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PubMed2026

Mechanism-Driven Diagnostic Development: A Specimen-Aware Framework Illustrated by Colorectal Cancer and Solid Tumours.

Translational oncology has moved rapidly from histopathology and single-analyte biomarkers toward multi-dimensional molecular profiling. Yet many clinically deployed tests still use reductionist biomarker strategies that under-represent cancer complexity. This review examines whether a mechanistic, multi-layered, and specimen-aware approach can improve cancer detection, classification, prognosis, minimal residual disease (MRD) assessment, and therapeutic selection. Evidence across solid tumours shows that genomic alterations alone incompletely explain tumour state, metastatic behaviour, immune evasion, or therapeutic vulnerability. Integrated genome and transcriptome analyses, proteogenomics, single-cell atlases, fragmentomic, methylation based cell-free DNA assays, metabolomics and microbiome assessments reveal clinically relevant biology that single modality tests cannot determine. Minimally invasive collected specimens can extend access to screening, diagnosis and longitudinal monitoring, but the choice of specimen should be matched to disease biology and analytes that represent mechanisms of oncogenesis. However, translation remains constrained by pre-analytical variability, contamination, differences in tumour shedding behaviour, clonal haematopoiesis, translation of generated models, incomplete external validation and uncertain downstream clinical utility for emerging platforms. This review provides a commentary on the future of cancer diagnostics, the considerations and barriers to clinical translation, the relationship between utility and dimensionality of biomarkers assessed and the emerging rationale towards mechanistically grounded integrated models.

biomarkers↗
PubMed2026

Multi‑omics approaches to decipher the molecular mechanisms of exercise‑mediated bone protection: From mechanistic insights to personalized exercise prescription (Review).

The global burden of bone metabolic disorders necessitates a shift from generic exercise recommendations toward personalized prescription strategies. Exercise confers skeletal protection through mechanotransduction, yet the underlying molecular networks remain incompletely understood. Multi‑omics technologies, including transcriptomics, proteomics, metabolomics and single‑cell spatial approaches, have revolutionized the capacity to decode exercise‑mediated bone adaptation at the systems level. The present review synthesizes current single‑omics landscapes and integrative multi‑omics analyses that elucidate the core regulatory networks, mechanobiological coupling mechanisms and multiorgan crosstalk that are implicated in the bone response to mechanical loading. Translational applications across clinical scenarios such as osteoporosis, osteoarthritis and disuse bone loss are evaluated, and the technical, analytical and translational challenges limiting clinical implementation are addressed. Finally, the present review provides a framework for translating multi‑omics molecular signatures into personalized exercise prescriptions for optimized skeletal health.

Humans↗
PubMed2026

Multi-omics and spatial transcriptomics reveal that S100A10 drives CD8+ T-cell exhaustion and immune evasion in hepatocellular carcinoma through cPLA2-5-LOX-mediated arachidonic acid metabolism and ferroptosis.

Immune evasion in hepatocellular carcinoma (HCC) represents a major biological barrier limiting the efficacy of immunotherapy, yet its molecular basis remains incompletely understood. Increasing evidence indicates that tumor metabolic reprogramming and ferroptosis-related signaling play critical roles in shaping an immunosuppressive tumor microenvironment (TME); however, the specific regulatory factors involved remain unclear. This study aims to systematically elucidate the functional role of S100 calcium-binding protein A10 (S100A10) in immune evasion in HCC, with a particular focus on the molecular mechanisms by which S100A10 regulates CD8+ T-cell exhaustion through arachidonic acid (AA) metabolism and ferroptosis, as well as its potential therapeutic implications. To this end, data from The Cancer Genome Atlas Liver Hepatocellular Carcinoma (TCGA-LIHC) cohort are integrated to analyze the expression patterns of S100A10, its prognostic value, and its association with the immune microenvironment. S100A10 overexpression and knockout models are established in HCCLM3 and MHCC97L cell lines, and S100A10-mediated metabolic pathway reprogramming is characterized using transcriptomic profiling, untargeted metabolomics, and ferroptosis-related functional assays. In parallel, single-cell RNA sequencing (scRNA-seq) and spatial transcriptomics are employed to delineate the cell-type specificity and spatial distribution of S100A10. Furthermore, human CD8+ T-cell co-culture systems and orthotopic mouse HCC models are used to evaluate the impact of S100A10 on immune function and responsiveness to anti-programmed cell death protein 1 (anti-PD-1) therapy. The results demonstrate that S100A10 is significantly upregulated in HCC and is closely associated with poor prognosis and an immunosuppressive state. Mechanistically, S100A10 activates cytosolic phospholipase A2-arachidonate 5-lipoxygenase (cPLA2-5-LOX)-mediated AA oxidative metabolism, leading to the accumulation of lipid peroxidation products and ferroptosis-associated signals, thereby driving CD8+ T-cell exhaustion and promoting immune evasion. Significantly, inhibition of S100A10 reshapes the tumor immune microenvironment (TIME) and enhances the therapeutic efficacy of anti-PD-1 treatment. Collectively, these findings identify S100A10 as a critical regulator of metabolic-immune coupling in HCC and provide a theoretical basis for combinatorial strategies targeting metabolism and immunotherapy.

