Early neoplastic lesions of the pancreas: initiation, progression, and opportunities for precancer interception

Pancreatic ductal adenocarcinoma (PDAC) is known to progress from one of two main precursor lesions: pancreatic intraepithelial neoplasia (PanIN) or intraductal papillary mucinous neoplasm (IPMN). The poor survival rates for patients with PDAC, even those diagnosed with localized disease, highlight the need for pancreatic cancer interception at the precursor stage. Although their basic biological drivers are well characterized, practical strategies for PanIN and IPMN interception remain elusive due to difficulties with detection, risk stratification, and low-morbidity intervention. Recently, advances in liquid biopsy, spatial multiomics analysis, and machine learning technology have provided deeper understanding of the molecular landscapes underlying pancreatic precursor development and progression. In this Review, we outline the different histologic phenotypes, clinical characteristics, and neoplastic cell–intrinsic and –extrinsic drivers of PanINs and IPMNs, with particular focus on current and potential future opportunities for pancreatic precancer interception.

PanINs, first described by Hulst over 100 years ago (10), are microscopic neoplastic lesions that arise in pancreatic ducts, characterized by mucinous epithelium with varying degrees of dysplasia. PanINs are currently defined as being smaller than 0.5 cm in the longest dimension on a standard histological section; recent 3D reconstructions of human pancreatic tissue challenge the validity of this definition, showing that the complex, branching architecture of PanINs (Figure 1) makes it difficult to accurately determine their size and spatial orientation in standard 2D sections (8, 11). The cell of origin of PanINs is still a topic of debate, although different studies have shown that PanINs can arise from both preexisting ductal cells, or from foci of acinar-to-ductal metaplasia (ADM). Recent evidence predominantly supports the latter, with epigenetic profiling indicating that PanINs possess an intermediate methylation profile that falls between normal ducts and PDAC and is more similar to acinar cells than ductal cells (12–16). Mouse models have further defined two distinct pathways for PDAC initiation: one in which PDAC arises from acinar cell–derived PanINs, and another wherein ductal epithelial cells give rise to PDAC directly without a PanIN intermediate (17–24).

Figure 1

PanIN and IPMN anatomy and histological features. (A) PanINs arise from small branches of the pancreatic ductal system and/or foci of acinar-to-ductal metaplasia. Representative H&E cross-sectional images of progression from low-grade to high-grade dysplasia are shown. (B and C) IPMNs involve the main pancreatic duct (most commonly intestinal or pancreatobiliary types) or side-branch ducts (most commonly gastric type). Intestinal and pancreatobiliary-type IPMNs are more likely to display high-grade dysplasia, while gastric IPMNs more commonly show low-grade dysplasia. Representative H&E cross-sectional images of the three histologic subtypes are shown. Scale bars: 300 μm (A) and 200 μm (B and C).

Current classifications, based on the 2014 Baltimore Consensus Meeting for Neoplastic Precursor Lesions in the Pancreas, separate PanINs into low-grade (LG-PanIN) and high-grade (HG-PanIN), with the LG category encompassing lesions historically classified as intermediate-grade (25). Histologic features distinguishing LG-PanIN from non-neoplastic ducts include papillary or micropapillary architecture, columnar epithelium with intracytoplasmic mucin, and cytologic atypia ranging from absent to mild (including loss of nuclear polarity, nuclear enlargement, hyperchromasia, or pseudostratification). By contrast, HG-PanIN displays more complex papillary architecture with possible cribriforming or tufting, along with severe cytologic atypia and mitotic activity (Figure 1) (25). Despite the commonly held theory that PanINs increase linearly with age, recent studies, notably including a cohort of presumed normal donor pancreata, have demonstrated that PanINs are very common even in younger individuals (9). Furthermore, in a cohort of 46 slabs of normal pancreas assessed via 3D modeling, Braxton et al. found that LG-PanINs were highly prevalent across the study population and within individual samples, with a mean of 13 PanINs per cubic centimeter of normal pancreas (8, 9). Importantly, although approximately 85% of PDACs are estimated to arise from PanINs based on histological observations and genetic analyses (26, 27), the vast majority of LG-PanINs never progress to HG-PanIN or PDAC (28, 29). By contrast, HG-PanINs are far rarer (seen in less than 5% of benign pancreata) and are often found in association with invasive PDAC (25, 30–32). Notably, studies involving high-risk individuals (HRIs) have demonstrated a higher overall PanIN prevalence with more frequent HG lesions. Indeed, one study showed a greater than 17-fold increase in mean PanIN lesions in benign pancreata from HRIs compared with age-matched controls; another revealed a nearly 3-fold increase in overall PanINs, and a nearly 5-fold increase in HG-PanINs, in pancreata from patients with familial versus sporadic pancreatic cancer (33, 34).

The basic biology of PanINs has been extensively characterized, with KRAS hotspot mutations recognized as a virtually universal early event (Figure 2) (35). Identification of specific KRAS variants can therefore aid in differentiating PanINs within the same specimen, although there is some evidence that a single PanIN can arise polyclonally (8). In the rarer cases of KRAS wild-type PanINs, alternative early driver mutations include GNAS or BRAF (35). Progression to HG-PanIN is associated with CDKN2A and/or TP53 inactivation, while inactivation of SMAD4 is typically seen in the context of associated PDAC and is only rarely found in isolated HG-PanIN (36). While some studies have shown SMAD4 loss in HG-PanINs, this is considered a late event in PanIN progression. These cases also most commonly contain invasive PDAC, raising the possibility of cancerization of ducts mimicking HG-PanIN (37, 38). Due to the microscopic nature of PanINs, molecular characterization has historically been through bulk collection (e.g., laser capture microdissection) and subsequent genomic and transcriptomic analyses. Bulk characterization is effective in defining overall genetic profiles of PanIN lesions and performing larger-scale comparisons between multiple PanINs and/or invasive PDAC within single specimens. However, this approach largely precludes a more nuanced investigation of the spatially and temporally defined genetic events that drive progression of a fraction of LG-PanINs.

Figure 2

Drivers of pancreatic precancer progression. Schematic of known neoplastic cell–intrinsic and –extrinsic drivers of pancreatic precursor lesion progression from initiation to invasive disease, based on data from studies involving PanINs and IPMNs. (A) Early driver mutations are found in oncogenes (most commonly KRAS), while later driver mutations occur in tumor suppressor genes. Gene expression drivers of IPMN progression include decreased exocrine function and increased basal-like expression programs, with increased expression of specific genes such as TFF1 that are associated with PanIN progression. Tumor suppressor promoter hypermethylation is evident even in low-grade lesions, with gradual increases with increasing grade. Chromosomal instability is evident in high-grade lesions but shows an abrupt increase in invasive disease. (B) Immune surveillance decreases and the microenvironment becomes more immunosuppressive with increasing grade, including increased expression of checkpoint markers and higher density of regulatory T cells.

