Integrative Multi-omics Analysis Research Articles

Attention-based GCN integrates multi-omics data for breast cancer subtype classification and patient-specific gene marker identification.

Breast cancer is a heterogeneous disease and can be divided into several subtypes with unique prognostic and molecular characteristics. The classification of breast cancer subtypes plays an important role in the precision treatment and prognosis of breast cancer. Benefitting from the relation-aware ability of a graph convolution network (GCN), we present a multi-omics integrative method, the attention-based GCN (AGCN), for breast cancer molecular subtype classification using messenger RNA expression, copy number variation and deoxyribonucleic acid methylation multi-omics data. In the extensive comparative studies, our AGCN models outperform state-of-the-art methods under different experimental conditions and both attention mechanisms and the graph convolution subnetwork play an important role in accurate cancer subtype classification. The layer-wise relevance propagation (LRP) algorithm is used for the interpretation of model decision, which can identify patient-specific important biomarkers that are reported to be related to the occurrence and development of breast cancer. Our results highlighted the effectiveness of the GCN and attention mechanisms in multi-omics integrative analysis and the implement of the LRP algorithm can provide biologically reasonable insights into model decision.

A multi-omics integrative analysis based on CRISPR screens re-defines the pluripotency regulatory network in ESCs

A comprehensive and precise definition of the pluripotency gene regulatory network (PGRN) is crucial for clarifying the regulatory mechanisms in embryonic stem cells (ESCs). Here, after a CRISPR/Cas9-based functional genomics screen and integrative analysis with other functional genomes, transcriptomes, proteomes and epigenome data, an expanded pluripotency-associated gene set is obtained, and a new PGRN with nine sub-classes is constructed. By integrating the DNA binding, epigenetic modification, chromatin conformation, and RNA expression profiles, the PGRN is resolved to six functionally independent transcriptional modules (CORE, MYC, PAF, PRC, PCGF and TBX). Spatiotemporal transcriptomics reveal activated CORE/MYC/PAF module activity and repressed PRC/PCGF/TBX module activity in both mouse ESCs (mESCs) and pluripotent cells of early embryos. Moreover, this module activity pattern is found to be shared by human ESCs (hESCs) and cancers. Thus, our results provide novel insights into elucidating the molecular basis of ESC pluripotency.

Integrative single-cell multiomics analyses dissect molecular signatures of intratumoral heterogeneities and differentiation states of human gastric cancer.

Human gastric cancer is a highly lethal disease, but the underlying multiomic molecular signatures remain largely unclear. Here, we performed multi-regional sampling, parallel single-cell multiomics sequencing and integrated analyses of human gastric cancer. We identified common transcriptomic alterations of gastric cancer cells, such as aberrant down-regulation of genes associated with normal stomach function and up-regulation of KRT7, PI3, S100A4, etc. Surprisingly, aberrant and prevalent up-regulation of genes highly expressed in normal colorectal epithelial cells were also identified in cancer cells, which may be partially regulated by promoter chromatin accessibility and DNA methylation levels. We revealed the single-cell DNA methylome landscape of gastric cancer, and identified candidate DNA methylation biomarkers, such as hypermethylated promoters of TMEM240 and HAGLROS, and hypomethylated promoters of TRPM2-AS and HRH1. Additionally, the relationships between genetic lineages, DNA methylation and transcriptomic clusters were systematically revealed at single-cell level. We showed that DNA methylation heterogeneities were mainly among different genetic lineages of cancer cells. Moreover, we found that DNA methylation levels of cancer cells with poorer differentiation states tend to be higher than those of cancer cells with better differentiation states in the primary tumor within the same patient, although still lower than in normal gastric epithelial cells. Cancer cells with poorer differentiation states also prevalently down-regulated MUC1 expression and immune-related pathways, and had poor infiltration of CD8+ T cells. Our study dissected the molecular signatures of intratumoral heterogeneities and differentiation states of human gastric cancer using integrative single-cell multiomics analyses.

