Pathway-level Analysis Research Articles

Optimal sampling strategy for probability estimation: An application to the Agricultural Quarantine Inspection Monitoring program.

Imported agricultural pests can cause substantial damage to agriculture, food security, and ecosystems. In the United States, the Agricultural Quarantine Inspection Monitoring (AQIM) program conducts random sampling to estimate the probabilities that cargo and passengers arriving at ports of entry carry pests. Assessing these risks accurately is critical to enable effective policies and operational procedures. This study introduces a pathway-level analysis with an objective function aligned with AQIM's goal, offering a new perspective compared to the current container-by-container approach, which relies on heuristics to set inspection rates. We formulate an optimization model that minimizes the mean squared error of the probability estimates that AQIM obtains. The central decision-making tradeoff that the model explores is whether it is preferable to sample more arriving containers (and fewer boxes per container) or more boxes per container (and fewer containers), given limited resources. We first derive an analytical solution for the optimal sampling strategy by leveraging several approximations. Then, we apply our model to a numerical case study of maritime cargo sampling at the Port of Long Beach. Across a wide range of parameter settings, the optimal strategy samples more containers (but fewer boxes per container) than the current AQIM protocol. The difference between the two strategies and the accuracy improvement with the optimal approach are larger if the pest statuses of boxes in the same container are more strongly correlated. We recommend that AQIM record box-level (beyond only container-level) inspection data, which could be used to estimate this correlation and other modelparameters.

GPS-Net: Discovering prognostic pathway modules based on network regularized kernel learning.

The search for prognostic biomarkers capable of predicting patient outcomes, by analyzing gene expression in tissue samples and other molecular profiles, remains largely focused on single-gene-based or global-gene-search approaches. Gene-centric approaches, while foundational, fail to capture the higher-order dependencies that reflect the activities of co-regulated processes, pathway alterations, and regulatory networks, all of which are crucial in determining the patient outcomes in complex diseases like cancer. Here, we introduce GPS-Net, a computational framework that fills the gap in efficiently identifying prognostic modules by incorporating the holistic pathway structures and the network of gene interactions. By innovatively incorporating advanced multiple kernel learning techniques and network-based regularization, the proposed method not only enhances the accuracy of biomarker and pathway identification but also significantly reduces computational complexity, as demonstrated by extensive simulation studies. Applying GPS-Net, we identified key pathways that are predictive of patient outcomes in a cancer immunotherapy study. Overall, our approach provides a novel framework that renders genome-wide pathway-level prognostic analysis both feasible and scalable, synergizing both mechanism-driven and data-driven methodologies for precision genomics.

Integrated microbiome and metabolomic analysis of Spodoptera litura under Metarhizium flavoviride qc1401 stress.

Metarhizium spp. have emerged as an alternative to chemical pesticides for protecting crops from insect pest. Here, we investigated midgut microbial community and metabolites of Spodoptera litura at three different timepoints after infection with Metarhizium flavoviride. The innate immune system of S. litura was activated with levels of polyphenol oxidase, carboxylesterase, multifunctional oxidase, and glutathione S-transferase activity significantly increasing. Exposure to the fungal pathogen also altered bacterial abundance and diversity in host's midgut, and these changes varied depending on the time elapsed since exposure. We identified more operational taxonomic units in the treated samples as compared to the control samples at all tested time points. A total of 372 metabolites were identified, and 88, 149, and 142 differentially accumulated metabolites (DAMs) were identified between the treatment and control groups at 3 timepoints after treatment, respectively. Based on the changes of DAMs in response to M. flavoviride infection at different timepoints and significantly enriched KEGG pathways, we speculated that "tyrosine metabolism," "galactose metabolism," "ATP-binding cassette transporters," "neuroactive ligand-receptor interaction," "purine metabolism," "arginine and proline metabolism," "beta-alanine metabolism," "lysosome," and "carbon metabolism" may participate in the metabolic-level defense response. An integrated pathway-level analysis of the 16S-rDNA and metabolomic data illustrated the connections and interdependencies between the metabolic responses of S. litura and the midgut microorganisms to M. flavoviride infection. This work emphasizes the value of integrated analyses of insect-pathogen interactions, provides a framework for future studies of critical microorganisms and metabolic determinants of these interactions, establishes a theoretical basis for the sustainable use of M. flavoviride.

