Hollingsworth, Michael A.

Abstract Purpose: The Pancreatic Cancer Detection Consortium (PCDC) performed a blinded Early Detection Research Network–defined phase II biomarker bakeoff study of blood-based biomarker panels. The aims were to evaluate panel performance, to compare the panels’ performance with that of cancer antigen 19-9 (CA19-9) alone, and to evaluate the performance of new combinations of the individual biomarkers. Experimental Design: Ten biomarkers representing eight biomarker panels and CA19-9 were evaluated using plasma, serum, and germline DNA from 140 stage I to IV pancreatic ductal adenocarcinoma (PDAC) cases and 140 controls from three tertiary care institutions, with controls frequency matched to cases on age and sex. LASSO regression was employed to explore new biomarker combinations. The primary metric was area under the receiver operating characteristic curve (AUC). Results: The study population was 51% female, with median age 67.3 (minimum: 45, maximum: 90) years. Biomarker panel AUCs ranged from 89.9 to 96.3; the AUC for serum CA19-9 alone was 91.7 [95% confidence interval (CI), 87.8–95.6]. Two panels had significantly higher AUCs than serum CA19-9 alone, the CA19-9/FUT2/3 panel (AUC = 96.3, P = 0.002), and the tissue factor pathway inhibitor/tenascin C (TFPI/TNC-FNIII-C) panel (AUC = 95, P = 0.01). Exploratory models to recombine biomarkers retained all but two biomarkers [optimism-corrected AUC = 96.4 (94–98.9)]. Conclusions: The CA19-9/FUT2/3 panel was the best performing panel in this biomarker bakeoff. Its evaluation in larger studies is warranted. Biomarker bakeoffs are an effective strategy for comparing the performance of promising biomarkers for pancreatic cancer early detection, and the PCDC is well poised to conduct such studies. Recommendations for performing such studies are provided.

Abstract Objective: The Pancreatic Cancer Detection Consortium (PCDC) performed a blinded EDRN defined phase 2 Biomarker Bakeoff study of eight promising biomarker panels using pancreatic cancer ductal adenocarcinoma (PDAC) case and general population healthy control samples from three institutions. The scientific questions were to determine whether panels could discriminate PDAC cases from controls with Receiver Operating Characteristic Area Under the Curve (AUC) > 0.8, compare panel performance head-to-head via 95% confidence intervals (CI), and determine whether the panel improved upon serum CA19-9 alone. A secondary goal was to perform modeling to discover a new panel consisting of individual biomarkers. Methods: Eligible biomarkers were required to have had a prior EDRN defined phase 2 validation study published and be ready for validation in a sample set from multiple institutions. Three institutions submitted a total of 140 cases and 140 controls frequency matched to cases on age and sex. Four consortium member institutions submitted for validation eight panels consisting of ten plasma, serum and DNA genotyping biomarkers in addition to CA19-9. Locked panel formulas were submitted prior to specimen distribution. The primary metric was AUC with bootstrapped 95% CIs. LASSO penalized regression was used for exploratory panel building. Nested cross validation and internal-external validation were performed, and optimism-corrected performance estimates were computed. Results: The overall sample (N=280) was 51% female, median age of 67 years, and 94% were of non-Hispanic white race and ethnicity. Controls had median BMI 27.3, 56% never smokers, and 12% type II diabetes mellitus (DM2); cases (staged I to IV) had median BMI 26.6, 42% never smokers, and 26% DM2. Overall panel AUC estimates ranged from 89.9 to 96.3 and all were significantly larger than the null hypothesis value of 0.8 (p<0.05). The apparent serum CA19-9 AUC was 91.7 (87.8, 95.6). Two panels were significantly different from serum CA19-9, a panel with FUT2/3 genotype with serum CA19-9 (p=0.002) and a panel containing tissue factor pathway inhibitor (TFPI), tenascin C (TNC), and plasma CA19-9 (p=0.01). In subset analyses by PDAC stage, no panel improved upon the null hypothesis value of 0.8 in stage I (p>0.05), all panels improved upon the null hypothesis value of 0.8 in stage II and III (p<0.05), and only serum CA19-9 did not improve upon the null hypothesis value of 0.8 in stage IV. All biomarkers but two microRNAs were retained in a LASSO model that had optimism corrected AUC=96.4 (95% CI: 94.0, 98.9). Conclusions: All submitted panels performed well overall in this blinded study, with the panel containing FUT2/3 genotype performing the best. Panel discovery improved upon serum CA19-9, though optimism corrected AUC 95% CIs overlap with those of apparent CA19-9 performance. Further work is needed to prioritize promising biomarkers and to improve detection of early stage PDAC. Citation Format: Ann L. Oberg, William R. Bamlet, Grant Izmirlian, Seetharaman Balasenthil, Surinder K. Batra, Masataka Hayashi, Daniel Herman, Michael A. Hollingsworth, Maneesh Jain, Ann M. Killary, Brianna M. Krusen, Suyu Liu, Gopalakrishnan Natarajan, Subrata Sen, Lynette M. Smith, Sudhir Srivastava, Brian M. Wolpin, Kenneth S. Zaret, Michael G. Goggins. Pancreatic Cancer Detection Consortium biomarker bake-off: A consortium Phase 2 blinded biomarker validation and panel discovery study [abstract]. In: Proceedings of the AACR Special Conference in Cancer Research: Advances in Pancreatic Cancer Research—Emerging Science Driving Transformative Solutions; Boston, MA; 2025 Sep 28-Oct 1; Boston, MA. Philadelphia (PA): AACR; Cancer Res 2025;85(18_Suppl_3):Abstract nr B070.

