A Serum-Induced Transcriptome and Serum Cytokine Signature Obtained at Diagnosis Correlates with the Development of Early Pancreatic Ductal Adenocarcinoma Metastasis

Recent advances in targeted and immune therapy for the treatment of cancer have created an urgency to identify systemic and tumor biomarkers to define carcinoma subsets that correlate with disease progression. An understanding of biomarkers will facilitate the advancement of personalized treatment by identifying which patients will benefit from targeted or immune therapy, monitoring the response to therapy, and predicting disease outcome (1). Once identified, systemic biomarkers will be more universally available for clinical analysis than tumor biomarkers. This study describes the identification of systemic indicators analyzed from serum that associate with pancreatic cancer metastatic progression.

Pancreatic ductal adenocarcinoma (PDAC) is an aggressive cancer with a 5-year survival of only 27%. However, there are some patients that do not experience PDAC progression for years after diagnosis and are long-term survivors (2). Advances in genomic technologies have enabled a molecular understanding of PDAC tumor subsets with different underlying pathology. Through RNA expression profiling, four subtypes of PDAC tumors [squamous, pancreatic progenitor, immunogenic, and aberrantly differentiated endocrine/exocrine (ADEX)] have been identified (3–6). Each tumor subset is associated with distinct histopathologic characteristics. Interestingly, a squamous subtype and an immunogenic subtype (with a macrophage and T-cell–coinhibitory signature) are associated with poor prognosis. Recently, a transcriptome analysis of paired pancreatic tumor and adjacent benign tissues revealed differentially expressed genes belonging to canonical pathways and molecular functions associated with inflammation (7). These data suggest that there are inflammatory related differences in tumor and nontumor pancreatic tissue.

Despite the accessibility of blood as surrogate biopsy material, identification of systemic biomarkers has been especially challenging. Inflammatory mediators present in serum or plasma have been associated with aggressive disease. For example, TNF and IL1β are elevated in advanced breast cancer and with more aggressive carcinomas (8). In aggressive inflammatory breast cancer, inflammatory mediators, IL6 and CXCL8 (IL8), are produced and secreted at high levels (9). Yet, the anti-inflammatory mediator, IL10, has also been elevated and associated with a negative prognosis in multiple cancers (10). Overall, the association of serum cytokine concentrations with cancer progression is difficult to interpret and remains ill-defined. In this study, we used a novel approach to identify systemic inflammatory indicators that associate with PDAC progression. Using baseline pretreatment serum from patients with PDAC with and without early metastatic progression, we analyzed serum-induced transcriptomes (induced in a normal reporter cell) and patient serum cytokine concentrations.

The serum-induced transcriptomes of patients with pancreatic cancer with early metastasis (<400 days from diagnosis) were compared with patients that did not develop metastasis or developed metastasis late (>500 days from diagnosis). We hypothesized that at baseline (before treatment), immune mediators would differ in patients with PDAC that experience early metastasis (referred to as progressed or P) as compared with patients that do not experience metastasis or experienced metastasis greater than 500 days from diagnosis (referred to as not progressed or N). To sensitively and comprehensively capture these differences, we employed a serum-induced transcriptome analysis (using pretreatment baseline serum) to induce transcription in a well-controlled reporter cell population consisting of peripheral blood mononuclear cells (PBMC) obtained from a healthy blood donor. This same serum-based transcriptome assay using PBMCs (obtained from the same PBMC donor as this study) has been used to capture inflammatory signatures in patients with type I diabetes and cervical cancer. In patients with recent onset type I diabetes, longitudinal plasma samples identified a signature of high proinflammatory mediators and low anti-inflammatory factors that was evident before the onset of type I diabetes (11, 12). For cervical cancer, the transcriptome signature identified an association of innate immune activation with metastatic cervical carcinoma (13).