Recently, advances in spatial transcriptomics and other similar techniques have allowed for more precise correlation between molecular changes and specific cellular phenotypes, as well as consideration of such changes in the context of the tissue microenvironment. Notably, Carpenter et al. found that within a cohort of donor pancreata, sporadic PanINs show predominantly similar gene expression profiles compared to established signatures from PDAC and PDAC-associated PanIN, an indication that even LG-PanIN lesions may have already acquired many features of malignancy (9). Bell et al. demonstrated a defined, gradual increase in expression of TFF1, a gene known to be overexpressed in both PanIN and PDAC, as LG-PanIN progresses to HG-PanIN (16). Furthermore, they showed that progression of HG-PanIN to PDAC is accompanied by increased proliferative capacity and decreased cancer-associated fibroblast–related (CAF-related) inflammatory signaling (16). At the DNA level, multiple studies have demonstrated a relatively abrupt, late accumulation of chromosomal changes as LG-PanIN progresses to HG-PanIN and PDAC, often including at least one chromothripsis event (8, 36, 39–41). As discussed above, methylation profiling of PanINs indicates an intermediate epigenetic state between normal ducts and PDAC, with more similarities to acinar cells than ductal cells. This suggests that epigenetic priming may help to facilitate progression from PanIN to PDAC, and it further supports the hypothesis that PanIN arises in the context of ADM (14, 16). Specifically, increased promoter hypermethylation in tumor suppressor genes, including RPRM, SARP2, and NPTX2, among others, has been correlated with increasing PanIN grade (42).

Aside from cell-intrinsic factors, the surrounding tissue microenvironment and immune cell milieu are perhaps equally as important to the initiation and progression of pancreatic precursor lesions. The ability of neoplastic cells to evade the host immune response has been recognized as a hallmark of cancer for over a decade (43, 44), yet challenges remain in characterizing the immune cell environment for different precancers and cancer types. This is particularly true for PanINs, given their small size and complex architecture. However, recent advances in machine learning, 3D tissue modeling, and spatial multiomics techniques have allowed for more holistic quantification of the immune cell response to PDAC precursors at various stages along their progression. Bell et al. applied a novel pipeline to a cohort of matched LG- and HG-PanINs and found that even LG lesions are accompanied by CAF subtypes typically seen in the PDAC microenvironment, including myofibroblastic CAFs, inflammatory CAFs, and antigen-presenting CAFs (16). This suggests that protumorigenic remodeling of the tumor microenvironment begins even in early precursor lesions (16). They further showed that the CAF-related inflammatory signaling in PanINs decreases during PDAC invasion and is accompanied by increased proliferation-related signaling, consistent with the densely fibrotic and immunosuppressive microenvironment that is characteristic of PDAC (45–49). Carpenter et al. used multiplex immunofluorescence to quantify the PanIN microenvironment in otherwise normal pancreata, finding that the stroma associated with PanINs preferentially contained myeloid cells, CD4⁺ T cells, activated fibroblasts, and collagen, compared with normal acinar tissue (9). Importantly, PanINs lacked regulatory T cells (Tregs), whereas invasive PDAC was found to be rich in Tregs, suggesting that immune surveillance in precursor lesions gives way to a more immunosuppressive microenvironment at the later stages of neoplastic progression (9).

IPMNs, as the most common pancreatic cyst and second most common PDAC precursor lesion (believed to give rise to <10% of PDACs overall) (7, 50), present distinct molecular characteristics and clinical implications. While substantial histologic overlap exists between PanINs and IPMNs, IPMNs overall are larger (>1 cm), typically more architecturally complex, and subclassified by location (main duct, branch duct, or mixed) and histologic subtype (gastric, intestinal, or pancreatobiliary) in addition to grade of dysplasia (HG or LG, analogous to PanIN; Figure 1) (25). Although the vast majority of IPMNs do not progress to PDAC, with overall risk of malignant transformation estimated at 33 per 100,000 cases, main-duct IPMNs carry up to 65% risk of malignant transformation (51). Gastric-type IPMNs are most commonly LG and derived from branch ducts, while intestinal and pancreatobiliary IPMNs more often involve the main duct and display HG dysplasia (52).

The macroscopic size of IPMNs allows for radiographic detection and risk stratification prior to potential surgical resection. While larger cysts can produce clinical symptoms leading to diagnosis, IPMNs are most often discovered incidentally on CT or MRI scans (52, 53). Indications for IPMN resection, rather than active surveillance, include main duct dilation, presence of mural nodule(s) or an associated mass, malignant cytology, or patient symptoms such as jaundice or clinical pancreatitis (52, 54). Larger cysts (>3 cm) without additional high-risk features are often approached with shorter-interval follow-up radiologic screening, transitioning to standard-frequency screening if no significant increase in size is observed (52, 54). Additional clinical history, such as family history of IPMN or PDAC, or known germline mutations associated with higher PDAC risk, are also considered when deciding whether and how frequently to screen for IPMNs; notably, a retrospective study by Skaro et al. demonstrated that patients with certain PDAC-associated germline mutations have increased risk of concurrent invasive carcinoma within IPMN resection specimens (55). Genetic predisposition is believed to contribute to IPMN development, given the frequent multifocality of IPMNs, tendency for additional IPMNs to arise in the remnant pancreas following resection, and increased risk for extrapancreatic neoplasia among IPMN patients (56–60).

Like PanINs, the majority of IPMNs (60%–80%) harbor KRAS hotspot driver mutations. However, other early IPMN driver mutations in genes such as GNAS (50%–70% of IPMNs) and KLF4 (21%–53% of IPMNs) are much rarer in PanINs (Figure 2) (30, 61, 62). Additional changes in RNF43, CDKN2A, and TP53 are believed to arise later in IPMN progression to HG dysplasia or invasive disease. Notably, GNAS and RNF43 changes are typical of PDAC arising from IPMN, but not of PDAC that lacks an associated IPMN (30). The specificity of GNAS mutations for IPMNs is especially useful in cyst fluid analysis, where they are considered diagnostic for IPMN (30). Interestingly, genomic analysis of entire IPMNs has shown that driver mutations in certain genes, including KLF4 and RNF43, can lead to clonal expansion within only LG lesions, while these clones are then selected against during IPMN progression (61, 63). Instead, HG lesions display multiple mutations in later driver genes, thereby suggesting a model of IPMN progression that combines early clonal selection with later convergent evolution (61, 63, 64). As with PanINs, epigenetic changes are also thought to contribute to IPMN progression. One study demonstrated increased promoter hypermethylation of the tumor suppressor genes ADAMTS1, BNC1, and CACNA1G in IPMN-advanced neoplasia versus LG-IPMNs (65).

The IPMN microenvironment displays stark differences between LG and HG or invasive lesions. Hernandez et al. applied multiplex immunofluorescence to human IPMN tissue to demonstrate that immune cell infiltration decreases as lesions progress from LG to HG to PDAC (66). Indicators of ongoing immune surveillance, including activated B cells and cytotoxic and memory T cells, were found at higher density in LG-IPMNs, while decreased immune surveillance was evident in certain LG-IPMNs that eventually progressed to HG-IPMN. This contrast highlights the potential role of immune infiltration in preventing IPMN progression (66). These findings are consistent with those from Roth et al., who performed comprehensive IHC assays across a range of human IPMN resection specimens to show that the active, T cell–rich microenvironment of LG-IPMNs becomes immunosuppressive and Treg dominant in invasive PDAC (67). Similarly, Jamouss et al. recently showed via IHC that progression to HG-IPMNs is accompanied by fewer cytotoxic T cells, increased macrophage density (specifically in HG foci), and overall increased expression of immune checkpoint markers, including PD-L1, TIM3, and VISTA (68). Overall, these findings suggest a potential role for immunomodulatory approaches to IPMN interception, taking advantage of the proimmunogenic microenvironment that is characteristic of LG lesions.