Arachidonic acid metabolism↗
PubMed2025

OmicsTweezer: A distribution-independent cell deconvolution model for multi-omics Data.

Cell deconvolution estimates cell type proportions from bulk omics data, enabling insights into tissue microenvironments and disease. However, practical applications are often hindered by batch effects between bulk data and referenced single-cell data, a challenge that is frequently overlooked. To address this discrepancy, we developed OmicsTweezer, a distribution-independent cell deconvolution model. By integrating optimal transport with deep learning, OmicsTweezer aligns simulated and real data in a shared latent space, effectively mitigating data shifts and inter-omics distribution differences. OmicsTweezer is versatile, capable of deconvolving bulk RNA-seq, bulk proteomics, and spatial transcriptomics. Extensive evaluations on simulated and real-world datasets demonstrate its robustness and accuracy. Furthermore, applications in prostate and colon cancer showcase OmicsTweezer's ability to identify biologically meaningful cell types. As a unified deconvolution framework for multi-omics data, OmicsTweezer offers an efficient and powerful tool for studying disease microenvironments.

Humans↗
PubMed2026

Association of MPO Expression with the Immune Microenvironment in Breast Cancer: Insights from Bioinformatics and Single-Cell Analyses.

Breast cancer remains a major cause of cancer-related mortality, and exploratory computational workflows can help prioritize immune-associated markers for further investigation. Here, we used the cancer genome atlas breast invasive carcinoma (TCGA-BRCA) bulk transcriptomic data and the public single-cell dataset GSE161529 to examine associations between myeloperoxidase (MPO) expression, clinical outcomes, immune infiltration, methylation, upstream-regulator annotations, single-cell expression patterns, virtual knockdown sensitivity outputs, drug-gene interaction retrieval, and absorption, distribution, metabolism, excretion, and toxicity (ADMET) annotation. MPO expression was lower in breast cancer tissues than in adjacent non-tumor tissues. Higher MPO expression was associated with a longer progression-free interval, whereas its associations with overall survival and disease-specific survival were not statistically significant. Receiver operating characteristic (ROC) analysis suggested tumor-normal separation within the analyzed public dataset, but this should not be interpreted as clinical diagnostic validation. Immune deconvolution and enrichment analyses indicated that MPO expression mainly tracked with immune- and myeloid-related transcriptional features, rather than establishing tumor-intrinsic regulation of the immune microenvironment. At single-cell resolution, the MPO signal was sparse, with only 85 MPO-positive cells detected before k-nearest neighbor (KNN)-based neighborhood expansion. Detectable MPO signal and MPO-associated scores were interpreted cautiously because they may be influenced by sparse expression, cell-type annotation uncertainty, dropout, doublets, or ambient RNA. In silico virtual knockdown suggested candidate immune- and inflammatory-related transcriptional changes, but these results were considered exploratory and require validation. Drug-gene interaction database (DGIdb)-based drug-gene retrieval and ADMET annotation were used only as preliminary chemical annotations and were not interpreted as therapeutic evidence. Overall, this study provides a reproducible in silico workflow for generating hypotheses about MPO-associated immune/myeloid features in breast cancer, which require external cohort validation and experimental confirmation.

Humans↗
PubMed2026

Multi-omics identification and functional validation of signal regulatory protein gamma as a prognostic biomarker and immune regulator in head and neck squamous cell carcinoma.

