Research Radar — 2026-05-27
Methods & AI
Computational
A Large-Scale Unified Deep Learning Model for Peptide Mass Spectrum Interpretation Trained on Multimodal Data
Nature Machine Intelligence Published 2026-05-25 research article DOI: 10.1038/s42256-026-01234-8
proteomics mass spectrometry deep learning peptide sequencing open search foundation model multimodal computational method
Summary: Introduces pUniFind, a large-scale unified deep learning model for proteomics that integrates peptide-spectrum scoring and open database searching into a single framework trained on multimodal mass spectrometry data. Traditional proteomics tools are fragmented — separate models handle peptide identification (matching spectra to known peptide sequences), de novo sequencing (reading peptides directly from spectra), and open searches (finding peptides with unexpected modifications). pUniFind unifies these tasks by training a transformer-based architecture on diverse mass spectrometry data types including collision-induced dissociation (CID), higher-energy collisional dissociation (HCD), and electron transfer dissociation (ETD) spectra from multiple instrument platforms. The multimodal training enables the model to learn generalizable representations of fragmentation patterns that transfer across instrument types and experimental conditions. pUniFind achieves state-of-the-art performance on peptide identification benchmarks while also enabling open modification searches that can detect unexpected post-translational modifications without prior specification — a critical capability for discovery proteomics. The model is designed to be scalable and can be fine-tuned on specific experimental datasets, making it a practical foundation model for mass spectrometry-based proteomics.
Why it matters: Proteomics has lagged behind genomics and transcriptomics in developing unified computational frameworks — most tools are task-specific and instrument-specific. pUniFind represents a step toward foundation models for proteomics that can handle multiple analytical tasks in a single framework. The ability to detect unexpected modifications in open search mode is particularly valuable for cancer proteomics, where aberrant post-translational modifications may drive signaling dysregulation that is invisible to standard database search approaches.
Why for Yiru: Integrating proteomics with transcriptomic and spatial data is increasingly important for understanding the functional state of the TME. A unified proteomics model that can identify both known and unexpected protein modifications across experimental conditions would add a critical layer of functional annotation to TME multi-omic studies, potentially revealing modification-driven immune evasion or drug resistance mechanisms.
AreTomoLive: Automated Reconstruction of Comprehensively Corrected and Denoised Cryo-Electron Tomograms in Real Time and at High Throughput
Nature Methods Published 2026-05-25 research article DOI: 10.1038/s41592-026-03093-y
cryo-electron tomography cryo-ET image processing automated pipeline denoising structural biology computational method real-time
Summary: Presents AreTomoLive, an accelerated preprocessing pipeline for cryo-electron tomography (cryo-ET) that streamlines tomographic alignment, motion correction, contrast transfer function (CTF) estimation, 3D reconstruction, and denoising — all in real time as data are acquired. Cryo-ET enables visualization of cellular structures in their native state at near-atomic resolution, but the computational processing pipeline has been a major bottleneck: raw tilt-series data require multiple computationally intensive steps that traditionally run offline after data collection is complete. AreTomoLive performs these steps concurrently with data acquisition, providing researchers with high-quality 3D tomograms within minutes rather than hours or days. The pipeline implements comprehensive corrections including per-particle motion correction, CTF estimation and correction across the tilt series, and advanced denoising algorithms that preserve high-resolution features. A key innovation is the GPU-accelerated implementation that achieves throughput matching modern detector speeds, enabling fully automated, push-button processing that reduces the barrier to cryo-ET for non-expert users. The pipeline is designed for high-throughput cryo-ET workflows where hundreds of tomograms are collected per session.
Why it matters: Cryo-ET is undergoing a transformation from a boutique technique requiring expert manual processing to a high-throughput structural biology method. AreTomoLive removes the computational bottleneck that has limited cryo-ET throughput — real-time reconstruction means researchers can assess data quality and adjust acquisition strategy during the experiment rather than discovering problems days later. This acceleration is essential for cryo-ET to scale to the cellular atlases and structural surveys that the field envisions.
Why for Yiru: Cryo-ET is increasingly used to study macromolecular complexes in their cellular context, including signaling complexes, membrane receptors, and chromatin structures relevant to cancer biology. High-throughput cryo-ET pipelines could enable structural surveys of TME-relevant complexes — such as immune synapses, receptor clustering, and extracellular matrix architecture — directly in tumour and immune cells without requiring purification.
