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A thermodynamic model of transcriptome formation.

The genome supplies information on both the quality and quantity of the transcriptome. However, as it remains unknown how a cell determines transcript levels from the genome sequences, despite comprehensive knowledge of the cellular components involved, the quantity information held by the genome cannot as yet be derived from nucleotide sequences. The model presented here explains on a thermodynamic basis how the components decode the genome to form and maintain the transcriptome. The model describes the level of a transcript as a pseudo-equilibrium between velocities of synthesis and degradation, both of which are controlled by sequence-specific interactions between protein factors and nucleic acids. Each of the transcript levels can be described by a single equation expressing a function of the activity concentrations of the protein factors. Quantitative information in the genome can thus be transformed into constants determined from the nucleotide sequences. Using this model, the transcriptome can be traced back to the protein factors and the state of chromosome packaging. The total description of transcript levels allows the model to be verified through comparison of derived hypotheses with comprehensive measurements of the transcriptome. The hypotheses thus derived in the present study are well supported by experimental microarray data, confirming the appropriateness of the model.

Gene Expression Regulation↗

Comprehensive Transcriptome Annotation of Thousands of HIV-1 Genomes.

Alternative splicing in HIV-1 has been a central focus of decades of research, uncovering key mechanisms of viral gene regulation, immune evasion, and therapeutic response - yet, no reference resource has existed to support transcriptome-wide analysis, limiting adoption of modern computational methods. We present HIV Atlas (https://ccb.jhu.edu/HIV_Atlas), the first reference-quality annotation of HIV-1 and SIV transcriptional diversity. We manually curated transcriptomes for HIV-1HXB2 and SIVmac239 and developed Vira, an automated annotation-transfer method specifically designed to address unique challenges of viral genome biology, to generate high-quality annotations for 2,077 complete HIV-1 genomes. Using the resources presented in our work, we evaluated conservation of splice sites, revealing near-perfect preservation of major donors and acceptors. Furthermore, using several public datasets, we demonstrate how HIV Atlas enhances methodology, improves the quality and novelty of results, and opens novel avenues for research, supporting more accurate and comprehensive analyses of bulk, single-cell, and spatial RNA-seq in HIV-1 studies.

Journal Article↗

Machine learning on multiple epigenetic features reveals H3K27Ac as a driver of gene expression prediction across patients with glioblastoma.

Epigenetic mechanisms play a crucial role in driving transcript expression and shaping the phenotypic plasticity of glioblastoma stem cells (GSCs), contributing to tumor heterogeneity and therapeutic resistance. These mechanisms dynamically regulate the expression of key oncogenic and stemness-associated genes, enabling GSCs to adapt to environmental cues and evade targeted therapies. Importantly, epigenetic reprogramming allows GSCs to transition between cellular states, including therapy-resistant mesenchymal-like phenotypes, underscoring the need for epigenetic-targeting strategies to disrupt these adaptive processes. Understanding these epigenetic drivers of gene expression provides a foundation for novel therapeutic interventions aimed at eradicating GSCs and improving glioblastoma outcomes. Using machine learning (ML), we employ cross-patient prediction of transcript expression in GSCs by combining epigenetic features from various sources, including ATAC-seq, CTCF ChIP-seq, RNAPII ChIP-seq, H3K27Ac ChIP-seq, and RNA-seq. We investigate different ML and deep learning (DL) models for this task and ultimately build our final pipeline using XGBoost. The model trained on one patient generalizes to other 11 patients with high performance. Notably, H3K27Ac alone from a single patient is sufficient to predict gene expression in all 11 patients. Furthermore, the distribution of H3K27Ac peaks across the genomes of all patients is remarkably similar. These findings suggest that GSCs share a common distributional pattern of enhancer activity characterized by H3K27Ac, which can be utilized to predict gene expression in GSCs across patients. In summary, while GSCs are known for their transcriptomic and phenotypic heterogeneity, we propose that they share a common epigenetic pattern of enhancer activation that defines their underlying transcriptomic expression pattern. This pattern can predict gene expression across patient samples, providing valuable insights into the biology of GSCs.

Glioblastoma↗

Effectors of mammalian telomere dysfunction: a comparative transcriptome analysis using mouse models.

