Subtype-Discordant Pancreatic Ductal Adenocarcinoma Tumors Show Intermediate Clinical and Molecular Characteristics

Patient-derived organoids of pancreatic ductal adenocarcinoma for subtype determination and clinical outcome prediction

BACKGROUND

Recently, two molecular subtypes of pancreatic ductal adenocarcinoma (PDAC) have been proposed: the "Classical" and "Basal-like" subtypes, with the former showing better clinical outcomes than the latter. However, the "molecular" classification has not been applied in real-world clinical practice. This study aimed to establish patient-derived organoids (PDOs) for PDAC and evaluate their application in subtype classification and clinical outcome prediction.

METHODS

We utilized tumor samples acquired through endoscopic ultrasound-guided fine-needle biopsy and established a PDO library for subsequent use in morphological assessments, RNA-seq analyses, and in vitro drug response assays. We also conducted a prospective clinical study to evaluate whether analysis using PDOs can predict treatment response and prognosis.

RESULTS

PDOs of PDAC were established at a high efficiency (> 70%) with at least 100,000 live cells. Morphologically, PDOs were classified as gland-like structures (GL type) and densely proliferating inside (DP type) less than 2 weeks after tissue sampling. RNA-seq analysis revealed that the "morphological" subtype (GL vs. DP) corresponded to the "molecular" subtype ("Classical" vs. "Basal-like"). The "morphological" classification predicted the clinical treatment response and prognosis; the median overall survival of patients with GL type was significantly longer than that with DP type (P < 0.005). The GL type showed a better response to gemcitabine than the DP type in vitro, whereas the drug response of the DP type was improved by the combination of ERK inhibitor and chloroquine.

CONCLUSIONS

PDAC PDOs help in subtype determination and clinical outcome prediction, thereby facilitating the bench-to-bedside precision medicine for PDAC.

Integrating metabolic profiling of pancreatic juice with transcriptomic analysis of pancreatic cancer tissue identifies distinct clinical subgroups

Introduction

Metabolic reprogramming is a hallmark feature of pancreatic ductal adenocarcinoma (PDAC). A pancreatic juice (PJ) metabolic signature has been reported to be prognostic of oncological outcome for PDAC. Integration of PJ profiling with transcriptomic and spatial characterization of the tumor microenvironment would help in identifying PDACs with peculiar vulnerabilities.

Methods

We performed a transcriptomic analysis of 26 PDAC samples grouped into 3 metabolic clusters (M_CL) according to their PJ metabolic profile. We analyzed molecular subtypes and transcriptional differences. Validation was performed by multidimensional imaging on tumor slides.

Results

Pancreatic juice metabolic profiling was associated with PDAC transcriptomic molecular subtypes (p=0.004). Tumors identified as M_CL1 exhibited a non-squamous molecular phenotype and demonstrated longer survival. Enrichment analysis revealed the upregulation of immune genes and pathways in M_CL1 samples compared to M_CL2, the group with worse prognosis, a difference confirmed by immunofluorescence on tissue slides. Enrichment analysis of 39 immune signatures by xCell confirmed decreased immune signatures in M_CL2 compared to M_CL1 and allowed a stratification of patients associated with longer survival.

Discussion

PJ metabolic fingerprints reflect PDAC molecular subtypes and the immune microenvironment, confirming PJ as a promising source of biomarkers for personalized therapy.

GATA6 identifies an immune-enriched phenotype linked to favorable outcomes in patients with pancreatic cancer undergoing upfront surgery

This study underscores GATA6's role in distinguishing classical and basal-like pancreatic ductal adenocarcinoma (PDAC) phenotypes. Retrospective studies associate GATA6 immunohistochemistry (IHC) expression with survival outcomes, warranting prospective validation. In a prospective treatment-naive cohort of patients with resected PDAC, GATA6 IHC proves a prognostic discriminator, associating high GATA6 expression with extended survival and the classical PDAC phenotype. However, GATA6's prognostic significance is numerically lower after gemcitabine-based neoadjuvant chemoradiotherapy compared to its significance in patients treated with upfront surgery. Furthermore, GATA6 is implicated in immunomodulation, although a comprehensive investigation of its immunological role is lacking. Treatment-naive PDAC tumors with varying GATA6 expression yield distinct immunological landscapes. Tumors highly expressing GATA6 show reduced infiltration of immunosuppressive regulatory T cells and M2 macrophages but increased infiltration of immune-stimulating, antigen-presenting, and activated T cells. Our findings caution against solely relying on GATA6 for molecular subtyping in clinical trials and open avenues for exploring immune-based combination therapies.

