Multi-omics Data Research Articles

REUNION: transcription factor binding prediction and regulatory association inference from single-cell multi-omics data.

Profiling of gene expression and chromatin accessibility by single-cell multi-omics approaches can help to systematically decipher how transcription factors (TFs) regulate target gene expression via cis-region interactions. However, integrating information from different modalities to discover regulatory associations is challenging, in part because motif scanning approaches miss many likely TF binding sites. We develop REUNION, a framework for predicting genome-wide TF binding and cis-region-TF-gene "triplet" regulatory associations using single-cell multi-omics data. The first component of REUNION, Unify, utilizes information theory-inspired complementary score functions that incorporate TF expression, chromatin accessibility, and target gene expression to identify regulatory associations. The second component, Rediscover, takes Unify estimates as input for pseudo semi-supervised learning to predict TF binding in accessible genomic regions that may or may not include detected TF motifs. Rediscover leverages latent chromatin accessibility and sequence feature spaces of the genomic regions, without requiring chromatin immunoprecipitation data for model training. Applied to peripheral blood mononuclear cell data, REUNION outperforms alternative methods in TF binding prediction on average performance. In particular, it recovers missing region-TF associations from regions lacking detected motifs, which circumvents the reliance on motif scanning and facilitates discovery of novel associations involving potential co-binding transcriptional regulators. Newly identified region-TF associations, even in regions lacking a detected motif, improve the prediction of target gene expression in regulatory triplets, and are thus likely to genuinely participate in the regulation. All source code is available at https://github.com/yangymargaret/REUNION.

Oncotree2vec - a method for embedding and clustering of tumor mutation trees.

Understanding the genomic heterogeneity of tumors is an important task in computational oncology, especially in the context of finding personalized treatments based on the genetic profile of each patient's tumor. Tumor clustering that takes into account the temporal order of genetic events, as represented by tumor mutation trees, is a powerful approach for grouping together patients with genetically and evolutionarily similar tumors and can provide insights into discovering tumor subtypes, for more accurate clinical diagnosis and prognosis. Here, we propose oncotree2vec, a method for clustering tumor mutation trees by learning vector representations of mutation trees that capture the different relationships between subclones in an unsupervised manner. Learning low-dimensional tree embeddings facilitates the visualization of relations between trees in large cohorts and can be used for downstream analyses, such as deep learning approaches for single-cell multi-omics data integration. We assessed the performance and the usefulness of our method in three simulation studies and on two real datasets: a cohort of 43 trees from six cancer types with different branching patterns corresponding to different modes of spatial tumor evolution and a cohort of 123 AML mutation trees. https://github.com/cbg-ethz/oncotree2vec.

An Innovative Multi-Omics Model Integrating Latent Alignment and Attention Mechanism for Drug Response Prediction.

By using omics, we can now examine all components of biological systems simultaneously. Deep learning-based drug prediction methods have shown promise by integrating cancer-related multi-omics data. However, the complex interaction between genes poses challenges in accurately projecting multi-omics data. In this research, we present a predictive model for drug response that incorporates diverse types of omics data, comprising genetic mutation, copy number variation, methylation, and gene expression data. This study proposes latent alignment for information mismatch in integration, which is achieved through an attention module capturing interactions among diverse types of omics data. The latent alignment and attention modules significantly improve predictions, outperforming the baseline model, with MSE = 1.1333, F1-score = 0.5342, and AUROC = 0.5776. High accuracy was achieved in predicting drug responses for piplartine and tenovin-6, while the accuracy was comparatively lower for mitomycin-C and obatoclax. The latent alignment module exclusively outperforms the baseline model, enhancing the MSE by 0.2375, the F1-score by 4.84%, and the AUROC by 6.1%. Similarly, the attention module only improves these metrics by 0.1899, 2.88%, and 2.84%, respectively. In the interpretability case study, panobinostat exhibited the most effective predicted response, with a value of -4.895. We provide reliable insights for drug selection in personalized medicine by identifying crucial genetic factors influencing drug response.

Integrated multi-omics analysis and machine learning to refine molecular subtypes, prognosis, and immunotherapy in lung adenocarcinoma.