Critical telomere shortening in the absence of telomerase in late generation Terc-/- mice (G3 Terc-/-) or loss of telomere capping due to abrogation of the DNA repair/telomere binding protein Ku86 (Ku86-/- mice) results in telomere dysfunction and organismal premature aging. Here, we report on genome-wide transcription in mouse G3 Terc-/-, Ku86-/- and G3 Terc-/-/Ku86-/- germ cells using high-density oligonucleotide microarrays. Although a few transcripts are modulated specifically in Ku86- or Terc-deficient cells, the observed transcriptional response is mainly inductive and qualitatively similar for all three genotypes, with highest transcriptional induction observed in double mutant G3 Terc-/-/Ku86-/- cells compared with either single mutant. Analysis of 92 known genes induced in G3 Terc-/-/Ku86-/- germ cells compared with wild-type cells shows predominance of genes involved in cell adhesion, cell-to-cell and cell-to-matrix communication, as well as increased metabolic turnover and augmented antioxidant responses. In addition, the data presented in this study support the view that telomere dysfunction induces a robust compensatory response to rescue impaired germ cell function through the induction of survival signals related to the PI3-kinase pathway, as well as by the coordinated upregulation of transcripts that are essential for mammalian spermatogenesis.

Animals↗

Identifying key palmitoylation-associated genes in endometriosis through genomic data analysis.

BACKGROUND: Palmitoylation, a post-translational lipid modification, has garnered increasing attention for its role in inflammatory processes and tumorigenesis. Emerging evidence suggests a potential association between palmitoylation and inflammatory responses in the pathogenesis of endometriosis. However, the precise mechanistic interplay remains elusive, necessitating further investigation. METHODS: This study integrated transcriptomic analysis and Mendelian randomization (MR) to identify a causal gene set implicated in endometriosis. Differentially expressed genes (DEGs) were first identified in the training dataset using the limma package in R. Weighted gene co-expression network analysis (WGCNA) was subsequently performed, leveraging Single Sample Gene Set Enrichment Analysis (ssGSEA)-derived scores of palmitoylation-related genes (PRGs) as phenotypic traits to identify key modular genes. The intersection of these key modular genes with DEGs yielded a refined gene set. Machine learning algorithms were then applied to further optimize gene selection, followed by external validation, immune infiltration analysis, RNA network construction, and exploration of potential targeted drug candidates. RESULTS: Through a rigorous screening process, VRK1, GALNT12, and RMI1 emerged as key genes associated with palmitoylation, exhibiting significant downregulation in endometriosis samples (P <&#x2009;0.05), indicative of a potential protective role. Immune infiltration analysis further revealed strong correlations between these genes and M2 macrophages as well as resting Natural Killer (NK) cells. Additionally, investigations into the targeted RNA network and drug association profiling provided novel insights, laying the groundwork for future high-quality validation studies. CONCLUSIONS: This study employed a comprehensive analytical framework to identify palmitoylation-associated key genes in endometriosis. The integration of immunoinfiltration analysis, RNA network construction, and drug association profiling offers valuable insights for advancing clinical diagnostics, disease monitoring, and therapeutic development in endometriosis.

Humans↗

Integrated multi-omics identification of m6A-SNP-related diagnostic biomarkers in amyotrophic lateral sclerosis.

BACKGROUND: Amyotrophic lateral sclerosis (ALS) lacks reliable and minimally invasive biomarkers for early diagnosis. m6A-associated single-nucleotide polymorphisms (m6A-SNPs) may influence RNA methylation and gene expression, offering opportunities to identify clinically relevant diagnostic markers. METHODS: We integrated eQTLGen cis-eQTL data, RMVar m6A-SNP annotations, and ALS transcriptomic datasets to identify m6A-SNP-related genes. Random Forest and LASSO regression were combined to screen robust diagnostic markers. A nomogram was constructed and validated using independent cohorts. Immune infiltration, predicted m6A modification sites, and potential RBP-SNP interactions were assessed. Peripheral blood samples from ALS patients were used for exploratory validation of gene expression and global m6A levels. RESULTS: We identified 109 ALS-associated m6A-SNP-related genes with cis-eQTL signals and narrowed these to seven candidate diagnostic markers (TMED5, OXR1, BRI3, FEM1C, SUZ12, EIF2AK4, and TJAP1). The seven-gene model outperformed the individual markers in the training cohort and retained moderate discrimination in the independent validation cohort. ALS samples showed differences in inferred immune-cell composition, including monocytes, neutrophils, and T-cell subsets. The selected SNP loci were located near predicted m6A sites and annotated RBP-binding regions. Exploratory clinical validation showed significant upregulation of FEM1C and SUZ12 at both mRNA and protein levels, accompanied by reduced global m6A modification. CONCLUSIONS: Through multi-omics integration and exploratory clinical validation, this study identifies m6A-SNP-related candidate markers associated with ALS. The findings support further evaluation of m6A-related signatures for ALS discrimination and molecular characterization, while larger independent cohorts and additional calibration are required before clinical application.