Results of this study show a baseline serum-induced transcriptome signature of innate type I IFN inflammation in patients with PDAC that experience early metastasis. This was in contrast to a transcriptome signature of regulated acute inflammation identified in patients that did not experience early metastasis. To correlate cytokine concentrations with transcriptome data, a canonical correlation of the transcriptome with serum cytokines was performed. From this correlation, both immune-activating and immune-regulating transcripts showed significant positive correlation in the not progressed PDAC group. These data were consistent with the transcriptome signature of regulated acute inflammation for this patient cohort. Individual serum cytokine concentrations did not correlate with the transcriptome data. However, when cytokine concentrations were calculated as a cytokine index (CI i.e., the sum of the concentration of acute inflammatory cytokines divided by the concentration of Th2 cytokines and myeloid differentiation factors), there was a Pearson correlation of <0.05 with the transcriptome index.

Although serum/plasma–induced transcriptomes have previously been applied in the cancer field (13), to our knowledge, this is the first analysis of cancer-associated peripheral immunity that describes cancer inflammation/immunity in the context of serum-induced transcription with serum cytokine concentrations. With further study, the serum-induced transcriptome and serum CI have the potential to be used as systemic biomarkers that will contribute to the identification of PDAC subsets that associate with disease progression.

Subjects were recruited through the pancreatic cancer program at the Medical College of Wisconsin, and blood and serum samples were collected into the Pancreatic Cancer Biorepository under an approved Institutional Review Board protocol (PRO00012151; Froedtert Memorial Hospital Wauwatosa, WI). Patients with early progressed PDAC had radiographic evidence of metastasis within 400 days of diagnosis. Not progressed or late-progressed patients had no radiographic evidence of metastasis within 500 days of diagnosis. For patient serum preparation, peripheral blood was drawn into nonanticoagulated tubes and serum was collected, centrifuged, and frozen at −80°C until analyzed.

A serum-induced transcription assay was conducted as described previously (12). For this study, commercial cryopreserved PBMCs from a Caucasian HLA-A2 male donor (UPN727, Cellular Technology Ltd.) were thawed and washed as per the manufacturer's protocol. PBMCs were cocultured with 40% subject serum in RPMI1640 medium supplemented with 100 U/mL penicillin and 100 μg/mL streptomycin at 37°C in 5% CO₂. Cultures were prepared in a Costar 24-well plate (Corning) using 5 × 10⁵ cells in 500 μL/well. After culture (9 hours), total RNA was extracted using TRIzol reagent (Invitrogen Life Technologies). Using purified total RNA (100 ng), cRNA was synthesized and amplified/labeled using the Affymetrix Express Kit, then fragmented and hybridized to the GeneChip Human Genome U133 plus 2.0 array in accordance with the Affymetrix GeneChip expression analysis technical manual (Affymetrix). After hybridization, arrays were washed and stained with Affymetrix fluidics protocol FS450_0001 and scanned with a 7G Affymetrix GeneChip Scanner. Image data were analyzed with Affymetrix Expression Console 1.1.2 software and normalized with Robust Multichip Analysis (www.bioconductor.org) to determine signal log ratios. The statistical significance of differential gene expression was determined through ANOVA and FDR using Partek Genomics Suite 6.5. Hierarchical clustering was conducted with Genesis (14). Pathway analysis was performed with the database for annotation, visualization, and integrated discovery (DAVID; ref. 15) and Integrated Pathway Analysis (IPA; ref. 16). The transcriptome index was calculated by subtracting the sum of the log₂ intensity of downregulated transcripts from the sum of the log₂ intensity of upregulated transcripts (relative to progressed PDAC). The data generated in this investigation are MIAME compliant (17) and have been deposited in the NCBI Gene Expression Omnibus (18), accessible through GSE107818.

Serum was analyzed in duplicate using the Human Cytokine/Human Chemokine Array 65-plex Panel (HD65; Eve Technologies).