Comprehensive analysis of KLF family reveals KLF6 as a promising prognostic and immune biomarker in pancreatic ductal adenocarcinoma

BACKGROUND

Pancreatic ductal adenocarcinoma (PDAC) is one of the deadliest tumors worldwide, with extremely aggressive and complicated biology. Krüppel-like factors (KLFs) encode a series of transcriptional regulatory proteins and play crucial roles in a variety of processes, including tumor cell differentiation and proliferation. However, the potential biological functions and possible pathways of KLFs in the progression of PDAC remain elusive.

METHODS

We systematically evaluated the transcriptional variations and expression patterns of KLFs in pancreatic cancer from the UCSC Xena. Based on difference analysis, the non-negative matrix factorization (NMF) algorithm was utilized to identify the immune characteristics and clinical significance of two different subtypes. The multivariate Cox regression was used to construct the risk model and then explore the differences in tumor immune microenvironment (TIME) and drug sensitivity between high and low groups. Through single-cell RNA sequencing (scRNA-seq) analysis, we screened KLF6 and further investigated its biological functions in pancreatic cancer and pan-cancer.

RESULTS

The KLFs exhibited differential expression and mutations in the transcriptomic profile of PDAC. According to the expression of KLFs, patients were classified into two distinct subtypes, each exhibiting significant differences in prognosis and TIME. Moreover, the KLF signature was developed using univariate Cox and Lasso regression, which proved to be a reliable and effective prognostic model. Furthermore, the KLF_Score was closely associated with immune infiltration, response to immunotherapy, and drug sensitivity and we screened small molecule compounds targeting prognostic genes separately. Through scRNA-seq analysis, KLF6 was selected to further demonstrate its role in the malignance of PC in vitro. Finally, pan-cancer analysis emphasized the biological significance of KLF6 in multiple types of tumors and its clinical utility in assessing cancer prognosis.

CONCLUSION

This study elucidated the pivotal role of KLF family genes in the malignant development of PC through comprehensive analysis and revealed that KLF6 would be a novel diagnostic biomolecule marker and potential therapeutic target for PDAC.

Establishment and analysis of artificial neural network diagnosis model for coagulation-related molecular subgroups in coronary artery disease

Background: Coronary artery disease (CAD) is the most common type of cardiovascular disease and cause significant morbidity and mortality. Abnormal coagulation cascade is one of the high-risk factors in CAD patients, but the molecular mechanism of coagulation in CAD is still limited. Methods: We clustered and categorized 352 CAD paitents based on the expression patterns of coagulation-related genes (CRGs), and then we explored the molecular and immunological variations across the subgroups to reveal the underlying biological characteristics of CAD patients. The feature genes between CRG-subgroups were further identified using a random forest model (RF) and least absolute shrinkage and selection operator (LASSO) regression, and an artificial neural network prediction model was constructed. Results: CAD patients could be divided into the C1 and C2 CRG-subgroups, with the C1 subgroup highly enriched in immune-related signaling pathways. The differential expressed genes between the two CRG-subgroups (DE-CRGs) were primarily enriched in signaling pathways connected to signal transduction and energy metabolism. Subsequently, 10 feature DE-CRGs were identified by RF and LASSO. We constructed a novel artificial neural network model using these 10 genes and evaluated and validated its diagnostic performance on a public dataset. Conclusion: Diverse molecular subgroups of CAD patients may each have a unique gene expression pattern. We may identify subgroups using a few feature genes, providing a theoretical basis for the precise treatment of CAD patients with different molecular subgroups.

Genome-scale CRISPR-Cas9 screening stratifies pancreatic cancer with distinct outcomes and immunotherapeutic efficacy

Pancreatic cancer (PC) was featured by dramatic heterogeneity and dismal outcomes. An ideal classification strategy capable of achieving risk stratification and individualized treatment is urgently needed to significantly improve prognosis. In this study, using the 105 prognostic cancer essential genes identified by genome-scale CRISPR-Cas9 screening and univariate Cox analysis, we established and verified three heterogeneous subtypes via non-negative matrix factorization (NMF) and nearest template prediction (NTP) algorithms in the TCGA-PAAD cohort (176 samples) and four multi-center cohorts (233 samples), respectively. Among them, C1 with the worst prognosis was enriched in immune-related pathways, possessed superior infiltration abundance of immune cells and immune checkpoint molecules expression, and might be most sensitive to immunotherapy. C3, owing a moderate prognosis, might be featured by proliferative biological function, and despite its highest immunogenicity, the defects in antigen processing and presentation ability coupled with barren immune environment render it ineffective for immunotherapy, while it had potential sensitivity to paclitaxel and methotrexate. Besides, C2 harbored the best prognosis and was characterized by metabolism-related functions. These results could deepen our understanding of PC molecular heterogeneity and provide a trustworthy reference for prognostic stratification management and precision medicine in clinical practice.

