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PubMed2026

The molecular similarity landscape of preclinical cancer models to patient tumors.

Selecting appropriate preclinical models is fundamental for translational oncology, yet a large-scale, multi-omic quantitative comparison of their similarity to primary human tumors is lacking. To address this, we integrated transcriptomic, proteomic, and genomic profiles from over 10,000 primary tumors from The Cancer Genome Atlas (TCGA) and the Clinical Proteomic Tumor Analysis Consortium (CPTAC), alongside 4,000 preclinical models. Using a robust computational framework, we revealed a clear hierarchy of transcriptomic and proteomic similarity to patient tumors: with patient-dervied xenografts (PDXs) having greater transcriptomic and proteomic similarity to patient tumors (>) compared with patient-derived organoids (PDOs), which are equal in hierarchy to that of PDX-dervied organoids (PDXOs) > cell lines. We also quantified high molecular conservation (Pearson correlation coefficient = 0.96) across paired in vitro to in vivo platform (organoids to PDX) transitions. Furthermore, genomic analysis demonstrated that whole-exome sequencing (WES) outperforms RNA-seq in detecting DNA variants, and it identified a clonal complexity hierarchy (cell lines > PDXOs > PDXs > PDOs) reflecting the effect of passaging history on intratumor heterogeneity. Ultimately, this study delivers a comprehensive quantitative benchmark, establishing a population-level hierarchy of molecular similarity between preclinical models and primary tumors and providing a data-driven reference for model selection. These findings offer a data-driven framework for selecting models that balance biological representativeness with experimental practicality.

Humans↗

PubMed2004

Expression profiling using a hexamer-based universal microarray.

We describe a transcriptional analysis platform consisting of a universal micro-array system (UMAS) combined with an enzymatic manipulation step that is capable of generating expression profiles from any organism without requiring a priori species-specific knowledge of transcript sequences. The transcriptome is converted to cDNA and processed with restriction endonucleases to generate low-complexity pools (approximately 80-120) of equal length DNA fragments. The resulting material is amplified and detected with the UMAS system, comprising all possible 4,096 (4(6)) DNA hexamers. Ligation to the arrays yields thousands of 14-mer sequence tags. The compendium of signals from all pools in the array-of-universal arrays comprises a full-transcriptome expression profile. The technology was validated by analysis of the galactose response of Saccharomyces cerevisiae, and the resulting profiles showed excellent agreement with the literature and real-time PCR assays. The technology was also used to demonstrate expression profiling from a hybrid organism in a proof-of-concept experiment where a T-cell receptor gene was expressed in yeast.

3' Untranslated Regions↗

PubMed2026

OmicsPred as a centralised resource for genetic prediction of multi-omic traits.

Genetic prediction of multi-omic data has emerged as a cost-effective alternative to direct omics profiling, particularly useful for identifying molecular features associated with disease susceptibility. However, despite its popularity, multi-omic imputation models are fragmented across studies, hindering findability, accessibility, interoperability and re-use. To address this, we developed OmicsPred (https://www.omicspred.org), a centralised platform for the deposition and dissemination of genetic prediction models of multi-omic traits. OmicsPred unifies the most commonly used molecular imputation models (e.g. from PredictDB) and other published studies totalling 3,339,469 prediction models spanning transcriptomic, proteomic, and metabolomic traits (as of May 2026). Each model is accompanied by metadata describing score development and predictive performance, and distributed in formats compatible with popular analytic tools, such as PGS Catalog Calculator and MetaXcan. To demonstrate the utility of the resource for systematic target discovery, we perform a multi-omic phenome-wide association analysis in Million Veterans Program data.

Journal Article↗

PubMed2005

Human muscle gene expression responses to endurance training provide a novel perspective on Duchenne muscular dystrophy.

