Integrative Profiling Research Articles

Multivariate time-series clustering of cardiopulmonary exercise test data for cardiovascular risk stratification

Abstract Aims Current clinical practice assesses cardiorespiratory fitness (CRF) by evaluating summary indexes of cardiopulmonary exercise tests (CPET). Yet, the raw time series recordings may hold additional information with clinical relevance. In this study, we investigated if the interpretation of raw CPET data could identify distinct CRF phenogroups and improve risk stratification. Methods In 1399 participants (mean age, 56.4 years; 37.7% women), who underwent maximal CPET by cycle ergometer, we collected baseline clinical characteristics and information on incident cardiovascular (CV) events (n=297) on average 4.3 years later. We employed dynamic time warping combined with k-medoids to identify phenogroups from raw CPET time-series. To evaluate the clinical significance of the derived phenogroups we compared clinical characteristics and incidence of CV events, while an external population cohort (n=171) was used to further validate the trained model. Results The optimal number of clusters was 5. We observed significant differences in age, use of medication, spirometry indexes and disease prevalence across all clusters. Cluster 5 was associated with the worst CV profile with higher use of antihypertensive medication and history of CV disease while cluster 1 represented the most favourable risk profile. After adjusting for traditional clinical covariables, clusters 4 (HR: 1.30; 95%CI: 1.02-1.65; p=0.033) and 5 (HR: 1.36; 95%CI: 1.08-1.72; p=0.0088) had significantly higher risk for incident CV events compared to clusters 1 and 2. Comparing clinical characteristics across clusters in the external validation cohort revealed similar patterns. Conclusion – Employing unsupervised machine learning on time series CPET recordings, we identified clinically meaningful CRF phenogroups. An integrative CRF profiling might facilitate CV risk stratification and consequently risk management.

Integrative biomarker discovery and immune profiling for ulcerative colitis: a multi-methodological approach

Background We aimed to pinpoint biomarkers, create a diagnostic model for ulcerative colitis (UC), and delve into its immune features to better understand this autoimmune condition. Methods The sequencing data for both the UC and the control groups were obtained from GEO, including both bulk and single-cell data. Using GSE87466 as training group, we applied differential analysis, WGCNA, PPI, LASSO, RF, and SVM-RFE for biomarker selection. A neural network shaped our diagnostic model, corroborated by GSE92415 as the validation cohort with ROC assessment. Immune cell profiling was conducted using CIBERSORT. Results 53 disease-associated genes were screened. Enrichment analysis highlighted roles in complement cascades and cell adhesion. Eight biomarkers were finally identified through multiple machine learning and PPI: B4GALNT2, PDZK1IP1, FAM195A, REG4, MTMR11, FLJ35024, CD55, and CD44. The diagnostic model had AUCs of 0.984 (training group) and 0.957 (validation group). UC tissues revealed heightened plasma cells, CD8 T cells, and other immune cells. Two unique UC immune patterns emerged, with certain T and NK cells central to immune modulation. Conclusion We identified eight biomarkers of UC by various methods, constructed a diagnostic model through neural networks, and explored the immune complexity of the disease, which contributes to the diagnosis and treatment of UC.

Real-World Performance of Integrative Clinical Genomics in Pediatric Precision Oncology

Despite significant improvement in the survival of pediatric patients with cancer, treatment outcomes for high-risk, relapsed, and refractory cancers remain unsatisfactory. Moreover, prolonged survival is frequently associated with long-term adverse effects due to intensive multimodal treatments. Accelerating the progress of pediatric oncology requires both therapeutic advances and strategies to mitigate the long-term cytotoxic side effects, potentially through targeting specific molecular drivers of pediatric malignancies. In this report, we present the results of integrative genomic and transcriptomic profiling of 230 patients with malignant solid tumors (the “primary cohort”) and 18 patients with recurrent or otherwise difficult-to-treat nonmalignant conditions (the “secondary cohort”). The integrative workflow for the primary cohort enabled the identification of clinically significant single nucleotide variants, small insertions/deletions, and fusion genes, which were found in 55% and 28% of patients, respectively. For 38% of patients, molecularly informed treatment recommendations were made. In the secondary cohort, known or potentially driving alteration was detected in 89% of cases, including a suspected novel causal gene for patients with inclusion body infantile digital fibromatosis. Furthermore, 47% of findings also brought therapeutic implications for subsequent management. Across both cohorts, changes or refinements to the original histopathological diagnoses were achieved in 4% of cases. Our study demonstrates the efficacy of integrating advanced genomic and transcriptomic analyses to identify therapeutic targets, refine diagnoses, and optimize treatment strategies for challenging pediatric and young adult malignancies and underscores the need for broad implementation of precision oncology in clinical settings.