IL-7 receptor expression is frequent in T-cell acute lymphoblastic leukemia and predicts sensitivity to JAK inhibition

AbstractT-cell acute lymphoblastic leukemia (T-ALL) is an aggressive hematological malignancy with a dismal prognosis related to refractory/relapsing diseases, raising the need for new targeted therapies. Activating mutations of interleukin-7–receptor pathway genes (IL-7Rp) play a proven leukemia-supportive role in T-ALL. JAK inhibitors, such as ruxolitinib, have recently demonstrated preclinical efficacy. However, prediction markers for sensitivity to JAK inhibitors are still lacking. Herein, we show that IL-7R (CD127) expression is more frequent (∼70%) than IL-7Rp mutations in T-ALL (∼30%). We compared the so-called nonexpressers (no IL-7R expression/IL-7Rp mutation), expressers (IL7R expression without IL-7Rp mutation), and mutants (IL-7Rp mutations). Integrative multiomics analysis outlined IL-7R deregulation in virtually all T-ALL subtypes, at the epigenetic level in nonexpressers, genetic level in mutants, and posttranscriptional level in expressers. Ex vivo data using primary-derived xenografts support that IL-7Rp is functional whenever the IL-7R is expressed, regardless of the IL-7Rp mutational status. Consequently, ruxolitinib impaired T-ALL survival in both expressers and mutants. Interestingly, we show that expressers displayed ectopic IL-7R expression and IL-7Rp addiction conferring a deeper sensitivity to ruxolitinib. Conversely, mutants were more sensitive to venetoclax than expressers. Overall, the combination of ruxolitinib and venetoclax resulted in synergistic effects in both groups. We illustrate the clinical relevance of this association by reporting the achievement of complete remission in 2 patients with refractory/relapsed T-ALL. This provides proof of concept for translation of this strategy into clinics as a bridge-to-transplantation therapy. IL7R expression can be used as a biomarker for sensitivity to JAK inhibition, thereby expanding the fraction of patients with T-ALL eligible for ruxolitinib up to nearly ∼70% of T-ALL cases.

Recent revelations and future directions using single-cell technologies in chronic lymphocytic leukemia.

Chronic lymphocytic leukemia (CLL) is a clinically and biologically heterogeneous disease with varying outcomes. In the last decade, the application of next-generation sequencing technologies has allowed extensive mapping of disease-specific genomic, epigenomic, immunogenetic, and transcriptomic signatures linked to CLL pathogenesis. These technologies have improved our understanding of the impact of tumor heterogeneity and evolution on disease outcome, although they have mostly been performed on bulk preparations of nucleic acids. As a further development, new technologies have emerged in recent years that allow high-resolution mapping at the single-cell level. These include single-cell RNA sequencing for assessment of the transcriptome, both of leukemic and non-malignant cells in the tumor microenvironment; immunogenetic profiling of B and T cell receptor rearrangements; single-cell sequencing methods for investigation of methylation and chromatin accessibility across the genome; and targeted single-cell DNA sequencing for analysis of copy-number alterations and single nucleotide variants. In addition, concomitant profiling of cellular subpopulations, based on protein expression, can also be obtained by various antibody-based approaches. In this review, we discuss different single-cell sequencing technologies and how they have been applied so far to study CLL onset and progression, also in response to treatment. This latter aspect is particularly relevant considering that we are moving away from chemoimmunotherapy to targeted therapies, with a potentially distinct impact on clonal dynamics. We also discuss new possibilities, such as integrative multi-omics analysis, as well as inherent limitations of the different single-cell technologies, from sample preparation to data interpretation using available bioinformatic pipelines. Finally, we discuss future directions in this rapidly evolving field.

Abstract 3683: Activation of purine anabolism creates a therapeutic vulnerability in hepatocellular carcinoma via m6A-mediated epitranscriptomic regulation

