Dynamic Interplay between Structural Variations and 3D Genome Organization in Pancreatic Cancer

DKK1-SE recruits AP1 to activate the target gene DKK1 thereby promoting pancreatic cancer progression

Super-enhancers are a class of DNA cis-regulatory elements that can regulate cell identity, cell fate, stem cell pluripotency, and even tumorigenesis. Increasing evidence shows that epigenetic modifications play an important role in the pathogenesis of various types of cancer. However, the current research is far from enough to reveal the complex mechanism behind it. This study found a super-enhancer enriched with abnormally active histone modifications in pancreatic ductal adenocarcinoma (PDAC), called DKK1-super-enhancer (DKK1-SE). The major active component of DKK1-SE is component enhancer e1. Mechanistically, AP1 induces chromatin remodeling in component enhancer e1 and activates the transcriptional activity of DKK1. Moreover, DKK1 was closely related to the malignant clinical features of PDAC. Deletion or knockdown of DKK1-SE significantly inhibited the proliferation, colony formation, motility, migration, and invasion of PDAC cells in vitro, and these phenomena were partly mitigated upon rescuing DKK1 expression. In vivo, DKK1-SE deficiency not only inhibited tumor proliferation but also reduced the complexity of the tumor microenvironment. This study identifies that DKK1-SE drives DKK1 expression by recruiting AP1 transcription factors, exerting oncogenic effects in PDAC, and enhancing the complexity of the tumor microenvironment.

Evaluation of the gene fusion landscape in early onset sporadic rectal cancer reveals association with chromatin architecture and genome stability

Gene fusions represent a distinct class of structural variants identified frequently in cancer genomes across cancer types. Several gene fusions exhibit gain of oncogenic function and thus have been the focus of development of efficient targeted therapies. However, investigation of fusion landscape in early-onset sporadic rectal cancer, a poorly studied colorectal cancer subtype prevalent in developing countries, has not been performed. Here, we present a comprehensive landscape of gene fusions in EOSRC and CRC using patient derived tumor samples and data from The Cancer Genome Atlas, respectively. Gene Ontology analysis revealed enrichment of unique biological process terms associated with 5'- and 3'- fusion partner genes. Extensive network analysis highlighted genes exhibiting significant promiscuity in fusion formation and their association with chromosome fragile sites. Investigation of fusion formation in the context of global chromatin architecture unraveled a novel mode of gene activation that arose from fusion between genes located in orthogonal chromatin compartments. The study provides novel evidence linking fusions to genome stability and architecture and unearthed a hitherto unidentified mode of gene activation in cancer.

Dynamic alternations of three-dimensional chromatin architecture contribute to phenotypic characteristics of breast muscle in chicken

Breast muscle growth rate and intramuscular fat (IMF) content show apparent differences between fast-growing broilers and slow-growing indigenous chickens. However, the underlying genetic basis of these phenotypic characteristics remains elusive. In this study, we investigate the dynamic alterations of three-dimensional genome architecture and chromatin accessibility in breast muscle across four key developmental stages from embryo to starter chick in Arbor Acres (AA) broilers and Yufen (YF) indigenous chickens. The limited breed-specifically up-regulated genes (Bup-DEGs) are embedded in breed-specific A compartment, while a majority of the Bup-DEGs involving myogenesis and adipogenesis are regulated by the breed-specific TAD reprogramming. Chromatin loops allow distal accessible regions to interact with myogenic genes, and those loops share an extremely low similarity between chicken with different growth rate. Moreover, AA-specific loop interactions promote the expression of 40 Bup-DEGs, such as IGF1, which contributes to myofiber hypertrophy. YF-specific loop interactions or distal accessible regions lead to increased expression of 5 Bup-DEGs, including PIGO, PEMT, DHCR7, TMEM38B, and DHDH, which contribute to IMF deposition. These results help elucidate the regulation of breast muscle growth and IMF deposition in chickens.