Screening lncRNAs essential for cardiomyocyte proliferation by integrative profiling of lncRNAs and mRNAs associated with heart development

BackgroundThe proliferation potential of mammalian cardiomyocytes declines markedly shortly after birth. Both long non-coding RNAs (lncRNAs) and mRNAs demonstrate altered expression patterns during cardiac development. However, the role of lncRNAs in the cell cycle arrest of cardiomyocytes remains inadequately understood. MethodThe expression pattern of lncRNAs and mRNAs was analyzed in mouse hearts exhibiting varying regenerative potentials on postnatal days (P) 1, 7, and 28. Weighted correlation network analysis (WGCNA) was employed to elucidate the co-expression relationship between lncRNAs and mRNAs. Protein-protein interaction (PPI) network was built using the STRING database, and hub lncRNAs were identified by CytoHubba. Molecular Complex Detection (MCODE) was used to screen core modules of the PPI network in Cytoscape. Upstream lncRNAs and miRNAs which may regulate mRNAs were predicted using miRTarBase and AnnoLnc2, respectively. Myocardial infarction (MI) was induced by ligation of the left anterior descending coronary artery. ResultsCompared with the P1 heart, 618 mRNAs and 414 lncRNAs displayed.transcriptional changes in the P7 heart, while 2358 mRNAs and 1290 lncRNAs showed from P7 to P28. Gene Ontology (GO) analysis revealed that module 1 in the both comparisons was enriched in the mitotic cell cycle process. 2810408I11Rik and 2010110K18Rik were identified as hub lncRNAs and their effects on the proliferation of cardiomyocytes were verified in vitro. Additionally, four lncRNA-miRNA-mRNA regulatory axes were predicted to explain the mechanism by which 2810408I11Rik and 2010110K18Rik regulate cardiomyocyte proliferation. Notably, the overexpression of 2810408I11Rik enhances cardiomyocyte proliferation and heart regeneration in the adult heart following MI. ConclusionThis study systematically analyzed the landscape of lncRNAs and mRNAs at P1, P7, and P28. These findings may enhance our understanding of the framework for heart development and could have significant implications for heart regeneration.

Integrative LC-MS and GC-MS metabolic profiling unveils dynamic changes during barley malting

Malting involves complex biochemical transformations affecting sensory and quality attributes. Despite extensive research on storage carbohydrates and proteins in malting, the lack of a detailed metabolic understanding of this process limits our ability to assess and enhance malt quality. This study employed untargeted GC-MS and LC-MS metabolite profiling across six malting timepoints to identify 4980 known metabolites, 82 % of which exhibited significant changes during the malting process. Here we identified stage-dependent metabolic shifts and dynamic chemical classes and pathways between each studied stage. These results can guide the fine-tuning of malting conditions to improve malt quality for beer production and other malt-based applications. Additionally, metabolites with antimicrobial properties were identified, underscoring the interplay between barley and microbial metabolic processes during malting. Further research into these microbial metabolites and cognate microbes may lead to novel malting assessment traits for high-quality and safe malted barley.

Identification of bio-markers for insulin resistance and sensitivity through multi-omics analysis

Aims: This study aims to identify multi-omics bio-markers for insulin resistance and sensitivity using machine learning approaches on a dataset integrated from several omics. Methods: The study included 362 patients with Insulin Resistance and Insulin Sensitivity from the Integrative Personal Omics Profiling (iPOP) database. Combining the multi-omics data from the Integrative Human Microbiome Project, this study used machine learning to reveal the relationship between insulin resistance and insulin sensitivity. Results: Of 362 patients 186 were insulin resistance and 176 were insulin sensitivity. 11,585 features were used, including clinical features, RNA transcripts, gut microbiota, cytokines, proteins, and metabolomics. We found 21 features capable of distinguishing insulin resistance from insulin sensitivity using a well-known artificial neural network (ANN) method. The model had an area under the receiver operating characteristic (AUC) of 0.97 in the validation dataset and 0.89 in the test dataset. The ANN model’s performance was compared with Random Forest model. Of the 21 new findings, two metabolites (methyl-uric acid and methylxanthine) are xenobiotics, and three RNA transcripts (SERPINF1, SLC2A2, and CHL1). Conclusion: A small number of multi-omics features identified from 11,585 potential candidates for a machine learning model can accurately predict insulin resistance and sensitivity.

Evolution of translational control and the emergence of genes and open reading frames in human and non-human primate hearts.