GPS-Net: discovering prognostic pathway modules based on network regularized kernel learning.

The search for prognostic biomarkers capable of predicting patient outcomes, by analyzing gene expression in tissue samples and other molecular profiles, remains largely on single-gene-based or global-gene-search approaches. Gene-centric approaches, while foundational, fail to capture the higher-order dependencies that reflect the activities of co-regulated processes, pathway alterations, and regulatory networks, all of which are crucial in determining the patient outcomes in complex diseases like cancer. Here, we introduce GPS-Net, a computational framework that fills the gap in efficiently identifying prognostic modules by incorporating the holistic pathway structures and the network of gene interactions. By innovatively incorporating advanced multiple kernel learning techniques and network-based regularization, the proposed method not only enhances the accuracy of biomarker and pathway identification but also significantly reduces computational complexity, as demonstrated by extensive simulation studies. Applying GPS-Net, we identified key pathways that are predictive of patient outcomes in a cancer immunotherapy study. Overall, our approach provides a novel framework that renders genome-wide pathway-level prognostic analysis both feasible and scalable, synergizing both mechanism-driven and data-driven for precision genomics.

Integrated Transcriptome and Metabolome Analysis Reveals Molecular Mechanisms Underlying Resistance to Phytophthora Root Rot.

Soybean production is significantly impacted by Phytophthora root rot (PRR), which is caused by Phytophthora sojae. The nucleotide-binding leucine-rich repeat (NLR) gene family plays a crucial role in plant disease resistance. However, current understanding of the function of soybean NLR genes in resistance to PRR is limited. To address this knowledge gap, transgenic soybean plants overexpressing the NLR gene (Glyma.18g283200) were generated to elucidate the molecular mechanism of resistance. Here, transcript changes and metabolic differences were investigated at three time points (12, 24, and 36 h) after P. sojae infection in hypocotyls of two soybean lines, Dongnong 50 (susceptible line, WT) and Glyma.18g283200 overexpression line (resistant line, OE). Based on the changes in differentially expressed genes (DEGs) in response to P. sojae infection in different lines and at different time points, it was speculated that HOPZ-ACTIVATED RESISTANCE 1 (ZAR1), valine, leucine, and isoleucine degradation, and phytohormone signaling may be involved in the defense response of soybean to P. sojae at the transcriptome level by GO term and KEGG pathway enrichment analysis. Differentially accumulated metabolites (DAMs) analysis revealed that a total of 223 and 210 differential metabolites were identified in the positive ion (POS) and negative ion (NEG) modes, respectively. An integrated pathway-level analysis of transcriptomics (obtained by RNA-seq) and metabolomics data revealed that isoflavone biosynthesis was associated with disease resistance. This work provides valuable insights that can be used in breeding programs aiming to enhance soybean resistance against PRR.

Association of Microsatellite Instability and Gene Expression Profile in Colorectal Carcinoma and Potential Implications for Therapy.

Background and Objective: In sporadic colorectal carcinomas (CRC), microsatellite instability (MSI) pathways play important roles. Previously, we showed differences in DNA methylation patterns in microsatellite stable (MSS) colorectal carcinomas and MSI-CRC. In the current study, we explore the similarities and differences in gene expression profiles in MSS and MSI at the gene level and at the pathway level to better understand CRC pathogenesis and/or the potential for therapeutic opportunities. Material and Methods: Seventy-one CRC patients (MSI = 18, MSS = 53) were studied. Paired tumor and adjacent normal tissues were used for genome-wide gene expression assays. Result: At the gene level, we compared the list of differentially expressed genes (fold change (FC) ≥ 3 and FDR < 0.05) in tumor tissues compared to corresponding normal tissue in CRC patients with MSI tumors (190 genes) and MSS tumors (129 genes). Of these, 107 genes overlapped. The list of genes that were differentially expressed in MSI tumors only showed enrichment predominantly in two broad categories of pathways-(a) Inflammation-related pathways including the interleukin-17 (IL-17) signaling pathway, tumor necrosis factor (TNF) signaling pathway, chemokine signaling, nuclear factor kappa B (NFκB) signaling, and cytokine-cytokine interactions, and (b) metabolism-related pathways, including retinol metabolism, steroid hormone biosynthesis, drug metabolism, pentose and glucoronate interconversions, and ascorbate and aldarate metabolism. The genes in inflammation-related pathways were up-regulated whereas genes in metabolism-related pathways were down-regulated in MSI tumor tissue. Pathway-level analysis also revealed similar results confirming the gene enrichment findings. For example, the 150 genes involved in the IL-17 signaling pathway were on average up-regulated by 1.19 fold (CI 1.16-1.21) in MSI compared to 1.14 fold (CI 1.13-1.16) in MSS patients (interaction p = 0.0009). Conclusions: We document an association between MSI status and differential gene expression that broadens our understanding of CRC pathogenesis. Furthermore, targeting one or more of these dysregulated pathways could provide the basis for improved therapies for MSI and MSS CRC.