Histologic examinations were performed on standard 4-μm thick hematoxylin and eosin (H&E)-stained sections of formalin-fixed, paraffin-embedded specimens. The pathologic features were evaluated by low-power examination of several representative slides from each case by a pathologist (A. Mackinnon). For IHC studies, paraffin blocks were available in all cases. Four-μm sections were stained using a Dako Autostainer Plus according to the manufacturer's protocol. Slides were dried at 60°C for 1 hour and deparaffinized. Heat-induced epitope retrieval was performed with Dako Envision FLEX target retrieval solution (high pH Tris/EDTA) at 100°C for 20 minutes. The primary antibody for CD8 (C8/144B) was obtained from Dako. Antibodies were incubated at room temperature for 60 minutes. Signals were detected using a Dako FLEX detection kit. Counterstaining was performed with Envision FLEX hematoxylin for 7 minutes at room temperature. Appropriate positive and negative controls were run concurrently. For IHC analysis, cytoplasmic and membranous expression of IHC staining was quantified for each case using an Automated Cellular Imaging System III (ACIS III, Dako) as described previously (19). For each patient, a portion of pancreatic cancer was quantitatively analyzed by scanning a representative whole slide. ACIS software collects individual, overlapping images at 400×, and then the software program tiles these images to create a montage of the entire scanned specimen. The software evaluates each individual 400× image and combines the results into an aggregate quantitative measurement corresponding to the entire tissue specimen. The ACIS system measures the intensity of the staining based on three related color parameters: the color defined by hue, the “darkness” defined as luminosity, and the density of the color defined as saturation. ACIS software for the analysis was programmed by an experienced user-pathologist (A. Mackinnon) by setting the color-specific thresholds to determine and calculate staining intensity and the ratio of positively stained cells to the entire area of selection. This was used to determine the approximate percentage of positive staining tissue in each specimen.

Data were compared using paired and unpaired nonparametric Student t test and Pearson correlation. Analyses were performed with Prism Graphpad 6.0 Software (GraphPad). Canonical correlation analyses between genes and cytokines were performed using R package CCA, Version 1.2, published 2014.07.02 (http://www.r-project.org/). A canonical cross-correlation was done between the log₂ intensity of 66 transcripts that met the statistical cutoff of <20% FDR (n = 66) and the concentration of 65 serum cytokines for each of the 5 patients in the progressed and not progressed PDAC cohorts.

Patients with resectable or borderline-resectable PDAC (i.e., not locally metastatically advanced and candidates for surgery) were included in this study. All patients were treated with neoadjuvant chemotherapy consisting of chemotherapy or chemotherapy and radiation (Table 1). Previous study of patients with resectable PDAC treated with neoadjuvant therapy showed a mean overall survival (OS) of 32 months (933 days from diagnosis; ref. 20). For this study, patients with early metastasis (i.e., progressed) were defined as those with radiographic evidence of metastasis within 400 days of diagnosis. Patients with late or no metastatic progression (i.e., not progressed) had no radiographic evidence of metastasis or had radiographic metastatic evidence that appeared 500 days after diagnosis (Tables 1 and 3). We hypothesized that mediators in the serum of patients with progressed and not progressed PDAC would induce unique transcriptional responses in a reporter population of normal PBMCs. A serum-based transcriptome assay extensively used to detect inflammatory signatures in type I diabetes was used to interrogate the hypothesis (11, 21, 22). Specifically, baseline serum (obtained at diagnosis and prior to treatment) was cocultured over PBMCs obtained from a standard normal donor. The induced transcription was measured on Affymetrix GeneChip Human Genome U133 Plus 2 arrays. An unsupervised principal component analysis (utilizing the complete unfiltered data) showed distinct clusters between progressed and not progressed samples (Supplementary Fig. S1). Of the greater than 47,000 transcripts interrogated, there were 727 unique transcripts that met thresholds of |log₂ ratio|of ≥0.05 (1.2-fold difference) with a P≤0.01. The heatmap in Fig. 1A shows the relative expression of the 727 transcripts in 5 progressed (P) and 5 not progressed (N) PDAC patients. The heatmap of the mean fold difference in expression shows a distinct segregation of up- and downregulated transcripts between the two patient cohorts (Fig. 1B).