Evolutionary innovations can be driven by changes in the rates of RNA translation and the emergence of new genes and small open reading frames (sORFs). In this study, we characterized the transcriptional and translational landscape of the hearts of four primate and two rodent species through integrative ribosome and transcriptomic profiling, including adult left ventricle tissues and induced pluripotent stem cell-derived cardiomyocyte cell cultures. We show here that the translational efficiencies of subunits of the mitochondrial oxidative phosphorylation chain complexes IV and V evolved rapidly across mammalian evolution. Moreover, we discovered hundreds of species-specific and lineage-specific genomic innovations that emerged during primate evolution in the heart, including 551 genes, 504 sORFs and 76 evolutionarily conserved genes displaying human-specific cardiac-enriched expression. Overall, our work describes the evolutionary processes and mechanisms that have shaped cardiac transcription and translation in recent primate evolution and sheds light on how these can contribute to cardiac development and disease.

Abstract C117: Integrative multi-omics profiling in patients of African descent diagnosed with prostate cancer reveals distinct tumor-promoting immunosuppressive niches at a single-cell level and spatial resolution

Abstract In the United States and globally, prostate cancer (PCa) mortality rates are the highest among men of African descent. 24% of the disparities in PCa remains even when controlled for access to care and stage at presentation. Moreover, the tumor microenvironment plays an essential role in tumor progression, aggressiveness, therapeutic response, and patient outcomes. Additionally, the association of the genomic findings with patient ancestry and other characteristics, such as tumor biology and transcriptomic alterations, remains poorly understood. Here, we performed a multi-Omics approach (N=447) to unravel the complexity of tumor heterogeneity and understand disease progression & distinct tumor biology influenced by genetic ancestry. We performed Whole Exome (Normal/tumor paired) Sequencing matched with Methyl Seq and Whole transcriptomic Sequencing for three datasets of our African, African American Men (AAM), and European Men (EAM). Additionally, we ran Spatial NanoString high-plex GeoMx-DSP for a total of 118 treatment-naive PCa patients (around 500 ROIs). Simultaneously, we added matched whole transcriptome Sequencing for these patients. The cohort comprised 87 AAM, 3 unknown, and 28 EAM self-reported individuals. To verify the self-reported race, the genomic ancestry was qualified using genotype and Admixture analysis. To further validate differentially expressed genes at the protein level, we performed multi-plex histological staining of 40 markers to determine the spatial resolution and neighborhood clustering within the tumor and the microenvironment. In parallel, we performed scMultiOmics sequencing (scRNA & scATAC-Seq) at a single-cell resolution (6000 cells/sample at 25K reads per cell) from an additional 12 patients (9 AAM & 3 EAM). We reconstructed a 3D model of the distinct tumor microenvironment at subcellular resolution using serially sectioned hematoxylin and eosin- stained tissue sections (CODA technology). Our results demonstrate that patients who self-report as AAM or Nigerian are assigned to high African (> 70%) Ancestry with either Yoruba (Nigeria) and/or Bantu subpopulation in the Sub-Saharan area. Additionally, high African Ancestry patients are diagnosed at a younger age and show advanced pathology stages compared to patients with European Ancestry. AAM of Yoruba descent expresses significantly higher immune-inflammatory signatures (STAT/IFNG-signaling pathway) compared to the Nigerian-Yoruba subpopulation. Our scRNA-Seq analysis shows that AAM has suppressive myeloid cells infiltrate within the tumor cells. The infiltrations of these cells change with age, Gleason Grade, and pathology stage. Moreover, AAM-tumor cells (scATAC-Seq) have increased open Chromatin accessibility with STAT motifs enrichment (p-value;0.0001), which could be the driver regulators of the immune-suppressive signature as well as influence the upregulation of STAT/IFNG signature within African Ancestry patients. Our study provides new insight into how genetic ancestry impacts immune signatures in AAM/African and contributes to PCa racial disparities. Citation Format: Isra Elhussin, Ezra Baraban1, Ashley Kiemen, Tamara L Lotan, Cathy Handy Marshall, Emmanuel Antonarakis, Moray J Campbell, Melissa Davis, Michael Dixon, Isaac Kim, Stefan Ambs, Rick Kittles, Adam B Murphy, Clayton Yates. Integrative multi-omics profiling in patients of African descent diagnosed with prostate cancer reveals distinct tumor-promoting immunosuppressive niches at a single-cell level and spatial resolution [abstract]. In: Proceedings of the 17th AACR Conference on the Science of Cancer Health Disparities in Racial/Ethnic Minorities and the Medically Underserved; 2024 Sep 21-24; Los Angeles, CA. Philadelphia (PA): AACR; Cancer Epidemiol Biomarkers Prev 2024;33(9 Suppl):Abstract nr C117.

