Master Regulators of Oncogenic KRAS Response in Pancreatic Cancer: An Integrative Network Biology Analysis

Spatial distribution and activation changes of T cells in pancreatic tumors according to KRAS mutation subtype

To enhance immunotherapy efficacy in pancreatic cancer, it is crucial to characterize its immune landscape and identify key factors driving immune alterations. To achieve this, we quantitatively analyzed the immune microenvironment using multiplex immunohistochemistry, assessing the spatial relationships between immune and tumor cells to correlate with patient survival rates and oncological factors. Additionally, through Whole Exome Sequencing analysis based on public data, we explored genetic mutations that could drive these compositions. Finally, we validated T cell (Tc) migration mechanisms using patient-derived tumor organoids with induced KRAS mutation subtypes. Through this approach, we obtained the following meaningful results. First, immune cells in pancreatic cancer are denser in stromal regions than near tumor cells, with higher Tc distribution linked to increased patient survival rates. Second, the distance between tumor and Tc was within 100 μm, with higher Tc density found within 15-30 μm of the tumor cells. Third, while increasing CAF levels correspond to higher Tc density, higher ECM density tends to decrease Tc presence. Fourth, compared to KRAS G12D, KRAS G12V mutation increases various immune cells, notably Tc, which is closely linked to a dramatic rise in vascular cells. Finally, Tc migration was enhanced in tumor organoids with the G12V mutation, attributed to a reduction in the secretion of immunosuppressive cytokines. Our results indicate that KRAS mutation subtypes influence immune cell composition and function in the pancreatic cancer microenvironment, leading to varied immunotherapy responses. This underscores the need for personalized immune therapeutics and research models specific to KRAS mutations.

Pancreatic cancer subtyping - the keystone of precision treatment

In recent years, the incidence and mortality rates of pancreatic cancer have been rising, posing a severe threat to human health. Tumor heterogeneity remains a critical barrier to advancing diagnosis and treatment efforts. The lack of specific early symptoms, limited early diagnostic methods, high biological complexity, and restricted therapeutic options contribute to the poor outcomes and prognosis of pancreatic cancer. Therefore, there is an urgent need to explore the different subtypes in-depth and develop personalized therapeutic strategies tailored to each subtype. Increasing evidence highlights the pivotal role of molecular subtyping in treating pancreatic cancer. This review focuses on recent advancements in classifying molecular subtypes and therapeutic approaches, discussed from the perspectives of gene mutations, genomics, transcriptomics, proteomics, metabolomics, and immunomics.

Trends in the Global Incidence of Pancreatic Cancer and a Brief Review of its Histologic and Molecular Subtypes

The global burden of pancreatic cancer has more than doubled in recent decades. It is now the sixth leading cause of cancer-related death worldwide, with an estimated 510,922 new cases and 467,409 deaths in 2022. The incidence of the disease continues to rise annually, with projections indicating a 95.4% increase in new cases by 2050, potentially reaching a total of 998,663 new cases globally. The overall five-year survival rate for pancreatic cancer is 10% worldwide, showing only a modest improvement compared to the past decade. The rising trends in the incidence rates are likely to continue as the global population ages and access to healthcare improves. The relatively low survival rate is primarily attributed to late-stage diagnoses and the lack of an effective screening method. Currently, population-based screening for asymptomatic individuals is not recommended, highlighting the importance of identifying and monitoring individuals at high risk for pancreatic cancer. Numerous studies have highlighted the differences in the molecular pathology of pancreatic cancer, underscoring the need for continued research to better understand these differences. The silent progression of the disease, poor prognosis, lack of screening options, and the necessity to improve our comprehension of its molecular characteristics emphasize the critical need for ongoing monitoring of disease trends at the population level. This review article analyses trends in the incidence of pancreatic cancer and its histological subtypes and provides an update on its molecular subtypes.

Comprehensive multi-omics profiling identifies novel molecular subtypes of pancreatic ductal adenocarcinoma

Pancreatic cancer, a highly fatal malignancy, is predicted to rank as the second leading cause of cancer-related death in the next decade. This highlights the urgent need for new insights into personalized diagnosis and treatment. Although molecular subtypes of pancreatic cancer were well established in genomics and transcriptomics, few known molecular classifications are translated to guide clinical strategies and require a paradigm shift. Notably, chronically developing and continuously improving high-throughput technologies and systems serve as an important driving force to further portray the molecular landscape of pancreatic cancer in terms of epigenomics, proteomics, metabonomics, and metagenomics. Therefore, a more comprehensive understanding of molecular classifications at multiple levels using an integrated multi-omics approach holds great promise to exploit more potential therapeutic options. In this review, we recapitulated the molecular spectrum from different omics levels, discussed various subtypes on multi-omics means to move one step forward towards bench-to-beside translation of pancreatic cancer with clinical impact, and proposed some methodological and scientific challenges in store.

Transcriptomic subtyping of gastrointestinal malignancies

Gastrointestinal (GI) cancers are highly heterogeneous at multiple levels. Tumor heterogeneity can be captured by molecular profiling, such as genetic, epigenetic, proteomic, and transcriptomic classification. Transcriptomic subtyping has the advantage of combining genetic and epigenetic information, cancer cell-intrinsic properties, and the tumor microenvironment (TME). Unsupervised transcriptomic subtyping systems of different GI malignancies have gained interest because they reveal shared biological features across cancers and bear prognostic and predictive value. Importantly, transcriptomic subtypes accurately reflect complex phenotypic states varying not only per tumor region, but also throughout disease progression, with consequences for clinical management. Here, we discuss methodologies of transcriptomic subtyping, proposed taxonomies for GI malignancies, and the challenges posed to clinical implementation, highlighting opportunities for future transcriptomic profiling efforts to optimize clinical impact.