Humans↗

A maize semi-dwarf mutant reveals a GRAS transcription factor involved in brassinosteroid signaling.

Brassinosteroids (BR) and gibberellins (GA) regulate plant height and leaf angle in maize (Zea mays). Mutants with defects in BR or GA biosynthesis or signaling identify components of these pathways and enhance our knowledge about plant growth and development. In this study, we characterized three recessive mutant alleles of GRAS transcription factor 42 (gras42) in maize, a GRAS transcription factor gene orthologous to the DWARF AND LOW TILLERING (DLT) gene of rice (Oryza sativa). These maize mutants exhibited semi-dwarf stature, shorter and wider leaves, and more upright leaf angle. Transcriptome analysis revealed a role for GRAS42 as a determinant of BR signaling. Analysis of the expression consequences from loss of GRAS42 in the gras42-mu1021149 mutant indicated a weak loss of BR signaling in the mutant, consistent with its previously demonstrated role in BR signaling in rice. Loss of BR signaling was also evident by the enhancement of weak BR biosynthetic mutant alleles in double mutants of nana plant1-1 and gras42-mu1021149. The gras42-mu1021149 mutant had little effect on GA-regulated gene expression, suggesting that GRAS42 is not a regulator of core GA signaling genes in maize. Single-cell expression data identified gras42 expressed among cells in the G2/M phase of the cell cycle consistent with its previously demonstrated role in cell cycle gene expression in Arabidopsis (Arabidopsis thaliana). Cis-acting natural variation controlling GRAS42 transcript accumulation was identified by expression genome-wide association study (eGWAS) in maize. Our results demonstrate a conserved role for GRAS42/SCARECROW-LIKE 28 (SCL28)/DLT in BR signaling, clarify the role of this gene in GA signaling, and suggest mechanisms of tillering and leaf angle control by BR.

Zea mays↗

Multi-omics analysis to uncover constitutive priming and dynamic metabolic reprogramming conferring white rust resistance in Brassica juncea.

White rust, caused by Albugo candida, is one of the most devastating diseases of Indian mustard (Brassica juncea), causing yield losses of up to 90%. Durable resistance sources within cultivated Brassica germplasm remain limited. In this study, near-isogenic lines (NILs) of B. juncea cv. Varuna harbouring resistance from an East European source (Donskaja-IV, possessing a single CC-NB-LRR protein-coding R gene) was used to investigate the molecular basis of resistance through integrated transcriptomic and metabolomic analyses at 48 and 96 hours post-inoculation (hpi). Transcriptomic profiling revealed that the resistant Varuna_WRR line exhibited significantly higher unique transcript expression (18.76%) compared to the susceptible parent (8.41%) during the progression of infection. Principal component analysis showed clear separation between genotypes based on infection status, time, and genetic background. In the resistant line, upregulated genes were enriched in ethylene-activated signaling, protein phosphorylation, endoplasmic reticulum stress response, pectin biosynthesis, and hypersensitive response at 48 hpi, shifting toward programmed cell death, protein ubiquitination, abscisic acid metabolism, and starch biosynthesis at 96 hpi. Conversely, the susceptible line displayed broad downregulation of primary metabolic processes, indicating metabolic exhaustion. Metabolomic analysis demonstrated that the resistant genotype accumulated higher levels of defense-related amino acids (proline, glutamine, glutamic acid, serine, threonine, glycine), carbohydrates, organic acids, and polyamines, supporting enhanced nitrogen assimilation, energy reserves, membrane stability, and signaling. Together, these findings indicate that constitutive priming and dynamic activation of defense signaling, protein turnover, and osmoprotectant accumulation underpin the enhanced resistance in Varuna_WRR against Albugo candida. This integrated multi-omics approach provides valuable insights for breeding durable white rust resistance in Brassica juncea.

