Supplementary Fig. S7F), supporting the concept that immune contexture varies by disease site and highlighting this as an important consideration for applying immunotherapies.
A highly immunosuppressive TiME is thought to represent a major obstacle to effective PDAC therapy (40, 41), but the extent of immune heterogeneity and spatial distribution of leukocytes within human PDAC has not been fully investigated. In this study, we leveraged a quantitative mIHC approach to interrogate density, complexity, and spatial relationships of myeloid and lymphoid lineages, as well as functional and/or differentiation status of leukocyte subpopulations in multiple histopathologic regions of a multi-institutional cohort of primary PDACs, consisting of 113 treatment-naive primary PDACs, 13 presurgically treated primary PDACs, and 9 PDAC distant metastases. Our results revealed that total leukocyte abundance and leukocyte subpopulation densities varied considerably across distinct histopathologic regions within single tumor resection specimens and also varied across patients.
Consistent with previous observations (12, 14, 41), CD8+ T cells and T helper cells together represented approximately one third of all intratumoral leukocytes in treatment-naive PDACs, with even higher densities found within stroma adjacent to invasive epithelium. A diverse assemblage of myeloid cells was present within tumor and adjacent stroma, with neutrophils/eosinophils being the predominant myeloid cell types in both, followed by CD68+ monocytes/macrophages and DCs. Neutrophils, monocytes, and macrophages all possess potent T cell–suppressive activities (42), consistent with data herein revealing paucity of T cells expressing indicators of activation, proliferation, or cytotoxicity across histopathologic regions. These observations support the assertion that, although human PDACs are not uniformly devoid of T cells, the myeloid microenvironment of PDAC is likely T cell suppressive, a point further bolstered by our observation that most CD8+ T cells were PD-1−EOMES− or PD-1−EOMES+. PD-1 is thought to play a critical role in regulating entry of T cells into a differentiation program following antigen experience and T cell receptor activation (43, 44), with PD-1+ T cells generally representing the target population for anti–PD-1 therapies (45). Thus, poor responses to PD-1 targeted immune therapies in PDAC observed thus far may be partly explained by most CD8+ T cells lacking expression of PD-1 and/or the highly T cell–suppressive TiME provided by myeloid subsets. These findings collectively support the notion that therapies aimed at relieving myeloid-based suppressive programs should be investigated.
In addition to high densities of putatively immunosuppressive myeloid subtypes, we also observed considerable abundance of DC-LAMP− DCs across histopathologic regions; these likely represent functionally immature cells with reduced capacity for T cell priming. However, cell surface markers evaluated herein could not further substratify DCs into monocyte-derived, conventional, or plasmacytoid DC subtypes. Moreover, it is worth noting that distinct myeloid subpopulations share expression of many cell surface markers, making definitive identification of subsets challenging, even when applying multiple biomarkers. Just as CD68+ cells may comprise both monocytes and macrophages, the CD68−HLA+DC-LAMP− cells identified as immature DCs could also reflect a fraction of monocytes. Despite the inability to fully resolve the complex identities and functional phenotypes of these populations, it is clear that as a whole, the treatment-naive PDAC TiME is generally enriched in myeloid cell types commonly associated with T cell suppression and/or lack of T cell activating capability.
Importantly, we observed substantial intrapatient leukocyte heterogeneity within tumor ROIs, an essential aspect to consider when conducting TiME studies. By utilizing a vTMA approach, we found that greater numbers of vTMA cores led to a better representation of the average leukocyte abundance in larger ROIs. We also noted that analysis of more tissue area is especially important for accurate estimation of the abundance of low-frequency populations, such as B cells, in our samples. In fact, highly variable B cell abundance across vTMA cores was evident in several cases where data appeared as segregated groups of averages, suggestive of clustered B cell organization across vTMAs, even though overt TLS structures were not present in vTMA ROIs. Altogether, the multiple approaches we took to describe inter- and intrapatient heterogeneity within the current study highlight the importance of histologic location and tissue area selection in bulk tumors for estimating abundance of multiple lineages of tumor-infiltrating leukocytes. Additional studies that continue to evaluate spatial distribution of cells across various tissue areas are warranted in order to further inform biological interpretation of human tissue analysis.