Transcription factor shapes chromosomal conformation and regulates gene expression in bacterial adaptation

Genomic mutations allow bacteria to adapt rapidly to adverse stress environments. The three-dimensional conformation of the genome may also play an important role in transcriptional regulation and environmental adaptation. Here, using chromosome conformation capture, we investigate the high-order architecture of the Zymomonas mobilis chromosome in response to genomic mutation and ambient stimuli (acetic acid and furfural, derived from lignocellulosic hydrolysate). We find that genomic mutation only influences the local chromosome contacts, whereas stress of acetic acid and furfural restrict the long-range contacts and significantly change the chromosome organization at domain scales. Further deciphering the domain feature unveils the important transcription factors, Ferric uptake regulator (Fur) proteins, which act as nucleoid-associated proteins to promote long-range (>200 kb) chromosomal communications and regulate the expression of genes involved in stress response. Our work suggests that ubiquitous transcription factors in prokaryotes mediate chromosome organization and regulate stress-resistance genes in bacterial adaptation.

DNA damage response and neoantigens: A favorable target for triple-negative breast cancer immunotherapy and vaccine development

Triple-negative breast cancer (TNBC) poses a significant clinical challenge due to its aggressive nature and limited therapeutic options. The interplay between DNA damage response (DDR) mechanisms and the emergence of neoantigens represents a promising avenue for developing targeted immunotherapeutic strategies and vaccines for TNBC. The DDR is a complex network of cellular mechanisms designed to maintain genomic integrity. In TNBC, where genetic instability is a hallmark, dysregulation of DDR components plays a pivotal role in tumorigenesis and progression. This review explores the intricate relationship between DDR and neoantigens, shedding light on the potential vulnerabilities of TNBC cells. Neoantigens, arising from somatic mutations in cancer cells, represent unique antigens that can be recognized by the immune system. TNBC's propensity for genomic instability leads to an increased mutational burden, consequently yielding a rich repertoire of neoantigens. The convergence of DDR and neoantigens in TNBC offers a distinctive opportunity for immunotherapeutic targeting. Immunotherapy has revolutionized cancer treatment by harnessing the immune system to selectively target cancer cells. The unique immunogenicity conferred by DDR-related neoantigens in TNBC positions them as ideal targets for immunotherapeutic interventions. This review also explores various immunotherapeutic modalities, including immune checkpoint inhibitors (ICIs), adoptive cell therapies, and cancer vaccines, that leverage the DDR and neoantigen interplay to enhance anti-tumor immune responses. Moreover, the potential for developing vaccines targeting DDR-related neoantigens opens new frontiers in preventive and therapeutic strategies for TNBC. The rational design of vaccines tailored to the individual mutational landscape of TNBC holds promise for precision medicine approaches. In conclusion, the convergence of DDR and neoantigens in TNBC presents a compelling rationale for the development of innovative immunotherapies and vaccines. Understanding and targeting these interconnected processes may pave the way for personalized and effective interventions, offering new hope for patients grappling with the challenges posed by TNBCs.

A comparative analysis of transcriptomics of newly diagnosed multiple myeloma: exploring drug repurposing

Multiple myeloma (MM) is an incurable malignant plasma cell disorder characterized by the infiltration of clonal plasma cells in the bone marrow compartment. Gene Expression Profiling (GEP) has emerged as a powerful investigation tool in modern myeloma research enabling the dissection of the molecular background of MM and allowing the identification of gene products that could potentially serve as targets for therapeutic intervention. In this study we investigated shared transcriptomic abnormalities across newly diagnosed multiple myeloma (NDMM) patient cohorts. In total, publicly available transcriptomic data of 7 studies from CD138+ cells from 281 NDMM patients and 44 healthy individuals were integrated and analyzed. Overall, we identified 28 genes that were consistently differentially expressed (DE) between NDMM patients and healthy donors (HD) across various studies. Of those, 9 genes were over/under-expressed in more than 75% of NDMM patients. In addition, we identified 4 genes (MT1F, PURPL, LINC01239 and LINC01480) that were not previously considered to participate in MM pathogenesis. Meanwhile, by mining three drug databases (ChEMBL, IUPHAR/BPS and DrugBank) we identified 31 FDA-approved and 144 experimental drugs that target 8 of these 28 over/under-expressed MM genes. Taken together, our study offers new insights in MM pathogenesis and importantly, it reveals potential new treatment options that need to be further investigated in future studies.