PANoptosis-related molecular subtype and prognostic model associated with the immune microenvironment and individualized therapy in pancreatic cancer

Background

PANoptosis is an inflammatory type of programmed cell death regulated by PANopotosome. Mounting evidence has shown that PANoptosis could be involved in cancer pathogenesis and the tumor immune microenvironment. Nevertheless, there have been no studies on the mechanism of PANoptosis on pancreatic cancer (PC) pathogenesis.

Methods

We downloaded the data on transcriptomic and clinical features of PC patients from the Cancer Genome Atlas (TCGA) and Gene Expression Omnibus databases. Additionally, the data on copy number variation (CNV), methylation and somatic mutations of genes in 33 types of cancers were obtained from TCGA. Next, we identified the PANoptosis-related molecular subtype using the consensus clustering analysis, and constructed and validated the PANoptosis-related prognostic model using LASSO and Cox regression analyses. Moreover, RT-qPCR was performed to determine the expression of genes involved in the model.

Results

We obtained 66 PANoptosis-related genes (PANRGs) from published studies. Of these, 24 PC-specific prognosis-related genes were identified. Pan-cancer analysis revealed complex genetic changes, including CNV, methylation, and mutation in PANRGs were identified in various cancers. By consensus clustering analysis, PC patients were classified into two PANoptosis-related patterns: PANcluster A and B. In PANcluster A, the patient prognosis was significantly worse compared to PANcluster B. The CIBERSORT algorithm showed a significant increase in the infiltration of CD8⁺ T cells, monocytes, and naïve B cells, in patients in PANcluster B. Additionally, the infiltration of macrophages, activated mast cells, and dendritic cells were higher in patients in PANcluster A. Patients in PANcluster A were more sensitive to erlotinib, selumetinib and trametinib, whereas patients in PANcluster B were highly sensitive to irinotecan, oxaliplatin and sorafenib. Moreover, we constructed and validated the PANoptosis-related prognostic model to predict the patient's survival. Finally, the GEPIA and Human Protein Atlas databases were analyzed, and RT-qPCR was performed. Compared to normal tissues, a significant increase in CXCL10 and ITGB6 (associated with the model) expression was observed in PC tissues.

Conclusion

We first identified the PANoptosis-related molecular subtypes and established a PANoptosis-related prognostic model for predicting the survival of patients with PC. These results would aid in exploring the mechanisms of PANoptosis in PC pathogenesis.

Pacpaint: a histology-based deep learning model uncovers the extensive intratumor molecular heterogeneity of pancreatic adenocarcinoma

Two tumor (Classical/Basal) and stroma (Inactive/active) subtypes of Pancreatic adenocarcinoma (PDAC) with prognostic and theragnostic implications have been described. These molecular subtypes were defined by RNAseq, a costly technique sensitive to sample quality and cellularity, not used in routine practice. To allow rapid PDAC molecular subtyping and study PDAC heterogeneity, we develop PACpAInt, a multi-step deep learning model. PACpAInt is trained on a multicentric cohort (n = 202) and validated on 4 independent cohorts including biopsies (surgical cohorts n = 148; 97; 126 / biopsy cohort n = 25), all with transcriptomic data (n = 598) to predict tumor tissue, tumor cells from stroma, and their transcriptomic molecular subtypes, either at the whole slide or tile level (112 µm squares). PACpAInt correctly predicts tumor subtypes at the whole slide level on surgical and biopsies specimens and independently predicts survival. PACpAInt highlights the presence of a minor aggressive Basal contingent that negatively impacts survival in 39% of RNA-defined classical cases. Tile-level analysis ( > 6 millions) redefines PDAC microheterogeneity showing codependencies in the distribution of tumor and stroma subtypes, and demonstrates that, in addition to the Classical and Basal tumors, there are Hybrid tumors that combine the latter subtypes, and Intermediate tumors that may represent a transition state during PDAC evolution.

Tumor heterogeneity: An oncogenic driver of PDAC progression and therapy resistance under stress conditions