Lung adenocarcinoma (LUAD) has a malignant characteristic that is highly aggressive and prone to metastasis. There is still a lack of suitable biomarkers to facilitate the refinement of precision-based therapeutic regimens. We used a combinationof10 known clustering algorithms and the omics data from 4 dimensions to identify high-resolution molecular subtypes of LUAD. Subsequently, consensus machine learning-related prognostic signature (CMRS) was developed based on subtypes related genes and an integrated program framework containing 10 machine learning algorithms. The efficiency of CMRS was analyzed from the perspectives of tumor microenvironment, genomic landscape, immunotherapy, drug sensitivity, and single-cell analysis. In terms of results,through multi-omics clustering, we identified 2 comprehensive omics subtypes (CSs) in which CS1 patients had worse survival outcomes, higher aggressiveness, mRNAsi and mutation frequency. Subsequently, we developed CMRS based on 13 key genes up-regulated in CS1. The prognostic predictive efficiency of CMRS was superior to most established LUAD prognostic signatures. CMRS demonstrated a strong correlation with tumor microenvironmental feature variants and genomic instability generation. Regarding clinical performance, patients in the high CMRS group were more likely to benefit from immunotherapy, whereas low CMRS were more likely to benefit from chemotherapy and targeted drug therapy. In addition, we evaluated that drugs such as neratinib, oligomycin A, and others may be candidates for patients in the high CMRS group. Single-cell analysis revealed that CMRS-related genes were mainly expressed in epithelial cells.The novel molecular subtypes identified in this study based on multi-omics data could provide new insights into the stratified treatment of LUAD, while the development of CMRS could serve as a candidate indicator of the degree of benefit of precision therapy and immunotherapy for LUAD.

Genetic correlation between smoking behavior and gastroesophageal reflux disease: insights from integrative multi-omics data

BackgroundObservational studies have preliminarily revealed an association between smoking and gastroesophageal reflux disease (GERD). However, little is known about the causal relationship and shared genetic architecture between the two. This study aims to explore their common genetic correlations by leveraging genome-wide association studies (GWAS) of smoking behavior—specifically, smoking initiation (SI), never smoking (NS), ever smoking (ES), cigarettes smoked per day (CPD), age of smoking initiation(ASI) and GERD.MethodsFirstly, we conducted global cross-trait genetic correlation analysis and heritability estimation from summary statistics (HESS) to explore the genetic correlation between smoking behavior and GERD. Then, a joint cross-trait meta-analysis was performed to identify shared “pleiotropic SNPs” between smoking behavior and GERD, followed by co-localization analysis. Additionally, multi-marker analyses using annotation (MAGMA) were employed to explore the degree of enrichment of single nucleotide polymorphism (SNP) heritability in specific tissues, and summary data-based Mendelian randomization (SMR) was further utilized to investigate potential functional genes. Finally, Mendelian randomization (MR) analysis was conducted to explore the causal relationship between the smoking behavior and GERD.ResultsConsistent genetic correlations were observed through global and local genetic correlation analyses, wherein SI, ES, and CPD showed significantly positive genetic correlations with GERD, while NS and ASI showed significantly negative correlations. HESS analysis also identified multiple significantly associated loci between them. Furthermore, three novel “pleiotropic SNPs” (rs4382592, rs200968, rs1510719) were identified through cross-trait meta-analysis and co-localization analysis to exist between SI, NS, ES, ASI, and GERD, mapping the genes MED27, HIST1H2BO, MAML3 as new pleiotropic genes between SI, NS, ES, ASI, and GERD. Moreover, both smoking behavior and GERD were found to be co-enriched in multiple brain tissues, with GMPPB, RNF123, and RBM6 identified as potential functional genes co-enriched in Cerebellar Hemisphere, Cerebellum, Cortex/Nucleus accumbens in SI and GERD, and SUOX identified in Caudate nucleus, Cerebellum, Cortex in NS and GERD. Lastly, consistent causal relationships were found through MR analysis, indicating that SI, ES, and CPD increase the risk of GERD, while NS and higher ASI decrease the risk.ConclusionWe identified genetic loci associated with smoking behavior and GERD, as well as brain tissue sites of shared enrichment, prioritizing three new pleiotropic genes and four new functional genes. Finally, the causal relationship between smoking behavior and GERD was demonstrated, providing insights for early prevention strategies for GERD.