Global gene expression profiling is used to generate novel insight into a variety of disease states. Such studies yield a bewildering number of data points, making it a challenge to validate which genes specifically contribute to a disease phenotype. Aerobic exercise training represents a plausible model for identification of molecular mechanisms that cause metabolic-related changes in human skeletal muscle. We carried out the first transcriptome-wide characterization of human skeletal muscle responses to 6 wk of supervised aerobic exercise training in 8 sedentary volunteers. Biopsy samples before and after training allowed us to identify approximately 470 differentially regulated genes using the Affymetrix U95 platform (80 individual hybridization steps). Gene ontology analysis indicated that extracellular matrix and calcium binding gene families were most up-regulated after training. An electronic reanalysis of a Duchenne muscular dystrophy (DMD) transcript expression dataset allowed us to identify approximately 90 genes modulated in a nearly identical fashion to that observed in the endurance exercise dataset. Trophoblast noncoding RNA, an interfering RNA species, was the singular exception-being up-regulated by exercise and down-regulated in DMD. The common overlap between gene expression datasets may be explained by enhanced alpha7beta1 integrin signaling, and specific genes in this signaling pathway were up-regulated in both datasets. In contrast to these common features, OXPHOS gene expression is subdued in DMD yet elevated by exercise, indicating that more than one major mechanism must exist in human skeletal muscle to sense activity and therefore regulate gene expression. Exercise training modulated diabetes-related genes, suggesting our dataset may contain additional and novel gene expression changes relevant for the anti-diabetic properties of exercise. In conclusion, gene expression profiling after endurance exercise training identified a range of processes responsible for the physiological remodeling of human skeletal muscle tissue, many of which were similarly regulated in DMD. Furthermore, our analysis demonstrates that numerous genes previously suggested as being important for the DMD disease phenotype may principally reflect compensatory integrin signaling.

Biopsy↗

PubMed2006

OMICS-driven biomarker discovery in nutrition and health.

While traditional nutrition research has dealt with providing nutrients to nourish populations, it nowadays focuses on improving health of individuals through diet. Modern nutritional research is aiming at health promotion and disease prevention and on performance improvement. As a consequence of these ambitious objectives, the disciplines "nutrigenetics" and "nutrigenomics" have evolved. Nutrigenetics asks the question how individual genetic disposition, manifesting as single nucleotide polymorphisms, copy-number polymorphisms and epigenetic phenomena, affects susceptibility to diet. Nutrigenomics addresses the inverse relationship, that is how diet influences gene transcription, protein expression and metabolism. A major methodological challenge and first pre-requisite of nutrigenomics is integrating genomics (gene analysis), transcriptomics (gene expression analysis), proteomics (protein expression analysis) and metabonomics (metabolite profiling) to define a "healthy" phenotype. The long-term deliverable of nutrigenomics is personalised nutrition for maintenance of individual health and prevention of disease. Transcriptomics serves to put proteomic and metabolomic markers into a larger biological perspective and is suitable for a first "round of discovery" in regulatory networks. Metabonomics is a diagnostic tool for metabolic classification of individuals. The great asset of this platform is the quantitative, non-invasive analysis of easily accessible human body fluids like urine, blood and saliva. This feature also holds true to some extent for proteomics, with the constraint that proteomics is more complex in terms of absolute number, chemical properties and dynamic range of compounds present. Apart from addressing the most complex "-ome", proteomics represents the only platform that delivers not only markers for disposition and efficacy but also targets of intervention. The Omics disciplines applied in the context of nutrition and health have the potential to deliver biomarkers for health and comfort, reveal early indicators for disease disposition, assist in differentiating dietary responders from non-responders, and, last but not least, discover bioactive, beneficial food components. This paper reviews the state-of-the-art of the three Omics platforms, discusses their implication in nutrigenomics and elaborates on applications in nutrition and health such as digestive health, allergy, diabetes and obesity, nutritional intervention and nutrient bioavailability. Proteomic developments, applications and potential in the field of nutrition have been specifically addressed in another review issued by our group.

Animals↗

PubMed2026

Decoding regional keratinization in human oral mucosa through high-resolution spatial transcriptomics.