Given the relative rarity of PDAC among the general population (13.5 estimated new cases per 100,000 individuals in the United States per year) (4), routine screening is not recommended (69). Instead, detection of PDAC or precursor lesions is either triggered by patient symptoms or occurs incidentally via unrelated radiographic studies. However, certain HRIs, defined either by family history (≥2 first-degree relatives with PDAC) or germline mutations in certain DNA repair–related genes, including BRCA1/2, PALB2, and ATM, are known to develop PDAC at higher rates than the general population (70). Depending on the clinical scenario, the American Gastroenterological Association recommends surveillance screening via MRI and/or EUS beginning at either 40 years, 50 years, or 10 years younger than the initial familial age of onset (69). These recommendations cite studies showing a significant survival benefit of PDAC screening in HRIs, although there is also a significant risk of morbidity from subsequent surgical procedures, and current radiological techniques are largely unable to detect PanIN lesions. While few pancreatic precancer interception strategies are currently employed, new advances in high-risk biomarker identification have already begun to inform numerous promising avenues.

PanIN interception. The current body of knowledge regarding PanIN progression suggests multiple strategic approaches for PanIN interception (Figure 3). Importantly, modeling studies using sequencing data from PanINs and associated PDAC estimate a substantial time interval, ranging from approximately 4 to 12 years, for PDAC development from either the common ancestral cell or from a HG-PanIN precursor (29, 71), providing a crucial window of time for PDAC interception. Given the heavy predominance of KRAS mutations in LG-PanINs, one theoretical approach involves using anti-KRAS therapy to target PanINs before they can progress to HG lesions or PDAC. Indeed, clinical trials are currently ongoing to evaluate the efficacy of KRAS-targeted vaccine-based therapy in pancreatic and other cancers (72, 73). A recent phase I clinical trial study also demonstrated the safety and immunogenicity of a mutant KRAS–targeted vaccine in HRIs prior to PDAC development, highlighting its promise as a novel strategy for precancer interception (74). Furthermore, considerable progress has been made in the development of targeted KRAS inhibitors in recent years, with two KRAS inhibitors already approved by the Food and Drug Administration for use in non–small cell lung cancer, and multiple others in clinical trials (75, 76). However, these therapies have been primarily investigated in the setting of invasive disease, rather than precancer. Ultimately, the practical utility of such a wide-net approach will be dictated by the presence and severity of therapy-related side effects, given the relatively high prevalence of LG-PanINs and rarity of progression to HG-PanIN. For interventions with higher cost and/or potential side-effects, a more logical inflection point to target is the transition from LG- to HG-PanIN, given the presumed large proportion of HG-PanINs that progress to invasive PDAC (29, 30). However, this approach inherently requires a clinically feasible detection strategy, including a measurable, specific diagnostic biomarker of HG-PanIN that has not yet progressed to PDAC.

Figure 3

Current and potential opportunities for pancreatic precancer interception. Schematic of current and theoretical strategies for PanIN and IPMN interception, categorized by lesion type and role in interception (diagnosis or intervention). PanIN detection strategies are limited but include certain imaging features and pancreatic juice analysis. Current IPMN detection strategies include imaging and cyst fluid analysis, with multiple composite biomarkers for risk stratification recently becoming available. Liquid biopsy analysis of peripheral blood has not been thoroughly studied for precursor lesions but is theoretically applicable. Potential intervention strategies for both PanINs and IPMNs include surgical resection, immune-based therapies, or KRAS-targeted therapies.

Although radiographic screening is useful for IPMNs, as discussed in detail later, radiology has not yet proven to have a consequential role in PanIN diagnosis. However, multiple studies have demonstrated a reproducible histologic pattern of lobulocentric atrophy associated with PanINs, particularly in HRIs for whom PanIN burden is often higher, that is detectable by EUS (6, 34). Furthermore, Kiemen et al. leveraged CODA, a novel histology- and machine learning–based pipeline for 3D tissue reconstruction, and subsequent re-review of prior CT scans, to demonstrate that larger PanIN lesions are in fact radiographically visible (11). It is worth noting, however, that many of these lesions may technically exceed the 1.0 cm definition for PanINs when considering their longest 3D axis (11). In recent years, multiple studies have attempted to define radiographic criteria for identification of HG-PanIN. CT and/or MRI features found to be associated with HG-PanIN include abrupt main pancreatic duct changes with distal pancreatic atrophy, enhancing mural nodules 5 mm or larger, and retention cysts/microcysts, although data are conflicting about the ability of the latter to distinguish between LG- and HG-PanIN (77–79). However, these studies are specifically performed in patients with concomitant IPMNs or other pancreatic tumors necessitating imaging studies, potentially limiting the generalizability of their findings for isolated HG-PanIN screening. Given the lack of symptoms associated with PanIN lesions, the patient population most likely to benefit from a possible radiographic screening approach are HRIs for whom routine screening is already recommended (69).

Noninvasive or minimally invasive methods for PDAC screening are also becoming more widely studied and utilized, and these could provide an alternative approach for detection and interception of HG-PanIN. Isolation and analysis of tumor cells or cell-free tumor DNA, RNA, proteins, or extracellular vesicles in circulation or other bodily fluids, also known as liquid biopsy, is a rapidly expanding field for the diagnosis and surveillance of a variety of tumor types, including PDAC (80, 81). These approaches carry multiple advantages, including minimizing potential surgical or procedural complications (80). However, many liquid biopsy techniques are still in the process of being validated and standardized for use in clinical settings, and downstream analysis and diagnosis is limited by the availability of known biomarkers for the disease of interest and sensitivity of testing methods. In addition, cell-free DNA (cfDNA) detection in peripheral blood is likely only applicable in cases of invasive disease where tumor cells have reached the bloodstream, and even then, the levels of detectable cfDNA in early invasive PDAC are typically low (81, 82). Detection of non-invasive precursor lesions would instead require more localized methods, such as pancreatic juice or cyst fluid analysis. While cyst fluid analysis is feasible for IPMNs, the small size of PanINs likely prohibits such an approach. Studies have shown that KRAS and TP53 mutations are detectable in pancreatic juice obtained from the duodenum in patients with PDAC during EUS surveillance (83–88). Importantly, even patients with HG dysplasia as their highest-grade lesion have been found to harbor higher concentrations of mutant TP53 and SMAD4 in pancreatic juice, highlighting the potential utility of this approach in detecting HG precursor lesions (89). However, at present, the lack of defined, sensitive biomarkers, and paucity of clinical validation, limits the utility of liquid biopsy as a screening method for HG-PanIN (90, 91). Moving forward, integration of novel tissue modeling techniques and spatial molecular analysis will ideally allow for characterization of precise, detectable biomarkers for preinvasive HG-PanIN lesions.