1957P Integrative proteomic profiling in high-grade serous ovarian carcinoma: Unraveling biomarkers and therapeutic targets

1957P Integrative proteomic profiling in high-grade serous ovarian carcinoma: Unraveling biomarkers and therapeutic targets

Integrative Multiomic Profiling of Triple-Negative Breast Cancer for Identifying Suitable Therapies.

Triple-negative breast cancer (TNBC) is a heterogeneous disease that carries the poorest prognosis of all breast cancers. Although novel TNBC therapies in development are frequently targeted toward tumors carrying a specific genomic, transcriptomic, or protein biomarker, it is poorly understood how these biomarkers are correlated. To better understand the molecular features of TNBC and their correlation with one another, we performed multimodal profiling on a cohort of 95 TNBC. Our approach involved quantifying tumor-infiltrating lymphocytes through hematoxylin and eosin staining, assessing the abundance of retinoblastoma, androgen receptor, and PDL1 proteins through IHC, and carrying out transcriptomic profiling using the NanoString BC360 platform, targeted DNA sequencing on a subset of cases, as well as evaluating associations with overall survival. Levels of RB1 mRNA and RB proteins are better correlated with markers of retinoblastoma functionality than RB1 mutational status. Luminal androgen receptor tumors clustered into two groups with transcriptomes that cluster with either basal or mesenchymal tumors. Tumors classified as PDL1-positive by the presence of immune or tumor cells showed similar biological characteristics. HER2-low TNBC showed no distinct biological phenotype when compared with HER2-zero. The majority of TNBC were classified as basal or HER2-enriched by PAM50, the latter showing significantly improved overall survival. Our study contributes new insights into biomarker utility for identifying suitable TNBC therapies and the intercorrelations between genomic, transcriptomic, protein, and cellular biomarkers. Additionally, our rich data resource can be used by other researchers to explore the interplay between DNA, RNA, and protein biomarkers in TNBC.

Integrative profiling of untreated primary membranous nephropathy at the single-cell transcriptome level.

Primary membranous nephropathy (PMN) is an autoimmune kidney disease. Despite the identification of certain autoantigens, the etiology and pathophysiology of PMN are still largely unknown. Five patients with biopsy-proven PMN were enrolled in this study. Their blood, kidney and urine samples were collected respectively to profile cellular, molecular and immunological alterations by using single-cell RNA sequencing (scRNA-seq). Experimental verifications were also implemented in kidney tissue. In the peripheral blood mononuclear cell (PBMC) samples, portions of B cells and plasma cells were increased in PMN patients. Cell-cell communication analysis suggests that APRIL (a proliferation-inducing ligand from B cells) might be a potential molecule that regulates the activity of plasma cells. In the kidney samples, scRNA-seq analysis showed that the infiltration of T cells, as well as the myeloid cells, appears abundant compared with healthy controls, suggesting that immune cells are actively recruited to kidney. Furthermore, we observed an enhanced interaction between inflammatory cells and podocytes, which might contribute to kidney injury. Accordingly, scRNA-seq analysis of urinary samples is partially reminiscent of the kidney cell landscape, especially T cells and myeloid cells, suggesting monitoring urinary samples is a promising method to monitor PMN development. Additionally, integrative analysis across the blood, kidney and urine identified LTB, HERP1, ANXA1, IL1RN and ICAM1 as common regulators of PMN. Finally, immune repertoire in PBMC also showed an elevated diversity of clonal type, implying the existence of autoreactive T-cell receptor/B-cell receptor. Our study comprehensively profiled the transcriptomic landscapes of blood, kidney and urine in patients with PMN using scRNA-seq. We depicted the alterations including cell compositions and cell-cell communication in PMN. These results offer important clues with regard to the diagnosis and pathogenesis of PMN and potential intervention of PMN progression.

Integrative transcriptomic profiling of ncRNAs and mRNAs in developing mouse lens.