BACKGROUND: Head and neck squamous cell carcinoma (HNSCC) comprises biologically diverse tumors, and durable responses to immune-checkpoint blockade are achieved by only a subset of patients. There remains a need for markers that connect clinical outcome with malignant-cell phenotypes and tissue-level immune organization. METHODS: We integrated The Cancer Genome Atlas HNSCC cohort (TCGA-HNSC), five Gene Expression Omnibus (GEO) validation cohorts, single-cell RNA sequencing, Visium spatial transcriptomics, cellular indexing of transcriptomes and epitopes by sequencing (CITE-seq)-informed protein-potential inference, pharmacogenomic screening, genetic-risk analysis and experimental validation. A reconstructed 296-pipeline survival modelling framework was used to prioritize prognostic hub genes across validation-cohort-specific analyses. RESULTS: SIRPG was repeatedly ranked among the top ten selected genes in all five validation cohorts. At single-cell resolution, SIRPG-high tumor cells showed stronger malignant-cell features, immune-inhibitory and metabolic programs, Scissor-positive risk association, CLCA2/P53-related perturbation signals and inferred SIRPG-CD47/signal regulatory protein (SIRP) communication. Spatial analyses placed this axis within an immune-checkpoint-coupled niche, supported by Maxspin/multiview intercellular spatial modelling (MISTy) spatial coupling, communication analysis by optimal transport (COMMOT)-inferred CD47-SIRPG communication and scProTrans-inferred CD47/SIRPG protein-potential overlap. Functionally, SIRPG knockdown reduced HNSCC cell viability and increased apoptosis, whereas re-expression of short hairpin RNA (shRNA)-resistant SIRPG restored the CLCA2-BAX/BCL2 protein response. CONCLUSION: Together, these findings identify SIRPG as an immune-related prognostic hub and context-dependent tumor-cell regulator associated with apoptosis, immune communication and spatial microenvironmental organization in HNSCC.

Humans↗
PubMed2025

LINNAEUS: Simultaneous Single-Cell Lineage Tracing and Cell Type Identification.

A key goal of biology is to understand the origin of the many cell types that can be observed during diverse processes such as development, regeneration, and disease. Single-cell RNA-sequencing (scRNA-seq) is commonly used to identify cell types in a tissue or organ. However, organizing the resulting taxonomy of cell types into lineage trees to understand the origins of cell states and relationships between cells remains challenging. Here we present LINNAEUS (Spanjaard et al, Nat Biotechnol 36:469-473. https://doi.org/10.1038/nbt.4124 , 2018; Hu et al, Nat Genet 54:1227-1237. https://doi.org/10.1038/s41588-022-01129-5 , 2022) (LINeage tracing by Nuclease-Activated Editing of Ubiquitous Sequences)-a strategy for simultaneous lineage tracing and transcriptome profiling in thousands of single cells. By combining scRNA-seq with computational analysis of lineage barcodes, generated by genome editing of transgenic reporter genes, LINNAEUS can be used to reconstruct organism-wide single-cell lineage trees. LINNAEUS provides a systematic approach for tracing the origin of novel cell types, or known cell types under different conditions.

Single-Cell Analysis↗
PubMed2026

Multiomics analysis reveals that senescent CXCL16+ macrophages promote lung adenocarcinoma progression through TGF-β signalling.