Cytokines and Pancreatic Ductal Adenocarcinoma: Exploring Their Relationship with Molecular Subtypes and Prognosis

Pancreatic ductal adenocarcinoma (PDAC) is characterized by its poor prognosis. The current challenge remains the absence of predictive biomarkers. Cytokines are crucial factors in the pathogenesis and prognosis of PDAC. Furthermore, there is growing interest in differentiating between molecular subtypes of PDAC. The aim of our study is to evaluate the association between the analyzed cytokines and the molecular subtypes of PDAC and to determine their prognostic value. Cytokine levels were measured in 73 patients, and molecular subtypes were analyzed in 34 of these patients. Transforming Growth Factor Beta 2 (TGF-β2) levels were independently associated with the basal-like and null subtypes. In patients with locally advanced and metastatic PDAC, elevated levels of interleukin (IL)-1α, IL-1β, IL-6, IL-8, IL-9, and IL-15 were associated with a higher risk of progression during first-line treatment, and increased levels of IL-1β, IL-6, IL-8, IL-9, and IL-15 were related to increased mortality. Furthermore, a significant association was observed between higher percentiles of IL-6 and IL-8 and shorter progression-free survival (PFS) during first-line treatment, and between higher percentiles of IL-8 and shorter overall survival (OS). In the multivariate analysis, only elevated levels of IL-8 were independently associated with a higher risk of progression during first-line treatment and mortality. In conclusion, the results of our study suggest that cytokine expression varies according to the molecular subtype of PDAC and that cytokines also play a relevant role in patient prognosis.

Ras-dependent activation of BMAL2 regulates hypoxic metabolism in pancreatic cancer

To identify drivers of malignancy in human pancreatic ductal adenocarcinoma (PDAC), we performed regulatory network analysis on a large collection of expression profiles from laser capture microdissected samples of PDAC and benign precursors. We discovered that BMAL2 plays a role in the initiation, progression, post resection survival, and KRAS activity in PDAC. Functional analysis of BMAL2 target genes led us to hypothesize that it plays a role in regulating the response to hypoxia, a critical but poorly understood feature of PDAC physiology. Knockout of BMAL2 in multiple human PDAC cell lines revealed effects on viability and invasion, particularly under hypoxic conditions. Loss of BMAL2 also affected glycolysis and other metabolic processes. We found that BMAL2 directly regulates hypoxia-responsive target genes. We also found that BMAL2 is necessary for the stabilization of HIF1A upon exposure to hypoxia, but destabilizes HIF2A under hypoxia. These data demonstrate that BMAL2 is a master transcriptional regulator of hypoxia responses in PDAC and may serve as a long-sought molecular switch that distinguishes HIF1A- and HIF2A-dependent modes of hypoxic metabolism.

Genomic analysis and filtration of novel prognostic biomarkers based on metabolic and immune subtypes in pancreatic cancer

PURPOSE

Patients with pancreatic cancer (PC) can be classified into various molecular subtypes and benefit from some precise therapy. Nevertheless, the interaction between metabolic and immune subtypes in the tumor microenvironment (TME) remains unknown. We hope to identify molecular subtypes related to metabolism and immunity in pancreatic cancer METHODS: Unsupervised consensus clustering and ssGSEA analysis were utilized to construct molecular subtypes related to metabolism and immunity. Diverse metabolic and immune subtypes were characterized by distinct prognoses and TME. Afterward, we filtrated the overlapped genes based on the differentially expressed genes (DEGs) between the metabolic and immune subtypes by lasso regression and Cox regression, and used them to build risk score signature which led to PC patients was categorized into high- and low-risk groups. Nomogram were built to predict the survival rates of each PC patient. RT-PCR, in vitro cell proliferation assay, PC organoid, immunohistochemistry staining were used to identify key oncogenes related to PC RESULTS: High-risk patients have a better response for various chemotherapeutic drugs in the Genomics of Drug Sensitivity in Cancer (GDSC) database. We built a nomogram with the risk group, age, and the number of positive lymph nodes to predict the survival rates of each PC patient with average 1-year, 2-year, and 3-year areas under the curve (AUCs) equal to 0.792, 0.752, and 0.751. FAM83A, KLF5, LIPH, MYEOV were up-regulated in the PC cell line and PC tissues. Knockdown of FAM83A, KLF5, LIPH, MYEOV could reduce the proliferation in the PC cell line and PC organoids CONCLUSION: The risk score signature based on the metabolism and immune molecular subtypes can accurately predict the prognosis and guide treatments of PC, meanwhile, the metabolism-immune biomarkers may provide novel target therapy for PC.

Epithelial to Mesenchymal Transition as Mechanism of Progression of Pancreatic Cancer: From Mice to Men

Owed to its aggressive yet subtle nature, pancreatic cancer remains unnoticed till an advanced stage so that in most cases the diagnosis is made when the cancer has already spread to other organs with deadly efficiency. The progression from primary tumor to metastasis involves an intricate cascade of events comprising the pleiotropic process of epithelial to mesenchymal transition (EMT) facilitating cancer spread. The elucidation of this pivotal phenotypic change in cancer cell morphology, initially heretic, moved from basic studies dissecting the progression of pancreatic cancer in animal models to move towards human disease, although no clinical translation of the concept emerged yet. Despite this transition, a full-blown mesenchymal phenotype may not be accomplished; rather, the plasticity of the program and its dependency on heterotopic signals implies a series of fluctuating modifications of cancer cells encompassing mesenchymal and epithelial features. Despite the evidence supporting the activation of EMT and MET during cancer progression, our understanding of the relationship between tumor microenvironment and EMT is not yet mature for a clinical application. In this review, we attempt to resume the knowledge on EMT and pancreatic cancer, aiming to include the EMT among the hallmarks of cancer that could potentially modify our clinical thinking with the purpose of filling the gap between the results pursued in basic research by animal models and those achieved in translational research by surrogate biomarkers, as well as their application for prognostic and predictive purposes.