Oral mucosa exhibits region-specific keratinization, essential for periodontal health, yet the spatial and molecular mechanisms driving these differences remain poorly understood. This study aimed to generate a high-resolution spatial transcriptomic atlas of the human oral mucosa around the mucogingival junction, to reveal stromal-epithelial interactions, that distinguish keratinized from non-keratinized programs. Formalin-fixed paraffin-embedded specimens from the mucogingival junction area of two healthy donors were analyzed with the 10 × Genomics Visium HD platform, yielding two keratinized and two non-keratinized regions. Spatial clustering, pseudotime trajectory inference, cell-type integration with a single-cell reference, and ligand-receptor network analysis were applied to delineate epithelial and stromal compartments. Sixteen reproducible clusters, recapitulating tissue architecture, were identified and revealed distinct transcriptional signatures, distinguishing gingiva from lining mucosa. Pseudotime analysis revealed bifurcating epithelial lineages, originating from a shared basal progenitor layer into keratinized and non-keratinized programs. Gingival keratinization was driven by stromal collagen ligands (COL1A1, COL1A2, COL6A1, COL6A2) engaging epithelial receptors (CD44, SDC1), further reinforced within the epithelium by desmosomal adhesion via DSG1-DSC2/3. Gingival keratinization emerges from integrated stromal collagen signaling and epithelial adhesion. This spatially resolved framework advances understanding of oral mucosal specialization and provides a foundation for biologically guided regenerative therapies.

Humans↗

PubMed2006

Molecular characterization of the developmental gene in eyes: through data-mining on integrated transcriptome databases.

OBJECTIVES: Our aim was to utilize publicly available and proprietary sources to discover candidate genes important for ocular development. DESIGN AND METHODS: The collated information on our 5092 non-redundant clusters was grouped and functional annotation was conducted using gene ontology (FatiGO) for categorizing them with respect to molecular function. The web-based viewer technological platform (H-InvDB) was employed for transcription analyses of in-house high quality fetal eye Expressed Sequence Tags (ESTs). Eye-specific ESTs were also analyzed across species by using EMBEST. RESULTS: According to adult eye cDNA libraries, nucleic acid binding and cell structure/cytoskeletal protein genes were the most abundant among the ESTs of fetal eyes. Using cDNA assembly in H-InvDB, 20 (80%) of the 25 most commonly expressed genes in the human eye are also expressed in extraocular tissues. The crystalline gamma S gene is highly expressed in the eye, but not in other tissues. We used EMBEST to compare human fetal eye and octopus eye ESTs and the expression similarity was low (1.6%). This indicated that our fetal eye library contains genes necessary for the developmental process and biological function of the eye, which may not be expressed in the fully developed octopus eyes. The human fetal eye cDNA library also contained highly abundant eye tissue genes, including alphaA-crystallin, eukaryotic translation elongation factor 1 alpha 1 (EEF1A1), bestrophin (VMD2), cystatin C, and transforming growth factor, beta-induced (BIGH3). CONCLUSIONS: Our annotated EST set provides a valuable resource for gene discovery and functional genomic analysis. This display will help to appreciate the strengths and weaknesses of the different technological platforms, so that in future studies the maximum amount of beneficial information can be derived from the appropriate use of each method.

Animals↗

PubMed2026

SCLC TumorMiner: A genomics platform for small cell lung cancer precision oncology.

Small cell lung cancer (SCLC) is among the most aggressive malignancies. Unlike many other cancers, it is not represented in The Cancer Genome Atlas, and available datasets are fragmented across institutions, disease stages, and treatment settings. RNA sequencing provides a powerful and cost-effective approach, but the high dimensionality of transcriptomic data and the heterogeneity of patient cohorts pose significant challenges. To address such challenges, we developed SCLC TumorMiner (https://discover.nci.nih.gov/SclcTumorMinerCDB/), which includes 50 tumor samples from relapsed patients at the National Cancer Institute (NCI) and 154 samples from untreated patients at the University of Cologne and Tongji University. SCLC TumorMiner enables molecular classification, genomic pathway analyses, risk stratification, identification of predictive cell-surface biomarkers such as DLL3 or TROP2, and drug-response biomarkers such as SLFN11. SCLC TumorMiner illustrates profound differences between untreated and relapsed patient samples. Additionally, "MyPatient", one of SCLC TumorMiner's modules, is presented as a medical assistant application prototype.

SCLC↗

PubMed2026

Odon: an ultra-fast viewer for spatial proteomics.