Whole genome sequencing in high-grade cervical intraepithelial neoplasia patients from different ethnic groups in China.

Cervical cancer (CC) is the fourth most common cancer in women worldwide. It develops through precancerous lesions (cervical intraepithelial neoplasia (CIN), graded from low-grade (CIN1) to high-grade (CIN2-3)). It is well established that precancerous and cancerous cervical lesions are caused by a persistent infection with high-risk types of the human papilloma virus (hrHPV). To have a deeper understanding of the pathogenesis of CIN and CC, we systematically analyzed the landscape of genomic alterations and HPV integration profiles in high-grade CIN2/3. We performed deep whole genome sequencing on exfoliated cervical cells and matched peripheral blood samples from a cohort of 51 Chinese patients (of whom 35 were HPV+) with high-grade CIN from 3 ethnic groups and constructed strict integrated workflow of genomic analysis. In addition, the HPV types and integration breakpoints in the exfoliated cervical cells from these patients were examined. Genomic analysis identified 6 significantly mutated genes (SMGs), including CDKN2A, PIK3CB, FAM20A, RABEP1, TMPRSS2 and SS18L1, in 51 CIN2/3 samples. As none of them had previously been identified as SMGs in the Cancer Genome Atlas cervical squamous cell carcinoma and endocervical adenocarcinoma (TCGA-CESC) cohort, future studies with larger sample size of CINs may be needed to validate our findings. Mutational signature analysis showed that mutational signatures of CINs were dramatically different from CCs, highlighting their different mutational processes and etiologies. Moreover, non-silent somatic mutations were detected in all of the CIN2/3 samples, and 88% of these mutations occurred in genes that also mutated in CCs of TCGA cohort. CIN2 samples had significantly less non-silent mutations than CIN3 samples (P = .0006). Gene ontology and pathway level analysis revealed that functions of mutated genes were significantly associated with tumorigenesis, thus these genes may be involved in the development and progression of CC. HPV integration breakpoints occurred in 28.6% of the CIN2/3 samples with HPV infection. Integrations of common high risk HPV types in CCs, including HPV16, 52, 58 and 68, also occurred in the CIN samples. Our results lay the groundwork for a deeper understanding of the molecular mechanisms underlying the pathogenesis of CC and pave the way for new tools for screening, diagnosis and treatment of cervical precancerous and cancerous lesions.

BIOM-63. PROTEOMIC BASED PROFILING OF CSF FOR CNSL MANAGEMENT

Abstract Central nervous system lymphoma (CNSL) is a rare subtype of non-Hodgkin lymphoma, primarily affecting the brain and spinal cord. CNSL exhibits a dismal prognosis, as reflected by a low 5-year survival rate of 30%, with the current standard of care. The advent of multi-agent intrathecal chemotherapy (MAITC) in conjunction with systemic therapy has demonstrated encouraging outcomes, enhancing both progression-free survival and overall survival in patients with CNSL. Given the potential morbidity associated with MAITC, identification of minimally-invasive biomarkers for guiding patient management are necessary. Leveraging the longitudinal, large volume of Cerebrospinal fluid (CSF) afforded through our MAITC program, the objective of this study is to identify CSF-based proteomic biomarkers that can serve as reliable indicators of MAITC treatment response and CNSL management. Patient-matched CSF samples from the Penn State Health Neuroscience biorepository were banked at pre- and post-therapeutic endpoint in 22 primary and 36 secondary CNSL patients. The samples were profiled using high-throughput fluid sample protocol coupled with mass-spectrometry that only requires 30 μl of CSF. More than 1000 unique proteins were detected using shotgun proteomics, and 797 proteins were present in 70% of the analyzed samples, which were used for downstream analysis. We were able to effectively discriminate between primary and secondary CNSL based on protein abundance. Furthermore, the findings suggest significant changes in the cellular processes related to glycoprotein metabolism and redox signalling in response to the treatment, indicating a potential impact on protein modification and the cellular response to oxidative stress. Our comprehensive, pathway-level analysis uncovered significant molecular alterations, shedding light on their potential prognostic significance in the context of CNS lymphoma. Future work is underway to identify biomarker signatures that would predict optimal response to MAITC therapy, exhibiting potential to improve clinical decision making.