Abstract Introduction Purines are building blocks for genomics and the abundance of purine nucleotides is controlled by purine synthesis and purine degradation. Imbalance of purine nucleotide pool in tumors has been shown, but how synthesis and degradation of purine integrates in tumors has not been well characterized. Hepatocellular carcinoma (HCC) is the most common liver cancer with a high mortality rate and limited treatments. Aberrant purine metabolism was observed in HCC, but the functional status of global purine metabolism in HCC and how that drive HCC fitness and therapeutic response remain unclear. Method HCC-specific purine metabolic changes were identified from the transcriptomic and metabolic data of tumor and paired non-tumor liver tissues obtained from 62 HCC patients from Thailand (discovery cohort) and validated in an additional 672 HCC with different race/ethnicities and etiologies. Correlation analysis of purine metabolic alteration and tumor-associated genomic, transcriptome and metabolome were conducted. The impacts of purine metabolism on HCC survival and therapeutic vulnerability were investigated. Biological functions of purine metabolic alterations were assessed in vitro and in vivo on multiple HCC cell lines. Results Using multi-omics integrative analyses, we found a tumor-specific activation of purine anabolism, induced by upregulation of purine de novo biosynthesis and inhibition of purine degradation, in HCC. A high purine anabolic status was associated with dysregulation of the DNA damage repairing (DDR) machinery in HCC, accompanied by unique somatic mutational signatures linked to patient prognosis. By examining drug responses to 180 molecular targeted agents in HCC cells, we found that increasing purine anabolism induced a therapeutic vulnerability of HCC to DDR targeting agents. Mechanistically, we found that excessive purine metabolites induced N6-methyladenosine (m6A) modification on DTL, a DDB1 cullin4 associated factor, thereby delaying the degradation of DTL mRNA transcriptomic remodeling of the DDR machinery and tumor fitness. Experimentally, we demonstrated that suppressing purine anabolism or exposing to DDR targeting agent, such as berzosertib, inhibited the growth of HCCs with high purine anabolism in vitro and in vivo. Conclusion Our study suggests that targeting purine anabolism or DDR may represent as attractive strategies in patients with high purine anabolic HCCs, and purine anabolic status could be a valuable biomarker to allocate systemic treatments for patients with advanced HCC. Citation Format: Man-Hsin Hung, Ching Wen Chang, Kathy Cheng Wang, Jittiporn Chaisaingmongkol, Mathuros Ruchirawat, Tim F. Greten, Xin Wei Wang. Activation of purine anabolism creates a therapeutic vulnerability in hepatocellular carcinoma via m6A-mediated epitranscriptomic regulation. [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2023; Part 1 (Regular and Invited Abstracts); 2023 Apr 14-19; Orlando, FL. Philadelphia (PA): AACR; Cancer Res 2023;83(7_Suppl):Abstract nr 3683.

Abstract 3512: Organ-specific effects of Smad4 in metastatic pancreatic cancer

Abstract Primary and metastatic tumors typically share the same driver gene mutations, but it is unclear if these mutations are functionally relevant across different anatomical sites. Among such mutations, inactivation of the tumor suppressor gene SMAD4 is a hallmark of pancreatic and other gastrointestinal cancers and has been associated with metastatic disease. Here, we develop a mouse model of pancreatic ductal adenocarcinoma that enables Smad4 depletion in the pre-malignant Kras-mutant pancreas and subsequent Smad4 reactivation in late-stage metastatic tumors. Whereas early Smad4 deficiency facilitated tumor formation, later Smad4 restoration had unexpected organ-specific outcomes: no effect on primary tumor growth, suppression of liver metastases, and promotion of lung metastases. Integrative multiomic analysis revealed a near-universal genomic deletion of the Cdkn2a/b locus and organ-specific changes in the tumor cells’ epigenomic state. In particular, the liver and lung differentially favored KLF vs. RUNX dominated chromatin programs, which were confirmed to underpin the divergent effects of Smad4 restoration in functional studies. Our results show how organ-dependent epigenomic changes lead to altered driver gene function in metastatic disease. This organ-specific gene-chromatin interplay may be a generalizable principle in cancer biology and invites a revised paradigm for precision oncology that considers anatomical site in the interpretation of tumor genetics. Citation Format: Kaloyan M. Tsanov, Francisco M. Barriga, Yu-Jui Ho, Direna Alonso-Curbelo, Geulah Livshits, Richard Koche, Timour Baslan, Alexandra N. Wuest, Janelle Simon, Sha Tian, Wei Luan, Scott W. Lowe. Organ-specific effects of Smad4 in metastatic pancreatic cancer [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2023; Part 1 (Regular and Invited Abstracts); 2023 Apr 14-19; Orlando, FL. Philadelphia (PA): AACR; Cancer Res 2023;83(7_Suppl):Abstract nr 3512.

Abstract 4515: PIONEER: harnessing multi-omics data to enhance immunotherapeutic target discovery and development