The molecular landscape of pancreatic ductal adenocarcinoma

Pancreatic ductal adenocarcinoma (PDAC) is predicted to become the second leading cause of cancer-related mortality within the next decade, with limited effective treatment options and a dismal long-term prognosis for patients. Surgical resection of early, localised disease provides the only chance for potentially curative treatment; however, most patients with PDAC present with advanced disease and are not suitable for surgery. Genomic analyses of PDAC tumour lesions have identified a small number of recurrent alterations that are detected across most tumours, and beyond that a large number that either occur at a low (<5%) prevalence or are patient-specific in nature. This molecular heterogeneity has presented a significant challenge for the characterisation of tumour subtypes and effective molecular biomarkers, which have not yet manifested clinical benefits for diagnosis, treatment or prognosis in PDAC. These challenges are compounded by the overall lack of tumour biopsies for sequencing, the invasive nature of tissue sampling and the confounding effects of low tumour cellularity in many PDAC biopsy specimens, which have limited the applications of molecular profiling in unresectable patients and for longitudinal tumour monitoring. Further investigation into alternative sources of tumour analytes that can be sampled using minimally invasive methods and used to complement molecular analyses from tissue sequencing are required.

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.

Personalized pancreatic cancer therapy: from the perspective of mRNA vaccine

Pancreatic cancer is characterized by inter-tumoral and intra-tumoral heterogeneity, especially in genetic alteration and microenvironment. Conventional therapeutic strategies for pancreatic cancer usually suffer resistance, highlighting the necessity for personalized precise treatment. Cancer vaccines have become promising alternatives for pancreatic cancer treatment because of their multifaceted advantages including multiple targeting, minimal nonspecific effects, broad therapeutic window, low toxicity, and induction of persistent immunological memory. Multiple conventional vaccines based on the cells, microorganisms, exosomes, proteins, peptides, or DNA against pancreatic cancer have been developed; however, their overall efficacy remains unsatisfactory. Compared with these vaccine modalities, messager RNA (mRNA)-based vaccines offer technical and conceptional advances in personalized precise treatment, and thus represent a potentially cutting-edge option in novel therapeutic approaches for pancreatic cancer. This review summarizes the current progress on pancreatic cancer vaccines, highlights the superiority of mRNA vaccines over other conventional vaccines, and proposes the viable tactic for designing and applying personalized mRNA vaccines for the precise treatment of pancreatic cancer.

Management of Advanced Pancreatic Cancer through Stromal Depletion and Immune Modulation

Pancreatic cancer is one of the leading causes of cancer-related deaths worldwide. Unfortunately, therapeutic gains in the treatment of other cancers have not successfully translated to pancreatic cancer treatments. Management of pancreatic cancer is difficult due to the lack of effective therapies and the rapid development of drug resistance. The cytotoxic agent gemcitabine has historically been the first-line treatment, but combinations of other immunomodulating and stroma-depleting drugs are currently undergoing clinical testing. Moreover, the treatment of pancreatic cancer is complicated by its heterogeneity: analysis of genomic alterations and expression patterns has led to the definition of multiple subtypes, but their usefulness in the clinical setting is limited by inter-tumoral and inter-personal variability. In addition, various cell types in the tumor microenvironment exert immunosuppressive effects that worsen prognosis. In this review, we discuss current perceptions of molecular features and the tumor microenvironment in pancreatic cancer, and we summarize emerging drug options that can complement traditional chemotherapies.

Molecular subclassification of gastrointestinal cancers based on cancer stem cell traits

Human gastrointestinal malignancies are highly heterogeneous cancers. Clinically, heterogeneity largely contributes to tumor progression and resistance to therapy. Heterogeneity within gastrointestinal cancers is defined by molecular subtypes in genomic and transcriptomic analyses. Cancer stem cells (CSCs) have been demonstrated to be a major source of tumor heterogeneity; therefore, assessing tumor heterogeneity by CSC trait-guided classification of gastrointestinal cancers is essential for the development of effective therapies. CSCs share critical features with embryonic stem cells (ESCs). Molecular investigations have revealed that embryonic genes and developmental signaling pathways regulating the properties of ESCs or cell lineage differentiation are abnormally active and might be oncofetal drivers in certain tumor subtypes. Currently, multiple strategies allow comprehensive identification of tumor subtype-specific oncofetal signatures and evaluation of subtype-specific therapies. In this review, we summarize current knowledge concerning the molecular classification of gastrointestinal malignancies based on CSC features and elucidate their clinical relevance. We also outline strategies for molecular subtype identification and subtype-based therapies. Finally, we explore how clinical implementation of tumor classification by CSC subtype might facilitate the development of more effective personalized therapies for gastrointestinal cancers.

A personalized approach to pancreatic ductal adenocarcinoma and its application in surgical practice

Pancreatic duct adenocarcinoma is an aggressive tumor which constitutes the fourth leading cause of cancer-related mortality in the USA. Despite the fact that surgery is an integral part of treatment, 5-year survival rates remain unfavorable, partly because of the complex genetic background, delayed diagnosis and also the absence of effective therapeutic approaches. To optimize surgery's results in recent years, the use of patients' genetic profile has been implemented through classification into subtypes; subtypes based on mutations which could efficiently lead oncologists to the path of targeted novel neoadjuvant regimens. This approach aims to achieve the most effective selection of patients undergoing surgery, to increase the number of potentially resectable tumors and also control micro-metastases, aiming to extend overall survival.

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.

Whole-Genome Analysis of De Novo Somatic Point Mutations Reveals Novel Mutational Biomarkers in Pancreatic Cancer

It is now known that at least 10% of samples with pancreatic cancers (PC) contain a causative mutation in the known susceptibility genes, suggesting the importance of identifying cancer-associated genes that carry the causative mutations in high-risk individuals for early detection of PC. In this study, we develop a statistical pipeline using a new concept, called gene-motif, that utilizes both mutated genes and mutational processes to identify 4211 3-nucleotide PC-associated gene-motifs within 203 significantly mutated genes in PC. Using these gene-motifs as distinguishable features for pancreatic cancer subtyping results in identifying five PC subtypes with distinguishable phenotypes and genotypes. Our comprehensive biological characterization reveals that these PC subtypes are associated with different molecular mechanisms including unique cancer related signaling pathways, in which for most of the subtypes targeted treatment options are currently available. Some of the pathways we identified in all five PC subtypes, including cell cycle and the Axon guidance pathway are frequently seen and mutated in cancer. We also identified Protein kinase C, EGFR (epidermal growth factor receptor) signaling pathway and P53 signaling pathways as potential targets for treatment of the PC subtypes. Altogether, our results uncover the importance of considering both the mutation type and mutated genes in the identification of cancer subtypes and biomarkers.