Circadian pattern subtyping unveiling distinct immune landscapes in breast cancer patients for better immunotherapy.

While epidemiological studies have established a firm link between circadian disruption and tumorigenesis, the role and mechanism are not fully understood, complicating the design of therapeutic targets related to circadian rhythms (CR). Here, we aimed to explore the intertumoral heterogeneity of CR and elucidate its impact on the tumor microenvironment (TME), drug sensitivity, and immunotherapy. Based on unsupervised clustering of 28 CR genes, two distinct CR subtypes (cluster-A and cluster-B) were identified in the TCGA cohort. We further constructed a circadian rhythm signature (CRS) based on the CR genes primarily responsible for clustering to quantify CR activity and to distinguish CR subtypes of individual patients from external datasets. CR subtypes were evaluated by TME characteristics, functional annotation, clinical features, and therapeutic response. The cluster-B (low-CRS) group was characterized by highly enriched immune-related pathways, high immune cell infiltration, and high anti-tumor immunity, while the cluster-A (high-CRS) group was associated with immunosuppression, synaptic transmission pathways, EMT activation, poor prognosis, and drug resistance. Immunohistochemistry (IHC) results demonstrated that high CD8+ T cell infiltration was associated with low-CR-protein expression. Importantly, patients with low CRS were more likely to benefit from immune checkpoint blockade (ICB) treatment, possibly due to their higher tumor mutation burden (TMB), increased immune checkpoint expression, and higher proportion of "hot" immunophenotype. In a nutshell, the cross talk in CR could reflect the TME immunoreactivity in breast cancer. Besides providing the first comprehensive pathway-level analysis of CR in breast cancer, this work highlights the potential clinical utility of CR for immunotherapy.

PATH-SURVEYOR: pathway level survival enquiry for immuno-oncology and drug repurposing

Pathway-level survival analysis offers the opportunity to examine molecular pathways and immune signatures that influence patient outcomes. However, available survival analysis algorithms are limited in pathway-level function and lack a streamlined analytical process. Here we present a comprehensive pathway-level survival analysis suite, PATH-SURVEYOR, which includes a Shiny user interface with extensive features for systematic exploration of pathways and covariates in a Cox proportional-hazard model. Moreover, our framework offers an integrative strategy for performing Hazard Ratio ranked Gene Set Enrichment Analysis and pathway clustering. As an example, we applied our tool in a combined cohort of melanoma patients treated with checkpoint inhibition (ICI) and identified several immune populations and biomarkers predictive of ICI efficacy. We also analyzed gene expression data of pediatric acute myeloid leukemia (AML) and performed an inverse association of drug targets with the patient’s clinical endpoint. Our analysis derived several drug targets in high-risk KMT2A-fusion-positive patients, which were then validated in AML cell lines in the Genomics of Drug Sensitivity database. Altogether, the tool offers a comprehensive suite for pathway-level survival analysis and a user interface for exploring drug targets, molecular features, and immune populations at different resolutions.

Pathway-level multi-omics analysis of the molecular mechanisms underlying the toxicity of long-term tacrolimus exposure

Tacrolimus (TAC)-based treatment is associated with nephrotoxicity and hepatotoxicity; however, the underlying molecular mechanisms responsible for this toxicity have not been fully explored. This study elucidated the molecular processes underlying the toxic effects of TAC using an integrative omics approach. Rats were sacrificed after 4 weeks of daily oral TAC administration at a dose of 5 mg/kg. The liver and kidney underwent genome-wide gene expression profiling and untargeted metabolomics assays. Molecular alterations were identified using individual data profiling modalities and further characterized by pathway-level transcriptomics-metabolomics integration analysis. Metabolic disturbances were mainly related to an imbalance in oxidant-antioxidant status, as well as in lipid and amino acid metabolism in the liver and kidney. Gene expression profiles also indicated profound molecular alterations, including in genes associated with a dysregulated immune response, proinflammatory signals, and programmed cell death in the liver and kidney. Joint-pathway analysis indicated that the toxicity of TAC was associated with DNA synthesis disruption, oxidative stress, and cell membrane permeabilization, as well as lipid and glucose metabolism. In conclusion, our pathway-level integration of transcriptome and metabolome and conventional analyses of individual omics profiles, provided a more comprehensive picture of the molecular changes resulting from TAC toxicity. This study also serves as a valuable resource for subsequent investigations aiming to understand the mechanism underlying the molecular toxicology of TAC.