The 727 unique transcripts were uploaded into DAVID and IPA for ontological analysis. Putative upstream regulators (upregulators) of gene transcription were identified in IPA (Fig. 1A; Table 2). Identification of upstream regulators was of particular interest as we hypothesized that mediators/regulators in patient serum are responsible for regulating transcription in normal donor PBMCs. Upstream regulators in progressed patients included those associated with activated innate immunity. Specifically, upstream regulators included IFN-regulatory factors (IRF3, IRF5, IRF7), stimulator of IFN genes [STING (TMEM173)], tank binding kinase (TBK), type I IFN (IFNA2), type III IFN (IFNL1 or IL29), IFN receptor subunits (IFNLR1 and IFNAR1), and transcription factors associated with IFN signaling (STAT1 and STAT2; Fig. 1A; Table 2). In contrast to the upstream regulators identified in patients with progressed PDAC, the upstream regulators of upregulated transcripts in not progressed PDAC was one of acute inflammation and TGFβ1/IL10 immune regulation. For this patient cohort, there were upstream regulators associated with acute inflammation (IL6, IL1, TNF, IL2, IL17A, MYD88) as well as mediators associated with immune regulation (PGE2, TGFβ, SMAD4, IL4, IL10, and FoxP3; Fig. 1A; Table 2). In summary, progressed patients have a serum-induced transcriptome signature consistent with activated innate inflammation, whereas not progressed patients have a serum-induced transcriptome signature consistent with acute inflammation and TGFβ and IL10 immune regulation.

To understand a global relationship of these genes, a transcriptome index was calculated as a method to ascribe a numerical value to the upregulated or downregulated transcripts depicted in the heatmap in Fig. 1A. Relative to progressed patients, the transcriptome index was calculated as the sum of the log₂ intensity of all downregulated transcripts subtracted from the sum of the log₂ intensity of all upregulated transcripts. Patients in the progressed cohort had transcriptome indices greater than 0, and patients in the not progressed patient cohort had transcriptome indices less than 0 (Table 3). Thus, the transcriptome index showed a numerical segregation of the patient cohorts.

Inflammation is a hallmark of cancer (23), yet markers of immune inflammation are difficult to interpret. To analyze transcripts in the context of inflammation, genes listed in IPA and DAVID belonging to ontology categories of inflammatory response, cytokine and cytokine–cytokine receptor interaction were identified (Supplementary Table S1). Within the original 727 transcripts, there were 194 related to inflammation. The heatmap in Fig. 1C shows the relative transcript expression of these inflammatory genes for each patient. Although there is variation of transcript expression between individuals within the progressed and not progressed patient cohorts, calculation of the mean fold difference showed a distinct segregation of gene expression between the two groups (Fig. 1D).

Similar to the transcriptome index calculated from 727 transcripts, an inflammatory transcriptome index was calculated from 194 inflammatory-associated transcripts. Analogous to the transcriptome signature, each of the progressed patients had an inflammatory transcriptome index greater than that of not progressed patients (Table 3). When the data from the transcriptome and inflammatory indices were analyzed together, the progressed and not progressed patient cohorts segregated (Fig. 1E). These data suggest that inflammation is a factor associated with disease progression.

To validate the results of the microarray analysis, qRT-PCR was done from cDNA made from the PBMCs (same donor PBMCs as used for the microarray analysis) following incubation with patient serum. Supplementary Fig. S2 shows a correlation of the log₂ intensity (Supplementary Fig. S2A) with PCR amplification (fold change based on ΔΔC_t value; Supplementary Fig. S2B) for several genes.

Inflammatory (IL6, CXCL8, TNF, IL1β) and regulatory (IL10, IL1RA) serum cytokine concentrations are elevated in patients with aggressive cancers (10), but to date, there are no validated serum mediators as markers of cancer progression. For this study, we hypothesized that serum immune mediators (cytokines, chemokines, and growth factors) analyzed in the context of each other would reflect a state of systemic immunity that would correlate with disease progression. For this assay, baseline (pretreatment) serum was analyzed using a luminex-based platform.

To understand serum mediators in context with the transcriptome, a canonical correlation between the log₂ intensity of individual transcripts and individual serum cytokine concentrations (pg/mL) was done. For this analysis, the transcriptome statistical stringency was increased to include a false discovery rate (FDR) of <20%. These criteria reduced the transcriptome list from 727 to 66 transcripts. The log₂ intensity of each of the 66 transcripts was correlated with the 65 serum cytokine concentrations analyzed for each patient. Figure 2 shows the correlation of transcriptome data (X) with serum cytokine concentrations (Y) in the progressed patient cohort (Fig. 2A) and the not progressed patient cohort (Fig. 2B). Values are translated into colors from blue (negative correlation) to red (positive correlation) to generate a correlation map. The maps show distinct differences in the progressed and not progressed patient groups. From the list of 66 transcripts subjected to canonical correlation, those that correlated significantly (Cor P > 0.94 or Cor P < −0.94) with cytokine concentrations in patient cohorts with progressed and not progressed PDAC are listed in Table 4.