3C methods in cancer research: recent advances and future prospects

In recent years, Hi-C technology has revolutionized cancer research by elucidating the mystery of three-dimensional chromatin organization and its role in gene regulation. This paper explored the impact of Hi-C advancements on cancer research by delving into high-resolution techniques, such as chromatin loops, structural variants, haplotype phasing, and extrachromosomal DNA (ecDNA). Distant regulatory elements interact with their target genes through chromatin loops. Structural variants contribute to the development and progression of cancer. Haplotype phasing is crucial for understanding allele-specific genomic rearrangements and somatic clonal evolution in cancer. The role of ecDNA in driving oncogene amplification and drug resistance in cancer cells has also been revealed. These innovations offer a deeper understanding of cancer biology and the potential for personalized therapies. Despite these advancements, challenges, such as the accurate mapping of repetitive sequences and precise identification of structural variants, persist. Integrating Hi-C with multiomics data is key to overcoming these challenges and comprehensively understanding complex cancer genomes. Thus, Hi-C is a powerful tool for guiding precision medicine in cancer research and treatment.

Modification of the Hi-C Technology for Molecular Genetic Analysis of Formalin-Fixed Paraffin-Embedded Sections of Tumor Tissues

Molecular genetic analysis of tumor tissues is the most important step towards understanding the mechanisms of cancer development; it is also necessary for the choice of targeted therapy. The Hi-C (high-throughput chromatin conformation capture) technology can be used to detect various types of genomic variants, including balanced chromosomal rearrangements, such as inversions and translocations. We propose a modification of the Hi-C method for the analysis of chromatin contacts in formalin-fixed paraffin-embedded (FFPE) sections of tumor tissues. The developed protocol allows to generate high-quality Hi-C data and detect all types of chromosomal rearrangements. We have analyzed various databases to compile a comprehensive list of translocations that hold clinical importance for the targeted therapy selection. The practical value of molecular genetic testing is its ability to influence the treatment strategies and to provide prognostic insights. Detecting specific chromosomal rearrangements can guide the choice of the targeted therapies, which is a critical aspect of personalized medicine in oncology.

Alteration of chromosome structure impacts gene expressions implicated in pancreatic ductal adenocarcinoma cells

BACKGROUND

Pancreatic ductal adenocarcinoma (PDAC) is a lethal malignancy with a five-year survival rate of approximately 10%. Genetic mutations are pivotal drivers in PDAC pathogenesis, but recent investigations also revealed the involvement of non-genetic alterations in the disease development. In this study, we undertook a multi-omics approach, encompassing ATAC-seq, RNA-seq, ChIP-seq, and Hi-C methodologies, to dissect gene expression alterations arising from changes in chromosome accessibility and chromatin three-dimensional interactions in PDAC.

RESULTS

Our findings indicate that chromosomal structural alterations can lead to abnormal expressions on key genes during PDAC development. Notably, overexpression of oncogenes FGFR2, FOXA2, CYP2R1, and CPOX can be attributed to the augmentation of promoter accessibility, coupled with long-range interactions with distal elements. Additionally, our findings indicate that chromosomal structural alterations caused by genomic instability can lead to abnormal expressions in PDACs. As an example, by analyzing chromosomal changes, we identified a putative oncogenic gene, LPAR1, which shows upregulated expression in both PDAC cell lines and clinical samples. The overexpression is correlated with alterations in LPAR1-associated 3D genome structure and chromatin state. We further demonstrated that high LPAR1 activity is required for enhanced PDAC cell migration in vitro.

CONCLUSIONS

Collectively, our findings reveal that the chromosomal conformational alterations, in addition to the well-known genetic mutations, are critical for PDAC tumorigenesis.