Abstract 4256: cBioPortal for Cancer Genomics

Abstract cBioPortal for Cancer Genomics is an open-source platform for interactive, exploratory analysis of large-scale clinico-genomic data sets. cBioPortal provides a suite of user-friendly visualizations and analyses, including OncoPrints, mutation “lollipop” plots, variant interpretation, group comparison, survival analysis, expression correlation analysis, alteration enrichment analysis, cohort and patient-level visualization. The public site (https://www.cbioportal.org) is accessed by &amp;gt;35,000 unique visitors each month and hosts data from &amp;gt;350 studies spanning individual labs and large consortia. In addition, at least 74 instances of cBioPortal are installed at academic institutions and companies worldwide. To better support all users, we unified our documentation (https://docs.cbioportal.org) and added a user guide and an ongoing series of ‘how-to’ videos to address common questions. In 2022 we added 32 studies (&amp;gt;38,000 samples) to the public site. In addition, we added a nonsynonymous tumor mutation burden (TMB) value for all samples and enhanced the TCGA PanCancer Atlas studies with DNA methylation and treatment data. All data is available in the cBioPortal Datahub: https://github.com/cBioPortal/datahub. We also host a dedicated instance for AACR Project GENIE, enabling access to the GENIE cohort of &amp;gt;165,000 clinically sequenced samples from 19 institutions (https://genie.cbioportal.org). The GENIE Biopharma Collaborative (BPC) enables the collection of comprehensive clinical annotations, including response, outcome, and treatment history. The first BPC cohorts are now available: ~2,000 non-small cell lung cancer samples and ~1,500 colorectal cancer samples. Support for multimodal data analysis has been a major focus, including several new integrations with external tools. Single cell data is now available in the CPTAC GBM study and can be visualized throughout cBioPortal, and via integration with cellxgene. On the patient page, H&amp;E and mIF images can be visualized via integration with Minerva, and the genomic overview now integrates IGV. We continue to enhance existing features. In the study view, users can now add charts comparing categorical vs continuous data, and the plots tab includes a heatmap option. We replaced the existing fusion data type with a generalized structural variant data type that supports detailed information including breakpoints and orientation, to enable new visualizations and analyses. Pathway level analysis has been extended with a new integration with NDEx. cBioPortal is fully open source (https://github.com/cBioPortal/). Development is a collaborative effort among groups at Memorial Sloan Kettering Cancer Center, Dana-Farber Cancer Institute, Children’s Hospital of Philadelphia, Princess Margaret Cancer Centre, Caris Life Sciences, Bilkent University and The Hyve. We welcome open source contributions from others in the cancer research community. Citation Format: Ino de Bruijn, Tali Mazor, Adam Abeshouse, Diana Baiceanu, Stephanie Carrero, Elena Garcia Lara, Benjamin Gross, David M. Higgins, Prasanna K. Jagannathan, Priti Kumari, Ritika Kundra, Bryan Lai, Xiang Li, James Lindsay, Aaron Lisman, Divya Madala, Ramyasree Madupuri, Angelica Ochoa, Yusuf Ziya Özgül, Oleguer Plantalech, Sander Rodenburg, Baby Anusha Satravada, Robert Sheridan, Lucas Sikina, Jessica Singh, S Onur Sumer, Yichao Sun, Pim van Nierop, Avery Wang, Manda Wilson, Hongxin Zhang, Gaofei Zhao, Sjoerd van Hagen, Ugur Dogrusoz, Allison Heath, Adam Resnick, Trevor J. Pugh, Chris Sander, Ethan Cerami, Jianjiong Gao, Nikolaus Schultz. cBioPortal for Cancer Genomics. [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 4256.