BACKGROUND: Lung adenocarcinoma (LUAD) is the most common histological subtype of lung cancer and remains a leading cause of cancer-related mortality worldwide. Although, immunotherapy has become a cornerstone of first-line treatment, only 20-30% of patients achieve a durable clinical benefit, largely because of the complexity and heterogeneity of the tumour immune microenvironment. Emerging evidence indicates that cellular senescence, particularly within immune cells, contributes to tumour progression by impairing antitumour immunity; however, its mechanistic role in LUAD remains incompletely understood. METHODS: We performed an integrative multiomics analysis incorporating genome-wide association studies (GWASs), bulk RNA sequencing, single-cell RNA sequencing, and spatial transcriptomics to characterize immune heterogeneity in LUAD. Cellular senescence was validated by performing staining for senescence-associated β-galactosidase and the canonical markers p16 and p21. SHAP analysis was applied to evaluate the contribution of CXCL16+ macrophages. Functional roles were assessed using coculture assays, in vitro and in vivo tumour models, orthotopic tumour implantation, and multiplex immunofluorescence staining of clinical specimens. RESULTS: A summary data-based on Mendelian randomization analysis integrating GWAS and TCGA data identified CXCL16 as a senescence-associated gene that is causally linked to the LUAD risk. Single-cell RNA sequencing revealed that CXCL16 is predominantly expressed in macrophages, and the pseudotime analysis together with β-galactosidase staining confirmed its association with macrophage senescence. Spatial transcriptomics and immunofluorescence staining showed the marked enrichment of CXCL16+ macrophages in LUAD tissues. The cell-cell communication analysis further revealed a strong association between the number of CXCL16+ macrophages and the activation of the TGF-β signalling pathway within the tumour microenvironment. Functionally, CXCL16+ macrophages promoted LUAD progression via TGF-β signalling, as validated in vitro and in subcutaneous and orthotopic tumour models. Molecular dynamics simulations additionally suggested that LUAD patients with high levels of CXCL16+ macrophage infiltration may exhibit increased sensitivity to bosutinib. CONCLUSIONS: CXCL16 promotes macrophage senescence, and senescent CXCL16+ macrophages drive LUAD progression through TGF-β signalling. These findings identify CXCL16+ macrophages as a biologically and therapeutically relevant immune cell population, highlighting a potential target for precision intervention in LUAD.

Humans↗
PubMed2026

The extracellular matrix in cancer-associated fibrosis: molecular mechanisms and clinical relevance.

The ECM is a dynamic component of the tumor microenvironment with a critical role in cancer progression, invasion, metastasis, immune exclusion, and response to therapy. Recent advances in proteomic analyses investigating the insoluble ECM fractions (termed "matrisome analysis"), along with single-cell RNA sequencing and spatial transcriptomics, have revealed cancer-specific patterns of ECM remodeling. These studies have identified a panel of recurrently upregulated ECM proteins, including annexin A1, fibrillin-1, fibronectin, periostin, and tenascin-C, actively contributing to tumor growth, invasion, angiogenesis, and immune exclusion. The expression of the cancer-associated ECM is largely driven by cancer-associated fibroblasts (CAFs), whose molecular diversity has been dissected through single-cell profiling and consolidated in emerging CAF atlases across cancers. By investigating the matrisome composition and CAF heterogeneity, these studies have unraveled the pivotal role of the stroma in shaping tumor biology. Based on these discoveries, ECM proteins and CAFs are now being explored as biomarkers and therapeutic targets. Future integration of multi-omics datasets with clinical outcomes will help to translate these insights into novel biomarkers for patient stratification and stroma-directed therapeutic interventions.

Humans↗
PubMed2026

Decoding tumor immune microenvironment heterogeneity by single-cell and spatial multi-omics: From immunotherapy resistance to translational biomarkers.

Immune checkpoint blockade has transformed cancer therapy, yet primary and acquired resistance remain major clinical challenges. Increasing evidence indicates that immunotherapy resistance cannot be fully explained by tumor-intrinsic alterations or conventional biomarkers such as PD-L1 expression, tumor mutational burden, or microsatellite instability. Instead, therapeutic response is shaped by the tumor immune microenvironment (TIME) as a heterogeneous, spatially organized, and dynamically evolving ecosystem. Single-cell omics has revealed diverse immune and stromal cell states, including progenitor and terminally exhausted T cells, suppressive myeloid programs, B-cell/TLS-associated immune-reactive states, and CAF-mediated exclusion phenotypes. Spatial transcriptomics, spatial proteomics, and imaging-based approaches further demonstrate that these cell states assemble into distinct immune niches, including immune-inflamed, T-cell-excluded, myeloid-suppressive, metabolic/hypoxic, and TLS-associated niches. These spatial ecosystems determine whether antitumor immune cells can access malignant cells, receive antigen-presenting support, or become restrained by stromal, vascular, metabolic, and myeloid barriers. In this review, we summarize how single-cell and spatial multi-omics redefine TIME heterogeneity in immunotherapy resistance, highlight ligand-receptor communication networks linking cell states to spatial immune dysfunction, and discuss emerging translational biomarkers for patient stratification. We further propose that future immunotherapy biomarkers should evolve from static single-marker assays toward longitudinal, spatially resolved, and interpretable multi-omics models that guide precision combination immunotherapy.