IPMN interception. The larger size of IPMNs compared with PanINs allows for easier detection, and thus a more diverse array of potential interception strategies (Figure 3). However, although an estimated 30% of resected IPMNs eventually demonstrate an invasive component (92–95), the molecular landscape at the transition point from IPMN to invasive PDAC has yet to be comprehensively mapped. Like PanINs, modeling studies based on IPMN sequencing data suggest an approximately 4-year interval between the development of HG dysplasia and invasive PDAC (96). IPMN histologic subtypes each carry some prognostic significance; for example, since gastric-type IPMNs are typically LG, identification of biomarkers for gastric differentiation could help categorize certain IPMNs as lower-risk, avoiding unnecessary intervention. However, the molecular drivers underlying IPMN phenotypes show substantial overlap, complicating the development of specific predictive biomarkers. One study found that KRAS, GNAS, and RNF43 mutations were all present within gastric, intestinal, and pancreatobiliary subtypes, although GNAS and RNF43 mutations were most common in intestinal and rarer in pancreatobiliary lesions (97). This is also illustrated by studies involving the NKX6-2 gene. Sans et al. leveraged spatial transcriptomics analysis of human and mouse IPMN tissue to identify NKX6-2 as a putative biomarker of lower-risk, gastric-type IPMNs that could feasibly be detected in cyst fluid analysis (98). By contrast, a separate spatial transcriptomics study concluded that NKX6-2 is a specific marker for HG gastric IPMNs, and thus is actually an indicator of higher-risk lesions (99). Finally, while gastric-type IPMNs are typically considered low-risk, Liffers et al. used methylation and transcriptomic analyses, as well as low-coverage whole-genome sequencing, to show that gastric-type IPMNs express higher MUCL3 (mucin-like 3) and harbor more recurrent copy number variations than PanINs, both of which could be associated with higher risk of progression (100). The same study also evaluated copy number changes within the different histologic IPMN subtypes, finding higher chromosomal instability among intestinal IPMNs, even in LG lesions (100). Numerous studies have demonstrated the feasibility of copy number variation analysis using cfDNA from liquid biopsy samples in multiple cancer types, including PDAC (81, 101–104), but its presence in LG lesions suggests limited utility as an isolated biomarker for higher-risk lesions. It is also important to consider the possibility of intralesional heterogeneity, with single IPMNs containing a range of grades and histologic subtypes; therefore, any single marker may not be representative of the entire lesion, especially for multiloculated cysts. Some genetic studies have even hypothesized that gastric-type IPMNs are a precursor to intestinal or pancreatobiliary types, while others have postulated a common lineage between gastric and pancreatobiliary types, with intestinal-type arising through a separate pathway (97, 100, 105–107). Overall, these studies highlight the need for biomarkers that more precisely correlate with histologic grade, rather than subtype alone. To this end, a recent study by Iyer et al. analyzed spatial transcriptomic profiles of IPMNs with a range of histologic grades, identifying specific RNA expression profiles that could categorize IPMNs as normal, low-risk, or high-risk, with the high-risk lesions displaying diminished exocrine function and increased basal-like gene expression programs (108).

Importantly, screening for IPMNs is more clinically feasible than for PanINs, and the larger size of IPMNs allows for molecular analyses via cyst fluid collection in many cases. Indeed, measurement of cyst fluid carcinoembryonic antigen (CEA) levels has been extensively investigated as a diagnostic tool to differentiate precancerous mucinous cysts — IPMNs and mucinous cystic neoplasm (MCN, another less common PDAC precursor) — from benign cysts such as serous cystadenomas. Recent data show that combining CEA measurement with next-generation sequencing for common IPMN driver mutations can increase diagnostic accuracy (52, 109). However, CEA is not reliably predictive of the grade of dysplasia within IPMNs (52). A recent meta-analysis examined the predictive ability of certain DNA mutations to identify pancreatic mucinous cysts, finding that KRAS and/or GNAS mutations were more sensitive and specific than CEA levels (110). Similarly to CEA, however, the authors found that KRAS and GNAS could not reliably distinguish cysts with or without HG dysplasia or PDAC; other mutations, including CDKN2A, PIK3CA, SMAD4, and TP53, were highly specific, but not sensitive for HG dysplasia or PDAC (110). Also currently under investigation is cyst fluid glucose measurement, which shows equivalent or better diagnostic accuracy than CEA with lower associated cost (52, 110). A newer, promising cyst fluid biomarker for IPMN risk stratification is Das-1, a murine mAb developed to react specifically with normal colonic epithelial cells (111, 112). Das et al. have demonstrated that cyst fluid ELISA using mAb Das-1 can identify high-risk IPMNs (i.e., any subtype with HG dysplasia, intestinal-type IPMNs with intermediate-grade dysplasia, or IPMNs with associated invasive carcinoma) with high sensitivity and specificity (110–112). Additionally, preliminary studies measuring MUC5AC protein levels in pancreatic cyst fluid from surgical specimens have shown high sensitivity and specificity for identifying mucinous cysts with HG dysplasia or PDAC, although this marker remains to be tested in the setting of EUS-guided fine needle–aspirated cyst fluid (110, 113–115).

While cyst fluid analyses have shown promise in the diagnosis and classification pancreatic cystic lesions, the inherent molecular complexity of such lesions limits the prognostic utility of any single biomarker. To address this, recent studies have sought to design more precisely predictive panels through integration of multiple molecular and clinical markers. Springer et al. initially demonstrated the ability of molecular features (mutations, loss of heterozygosity, and aneuploidy) to identify different pancreatic cystic lesions, finding that composite molecular and clinical markers performed better than single or composite molecular features alone (116). They further expanded upon this approach with CompCyst, a machine learning–based method to guide pancreatic cyst management that showed greater concordance with gold standard pathologic diagnosis than conventional clinical management approaches (116, 117). Similarly, Singhi et al. and later Nikiforova et al. developed and refined the PancreaSeq targeted DNA/RNA sequencing panel for pancreatic cyst fluid analysis, achieving high sensitivity and specificity for detecting advanced neoplasia through a risk score that integrates characterization of mutations (GNAS, TP53, SMAD4, CTNNB1, mTOR pathway genes), mutant allele frequency, copy number changes, gene fusions, and gene expression profiles (118, 119). Moving forward, improvements in molecular characterization techniques and machine learning approaches will allow for development of such composite biomarkers with even greater degrees of complexity and precision.

Microenvironmental- and immune-based approaches to precursor interception. As discussed above, the microenvironments of both LG-PanINs and LG-IPMNs display evidence of immune surveillance that transitions to immunosuppression as these lesions progress to HG dysplasia and especially invasive disease (9, 16, 66–68). Importantly, these findings suggest that if timed correctly, proimmunogenic therapies could be deployed to intercept PDAC precursor lesions prior to development of HG dysplasia and invasive cancer. One such approach, using mutant KRAS–targeted vaccines in patients with evidence of cystic pancreatic neoplasia, or those at high risk of PDAC development, is currently being investigated through early-phase clinical trials (73). Although PDAC is known to possess a relatively low tumor mutational burden that seems to limit its responsiveness to immune checkpoint inhibitor (ICI) therapy (120), recent evidence showing increased immune checkpoint marker expression in HG-IPMNs (68) suggests a possible role for ICIs in targeting precursor lesions. Additional immune-based approaches currently under investigation for PDAC treatment, including oncolytic virus therapy and chimeric antigen receptor T cell (CAR-T) therapy, could theoretically be applied to target precursor lesions, especially given the similar molecular profiles between HG precursors and invasive PDAC (45). Overall, application of any immunomodulatory approaches would require careful consideration of the threshold of acceptable off-target effects, which may be lower in the setting of cancer prevention versus active cancer treatment. As with other precursor interception strategies, more precise stratification of precursor lesions through novel biomarkers could aid in identifying higher-risk LG lesions that justify immune-based interventions.