The prognostic significance and immune characteristics of bone morphogenetic proteins (BMPs) family: A pan-cancer multi-omics analysis.

Bone morphogenetic proteins (BMPs) are a group of cancer-related proteins vital for development and progression of certain cancer types. Nevertheless, function of BMP family in pan-cancer was not detailedly researched. Investigating expression pattern and prognostic value of the BMPs family (BMP1-8A and BMP8B) expression across multiple cancer types. Our research integrated multi-omics data for exploring potential associations between BMPs expression and prognosis, clinicopathological characteristics, copy number or somatic mutations, immune characteristics, tumor microenvironment (TME), tumor mutation burden (TMB), microsatellite instability (MSI), immune checkpoint genes and drug sensitivity in The Cancer Genome Atlas (TCGA) tumors. Furthermore, association of BMPs expression and immunotherapy effectiveness was investigated in some confirmatory cohorts (GSE111636, GSE78220, GSE67501, GSE176307, IMvigor210 and mRNA sequencing data from currently undergoing TRUCE01 clinical research included), and biological function and potential signaling pathways of BMPs in bladder cancer (BCa) was explored via Gene Set Enrichment Analysis (GSEA). Eventually, immune infiltration analysis was done via BMPs expression, copy number or somatic mutations in BCa, as well as validation of the expression levels by reverse transcription-quantitative PCR and western blot, and in vitro functional experiments of BMP8A. Discoveries displayed BMPs expression was related to prognosis, clinicopathological characteristics, mutations, TME, TMB, MSI and immune checkpoint genes of TCGA tumors. Anticancer drug sensitivity analysis displayed BMPs were associated with various drug sensitivities. What's more, it was discovered that expression level of certain BMP family members related to objective response to immunotherapy. By GSEA, we discovered multiple immune-associated functions and pathways were enriched. Immune infiltration analysis on BCa also displayed significant associations among BMPs copy number variations, mutation status and infiltration level of diverse immune cells. Furthermore, differential expression validation and in vitro phenotypic experiment indicated that BMP8A significantly promoted BCa cell proliferation, migration and invasion. Current results confirmed significance of both BMPs expression and genomic alteration in the prognosis and treatment of diverse cancer types, and suggested that BMPs may be vital for BCa and can possibly be utilized as biomarkers for immunotherapy.

MOI is a comprehensive database collecting processed multi-omics data associated with viral infection

Viral infections pose significant public health challenges, exemplified by the global impact of COVID-19 caused by SARS-CoV-2. Understanding the intricate molecular mechanisms governing virus-host interactions is pivotal for effective intervention strategies. Despite the burgeoning multi-omics data on viral infections, a centralized database elucidating host responses to viruses remains lacking. In response, we have developed a comprehensive database named ‘MOI’ (available at http://www.fynn-guo.cn/), specifically designed to aggregate processed Multi-Omics data related to viral Infections. This meticulously curated database serves as a valuable resource for conducting detailed investigations into virus-host interactions. Leveraging high-throughput sequencing data and metadata from PubMed and Gene Expression Omnibus (GEO), MOI comprises over 3200 viral-infected samples, encompassing human and murine infections. Standardized processing pipelines ensure data integrity, including bulk RNA sequencing (RNA-seq), single-cell RNA-seq (scRNA-seq), Chromatin Immunoprecipitation sequencing (ChIP-seq), and Assay for Transposase-Accessible Chromatin using sequencing (ATAC-seq). MOI offers user-friendly interfaces presenting comprehensive cell marker tables, gene expression data, and epigenetic landscape charts. Analytical tools for DNA sequence conversion, FPKM calculation, differential gene expression, and Gene Ontology (GO)/ Kyoto Encyclopedia of Genes and Genomes (KEGG) enrichment enhance data interpretation. Additionally, MOI provides 16 visualization plots for intuitive data exploration. In summary, MOI serves as a valuable repository for researchers investigating virus-host interactions. By centralizing and facilitating access to multi-omics data, MOI aims to advance our understanding of viral pathogenesis and expedite the development of therapeutic interventions.