To interrogate potential impact of standard cytotoxic regimens on the PDAC TiME, we examined immune contexture and T cell functionality in presurgically treated PDACs. We found that high intratumoral CD8+/CD68+ cell ratios correlated with longer median OS (25.4 months OS compared with 14.8 months in patients with low CD8+/CD68+ cell ratios). Moreover, presurgically treated patients whose tumors were lymphoid enriched had significantly improved survival outcomes. These results are consistent with our prior evaluation of treated PDACs (8, 16), thus underscoring the robustness of these metrics in predicting clinical outcome independent of treatment status or modality of presurgical therapy. Interestingly, lymphoid enrichment alone was not strongly correlated with survival in treatment-naive patients as it was in presurgically treated patients, thus highlighting this as a feature that could have unique utility as a predictive biomarker in the context of therapy. That said, relative distribution of CD163+ monocytes/macrophages skewed higher within tumor regions of presurgically treated specimens on average compared with therapy-naive specimens, perhaps indicating the potential for sustained T cell suppression, even in the context of therapy. In agreement with this, we observed limited positivity of PD-1, EOMES, Ki-67, and GzmB in CD8+ T cells in presurgically treated samples. Notably, in TLSs of presurgically treated PDACs, PD-1−EOMES+ late effector/memory cells were the dominant CD8+ T cell population, and PD-1+EOMES+ cells were modestly expanded compared with T cells in TLSs of treatment-naive patients, consistent with enhanced frequency of T cell activation events. However, CD8+ T cell proliferation was similarly low in TLSs of treatment-naive and presurgically treated cohorts, consistent with recent reports revealing that T cell effector differentiation and proliferation can be unlinked (46). Taken together, our data indicate that conventional standard-of-care approaches may not substantially enhance intratumoral T cell activity in PDAC; however, it is important to acknowledge that this presurgically treated population represents a small number of patients treated with a variety of therapeutic agents (Supplementary Table S2). Future studies controlling for neoadjuvant therapy type and evaluating matched pre- and posttreatment samples will be needed to delineate drug-specific effects on the TiME.
Tumor cell intrinsic and extrinsic features evolve as tumor progression ensues and underlie the tremendous heterogeneity thought to limit success of cytotoxic, targeted, and immune therapy approaches. It is currently unclear how PDAC immune contexture evolves as tumors acquire additional genomic alterations or how immune complexity varies in primary tumors versus distant metastases—both of these represent important considerations for patient stratification. Our data revealed modest enrichment in intratumoral neutrophils/eosinophils in PDACs harboring mutations in TP53, but we observed no other overt changes in immune complexity associated with molecular status, indicating perhaps that the TiME of PDACs is established early during neoplastic development. In an exploratory comparison of primary PDACs and unmatched metastases, we found that metastases clustered separately from primary tumors. Although sample number in this comparison was limited, and primary and metastatic tumors were not derived from matched patients, these data indicate that differences in immune complexity likely do exist between primary and metastatic lesions and may be significant for therapeutic responsiveness. Future studies with larger cohorts and paired primary and metastatic specimens are warranted to study these differences in greater detail and should also aim to investigate features of T cell phenotype. Consensus sets of protein biomarkers reflecting T cell functional state are now coming into focus (43, 44, 46, 47); these will undoubtedly reveal novel mechanisms for inducing T cell memory, proliferation, and/or sustaining cytotoxicity that surely will lead to new therapeutic opportunities.
Collectively, data presented herein provide a comprehensive atlas characterizing regional leukocyte contexture within treatment-naive and presurgically treated PDACs that can promote hypothesis generation, serve as a resource for future investigations, inform on the response to therapies when untreated baseline samples are not available, as we have recently reported in Byrne and colleagues (48), and provide metrics for stratification of patient therapy.
Human PDAC specimens were obtained in accordance with the Declaration of Helsinki and were acquired with written informed consent and institutional review board (IRB) approval from Dana-Farber/Harvard Cancer Center and the Oregon Pancreas Tissue Registry under Oregon Health & Science University (OHSU) IRB protocol #3609. Additional PDAC archival resection and prestudy biopsy specimens were collected from consented patients enrolled in the multicenter phase Ib PRINCE clinical trial (NCT03214250, sponsored by Parker Institute for Cancer Immunotherapy). Healthy normal pancreas was acquired through organ transplant programs at University of California, San Francisco and OHSU. Clinical characteristics are provided in Supplementary Tables S1 to S3.