Conflict of interest: The authors have declared that no conflict of interest exists. Copyright: © 2025, Pedro et al. This is an open access article published under the terms of the Creative Commons Attribution 4.0 International License.

Reference information: J Clin Invest. 2025;135(14):e191937. https://doi.org/10.1172/JCI191937.

1. Kenner BJ, et al. Early detection of pancreatic cancer-a defined future using lessons from other cancers: a white paper. Pancreas. 2016;45(8):1073–1079.
   View this article via: CrossRef PubMed Google Scholar
2. Rahib L, et al. Estimated projection of US Cancer incidence and death to 2040. JAMA Netw Open. 2021;4(4):e214708.
   View this article via: CrossRef PubMed Google Scholar
3. Domchek SM, Vonderheide RH. Advancing cancer interception. Cancer Discov. 2024;14(4):600–604.
   View this article via: CrossRef PubMed Google Scholar
4. American Cancer Society. Cancer Facts & Figures 2024. https://www.cancer.org/content/dam/cancer-org/research/cancer-facts-and-statistics/annual-cancer-facts-and-figures/2024/2024-cancer-facts-and-figures-acs.pdf Accessed May 6, 2025.
5. Sohal DP, et al. Pancreatic adenocarcinoma: treating a systemic disease with systemic therapy. J Natl Cancer Inst. 2014;106(3):dju011.
   View this article via: CrossRef PubMed Google Scholar
6. Hruban RH, et al. Precursors to pancreatic cancer. Gastroenterol Clin North Am. 2007;36(4):831–849, vi.
   View this article via: CrossRef PubMed Google Scholar
7. Wood LD, et al. Pancreatic cancer: pathogenesis, screening, diagnosis, and treatment. Gastroenterology. 2022;163(2):386–402.
   View this article via: CrossRef PubMed Google Scholar
8. Braxton AM, et al. 3D genomic mapping reveals multifocality of human pancreatic precancers. Nature. 2024;629(8012):679–687.
   View this article via: CrossRef PubMed Google Scholar
9. Carpenter ES, et al. Analysis of donor pancreata defines the transcriptomic signature and microenvironment of early neoplastic lesions. Cancer Discov. 2023;13(6):1324–1345.
   View this article via: CrossRef PubMed Google Scholar
10. Hulst SPL. Zur Kenntnis der Genese des Adenokarzinoms und Karzinoms des Pankreas. Virchows Arch Path Anat. 1905;180:288–316.
    View this article via: CrossRef Google Scholar
11. Kiemen AL, et al. PanIN or IPMN? Redefining lesion size in 3 dimensions. Am J Surg Pathol. 2024;48(7):839–845.
    View this article via: CrossRef PubMed Google Scholar
12. Jiang T, et al. Clinical significance of pancreatic ductal metaplasia. J Pathol. 2022;257(2):125–139.
    View this article via: CrossRef PubMed Google Scholar
13. Pin CL, et al. Acinar cell reprogramming: a clinically important target in pancreatic disease. Epigenomics. 2015;7(2):267–281.
    View this article via: CrossRef PubMed Google Scholar
14. Lo EKW, et al. Comprehensive DNA methylation analysis indicates that pancreatic intraepithelial neoplasia lesions are acinar-derived and epigenetically primed for carcinogenesis. Cancer Res. 2024;83(11):1905–1916.
    View this article via: CrossRef PubMed Google Scholar
15. Zheng C, et al. Cell of origin of pancreatic cancer: novel findings and current understanding. Pancreas. 2024;53(3):e288–e297.
    View this article via: CrossRef PubMed Google Scholar
16. Bell ATF, et al. PanIN and CAF transitions in pancreatic carcinogenesis revealed with spatial data integration. Cell Syst. 2024;15(8):753–769.
    View this article via: CrossRef PubMed Google Scholar
17. Alonso-Curbelo D, et al. A gene-environment-induced epigenetic program initiates tumorigenesis. Nature. 2021;590(7847):642–648.
    View this article via: CrossRef PubMed Google Scholar
18. Del Poggetto E, et al. Epithelial memory of inflammation limits tissue damage while promoting pancreatic tumorigenesis. Science. 2021;373(6561):eabj0486.
    View this article via: CrossRef PubMed Google Scholar
19. Li AL, et al. A single-cell transcriptomic census of mammalian olfactory epithelium aging. Dev Cell. 2024;59(22):3043–3058.
    View this article via: CrossRef PubMed Google Scholar
20. Grimont A, et al. Uncertain beginnings: acinar and ductal cell plasticity in the development of pancreatic cancer. Cell Mol Gastroenterol Hepatol. 2022;13(2):369–382.
    View this article via: CrossRef PubMed Google Scholar
21. Bailey JM, et al. p53 mutations cooperate with oncogenic Kras to promote adenocarcinoma from pancreatic ductal cells. Oncogene. 2015;35(32):4282–4288.
    View this article via: CrossRef PubMed Google Scholar
22. Lee AYL, et al. Cell of origin affects tumour development and phenotype in pancreatic ductal adenocarcinoma. Gut. 2024;68(3):487–498.
    View this article via: CrossRef PubMed Google Scholar
23. Ferreira RMM, et al. Duct- and acinar-derived pancreatic ductal adenocarcinomas show distinct tumor progression and marker expression. Cell Rep. 2017;21(4):966–978.
    View this article via: CrossRef PubMed Google Scholar
24. Flowers BM, et al. Cell of origin influences pancreatic cancer subtype. Cancer Discov. 2021;11(3):660–677.
    View this article via: CrossRef PubMed Google Scholar
25. Basturk O, et al. A revised classification system and recommendations from the Baltimore Consensus Meeting for Neoplastic Precursor Lesions in the Pancreas. Am J Surg Pathol. 2015;39(12):1730–1741.
    View this article via: CrossRef PubMed Google Scholar
26. Halbrook CJ, et al. Pancreatic cancer: advances and challenges. Cell. 2023;186(8):1729–1754.
    View this article via: CrossRef PubMed Google Scholar
27. Singhi AD, et al. Early detection of pancreatic cancer: opportunities and challenges. Gastroenterology. 2019;156(7):2024–2040.
    View this article via: CrossRef PubMed Google Scholar
28. Hruban RH, et al. Progression model for pancreatic cancer. Clin Cancer Res. 2000;6(8):2969–2972.
    View this article via: PubMed Google Scholar
29. Peters MLB, et al. Progression to pancreatic ductal adenocarcinoma from pancreatic intraepithelial neoplasia: results of a simulation model. Pancreatology. 2018;18(8):928–934.
    View this article via: CrossRef PubMed Google Scholar
30. Pittman ME, et al. Classification, morphology, molecular pathogenesis, and outcome of premalignant lesions of the pancreas. Arch Pathol Lab Med. 2017;141(12):1606–1614.
    View this article via: CrossRef PubMed Google Scholar
31. Andea A, et al. Clinicopathological correlates of pancreatic intraepithelial neoplasia: a comparative analysis of 82 cases with and 152 cases without pancreatic ductal adenocarcinoma. Mod Pathol. 2003;16(10):996–1006.
    View this article via: CrossRef PubMed Google Scholar
32. Matsuda Y, et al. The prevalence and clinicopathological characteristics of high-grade pancreatic intraepithelial neoplasia: autopsy study evaluating the entire pancreatic parenchyma. Pancreas. 2017;46(5):658–664.
    View this article via: CrossRef PubMed Google Scholar