Multi-omics data analysis reveals the complex roles of age in differentiated thyroid cancer

AimsAge is a major risk factor for differentiated thyroid cancer (DTC); however, the mechanisms underlying aging-regulated progression of DTC remains unclear. MethodsBased on multi-omics data (transcriptional files, somatic mutation files, methylation files) derived from the TCGA database, we comprehensively investigated the genomic and biological features associated with aging in patients with DTC. ResultsWe confirmed that age was an independent risk factor for overall survival and progression-free survival of patients with DTC, and confirmed that 55 years of age (adopted in the 8th AJCC staging system) is an appropriate cutoff for patients with DTC rather than 45 years (adopted in the 7th AJCC staging system). Using 55 years as the cutoff, we demonstrated DNA methylation-driven transcriptional regulation during aging, and identified the landscape of somatic mutations in young and old patients with DTC along with two aging-related mutations: TTN and EIF1AX. Subsequently, we investigated the infiltration of immune cells in DTC, and found that old patients exhibited decreased CD8+ T cells infiltration with lower cytotoxicity. Finally, we constructed a prognosis prediction model based on three age-related genes (PTK2B, E2F1, and GHR) that showed satisfactory performance in predicting patients prognosis. ConclusionsWe comprehensively investigated the complex interplay between age and biological features of DTC, which may provide new insights into the role of aging in DTC.

MDDOmics: multi-omics resource of major depressive disorder.

Major depressive disorder (MDD) is a pressing global health issue. Its pathogenesis remains elusive, but numerous studies have revealed its intricate associations with various biological factors. Consequently, there is an urgent need for a comprehensive multi-omics resource to help researchers in conducting multi-omics data analysis for MDD. To address this issue, we constructed the MDDOmics database (Major Depressive Disorder Omics, (https://www.csuligroup.com/MDDOmics/), which integrates an extensive collection of published multi-omics data related to MDD. The database contains 41 222 entries of MDD research results and several original datasets, including Single Nucleotide Polymorphisms, genes, non-coding RNAs, DNA methylations, metabolites and proteins, and offers various interfaces for searching and visualization. We also provide extensive downstream analyses of the collected MDD data, including differential analysis, enrichment analysis and disease-gene prediction. Moreover, the database also incorporates multi-omics data for bipolar disorder, schizophrenia and anxiety disorder, due to the challenge in differentiating MDD from similar psychiatric disorders. In conclusion, by leveraging the rich content and online interfaces from MDDOmics, researchers can conduct more comprehensive analyses of MDD and its similar disorders from various perspectives, thereby gaining a deeper understanding of potential MDD biomarkers and intricate disease pathogenesis. Database URL: https://www.csuligroup.com/MDDOmics/.

Enhancer demethylation-regulated gene score identified molecular subtypes, inspiring immunotherapy or CDK4/6 inhibitor therapy in oesophageal squamous cell carcinoma

The 5-year survival rate of oesophageal squamous cell carcinoma (ESCC) is approximately 20%. The prognosis and drug response exhibit substantial heterogeneity in ESCC, impeding progress in survival outcomes. Our goal is to identify a signature for tumour subtype classification, enabling precise clinical treatments. Utilising pre-treatment multi-omics data from an ESCC dataset (n=310), an enhancer methylation-eRNA-target gene regulation network was constructed and validated by invitro experiments. Four machine learning methods collectively identified core target genes, establishing an Enhancer Demethylation-Regulated Gene Score (EDRGS) model for classification. The molecular function of EDRGS subtyping was explored in scRNA-seq (n=60) and bulk-seq (n=310), and the EDRGS's potential to predict treatment response was assessed in datasets of various cancer types. EDRGS stratified ESCCs into EDRGS-high/low subtypes, with EDRGS-high signifying a less favourable prognosis in ESCC and nine additional cancer types. EDRGS-high exhibited an immune-hot but immune-suppressive phenotype with elevated immune checkpoint expression, increased T cell infiltration, and IFNγ signalling in ESCC, suggesting a better response to immunotherapy. Notably, EDRGS outperformed PD-L1 in predicting anti-PD-1/L1 therapy effectiveness in ESCC (n=42), kidney renal clear cell carcinoma (KIRC, n=181), and bladder urothelial carcinoma (BLCA, n=348) cohorts. EDRGS-low showed a cell cycle-activated phenotype with higher CDK4 and/or CDK6 expression, demonstrating a superior response to the CDK4/6 inhibitor palbociclib, validated in ESCC (n=26), melanoma (n=18), prostate cancer (n=15) cells, and PDX models derived from patients with pancreatic cancer (n=30). Identification of EDRGS subtypes enlightens ESCC categorisation, offering clinical insights for patient management in immunotherapy (anti-PD-1/L1) and CDK4/6 inhibitor therapy across cancer types. This study was supported by funding from the National Key R&D Program of China (2021YFC2501000, 2020YFA0803300), the National Natural Science Foundation of China (82030089, 82188102), the CAMS Innovation Fund for Medical Sciences (2021-I2M-1-018, 2022-I2M-2-001, 2021-I2M-1-067), the Fundamental Research Funds for the Central Universities (3332021091).