Pancreatic ductal adenocarcinoma (PDAC) is a clinically challenging disease usually diagnosed at advanced or metastasized stage. By this year end, there are an expected increase in 62,210 new cases and 49,830 deaths in the United States, with 90% corresponding to PDAC subtype alone. Despite advances in cancer therapy, one of the major challenges combating PDAC remains tumor heterogeneity between PDAC patients and within the primary and metastatic lesions of the same patient. This review describes the PDAC subtypes based on the genomic, transcriptional, epigenetic, and metabolic signatures observed among patients and within individual tumors. Recent studies in tumor biology suggest PDAC heterogeneity as a major driver of disease progression under conditions of stress including hypoxia and nutrient deprivation, leading to metabolic reprogramming. We therefore advance our understanding in identifying the underlying mechanisms that interfere with the crosstalk between the extracellular matrix components and tumor cells that define the mechanics of tumor growth and metastasis. The bilateral interaction between the heterogeneous tumor microenvironment and PDAC cells serves as another important contributor that characterizes the tumor-promoting or tumor-suppressing phenotypes providing an opportunity for an effective treatment regime. Furthermore, we highlight the dynamic reciprocating interplay between the stromal and immune cells that impact immune surveillance or immune evasion response and contribute towards a complex process of tumorigenesis. In summary, the review encapsulates the existing knowledge of the currently applied treatments for PDAC with emphasis on tumor heterogeneity, manifesting at multiple levels, impacting disease progression and therapy resistance under stress.

Morphology-guided transcriptomic analysis of human pancreatic cancer organoids reveals microenvironmental signals that enhance invasion

Pancreatic ductal adenocarcinoma (PDAC) frequently presents with metastasis, but the molecular programs in human PDAC cells that drive invasion are not well understood. Using an experimental pipeline enabling PDAC organoid isolation and collection based on invasive phenotype, we assessed the transcriptomic programs associated with invasion in our organoid model. We identified differentially expressed genes in invasive organoids compared with matched noninvasive organoids from the same patients, and we confirmed that the encoded proteins were enhanced in organoid invasive protrusions. We identified 3 distinct transcriptomic groups in invasive organoids, 2 of which correlated directly with the morphological invasion patterns and were characterized by distinct upregulated pathways. Leveraging publicly available single-cell RNA-sequencing data, we mapped our transcriptomic groups onto human PDAC tissue samples, highlighting differences in the tumor microenvironment between transcriptomic groups and suggesting that non-neoplastic cells in the tumor microenvironment can modulate tumor cell invasion. To further address this possibility, we performed computational ligand-receptor analysis and validated the impact of multiple ligands (TGF-β1, IL-6, CXCL12, MMP9) on invasion and gene expression in an independent cohort of fresh human PDAC organoids. Our results identify molecular programs driving morphologically defined invasion patterns and highlight the tumor microenvironment as a potential modulator of these programs.

Spatially Resolved Single-Cell Assessment of Pancreatic Cancer Expression Subtypes Reveals Co-expressor Phenotypes and Extensive Intratumoral Heterogeneity

Pancreatic ductal adenocarcinoma (PDAC) has been classified into classical and basal-like transcriptional subtypes by bulk RNA measurements. However, recent work has uncovered greater complexity to transcriptional subtypes than was initially appreciated using bulk RNA expression profiling. To provide a deeper understanding of PDAC subtypes, we developed a multiplex immunofluorescence (mIF) pipeline that quantifies protein expression of six PDAC subtype markers (CLDN18.2, TFF1, GATA6, KRT17, KRT5, and S100A2) and permits spatially resolved, single-cell interrogation of pancreatic tumors from resection specimens and core needle biopsies. Both primary and metastatic tumors displayed striking intratumoral subtype heterogeneity that was associated with patient outcomes, existed at the scale of individual glands, and was significantly reduced in patient-derived organoid cultures. Tumor cells co-expressing classical and basal markers were present in > 90% of tumors, existed on a basal-classical polarization continuum, and were enriched in tumors containing a greater admixture of basal and classical cell populations. Cell-cell neighbor analyses within tumor glands further suggested that co-expressor cells may represent an intermediate state between expression subtype poles. The extensive intratumoral heterogeneity identified through this clinically applicable mIF pipeline may inform prognosis and treatment selection for patients with PDAC.

SIGNIFICANCE

A high-throughput pipeline using multiplex immunofluorescence in pancreatic cancer reveals striking expression subtype intratumoral heterogeneity with implications for therapy selection and identifies co-expressor cells that may serve as intermediates during subtype switching.

GATA6 and CK5 Stratify the Survival of Patients With Pancreatic Cancer Undergoing Neoadjuvant Chemotherapy