NOGEA: A Network-oriented Gene Entropy Approach for Dissecting Disease Comorbidity and Drug Repositioning

Rapid development of high-throughput technologies has permitted the identification of an increasing number of disease-associated genes (DAGs), which are important for understanding disease initiation and developing precision therapeutics. However, DAGs often contain large amounts of redundant or false positive information, leading to difficulties in quantifying and prioritizing potential relationships between these DAGs and human diseases. In this study, a network-oriented gene entropy approach (NOGEA) is proposed for accurately inferring master genes that contribute to specific diseases by quantitatively calculating their perturbation abilities on directed disease-specific gene networks. In addition, we confirmed that the master genes identified by NOGEA have a high reliability for predicting disease-specific initiation events and progression risk. Master genes may also be used to extract the underlying information of different diseases, thus revealing mechanisms of disease comorbidity. More importantly, approved therapeutic targets are topologically localized in a small neighborhood of master genes on the interactome network, which provides a new way for predicting drug-disease associations. Through this method, 11 old drugs were newly identified and predicted to be effective for treating pancreatic cancer and then validated by in vitro experiments. Collectively, the NOGEA was useful for identifying master genes that control disease initiation and co-occurrence, thus providing a valuable strategy for drug efficacy screening and repositioning. NOGEA codes are publicly available at https://github.com/guozihuaa/NOGEA.

Molecular profiling of ctDNA in pancreatic cancer: Opportunities and challenges for clinical application

Pancreatic ductal adenocarcinoma (PDAC) is predicted to become the second leading cause of cancer-related mortality within the next decade, with limited effective treatment options and a dismal long-term prognosis for patients. Genomic profiling has not yet manifested clinical benefits for diagnosis, treatment or prognosis in PDAC, due to the lack of available tissues for sequencing and the confounding effects of low tumour cellularity in many biopsy specimens. Increasing focus is now turning to the use of minimally invasive liquid biopsies to enhance the characterisation of actionable PDAC tumour genomes. Circulating tumour DNA (ctDNA) is the most comprehensively studied liquid biopsy analyte in blood and can provide insight into the molecular profile and biological characteristics of individual PDAC tumours, in real-time and in advance of traditional imaging modalities. This can pave the way for identification of new therapeutic targets, novel risk variants and markers of tumour response, to supplement diagnostic screening and provide enhanced scrutiny in treatment stratification. In the roadmap towards the application of precision medicine for clinical management in PDAC, ctDNA analyses may serve a leading role in streamlining candidate biomarkers for clinical integration. In this review, we highlight recent developments in the use of ctDNA-based liquid biopsies for PDAC and provide new insights into the technical, analytical and biological challenges that must be overcome for this potential to be realised.

Intron retention is a robust marker of intertumoral heterogeneity in pancreatic ductal adenocarcinoma

Pancreatic ductal adenocarcinoma (PDAC) is an aggressive cancer with a 5-year survival rate of <8%. Unsupervised clustering of 76 PDAC patients based on intron retention (IR) events resulted in two clusters of tumors (IR-1 and IR-2). While gene expression-based clusters are not predictive of patient outcome in this cohort, the clusters we developed based on intron retention were associated with differences in progression-free interval. IR levels are lower and clinical outcome is worse in IR-1 compared with IR-2. Oncogenes were significantly enriched in the set of 262 differentially retained introns between the two IR clusters. Higher IR levels in IR-2 correlate with higher gene expression, consistent with detention of intron-containing transcripts in the nucleus in IR-2. Out of 258 genes encoding RNA-binding proteins (RBP) that were differentially expressed between IR-1 and IR-2, the motifs for seven RBPs were significantly enriched in the 262-intron set, and the expression of 25 RBPs were highly correlated with retention levels of 139 introns. Network analysis suggested that retention of introns in IR-2 could result from disruption of an RBP protein-protein interaction network previously linked to efficient intron removal. Finally, IR-based clusters developed for the majority of the 20 cancer types surveyed had two clusters with asymmetrical distributions of IR events like PDAC, with one cluster containing mostly intron loss events. Taken together, our findings suggest IR may be an important biomarker for subclassifying tumors.

Identification of potential crucial genes associated with the pathogenesis and prognosis of pancreatic adenocarcinoma

Pancreatic adenocarcinoma (PAAD) is a type of malignant tumor with the highest mortality rate among all neoplasms worldwide, and its exact pathogenesis is still poorly understood. Timely diagnosis and treatment are of great importance in order to decrease the mortality rate of PAAD. Therefore, identifying new biomarkers for diagnosis and prognosis is essential to enable early detection of PAAD and to improve the overall survival (OS) rate. In order to screen and integrate differentially expressed genes (DEGs) between PAAD and normal tissues, a total of seven datasets were downloaded from the Gene Expression Omnibus database and the ‘limma’ and ‘robustrankggreg’ packages in R software were used. The Gene Ontology and Kyoto Encyclopedia of Genes and Genomes analysis of the DEGs was performed using the Database for Annotation, Visualization and Integrated Discovery website, and the protein-protein interaction network analysis was performed using the Search Tool for the Retrieval of Interacting Genes/Proteins database. A gene prognostic signature was constructed using the Cox regression model. A total of 10 genes (CDK1, CCNB1, CDC20, ASPM, UBE2C, TPX2, TOP2A, NUSAP1, KIF20A and DLGAP5) that may be associated with pancreatic adenocarcinoma were identified. According to the differentially expressed genes in The Cancer Genome Atlas, the present study set up four prognostic signatures (matrix metalloproteinase 12, sodium voltage-gated channel α subunit 11, tetraspanin 1 and SH3 domain and tetratricopeptide repeats-containing 2), which effectively predicted OS. The hub genes that were highly associated with the occurrence, development and prognosis of PAAD were identified, which may be helpful to further understand the molecular basis of pancreatic cancer and guide the synthesis of drugs for PPAD.