Abstract Introduction: Immunotherapeutic strategies have produced remarkable results in some malignancies. However, optimal cell surface targets in many childhood cancers remain elusive and tools for novel target discovery are limited. We developed a proteogenomic approach to identify high confidence cell surface proteins for immunotherapy development and applied it to neuroblastoma, an often fatal childhood cancer. Through the Pediatric Immunotherapy Discovery and Development Network (PI-DDN), we have extended this approach to 14 high-risk childhood cancers. This effort includes MS-based surfaceome data generation for 175 patient-derived xenograft (PDX) models, 30 primary patient tumors, and 10 human derived cell line models. Methods/Results: To optimize the utility of our approach and data, we are developing a web-based application called PIONEER (Pediatric Integrative Omics Network Enhancing Early Research). The goal of PIONEER is to disseminate data to the broader scientific community and to provide the necessary analysis, query and visualization tools to make these data accessible to everyone, regardless of computational expertise. A pilot version of PIONEER was developed using R Shiny Dashboard and is derived from our neuroblastoma efforts. The application is comprised of two main categories: (1) target discovery data and prioritization (2) target validation and preclinical development. Modules include proteomics, transcriptomics, epigenomics, multi-omics, validation and pre-clinical drug development. Cancer ‘omics data currently housed in PIONEER include tumor and cancer cell line mass-spectrometry based proteomics, RNA-sequencing, and chromatin immunoprecipitation (ChIP) sequencing. Extensive normal tissue expression data from GTEx and mass spectrometry will be integrated. Surface proteins are prioritized through an integrative multi-omic analysis of tumor and normal tissue data. Users can perform queries and cancer subtype and cross-histotype studies, apply custom cutoffs, and generate plots for visualization. We are currently adding functionality to support automatic data analysis and integration for surface proteins (SPACE: Surface Protein Analysis for Collaborative Efforts). Through the target validation and preclinical development modules, users can view an antibody repository, immunofluorescence, immunohistochemistry, drugs in development for each protein, and efficacy in patient derived xenograft models. PIONEER will be deployed using R Connect; data for additional histotypes will be incorporated as available. Conclusion: PIONEER will provide a comprehensive characterization of the surfaceome of high-risk pediatric cancers and a web-based application for data integration, visualization and sharing. This interface facilitates the discovery of optimal immunotherapeutic drug targets in high-risk childhood cancers. Citation Format: Amber K. Weiner, Hemma Murali, Rawan Shraim, Karina L. Conkrite, Alexander B. Radaoui, Daniel Martinez, Brian Mooney, Sandra E. Spencer Miko, Gian Negri, Alberto Delaidelli, Caitlyn de Jong, Yuankun Zhu, Allison P. Heath, Jennifer Pogoriler, Yael P. Mosse, Deanne M. Taylor, Poul H. Sorensen, Gregg B. Morin, Benjamin A. Garcia, John M. Maris, Sharon J. Diskin. PIONEER: harnessing multi-omics data to enhance immunotherapeutic target discovery and development. [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2023; Part 1 (Regular and Invited Abstracts); 2023 Apr 14-19; Orlando, FL. Philadelphia (PA): AACR; Cancer Res 2023;83(7_Suppl):Abstract nr 4515.

Abstract 4859: Bi-steric mTORC1 inhibitors are superior to rapamycin and induce apoptotic cell death in tumor models with hyperactivated mTORC1

Abstract The PI3K-mTOR pathway is one of the most commonly dysregulated pathways in human tumors. Rapalogs have been used extensively in human clinical trials but exhibit modest clinical benefit for the most part, possibly due to their lack of effect on 4E-BP1, a key target downstream of mTORC1. ATP-competitive mTOR inhibitors can fully inhibit mTORC1 but are poorly tolerated possibly due to their inhibition of mTORC2. A new class of selective mTORC1 inhibitors has been developed and termed ‘bi-steric’, which comprise a rapamycin-like core moiety covalently linked to an mTOR active-site inhibitor. We report that bi-steric mTORC1-selective inhibitors (RMC-4627 and RMC-6272) have greater activity than rapalogs for tumors with mTORC1 hyperactivation. RMC-4627 and RMC-6272 showed more effective growth inhibition in multiple tumor cell lines with mTORC1 dysfunction compared to rapamycin. Both RMC-4627 and RMC-6272 at ~1 nM showed near complete inhibition of p4E-BP1T37/46, which was not seen with rapamycin treatment. Rapamycin and both bi-steric compounds markedly reduced kidney tumor burden and human bladder cancer PDX tumor size after four weeks of treatment. Tumor regrowth assessed after two-month treatment cessation was significantly reduced in the RMC-6272 group as compared to the rapamycin group. Finally, TUNEL staining showed RMC-6272 treatment led to a greater induction of apoptosis in kidney tumor cells and human bladder cancer PDX tumors relative to rapamycin after a single dose of each compound. This is the first time that clear evidence for cell death after mTORC1 inhibition has been seen. Integrative multi-omic analysis reveals differential global rewiring induced by RMC-6272 in comparison to rapamycin. Genes involved in cell cycle transition were downregulated by RMC-6272 vs. rapamycin. Proteins downregulated by RMC-6272 treatment relative to rapamycin were enriched for translation and ribosome-related proteins, consistent with effects of non-phosphorylated 4E-BP1 on synthesis of those proteins. Multiple purine metabolites were decreased to a greater extent in RMC-6272 treated cells as compared to rapamycin treatment, including deoxyadenosine, AICAR, AMP, IMP, and GMP. Notably, there was no significant change in R5P, suggesting that downregulation of the purine de novo synthesis pathway was the cause of these changes. Strong tumor cell apoptosis was induced by the suppression of de novo purine synthesis through mTORC1-PRPS1 axis. PRPS1, the rate-limit enzyme involving in purine synthesis was regulated by mTORC1 activity. In summary, RMC-4627 and RMC-6272 demonstrate improved in vitro and in vivo inhibition of mTORC1 in comparison to rapamycin, and induced more cell death in preclinical models, indicating the potential of bi-steric mTORC1-selective inhibitors as a novel therapeutic strategy to treat tumors with mTORC1 dysregulation. Citation Format: Heng Du, Yu Chi Yang, Heng-Jia Liu, Min Yuan, John Asara, Kwok-Kin Wong, Mallika Singh, David Kwiatkowski. Bi-steric mTORC1 inhibitors are superior to rapamycin and induce apoptotic cell death in tumor models with hyperactivated mTORC1. [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2023; Part 1 (Regular and Invited Abstracts); 2023 Apr 14-19; Orlando, FL. Philadelphia (PA): AACR; Cancer Res 2023;83(7_Suppl):Abstract nr 4859.

