Faculty Bibliography

BACKGROUND & AIMS/OBJECTIVE:Pancreatic ductal adenocarcinoma (PDAC) has a complex tumor microenvironment enriched with tumor-associated macrophages. Triggering receptor expressed on myeloid cells 2 (TREM2) is highly expressed by a subset of macrophages in PDAC. However, the functional role of TREM2 in PDAC progression remains elusive. METHODS:) that enables the genetic depletion of TREM2 in the context of spontaneous PDAC development. Single-cell RNA-sequencing analysis was used to identify changes in the tumor immune microenvironment on TREM2 depletion. We evaluated the impacts of TREM2 depletion on the tumor immune microenvironment to elucidate the functions of TREM2 in macrophages and PDAC development. RESULTS:mice. Single-cell analysis revealed that TREM2 depletion enhanced proinflammatory macrophages and exacerbated pathogenic inflammation in PDAC. Specifically, TREM2 functions as a key braking mechanism for the NLRP3/nuclear factor-κB/interleukin (IL)-1β inflammasome pathway, opposing to microbial lipopolysaccharide as the key activator of this pathway. TREM2 deficiency orchestrated with microbial lipopolysaccharide to trigger IL-1β upregulation and pathogenic inflammation, thereby fueling PDAC development. Notably, IL-1β inhibition or microbiome ablation not only reversed the accelerated PDAC progression caused by TREM2 depletion, but also further inhibited PDAC progression in the TREM2-depleted context. CONCLUSIONS:TREM2 depletion accelerates tumor progression by enhancing proinflammatory macrophages and IL-1β-mediated pathogenic inflammation in PDAC. The accelerated tumor progression by TREM2 depletion can be reversed by blocking IL-1β-associated pathogenic inflammation.

BACKGROUND:Classification and risk stratification of pancreatic cysts are challenging because of limited radiographic and cytomorphologic features. Although molecular profiling has emerged as an ancillary test for pancreatic cyst fluid (PCF), additional high-sensitivity and -specificity biomarkers are still needed for improved classification. METHODS:In this study, PCF from 93 patients, including intraductal papillary mucinous neoplasms (n = 65), mucinous cystic neoplasms (n = 9), serous cystadenomas (n = 9), pancreatic cyst not otherwise specified (n = 8), and pseudocysts (n = 2), were evaluated for biomarkers. Molecular profiling by next-generation sequencing was performed, and a subset of the cases (n = 32) were interrogated with 2083 microRNAs (miRNAs) to evaluate their use for pancreatic cyst risk stratification. RESULTS:As independent PCF biomarkers in 32 cases with histologic diagnoses, three miRNAs performed significantly better than mutant KRAS, mutant GNAS, carcinoembryonic antigen (CEA), and serum carbohydrate antigen 19-9 (CA19-9) in discriminating high-risk from low-risk cysts. The three elevated miRNAs in combination with mutant KRAS, mutant GNAS, and serum CA19-9 displayed similar diagnostic performance (miR-4461: area under the curve [AUC], 0.950; 95% confidence interval [CI], 0.800-1; miR-6723-5p: AUC, 0.958; 95% CI, 0.850-1; miR-6755-3p: AUC, 0.942; 95% CI, 0.816-1) in discriminating high-risk from low-risk cysts, when compared to mutant KRAS, mutant GNAS, CEA, and serum CA19-9 (AUC, 0.950; 95% CI, 0.825-1). In the absence of CA19-9, the three-marker panel of KRAS, GNAS, and miRNAs showed marginally improved performance compared with KRAS, GNAS, and CEA, which highlights the potential utility of miRNAs as biomarkers in PCF analysis. CONCLUSIONS:These findings demonstrate that a multiomics biomarker approach with elevated PCF miRNAs with mutant KRAS, mutant GNAS, and serum CA19-9 may help in better detecting high-risk cysts for early clinical intervention.

Advances in spatial profiling have resulted in the generation of multi-omic atlases that span biological scales. In general, multiple workflows are required for image registration, coordinate registration, and spot deconvolution to integrate modalities. To improve the throughput of registration of multi-omic cohorts, we introduce PIVOT, a user-friendly and open-source interface for streamlined nonlinear registration. We demonstrate PIVOT's strengths through registration of three multi-omic datasets, and show comparison of its performance to existing workflows.

IMPORTANCE/UNASSIGNED:Neoadjuvant therapy (NT) is an increasingly used treatment strategy for patients with localized pancreatic ductal adenocarcinoma (PDAC). Little research has been conducted on cancer care delivery during NT, and the standards for optimal delivery of NT have not been defined. OBJECTIVE/UNASSIGNED:To develop consensus best practices for delivering NT to patients with localized PDAC. DESIGN, SETTING, AND PARTICIPANTS/UNASSIGNED:This study used a modified Delphi approach consisting of 2 rounds of voting, and a series of virtual conferences (from October to December 2023) to reach expert consensus on candidate best practice statements generated from a systematic review of the literature and expert opinion. An interdisciplinary panel was formed including 47 North American experts from surgical, medical, and radiation oncology, radiology, pathology, gastroenterology, integrative oncology, anesthesia, pharmacy, nursing, cancer care delivery research, and nutrition as well as patient and caregiver stakeholders. MAIN OUTCOME AND MEASURES/UNASSIGNED:Statements that reached 75% agreement or greater were included in final consensus statements. RESULTS/UNASSIGNED:Of the 47 participating panel members, 27 (57.64%) were male, and the mean (SD) age was 47.6 (8.2) years. Physicians reported completing training a mean (SD) 14.6 (8.6) years prior and seeing a mean (SD) 110.6 (38.4) patients with PDAC annually; 35 (77.7%) were in academic practice. Final consensus was reached on 82 best practices for delivering NT. Of these, 38 statements focused on pre-NT practices, including diagnosis and staging (n = 15), evaluation and optimization (n = 20), and decision-making (n = 3); 29 statements defined best practices during NT, including initiation (n = 3), delivery of therapy (n = 8), restaging practices (n = 12), and management of complications during NT (n = 6); and 15 best practices were identified to guide treatment post-NT, focusing on surgery (n = 7), pathology (n = 4), and follow-up (n = 3). CONCLUSIONS/UNASSIGNED:Using a modified Delphi consensus technique, best practice guidelines were developed focusing on the optimal standards for delivering NT to patients with localized PDAC. Given the prognostic importance of completing multimodality therapy, efforts to standardize and optimize the delivery of NT represent an immediate opportunity to decrease care variation and improve outcomes for patients with PDAC. Future research should focus on validating and implementing best practice standards into clinical practice.