LncRNA FAM66C inhibits pancreatic cancer progression by sponging miR-574-3p

Background

Pancreatic cancer is an extensively concerned human malignancy around the globe, yet the potential therapeutic target remains to be further determined. MicroRNA and LncRNA have been reported to be involved in progression of pancreatic cancer, while the biological role of microRNA-574-3p (miR-574-3p) and FAM66C in pancreatic cancer development is poorly investigated.

Methods

Quantitative real-time PCR (qPCR) analysis was employed to detect the expression of miR-574-3p and FAM66C in pancreatic normal or cancerous tissues and cells. The proliferative and apoptosis signaling molecules were also examined via qPCR and western blot separately. Additionally, cell proliferation and apoptosis assay were performed via CCK8, colony formation and Annexin V-FITC apoptosis assay. Interaction between miR-574-3p and FAM66C was interrogated by luciferase reporter assay and RNA immunoprecipitation. Even more, a pancreatic cancer xenograft mice assay was implemented to illustrate the coordinating role of miR-574-3p and FAM66C in pancreatic cancer proliferation.

Results

We found that levels of miR-574-3p were significantly higher in cancer tissues and cells compared to normal (P<0.05). Remarkably, the results indicated that depletion of miR-574-3p inhibited proliferation and promoted apoptosis of human pancreatic cancer cell lines. Additionally, FAM66C was demonstrated to interact with miR-574-3p and inhibit its expression. Significantly, FAM66C was proved to act as a tumor suppressor role via inhibiting cell proliferation and promoting cell apoptosis in pancreatic cancer. Moreover, FAM66C coordinated with miR-574-3p to regulate progression of xenograft tumor in the nude mice.

Conclusions

FAM66C-miR-574-3p axis mediates progression of pancreatic and might be the promising therapeutic target for pancreatic cancer patients.

Cancer classification from time series microarray data through regulatory Dynamic Bayesian Networks

Genomic profiling of cancer studies has generated comprehensive gene expression patterns for diverse phenotypes. Computational methods which employ transcriptomics datasets have been proposed to model gene expression data. Dynamic Bayesian Networks (DBNs) have been used for modeling time series datasets and for the inference of regulatory networks. Furthermore, cancer classification through DBN-based approaches could reveal the importance of exploiting knowledge from statistically significant genes and key regulatory molecules. Although microarray datasets have been employed extensively by several classification methods for decision making, the use of new knowledge from the pathway level has not been addressed adequately in the literature in terms of DBNs for cancer classification. In the present study, we identify the genes that act as regulators and mediate the activity of transcription factors that have been found in all promoters of our differentially expressed gene sets. These features serve as potential priors for distinguishing tumor from normal samples using a DBN-based classification approach. We employed three microarray datasets from the Gene Expression Omnibus (GEO) public functional repository and performed differential expression analysis. Promoter and pathway analysis of the identified genes revealed the key regulators which influence the transcription mechanisms of these genes. We applied the DBN algorithm on selected genes and identified the features that can accurately classify the samples into tumors and controls. Both accuracy and Area Under the Curve (AUC) were high for the gene sets comprising of the differentially expressed genes along with their master regulators (accuracy: 70.8%-98.5%; AUC: 0.562-0.985).

Different shades of pancreatic ductal adenocarcinoma, different paths towards precision therapeutic applications

BACKGROUND

Different histological and molecular subtypes of pancreatic ductal adenocarcinoma (PDAC), with different molecular composition and survival statistics, have recently been recognised.

MATERIALS AND METHODS

This review describes the currently available studies regarding molecular and histological subtypes in PDAC. Studies from major cohorts such as International Cancer Genome Consortium as well as smaller cohorts are reviewed. We discuss where the described subtypes overlap, where the discrepancies are and which paths forward could be taken regarding diagnosis, ontogeny and therapy.

RESULTS

Four molecular subtypes with strong overlap among the different studies can be found, next to a list of mixed findings. Two of the four subtypes (epithelial classical and mesenchymal basal-like) were represented in every study and were often discriminated in other solid tumours as well. These two subtypes differ substantially in prognosis. One biomarker has been discovered, only discriminating these two subtypes, and insights into subtype-specific therapeutic vulnerabilities are scarce.

CONCLUSION

Subtypes can be reproducibly detected in cohorts of PDAC patients and two of them directly relate with prognosis. A consensus on the subtypes is warranted. Further discovery and validation studies are needed to identify strong biomarkers, to comprehend subtype ontogeny and to define strategies for precision medicine.

Immunophenotypes of pancreatic ductal adenocarcinoma: Meta-analysis of transcriptional subtypes

Pancreatic ductal adenocarcinoma (PDAC) is the most common malignancy of the pancreas and has one of the highest mortality rates of any cancer type with a 5-year survival rate of <5%. Recent studies of PDAC have provided several transcriptomic classifications based on separate analyses of individual patient cohorts. There is a need to provide a unified transcriptomic PDAC classification driven by therapeutically relevant biologic rationale to inform future treatment strategies. Here, we used an integrative meta-analysis of 353 patients from four different studies to derive a PDAC classification based on immunologic parameters. This consensus clustering approach indicated transcriptomic signatures based on immune infiltrate classified as adaptive, innate and immune-exclusion subtypes. This reveals the existence of microenvironmental interpatient heterogeneity within PDAC and could serve to drive novel therapeutic strategies in PDAC including immune modulation approaches to treating this disease.