Relevant protein expression of GATA6, CK5, vimentin, and mucins using immunohistochemistry was assessed for predicting the prognosis of and chemotherapy efficacy in patients with pancreatic cancers (PCs). The protein expression was examined in 159 PCs resected after neoadjuvant chemotherapy (NAC-PCs) and compared with that of 120 matched biopsy specimens taken before NAC. KRAS mutations were assessed by digital PCR. NAC-PCs were classified by GATA6 expression initially and CK5 expression subsequently into 4 types: classical-type (n = 22) with GATA6-high (≥50%)/CK5-low (<10%) PCs; hybrid-type (n = 45) with GATA6-high/CK5-high (≥10%) PCs; basal-like-type (n = 53) with GATA6-low (<50%)/CK5-high (≥30%) PCs; and null-type (n = 39) with GATA6-low/CK5-low (<30%) PCs, which resulted in clear stratification of patient prognosis. The classical-type was associated with the most favorable prognosis, whereas the null-type was associated with the worst prognosis (multivariate hazard ratio: 3.56; 95% CI: 1.63-7.77; P = .0015). The hybrid and basal-like types correlated with in-between levels of prognosis. The risk of hepatic recurrence was lower in the classical-type than in null (multivariate odds ratio [mOR]: 0.18; 95% CI: 0.04-0.96; P = .0449) and basal-like (mOR: 0.24; 95% CI: 0.05-1.16; P =.0750) types. By contrast, the risk of locoregional recurrence was higher in the classical-type than in the basal-like-type (mOR: 5.03; 95% CI: 1.20-21.1; P = .0272). The hybrid-type was subclassified into transition and coexpression patterns with different gastric mucin expression levels. High levels of vimentin (≥10%, n = 30) in pre-NAC-PC tissues was associated with poor prognosis (P = .0256). Phenotypic transitions between pre-NAC and post-NAC-PCs were common (73/120; 61%). PCs with NAC regression grades 2 and 3 showed a transition to poorer prognostic phenotypes (P = .0497). KRAS mutations were not associated with these phenotypes. In conclusion, GATA6 and CK5 immunohistochemical expression phenotypes may stratify the survival of patients with NAC-PCs and reflect post-NAC phenotypic transitions associated with poor prognosis. Prompt evaluation of immunohistochemical phenotypes may contribute to designing a precision therapeutic strategy for patients with PCs.

Heterogeneity of Glycan Biomarker Clusters as an Indicator of Recurrence in Pancreatic Cancer

Outcomes following tumor resection vary dramatically among patients with pancreatic cancer. A challenge in defining predictive biomarkers is to discern within the complex tumor tissue the specific subpopulations and relationships that drive recurrence. Multiplexed immunofluorescence is valuable for such studies when supplied with markers of relevant subpopulations and analysis methods to sort out the intra-tumor relationships that are informative of tumor behavior. We hypothesized that the glycan biomarkers CA19-9 and STRA, which detect separate subpopulations of cancer cells, define intra-tumoral features associated with recurrence. We probed this question using automated signal thresholding and spatial cluster analysis applied to the immunofluorescence images of the STRA and CA19-9 glycan biomarkers in whole-block tumor sections. The tumors (N = 22) displayed extreme diversity between them in the amounts of the glycans and in the levels of spatial clustering, but neither the amounts nor the clusters of the individual and combined glycans associated with recurrence. The combined glycans, however, marked divergent types of spatial clusters, alternatively only STRA, only CA19-9, or both. The co-occurrence of more than one cluster type within a tumor associated significantly with disease recurrence, in contrast to the independent occurrence of each type of cluster. In addition, intra-tumoral regions with heterogeneity in biomarker clusters spatially aligned with pathology-confirmed cancer cells, whereas regions with homogeneous biomarker clusters aligned with various non-cancer cells. Thus, the STRA and CA19-9 glycans are markers of distinct and co-occurring subpopulations of cancer cells that in combination are associated with recurrence. Furthermore, automated signal thresholding and spatial clustering provides a tool for quantifying intra-tumoral subpopulations that are informative of outcome.

Heterogeneity of glycan biomarker clusters as an indicator of recurrence in pancreatic cancer

Introduction

Outcomes following tumor resection vary dramatically among patients with pancreatic ductal adenocarcinoma (PDAC). A challenge in defining predictive biomarkers is to discern within the complex tumor tissue the specific subpopulations and relationships that drive recurrence. Multiplexed immunofluorescence is valuable for such studies when supplied with markers of relevant subpopulations and analysis methods to sort out the intra-tumor relationships that are informative of tumor behavior. We hypothesized that the glycan biomarkers CA19-9 and STRA, which detect separate subpopulations of cancer cells, define intra-tumoral features associated with recurrence.

Methods

We probed this question using automated signal thresholding and spatial cluster analysis applied to the immunofluorescence images of the STRA and CA19-9 glycan biomarkers in whole-block sections of PDAC tumors collected from curative resections.

Results

The tumors (N = 22) displayed extreme diversity between them in the amounts of the glycans and in the levels of spatial clustering, but neither the amounts nor the clusters of the individual and combined glycans associated with recurrence. The combined glycans, however, marked divergent types of spatial clusters, alternatively only STRA, only CA19-9, or both. The co-occurrence of more than one cluster type within a tumor associated significantly with disease recurrence, in contrast to the independent occurrence of each type of cluster. In addition, intra-tumoral regions with heterogeneity in biomarker clusters spatially aligned with pathology-confirmed cancer cells, whereas regions with homogeneous biomarker clusters aligned with various non-cancer cells.