In recent years, burgeoning research has underscored the pivotal role of non-coding RNA in orchestrating the growth, development, and pathogenesis of various diseases across organisms. However, despite these advances, our understanding of the specific contributions of long non-coding RNAs (lncRNAs) and circular RNAs (circRNAs) to lens development remains notably limited. Clarifying the intricate gene regulatory networks is imperative for unraveling the molecular underpinnings of lens-related disorders. In this study, we aimed to address this gap by conducting a comprehensive analysis of the expression profiles of messenger RNAs (mRNAs), lncRNAs, and circRNAs at critical developmental time points of the mouse lens, encompassing both embryonic (E10.5, E12.5, and E16.5) and postnatal stages (P0.5, P10.5, and P60). Leveraging RNA-sequencing technology, we identified key transcripts pivotal to lens development. Our analysis revealed differentially expressed (DE) mRNAs, lncRNAs, and circRNAs across various developmental stages. Particularly noteworthy, there were 1831 co-differentially expressed (CO-DE) mRNAs, 150 CO-DE lncRNAs, and 13 CO-DE circRNAs identified during embryonic stages. Gene Ontology (GO) enrichment analysis unveiled associations primarily related to lens development, DNA conformational changes, and angiogenesis among DE mRNAs and lncRNAs. Furthermore, employing protein-protein interaction networks, mRNA-lncRNA co-expression networks, and circRNA-microRNA-mRNA networks, we predicted candidate key molecules implicated in lens development. Our findings underscore the pivotal roles of lncRNAs and circRNAs in this process, offering fresh insights into the pathogenesis of lens-related disorders and paving the way for future exploration in this field.

Integrative transcriptomic and proteomic profiling of the effects of cell confluency on gene expression

In this study we examine the impact of cell confluency on gene expression. We focused on Argonaute (AGO) protein dynamics and associated gene and protein expression in HEK293, A375, and SHSY5Y cell lines. As a consequence of cell confluency, AGO2 protein translocates into the nucleus. Therefore, we generated transcriptomic data using RNA sequencing to compare gene expression in subconfluent versus confluent cells, which highlighted significant alterations in gene regulation patterns directly corresponding to changes in cell density. Our study also encompasses miRNA profiling data obtained through small RNA sequencing, revealing miRNA expressional changes dependent on cellular confluency, as well as cellular localization. Finally, we derived proteomic data from mass spectrometry analyses following AGO1-4 immunoprecipitation, providing a comprehensive view of AGO interactome in both nuclear and cytoplasmic compartments under varying confluency. These datasets offer a detailed exploration of the cellular and molecular dynamics, influenced by cell confluency, presenting a valuable resource for further research in cellular biology, particularly in understanding the basic mechanisms of cell density in cancer cells.

Integrative multi-region molecular profiling of primary prostate cancer in men with synchronous lymph node metastasis

Localized prostate cancer is frequently composed of multiple spatially distinct tumors with significant inter- and intra-tumoral molecular heterogeneity. This genomic diversity gives rise to many competing clones that may drive the biological trajectory of the disease. Previous large-scale sequencing efforts have focused on the evolutionary process in metastatic prostate cancer, revealing a potential clonal progression to castration resistance. However, the clonal origin of synchronous lymph node (LN) metastases in primary disease is still unknown. Here, we perform multi-region, targeted next generation sequencing and construct phylogenetic trees in men with prostate cancer with synchronous LN metastasis to better define the pathologic and molecular features of primary disease most likely to spread to the LNs. Collectively, we demonstrate that a combination of histopathologic and molecular factors, including tumor grade, presence of extra-prostatic extension, cellular morphology, and oncogenic genomic alterations are associated with synchronous LN metastasis.

Abstract PO5-15-07: Integrative proteomics, phosphoproteomics, and RNA profiling revealed novel timeline-dependent vulnerabilities in HER2-positive breast cancer