33. Shi C, et al. Increased prevalence of precursor lesions in familial pancreatic cancer patients. Clin Cancer Res. 2009;15(24):7737–7743.
    View this article via: CrossRef PubMed Google Scholar
34. Brune K, et al. Multifocal neoplastic precursor lesions associated with lobular atrophy of the pancreas in patients having a strong family history of pancreatic cancer. Am J Surg Pathol. 2006;30(9):1067–1076.
    View this article via: PubMed Google Scholar
35. Kanda M, et al. Presence of somatic mutations in most early-stage pancreatic intraepithelial neoplasia. Gastroenterology. 2012;142(4):730–733.
    View this article via: CrossRef PubMed Google Scholar
36. Hosoda W, et al. Genetic analyses of isolated high-grade pancreatic intraepithelial neoplasia (HG-PanIN) reveal paucity of alterations in TP53 and SMAD4. J Pathol. 2017;242(1):16–23.
    View this article via: CrossRef PubMed Google Scholar
37. Wilentz RE, et al. Loss of expression of Dpc4 in pancreatic intraepithelial neoplasia: evidence that DPC4 inactivation occurs late in neoplastic progression. Cancer Res. 2000;60(7):2002–2006.
    View this article via: PubMed Google Scholar
38. Hutchings D, et al. Cancerization of the pancreatic ducts: demonstration of a common and under-recognized process using immunolabeling of paired duct lesions and invasive pancreatic ductal adenocarcinoma for p53 and Smad4 expression. Am J Surg Pathol. 2018;42(11):1556–1561.
    View this article via: CrossRef PubMed Google Scholar
39. Hata T, et al. Genome-wide somatic copy number alterations and mutations in high-grade pancreatic intraepithelial neoplasia. Am J Pathol. 2018;188(7):1723–1733.
    View this article via: CrossRef PubMed Google Scholar
40. Hong S, et al. Genome-wide somatic copy number alterations in low-grade PanINs and IPMNs from individuals with a family history of pancreatic cancer. Clin Cancer Res. 2012;18(16):4303–4312.
    View this article via: CrossRef PubMed Google Scholar
41. Notta F, et al. A renewed model of pancreatic cancer evolution based on genomic rearrangement patterns. Nature. 2016;538(7625):378–382.
    View this article via: CrossRef PubMed Google Scholar
42. Sato N, et al. CpG island methylation profile of pancreatic intraepithelial neoplasia. Mod Pathol. 2007;21(3):238–244.
    View this article via: CrossRef PubMed Google Scholar
43. Hanahan D, Weinberg RA. Hallmarks of cancer: the next generation. Cell. 2011;144(5):646–674.
    View this article via: CrossRef PubMed Google Scholar
44. Hanahan D. Hallmarks of cancer: new dimensions. Cancer Discov. 2024;12(1):31–46.
    View this article via: CrossRef PubMed Google Scholar
45. Farhangnia P, et al. Current and future immunotherapeutic approaches in pancreatic cancer treatment. J Hematol Oncol. 2024;17(1):40.
    View this article via: CrossRef PubMed Google Scholar
46. Zheng L, et al. Vaccine-induced intratumoral lymphoid aggregates correlate with survival following treatment with a neoadjuvant and adjuvant vaccine in patients with resectable pancreatic adenocarcinoma. Clin Cancer Res. 2024;27(5):1278–1286.
    View this article via: CrossRef PubMed Google Scholar
47. Jiang H, et al. Targeting focal adhesion kinase renders pancreatic cancers responsive to checkpoint immunotherapy. Nat Med. 2016;22(8):851–860.
    View this article via: CrossRef PubMed Google Scholar
48. Laheru D, Jaffee EM. Immunotherapy for pancreatic cancer - science driving clinical progress. Nat Rev Cancer. 2005;5(6):459–467.
    View this article via: CrossRef PubMed Google Scholar
49. O’reilly EM, et al. Durvalumab with or without tremelimumab for patients with metastatic pancreatic ductal adenocarcinoma: a phase 2 randomized clinical trial. JAMA Oncol. 2019;5(10):1431–1438.
    View this article via: CrossRef PubMed Google Scholar
50. Kromrey M, et al. Prospective study on the incidence, prevalence and 5-year pancreatic-related mortality of pancreatic cysts in a population-based study. Gut. 2017;67(1):138–145.
    View this article via: CrossRef PubMed Google Scholar
51. Gardner TB, et al. Pancreatic cyst prevalence and the risk of mucin-producing adenocarcinoma in US adults. Am J Gastroenterol. 2013;108(10):1546–1550.
    View this article via: CrossRef PubMed Google Scholar
52. Gardner TB, et al. Diagnosis and management of pancreatic cysts. Gastroenterology. 2024;167(3):454–468.
    View this article via: CrossRef PubMed Google Scholar
53. Laffan TA, et al. Prevalence of unsuspected pancreatic cysts on MDCT. AJR Am J Roentgenol. 2008;191(3):802–807.
    View this article via: CrossRef PubMed Google Scholar
54. Hasan A, et al. Overview and comparison of guidelines for management of pancreatic cystic neoplasms. World J Gastroenterol. 2019;25(31):4405–4413.
    View this article via: CrossRef PubMed Google Scholar
55. Skaro M, et al. Prevalence of germline mutations associated with cancer risk in patients with intraductal papillary mucinous neoplasms. Gastroenterology. 2019;156(6):1905–1913.
    View this article via: CrossRef PubMed Google Scholar
56. Reid-Lombardo KM, et al. Frequency of extrapancreatic neoplasms in intraductal papillary mucinous neoplasm of the pancreas: implications for management. Ann Surg. 2010;251(1):64–69.
    View this article via: CrossRef PubMed Google Scholar
57. Kang MJ, et al. Long-term prospective cohort study of patients undergoing pancreatectomy for intraductal papillary mucinous neoplasm of the pancreas: implications for postoperative surveillance. Ann Surg. 2014;260(2):356–363.
    View this article via: CrossRef PubMed Google Scholar
58. Panic N, et al. Risk for colorectal adenomas among patients with pancreatic intraductal papillary mucinous neoplasms: a prospective case-control study. J Gastrointestin Liver Dis. 2015;24(4):445–450.
    View this article via: CrossRef PubMed Google Scholar
59. Pea A, et al. Targeted DNA sequencing reveals patterns of local progression in the pancreatic remnant following resection of intraductal papillary mucinous neoplasm (IPMN) of the pancreas. Ann Surg. 2017;266(1):133–141.
    View this article via: CrossRef PubMed Google Scholar
60. Matthaei H, et al. Clinicopathological characteristics and molecular analyses of multifocal intraductal papillary mucinous neoplasms of the pancreas. Ann Surg. 2012;255(2):326–333.
    View this article via: CrossRef PubMed Google Scholar
61. Fujikura K, et al. Multiregion whole-exome sequencing of intraductal papillary mucinous neoplasms reveals frequent somatic KLF4 mutations predominantly in low-grade regions. Gut. 2020;70(5):928–939.
    View this article via: CrossRef PubMed Google Scholar
62. Oi H, et al. Genetic assessment of IPMN for predicting concomitant pancreatic ductal adenocarcinoma. Pancreas. 2024;53(10):e790–e795.
    View this article via: CrossRef PubMed Google Scholar