Multi-omics integration reveals potential stage-specific druggable targets in T-cell acute lymphoblastic leukemia

T-cell acute lymphoblastic leukemia (T-ALL), a heterogeneous hematological malignancy, is caused by the developmental arrest of normal T-cell progenitors. The development of targeted therapeutic regimens is impeded by poor knowledge of the stage-specific aberrances in this disease. In this study, we performed multi-omics integration analysis, which included mRNA expression, chromatin accessibility, and gene-dependency database analyses, to identify potential stage-specific druggable targets and repositioned drugs for this disease. This multi-omics integration helped identify 29 potential pathological genes for T-ALL. These genes exhibited tissue-specific expression profiles and were enriched in the cell cycle, hematopoietic stem cell differentiation, and the AMPK signaling pathway. Of these, four known druggable targets (CDK6, TUBA1A, TUBB, and TYMS) showed dysregulated and stage-specific expression in malignant T cells and may serve as stage-specific targets in T-ALL. The TUBA1A expression level was higher in the early T cell precursor (ETP)-ALL cells, while TUBB and TYMS were mainly highly expressed in malignant T cells arrested at the CD4 and CD8 double-positive or single-positive stage. CDK6 exhibited a U-shaped expression pattern in malignant T cells along the naïve to maturation stages. Furthermore, mebendazole and gemcitabine, which target TUBA1A and TYMS, respectively, exerted stage-specific inhibitory effects on T-ALL cell lines, indicating their potential stage-specific antileukemic role in T-ALL. Collectively, our findings might aid in identifying potential stage-specific druggable targets and are promising for achieving more precise therapeutic strategies for T-ALL.

Focal adhesion is associated with lithium response in bipolar disorder: evidence from a network-based multi-omics analysis

Lithium (Li) is one of the most effective drugs for treating bipolar disorder (BD), however, there is presently no way to predict response to guide treatment. The aim of this study is to identify functional genes and pathways that distinguish BD Li responders (LR) from BD Li non-responders (NR). An initial Pharmacogenomics of Bipolar Disorder study (PGBD) GWAS of lithium response did not provide any significant results. As a result, we then employed network-based integrative analysis of transcriptomic and genomic data. In transcriptomic study of iPSC-derived neurons, 41 significantly differentially expressed (DE) genes were identified in LR vs NR regardless of lithium exposure. In the PGBD, post-GWAS gene prioritization using the GWA-boosting (GWAB) approach identified 1119 candidate genes. Following DE-derived network propagation, there was a highly significant overlap of genes between the top 500- and top 2000-proximal gene networks and the GWAB gene list (Phypergeometric = 1.28E–09 and 4.10E–18, respectively). Functional enrichment analyses of the top 500 proximal network genes identified focal adhesion and the extracellular matrix (ECM) as the most significant functions. Our findings suggest that the difference between LR and NR was a much greater effect than that of lithium. The direct impact of dysregulation of focal adhesion on axon guidance and neuronal circuits could underpin mechanisms of response to lithium, as well as underlying BD. It also highlights the power of integrative multi-omics analysis of transcriptomic and genomic profiling to gain molecular insights into lithium response in BD.