Brassica juncea↗

Comparative transcriptional profiling of two contrasting rice genotypes under salinity stress during the vegetative growth stage.

Rice (Oryza sativa), a salt-sensitive species, has considerable genetic variation for salt tolerance within the cultivated gene pool. Two indica rice genotypes, FL478, a recombinant inbred line derived from a population developed for salinity tolerance studies, and IR29, the sensitive parent of the population, were selected for this study. We used the Affymetrix rice genome array containing 55,515 probe sets to explore the transcriptome of the salt-tolerant and salt-sensitive genotypes under control and salinity-stressed conditions during vegetative growth. Response of the sensitive genotype IR29 is characterized by induction of a relatively large number of probe sets compared to tolerant FL478. Salinity stress induced a number of genes involved in the flavonoid biosynthesis pathway in IR29 but not in FL478. Cell wall-related genes were responsive in both genotypes, suggesting cell wall restructuring is a general adaptive mechanism during salinity stress, although the two genotypes also had some differences. Additionally, the expression of genes mapping to the Saltol region of chromosome 1 were examined in both genotypes. Single-feature polymorphism analysis of expression data revealed that IR29 was the source of the Saltol region in FL478, contrary to expectation. This study provides a genome-wide transcriptional analysis of two well-characterized, genetically related rice genotypes differing in salinity tolerance during a gradually imposed salinity stress under greenhouse conditions.

Base Sequence↗

Modulation of yeast genome expression in response to defective RNA polymerase III-dependent transcription.

We used genome-wide expression analysis in Saccharomyces cerevisiae to explore whether and how the expression of protein-coding, RNA polymerase (Pol) II-transcribed genes is influenced by a decrease in RNA Pol III-dependent transcription. The Pol II transcriptome was characterized in four thermosensitive, slow-growth mutants affected in different components of the RNA Pol III transcription machinery. Unexpectedly, we found only a modest correlation between altered expression of Pol II-transcribed genes and their proximity to class III genes, a result also confirmed by the analysis of single tRNA gene deletants. Instead, the transcriptome of all of the four mutants was characterized by increased expression of genes known to be under the control of the Gcn4p transcriptional activator. Indeed, GCN4 was found to be translationally induced in the mutants, and deleting the GCN4 gene eliminated the response. The Gcn4p-dependent expression changes did not require the Gcn2 protein kinase and could be specifically counteracted by an increased gene dosage of initiator tRNA(Met). Initiator tRNA(Met) depletion thus triggers a GCN4-dependent reprogramming of genome expression in response to decreased Pol III transcription. Such an effect might represent a key element in the coordinated transcriptional response of yeast cells to environmental changes.

Basic-Leucine Zipper Transcription Factors↗

Novel RNAs identified from an in-depth analysis of the transcriptome of human chromosomes 21 and 22.

In this report, we have achieved a richer view of the transcriptome for Chromosomes 21 and 22 by using high-density oligonucleotide arrays on cytosolic poly(A)(+) RNA. Conservatively, only 31.4% of the observed transcribed nucleotides correspond to well-annotated genes, whereas an additional 4.8% and 14.7% correspond to mRNAs and ESTs, respectively. Approximately 85% of the known exons were detected, and up to 21% of known genes have only a single isoform based on exon-skipping alternative expression. Overall, the expression of the well-characterized exons falls predominately into two categories, uniquely or ubiquitously expressed with an identifiable proportion of antisense transcripts. The remaining observed transcription (49.0%) was outside of any known annotation. These novel transcripts appear to be more cell-line-specific and have lower and less variation in expression than the well-characterized genes. Novel transcripts were further characterized based on their distance to annotations, transcript size, coding capacity, and identification as antisense to intronic sequences. By RT-PCR, 126 novel transcripts were independently verified, resulting in a 65% verification rate. These observations strongly support the argument for a re-evaluation of the total number of human genes and an alternative term for "gene" to encompass these growing, novel classes of RNA transcripts in the human genome.

Cell Line↗

Functional genomics: lessons from yeast.