Conclusion

Thus, the STRA and CA19-9 glycans are markers of distinct and co-occurring subpopulations of cancer cells that in combination are associated with recurrence. Furthermore, automated signal thresholding and spatial clustering provides a tool for quantifying intra-tumoral subpopulations that are informative of outcome.

The Microbiome in PDAC-Vantage Point for Future Therapies?

Microorganisms have been increasingly implicated in the pathogenesis of malignant diseases, potentially affecting different hallmarks of cancer. Despite the fact that we have recently gained tremendous insight into the existence and interaction of the microbiome with neoplastic cells, we are only beginning to understand and exploit this knowledge for the treatment of human malignancies. Pancreatic ductal adenocarcinoma (PDAC) is an aggressive solid tumor with limited therapeutic options and a poor long-term survival. Recent data have revealed fascinating insights into the role of the tumoral microbiome in PDAC, with profound implications for survival and potentially therapeutic outcomes. In this review, we outline the current scientific knowledge about the clinical and translational role of the microbiome in PDAC. We describe the microbial compositions in healthy and tumoral pancreatic tissue and point out four major aspects of the microbiome in PDAC: pathogenesis, diagnosis, treatment, and prognosis. However, caution must be drawn to inherent pitfalls in analyzing the intratumoral microbiome. Among others, contamination with environmental microbes is one of the major challenges. To this end, we discuss different decontamination approaches that are crucial for clinicians and scientists alike to foster applicability and physiological relevance in this translational field. Without a definition of an exact and reproducible intratumoral microbial composition, the exploitation of the microbiome as a diagnostic or therapeutic tool remains theoretical.

The limits of molecular signatures for pancreatic ductal adenocarcinoma subtyping

Molecular signatures have been suggested as biomarkers to classify pancreatic ductal adenocarcinoma (PDAC) into two, three, four or five subtypes. Since the robustness of existing signatures is controversial, we performed a systematic evaluation of four established signatures for PDAC stratification across nine publicly available datasets. Clustering revealed inconsistency of subtypes across independent datasets and in some cases a different number of PDAC subgroups than in the original study, casting doubt on the actual number of existing subtypes. Next, we built sixteen classification models to investigate the ability of the signatures for tumor subtype prediction. The overall classification performance ranged from ∼35% to ∼90% accuracy, suggesting instability of the signatures. Notably, permuted subtypes and random gene sets achieved very similar performance. Cellular decomposition and functional pathway enrichment analysis revealed strong tissue-specificity of the predicted classes. Our study highlights severe limitations and inconsistencies that can be attributed to technical biases in sample preparation and tumor purity, suggesting that PDAC molecular signatures do not generalize across datasets. How stromal heterogeneity and immune compartment interplay in the diverging development of PDAC is still unclear. Therefore, a more mechanistic or a cross-platform multi-omic approach seems necessary to extract more robust and clinically exploitable insights.

Integrative analysis of KRAS wildtype metastatic pancreatic ductal adenocarcinoma reveals mutation and expression-based similarities to cholangiocarcinoma

Oncogenic KRAS mutations are absent in approximately 10% of patients with metastatic pancreatic ductal adenocarcinoma (mPDAC) and may represent a subgroup of mPDAC with therapeutic options beyond standard-of-care cytotoxic chemotherapy. While distinct gene fusions have been implicated in KRAS wildtype mPDAC, information regarding other types of mutations remain limited, and gene expression patterns associated with KRAS wildtype mPDAC have not been reported. Here, we leverage sequencing data from the PanGen trial to perform comprehensive characterization of the molecular landscape of KRAS wildtype mPDAC and reveal increased frequency of chr1q amplification encompassing transcription factors PROX1 and NR5A2. By leveraging data from colorectal adenocarcinoma and cholangiocarcinoma samples, we highlight similarities between cholangiocarcinoma and KRAS wildtype mPDAC involving both mutation and expression-based signatures and validate these findings using an independent dataset. These data further establish KRAS wildtype mPDAC as a unique molecular entity, with therapeutic opportunities extending beyond gene fusion events.

KRAS wildtype metastatic pancreatic ductal adenocarcinoma (mPDAC) could represent a distinct molecular entity from other PDACs. Here, the authors analyse KRAS wildtype mPDAC tumours using genomics and transcriptomics and find molecular similarities with cholangiocarcinomas.