Cancer-associated fibroblasts (CAFs) are a multifaceted cell population essential for shaping the tumor microenvironment (TME) and influencing therapy responses. Characterizing the spatial organization and interactions of CAFs within complex tissue environments provides critical insights into tumor biology and immunobiology. In this study, through integrative analyses of over 14 million cells from 10 cancer types across 7 spatial transcriptomics and proteomics platforms, we discover, validate, and characterize four distinct spatial CAF subtypes. These subtypes are conserved across cancer types and independent of spatial omics platforms. Notably, they exhibit distinct spatial organizational patterns, neighboring cell compositions, interaction networks, and transcriptomic profiles. Their abundance and composition vary across tissues, shaping TME characteristics, such as levels, distribution, and state composition of tumor-infiltrating immune cells, tumor immune phenotypes, and patient survival. This study enriches our understanding of CAF spatial heterogeneity in cancer and paves the way for novel approaches to target and modulate CAFs.

Normal aging alters brain functions and phenotypes. However, it is not well understood how astrocytes are impacted by aging, nor how they contribute to neuronal dysfunction and disease risk as organisms age. Here, we examine the transcriptional, cell biology, and functional differences in astrocytes across normal aging. Astrocytes at baseline are heterogenous, responsive to their environments, and critical regulators of brain microenvironments and neuronal function. With increasing age, astrocytes adopt different immune-related and senescence-associated states, which relate to organelle dysfunction and loss of homeostasis maintenance, both cell autonomously and non-cell autonomously. These perturbed states are increasingly associated with age-related dysfunction and the onset of neurodegeneration, suggesting that astrocyte aging is a compelling target for future manipulation in the prevention of disease.

In the current study, we assessed whether repeated measurements of a panel of protein biomarkers with relevance to pancreatic ductal adenocarcinoma (PDAC) improves lead time performance for earlier detection over a single timepoint measurement. Specifically, CA125, CEA, LRG1, REG3A, THBS2, TIMP1, TNRFSF1A as well as CA19-9 were assayed in serially collected pre-diagnostic plasma from 242 PDAC cases and 242 age- and sex-matched non-case control participants in the PLCO cohort. We compared performance estimates of a parametric empirical Bayes (PEB) algorithm, which incorporates participant biomarker history, to that of a single-threshold (ST) method. We demonstrated improvements in AUC estimates (2-13 %) for all biomarkers when considering the PEB approach compared to ST. For CA19-9, the PEB^CA19-9 yielded an AUC of 0.88 when at least one repeat measurement was within 3 years of clinical diagnosis. At a specificity of 98.5 %, the PEB^CA19-9 identified 15 of the 41 PDAC cases and signaled positive at an average lead-time of 1.09 years whereas the ST approach captured 11 of the 41 PDAC cases with an average positive signal at 0.48 years. Among CA19-9 low individuals, a PEB algorithm based on repeat measurements of TIMP1 yielded an additional 14 % sensitivity at 98.5 % specificity. An adaptive algorithm that considers repeated CA19-9 measurements improves sensitivity and lead-time detection of PDAC compared to a single-threshold method. Additional protein biomarkers may improve sensitivity for earlier detection of PDAC among cases with low CA19-9.

Glioblastoma (GBM) is the most aggressive primary brain tumor in adults, characterized by a dismal prognosis and limited therapeutic options. Its highly invasive nature and pronounced intratumoral heterogeneity underscores the urgent need for innovative and targeted therapeutic strategies. One promising approach is synthetic lethality, which exploits cancer-specific genetic vulnerabilities to selectively eliminate tumor cells. A well-characterized example involves the deletion of methylthioadenosine phosphorylase (MTAP), commonly observed in GBM and other malignancies. This review focuses on synthetic lethality targeting protein arginine methyltransferase 5 (PRMT5) in MTAP-deleted GBM. Loss of MTAP leads to the accumulation of methylthioadenosine (MTA), a metabolite that partially inhibits PRMT5, thereby creating a selective vulnerability to PRMT5 inhibition which is used to inhibit the residual function of PRMT5. We critically evaluate preclinical and clinical data on both first- and second-generation PRMT5 inhibitors, with particular emphasis on MTA-cooperative compounds that selectively exploit MTAP deficiency. Despite promising anti-tumor activity in vitro, the clinical efficacy of PRMT5 inhibitors is often limited by the tumor microenvironment, particularly the impact of non-malignant cells that attenuate drug activity. Finally, we explore rational combination strategies that integrate PRMT5 inhibition with existing therapies to enhance clinical outcomes in GBM.