Improving the prognosis of pancreatic cancer: insights from epidemiology, genomic alterations, and therapeutic challenges

Pancreatic cancer, notorious for its late diagnosis and aggressive progression, poses a substantial challenge owing to scarce treatment alternatives. This review endeavors to furnish a holistic insight into pancreatic cancer, encompassing its epidemiology, genomic characterization, risk factors, diagnosis, therapeutic strategies, and treatment resistance mechanisms. We delve into identifying risk factors, including genetic predisposition and environmental exposures, and explore recent research advancements in precursor lesions and molecular subtypes of pancreatic cancer. Additionally, we highlight the development and application of multi-omics approaches in pancreatic cancer research and discuss the latest combinations of pancreatic cancer biomarkers and their efficacy. We also dissect the primary mechanisms underlying treatment resistance in this malignancy, illustrating the latest therapeutic options and advancements in the field. Conclusively, we accentuate the urgent demand for more extensive research to enhance the prognosis for pancreatic cancer patients.

Three-Dimensional Gene Regulation Network in Glioblastoma Ferroptosis

Ferroptosis is an iron-dependent form of cell death, which is reported to be associated with glioma progression and drug sensitivity. Targeting ferroptosis is a potential therapeutic approach for glioma. However, the molecular mechanism of glioma cell ferroptosis is not clear. In this study, we profile the change of 3D chromatin structure in glioblastoma ferroptosis by using HiChIP and study the 3D gene regulation network in glioblastoma ferroptosis. A combination of an analysis of HiChIP and RNA-seq data suggests that change of chromatin loops mediated by 3D chromatin structure regulates gene expressions in glioblastoma ferroptosis. Genes that are regulated by 3D chromatin structures include genes that were reported to function in ferroptosis, like HDM2 and TXNRD1. We propose a new regulatory mechanism governing glioblastoma cell ferroptosis by 3D chromatin structure.

Chromosome conformation capture technologies as tools to detect structural variations and their repercussion in chromatin 3D configuration

3D genome organization regulates gene expression in different physiological and pathological contexts. Characterization of chromatin structure at different scales has provided information about how the genome organizes in the nuclear space, from chromosome territories, compartments of euchromatin and heterochromatin, topologically associated domains to punctual chromatin loops between genomic regulatory elements and gene promoters. In recent years, chromosome conformation capture technologies have also been used to characterize structural variations (SVs) de novo in pathological conditions. The study of SVs in cancer, has brought information about transcriptional misregulation that relates directly to the incidence and prognosis of the disease. For example, gene fusions have been discovered arising from chromosomal translocations that upregulate oncogenes expression, and other types of SVs have been described that alter large genomic regions encompassing many genes. However, studying SVs in 2D cannot capture all their regulatory implications in the genome. Recently, several bioinformatic tools have been developed to identify and classify SVs from chromosome conformation capture data and clarify how they impact chromatin structure in 3D, resulting in transcriptional misregulation. Here, we review recent literature concerning bioinformatic tools to characterize SVs from chromosome conformation capture technologies and exemplify their vast potential to rebuild the 3D landscape of genomes in cancer. The study of SVs from the 3D perspective can produce essential information about drivers, molecular targets, and disease evolution.

ITGA3 acts as a purity-independent biomarker of both immunotherapy and chemotherapy resistance in pancreatic cancer: bioinformatics and experimental analysis

Contribution of integrin superfamily genes to treatment resistance remains uncertain. Genome patterns of thirty integrin superfamily genes were analyzed of using bulk and single-cell RNA sequencing, mutation, copy number, methylation, clinical information, immune cell infiltration, and drug sensitivity data. To select the integrins that are most strongly associated with treatment resistance in pancreatic cancer, a purity-independent RNA regulation network including integrins were constructed using machine learning. The integrin superfamily genes exhibit extensive dysregulated expression, genome alterations, epigenetic modifications, immune cell infiltration, and drug sensitivity, as evidenced by multi-omics data. However, their heterogeneity varies among different cancers. After constructing a three-gene (TMEM80, EIF4EBP1, and ITGA3) purity-independent Cox regression model using machine learning, ITGA3 was identified as a critical integrin subunit gene in pancreatic cancer. ITGA3 is involved in the molecular transformation from the classical to the basal subtype in pancreatic cancer. Elevated ITGA3 expression correlated with a malignant phenotype characterized by higher PD-L1 expression and reduced CD8+ T cell infiltration, resulting in unfavorable outcomes in patients receiving either chemotherapy or immunotherapy. Our findings suggest that ITGA3 is an important integrin in pancreatic cancer, contributing to chemotherapy resistance and immune checkpoint blockade therapy resistance.