Experimental pancreatic cancer develops in soft pancreas: novel leads for an individualized diagnosis by ultrafast elasticity imaging

Rapid, easy and early pancreatic cancer diagnosis and therapeutic follow up continue to necessitate an increasing attention towards the development of effective treatment strategies for this lethal disease. The non invasive quantitative assessment of pancreatic heterogeneity is limited. Here, we report the development of a preclinical imaging protocol using ultrasonography and shear wave technology in an experimental in situ pancreatic cancer model to measure the evolution of pancreatic rigidity.

Methods: Intrapancreatic tumors were genetically induced by mutated Kras and p53 in KPC mice. We evaluated the feasiblity of a live imaging protocol by assessing pancreas evolution with Aixplorer technology accross 36 weeks. Lethality induced by in situ pancreatic cancer was heterogeneous in time.

Results: The developed method successfully detected tumor mass from 26 weeks onwards at minimal 0.029 cm³ size. Elastography measurements using shear wave methodology had a wide detection range from 4.7kPa to 166.1kPa. Protumorigenic mutations induced a significant decrease of the rigidity of pancreatic tissue before tumors developed in correlation with the detection of senescent marker p16-positive cells. An intratumoral increased rigidity was quantified and found surprisingly heterogeneous. Tumors also presented a huge inter-individual heterogeneity in their rigidity parameters; tumors with low and high rigidity at detection evolve very heterogeneously in their rigidity parameters, as well as in their volume. Increase in rigidity in tumors detected by ultrafast elasticity imaging coincided with detection of tumors by echography and with the detection of the inflammatory protumoral systemic condition by non invasive follow-up and of collagen fibers by post-processing tumoral IHC analysis.

Conclusion: Our promising results indicate the potential of the shear wave elastography to support individualization of diagnosis in this most aggressive disease.

Master regulator analysis of paragangliomas carrying SDHx, VHL, or MAML3 genetic alterations

BACKGROUND

Succinate dehydrogenase (SDH) loss and mastermind-like 3 (MAML3) translocation are two clinically important genetic alterations that correlate with increased rates of metastasis in subtypes of human paraganglioma and pheochromocytoma (PPGL) neuroendocrine tumors. Although hypotheses propose that succinate accumulation after SDH loss poisons dioxygenases and activates pseudohypoxia and epigenomic hypermethylation, it remains unclear whether these mechanisms account for oncogenic transcriptional patterns. Additionally, MAML3 translocation has recently been identified as a genetic alteration in PPGL, but is poorly understood. We hypothesize that a key to understanding tumorigenesis driven by these genetic alterations is identification of the transcription factors responsible for the observed oncogenic transcriptional changes.

METHODS

We leverage publicly-available human tumor gene expression profiling experiments (N = 179) to reconstruct a PPGL tumor-specific transcriptional network. We subsequently use the inferred transcriptional network to perform master regulator analyses nominating transcription factors predicted to control oncogenic transcription in specific PPGL molecular subtypes. Results are validated by analysis of an independent collection of PPGL tumor specimens (N = 188). We then perform a similar master regulator analysis in SDH-loss mouse embryonic fibroblasts (MEFs) to infer aspects of SDH loss master regulator response conserved across species and tissue types.

RESULTS

A small number of master regulator transcription factors are predicted to drive the observed subtype-specific gene expression patterns in SDH loss and MAML3 translocation-positive PPGL. Interestingly, although EPAS1 perturbation is detectible in SDH-loss and VHL-loss tumors, it is by no means the most potent factor driving observed patterns of transcriptional dysregulation. Analysis of conserved SDH-loss master regulators in human tumors and MEFs implicated ZNF423, a known modulator of retinoic acid response in neuroblastoma. Subsequent functional analysis revealed a blunted cell death response to retinoic acid in SDH-loss MEFs and blunted differentiation response in SDH-inhibited SH-SY5Y neuroblastoma cells.

CONCLUSIONS

The unbiased analyses presented here nominate specific transcription factors that are likely drivers of oncogenic transcription in PPGL tumors. This information has the potential to be exploited for targeted therapy. Additionally, the observation that SDH loss or inhibition results in blunted retinoic acid response suggests a potential developmental etiology for this tumor subtype.

A framework for identifying dysregulated chromatin regulators as master regulators in human cancer

MOTIVATION

Chromatin regulators (CRs) are frequently dysregulated to reprogram the epigenetic landscape of the cancer genome. However, the underpinnings of the dysregulation of CRs and their downstream effectors remain to be elucidated.

RESULTS

Here, we designed an integrated framework based on multi-omics data to identify candidate master regulatory CRs affected by genomic alterations across eight cancer types in The Cancer Genome Atlas. Most of them showed consistent activated or repressed (i.e. oncogenic or tumor-suppressive) roles in cancer initiation and progression. In order to further explore the insight mechanism of the dysregulated CRs, we developed an R package ModReg based on differential connectivity to identify CRs as modulators of transcription factors (TFs) involved in tumorigenesis. Our analysis revealed that the connectivity between TFs and their target genes (TGs) tended to be disrupted in the patients who had a high expression of oncogenic CRs or low-expression of tumor-suppressive CRs. As a proof-of-principle study, 14 (82.4%) of the top-ranked 17 driver CRs in liver cancer were able to be validated by literature mining or experiments including shRNA knockdown and dCas9-based epigenetic editing. Moreover, we confirmed that CR SIRT7 physically interacted with TF NFE2L2, and positively modulated the transcriptional program of NFE2L2 by affecting ∼64% of its TGs.

AVAILABILITY AND IMPLEMENTATION

ModReg is freely accessible at http://cis.hku.hk/software/ModReg.tar.gz.

SUPPLEMENTARY INFORMATION

Supplementary data are available at Bioinformatics online.