MP50-11 INTEGRATIVE MULTI-OMICS PROFILING IN PENILE SQUAMOUS CELL CARCINOMA

You have accessJournal of UrologyCME1 Apr 2023MP50-11 INTEGRATIVE MULTI-OMICS PROFILING IN PENILE SQUAMOUS CELL CARCINOMA Timothy Shaw, Alyssa Obermayer, Dalia Ercan, Paul Stewart, Jad Chahoud, Peter Johnstone, Philippe Spiess, Steven Eschrich, and Daniel Grass Timothy ShawTimothy Shaw More articles by this author , Alyssa ObermayerAlyssa Obermayer More articles by this author , Dalia ErcanDalia Ercan More articles by this author , Paul StewartPaul Stewart More articles by this author , Jad ChahoudJad Chahoud More articles by this author , Peter JohnstonePeter Johnstone More articles by this author , Philippe SpiessPhilippe Spiess More articles by this author , Steven EschrichSteven Eschrich More articles by this author , and Daniel GrassDaniel Grass More articles by this author View All Author Informationhttps://doi.org/10.1097/JU.0000000000003298.11AboutPDF ToolsAdd to favoritesDownload CitationsTrack CitationsPermissionsReprints ShareFacebookLinked InTwitterEmail Abstract INTRODUCTION AND OBJECTIVE: Penile squamous cell carcinoma (PSCC) is rare and about 50% of cases are related to HPV. Relapse after definitive therapy or first-line chemotherapy portends poor survival, thus we hypothesized novel targeting strategies in PSCC may be revealed by relating molecular pathways and patient survival. METHODS: We performed RNA-Seq on 37 PSCC tumors (HPV+=16, HPV-=16, unknown=5) from patients at our institution; 16 samples also underwent bulk proteomics with mass spectrometry. PATH-SURVEIOR is a framework for analyzing and visualizing “on-the-fly” associations of pathways with clinical endpoints. We applied PATH-SURVEIOR to perform single sample gene set enrichment (ssGSEA) on the PSCC RNA and protein expression data. Pathway scores and gene expression were dichotomized by the median to group samples as above/below for each pathway or individual gene expression. Multi-omics integration of pathways related to HPV status was assessed with DRPPM-EASY. TCGA data was downloaded from cBioportal. Uniform manifold projection (UMAP) was performed with the umap R package. RESULTS: We first performed an unsupervised analysis on PSCC and TCGA RNA data and UMAP found similarities with PSCC and other SCCs. Next, we associated pathways/genes and overall survival (OS) through univariable Cox regression and filtered results by hazard ratio (HR>1) and p-value<0.05, followed by HR ranking to define pathways/genes related with high-risk patients. Genes related to OS risk in PSCC included higher levels of DLL4, MET, and MSLN. Also, a cisplatin resistance gene set was linked to poor OS in a subset of PSCC patients. Systematic analysis of gene sets found expression associated with HPV as one prognostic feature based on an unsupervised pathway-level survival analysis; projection of this gene-set feature in the TCGA manifold space revealed an HPV-related cluster that included PSCC and other HPV-related cancers. By integrative GSEA of RNA and proteomics data, we found E2F and DNA damage repair pathways were enriched in HPV+ tumors, whereas HPV-tumors had features of EMT and inflammation. Whereas most RNAs and proteins aligned in direction of expression, several genes (e.g., TP53, MAGI3) were discordant to protein expression in HPV+ tumors, which may imply HPV-mediated translational regulation of protein subsets. CONCLUSIONS: Integrative analysis relating multi-omics data to patient survival from PSCC and TCGA samples identified features consistent with other HPV-related cancers, as well as potential novel targets to consider in PSCC. Source of Funding: Congressionally Directed Medical Research Programs: Rare Cancer Research Programs © 2023 by American Urological Association Education and Research, Inc.FiguresReferencesRelatedDetails Volume 209Issue Supplement 4April 2023Page: e689 Advertisement Copyright & Permissions© 2023 by American Urological Association Education and Research, Inc.MetricsAuthor Information Timothy Shaw More articles by this author Alyssa Obermayer More articles by this author Dalia Ercan More articles by this author Paul Stewart More articles by this author Jad Chahoud More articles by this author Peter Johnstone More articles by this author Philippe Spiess More articles by this author Steven Eschrich More articles by this author Daniel Grass More articles by this author Expand All Advertisement PDF downloadLoading ...