Humans↗
PubMed2027

Systematic Dissection of Key Driver Perturbation Signatures in Single Cells via ECCITE-seq.

CRISPR screens, such as expanded CRISPR-compatible cellular indexing of transcriptomes and epitopes by sequencing (ECCITE-seq), enable the simultaneous measurement of transcriptomes, gRNA identity, and cell-surface protein expression at single-cell resolution to systematically interrogate gene function. This platform provides a powerful and scalable experimental approach for validating disease-associated regulators identified by large-scale association studies and other computational methods, including network-based analyses of multi-omics data. Here, as an example application, we describe an ECCITE-seq framework to characterize the transcriptomic consequences of perturbing multiple neuronal key driver genes associated with Alzheimer's disease (AD) in human-induced pluripotent stem cell (hiPSC)-derived neurons. More broadly, by integrating customized pooled gRNA libraries with different CRISPR effectors across multiple cell types, this approach allows for the assessment of the regulatory impact of candidate genes implicated in development and disease processes.

Humans↗
PubMed2025

MOADE: a multimodal autoencoder for dissociating bulk multi-omics data.

In single cell biology, the complexity of tissues may hinder lineage cell mapping or tumor microenvironment decomposition, requiring digital dissociation of bulk tissues. Many deconvolution methods focus on transcriptomic assay, not easily applicable to other omics due to ambiguous cell markers and reference-to-target difference. Here, we present MOADE, a multimodal autoencoder pipeline linking multi-dimensional features to jointly predict personalized multi-omic profiles and cellular compositions, using pseudo-bulk data constructed by internal non-transcriptomic reference and external scRNA-seq data. MOADE is evaluated through rigorous simulation experiments and real multi-omic data from multiple tissue types, outperforming nine deconvolution pipelines with superior generalizability and fidelity.

Humans↗
PubMed2026

A Multi-omics Regulated Cell Death Framework Defines Immune Phenotypes and Guides Precision Therapy in Colorectal Cancer.

Colorectal cancer (CRC) is molecularly and immunologically heterogeneous, contributing to variable treatment response. Because regulated cell death (RCD) intersects with tumor metabolism, immune regulation, and therapeutic susceptibility, we built an RCD-centered framework for CRC stratification. Multi-cohort transcriptomic data were used to infer RCD subtypes with non-negative matrix factorization (NMF) and non-negative least squares (NNLS). Genomic, bulk RNA-seq, single-cell RNA-seq, and spatial transcriptomic datasets were integrated to characterize subtype-associated biology. Machine-learning models were developed for immunotherapy response and survival-risk estimation. Candidate compounds were screened by GDSC2-based drug-sensitivity modeling and molecular docking, and FSTL3 was functionally assessed in vitro. The framework separated CRC samples into two RCD-related phenotypes resembling immune-hot and immune-cold states. RCD1 showed immune activation and higher mutational burden, whereas RCD2 showed immune-suppressed features, intratumoral heterogeneity, and aggressive biology. RCD-associated signatures showed potential for predicting immunotherapy response and survival risk. Dasatinib was prioritized for immune-cold, high-risk tumors, with preliminary evidence supporting its activity in CRC cells, while functional assays suggested a role for FSTL3 in growth, invasion, epithelial-mesenchymal transition, and apoptosis regulation. These findings suggest that RCD-based multi-omics analysis may refine CRC stratification and help generate therapeutic hypotheses.

Colorectal cancer↗
PubMed2026

Exploration and experimental verification of triaptosis-related prognostic genes and cells in gastric cancer.