Interestingly, in the progressed group, there were two genes related to mitochondrial metabolic function that significantly correlated with serum cytokines (Table 4). With reference to the cytokine signature, the electron transfer flavoprotein dehydrogenase gene (ETFDH) was negatively correlated and thioredoxin (TXN) was positively correlated. Both transcripts were upregulated in the progressed group when compared with the not progressed group with a mean fold difference of 1.48 for ETFDH and 1.61 for TXN. ETFDH is a mitochondrial enzyme involved in energy production by fat and protein catabolism, and thioredoxin is a redox-active protein important in the regulation of mitochondrial membrane potential. Recent studies have reported that metabolic reprogramming may enable PDAC cells to maintain adequate intracellular nutrient levels despite limited supplies (24, 25). Furthermore, autophagy, microlipophagy, and electron transport chain activity were reported to be critical for survival of tumor-initiating cells (26). Together, these data suggest that metabolic states may play a role in progression.

In the not progressed group, there was a positive correlation of transcripts related to activated inflammation (i.e., SASH1, CXCL5, FCAR, CD14, VCAN, CLEC5A, C5AR1, and IL1R). There were also positive correlations of cytokine concentrations with transcripts associated with TGFβ activation and signaling (i.e., SMAD4 and ITGB8) in the not progressed patient cohort. These data are consistent with the transcriptome data suggesting not progressed patients have a regulated acute inflammatory signature (Table 2).

A CI was calculated as a strategy to globally assess serum cytokine concentrations. The CI was calculated as the sum of the concentrations of acute phase cytokines (IL1α, IL1β, IL6, IL17A, IL28A IFNA2 IFNγ, and TNFα) divided by the sum of concentrations of Th type 2 (Th2) cytokines and myeloid growth factors (IL4, IL5, IL10, IL13, GM-CSF, FLT3L, and G-CSF). A Pearson correlation of the CI with the transcriptome index was significant at P = 0.0332. (R² = 0.452; Fig. 3A; Supplementary Table S2). There was no significance observed when individual serum cytokine concentrations were correlated with the transcriptome index (Supplementary Table S2). Figure 3 shows separation of the progressed and not progressed patient cohorts when the CI was graphed with the transcriptome index (Fig. 3A) and inflammatory transcriptome index (Fig. 3B). To substantiate the CI as an indicator for PDAC metastatic progression, it was calculated from baseline serum from additional patients [i.e., progressed (n = 17) and not progressed (n = 18); Table 3]. From this validation cohort, the CI showed statistical significance (P = 0.0487) between the progressed and not progressed PDAC cohorts (Fig. 3C). The mean CI of the progressed group was 2.3 and the mean CI of the not progressed group was 3.9 (Table 3). It is important to note that some progressed patients had a CI greater than 3.9 (P8, P15, and P16) and some not progressed patients had a CI less than 2.3 (N7, N8, and N17). These data suggest that a higher CI may be a favorable indicator of delayed PDAC progression only in some patients as the CI did not completely segregate the 2 patient cohorts. Furthermore, this index requires validation with larger cohorts of patients with early and late metastatically progressed PDAC.