An integrated multiomics analysis of rectal cancer patients identified POU2F3 as a putative druggable target and entinostat as a cytotoxic enhancer of 5-fluorouracil.

Rectal cancer (RC) accounts for one-third of colorectal cancers (CRC), and 40% of these are locally advanced rectal cancers (LARC). The use of neoadjuvant chemoradiotherapy (nCRT) significantly reduces the rate of local recurrence compared to adjuvant therapy or surgery alone. However, after nCRT, up to 40%-60% of patients show a poor pathological response, while only about 20% achieve a pathological complete response. In this scenario, the identification of novel predictors of tumor response to nCRT is urgently needed to reduce LARC mortality and to spare poorly responding patients from unnecessary treatments. Therefore, by combining gene and microRNA expression datasets with proteomic data from LARC patients, we developed an integrated network centered on seven hub-genes putatively involved in the response to nCRT. In an independent validation cohort of LARC patients, we confirmed that differential expression of NFKB1, TRAF6 and STAT3 is correlated with the response to nCRT. In addition, the functional enrichment analysis also revealed that these genes are strongly related to hallmarks of cancer and inflammation, whose dysfunction may causatively affect LARC patient's response to nCRT. Furthermore, by constructing the transcription factor-module network, we hypothesized a protective role of POU2F3 gene, which could be used as a new drug target in LARC patients. Finally, we identified and tested in vitro entinostat, a histone deacetylase inhibitor, as a chemical compound that could be combined with a classical therapeutic regimen in order to design more efficient therapeutic strategies in LARC management.

Multi-omics analysis identifies drivers of protein phosphorylation

BackgroundPhosphorylation of proteins is a key step in the regulation of many cellular processes including activation of enzymes and signaling cascades. The abundance of a phosphorylated peptide (phosphopeptide) is determined by the abundance of its parent protein and the proportion of target sites that are phosphorylated.ResultsWe quantified phosphopeptides, proteins, and transcripts in heart, liver, and kidney tissue samples of mice from 58 strains of the Collaborative Cross strain panel. We mapped ~700 phosphorylation quantitative trait loci (phQTL) across the three tissues and applied genetic mediation analysis to identify causal drivers of phosphorylation. We identified kinases, phosphatases, cytokines, and other factors, including both known and potentially novel interactions between target proteins and genes that regulate site-specific phosphorylation. Our analysis highlights multiple targets of pyruvate dehydrogenase kinase 1 (PDK1), a regulator of mitochondrial function that shows reduced activity in the NZO/HILtJ mouse, a polygenic model of obesity and type 2 diabetes.ConclusionsTogether, this integrative multi-omics analysis in genetically diverse CC strains provides a powerful tool to identify regulators of protein phosphorylation. The data generated in this study provides a resource for further exploration.

Identification of immunotherapy and chemotherapy-related molecular subtypes in colon cancer by integrated multi-omics data analysis.

Colon cancer is a highly heterogeneous disease, and identifying molecular subtypes can provide insights into deregulated pathways within tumor subsets, which may lead to personalized treatment options. However, most prognostic models are based on single-pathway genes. In this study, we aimed to identify three clinically relevant subtypes of colon cancer based on multiple signaling pathways-related genes. Integrative multi-omics analysis was used to explain the biological processes contributing to colon cancer aggressiveness, recurrence, and progression. Machine learning methods were employed to identify the subtypes and provide medication guidance for distinct subtypes using the L1000 platform. We developed a robust prognostic model (MKPC score) based on gene pairs and validated it in one internal test set and three external test sets. Risk-related genes were extracted and verified by qPCR. Three clinically relevant subtypes of colon cancer were identified based on multiple signaling pathways-related genes, which had significantly different survival state (Log-Rank test, p<0.05). Integrative multi-omics analysis revealed biological processes contributing to colon cancer aggressiveness, recurrence, and progression. The developed MKPC score, based on gene pairs, was robust in predicting prognosis state (Log-Rank test, p<0.05), and risk-related genes were successfully verified by qPCR (t test, p<0.05). An easy-to-use web tool was created for risk scoring and therapy stratification in colon cancer patients, and the practical nomogram can be extended to other cancer types. In conclusion, our study identified three clinically relevant subtypes of colon cancer and developed a robust prognostic model based on gene pairs. The developed web tool is a valuable resource for researchers and clinicians in risk scoring and therapy stratification in colon cancer patients, and the practical nomogram can be extended to other cancer types.

MOPA: An integrative multi-omics pathway analysis method for measuring omics activity.