Functional genomics represents a systematic approach to elucidating the function of the novel genes revealed by complete genome sequences. Such an approach should adopt a hierarchical strategy since this will both limit the number of experiments to be performed and permit a closer and closer approximation to the function of any individual gene to be achieved. Moreover, hierarchical analyses have, in their early stages, tremendous integrative power and functional genomics aims at a comprehensive and integrative view of the workings of living cells. The first draft of the human genome sequence has just been produced, and the complete genome sequences of a number of eukaryotic human pathogens (including the parasitic protozoa Plasmodium, Leishmania, and Trypanosoma) will soon be available. However, the most rapid progress in the elucidation of gene function will initially be made using model organisms. Yeast is an excellent eukaryotic model and at least 40% of single-gene determinants of human heritable diseases find homologues in yeast. We have adopted a systematic approach to the functional analysis of the Saccharomyces cerevisiae genome. A number of the approaches for the functional analysis of novel yeast genes are discussed. The different approaches are grouped into four domains: genome, transcriptome, proteome, and metabolome. The utility of genetic, biochemical, and physico-chemical methods for the analysis of these domains is discussed, and the importance of framing precise biological questions, when using these comprehensive analytical methods, is emphasized. Finally, the prospects for elucidating the function of protozoan genes by using the methods pioneered with yeast, and even exploiting Saccharomyces itself, as a surrogate, are explored.

Fungal Proteins↗

A pan-cancer single-cell atlas uncovers the role of sex hormones and chromosomes in sex-divergent reprogramming of the tumor microenvironment.

BACKGROUND: Sex bias is pervasive in tumors; however, how sex chromosomes and hormone-responsive signaling shape the tumor microenvironment (TME) remains insufficiently characterized. Considering the critical impact of the TME on tumor progression and response to immunotherapy, a pan-cancer investigation of sex-specific and cancer-context-dependent TME features is warranted. METHOD: Based on stringent inclusion criteria, we constructed a high-resolution pan-cancer single-cell sequencing atlas by integrating 31 publicly available single-cell RNA-seq datasets, comprising a total of 1,831,436 cells by integrating 468 samples from eight types of non-sex-specific solid tumors (282 males and 186 females). After correcting for batch effects, we identified major and minor cellular subsets. Multiple computational approaches were applied to investigate sex-associated differences in cellular composition, gene expression, pathway activity, malignant cell states and intercellular communication. RESULTS: We systematically compared sex-specific TME features across eight common solid malignancies. Male-biased CD8+ T cell exhaustion emerged as a recurrent but non-uniform feature, with its magnitude varying across cancer types and being modified by tissue-specific contexts. This pattern was associated with androgen-response signature scores and expression-based loss of the Y chromosome (LOY) scores. M2-like macrophage polarization showed a more cancer-type-dependent pattern; although female-biased enrichment was observed in selected malignancies, it did not represent a uniform pan-cancer feature. Expression-based X chromosome inactivation (XCI)/XCI escape-related programs, estrogen-response signature scores and stromal components, including fibroblasts and endothelial cells, were associated with macrophage and immune-regulatory states in specific tumor contexts. Tumor cells of male origin displayed higher genomic instability and more aggressive phenotypes, with androgen-response signatures and LOY contributing to the development of a male biased malignant state. Furthermore, expression-based LOY scores in malignant cells were associated with CD8+ T cell exhaustion based on transcriptomic proxies. CONCLUSION: Our study uncovers extensive but heterogeneous sex-specific differences in the TME across multiple cancer types. We propose a regulatory framework linking sex chromosomes, hormone-responsive signaling and TME interactions, which is consistent with recurrent male-biased CD8&#x207a; T cell exhaustion and context-dependent M2-like macrophage polarization. Importantly, the magnitude and, in some cancers, the direction of these sex-biased features are modified by tissue-specific contexts. These findings underscore the need to include sex chromosome and hormone status as essential biological variables in studies of the tumor microenvironment and the design of immunotherapies.

Tumor Microenvironment↗

Arabidopsis ABA INSENSITIVE4 regulates lipid mobilization in the embryo and reveals repression of seed germination by the endosperm.