Abstract Background: Most personalized breast cancer therapies leverage biomarkers identified in the pre-treatment setting. They do not always reflect the dynamic nature of early indicators of treatment response/resistance. Herein, we have a unique cohort of newly diagnosed HER2-positive breast tumors treated with a single low dose of trastuzumab (50mg), and the corresponding residual tumors that did not achieve pathologic complete response (pCR) to HER2-targeted neoadjuvant therapy. We hypothesize that integrative molecular profiling with proteomics and transcriptomics of these breast tumors could reveal novel time-course dependent contributors of therapy response and resistance. Method: This correlative biomarker study was a secondary objective of a parent study that evaluated 64uc-DOTA-trastuzumab PET imaging to predict response to trastuzumab and pertuzumab-based neoadjuvant therapy in HER2-positive breast cancer patients. Eighteen newly diagnosed breast cancer patients were enrolled, and pCR was achieved in 13 patients (72%). This correlative biomarker study was performed on breast tumors collected within 72 hours following administering a low dose trastuzumab (50mg) to the subjects prior to imaging and neoadjuvant therapy. Deep expression proteomics and phosphoproteomics data acquisition were performed on a Thermo Orbitrap Eclipse mass spectrometer. RNA profiling was performed using Nanostring breast cancer profiler. Results: Our multi-omic approach identified increased abundance or activity in the pCR group on the following (p < 0.05): Her2 protein and ERBB2 transcript levels, increased phosphorylation of ERBB2 (T1052), increase in Golgi trafficking activities, posttranslational modification with protein glycosylation, and lipid biosynthesis. Interestingly, compared to the non-pCR group, the pCR group showed lower activity level in ERBB2’s downstream pathways, such as MAPK, SRC, and PI3K. In the non-pCR group, the residual tumor showed decrease in the following (p < 0.05): Golgi trafficking, glycosylation, antigen processing, and Her2 protein and phosphorylation (T1166). The pCR group had a negative enrichment for SGK1, a kinase which mediates PI3K pathway independently of AKT. SGK1 abundance was highest in residual tumors following neoadjuvant therapy. Conclusion: Using integrative proteomic and RNA profiling, we identified several in-vivo biological pertubations following administration of low dose trastuzumab. Increased Her2 abundance and ERBB2 transcript levels are associated with pCR, in keeping with previous reports of known pCR predictors. Decreased abundance of ERBB’s downstream signaling proteins in the pCR group suggests that early treatment response to trastuzumab may be a predictor of pCR. Our integrative approach identified other dynamic biomarkers. For example, compared to non-pCR group, Golgi trafficking is more active in pCR group tumors, and lowest in residual tumors following neoadjuvant therapy. This suggests that novel Golgi-targeting agents may be best utilized in neoadjuvant setting. Further, we identified SGK1 may be an important kinase for mediating therapy resistance early in treatment course, since its level is higher in non-pCR group following early exposure to trastuzumab compared to pCR group, with highest level detected in the residual tumors. These findings warrant orthogonal validation using in-vitro functional assays. Citation Format: Christina Wei, Lixin Yang, Krystine Mansfield, Jessica Liu, Ritin Sharma, Russell Rockne, Raju Pillai, Patrick Pirrotte, Joanne Mortimer. Integrative proteomics, phosphoproteomics, and RNA profiling revealed novel timeline-dependent vulnerabilities in HER2-positive breast cancer [abstract]. In: Proceedings of the 2023 San Antonio Breast Cancer Symposium; 2023 Dec 5-9; San Antonio, TX. Philadelphia (PA): AACR; Cancer Res 2024;84(9 Suppl):Abstract nr PO5-15-07.

Abstract PO3-24-01: Mapping inter- and intra-tumor heterogeneity in Ductal Carcinoma in situ and invasive breast cancer using integrative multi-omic profiling

Abstract Background: Molecular profiling of ductal carcinoma in situ (DCIS) has shown some prognostic utility in the clinic. However, there is still an incomplete understanding of the diversity of molecular mechanisms by which DCIS progresses to invasive breast cancer (IBC).Here, we utilize integrative multi-omic profiling of co-occurring DCIS and IBC in humans as a model for mapping the relationship between tumor mutations, global gene expression and morphological changes in DCIS and IBC. Methods: We performed targeted panel DNA sequencing (Tempus xT), whole transcriptome profiling and hyperplex immunofluorescence (Lunaphore COMET) on a cohort of 39 pairs of co-occurring DCIS and IBC lesions. We performed an integrative analysis of the above multi-modal data to uncover similar and dissimilar molecular trajectories in DCIS/IBC pairs. DAVID, KEGG and Reactome databases were utilized to analyze differential pathway activity between DCIS and IBC lesions. Results: IBC samples significantly overexpressed pathways associated with cycling cells and proliferation. For DCIS, many of the enriched pathways were related to CYP enzymes and xenobiotic metabolism. We also assessed enrichment of specific transcription factor binding sites (TFBS) with the associated differentially expressed genes. The top site enriched in IBC was a promoter regulatory element of unknown function (M120 motif KRCTCNNNNMANAGC) as well as sites regulated by heat shock transcription factor 4 (HSF4). Top TFBS enriched in DCIS included MEF2, HMEF2, AR and CEBPE. The top pathways associated with IBC using DAVID were related to extracellular matrix (ECM) organization and regulation. Interestingly, the top pathways associated with DCIS also included several pathways associated with ECM organization. On Reactome pathways diagrams for ECM formation distinct components of ECM formation and organization were enriched in IBC vs DCIS lesions, with invadopodia formation components and proteoglycans specifically enriched in IBC and integrin/laminin signaling enriched in DCIS. Among the 39 cases profiled, we observed distinct patterns of DCIS/IBC pairs with highly similar mutational profiles indicative of derivation from a shared progenitor as well as pairs with little-to-no molecular concordance that likely arose independently. Even within mutationally similar DCIS/IBC pairs there were different degrees of gene expression concordance. We subdivided mutationally similar pairs into two groups based on gene expression similarity (Euclidean Distance) and identified 115 genes that differed between these groups (q < 0.05), with the dissimilar DCIS lesions hallmarked by upregulation of a variety of pathways related to B-cell function, complement cascade and cell cycle progression. Specific mutations also were related to conserved transcriptional programs in these lesions, with MCL-1 amplified tumors showing a common gene expression signature of 357 altered genes (q < 0.05) with key differences in extracellular matrix organization and adaptive immune response pathways. Dissimilarly, tumors with CKS1B copy number alterations associated with 627 differentially expressed genes (q < 0.05) with the top targets related to endothelial function and angiogenesis. Conclusions: Integrative multi-modal analysis helps us better understand tumor progression at multiple levels of biomolecular dysregulation and shows the complex interplay among genome alterations, gene expression reprogramming and tissue morphology. Such approaches may yield future biomarker signatures that better encapsulate the diverse mechanisms by which DCIS lesions evolve. Citation Format: Henry Kaplan, Alexa Dowdell, Racheli Ben Shimoli, Angela Crabtree, Ann Berry, Fred Robinson, Christopher Carney, Carlo Bifulco, Brian Piening. Mapping inter- and intra-tumor heterogeneity in Ductal Carcinoma in situ and invasive breast cancer using integrative multi-omic profiling [abstract]. In: Proceedings of the 2023 San Antonio Breast Cancer Symposium; 2023 Dec 5-9; San Antonio, TX. Philadelphia (PA): AACR; Cancer Res 2024;84(9 Suppl):Abstract nr PO3-24-01.

