Metabolomic and Immune-Microbiome Profiling for Unresectable Pancreatic Cancer

Overview

Brief Summary The goal of this observational study is to identify biomarkers and develop a personalised treatment stratification model for patients with unresectable pancreatic ductal adenocarcinoma (PDAC) in Taiwan. The main questions it aims to answer are: * What serum metabolomic profiles predict treatment response and patient survival? * How do immune response markers and gut microbiome composition correlate with therapeutic outcomes? * Can a combined multi-omic stratification algorithm enhance personalised therapy planning? Participants, who have been diagnosed with unresectable locally advanced or metastatic PDAC and are undergoing systemic therapy and chemoradiotherapy, will: * Provide serum samples for comprehensive metabolomic profiling via high-performance liquid chromatography-mass spectrometry. * Undergo immune profiling through flow cytometry. * Provide stool samples for gut microbiome analysis using 16S rRNA sequencing. * Be followed longitudinally to correlate these multi-omic findings with clinical outcomes. Researchers anticipate that integrating these multi-omic analyses will facilitate personalised therapy approaches, potentially improving patient outcomes.

Detailed Description \<Pancreatic cancer and current treatment landscape\> Pancreatic ductal adenocarcinoma (PDAC) is a highly lethal malignancy characterised by nearly equal incidence and mortality rates. Globally, in 2022, there were 511,000 new cases and 467,000 deaths, making PDAC the sixth leading cause of cancer mortality among both sexes combined. In Taiwan, pancreatic cancer ranks as the seventh leading cause of cancer death, accounting for 2,769 deaths in 2022. Over 80% of patients are diagnosed at an unresectable stage (locally advanced or metastatic), for which five-year survival is below 5%. Current systemic treatments such as FOLFIRINOX or gemcitabine-based chemotherapy provide only modest improvements in median survival, and most advanced PDAC ultimately develop therapeutic resistance. No effective stratification exists to guide individualised therapy in unresectable cases - all patients typically receive similar empiric regimens despite substantial biological heterogeneity. \<Preliminary Data on Dose Escalation in Radiotherapy\> Between 2015 and 2022, 231 patients with pancreatic cancer underwent locoregional radiotherapy at Linkou Chang Gung Memorial Hospital (CGMH). After applying exclusion criteria (e.g., prior surgery, small cell or neuroendocrine histology, insufficient radiotherapy dose, and inadequate follow-up), 145 patients were included in a retrospective analysis. Kaplan-Meier estimates indicated that patients receiving proton beam therapy (PBT)-either alone or in combination with X-ray radiotherapy (XRT)-demonstrated significantly better local control (LC) than those treated with XRT alone. In multivariate analysis, PBT was associated with a hazard ratio of 0.52 (95% CI 0.28-0.97; p = 0.039) for improved LC, underscoring the potential efficacy of proton-based modalities in unresectable PDAC. Additionally, dose escalation correlated strongly with improved local outcomes. Patients prescribed ≥5940 cGy showed significantly higher one- and two-year local control (LC) rates of 100% and 88.4%, respectively, as well as an extended median LC of 28.1 months, compared to those receiving 4500-5940 cGy, which had one- and two-year LC rates of 73.3% and 40.8%, respectively, and a median LC of 18.1 months. These data highlight a dose-response relationship in locally advanced PDAC. While higher-dose PBT seems beneficial, intensifying the dose may increase toxicity and may not be suitable for all patients. Importantly, no established biomarker exists to identify those most likely to benefit from dose escalation. \<Research Gap in Unresectable PDAC\> The only widely used biomarker in PDAC is serum CA19-9, which has moderate prognostic and diagnostic value but has significant limitations. CA19-9 is false-negative in 5-10% of individuals who are Lewis-antigen negative and cannot produce the antigen. It also has poor specificity, being elevated in benign biliary obstruction and other conditions. Thus, patients (especially those without Lewis antigen expression or with jaundice) cannot rely on CA19-9 for stratification. In the absence of any robust alternative biomarker to predict which unresectable PDAC patients will benefit from intensive treatment versus those who might avoid futile toxicity, the critical research gap is to identify new biomarkers that can classify advanced PDAC patients into subgroups predictive of treatment response or prognosis. This gap is particularly pressing for unresectable PDAC, where inappropriate therapy can lead to significant toxicity without meaningful benefit. \<Metabolomic Alterations in PDAC\> Metabolomics offers a high-throughput and quantitative perspective on tumor phenotype, bridging genotype and clinical outcome. In particular, it presents a novel approach to address this gap by capturing global biochemical changes related to tumor presence, host metabolic response, and cachexia. Once validated, metabolic biomarkers could be translated into rapid and cost-effective assays. Notably, recent studies indicate that metabolite panels can detect PDAC with high accuracy (e.g., plasma metabolic signatures that distinguish PDAC from healthy controls, achieving an AUC of approximately 0.92) and can stratify patients by survival duration. These findings suggest that metabolomic profiling can provide prognostic insights beyond conventional markers. Regarding treatment response, preliminary metabolomic studies indicate that metabolic profiles can distinguish PDAC patients who respond to chemotherapy from those who exhibit disease progression. Recent plasma metabolomic analyses revealed that patients with progressive diseases during chemotherapy show distinct alterations (e.g., reduced amino acids with FOLFIRINOX and elevated glycolytic and bile acid metabolites with gemcitabine) compared to responders. These profiles suggest a potential stratification tool: participants' pretreatment metabolomic signatures could predict therapy efficacy or resistance. In Aim 1, the investigators will utilize ultra-performance LC-MS/MS to capture a wide range of metabolites in patient sera, aiming to identify metabolic subtypes of unresectable PDAC that correlate with survival or treatment response. This approach builds on emerging evidence that