Infiltration of CD3⁺CD8⁺ cells in the tumor core (CT) and invasive margins (IM; referred to as the immunoscore) has been shown to correlate with OS in multiple carcinomas including bladder, breast, colorectal, esophagus, head and neck, liver, lung, melanoma, and ovarian (27). Often, there is only 5%–20% cellularity within the extracellular matrix-rich stroma of pancreatic cancer (28). Despite the presence of patchy disease, a better disease-specific survival and OS has been associated with the presence of CD8⁺ T cells in the PDAC CT and CD3⁺ T cells in the CT and IM (29). High tumor infiltration of both CD4⁺ and CD8⁺ T cells also correlated with a significant increase in survival (30). Thus, for this study, it was on interest to determine whether CD8⁺ T cells could be detected in the resected tumors of our patients with PDAC. Available tumor samples were analyzed in 4 progressed patients (P1, P3, P5, and P6) and 8 not progressed patients (N1–N8). Tumor sections were stained with anti-CD8, and the percentage of CD8⁺ T cells was determined in multiple regions of the tissue. The CD8⁺ T-cell density score represents the average percent of CD8⁺ T cells from each case (Table 3). Figure 4 shows the CD8⁺ T-cell density graphed against the inflammatory transcriptome index (Fig. 4A) and the CI (Fig. 4B). Figure 4C shows representative tumor sections stained with hematoxylin/eosin (panel 1) and anti-CD8 (panel 2) From these limited data, a few interesting observations were made. Two of the not progressed patients had few T cells detected in tumor (N4 and N7; Table 3). Patient N4 relapsed with metastasis 1,824 days since diagnosis and patient N7 is beyond 1,000 days without relapse (Tables 1 and 3). The average number of CD8⁺ T cells in patient P6 was greater than patients N4 and N7 (Table 3). Patient P6 relapsed 308 days from diagnosis. It could be that PDAC T-cell infiltration is a useful indicator of PDAC progression, but it may not serve as a predictor of PDAC progression for all patients. This hypothesis requires further testing.

There are multiple indicators that associate with the development and progression of pancreatic cancer. The presence of somatic mutations in tumor cells (e.g., KRAS, TP53, CDKN2A, SMAD4, RNF43, ARID1A, TGFβR2, GNAS, RREB1, and PBRM1; ref. 4), tumor RNA expression profiles (3–6), tumor-immune cell infiltration (27, 29, 30) and metabolic function (24–26) all contribute to identifying more and less aggressive subsets of PDAC. The main objective of this study was to query the systemic response in PDAC to further enhance knowledge of PDAC disease progression.

Identification of systemic indicators such as inflammatory mediators present in the serum and their association with PDAC progression has been a difficult task. In this study, serum was analyzed using novel strategies that allowed for a global analysis of indicators. An in-depth analysis of a serum-induced transcriptome and serum cytokine concentrations was conducted on a pilot sample of patients with PDAC to investigate these associations with disease progression. Remarkably, despite the limitation in sample size, the baseline serum–induced transcriptome showed a difference in immune activation and immune regulation in patients with PDAC that do and do not develop early metastasis (Fig. 1A and B). Ontological analysis of the baseline serum–induced transcriptome from progressed patients that experience early metastasis showed putative upstream regulators consistent with induction of type I IFN production through the stimulator of IFN genes pathway (STING). The presence of activators of innate immunity in this patient cohort is counter intuitive because activation of the STING pathway and the secretion of type I IFNs, inflammatory cytokines, and chemokines promote maturation of dendritic cells, which prime cancer antigen-specific T cells to produce an adaptive anticancer response (31). It is possible that these immune upstream regulators are present in response to innate activation signals, but there is immune dysregulation that prevents activation of Th1 adaptive immunity in this patient cohort. Utilizing the same serum-induced transcriptome assay and the same reporter PBMC cells, an innate immune activation signature was also identified in patients with metastatic cervical carcinoma (13). Remarkably, and similar to patients with progressed PDAC, the transcriptome upstream regulators for patients with metastatic cervical cancer included IRF5, IRF7, IFNL1, IFNA2, and IFNL1.