Pathways are composed of proteins forming a network to represent specific biological mechanisms and are often used to measure enrichment scores based on a list of genes in means to measure their biological activity. The pathway analysis is a de facto standard downstream analysis procedure in most genomic and transcriptomic studies. Here, we present MOPA (Multi-Omics Pathway Analysis), which is a multi-omics integrative method that scores individual pathways in a sample wise manner in terms of enriched multi-omics regulatory activity, which we refer to mES (multi-omics Enrichment Score). The mES score reflects the strength of regulatory relations between multi-omics in units of pathways. In addition, MOPA is able to measure how much each omics contribute to mES that may be used to observe what kind of omics are active in a pathway within a sample group (e.g., subtype, gender), which we refer to OCR (Omics Contribution Rate). Using nine different cancer types, 93 clinical features and three types of omics (i.e., gene expression, miRNA and methylation), MOPA was used to search for clinical features that were explainable in context of multi-omics. By evaluating the performance of MOPA, we showed that it yielded higher or at least equal performance compared to previous single and multi-omics pathway analysis tools. We find that the advantage of MOPA is the ability to explain pathways in terms of omics relation using mES and OCR. As one of the results, the TGF-beta signaling pathway was captured as an important pathway that showed distinct mES and OCR values specific to the CMS4 subtype in colon adenocarcinoma. The mES and OCR metrics suggested that the mRNA and miRNA expressions were significantly different from the other subtypes, which was concordant with previous studies. The MOPA software is available at https://github.com/jaeminjj/MOPA.

The X-linked epigenetic regulator UTX controls NK cell-intrinsic sex differences.

Viral infection outcomes are sex biased, with males generally more susceptible than females. Paradoxically, the numbers of antiviral natural killer (NK) cells are increased in males. We demonstrate that while numbers of NK cells are increased in male mice, they display decreased effector function compared to females in mice and humans. These differences were not solely dependent on gonadal hormones, because they persisted in gonadectomized mice. Kdm6a (which encodes the protein UTX), an epigenetic regulator that escapes X inactivation, was lower in male NK cells, while NK cell-intrinsic UTX deficiency in female mice increased NK cell numbers and reduced effector responses. Furthermore, mice with NK cell-intrinsic UTX deficiency showed increased lethality to mouse cytomegalovirus. Integrative multi-omics analysis revealed a critical role for UTX in regulating chromatin accessibility and gene expression critical for NK cell homeostasis and effector function. Collectively, these data implicate UTX as a critical molecular determinant of sex differences in NK cells.

Proteomics and metabolomics profiling reveal panels of circulating diagnostic biomarkers and molecular subtypes in stable COPD

BackgroundChronic obstructive pulmonary disease (COPD) is a complex and heterogeneous disease with high morbidity and mortality, especially in advanced patients. We aimed to develop multi-omics panels of biomarkers for the diagnosis and explore its molecular subtypes.MethodsA total of 40 stable patients with advanced COPD and 40 controls were enrolled in the study. Proteomics and metabolomics techniques were applied to identify potential biomarkers. An additional 29 COPD and 31 controls were enrolled for validation of the obtained proteomic signatures. Information on demographic, clinical manifestation, and blood test were collected. The ROC analyses were carried out to evaluate the diagnostic performance, and experimentally validated the final biomarkers on mild-to-moderate COPD. Next, molecular subtyping was performed using proteomics data.ResultsTheophylline, palmitoylethanolamide, hypoxanthine, and cadherin 5 (CDH5) could effectively diagnose advanced COPD with high accuracy (auROC = 0.98, sensitivity of 0.94, and specificity of 0.95). The performance of the diagnostic panel was superior to that of other single/combined results and blood tests. Proteome based stratification of COPD revealed three subtypes (I–III) related to different clinical outcomes and molecular feature: simplex COPD, COPD co-existing with bronchiectasis, and COPD largely co-existing with metabolic syndrome, respectively. Two discriminant models were established using the auROC of 0.96 (Principal Component Analysis, PCA) and 0.95 (the combination of RRM1 + SUPV3L1 + KRT78) in differentiating COPD and COPD with co-morbidities. Theophylline and CDH5 were exclusively elevated in advanced COPD but not in its mild form.ConclusionsThis integrative multi-omics analysis provides a more comprehensive understanding of the molecular landscape of advanced COPD, which may suggest molecular targets for specialized therapy.