Pancreatic Cancer: Pathogenesis, Screening, Diagnosis, and Treatment

Pancreatic ductal adenocarcinoma (PDAC) is a clinically challenging cancer, due to both its late stage at diagnosis and its resistance to chemotherapy. However, recent advances in our understanding of the biology of PDAC have revealed new opportunities for early detection and targeted therapy of PDAC. In this review, we discuss the pathogenesis of PDAC, including molecular alterations in tumor cells, cellular alterations in the tumor microenvironment, and population-level risk factors. We review the current status of surveillance and early detection of PDAC, including populations at high risk and screening approaches. We outline the diagnostic approach to PDAC and highlight key treatment considerations, including how therapeutic approaches change with disease stage and targetable subtypes of PDAC. Recent years have seen significant improvements in our approaches to detect and treat PDAC, but large-scale, coordinated efforts will be needed to maximize the clinical impact for patients and improve overall survival.

Challenges in genomic analysis of model systems and primary tumors of pancreatic ductal adenocarcinoma

Pancreatic ductal adenocarcinoma (PDAC) is characterized by aggressive tumor behavior and poor prognosis. Recent next-generation sequencing (NGS)-based genomic studies have provided novel treatment modes for pancreatic cancer via the identification of cancer driver variants and molecular subtypes in PDAC. Genome-wide approaches have been extended to model systems such as patient-derived xenografts (PDXs), organoids, and cell lines for pre-clinical purposes. However, the genomic characteristics vary in the model systems, which is mainly attributed to the clonal evolution of cancer cells during their construction and culture. Moreover, fundamental limitations such as low tumor cellularity and the complex tumor microenvironment of PDAC hinder the confirmation of genomic features in the primary tumor and model systems. The occurrence of these phenomena and their associated complexities may lead to false insights into the understanding of mechanisms and dynamics in tumor tissues of patients. In this review, we describe various model systems and discuss differences in the results based on genomics and transcriptomics between primary tumors and model systems. Finally, we introduce practical strategies to improve the accuracy of genomic analysis of primary tissues and model systems.

PRMT5: An Emerging Target for Pancreatic Adenocarcinoma

Simple Summary

The burden of pancreatic ductal adenocarcinoma (PDAC) increases with rising incidence, yet 5-year overall survival remains poor at 17%. Routine comprehensive genomic profiling of PDAC only finds 2.5% of patients who may benefit and receive matched targeted therapy. Protein arginine methyltransferase 5 (PRMT5) as an anti-cancer target has gained significant interest in recent years and high levels of PRMT5 protein are associated with worse survival outcomes across multiple cancer types. Inhibition of PRMT5 in pre-clinical models can lead to cancer growth inhibition. However, PRMT5 is involved in multiple cellular processes, thus determining its mechanism of action is challenging. While past reviews on PRMT5 have focused on its role in diverse cellular processes and past research studies have focused mainly on haematological malignancies and glioblastoma, this review provides an overview of the possible biological mechanisms of action of PRMT5 inhibition and its potential as a treatment in pancreatic cancer.

Abstract

The overall survival of pancreatic ductal adenocarcinoma (PDAC) remains poor and its incidence is rising. Targetable mutations in PDAC are rare, thus novel therapeutic approaches are needed. Protein arginine methyltransferase 5 (PRMT5) overexpression is associated with worse survival and inhibition of PRMT5 results in decreased cancer growth across multiple cancers, including PDAC. Emerging evidence also suggests that altered RNA processing is a driver in PDAC tumorigenesis and creates a partial dependency on this process. PRMT5 inhibition induces altered splicing and this vulnerability can be exploited as a novel therapeutic approach. Three possible biological pathways underpinning the action of PRMT5 inhibitors are discussed; c-Myc regulation appears central to its action in the PDAC setting. Whilst homozygous MTAP deletion and symmetrical dimethylation levels are associated with increased sensitivity to PRMT5 inhibition, neither measure robustly predicts its growth inhibitory response. The immunomodulatory effect of PRMT5 inhibitors on the tumour microenvironment will also be discussed, based on emerging evidence that PDAC stroma has a significant bearing on disease behaviour and response to therapy. Lastly, with the above caveats in mind, current knowledge gaps and the implications and rationales for PRMT5 inhibitor development in PDAC will be explored.

Prognostic Relevance of Pancreatic Adenocarcinoma Whole-Tumor Transcriptomic Subtypes and Components

PURPOSE

Our team previously defined six quantitative transcriptomic components, and a classification in five subtypes by association of these components. In this study, we compared the robustness of quantitative components and qualitative classifications from different transcriptomic profiling techniques, investigated their clinical relevance, and proposed a new prognostic model.

EXPERIMENTAL DESIGN

A total of 210 patients from a multicentric cohort and 149 patients from a monocentric cohort were included in this study. RNA microarray profiles were obtained from 165 patients of the multicentric cohort. RNA sequencing (RNA-seq) profiles were obtained from all the patients.