BACKGROUND: Triaptosis is a recently characterized form of programmed cell death with unclear implications in cancer. This study aimed to investigate the prognostic significance and biological relevance of triaptosis in gastric cancer (GC). METHODS: Transcriptomic and clinical data from TCGA-STAD and GSE62254, and single-cell RNA sequencing data from GSE183904 were analyzed. Triaptosis-related gene (TRG) scores were calculated using single-sample gene set enrichment analysis. Differentially expressed genes identified in TRG-score and GC-versus-normal comparisons underwent functional enrichment, Cox regression, and least absolute shrinkage and selection operator regression to develop an externally validated signature. Immune profiles, pathway activity, somatic mutations, tumor mutational burden (TMB), predicted drug sensitivity, and clinical features were compared by risk group. Single-cell analyses assessed TRG activity, prognostic gene expression, cell-cell communication, and pseudotime. Reverse transcription-quantitative PCR and Western blotting assessed mRNA expression and protein levels, respectively. RESULTS: A TRG-based prognostic model comprising ASPN, GRB14, and VTN was developed and externally validated, effectively distinguishing patients into two distinct risk groups with notably different survival outcomes. mRNA expression of all three genes and their protein levels were significantly higher in SGC-7901 cells than in GES-1 cells. High-risk patients had higher stromal scores and distinct immune profiles; 15 immune cell types differed between groups. Single-cell analysis revealed fibroblasts and pericytes among high-TRG-active cell types. Prognostic genes were significantly overexpressed in fibroblasts, which also showed high TRG activity. Fibroblasts demonstrated enhanced communication with pericytes, whereas tumor-derived fibroblasts showed weaker communication with macrophages, indicating immune microenvironment remodeling. CONCLUSION: The three-gene prognostic signature predicted GC prognosis and was associated with distinct immune and genomic features, suggesting potential value for risk stratification and personalized treatment.

Humans↗
PubMed2026

Omics in optic neuropathies: From molecular landscapes to personalized therapeutics.

Optic neuropathies comprise a heterogeneous group of disorders involving transient or permanent injury to retinal ganglion cells (RGCs) and their axons. Clinically, these neurodegenerative conditions manifest as dyschromatopsia, decreased visual acuity, and visual field defects, and in severe cases may ultimately lead to blindness and disability. The marked heterogeneity across disease subtypes, incompletely understood etiologies, and complex pathogenic mechanisms pose substantial challenges to precise diagnosis and effective treatment. Recent advances in omics technologies - including genomics, transcriptomics, proteomics, metabolomics, lipidomics, single-cell and spatial sequencing, and integrative multi-omics approaches - have ushered optic nerve degenerative disease research into an era of high-resolution comprehensive investigation. In this review, we summarize representative applications of omics approaches to elucidate genetic alterations, signaling dysregulation, metabolic reprogramming, and immune responses in optic neuropathies. We further discuss the emerging potential of multi-omics in identifying early diagnostic biomarkers and informing individualized therapeutic strategies. Finally, we provide a forward-looking perspective on the future trajectory of omics technologies and their prospects in both fundamental research and clinical translation, with the overarching aim of accelerating the bench-to-bedside transition in this critical eye disease field.

biomarkers↗
PubMed2026

Hyperprogression Upon Cemiplimab Alone or With Short Course Chemotherapy in PD-L1 ≥ 50% Non-small Cell Lung Cancer: A Biomarker Guided Multicenter International Phase 2 Trial-HYPERBOLIC Study.

BACKGROUND: Immune checkpoint inhibitor (ICI) monotherapy is the standard first-line treatment for advanced non-small cell lung cancer (NSCLC) with PD-L1 ≥ 50%; however, up to 30% of patients experience early progression or death, including cases of hyperprogressive disease (HPD). High baseline levels (≥ 30.5%) of circulating CD10- low-density neutrophils (LDNs) have been associated with increased HPD occurrence. Emerging evidence suggests that combining ICI with platinum-based chemotherapy (PCT) may mitigate the risk of HPD. Currently, no prospective studies have addressed HPD prevention in this context. PATIENTS AND METHODS: HYPERBOLIC (NCT07274384) is a phase 2, randomized, open-label, multicenter, international trial evaluating whether adding 3 cycles of PCT to first-line cemiplimab reduces HPD rate in stage IV NSCLC with PD-L1 ≥ 50% and CD10- LDNs (identified by flow cytometry as CD15⁺CD11b⁺ within the PBMC fraction, with immature cells defined by loss of CD10) ≥ 30.5%. Seventy-four patients will be randomized (1:1 ratio) to receive cemiplimab alone or cemiplimab plus 3 PCT cycles, followed by cemiplimab maintenance. Randomization will be stratified by Lung Immune Prognostic Index. The first computed tomography scan at week 7 after treatment start will assess HPD occurrence, defined as RECIST v 1.1. disease progression with a delta tumor growth rate (ΔTGR) ≥ 50% and/or TGR ratio ≥ 2. The primary endpoint will be the combined rate of HPD and early death (death within 12 weeks with no radiological evaluation). Secondary endpoints will be HPD rate according to alternative definitions, overall survival, progression free survival, objective response rate, and safety. An extensive translational research platform will include spatial transcriptomics of tumor tissue, single-cell RNA sequencing of PBMCs, circulating-free DNA and plasma factors profiling, and saliva/stool microbiome genomics and metabolomics, to longitudinally explore tumor-host dynamic interactions during treatment. CONCLUSION: to our knowledge, HYPERBOLIC is the first prospective, biomarker-driven trial investigating early treatment escalation based on HPD risk in PD-L1-high NSCLC.