mIHC Staining and Image Acquisition
mIHC was performed on 4- to 5-μm FFPE sections, as previously described (8, 18). Briefly, tissues were fixed with 10% neutral buffered formalin, dehydrated in ethanol, and paraffin embedded using standard protocols. Slides were deparaffinized and stained with hematoxylin (S3301; Dako), followed by digital whole-slide scanning at 20× magnification on an Aperio AT2 scanner (Leica Biosystems). Tissues were then subject to 20 minutes of heat-mediated antigen retrieval in pH 6.0 Citra solution (HK080; BioGenex), followed by endogenous peroxidase blocking at room temperature (RT) in either 0.6% H2O2 for 20 minutes or Dako Dual Endogenous Enzyme Block (S2003; Dako) for 10 minutes. Protein blocking was performed for 10 minutes at RT with 5% normal goat serum and 2.5% bovine serum albumin in PBS. Tissues were incubated in primary antibodies for 30 to 60 minutes at RT or overnight at 4°C; antibodies and staining conditions are listed in Supplementary Table S4. Slides were then washed in TBST, and anti-rat, anti-mouse, or anti-rabbit Histofine Simple Stain MAX PO horseradish peroxidase–conjugated polymer (Nichirei Biosciences) was applied for 30 minutes at RT, followed by signal detection with aminoethyl carbazole chromogen (Vector Laboratories). Human tonsil was used in all rounds of mIHC as a staining control. Slides were digitally scanned after each chromogen development, followed by chromogen removal in 100% ethanol. For staining cycles with two rounds of antibody development, enzyme and protein blocking was repeated between rounds after chromogen removal, and tissue sections were taken through all steps described above from primary antibody application through slide scanning. All new staining cycles were started at the heat-mediated antigen retrieval step, in order to strip all antibodies from the previous cycle.
For survival analyses, Kaplan–Meier curves were generated to demonstrate time to event, and log-rank test was used to evaluate statistical significance. DFS was defined as time between surgery and disease recurrence. Cases with gross residual disease (R2 margin status) following surgery and cases with unknown date of disease recurrence were excluded from DFS analysis. OS was defined as time between date of surgical resection and date of death from any cause or date of last clinical contact. For presurgically treated cases, OS was defined as time from start of therapy to date of death from any cause or last clinical contact. To compare leukocyte densities across samples and histopathologic region types, Mann–Whitney U tests, Kruskal–Wallis tests, mixed-effects models, and multiple t tests with adjustment for multiple comparisons were used. Specific tests are indicated in figure legends. Correlations between continuous variables were measured using Spearman rank correlation. P values <0.05 were considered statistically significant, with *, P ≤ 0.05, **, P ≤ 0.01, ***, P ≤ 0.001, and ****, P ≤ 0.0001, unless otherwise specified in legend. Statistical analyses were performed using GraphPad Prism 8 (GraphPad Software) and SAS 9.4 (SAS Institute).
Additional detailed methods can be found in the Supplementary Materials and Methods.
S.A. Väyrynen reports grants from Finnish Cultural Foundation and grants from Orion Research Foundation during the conduct of the study. M.A. Tempero reports personal fees from AstraZeneca, personal fees from Bristol-Myers Squibb, grants from Celgene, personal fees from GlaxoSmithKline, grants from Halozyme, personal fees from ISPEN, personal fees from Karyopharm Therapeutics, personal fees from Merck & Co., personal fees from Seagen, and personal fees from Swedish Orphan Biovitrum outside the submitted work. R.H. Vonderheide reports having received consulting fees or honoraria from Celldex, Lilly, Medimmune, and Verastem. R.H. Vonderheide is an inventor of a licensed patent relating to cancer cellular immunotherapy and receives royalties from Children's Hospital Boston for a licensed research-only monoclonal antibody. E.M. Jaffee is a paid consultant for Adaptive Biotech, CSTONE, Achilles, DragonFly, and Genocea. E.M. Jaffee receives funding from Lustgarten Foundation and AduroBiotech and through a licensing agreement between AduroBiotech and JHU has the potential to receive royalties on GVAX. E.M. Jaffee is the chief medical advisor for Lustgarten and serves on the National Cancer Advisory Board and as an advisor to the Parker Institute for Cancer Immunotherapy (PICI). R.C. Sears reports grants from NIH during the conduct of the study, personal fees from Novartis, and personal fees from RAPPTA Therapeutics outside the submitted work. M. Mori reports grants from NCI and grants from AACR during the conduct of the study. B.M. Wolpin reports research funding from Celgene and Eli Lilly and consulting for BioLineRx, Celgene, G1 Therapeutics, and GRAIL. L.M. Coussens is a paid consultant for Cell Signaling Technologies, AbbVie, and Shasqi; received reagent and/or research support from Plexxikon, Pharmacyclics, Acerta Pharma LLC, Deciphera Pharmaceuticals LLC, Genentech, Roche Glycart AG, Syndax Pharmaceuticals, Innate Pharma, NanoString Technologies, and Cell Signaling Technologies; is a member of the Scientific Advisory Boards of Syndax Pharmaceuticals, Carisma Therapeutics, Zymeworks, Verseau Therapeutics, Cytomix Therapeutics, and Kineta; and is a member of the Lustgarten Therapeutics Advisory working group. No disclosures were reported by the other authors.