Integrated Assays of Genome-Wide Association Study, Multi-Omics Co-Localization, and Machine Learning Associated Calcium Signaling Genes with Oilseed Rape Resistance to Sclerotinia sclerotiorum.

Sclerotinia sclerotiorum (Ss) is one of the most devastating fungal pathogens, causing huge yield loss in multiple economically important crops including oilseed rape. Plant resistance to Ss pertains to quantitative disease resistance (QDR) controlled by multiple minor genes. Genome-wide identification of genes involved in QDR to Ss is yet to be conducted. In this study, we integrated several assays including genome-wide association study (GWAS), multi-omics co-localization, and machine learning prediction to identify, on a genome-wide scale, genes involved in the oilseed rape QDR to Ss. Employing GWAS and multi-omics co-localization, we identified seven resistance-associated loci (RALs) associated with oilseed rape resistance to Ss. Furthermore, we developed a machine learning algorithm and named it Integrative Multi-Omics Analysis and Machine Learning for Target Gene Prediction (iMAP), which integrates multi-omics data to rapidly predict disease resistance-related genes within a broad chromosomal region. Through iMAP based on the identified RALs, we revealed multiple calcium signaling genes related to the QDR to Ss. Population-level analysis of selective sweeps and haplotypes of variants confirmed the positive selection of the predicted calcium signaling genes during evolution. Overall, this study has developed an algorithm that integrates multi-omics data and machine learning methods, providing a powerful tool for predicting target genes associated with specific traits. Furthermore, it makes a basis for further understanding the role and mechanisms of calcium signaling genes in the QDR to Ss.

Integrative multi-omics analysis reveals cellular and molecular insights into primary Sjögren's syndrome

ObjectiveThis study aims to comprehensively analyze genomic, transcriptomic, proteomic, and single-cell sequencing data to unravel the molecular basis of primary Sjögren's syndrome (pSS) and explore potential therapeutic targets. MethodsMendelian randomization and single-cell RNA sequencing were employed to analyze pSS data. Differentially expressed genes specific to different blood cell types were identified. Integration of multiomics data facilitated the exploration of genetic regulatory relationships. ResultsThe analysis revealed distinct cell clusters representing various immune cell subsets. Several genes, including cathepsin S (CTSS) and glutathione S-transferase omega 1 (GSTO1), were identified as potential biomarkers and therapeutic targets for pSS. Diagnostic utility analysis demonstrated the discriminatory power of CTSS and GSTO1 in distinguishing pSS patients from healthy controls. ConclusionThe findings highlight the importance of integrating multiomics data for understanding pSS pathogenesis. CTSS and GSTO1 show promise as diagnostic biomarkers and potential therapeutic targets for pSS. Further investigations are warranted to elucidate the underlying mechanisms and develop targeted therapies for this complex autoimmune disease.

Multi omics analysis of mitophagy subtypes and integration of machine learning for predicting immunotherapy responses in head and neck squamous cell carcinoma.