metabolomic profiling can refine PDAC classification and reveal novel therapeutic targets. \<Immune Profiling and Tumor Microenvironment\> PDAC is characterised by a dense desmoplastic stroma and an immunosuppressive tumor microenvironment (TME) that limits the effectiveness of conventional immunotherapy. While checkpoint inhibitors are effective in microsatellite-instability-high PDAC, most PDAC cases are refractory to immunotherapy due to factors such as low tumor immunogenicity and an abundance of suppressive myeloid cells. Recent comprehensive immune profiling has identified at least seven distinct immune subtypes of PDAC tumors, ranging from T cell-rich "inflamed" to immune-desert and various immunosuppressive phenotypes. Interestingly, one-third of patients exhibited a combined myeloid- and metabolite-driven immunosuppressive profile, while a minority (\~3%) had an immune-desert profile devoid of significant lymphocyte infiltration. These findings demonstrate that advanced PDAC is not a uniform entity; substantial immune heterogeneity may impact treatment responses. Peripheral immune cell populations can partially reflect the TME and thus serve as accessible biomarkers. In PDAC, elevated levels of myeloid-derived suppressor cells and regulatory T cells in circulation correlate with poorer prognosis, whereas higher numbers of effector T cells are associated with better outcomes. In Aim 3, the investigators will perform detailed immune profiling, including flow cytometry of peripheral blood mononuclear cells (PBMCs) to quantify T cell, B cell, NK cell, and myeloid subsets, as well as analysis of tumor tissue (where available) for immune infiltrates and checkpoint molecule expression. By integrating immune data with metabolomic and microbiome findings, the investigators aim to define composite biomarker signatures. The investigators anticipate that linking systemic immune features (e.g., cytokine levels or immune cell ratios) with metabolic and microbial markers will offer a holistic perspective on each patient's disease state. This integrated approach is innovative and may reveal mechanisms-such as metabolite-mediated immune suppression-that underlie therapy resistance \<Gut Microbiome and PDAC\> The gastrointestinal microbiome profoundly influences host metabolism and immunity and is increasingly implicated in pancreatic cancer progression. Bacteria can translocate from the gut to the pancreatic tumor microenvironment, modulating local immune responses and even the metabolism of chemotherapeutic agents. Notably, a high intratumoral microbial diversity has been correlated with prolonged survival in PDAC patients. For example, Riquelme et al. demonstrated that PDAC patients with a more diverse tumor microbiome exhibited significantly improved survival, whereas short-term survivors did not show a dominant microbial profile. Moreover, transplanting stool from long-term survivor PDAC patients into mice altered the tumor immune microenvironment and slowed tumour growth, suggesting a causal role of the gut microbiota in PDAC outcomes. The microbiome also produces metabolites (such as short-chain fatty acids, SCFAs) that can influence cancer biology. For instance, the bacterial metabolite butyrate has been observed to enhance anti-tumor immunity and chemosensitivity in PDAC models. Conversely, dysbiosis may create an immunosuppressive milieu that fosters therapeutic resistance. In Aim 2, the investigators will profile participants' gut microbiota through 16S rRNA sequencing to identify microbial signatures associated with the metabolic profiles (Aim 1) and clinical outcomes. By correlating microbial diversity and specific taxa with host metabolite patterns, the investigators will explore microbiome-metabolome interactions in PDAC. Identifying gut microbiome correlations with the PDAC phenotype may uncover potentially novel biomarkers (e.g., bacterial taxa or derived metabolites) and therapeutic targets (e.g., microbiota modulation) for advanced disease. \<Integrative multi-omics and research gap\> Each of the domains discussed - metabolism, immune microenvironment, and microbiome - offers a distinct perspective on tumour biology and has demonstrated potential as a source of biomarkers. Importantly, these domains are interconnected; they interact extensively. For instance, tumour metabolic byproducts can influence immune cell function, while gut microbes produce metabolites that circulate and impact both metabolism and immunity. Therefore, an integrative approach may provide deeper insights than any single modality alone. A recent integrative study in PDAC patients supports this idea: Guo et al. combined metabolomic and gut microbiota analyses and found notable differences between resectable and unresectable PDAC cases in both microbial composition and metabolite profiles. They identified key metabolites (such as certain fatty acids and carnitine derivatives) that were significantly altered in advanced disease, some of which correlated strongly with specific gut bacteria. Notably, several of these metabolites and microbes were linked to patient survival - for instance, higher levels of Faecalibacterium (a gut commensal) and certain short-chain fatty acids correlated with longer survival. This study illustrates the feasibility and value of an integrative metabolome-microbiome analysis in PDAC. It also indicates that multi-omics signatures can reflect disease aggressiveness and prognosis. Despite these insights, significant gaps remain. To date, no study has utilised an integrated approach combining metabolomics, immunology, and microbiome profiling specifically to stratify treatment responses in unresectable pancreatic cancer. Previous research either concentrates on a single domain or investigates broad disease differences (resectable versus advanced) instead of predicting therapy outcomes within an advanced PDAC cohort. Additionally, there is an absence of prospective studies that collect multi-omic data longitudinally alongside treatment, which is vital for creating clinically actionable stratification tools. This study directly addresses this shortcoming by undertaking a comprehensive multi-omic characterisation of unresectable PDAC patients in a prospective manner, with the explicit aim of identifying biomarkers that predict treatment efficacy or failure. By bridging this gap, the investigators intend to facilitate a more personalised approach to treating pancreatic cancer, advancing beyond the current empirical paradigm.