Molecular subtypes of pancreatic cancer

Cancers that appear morphologically similar often have dramatically different clinical features, respond variably to therapy and have a range of outcomes. Compelling evidence now demonstrates that differences in the molecular pathology of otherwise indistinguishable cancers substantially impact the clinical characteristics of the disease. Molecular subtypes now guide preclinical and clinical therapeutic development and treatment in many cancer types. The ability to predict optimal therapeutic strategies ahead of treatment improves overall patient outcomes, minimizing treatment-related morbidity and cost. Although clinical decision making based on histopathological criteria underpinned by robust data is well established in many cancer types, subtypes of pancreatic cancer do not currently inform treatment decisions. However, accumulating molecular data are defining subgroups in pancreatic cancer with distinct biology and potential subtype-specific therapeutic vulnerabilities, providing the opportunity to define a de novo clinically applicable molecular taxonomy. This Review summarizes current knowledge concerning the molecular subtyping of pancreatic cancer and explores future strategies for using a molecular taxonomy to guide therapeutic development and ultimately routine therapy with the overall goal of improving outcomes for this disease.

MR4Cancer: a web server prioritizing master regulators for cancer

MOTIVATION

During cancer stage transition, a master regulator (MR) refers to the key gene controlling cancer initiation and progression by orchestrating the associated target genes (termed as its regulon). Due to their inherent importance, MRs can serve as critical biomarkers for cancer diagnosis and prognosis, and therapeutic targets. However, it is challenging to infer key MRs that might explain gene expression profile changes between two groups due to lack of context-specific regulons, whose expression level can collectively reflect the activity of likely MRs. There is also a need to design an easy-to-use tool of MR identification for research community.

RESULTS

First, we generated cancer-specific regulons for 26 cancer types by analyzing high-throughput omics data from TCGA, and extracted noncancer-specific regulons from public databases. We subsequently developed a web server MR4Cancer, integrating the regulons with statistical inference to identify and prioritize MRs driving a phenotypic divergence of interest. Based on the input gene list (e.g. differentially expressed genes) or expression profile with two groups, MR4Cancer outputs ranked MRs by enrichment testing against the predefined regulons. Gene Ontology and canonical pathway analyses are also conducted to elucidate the function of likely MRs. Moreover, MR4Cancer provides dynamic network visualization for MR-target relations, and users can interactively interrogate the network to produce new hypotheses and high-quality figures for publication. Finally, the presented case studies highlighted the performance of MR4Cancer. We expect this user-friendly and powerful web tool will provide researchers novel insights into tumorigenesis and therapeutic intervention.

AVAILABILITY AND IMPLEMENTATION

http://cis.hku.hk/MR4Cancer.

SUPPLEMENTARY INFORMATION

Supplementary data are available at Bioinformatics online.

Identification of hub genes with diagnostic values in pancreatic cancer by bioinformatics analyses and supervised learning methods

Background

Pancreatic cancer is one of the most lethal tumors with poor prognosis, and lacks of effective biomarkers in diagnosis and treatment. The aim of this investigation was to identify hub genes in pancreatic cancer, which would serve as potential biomarkers for cancer diagnosis and therapy in the future.

Methods

Combination of two expression profiles of GSE16515 and GSE22780 from Gene Expression Omnibus (GEO) database was served as training set. Differentially expressed genes (DEGs) with top 25% variance followed by protein-protein interaction (PPI) network were performed to find candidate genes. Then, hub genes were further screened by survival and cox analyses in The Cancer Genome Atlas (TCGA) database. Finally, hub genes were validated in GSE15471 dataset from GEO by supervised learning methods k-nearest neighbor (kNN) and random forest algorithms.

Results

After quality control and batch effect elimination of training set, 181 DEGs bearing top 25% variance were identified as candidate genes. Then, two hub genes, MMP7 and ITGA2, correlating with diagnosis and prognosis of pancreatic cancer were screened as hub genes according to above-mentioned bioinformatics methods. Finally, hub genes were demonstrated to successfully differ tumor samples from normal tissues with predictive accuracies reached to 93.59 and 81.31% by using kNN and random forest algorithms, respectively.

Conclusions

All the hub genes were associated with the regulation of tumor microenvironment, which implicated in tumor proliferation, progression, migration, and metastasis. Our results provide a novel prospect for diagnosis and treatment of pancreatic cancer, which may have a further application in clinical.

Electronic supplementary material

The online version of this article (10.1186/s12957-018-1519-y) contains supplementary material, which is available to authorized users.

Novel Targets in Pancreatic Cancer Therapy - Current Status and Ongoing Translational Efforts

Pancreatic ductal adenocarcinoma (PDAC, pancreatic cancer) carries one of the poorest overall prognoses of all human malignancies known to date. Despite the introduction of novel therapeutic regimens, the outcome has not markedly improved over the past decades, the incidence rates are almost identical to the mortality rates, and PDAC is projected to soon become the second most common cause of cancer-related mortality in Western countries. Despite this clear medical need to develop novel therapeutic strategies against this dire malady, this need has so far not been addressed with sufficient institutional attention and support in terms of research funding and strategical programs. Given the still growing life expectancy and projected demographic changes with a growing proportion of senior citizens in many European societies, this discrepancy is likely to become even more pressing in the future. This article provides a brief overview of ongoing preclinical efforts to identify novel targets and, based on this, to develop novel strategies to treat advanced pancreatic cancer and improve survival and the quality of life of patients suffering from this malignancy.

Signal-Targeted Therapies and Resistance Mechanisms in Pancreatic Cancer: Future Developments Reside in Proteomics

For patients with metastatic pancreatic cancer that are not eligible for surgery, signal-targeted therapies have so far failed to significantly improve survival. These therapeutic options have been tested in phase II/III clinical trials mostly in combination with the reference treatment gemcitabine. Innovative therapies aim to annihilate oncogenic dependency, or to normalize the tumoural stroma to allow immune cells to function and/or re-vascularisation to occur. Large scale transcriptomic and genomic analysis revealed that pancreatic cancers display great heterogeneity but failed to clearly delineate specific oncogene dependency, besides oncogenic Kras. Beyond these approaches, proteomics appears to be an appropriate approach to classify signal dependency and to identify specific alterations at the targetable level. However, due to difficulties in sampling, proteomic data for this pathology are scarce. In this review, we will discuss the current state of clinical trials for targeted therapies against pancreatic cancer. We will then highlight the most recent proteomic data for pancreatic tumours and their metastasis, which could help to identify major oncogenic signalling dependencies, as well as provide future leads to explain why pancreatic tumours are intrinsically resistant to signal-targeted therapies. We will finally discuss how studies on phosphatidylinositol-3-kinase (PI3K) signalling, as the paradigmatic pro-tumoural signal downstream of oncogenic Kras in pancreatic cancer, would benefit from exploratory proteomics to increase the efficiency of targeted therapies.