Mitophagy serves as a critical mechanism for tumor cell death, significantly impacting the progression of tumors and their treatment approaches. There are significant challenges in treating patients with head and neck squamous cell carcinoma, underscoring the importance of identifying new targets for therapy. The function of mitophagy in head and neck squamous carcinoma remains uncertain, thus investigating its impact on patient outcomes and immunotherapeutic responses is especially crucial. We initially analyzed the differential expression, prognostic value, intergene correlations, copy number variations, and mutation frequencies of mitophagy-related genes at the pan-cancer level. Through unsupervised clustering, we divided head and neck squamous carcinoma into three subtypes with distinct prognoses, identified the signaling pathway features of each subtype using ssGSEA, and characterized subtype B as having features of an immune desert using various immune infiltration calculation methods. Using multi-omics data, we identified the genomic variation characteristics, mutated gene pathway features, and drug sensitivity features of the mitophagy subtypes. Utilizing a combination of 10 machine learning algorithms, we have developed a prognostic scoring model called Mitophagy Subgroup Risk Score (MSRS), which is used to predict patient survival and the response to immune checkpoint blockade therapy. Simultaneously, we applied MSRS to single-cell analysis to explore intercellular communication. Through laboratory experiments, we validated the biological function of SLC26A9, one of the genes in the risk model. In summary, we have explored the significant role of mitophagy in head and neck tumors through multi-omics data, providing new directions for clinical treatment.

The Applications of Artificial Intelligence (AI)-Driven Tools in Virus-Like Particles (VLPs) Research.

Viral-like particles (VLPs) represent versatile nanoscale structures mimicking the morphology and antigenic characteristics of viruses, devoid of genetic material, making them promising candidates for various biomedical applications. The integration of artificial intelligence (AI) into VLP research has catalyzed significant advancements in understanding, production, and therapeutic applications of these nanostructures. This comprehensive review explores the collaborative utilization of AI tools, computational methodologies, and state-of-the-art technologies within the VLP domain. AI's involvement in bioinformatics facilitates sequencing and structure prediction, unraveling genetic intricacies and three-dimensional configurations of VLPs. Furthermore, AI-enabled drug discovery enables virtual screening, demonstrating promise in identifying compounds to inhibit VLP activity. In VLP production, AI optimizes processes by providing strategies for culture conditions, nutrient concentrations, and growth kinetics. AI's utilization in image analysis and electron microscopy expedites VLP recognition and quantification. Moreover, network analysis of protein-protein interactions through AI tools offers an understanding of VLP interactions. The integration of multi-omics data via AI analytics provides a comprehensive view of VLP behavior. Predictive modeling utilizing machine learning algorithms aids in forecasting VLP stability, guiding optimization efforts. Literature mining facilitated by text mining algorithms assists in summarizing information from the VLP knowledge corpus. Additionally, AI's role in laboratory automation enhances experimental efficiency. Addressing data security concerns, AI ensures the protection of sensitive information in the digital era of VLP research. This review serves as a roadmap, providing insights into AI's current and future applications in VLP research, thereby guiding innovative directions in medicine and beyond.

Multinomial Logistic Regression with Adaptive Regularization for Cancer Subtype Classification via Multi-omics Data

Background: Integrating multi-omics data for cancer classification brings complementary biological insights while also facing challenges such as data integration, gene grouping, and adaptive weight construction. Objective: This paper aims to address the challenges faced by the cancer subtype classification and gene screening based on multi-omics data. Methods: Multinomial logistic regression with adaptive regularization (MLRAR) was proposed by integrating DNA methylation, gene mutation, and RNA-seq information. A data preprocessing strategy that effectively utilizes multi-omics information was presented, and the local maximum quasiclique merging (lmQCM) algorithm was implemented to group genes. Biological pathway information was utilized to evaluate the significance of gene groups, while the significance of each gene within a group was evaluated by integrating mutation information, information theory, and methylation information. Results: Compared to MRlasso, MRGL, MSGL, MROGL, AMRSOGL, and AGLRMR, the proposed method yielded improvements in subtype classification accuracy of breast cancer by 2.6%, 2.9%, 3.5%, 2.3%, 2.0%, and 1.8%, respectively. In addition, MLRAR also achieved significant improvements in ovarian cancer by 8.2%, 5.0%, 6.8%, 5.2%, 12.7%, and 6.3%, respectively. Conclusion: The proposed method can effectively deal with data integration, gene grouping, and adaptive weight construction.