MOTIVATION: Multiplexed spatial proteomics and spatial transcriptomics generate large, high-dimensional imaging datasets that are challenging to visualize efficiently, particularly at whole-slide and cohort scale. Visualization is an essential step for rapid detection of staining artefacts, such as protein aggregates or non-specific staining. RESULTS: Here, we present Odon, a native Rust desktop viewer designed for rapid, interactive exploration of multiplex imaging data on a standard laptop. Odon is primarily built around the OME-Zarr imaging format, and supports annotations via GeoJSON and GeoParquet, with secondary support for SpatialData, Xenium containers, and TIFF. Data can be stored locally or streamed directly from HTTP or S3-compatible object storage using viewport-driven tile loading. Odon incorporates a highly optimized rendering engine designed for viewport-driven tile loading and GPU-based compositing. In scripted benchmarks using synthetic multiplex OME-Zarr datasets, Odon showed lower peak memory use, lower affine-derived zoom-step error, and faster warm-start image loading than napari and QuPath under the tested conditions. Its GPU-based compositing pipeline also enables smooth rendering and interaction with >1 000 000 segmented cells. Odon further supports integrated visual analytics, including live thresholding and cell selection, and a mosaic mode for simultaneous viewing of hundreds of regions of interest in cohort and tissue microarray studies. Together, these features establish Odon as a high-performance platform for scalable visualization of spatial proteomics data. AVAILABILITY AND IMPLEMENTATION: Source code and compiled installers are available at https://github.com/alexcoulton/odon.

Proteomics↗

PubMed2005

Analysis of the biliary transcriptome in experimental biliary atresia.

BACKGROUND & AIMS: Discovery of the pathogenic mechanisms of biliary atresia has been limited by the inability to study extrahepatic biliary tissues from patients at early phases of disease. Here, we used a rotavirus-induced model of biliary atresia to investigate the entire biliary transcriptome for molecular networks activated at the onset and different phases of progression to duct obstruction. METHODS: We injected Balb/c mice with saline or rotavirus intraperitoneally within 24 hours of birth, microdissected the gallbladder and extrahepatic bile ducts en bloc 3, 7, and 14 days later, generated biotinylated RNA pools, and hybridized them against microarrays containing 45,101 gene products. RESULTS: Data filtering, cluster analysis, and functional assignment of the gene expression platform revealed 2 unique patterns of expression. The first was an overarching expression of genes regulating immunity, enzymes, and structural proteins at all phases of atresia. Within this pattern, the sequential expression of the interferon inducers Irf7 and Irf9 at the onset of injury, and interferon-gamma and interferon-gamma-activated genes (Stat1, Igtp, Cxcl9, Cxcl10) at the time of duct obstruction, pointed to a prominent proinflammatory circuit. The second was the time-restricted expression of genes regulating biological networks previously unrecognized in biliary atresia, such as the complement components C3ar-1 and C1q-alpha/beta. CONCLUSIONS: The coordinate expression of functionally related genes in the biliary transcriptome underscores a predominant proinflammatory footprint and provides a basis for identification of gene groups that may play regulatory roles in the pathogenesis of duct injury and obstruction in experimental biliary atresia.

Animals↗

PubMed2026

The molecular landscape of chordoma: Current frontiers from multi-omics to artificial intelligence.

Chordoma is a rare and aggressive malignant bone tumor of the axial skeleton that has historically challenged clinicians due to its complex anatomical locations and a high recurrence rate of up to 85%. This review synthesizes the most recent advances in chordoma research and offers an overview of how multi-omics, advanced immunology, and artificial intelligence are reshaping the treatment paradigm. Central to its pathogenesis is the T-box transcription factor Brachyury, which this review highlights as both the pathognomonic diagnostic marker and the primary therapeutic vulnerability. Cutting-edge innovations targeting this driver include covalent small-molecule binders, targeted protein degradation, and peptide-centric CAR-T cells designed to attack the intracellular oncoprotein. The tumor immune microenvironment is functionally dynamic, and new dimensions in cellular therapy, such as dual-specific CAR constructs and NK-cell platforms, are being engineered to neutralize immunosuppressive factors. Beyond biological insights, the review emphasizes the role of computational biology, specifically how deep-learning and machine-learning models achieve expert-level precision in tumor segmentation and personalized survival forecasting. By integrating genomic, transcriptomic, epigenomic, and proteomic data, multiomics approaches can fully elucidate chordoma subtypes and underlying resistance mechanisms, ultimately paving the way for more precise and personalized therapeutic strategies.