Integrative multi-omics profiling in human decedents receiving pig heart xenografts.

In a previous study, heart xenografts from 10-gene-edited pigs transplanted into two human decedents did not show evidence of acute-onset cellular- or antibody-mediated rejection. Here, to better understand the detailed molecular landscape following xenotransplantation, we carried out bulk and single-cell transcriptomics, lipidomics, proteomics and metabolomics on blood samples obtained from the transplanted decedents every 6 h, as well as histological and transcriptomic tissue profiling. We observed substantial early immune responses in peripheral blood mononuclear cells and xenograft tissue obtained from decedent 1 (male), associated with downstream T cell and natural killer cell activity. Longitudinal analyses indicated the presence of ischemia reperfusion injury, exacerbated by inadequate immunosuppression of T cells, consistent with previous findings of perioperative cardiac xenograft dysfunction in pig-to-nonhuman primate studies. Moreover, at 42 h after transplantation, substantial alterations in cellular metabolism and liver-damage pathways occurred, correlating with profound organ-wide physiological dysfunction. By contrast, relatively minor changes in RNA, protein, lipid and metabolism profiles were observed in decedent 2 (female) as compared to decedent 1. Overall, these multi-omics analyses delineate distinct responses to cardiac xenotransplantation in the two human decedents and reveal new insights into early molecular and immune responses after xenotransplantation. These findings may aid in the development of targeted therapeutic approaches to limit ischemia reperfusion injury-related phenotypes and improve outcomes.

Identification and clinicopathological analysis of potential p73-regulated biomarkers in colorectal cancer via integrative bioinformatics.

This study aims to decipher crucial biomarkers regulated by p73 for the early detection of colorectal cancer (CRC) by employing a combination of integrative bioinformatics and expression profiling techniques. The transcriptome profile of HCT116 cell line p53 p73 and p53 p73 knockdown was performed to identify differentially expressed genes (DEGs). This was corroborated with three CRC tissue expression datasets available in Gene Expression Omnibus. Further analysis involved KEGG and Gene ontology to elucidate the functional roles of DEGs. The protein-protein interaction (PPI) network was constructed using Cytoscape to identify hub genes. Kaplan-Meier (KM) plots along with GEPIA and UALCAN database analysis provided the insights into the prognostic and diagnostic significance of these hub genes. Machine/deep learning algorithms were employed to perform TNM-stage classification. Transcriptome profiling revealed 1289 upregulated and 1897 downregulated genes. When intersected with employed CRC datasets, 284 DEGs were obtained. Comprehensive analysis using gene ontology and KEGG revealed enrichment of the DEGs in metabolic process, fatty acid biosynthesis, etc. The PPI network constructed using these 284 genes assisted in identifying 20 hub genes. Kaplan-Meier, GEPIA, and UALCAN analyses uncovered the clinicopathological relevance of these hub genes. Conclusively, the deep learning model achieved TNM-stage classification accuracy of 0.78 and 0.75 using 284 DEGs and 20 hub genes, respectively. The study represents a pioneer endeavor amalgamating transcriptomics, publicly available tissue datasets, and machine learning to unveil key CRC-associated genes. These genes are found relevant regarding the patients' prognosis and diagnosis. The unveiled biomarkers exhibit robustness in TNM-stage prediction, thereby laying the foundation for future clinical applications and therapeutic interventions in CRC management.