Circadian-driven tissue specificity is constrained under caloric restricted feeding conditions

Tissue specificity is a fundamental property of an organ that affects numerous biological processes, including aging and longevity, and is regulated by the circadian clock. However, the distinction between circadian-affected tissue specificity and other tissue specificities remains poorly understood. Here, using multi-omics data on circadian rhythms in mice, we discovered that approximately 35% of tissue-specific genes are directly affected by circadian regulation. These circadian-affected tissue-specific genes have higher expression levels and are associated with metabolism in hepatocytes. They also exhibit specific features in long-reads sequencing data. Notably, these genes are associated with aging and longevity at both the gene level and at the network module level. The expression of these genes oscillates in response to caloric restricted feeding regimens, which have been demonstrated to promote longevity. In addition, aging and longevity genes are disrupted in various circadian disorders. Our study indicates that the modulation of circadian-affected tissue specificity is essential for understanding the circadian mechanisms that regulate aging and longevity at the genomic level.

Predicting regulatory mutations and their target genes by new computational integrative analysis: A study of follicular lymphoma

Mutations in DNA regulatory regions are increasingly being recognized as important drivers of cancer and other complex diseases. These mutations can regulate gene expression by affecting DNA-protein binding and epigenetic profiles, such as DNA methylation in genome regulatory elements. However, identifying mutation hotspots associated with expression regulation and disease progression in non-coding DNA remains a challenge. Unlike most existing approaches that assign a mutation score to individual single nucleotide polymorphisms (SNP), a mutation block (MB)-based approach was introduced in this study to assess the collective impact of a cluster of SNPs on transcription factor-DNA binding affinity, differential gene expression (DEG), and nearby DNA methylation. Moreover, the long-distance target genes of functional MBs were identified using a new permutation-based algorithm that assessed the significance of correlations between DNA methylation at regulatory regions and target gene expression. Two new Python packages were developed. The Differential Methylation Region (DMR-analysis) analysis tool was used to detect DMR and map them to regulatory elements. The second tool, an integrated DMR, DEG, and SNP analysis tool (DDS-analysis), was used to combine the omics data to identify functional MBs and long-distance target genes. Both tools were validated in follicular lymphoma (FL) cohorts, where not only known functional MBs and their target genes (BCL2 and BCL6) were recovered, but also novel genes were found, including CDCA4 and JAG2, which may be associated with FL development. These genes are linked to target gene expression and are significantly correlated with the methylation of nearby DNA sequences in FL. The proposed computational integrative analysis of multiomics data holds promise for identifying regulatory mutations in cancer and other complex diseases.

NNBGWO-BRCA marker: Neural Network and binary grey wolf optimization based Breast cancer biomarker discovery framework using multi-omics dataset

Background and ObjectiveBreast cancer is a multifaceted condition characterized by diverse features and a substantial mortality rate, underscoring the imperative for timely detection and intervention. The utilization of multi-omics data has gained significant traction in recent years to identify biomarkers and classify subtypes in breast cancer. This kind of research idea from part to whole will also be an inevitable trend in future life science research. Deep learning can integrate and analyze multi-omics data to predict cancer subtypes, which can further drive targeted therapies. However, there are few articles leveraging the nature of deep learning for feature selection. Therefore, this paper proposes a Neural Network and Binary grey Wolf Optimization based BReast CAncer bioMarker (NNBGWO-BRCAMarker) discovery framework using multi-omics data to obtain a series of biomarkers for precise classification of breast cancer subtypes. MethodsNNBGWO-BRCAMarker consists of two phases: in the first phase, relevant genes are selected using the weights obtained from a trained feedforward neural network; in the second phase, the binary grey wolf optimization algorithm is leveraged to further screen the selected genes, resulting in a set of potential breast cancer biomarkers. ResultsThe SVM classifier with RBF kernel achieved a classification accuracy of 0.9242 ± 0.03 when trained using the 80 biomarkers identified by NNBGWO-BRCAMarker, as evidenced by the experimental results. We conducted a comprehensive gene set analysis, prognostic analysis, and druggability analysis, unveiling 25 druggable genes, 16 enriched pathways strongly linked to specific subtypes of breast cancer, and 8 genes linked to prognostic outcomes. ConclusionsThe proposed framework successfully identified 80 biomarkers from the multi-omics data, enabling accurate classification of breast cancer subtypes. This discovery may offer novel insights for clinicians to pursue in further studies.