Cancer cachexia is a multifactorial syndrome involving muscle and fat wasting, inflammation, and metabolic dysfunction. Across cancer subtypes, pancreatic cancer has one of the highest cachexia incidence rates at ∼80%. Given the advanced age of most pancreatic cancer patients, we sought to query cancer-associated muscle wasting using an age-matched murine model. We found that histamine and histamine decarboxylase (HDC) activity were specifically elevated in the muscles of aged tumor-bearing mice. We further found that (1) wasting stimuli induced histamine production and enhanced HDC activity; (2) exogenous histamine was sufficient to induce atrophy-associated gene expression; (3) inhibition of HDC activity by α-fluoromethylhistidine (FMH) protected against atrophy; (4) treatment of tumor-bearing mice with FMH rescued muscle wasting; and (5) a calcineurin inhibitor was able to rescue histamine-associated increases in calcium/atrogene signaling. In summary, we present a novel metabolic pathway that has significant implications for the treatment of cachectic cancer patients.

Abstract A C57BL/6 mouse transgenic for human MUC1 (MUC1.Tg) was developed to evaluate MUC1-specific tumor immunity in an animal that expresses MUC1 as a normal self protein. Previous studies showed that MUC1.Tg mice, challenged with syngeneic tumors expressing MUC1 (B16.MUC1), developed progressively growing MUC1-positive tumors, whereas wild-type C57BL/6 (wt) mice developed MUC1-negative tumors at a significantly slower rate. The results of a limiting dilution CTL frequency assay were not informative, in that similar numbers of MUC1-specific CTL precursors (CTL) were detected in MUC1.Tg and wt mice. Tumor immunity in vivo was characterized by an adoptive transfer method to evaluate the degree of MUC1 or non-MUC1 tumor immunity in wt or MUC1.Tg mice. The results revealed that wt mice developed protective tumor immunity mediated by MUC1-specific CD4+ lymphocytes, while MUC1.Tg mice were functionally tolerant to MUC1 in vivo. The potential of adoptive immunotherapy to provide immunity to tumors expressing MUC1 and to produce undesirable autoimmunity in recipient MUC1.Tg mice expressing MUC1 as a self Ag was evaluated. Adoptive transfer of immune cells from wt mice primed in vivo with B16.MUC1 tumor cells into MUC1.Tg recipients resulted in significant increases in the survival of MUC1.Tg recipients compared with unmanipulated control MUC1.Tg mice challenged with B16.MUC1 tumor cells. This response was specific for MUC1 since control tumors developed at equivalent rates in recipient or control MUC1.Tg mice. No gross or histologic evidence of autoimmunity was observed in recipient MUC1.Tg mice, indicating that tumor immune responses mediated by MUC1-specific CD4+ lymphocytes spare nontransformed epithelia-expressing MUC1.