S.M. Liudahl: Conceptualization, data curation, formal analysis, investigation, visualization, methodology, writing–original draft, writing–review and editing. C.B. Betts: Conceptualization, data curation, formal analysis, investigation, visualization, methodology, writing–review and editing. S. Sivagnanam: Conceptualization, data curation, software, formal analysis, investigation, visualization, methodology, writing–review and editing. V. Morales-Oyarvide: Data curation, formal analysis. A. da Silva: Data curation. C. Yuan: Data curation, formal analysis. S. Hwang: Investigation. A. Grossblatt-Wait: Data curation. K.R. Leis: Investigation. W. Larson: Formal analysis. M.B. Lavoie: Formal analysis. P. Robinson: Investigation. A. Dias Costa: Data curation, formal analysis. S.A. Vayrynen: Data curation, formal analysis. T.E. Clancy: Resources. D.A. Rubinson: Data curation. J. Link: Resources, data curation. D. Keith: Data curation. W. Horton: Visualization. M.A. Tempero: Resources, writing–review and editing. R.H. Vonderheide: Writing–review and editing. E.M. Jaffee: Writing–review and editing. B. Sheppard: Resources.J. Goecks: Supervision. R.C. Sears: Supervision, writing–review and editing. B.S. Park: Formal analysis, writing–review and editing.M. Mori: Formal analysis, writing–review and editing. J.A. Nowak: Conceptualization, resources, data curation, formal analysis, supervision, project administration, writing–review and editing. B.M. Wolpin: Conceptualization, resources, funding acquisition, project administration, writing–review and editing. L.M. Coussens: Conceptualization, resources, supervision, funding acquisition, writing–original draft, project administration, writing–review and editing.
We thank members of the Coussens and Wolpin-Nowak laboratories for critical feedback; Mr. Justin Tibbitts, Ms. Teresa Beechwood, and Dr. Jacklyn Woods for laboratory management; and Ms. Cathy Love for administrative assistance. We also thank the Parker Institute for Cancer Immunotherapy PRINCE trial translational team, the OHSU Histopathology Shared Resource, OHSU Surgical Pathology, the OHSU Cancer Registry, and the OHSU Knight Diagnostic Laboratories. We also thank all patients who donated tissue samples that made this study possible.
The study and analyses were funded by a Stand Up To Cancer—Lustgarten Foundation Pancreatic Cancer Dream Team Translational Research Grant (Grant Number: SU2C-AACR-DT14-14) and the Brenden-Colson Center for Pancreatic Care at OHSU. Stand Up To Cancer is a division of the Entertainment Industry Foundation. The indicated grant is administered by the American Association for Cancer Research. L.M. Coussens acknowledges funding from the National Institutes of Health (1U01 CA224012, U2C CA233280, R01 CA223150, R01 CA226909, R21 HD099367), the Knight Cancer Institute, and the Brenden-Colson Center for Pancreatic Care at OHSU. Analytic methods used for image analysis at OHSU were developed and carried out with major support from the National Institutes of Health, National Cancer Institute Human Tumor Atlas Network (HTAN) Research Center (U2C CA233280), and the Prospect Creek Foundation to the OHSU SMMART (Serial Measurement of Molecular and Architectural Responses to Therapy) Program. B.M. Wolpin acknowledges funding from the Hale Family Center for Pancreatic Cancer Research, Lustgarten Foundation Dedicated Laboratory program, NIH grant U01 CA210171, NIH grant P50 CA127003, Stand Up To Cancer, Pancreatic Cancer Action Network, Noble Effort Fund, Wexler Family Fund, Promises for Purple, and McCarthy Strong. S.A. Väyrynen is supported by the Finnish Cultural Foundation and Orion Research Foundation. R.H. Vonderheide receives research funding from Apexigen, Fibrogen, Inovio, Janssen, and Lilly. R.C. Sears acknowledges funding from the NIH (1U01 CA224012, U2C CA233280, U54 CA209988, R01 CA196228, and R01 CA186241) and the Brenden-Colson Center for Pancreatic Care at OHSU. This study was also made possible with support from the Oregon Clinical & Translational Research Institute (OCTRI), which is supported by the National Center for Advancing Translational Sciences, National Institutes of Health, through Grant UL1TR002369.
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