Integrating taxonomic signals from MAGs and contigs improves read annotation and taxonomic profiling of metagenomes.

Metagenomic analysis typically includes read-based taxonomic profiling, assembly, and binning of metagenome-assembled genomes (MAGs). Here we integrate these steps in Read Annotation Tool (RAT), which uses robust taxonomic signals from MAGs and contigs to enhance read annotation. RAT reconstructs taxonomic profiles with high precision and sensitivity, outperforming other state-of-the-art tools. In high-diversity groundwater samples, RAT annotates a large fraction of the metagenomic reads, calling novel taxa at the appropriate, sometimes high taxonomic ranks. Thus, RAT integrative profiling provides an accurate and comprehensive view of the microbiome from shotgun metagenomics data. The package of Contig Annotation Tool (CAT), Bin Annotation Tool (BAT), and RAT is available at https://github.com/MGXlab/CAT_pack (from CATpack v6.0). The CAT pack now also supports Genome Taxonomy Database (GTDB) annotations.

Abstract LT09: Integrative Multi-Omics Enhancer Activity Profiling Identifies Therapeutic Vulnerabilities in Cholangiocarcinoma of Different Etiologies

Abstract Objectives: Cholangiocarcinoma (CCA) is a heterogeneous malignancy with high mortality and dismal prognosis, and an urgent clinical need for new therapies. Knowledge of the CCA epigenome is largely limited to aberrant DNA methylation. Dysregulation of enhancer activities has been identified to affect carcinogenesis and leveraged for new therapies but is uninvestigated in CCA. Our aim is to identify potential therapeutic targets in different subtypes of CCA through enhancer profiling. Design: Integrative multi-omics enhancer activity profiling of diverse CCA was performed. A panel of diverse CCA cell lines, and patient-derived and cell line-derived xenografts were used to study identified enriched pathways and vulnerabilities. NanoString, multiplex immunohistochemistry staining, and single-cell spatial transcriptomics were used to explore immunogenicity of diverse CCA. Results: We identified 3 distinct groups, associated with different etiologies and unique pathways. Drug inhibitors of identified pathways reduced tumor growth in in-vitro and in-vivo models. The first group (ESTRO), with mostly fluke-associated CCAs, displayed activation in estrogen signaling and were sensitive to MTOR inhibitors. Another group (OXPHO), with mostly BAP1 and IDH-mutant CCAs, displayed activated oxidative phosphorylation pathways, and were sensitive oxidative phosphorylation inhibitors. Immune-related pathways were activated in the final group (IMMUN), made up of an immunogenic CCA subtype and CCA with aristolochic acid (AA) mutational signatures. Intra-tumor differences in AA mutation load were correlated to intra-tumor variation of different immune cell populations. Conclusion: Our study elucidates the mechanisms underlying enhancer dysregulation and deepens understanding of different tumorigenesis processes in distinct CCA subtypes, with potential significant therapeutics and clinical benefits. Citation Format: Jing Han Hong, Chern Han Yong, Hong Lee Heng, Jason Yongsheng Chan, Mai Chan Lau, Jianfeng Chen, Jing Yi Lee, Abner Herbert Lim, Zhimei Li, Peiyong Guan, Pek Lim Chu, Sheng Rong Ng, Xiaosai Yao, Peili Wang, Rong Xiao, Xian Zeng, Yichen Sun, Wei Liu, Joanna Koh, Felicia Yu Ting Wee, Jeffrey Chun Tatt Lim, Cedric Chuan Young Ng, Xiu Yi Kwek, Poramate Klanrit, Yaojun Zhang, Jiaming Lai, David Wai Meng Tai, Chawalit Pairojkul, Simona Dima, Irinel Popescu, Sen-Yung Hsieh, Ming-Chin Yu, Joe Yeong, Sarinya Kongpetch, Apinya Jusakul, Watcharin Loilome, Patrick Tan, Jing Tan, Bin Tean Teh. Integrative Multi-Omics Enhancer Activity Profiling Identifies Therapeutic Vulnerabilities in Cholangiocarcinoma of Different Etiologies [abstract]. In: Proceedings of Frontiers in Cancer Science; 2023 Nov 6-8; Singapore. Philadelphia (PA): AACR; Cancer Res 2024;84(8_Suppl):Abstract nr LT09.