As the most lethal major cancer, pancreatic cancer is a global healthcare challenge. Personalized medicine utilizing cutting-edge multi-omics data holds potential for major breakthroughs in tackling this critical problem. Radiomics and deep learning, two trendy quantitative imaging methods that take advantage of data science and modern medical imaging, have shown increasing promise in advancing the precision management of pancreatic cancer via diagnosing of precursor diseases, early detection, accurate diagnosis, and treatment personalization and optimization. Radiomics employs manually-crafted features, while deep learning applies computer-generated automatic features. These two methods aim to mine hidden information in medical images that is missed by conventional radiology and gain insights by systematically comparing the quantitative image information across different patients in order to characterize unique imaging phenotypes. Both methods have been studied and applied in various pancreatic cancer clinical applications. In this review, we begin with an introduction to the clinical problems and the technology. After providing technical overviews of the two methods, this review focuses on the current progress of clinical applications in precancerous lesion diagnosis, pancreatic cancer detection and diagnosis, prognosis prediction, treatment stratification, and radiogenomics. The limitations of current studies and methods are discussed, along with future directions. With better standardization and optimization of the workflow from image acquisition to analysis and with larger and especially prospective high-quality datasets, radiomics and deep learning methods could show real hope in the battle against pancreatic cancer through big data-based high-precision personalization.

Abstract Adenosquamous cancer of the pancreas (ASCP) is a subtype of pancreatic cancer that has a worse prognosis and greater metastatic potential than the more common pancreatic ductal adenocarcinoma (PDAC) subtype. To distinguish the genomic landscape of ASCP and identify actionable targets for this lethal cancer, we applied DNA content flow cytometry to a series of 15 tumor samples including five patient-derived xenografts (PDX). We interrogated purified sorted tumor fractions from these samples with whole-genome copy-number variant (CNV), whole-exome sequencing, and Assay for Transposase-Accessible Chromatin using sequencing (ATAC-seq) analyses. These identified a variety of somatic genomic lesions targeting chromatin regulators in ASCP genomes that were superimposed on well-characterized genomic lesions including mutations in TP53 (87%) and KRAS (73%), amplification of MYC (47%), and homozygous deletion of CDKN2A (40%) that are common in PDACs. Furthermore, a comparison of ATAC-seq profiles of three ASCP and three PDAC genomes using flow-sorted PDX models identified genes with accessible chromatin unique to the ASCP genomes, including the lysine methyltransferase SMYD2 and the pancreatic cancer stem cell regulator RORC in all three ASCPs, and a FGFR1-ERLIN2 fusion associated with focal CNVs in both genes in a single ASCP. Finally, we demonstrate significant activity of a pan FGFR inhibitor against organoids derived from the FGFR1-ERLIN2 fusion–positive ASCP PDX model. Our results suggest that the genomic and epigenomic landscape of ASCP provide new strategies for targeting this aggressive subtype of pancreatic cancer. Significance: These data provide a unique description of the ASCP genomic and epigenomic landscape and identify candidate therapeutic targets for this dismal cancer.

Abstract MUC1 is a heterodimeric transmembrane glycoprotein that is overexpressed and aberrantly glycosylated in ductal adenocarcinomas. Differential phosphorylation of the MUC1 cytoplasmic tail (MUC1CT) has been associated with signaling events that influence the proliferation and metastasis of cancer cells. We identified a novel tyrosine phosphorylation site (HGRYVPP) in the MUC1CT by mass spectrometric analysis of MUC1 from human pancreatic adenocarcinoma cell lines. Analyses in vitro and in vivo showed that platelet-derived growth factor receptor β (PDGFRβ) catalyzed phosphorylation of this site and of tyrosine in the RDTYHPM site. Stimulation of S2-013.MUC1F cells with PDGF-BB increased nuclear colocalization of MUC1CT and β-catenin. PDGF-BB stimulation had no significant effect on cell proliferation rate; however, it enhanced invasion in vitro through Matrigel and in vivo tumor growth and metastases. Invasive properties of the cells were significantly altered on expression of phosphorylation-abrogating or phosphorylation-mimicking mutations at these sites. We propose that interactions of MUC1 and PDGFRβ induce signal transduction events that influence the metastatic properties of pancreatic adenocarcinoma. [Cancer Res 2007;67(11):5201–10]