Cooperative driver pathways discovery by multiplex network embedding.

Cooperative driver pathways discovery helps researchers to study the pathogenesis of cancer. However, most discovery methods mainly focus on genomics data, and neglect the known pathway information and other related multi-omics data; thus they cannot faithfully decipher the carcinogenic process. We propose CDPMiner (Cooperative Driver Pathways Miner) to discover cooperative driver pathways by multiplex network embedding, which can jointly model relational and attribute information of multi-type molecules. CDPMiner first uses the pathway topology to quantify the weight of genes in different pathways, and optimizes the relations between genes and pathways. Then it constructs an attributed multiplex network consisting of micro RNAs, long noncoding RNAs, genes and pathways, embeds the network through deep joint matrix factorization to mine more essential information for pathway-level analysis and reconstructs the pathway interaction network. Finally, CDPMiner leverages the reconstructed network and mutation data to define the driver weight between pathways to discover cooperative driver pathways. Experimental results on Breast invasive carcinoma and Stomach adenocarcinoma datasets show that CDPMiner can effectively fuse multi-omics data to discover more driver pathways, which indeed cooperatively trigger cancers and are valuable for carcinogenesis analysis. Ablation study justifies CDPMiner for a more comprehensive analysis of cancer by fusing multi-omics data.

COVID-19 and non-alcoholic fatty liver disease: Biological insights from multi-omics data.

We explored the shared pathophysiological mechanisms between COVID-19 and non-alcoholic fatty liver disease (NAFLD) by integrating multi-omics data. We studied common genetic risk factors and underlying biological processes using functional enrichment analysis. To understand the sex-specific pathways involved in the clinical course of SARS-CoV-2 infection, we processed sex-stratified data from COVID-19 genome-wide association datasets. We further explored the transcriptional signature of the liver cells in healthy and COVID-19 tissue specimens. We also integrated genetic and metabolomic information. We found that COVID-19 and NAFLD share biological disease mechanisms, including pathways that regulate the inflammatory and lipopolysaccharide response. Single-cell transcriptomics revealed enrichment of complement-related pathways in Kupffer cells, syndecan-mediated signalling in plasma cells, and epithelial-to-mesenchymal transition in hepatic stellate cells. The strategy of pathway-level analysis of genomic and metabolomic data uncovered l-lactic acid, Krebs cycle intermediate compounds, arachidonic acid and cortisol among the most prominent shared metabolites.

Metabolic remodeling of pyrimidine synthesis pathway and serine synthesis pathway in human glioblastoma

Glioblastoma is the most common brain tumor with dismal outcomes in adults. Metabolic remodeling is now widely acknowledged as a hallmark of cancer cells, but glioblastoma-specific metabolic pathways remain unclear. Here we show, using a large-scale targeted proteomics platform and integrated molecular pathway-level analysis tool, that the de novo pyrimidine synthesis pathway and serine synthesis pathway (SSP) are the major enriched pathways in vivo for patients with glioblastoma. Among the enzymes associated with nucleotide synthesis, RRM1 and NME1 are significantly upregulated in glioblastoma. In the SSP, SHMT2 and PSPH are upregulated but the upstream enzyme PSAT1 is downregulated in glioblastoma. Kaplan–Meier curves of overall survival for the GSE16011 and The Cancer Genome Atlas datasets revealed that high SSP activity correlated with poor outcome. Enzymes relating to the pyrimidine synthesis pathway and SSP might offer therapeutic targets for new glioblastoma treatments.

Pilot Study Showing Feasibility of Phosphoproteomic Profiling of Pathway-Level Molecular Alterations in Barrett's Esophagus.

(1) Background: Barrett’s esophagus is a major risk factor for esophageal adenocarcinoma. In this pilot study, we employed precision mass spectrometry to map global (phospho)protein perturbations in Barrett’s esophagus lesions and adjacent normal tissue to glean insights into disease progression. (2) Methods: Biopsies were collected from two small but independent cohorts. Comparative analyses were performed between Barrett’s esophagus samples and adjacent matched (normal) tissues from patients with known pathology, while specimens from healthy patients served as additional controls. (3) Results: We identified and quantified 6810 proteins and 6395 phosphosites in the discovery cohort, revealing hundreds of statistically significant differences in protein abundances and phosphorylation states. We identified a robust proteomic signature that accurately classified the disease status of samples from the independent patient cohorts. Pathway-level analysis of the phosphoproteomic profiles revealed the dysregulation of specific cellular processes, including DNA repair, in Barrett’s esophagus relative to paired controls. Comparative analysis with previously published transcriptomic profiles provided independent evidence in support of these preliminary findings. (4) Conclusions: This pilot study establishes the feasibility of using unbiased quantitative phosphoproteomics to identify molecular perturbations associated with disease progression in Barrett’s esophagus to define potentially clinically actionable targets warranting further assessment.