CD10↗
PubMed2025

CountASAP: a lightweight, easy to use python package for processing ASAPseq data.

BACKGROUND: Declining sequencing costs coupled with the increasing availability of easy-to-use kits for the isolation of DNA and RNA transcripts from single cells have driven a rapid proliferation of studies centered around genomic and transcriptomic data. Simultaneously, a wealth of new techniques have been developed that utilize single cell technologies to interrogate a broad range of cell-biological processes. One recently developed technique, transposase-accessible chromatin with sequencing (ATAC) with select antigen profiling by sequencing (ASAPseq), provides a combination of chromatin accessibility assessments with measurements of cell-surface marker expression levels. While software exists for the characterization of these datasets, there currently exists no tool explicitly designed to reformat ASAP surface marker FASTQ data into a count matrix which can then be used for these downstream analyses. RESULTS: To address this lack of a dedicated tool for ASAPseq data processing, we created CountASAP, an easy-to-use Python package purposefully designed to transform FASTQ files from ASAP experiments into count matrices compatible with commonly-used downstream bioinformatic analysis packages. CountASAP takes advantage of the independence of the relevant data structures to perform fully parallelized matches of each sequenced read to user-supplied input ASAP oligos and unique cell-identifier sequences. We directly compare the performance and user-friendliness of CountASAP to existing tools using similarly-structured data from a more common sequencing experiment: cellular indexing of transcriptomes and epitopes by sequencing (CITEseq). Further benchmarking against existing tools helps to identify proper defaults for CountASAP and assess the agreement of outputs from all tested software. A final test using a novel ASAPseq dataset provides evidence that CountASAP can generate biologically meaningful results that correlate well with paired chromatin accessibility data. CONCLUSIONS: CountASAP shows good agreement with existing, well-tested data processing tools in the analysis of similarly-structured benchmarking data. CountASAP runs efficiently on a standard laptop, has user-friendly documentation, a one-step installation, and represents the first and only tool designed specifically for the processing of ASAPseq data.

Software↗
PubMed2026

A single-nucleus transcriptome atlas of soybean anthers.

Anther development is crucial for plant sexual reproduction. However, a high-resolution, cell-type-specific transcriptomic atlas of this process is lacking for the legume crop soybean (Glycine max). Here, we construct a comprehensive transcriptional atlas of developing soybean anthers using single-nucleus RNA sequencing (snRNA-seq). We identify and characterize nine distinct cell types spanning both somatic and reproductive lineages. Our analysis reveals robust transcriptional continuity across anther developmental stages and dynamic reprogramming during key transitions. Notably, the shift from diploid meiocytes to haploid unicellular microspores is marked by the induction of previously inactive genes, despite an overall reduction in transcript abundance. Subsequently, within bicellular microspores, generative and vegetative cell lineages exhibit sharply divergent transcriptional programs: generative cells specialize in mRNA export and turnover, whereas vegetative cells up-regulate translational machinery. Evolutionary analysis further indicates that generative-cell-specific genes are subject to more relaxed purifying selection compared to those specific to vegetative cells. Functional validation using mutants generated by CRISPR/Cas9-mediated genome editing and EMS mutagenesis reveals the essential roles of OSD1A and PKSA in pollen development and fertility. This high-resolution atlas provides fundamental insights into the transcriptional regulation of soybean anther development and serves as a valuable resource for manipulating male fertility to advance hybrid breeding programs. The data are available at https://databases.genedenovo.com/pollen.

Glycine max↗