63. Kuboki Y, et al. Single-cell sequencing defines genetic heterogeneity in pancreatic cancer precursor lesions. J Pathol. 2019;247(3):347–356.
    View this article via: CrossRef PubMed Google Scholar
64. Fischer CG, et al. Intraductal papillary mucinous neoplasms arise from multiple independent clones, each with distinct mutations. Gastroenterology. 2019;157(4):1123–1137.
    View this article via: CrossRef PubMed Google Scholar
65. Chhoda A, et al. Utility of promoter hypermethylation in malignant risk stratification of intraductal papillary mucinous neoplasms. Clin Epigenet. 2023;15(1):28.
    View this article via: CrossRef PubMed Google Scholar
66. Hernandez S, et al. Diminished immune surveillance during histologic progression of intraductal papillary mucinous neoplasms offers a therapeutic opportunity for cancer interception. Clin Cancer Res. 2024;28(9):1938–1947.
    View this article via: CrossRef PubMed Google Scholar
67. Roth S, et al. Evolution of the immune landscape during progression of pancreatic intraductal papillary mucinous neoplasms to invasive cancer. EBioMedicine. 2020;54:102714.
    View this article via: CrossRef PubMed Google Scholar
68. Jamouss KT, et al. Tumor immune microenvironment alterations associated with progression in human intraductal papillary mucinous neoplasms. J Pathol. 2025;266(1):40–50.
    View this article via: CrossRef PubMed Google Scholar
69. Aslanian HR, et al. AGA clinical practice update on pancreas cancer screening in high-risk individuals: expert review. Gastroenterology. 2020;159(1):358–362.
    View this article via: CrossRef PubMed Google Scholar
70. Abe K, et al. Hereditary pancreatic cancer. Int J Clin Oncol. 2021;26(10):1784–1792.
    View this article via: CrossRef PubMed Google Scholar
71. Makohon-Moore AP, et al. Precancerous neoplastic cells can move through the pancreatic ductal system. Nature. 2018;561(7722):201–205.
    View this article via: CrossRef PubMed Google Scholar
72. Pant S, et al. Lymph-node-targeted, mKRAS-specific amphiphile vaccine in pancreatic and colorectal cancer: the phase 1 AMPLIFY-201 trial. Nat Med. 2024;30(2):531–542.
    View this article via: CrossRef PubMed Google Scholar
73. Chung V, et al. Novel therapies for pancreatic cancer. [published online November 26, 2024]. JCO Oncol Pract. https://doi.org/10.1200/OP.24.00279.
    View this article via: PubMed Google Scholar
74. Haldar SD, et al. Abstract CT022: mutant KRAS peptide-based vaccine in patients at high risk of developing pancreatic cancer: Preliminary analysis from a phase I study. Cancer Res. 2024;84(7_suppl):CT022.
75. Molina-Arcas M, Downward J. Exploiting the therapeutic implications of KRAS inhibition on tumor immunity. Cancer Cell. 2024;42(3):338–357.
    View this article via: CrossRef PubMed Google Scholar
76. Long S, et al. Evaluation of KRAS inhibitor-directed therapies for pancreatic cancer treatment. Front Oncol. 2024;14:1402128.
    View this article via: CrossRef PubMed Google Scholar
77. Kim MC, et al. CT findings and clinical effects of high grade pancreatic intraepithelial neoplasia in patients with intraductal papillary mucinous neoplasms. PLoS One. 2024;19(4):e0298278.
    View this article via: CrossRef PubMed Google Scholar
78. Vullierme M, et al. Non-branched microcysts of the pancreas on MR imaging of patients with pancreatic tumors who had pancreatectomy may predict the presence of pancreatic intraepithelial neoplasia (PanIN): a preliminary study. Eur Radiol. 2019;29(11):5731–5741.
    View this article via: CrossRef PubMed Google Scholar
79. Sagami R, et al. Pre-operative imaging and pathological diagnosis of localized high-grade pancreatic intra-epithelial neoplasia without invasive carcinoma. Cancers (Basel). 2021;13(5):945.
    View this article via: CrossRef PubMed Google Scholar
80. Wan JCM, et al. Liquid biopsies come of age: towards implementation of circulating tumour DNA. Nat Rev Cancer. 2017;17(4):223–238.
    View this article via: CrossRef PubMed Google Scholar
81. Okada T, et al. Digital PCR-based plasma cell-free DNA mutation analysis for early-stage pancreatic tumor diagnosis and surveillance. J Gastroenterol. 2020;55(12):1183–1193.
    View this article via: CrossRef PubMed Google Scholar
82. Cohen JD, et al. Detection and localization of surgically resectable cancers with a multi-analyte blood test. Science. 2018;359(6378):926–930.
    View this article via: CrossRef PubMed Google Scholar
83. Kondo H, et al. Detection of point mutations in the K-ras oncogene at codon 12 in pure pancreatic juice for diagnosis of pancreatic carcinoma. Cancer. 1994;73(6):1589–1594.
    View this article via: CrossRef PubMed Google Scholar
84. Kanda M, et al. Mutant TP53 in duodenal samples of pancreatic juice from patients with pancreatic cancer or high-grade dysplasia. Clin Gastroenterol Hepatol. 2013;11(6):719–30.
    View this article via: CrossRef PubMed Google Scholar
85. Nuzhat Z, et al. Exosomes in pancreatic juice as valuable source of biomarkers for early diagnosis of pancreatic cancer. Transl Cancer Res. 2017;6(suppl 8):S1339–S1351.
    View this article via: CrossRef Google Scholar
86. Yu J, et al. Digital next-generation sequencing identifies low-abundance mutations in pancreatic juice samples collected from the duodenum of patients with pancreatic cancer and intraductal papillary mucinous neoplasms. Gut. 2016;66(9):1677–1687.
    View this article via: CrossRef PubMed Google Scholar
87. Okada T, et al. Utility of “liquid biopsy” using pancreatic juice for early detection of pancreatic cancer. Endosc Int Open. 2018;06(12):E1454–E1461.
    View this article via: CrossRef Google Scholar
88. Wang K, et al. Liquid biopsy techniques and pancreatic cancer: diagnosis, monitoring, and evaluation. Mol Cancer. 2023;22(1):167.
    View this article via: CrossRef PubMed Google Scholar
89. Suenaga M, et al. Pancreatic juice mutation concentrations can help predict the grade of dysplasia in patients undergoing pancreatic surveillance. Clin Cancer Res. 2018;24(12):2963–2974.
    View this article via: CrossRef PubMed Google Scholar
90. Takahashi K, et al. Current status of molecular diagnostic approaches using liquid biopsy. J Gastroenterol. 2023;58(9):834–847.
    View this article via: CrossRef PubMed Google Scholar
91. Wang Q, et al. Single-cell omics: a new perspective for early detection of pancreatic cancer? Eur J Cancer. 2023;190:112940.
    View this article via: CrossRef PubMed Google Scholar
92. Grützmann R, et al. Intraductal papillary mucinous tumors of the pancreas: biology, diagnosis, and treatment. Oncologist. 2010;15(12):1294–1309.
    View this article via: CrossRef PubMed Google Scholar
93. Strauss A, et al. Intraductal papillary mucinous neoplasms of the pancreas: radiological predictors of malignant transformation and the introduction of bile duct dilation to current guidelines. Br J Radiol. 2016;89(1061):20150853.