Cutaneous neurofibromas in the genomics era: current understanding and open questions

Cutaneous neurofibromas (cNF) are a nearly ubiquitous symptom of neurofibromatosis type 1 (NF1), a disorder with a broad phenotypic spectrum caused by germline mutation of the neurofibromatosis type 1 tumour suppressor gene (NF1). Symptoms of NF1 can include learning disabilities, bone abnormalities and predisposition to tumours such as cNFs, plexiform neurofibromas, malignant peripheral nerve sheath tumours and optic nerve tumours. There are no therapies currently approved for cNFs aside from elective surgery, and the molecular aetiology of cNF remains relatively uncharacterised. Furthermore, whereas the biallelic inactivation of NF1 in neoplastic Schwann cells is critical for cNF formation, it is still unclear which additional genetic, transcriptional, epigenetic, microenvironmental or endocrine changes are important. Significant inroads have been made into cNF understanding, including NF1 genotype-phenotype correlations in NF1 microdeletion patients, the identification of recurring somatic mutations, studies of cNF-invading mast cells and macrophages, and clinical trials of putative therapeutic targets such as mTOR, MEK and c-KIT. Despite these advances, several gaps remain in our knowledge of the associated pathogenesis, which is further hampered by a lack of translationally relevant animal models. Some of these questions may be addressed in part by the adoption of genomic analysis techniques. Understanding the aetiology of cNF at the genomic level may assist in the development of new therapies for cNF, and may also contribute to a greater understanding of NF1/RAS signalling in cancers beyond those associated with NF1. Here, we summarise the present understanding of cNF biology, including the pathogenesis, mutational landscape, contribution of the tumour microenvironment and endocrine signalling, and the historical and current state of clinical trials for cNF. We also highlight open access data resources and potential avenues for future research that leverage recently developed genomics-based methods in cancer research.

Single-gene prognostic signatures for advanced stage serous ovarian cancer based on 1257 patient samples

OBJECTIVE

We sought to identify stable single-gene prognostic signatures based on a large collection of advanced stage serous ovarian cancer (AS-OvCa) gene expression data and explore their functions.

METHODS

The empirical Bayes (EB) method was used to remove the batch effect and integrate 8 ovarian cancer datasets. Univariate Cox regression was used to evaluate the association between gene and overall survival (OS). The Database for Annotation, Visualization and Integrated Discovery (DAVID) tool was used for the functional annotation of genes for Gene Ontology (GO) terms and Kyoto Encyclopedia of Genes and Genomes (KEGG) pathways.

RESULTS

The batch effect was removed by the EB method, and 1257 patient samples were used for further analysis. We selected 341 single-gene prognostic signatures with FDR < 0.05, in which 110 and 231 genes were positively and negatively associated with OS, respectively. The functions of these genes were mainly involved in extracellular matrix organization, focal adhesion and DNA replication which are closely associated with cancer.

CONCLUSION

We used the EB method to remove the batch effect of 8 datasets, integrated these datasets and identified stable prognosis signatures for AS-OvCa.

Contrasting roles of H3K4me3 and H3K9me3 in regulation of apoptosis and gemcitabine resistance in human pancreatic cancer cells

Background

Pancreas ductal adenocarcinoma (PDAC) has the most dismal prognosis among all human cancers since it is highly resistant to chemotherapy, radiotherapy and immunotherapy. The anticipated consequence of all therapies is induction of tumor apoptosis. The highly resistance nature of PDACs to all therapies suggests that the intrinsic tumor cell factors, likely the deregulated apoptosis pathway, are key mechanisms underlying PDAC non-response to these therapies, rather than the therapeutic agents themselves. The aim of this study is to test the hypothesis that epigenetic dysregulation of apoptosis mediators underlies PDAC resistance to gemcitabine, the standard chemotherapy for human PDAC.

Methods

PDAC cells were analyzed for apoptosis sensitivity in the presence of a selective epigenetic inhibitor. The epigenetic regulation of apoptosis regulators was determined by Western Blotting and quantitative PCR. The specific epigenetic modification of apoptosis regulator promoter chromatin was determined by chromatin immunoprecipitation in PDAC cells.

Results

Inhibition of histone methyltransferase (HMTase) by a selective HMTase inhibitor, verticillin A, significantly increased human PDAC cell sensitivity to gemcitabine-induced growth suppression. Verticillin A treatment decreased FLIP, Mcl-1, Bcl-x and increased Bak, Bax and Bim protein level in the tumor cells, resulting in activation of caspases, elevated cytochrome C release and increased apoptosis as determined by upregulated PARP cleavage in tumor cells. Analysis of human PDAC specimens indicated that the expression levels of anti-apoptotic mediators Bcl-x, Mcl-1, and FLIP were significantly higher, whereas the expression levels of pro-apoptotic mediators Bim, Bak and Bax were dramatically lower in human PDAC tissues as compared to normal pancreas. Verticillin A downregulated H3K4me3 levels at the BCL2L1, CFLAR and MCL-1 promoter to decrease Bcl-x, FLIP and Mcl-1 expression level, and inhibited H3K9me3 levels at the BAK1, BAX and BCL2L11 promoter to upregulate Bak, Bax and Bim expression level.

Conclusion

We determined that PDAC cells use H3K4me3 to activate Bcl-x, FLIP and Mcl-1, and H3K9me3 to silence Bak, Bax and Bim to acquire an apoptosis-resistant phenotype. Therefore, selective inhibition of H3K4me3 and H3K9me3 is potentially an effective approach to overcome PDAC cells resistance to gemcitabine.