Although the transcriptome data are striking, this method of analysis is costly and may not be clinically feasible. The ability to identify alternative inflammatory states from serum cytokine analysis is of great clinical relevance. To date, serum cytokine analysis has shown that both immune-stimulatory and immunosuppressive cytokines are present in the serum of patients with multiple aggressive cancers (10). Most notably, both high concentrations of the acute inflammatory cytokine IL6, as well as the inflammation regulatory cytokine IL10, are associated with poor outcome. Our data showed no correlation of individual cytokines with the transcriptome index (Supplementary Table S2). Using the upstream regulators identified from the transcriptional data as a guide, cytokines were clustered into acute inflammatory cytokines and regulatory cytokines and myeloid differentiation factors to mathematically calculate a CI. A Pearson correlation of the CI with the transcriptome index yielded a correlation of P < 0.05 (R² = 0.452; Fig. 3A). Analysis from a larger cohort of 17 progressed and 18 not progressed patients yielded a mean CI that was statistically significant between the progressed and not progressed patient cohorts (Fig. 3C; Table 3). It is important to note that not every patient in the progressed cohort had a low CI and not every patient in the not progressed cohort had a high CI. With further study, the CI may be shown to be an indicator of favorable outcome in some patients within a particular subset of PDAC that is yet to be defined.

To date, most of the information defining subsets of PDAC is derived from tumor. Often, tumor samples are not available and mutational, transcriptional, immunogenic, and metabolic tumor parameters cannot be assessed. Therefore, further investigation of systemic indicators of PDAC progression is of critical importance. However, when tumor is available, it is conceivable that more and less aggressive subsets of PDAC can be further defined by combining tumor analysis with systemic analysis. The subtype of tumor (i.e., squamous, pancreatic progenitor, immunogenic, and ADEX), tumor immunogenicity (i.e., detection of tumor-infiltrating CD3⁺, CD8⁺, and CD4⁺ T cells), tumor mitochondrial metabolism, and the systemic inflammatory profile (serum-induced transcriptome or CI) may need to be assessed together to identify specific subsets of PDAC with a predictable outcome.

In summary, both the transcriptome index and the CI consider multiple immune-associated genes or cytokines that reflect a global state of systemic inflammation. The ability to segregate patients with PDAC that experience early metastasis from those that do not through analysis of baseline cytokine concentrations and baseline transcriptome indices is conceptually novel. An in-depth analysis of this pilot sample of patients has introduced the serum-induced transcriptome and the CI as potential indicators of PDAC progression. Further study of these systemic indicators alone or in combination with tumor characteristics should lead to a better understanding of subsets of PDAC and their association with progression and survival.

M.B. Dwinell has ownership interest in Protein Foundry, LLC. No potential conflicts of interest were disclosed by the other authors.

Conception and design: S. Tsai, B.D. Johnson, M.J. Hessner, J.A. Gershan

Development of methodology: A. Mackinnon, M.J. Hessner, J.A. Gershan

Acquisition of data (provided animals, acquired and managed patients, provided facilities, etc.): S. Tsai, L. McOlash, M.L. Kaldunski, M. Aldakkak, A. Mackinnon

Analysis and interpretation of data (e.g., statistical analysis, biostatistics, computational analysis): S. Tsai, L. McOlash, S. Jia, J. Zhang, P. Simpson, M. Aldakkak, M.B. Dwinell, A. Mackinnon, M.J. Hessner, J.A. Gershan

Writing, review, and/or revision of the manuscript: S. Tsai, P. Simpson, M.B. Dwinell, B.D. Johnson, A. Mackinnon, M.J. Hessner, J.A. Gershan

Administrative, technical, or material support (i.e., reporting or organizing data, constructing databases): L. McOlash, M.L. Kaldunski, M. Aldakkak, J. Grewal, K. Palen, J.A. Gershan

Study supervision: M.J. Hessner, J.A. Gershan

J.A. Gershan was supported by the Advancing a Healthier Wisconsin Program and the Medical College of Wisconsin Cancer Center, the Midwest Athletes for Childhood Cancer (MACC) fund, and the Children's Hospital of Wisconsin Children's Research Institute. This work was supported by the Juvenile Diabetes Research Foundation International (grant numbers 2-SRA-2015-109-Q-R and 3-SRA-2018-478-S-B to M.J. Hessner); NIH (R56DK108802 and DP3DK098161 to M.J. Hessner), 1-UL1-RR031973, and The Clinical and Translational Science Institute of Southeast Wisconsin. This research was also supported in part by a Seed Grant FP9214 from the A Healthier Wisconsin Endowment to the MCW Cancer Center (to M.B. Dwinell).

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