Regulation of seed germination requires coordinate action by the embryo and surrounding endosperm. We used Arabidopsis thaliana to establish the relative roles of embryo and endosperm in the control of seed germination and seedling establishment. We previously showed that endospermic oil reserves are used postgerminatively via gluconeogenesis to fuel seedling establishment and that lipid breakdown is repressed by abscisic acid (ABA) in embryo but not endosperm tissues. Here, we use RNA amplification to describe the transcriptome of the endosperm and compare the hormone responses of endosperm and embryo tissues. We show that the endosperm responds to both ABA and gibberellin but that ABA in particular regulates nuclear but not plastid-encoded photosynthetic gene expression in the embryo. We also show that ABA INSENSITIVE4 (ABI4) expression is confined to the embryo, accounts for the major differences in embryo response to ABA, and defines a role for ABI4 as a repressor of lipid breakdown. Furthermore, ABI5 expression in the endosperm defines a second region of altered ABA signaling in the micropylar endosperm cap. Finally, embryo and endosperm ABA signaling mutants demonstrate the spatial specificity of ABA action in seed germination. We conclude that the single cell endosperm layer plays an active role in the regulation of seed germination in Arabidopsis.

Abscisic Acid↗

IGF1R deficiency mitigates acute lung injury by promoting anti-inflammatory transcriptional profiles.

BACKGROUND: Acute lung injury (ALI), acute respiratory distress syndrome (ARDS) and COVID-19 are characterized by hyperinflammation, commonly referred to as "cytokine storm". The insulin-like growth factor (IGF) pathway, particularly the type 1 receptor (IGF1R), plays a critical role in lung homeostasis and has been implicated in the pathogenesis of pulmonary inflammatory diseases. In mice, widespread Igf1r deficiency attenuates lung inflammation and alveolar damage in bleomycin (BLM)-induced ALI. METHODS: We analyzed single-cell RNA sequencing datasets from lung tissue of COVID-19 cases and control donors as well as mouse lungs to determine Igf1r and IGF family expression across pulmonary cell types. Furthermore, we conducted bulk RNA sequencing on lungs from Igf1r-deficient mice three days after BLM or saline instillation, followed by differential expression and functional enrichment analyses. Findings were further tested through protein detection, assessment of DNA damage and methylation in lung tissues, and functional assays using Igf1r-deficient primary mouse embryonic fibroblasts (MEFs). RESULTS: IGF1R was broadly expressed across multiple cell types in both human and mouse lungs under normal and pathological conditions. Other IGF family members showed cell-type-specific expression, which was modulated by lung injury. Transcriptomic profiling revealed differentially expressed genes between BLM-challenged and control mouse lungs, detecting biological processes and signaling pathways involved in ALI pathobiology. Igf1r deficiency in BLM-challenged mice reversed a large fraction of the transcriptional changes triggered by BLM, including "cytokine storm"-related gene expression. Functional enrichment analysis additionally revealed significant modulation of pathways related to DNA damage, metabolic reprogramming, mitochondrial homeostasis, and epigenetic regulation. In vitro, Igf1r-deficient MEFs exhibited decreased mitochondrial respiration and glycolysis, protection against BLM-induced nuclear damage and mitochondrial accumulation, and decreased histone H3 acetylation. Moreover, Igf1r-deficient mouse lungs displayed increased global DNA methylation following BLM challenge. CONCLUSIONS: IGF1R is a key modulator of the inflammatory and molecular response to ALI pathogenesis. IGF1R deficiency dampens the "cytokine storm", modifies transcriptional and epigenetic profiles and promotes protective cellular responses. These findings highlight IGF1R signaling as a potential therapeutic target in ARDS and related lung injuries.

Animals↗

Rhythm profiling using COFE reveals multi-omic circadian rhythms in human cancers in vivo.

The study of ubiquitous circadian rhythms in human physiology requires regular measurements across time. Repeated sampling of the different internal tissues that house circadian clocks is both practically and ethically infeasible. Here, we present a novel unsupervised machine learning approach (COFE) that can use single high-throughput omics samples (without time labels) from individuals to reconstruct circadian rhythms across cohorts. COFE can simultaneously assign time labels to samples and identify rhythmic data features used for temporal reconstruction, while also detecting invalid orderings. With COFE, we discovered widespread de novo circadian gene expression rhythms in 11 different human adenocarcinomas using data from The Cancer Genome Atlas (TCGA) database. The arrangement of peak times of core clock gene expression was conserved across cancers and resembled a healthy functional clock except for the mistiming of a few key genes. Moreover, rhythms in the transcriptome were strongly associated with the cancer-relevant proteome. The rhythmic genes and proteins common to all cancers were involved in metabolism and the cell cycle. Although these rhythms were synchronized with the cell cycle in many cancers, they were uncoupled with clocks in healthy matched tissue. The targets of most of FDA-approved and potential anti-cancer drugs were rhythmic in tumor tissue with different amplitudes and peak times. These findings emphasize the utility of considering "time" in cancer therapy, and suggest a focus on clocks in healthy tissue rather than free-running clocks in cancer tissue. Our approach thus creates new opportunities to repurpose data without time labels to study circadian rhythms.