Neoantigens: promising targets for cancer therapy

Recent advances in neoantigen research have accelerated the development and regulatory approval of tumor immunotherapies, including cancer vaccines, adoptive cell therapy and antibody-based therapies, especially for solid tumors. Neoantigens are newly formed antigens generated by tumor cells as a result of various tumor-specific alterations, such as genomic mutation, dysregulated RNA splicing, disordered post-translational modification, and integrated viral open reading frames. Neoantigens are recognized as non-self and trigger an immune response that is not subject to central and peripheral tolerance. The quick identification and prediction of tumor-specific neoantigens have been made possible by the advanced development of next-generation sequencing and bioinformatic technologies. Compared to tumor-associated antigens, the highly immunogenic and tumor-specific neoantigens provide emerging targets for personalized cancer immunotherapies, and serve as prospective predictors for tumor survival prognosis and immune checkpoint blockade responses. The development of cancer therapies will be aided by understanding the mechanism underlying neoantigen-induced anti-tumor immune response and by streamlining the process of neoantigen-based immunotherapies. This review provides an overview on the identification and characterization of neoantigens and outlines the clinical applications of prospective immunotherapeutic strategies based on neoantigens. We also explore their current status, inherent challenges, and clinical translation potential.

Developing and validating a survival prediction model based on blood exosomal ceRNA network in patients with PAAD

BACKGROUND

Among the most lethal cancers, pancreatic adenocarcinoma (PAAD) is an essential component of digestive system malignancies that still lacks effective diagnosis and treatment methods. As exosomes and competing endogenous RNA (ceRNA) regulatory networks in tumors go deeper, we expect to construct a ceRNA regulatory network derived from blood exosomes of PAAD patients by bioinformatics methods and develop a survival prediction model based on it.

METHODS

Blood exosome sequencing data of PAAD patients and normal controls were downloaded from the exoRbase database, and the expression profiles of exosomal mRNA, lncRNA, and circRNA were differentially analyzed by R. The related mRNA, circRNA, lncRNA, and their corresponding miRNA prediction data were imported into Cytoscape software to visualize the ceRNA network. Then, we conducted GO and KEGG enrichment analysis of mRNA in the ceRNA network. Genes that express differently in pancreatic cancer tissues compared with normal tissues and associate with survival (P < 0.05) were determined as Hub genes by GEPIA. We identified optimal prognosis-related differentially expressed mRNAs (DEmRNAs) and generated a risk score model by performing univariate and multivariate Cox regression analyses.

RESULTS

205 DEmRNAs, 118 differentially expressed lncRNAs (DElncRNAs), and 98 differentially expressed circRNAs (DEcircRNAs) were screened out. We constructed the ceRNA network, and a total of 26 mRNA nodes, 7 lncRNA nodes, 6 circRNA nodes, and 16 miRNA nodes were identified. KEGG enrichment analysis showed that the DEmRNAs in the regulatory network were mainly enriched in Human papillomavirus infection, PI3K-Akt signaling pathway, Osteoclast differentiation, and ECM-receptor interaction. Next, six hub genes (S100A14, KRT8, KRT19, MAL2, MYO5B, PSCA) were determined through GEPIA. They all showed significantly increased expression in cancer tissues compared with control groups, and their high expression pointed to adverse survival. Two optimal prognostic-related DEmRNAs, MYO5B (HR = 1.41, P < 0.05) and PSCA (HR = 1.10, P < 0.05) were included to construct the survival prediction model.

CONCLUSION

In this study, we successfully constructed a ceRNA regulatory network in blood exosomes from PAAD patients and developed a two-gene survival prediction model that provided new targets which shall aid in diagnosing and treating PAAD.