Refining Classification of Cholangiocarcinoma Subtypes via Proteogenomic Integration Reveals New Therapeutic Prospects

Intrahepatic cholangiocarcinomas (iCCs) are characterized by their rarity, difficult diagnosis, and overall poor prognosis. The iCC molecular classification for developing precision medicine strategies was investigated. Comprehensive genomic, transcriptomic, proteomic, and phosphoproteomic analyses were performed on treatment-naïve tumor samples from 102 patients with iCC who underwent surgical resection with curative intent. An organoid model was constructed for testing therapeutic potential. Three clinically supported subtypes (stem-like, poorly immunogenic, and metabolism) were identified. NCT-501 (ALDH1A1 inhibitor) exhibited synergism with nab-paclitaxel in the organoid model for stem-like subtype. The oncometabolite dysregulations were associated with different clinical outcomes in stem-like and metabolism subtypes. Poorly immunogenic subtype harbors the non-T-cell tumor infiltration. Integrated multiomics analysis reproduced not only the three subtypes but also showed heterogeneity in iCC. This large-scale proteogenomic analysis provides information beyond that obtained with genomic analysis, allowing the functional impact of genomic alterations to be discerned. These findings may assist in the stratification of patients with iCC and in developing rational therapeutic strategies.

Integrative analysis of multi-omics and imaging data with incorporation of biological information via structural Bayesian factor analysis.

With the rapid development of modern technologies, massive data are available for the systematic study of Alzheimer's disease (AD). Though many existing AD studies mainly focus on single-modality omics data, multi-omics datasets can provide a more comprehensive understanding of AD. To bridge this gap, we proposed a novel structural Bayesian factor analysis framework (SBFA) to extract the information shared by multi-omics data through the aggregation of genotyping data, gene expression data, neuroimaging phenotypes and prior biological network knowledge. Our approach can extract common information shared by different modalities and encourage biologically related features to be selected, guiding future AD research in a biologically meaningful way. Our SBFA model decomposes the mean parameters of the data into a sparse factor loading matrix and a factor matrix, where the factor matrix represents the common information extracted from multi-omics and imaging data. Our framework is designed to incorporate prior biological network information. Our simulation study demonstrated that our proposed SBFA framework could achieve the best performance compared with the other state-of-the-art factor-analysis-based integrative analysis methods. We apply our proposed SBFA model together with several state-of-the-art factor analysis models to extract the latent common information from genotyping, gene expression and brain imaging data simultaneously from the ADNI biobank database. The latent information is then used to predict the functional activities questionnaire score, an important measurement for diagnosis of AD quantifying subjects' abilities in daily life. Our SBFA model shows the best prediction performance compared with the other factor analysis models. Code are publicly available at https://github.com/JingxuanBao/SBFA. qlong@upenn.edu.

Identification of a necroptosis-related gene signature as a novel prognostic biomarker of cholangiocarcinoma.

Cholangiocarcinoma (CHOL) is the most prevalent type of malignancy and the second most common form of primary liver cancer, resulting in high rates of morbidity and mortality. Necroptosis is a type of regulated cell death that appears to be involved in the regulation of several aspects of cancer biology, including tumorigenesis, metastasis, and cancer immunity. This study aimed to construct a necroptosis-related gene (NRG) signature to investigate the prognosis of CHOL patients using an integrated bioinformatics analysis. CHOL patient data were acquired from the Gene Expression Omnibus (GEO) (GSE89748, GSE107943) and The Cancer Genome Atlas (TCGA) databases, with NRGs data from the necroptosis pathway in the Kyoto Encyclopedia of Genes and Genomes (KEGG) database. Univariate and multivariate regression analyses were performed to establish the NRG signatures. Kaplan-Meier (KM) curves were used to evaluate the prognosis of patients with CHOL. Functional enrichment analysis was performed to identify key NRG-associated biological signaling pathways. We also applied integrative multi-omics analysis to the high- and low-risk score groups. Spearman's rank correlation was used to clarify the relationship between the NRG signature and immune infiltration. 65 differentially expressed (DE) NRGs were screened, five of which were selected to establish the prognostic signature of NRGS based on multivariate Cox regression analysis. We observed that low-risk patients survived significantly longer than high-risk patients. We found that patients with high-risk scores experienced higher immune cell infiltration, drug resistance, and more somatic mutations than patients with low-risk scores. We further found that sensitivities to GW843682X, mitomycin C, rapamycin, and S-trityl-L-cysteine were significantly higher in the low-risk group than in the high-risk group. Finally, we validated the expression of five NRGs in CHOL tissues using the TCGA database, HPA database and our clinical data. These findings demonstrate that the five-NRG prognostic signature for CHOL patients is reasonably accurate and valid, and it may prove to be of considerable value for the treatment and prognosis of CHOL patients in the future.