Humans↗

Comprehensive Multiplatform Tyrosine Kinase Profiling Reveals Novel Actionable FGFR Aberrations across Sarcomas Affecting the Young.

Limited targeted agents are approved for pediatric sarcomas. Tyrosine kinase (TK) inhibitors (TKi) have shown clinical efficacy in some, but not all, young patients with sarcoma. A major obstacle preventing further advances and clinical implementation is the lack of predictive response biomarkers to guide TK-targeted treatments. TK-activating fusions or mutations are rare in these patients. RNA overexpression of TKs is a frequent feature. The unresolved question is when upregulated TK expression is associated with kinase activation and signaling dependence. We explored the TK molecular landscape of 107 patients with sarcoma from the ZERO Childhood Cancer Precision Medicine Program (ZERO) using whole-genome and -transcriptome sequencing. Phosphoproteomic analyses of tyrosine phosphorylation (pY) and functional in vitro and in vivo assays were performed in cell lines and patient-derived xenografts (PDX). Our analysis shows that although novel genomic driver lesions are rare, when present they are therapeutically actionable as exemplified by a novel LSM1-FGFR1 fusion identified in a patient with osteosarcoma. We further show that in certain contexts, TK RNA expression can indicate TK pathway activity and predict TKi sensitivity. We highlight the utility of FGFR inhibitors in PAX3-FOXO1 fusion-positive rhabdomyosarcomas (FP-RMS) characterized by high FGFR4 and FGF8 RNA expression levels and FGFR4 activation (FGFR4_pY). We demonstrate marked tumor growth inhibition in all FP-RMS PDXs treated with single-agent FGF401 (FGFR4-specific inhibitor) and single-agent lenvatinib (multikinase FGFR inhibitor) and report a clinical response to lenvatinib in a patient with relapsed metastatic FP-RMS. Altogether, we identified new patients with sarcoma who may benefit from FGFR inhibitors, most notably FP-RMS via FGFR4/FGF8 coexpression.

Journal Article↗

Stage-Independent Real-Time Subtype Classification and Comprehensive Biopsy Profiling of Urothelial Carcinomas by the Lund Taxonomy System.

Bladder cancer is a heterogeneous malignancy with diverse clinical outcomes, and conventional pathological assessment alone is insufficient to capture its underlying biology. Gene expression profiling can stratify tumors into molecular subtypes with prognostic and predictive potential, but the reliability of transcriptomic classification and its clinical utility remains to be established. The translational/observational UROSCANSEQ study (ISRCTN15459149) prospectively evaluates RNA-based Lund Taxonomy (LundTax) molecular subtype classification in a clinical setting. Among 784 consecutive biopsies collected between 2018 and 2022, RNA sequencing was successful for 90% of all biopsies, encompassing 662 bladder cancer patients with a stage distribution of 48% Ta, 27% T1, 24% &#x2265;T2, and 1% CIS. We demonstrate that the LundTax subtype classification algorithm, applied to individual samples, accurately identifies cancer cell phenotypes with characteristic gene and protein expression patterns in a manner robust to RNA quality, data preprocessing strategies, and batch effects, supporting its clinical feasibility across both non-muscle-invasive and muscle-invasive disease. We further extend the LundTax framework by incorporating single-sample molecular risk scores reflecting tumor grade, proliferation, and progression risk, as well as tumor microenvironment signatures. Both risk scores and overall immune and stromal content in biopsies were significantly associated with an increased risk of clinical progression in noninvasive disease. In a separate analysis of the relative cellular composition of the tumor microenvironment, however, only the fraction of natural killer cells remained significant. Together, the expanded LundTax system provides a comprehensive molecular portrait of individual tumor biopsies. By explicitly separating cancer cell-intrinsic phenotypes, prognostic indexes, and microenvironmental signals, the framework minimizes biological confounding and establishes a strong foundation for future studies evaluating clinical outcomes and treatment responses.

Humans↗