Humans↗

PubMed2002

Profiling alternative splicing on fiber-optic arrays.

The human transcriptome is marked by extensive alternative mRNA splicing and the expression of many closely related genes, which may be difficult to distinguish using standard microarray techniques. Here we describe a sensitive and specific assay for parallel analysis of mRNA isoforms on a fiber-optic microarray platform. The method permits analysis of mRNA transcripts without prior RNA purification or cDNA synthesis. Using an endogenously expressed viral transcript as a model, we demonstrated that the assay readily detects mRNA isoforms from as little as 10-100 pg of total cellular RNA or directly from a few cells. Multiplexed analysis of human cancer cell lines revealed differences in mRNA splicing and suggested a potential autocrine mechanism in the development of choriocarcinomas. Our approach may be useful in the large-scale analysis of the role of alternative splicing in development and disease.

Alternative Splicing↗

PubMed2004

A global approach combining proteome analysis and phenotypic screening with RNA interference yields novel apoptosis regulators.

Global approaches like proteome or transcriptome analyses have been performed extensively to identify candidate genes or proteins involved in biological and pathological processes. Here we describe the identification of proteins implicated in the regulation of apoptosis using proteome analysis and the functional validation of targets by RNA interference. A high-throughput platform for the validation of synthetic small interfering RNAs (siRNAs) by quantitative real-time PCR was established. Genes of the identified factors were silenced by automated siRNA transfection, and their role in apoptotic signaling was investigated. Using this strategy, nine new modulators of apoptosis were identified. A subsequent detailed study demonstrated that hepatoma-derived growth factor (HDGF) is required for TNFalpha-induced release of pro-apoptotic factors from mitochondria. The strategy described here may be used for hypothesis-free, global gene function analysis.

Apoptosis↗

PubMedDate not supplied

Concatenation cDNA sequencing for transcriptome analysis.

We describe a high-throughput cDNA sequencing pipeline (http://www.hgsc.bcm.tmc.edu/projects/cdna) built in response to the emerging need for rapid sequencing of large cDNA collections. Using this strategy cDNA inserts are purified and joined through concatenation into large molecules. These 'pseudo-BACs' are subjected to random shotgun sequencing whereby the majority of cDNA inserts in the pool are sequenced. Using this concatenation cDNA sequencing platform, we have contributed more than 13000 full-length cDNA sequences from human and mouse to the Mammalian Gene Collection (MGC).

Cloning, Molecular↗

PubMed2006

A systems approach to model metastatic progression.

Proteomic profiling of human disease has seen much early activity with the accessibility of the newest generation of high-throughput platforms and technologies. Nevertheless, the nature of the dynamic physiologic milieu and high dimensionality of the data has complicated major diagnostic and prognostic breakthroughs. Our recent article in Cancer Cell delineates an integrative model for culling a molecular signature of metastatic progression in prostate cancer from proteomic and transcriptomic analyses and shows its facility as a predictor of prognosis. The study leveraged direct proteomic analysis of tumor tissue extracts, differential feature selection characterizing the proteomic alterations of prostate cancer subclasses, and integration with public and study-derived genomic data to construct a multiplex gene signature representing progression of indolent cancer to aggressive disease. This further predicted clinical outcome in a variety of solid tumors. This review describes the context of the work, the framework for the analysis itself, and a look forward to the promise of this systems approach to human disease.

Computational Biology↗

PubMed2006

Critical appraisal of DNA microarrays in psychiatric genomics.