RESULTS

For the patients with both RNA microarray and RNA-seq profiles, the concordance in subtype assignment was partial with an 82.4% coherence rate. The correlation between the two technique projections of the six components ranged from 0.85 to 0.95, demonstrating an advantage of robustness. On the basis of the Akaike information criterion, the RNA components showed more prognostic value in univariate or multivariate models than the subtypes. Using the monocentric cohort for training, we developed a multivariate Cox regression model using all six components and clinicopathologic characteristics (node invasion and resection margins) on disease-free survival (DFS). This prognostic model was highly associated with DFS (P < 0.001). The evaluation of the model in the multicentric cohort showed significant association with DFS and overall survival (P < 0.001).

CONCLUSIONS

We described the advantage of the prognostic value and robustness of the whole-tumor transcriptomic components than subtypes. We created and validated a new DFS-based multivariate Cox regression prognostic model, including six pancreatic adenocarcinoma transcriptomic component levels and pathologic characteristics.

Genomic Heterogeneity of Pancreatic Ductal Adenocarcinoma and Its Clinical Impact

Pancreatic ductal adenocarcinoma (PDAC) is one of the leading causes of cancer death due to limited advances in recent years in early diagnosis and personalized therapy capable of overcoming tumor resistance to chemotherapy. In the last decades, significant advances have been achieved in the identification of recurrent genetic and molecular alterations of PDAC including those involving the KRAS, CDKN2A, SMAD4, and TP53 driver genes. Despite these common genetic traits, PDAC are highly heterogeneous tumors at both the inter- and intra-tumoral genomic level, which might contribute to distinct tumor behavior and response to therapy, with variable patient outcomes. Despite this, genetic and genomic data on PDAC has had a limited impact on the clinical management of patients. Integration of genomic data for classification of PDAC into clinically defined entities-i.e., classical vs. squamous subtypes of PDAC-leading to different treatment approaches has the potential for significantly improving patient outcomes. In this review, we summarize current knowledge about the most relevant genomic subtypes of PDAC including the impact of distinct patterns of intra-tumoral genomic heterogeneity on the classification and clinical and therapeutic management of PDAC.

The Role of lncRNAs in the Stem Phenotype of Pancreatic Ductal Adenocarcinoma

Pancreatic ductal adenocarcinoma is one of the deadliest tumors. This neoplasia is characterized by an important cellular and phenotypic heterogeneity. In particular, it has been shown that at least two subtypes can be found: basal-like, which presents stem-like properties, and classical. Cancer stem cells have been isolated and characterized from these tumors, showing their dependance on general and tissue-specific stem transcription factors and signaling pathways. Nevertheless, little is known about their tissue microenvironment and cell non-autonomous regulators, such as long-non-coding RNAs. (lncRNAs). In this review, we summarize the current knowledge about the positive and negative effects of lncRNAs in the stemness phenotype of pancreatic ductal adenocarcinoma cancer (PDAC).

Tumour and stroma RNA signatures predict more accurately distant recurrence than clinicopathological factors in resected pancreatic adenocarcinoma

BACKGROUND

Few patients with pancreatic adenocarcinoma (PAC) are eligible for surgery. Patients with early relapse have a poor prognosis and might be better candidates for a medical approach. Clinical and pathological parameters only partially predict recurrence and are only obtained after surgery. PAC subtypes based on gene expression were proposed, and we assessed if they could predict the risk and type of recurrence independently of clinicopathological parameters.

METHODS

Patients with curative-intent surgery for PAC without pretreatment were selected and divided into two independent cohorts defined as discovery (n = 381) and validation (n = 149) cohorts. Transcriptomic analyses were performed on formalin-fixed paraffin-embedded surgical samples to characterise tumour and stroma compartments using previously defined signatures. We associated molecular and clinicopathological characteristics with general, distant, and local recurrences using Cox regression analyses.

RESULTS

We found that tumour biology predicted distant recurrence contrary to local recurrence, which was directly related to resection margin status. Pure basal-like and stroma-activated subtypes were strongly associated with distant recurrence, independently of clinicopathological factors (hazard ratios [HRs] = 5.85, p < 0.001 and HR = 1.75, p = 0.007, respectively). By dissecting tumoural and stromal compartments, we demonstrated that the basal-like tumour component positively correlated with distant recurrence in both cohorts (HR = 1.45, p < 0.001 and HR = 1.90, p < 0.001), whereas the inactive structural stroma component was protective against distant recurrence (HR = 0.68, p < 0.001 and HR = 0.72, p < 0.001).

CONCLUSIONS

In addition to suggesting a different mechanism for local and distant relapse (incomplete resection and high metastatic potential, respectively), our results show the potency of molecular phenotype to predict patient outcome regarding distant recurrences.