DIPG-27. MATCHED PRIMARY-RELAPSE PEDHGG PATIENT-DERIVED XENOGRAFT MODELS TO IDENTIFY THERAPY RESISTANCE PROFILES

Abstract BACKGROUND We established patient-derived xenografts (PDXs) from matched primary-relapse pairs of pediatric high-grade gliomas (pedHGGs) in collaboration with the Swedish Childhood Tumor Biobank. Further, we used human iPSC derived cortical organoids to evaluate interactions between glial and tumor cells under treatment pressure potentially lost in xenograft models. Using this setup, we hope to follow tumor relapse evolution and to identify novel mechanisms behind treatment evasion and relapse formation. METHODS Tumoral singel cell suspensions from matched primary-relapse pairs are injected orthotopically in immunodeficient NOG mice. We establish in vitro 2D models in stem cell conditions from the first generation of animals transplanted. Lentivirus-based labelling with GFP/Luciferase was done on PDX-derived cell lines to allow cell tracking and to quantify efficacy of drug screening efforts. Co-culture of these cells with normal cells in 3D assembloids were performed using 150-250 days old cortical organoids. Single-cell RNA sequencing and bulk RNA sequencing (RNA-seq) on patient biopsies, their corresponding PDXs from first generation of mice, and PDX-derived cell lines, was performed to track potential changes in vivo and in vitro and to further validate primary-relapse specific evolution. RESULTS 2D and 3D cell cultures offered efficient labeling and we quantified differences in sensitivity to standard therapy between primary and relapse samples. PDX models and assembloids highlighted pedHGG growth patterns, modes of invasion and cell interactions, especially astrocyte-tumor cell interactions. These 3D models also allow for further validation of targeted drug testing based on multiomics data (WGS, methylation and RNA-seq) from the patient biopsies. CONCLUSION PedHGG primary-relapse in vitro 2D cell line/ in vivo 3D assembloid models together with PDXs is a robust approach to study different aspects of relapse-specific tumor biology. Our results offer insights aiding the development of novel therapeutics inhibiting relapse mechanisms already in the first line of treatment.

METB-12. GIFT FROM A CHILD POSTMORTEM SPECIMEN DATASET EXTENSION OF THE PEDIATRIC BRAIN TUMOR ATLAS EMPOWERS PEDIATRIC BRAIN TUMOR RESEARCH

Abstract BACKGROUND Pediatric brain and spinal cord tumors are the leading cause of cancer-related mortality in children. An incomplete understanding of brain tumor biology and associated limited access to high-quality biological samples for research are the main factors driving the lack of clinical therapeutic development for pediatric brain tumors that recur or progress. Post-mortem tissue donation provides an unprecedented resource for addressing some of these limitations. METHODS The Gift from a Child (GFAC) program by the Swifty Foundation has a unique mission to increase post-mortem pediatric brain tissue donations through advocacy as well as the education of clinicians and families. Through GFAC’s strategic collaboration with the Children’s Brain Tumor Network (CBTN), CBTN has leveraged postmortem tissue to expand the Pediatric Brain Tumor Atlas (PBTA), a cross-histology multi-omics atlas resource. As part of the effort CBTN has sequenced and released data for over 350 post-mortem pediatric brain tumor specimens including multiple brain region sampling cases with specimen and sequencing quality metrics. RESULTS Here we present an assessment of postmortem samples and available multi-omic data on postmortem samples within the PBTA dataset. Data have been harmonized and released with no publication embargo. To access data, researchers can utilize existing open source data resources and platforms including PedCbioPortal and OpenPedCan to: (1) Identify tumor spatial and temporal specific alterations (2) Establish tumor evolution trajectory leading to therapeutic resistance and tumor progression; (3) Understand tumor heterogeneity longitudinally across multiple ‘omics layers; and (4) Identify and request specimens and derived tumor models. CONCLUSIONS Together, we present the largest deeply characterized cohort of postmortem pediatric brain tumor samples as powerful expansion of the PBTA cohort of >3,000 pediatric brain tumors. CBTN’s open-science model supported by the GFAC mission highlights the value and utility of autopsy-based specimen collection on behalf of improving outcomes for children with brain tumors.