Transcriptome profiling using DNA microarrays are data-driven approaches with the potential to uncover unanticipated relationships between gene expression alterations and psychiatric disorders. Studies to date have yielded both convergent and divergent findings. Differences may be explained, at least in part, by the use of a variety of microarray platforms and analytical approaches. Consistent findings across studies suggest, however, that important relationships may exist between altered gene expression and genetic susceptibility to psychiatric disorders. For example, GAD67, RGS4, DTNBP1, NRG1, and GABRAB2 show expression alterations in the postmortem brain of subjects with schizophrenia, and these genes have been also implicated as putative, heritable schizophrenia susceptibility genes. Thus, we propose that for some genes, altered expression in the postmortem human brain may have a dual origin: polymorphisms in the candidate genes themselves or upstream genetic-environmental factors that converge to alter their expression level. We hypothesize that certain gene products, which function as "molecular hubs," commonly show altered expression in psychiatric disorders and confer genetic susceptibility for one or more diseases. Microarray gene expression studies are ideally suited to reveal these putative disease-associated molecular hubs and to identify promising candidates for genetic association studies.

DNA↗

PubMed2025

The Elements of Life, Photosynthesis and Genomics.

I am a Professor of Biochemistry, Biophysics and Structural Biology and Plant and Microbial Biology at the University of California in Berkeley. I was born and raised in India, emigrated to the United States to attend university, earning a B.S. in Molecular Biology and a Ph.D. in Biochemistry at the University of Wisconsin in Madison. Following post-doctoral studies with Lawrence Bogorad at Harvard University where I became interested in genetic control of trace element quotas, I joined the department of Chemistry and Biochemistry at UCLA. One of the first to appreciate essential trace metals as potential regulators of gene expression, I articulated the details of the nutritional Cu regulon in Chlamydomonas. In parallel, I used genetic approaches to discover the genes governing missing steps in tetrapyrrole metabolism, including the attachment of heme to apocytochromes in the thylakoid lumen and the factors catalyzing the formation of ring V in chlorophyll. After biochemistry and classical genetics, I embraced genomics, taking a leadership role on the Joint Genome Institute's efforts on the Chlamydomonas genome and more recently, contributing to high quality assemblies of several genomes in the green algal radiation, and large transcriptomic and proteomic datasets - focusing on the diel metabolic cycle in synchronized cultures and acclimation to key environmental and nutritional stressors - that are well-used and appreciated by the community. A new venture in Berkeley is the promotion of Auxenochlorella protothecoides as the true "green yeast" and as a platform for engineering algae to produce useful bioproducts.

Photosynthesis↗

PubMed2026

Integrative Multiomics and Drug Sensitivity Profiling Reveal Potential Biomarkers and Therapeutic Strategies in Pediatric Solid Tumors.

UNLABELLED: Cure rates for childhood malignancies using established therapy protocols have increased to an average of 80% but have reached a plateau. Moreover, survival rates are particularly low for some pediatric tumors-such as high-risk group 3 medulloblastomas, osteosarcomas, Ewing sarcomas, high-risk neuroblastomas, and high-grade gliomas-and dismal for patients with relapsed malignancies. A functional drug response profiling platform for pediatric solid and brain tumors has been established within the INFORM program to identify patient-specific vulnerabilities and biomarkers and to unravel molecular mechanisms associated with drug response profiles for clinical translation. In this study, we performed a multiomics analysis using drug sensitivity profiles, as well as genomic and transcriptomic data, of 81 pediatric solid tumor samples. The integrative analysis suggested two multiomics signatures associated with drug sensitivity. One signature distinguished neuroblastoma samples with sensitivity to navitoclax, a BCL2 family inhibitor. A second signature was specific to a subset of Wilms tumors harboring the SIX1 (Q177R) hotspot mutation that displayed high expression of MGAM, PTPN14, STAT4, and KDM2B and high sensitivity to MEK inhibitors. A patient-specific causal interaction network analysis suggested possible molecular interactions between MEK inhibitors and the SIX1 mutation in Wilms tumor samples. In conclusion, the integration of drug sensitivity profiling and multiomics data revealed potential biomarkers that may be associated with drug sensitivity in pediatric solid tumors. Patient-specific causal interaction network analysis further elucidated the interaction between inhibitors and signature biomarkers, providing insights that may inform clinical translation. SIGNIFICANCE: The combination of multiomics analysis and drug sensitivity profiling identified two signatures related to drug sensitivity in pediatric solid tumors, contributing to the advancement of functional precision medicine and personalized treatment strategies. This article is part of a special series: Driving Cancer Discoveries with Computational Research, Data Science, and Machine Learning/AI .

Humans↗