    View this article via: CrossRef PubMed Google Scholar
94. Adsay NV, et al. Intraductal papillary-mucinous neoplasms of the pancreas: an analysis of in situ and invasive carcinomas in 28 patients. Cancer. 2001;94(1):62–77.
    View this article via: CrossRef PubMed Google Scholar
95. Sohn TA, et al. Intraductal papillary mucinous neoplasms of the pancreas: an updated experience. Ann Surg. 2004;239(6):788–797; discussion 797.
    View this article via: CrossRef PubMed Google Scholar
96. Noë M, et al. Genomic characterization of malignant progression in neoplastic pancreatic cysts. Nat Commun. 2020;11(1):4085.
    View this article via: CrossRef PubMed Google Scholar
97. Kobayashi T, et al. Pathways for the development of multiple epithelial types of intraductal papillary mucinous neoplasm of the pancreas. J Gastroenterol. 2021;56(6):581–592.
    View this article via: CrossRef PubMed Google Scholar
98. Sans M, et al. Spatial transcriptomics of intraductal papillary mucinous neoplasms of the pancreas identifies NKX6-2 as a driver of gastric differentiation and indolent biological potential. Cancer Discov. 2023;13(8):1844–1861.
    View this article via: CrossRef PubMed Google Scholar
99. Agostini A, et al. Identification of spatially-resolved markers of malignant transformation in intraductal papillary mucinous neoplasms. Nat Commun. 2024;15(1):2764.
    View this article via: CrossRef PubMed Google Scholar
100. Liffers S, et al. Molecular heterogeneity and commonalities in pancreatic cancer precursors with gastric and intestinal phenotype. Gut. 2023;72(3):522–534.
     View this article via: CrossRef PubMed Google Scholar
101. Sharbatoghli M, et al. Copy number variation of circulating tumor DNA (ctDNA) detected using NIPT in neoadjuvant chemotherapy-treated ovarian cancer patients. Front Genet. 2022;13:938985.
     View this article via: CrossRef PubMed Google Scholar
102. Tosello V, et al. Binary classification of copy number alteration profiles in liquid biopsy with potential clinical impact in advanced NSCLC. Sci Rep. 2024;14(1):18545.
     View this article via: CrossRef PubMed Google Scholar
103. Shirai R, et al. Quantitative assessment of copy number alterations by liquid biopsy for neuroblastoma. Genes Chromosomes Cancer. 2022;61(11):662–669.
     View this article via: CrossRef PubMed Google Scholar
104. Martignano F, et al. Nanopore sequencing from liquid biopsy: analysis of copy number variations from cell-free DNA of lung cancer patients. Mol Cancer. 2021;20(1):32.
     View this article via: CrossRef PubMed Google Scholar
105. Noë M, Brosens LAA. Gastric- and intestinal-type IPMN: two of a kind? Virchows Arch. 2020;477(1):17–19.
     View this article via: CrossRef PubMed Google Scholar
106. Omori Y, et al. How does intestinal-type intraductal papillary mucinous neoplasm emerge? CDX2 plays a critical role in the process of intestinal differentiation and progression. Virchows Arch. 2020;477(1):21–31.
     View this article via: CrossRef PubMed Google Scholar
107. Iyer MK, et al. Digital spatial profiling of intraductal papillary mucinous neoplasms: toward a molecular framework for risk stratification. Sci Adv. 2023;9(11):eade4582.
     View this article via: CrossRef PubMed Google Scholar
108. Iyer MK, et al. Spatial transcriptomics of intraductal papillary mucinous neoplasms reveals divergent indolent and malignant states. Clin Cancer Res. 2025;31(9):1796–1808.
     View this article via: CrossRef PubMed Google Scholar
109. Belfrage H, et al. Next-generation sequencing improves diagnostic accuracy of imaging and carcinoembryonic antigen alone for pancreatic cystic neoplasms. Pancreatology. 2024;24(8):1322–1331.
     View this article via: CrossRef PubMed Google Scholar
110. Pflüger MJ, et al. Predictive ability of pancreatic cyst fluid biomarkers: a systematic review and meta-analysis. Pancreatology. 2023;23(7):868–877.
     View this article via: CrossRef PubMed Google Scholar
111. Das KK, et al. Cross validation of the monoclonal antibody Das-1 in identification of high-risk mucinous pancreatic cystic lesions. Gastroenterology. 2019;157(3):720–730.
     View this article via: CrossRef PubMed Google Scholar
112. Das KK, et al. mAb Das-1 is specific for high-risk and malignant intraductal papillary mucinous neoplasm (IPMN). Gut. 2013;63(10):1626–1634.
     View this article via: PubMed Google Scholar
113. Haab BB, et al. Glycosylation variants of mucins and CEACAMs as candidate biomarkers for the diagnosis of pancreatic cystic neoplasms. Ann Surg. 2010;251(5):937–945.
     View this article via: CrossRef PubMed Google Scholar
114. Jabbar KS, et al. Highly accurate identification of cystic precursor lesions of pancreatic cancer through targeted mass spectrometry: a phase IIc diagnostic study. J Clin Oncol. 2018;36(4):367–375.
     View this article via: CrossRef PubMed Google Scholar
115. Jabbar KS, et al. Proteomic mucin profiling for the identification of cystic precursors of pancreatic cancer. J Natl Cancer Inst. 2014;106(2):djt439.
     View this article via: CrossRef PubMed Google Scholar
116. Springer S, et al. A combination of molecular markers and clinical features improve the classification of pancreatic cysts. Gastroenterology. 2015;149(6):1501–1510.
     View this article via: CrossRef PubMed Google Scholar
117. Springer S, et al. A multimodality test to guide the management of patients with a pancreatic cyst. Sci Transl Med. 2019;11(501):eaav4772.
     View this article via: CrossRef PubMed Google Scholar
118. Nikiforova MN, et al. A combined DNA/RNA-based next-generation sequencing platform to improve the classification of pancreatic cysts and early detection of pancreatic cancer arising from pancreatic cysts. Ann Surg. 2024;278(4):e789–e797.
     View this article via: CrossRef PubMed Google Scholar
119. Singhi AD, et al. Preoperative next-generation sequencing of pancreatic cyst fluid is highly accurate in cyst classification and detection of advanced neoplasia. Gut. 2017;67(12):2131–2141.
     View this article via: CrossRef PubMed Google Scholar
120. Yarchoan M, et al. Tumor mutational burden and response rate to PD-1 inhibition. N Engl J Med. 2017;377(25):2500–2501.
     View this article via: CrossRef PubMed Google Scholar
121. Chari ST. Detecting early pancreatic cancer: problems and prospects. Semin Oncol. 2007;34(4):284–294.
     View this article via: CrossRef PubMed Google Scholar
122. Sharma A, et al. Model to determine risk of pancreatic cancer in patients with new-onset diabetes. Gastroenterology. 2018;155(3):730–739.
     View this article via: CrossRef PubMed Google Scholar
123. Eisenhauer EA, et al. New response evaluation criteria in solid tumours: revised RECIST guideline (version 1.1). Eur J Cancer. 2009;45(2):228–247.
     View this article via: CrossRef PubMed Google Scholar