Elucidating gene expression patterns across multiple biological contexts through a large-scale investigation of transcriptomic datasets

Distinct gene expression patterns within cells are foundational for the diversity of functions and unique characteristics observed in specific contexts, such as human tissues and cell types. Though some biological processes commonly occur across contexts, by harnessing the vast amounts of available gene expression data, we can decipher the processes that are unique to a specific context. Therefore, with the goal of developing a portrait of context-specific patterns to better elucidate how they govern distinct biological processes, this work presents a large-scale exploration of transcriptomic signatures across three different contexts (i.e., tissues, cell types, and cell lines) by leveraging over 600 gene expression datasets categorized into 98 subcontexts. The strongest pairwise correlations between genes from these subcontexts are used for the construction of co-expression networks. Using a network-based approach, we then pinpoint patterns that are unique and common across these subcontexts. First, we focused on patterns at the level of individual nodes and evaluated their functional roles using a human protein–protein interactome as a referential network. Next, within each context, we systematically overlaid the co-expression networks to identify specific and shared correlations as well as relations already described in scientific literature. Additionally, in a pathway-level analysis, we overlaid node and edge sets from co-expression networks against pathway knowledge to identify biological processes that are related to specific subcontexts or groups of them. Finally, we have released our data and scripts at https://zenodo.org/record/5831786 and https://github.com/ContNeXt/, respectively and developed ContNeXt (https://contnext.scai.fraunhofer.de/), a web application to explore the networks generated in this work.

Research on Voxel-Based Features Detection and Analysis of Alzheimer’s Disease Using Random Survey Support Vector Machine

Alzheimer’s disease (AD) is a degenerative disease of the central nervous system characterized by memory and cognitive dysfunction, as well as abnormal changes in behavior and personality. The research focused on how machine learning classified AD became a recent hotspot. In this study, we proposed a novel voxel-based feature detection framework for AD. Specifically, using 649 voxel-based morphometry (VBM) methods obtained from MRI in Alzheimer’s Disease Neuroimaging Initiative (ADNI), we proposed a feature detection method according to the Random Survey Support Vector Machines (RS-SVM) and combined the research process based on image-, gene-, and pathway-level analysis for AD prediction. Particularly, we constructed 136, 141, and 113 novel voxel-based features for EMCI (early mild cognitive impairment)-HC (healthy control), LMCI (late mild cognitive impairment)-HC, and AD-HC groups, respectively. We applied linear regression model, least absolute shrinkage and selection operator (Lasso), partial least squares (PLS), SVM, and RS-SVM five methods to test and compare the accuracy of these features in these three groups. The prediction accuracy of the AD-HC group using the RS-SVM method was higher than 90%. In addition, we performed functional analysis of the features to explain the biological significance. The experimental results using five machine learning indicate that the identified features are effective for AD and HC classification, the RS-SVM framework has the best classification accuracy, and our strategy can identify important brain regions for AD.

Prognostic Pathways Guide Drug Indications in Pan-Cancers.

Pathway-level analysis is a powerful approach enabling the interpretation of post-genomic data at a higher level than that of individual molecules. Molecular-targeted therapy focusing on cascade signaling pathways has become a new paradigm in anticancer therapy, instead of a single protein. However, the approaches to narrowing down the long list of biological pathways are limited. Here, we proposed a strategy for in silico Drug Prescription on biological pathways across pan-Cancers (CDP), by connecting drugs to candidate pathways. Applying on a list of 120 traditional Chinese medicines (TCM), we especially identified the “TCM–pathways–cancers” triplet and constructed it into a heterogeneous network across pan-cancers. Applying them into TCMs, the computational prescribing methods deepened the understanding of the efficacy of TCM at the molecular level. Further applying them into Western medicines, CDP could promote drug reposition avoiding time-consuming developments of new drugs.