Multiplexed Perturbation Enables Scalable Pooled Screens
Nature Methods Published 2026-05-25 research article DOI: 10.1038/s41592-026-03095-w
CRISPR screen multiplexed perturbation pooled screen functional genomics CRISPRi high-throughput computational method
Summary: Demonstrates that delivering multiple sgRNAs per cell at high multiplicity of infection (MOI) maintains CRISPRi screen performance while dramatically reducing the number of cells required — making pooled perturbation screens scalable to contexts where cell numbers are limiting. Traditional pooled CRISPR screens require one perturbation per cell to maintain genotype-phenotype linkage, necessitating large cell populations (typically millions of cells) to ensure adequate representation of each sgRNA. This constraint has limited pooled screens in primary cells, patient-derived organoids, and rare cell populations. The authors show that CRISPRi — which suppresses gene expression rather than cutting DNA — tolerates multiple sgRNAs per cell without compromising screen performance, because each sgRNA independently reduces expression of its target gene without creating the confounding double-strand breaks that plague CRISPRko multiplexing. A key insight is the development of a computational framework that deconvolves the contribution of individual sgRNAs from cells carrying multiple perturbations, enabling accurate hit calling even with high MOI. The approach reduces cell requirements by up to 10-fold compared to standard single-sgRNA screens, enabling genome-wide screens in cell populations as small as a few hundred thousand cells.
Why it matters: Cell number requirements have been a fundamental barrier to applying CRISPR screens in physiologically relevant model systems — primary tumour cells, rare immune subsets, and patient-derived organoids often cannot be expanded to the millions of cells needed for standard screens. Multiplexed CRISPRi screening removes this barrier, democratizing functional genomics for contexts where cell numbers are limiting. This is particularly important for cancer research, where understanding gene function in patient-derived models is essential for translating genomic findings into therapeutic targets.
Why for Yiru: Functional genomics in TME-relevant contexts — primary tumour cells, tumour-infiltrating lymphocytes, cancer-associated fibroblasts — has been limited by the cell numbers required for standard CRISPR screens. Multiplexed CRISPRi screening could enable genome-wide functional screens in these rare populations, identifying genes that regulate anti-tumour immunity, immune evasion, and therapy resistance in physiologically relevant cell types.
HELIX: A Scalable Model for Predicting Context-Dependent Regulation of RNA Splicing and Isoform Usage
Nature Computational Science Published 2026-05-19 research article DOI: 10.1038/s43588-026-00988-w
RNA splicing isoform deep learning transcriptomics gene regulation tissue-specific computational method
Summary: Introduces HELIX, a deep learning framework that predicts tissue- and context-specific RNA splicing and full-length transcript isoform usage from genomic sequence features. Alternative splicing generates multiple mRNA isoforms from a single gene, massively expanding the functional diversity of the proteome, yet predicting which isoforms are expressed in which cellular contexts has remained challenging. HELIX addresses this by modeling the combinatorial effects of splice site sequences, regulatory element motifs, RNA secondary structure, and cell-type-specific trans-factor expression levels to predict isoform usage across diverse tissues and conditions. The model is trained on large-scale RNA-seq data from GTEx and other consortia, learning context-dependent splicing codes that generalize to unseen tissues and disease states. HELIX can predict how disease-associated genetic variants affect splicing — identifying variants that create or disrupt splice sites or regulatory elements — and can forecast how splicing patterns change during development, differentiation, or disease progression. The framework is designed to be scalable and can incorporate new data types as they become available.
Why it matters: Alternative splicing is a major source of proteomic diversity and is frequently dysregulated in disease — yet most genomic analyses focus on gene-level expression, missing isoform-level changes. HELIX provides a predictive framework for understanding how genetic variation and cellular context jointly determine splicing outcomes, bridging the gap between DNA sequence and isoform-level function. This has direct applications for interpreting non-coding variants from GWAS and for understanding splicing dysregulation in cancer.
Why for Yiru: Isoform-level analysis is increasingly important in TME studies — immune checkpoint receptors (PD-L1, CTLA-4), cytokines, and signaling molecules often have functionally distinct isoforms that are differentially expressed across tumour, immune, and stromal compartments. A tool that predicts isoform usage from sequence and context could identify cancer-specific splicing events that generate neo-epitopes or alter protein function in ways relevant to immunotherapy response.
Uncovering Spatially Resolved Functional Genomics with CRISPR Screen Sequencing
Cell Published 2026-05-26 research article DOI: 10.1016/j.cell.2026.04.049
spatial transcriptomics CRISPR screen functional genomics perturbation sequencing spatial biology computational method
Summary: Presents SPAC-seq, a sequencing-based spatial CRISPR screening technology that captures high-throughput perturbation libraries alongside spatial whole transcriptomes, and TARDIS, a companion computational framework for analyzing spatially resolved perturbation effects. Current CRISPR screens measure how genetic perturbations affect cell-autonomous phenotypes (proliferation, gene expression) but lose spatial context — they cannot reveal how a perturbation in one cell affects neighboring cells or how tissue architecture modulates perturbation effects. SPAC-seq solves this by barcoding perturbations and spatial positions simultaneously, enabling the reconstruction of which sgRNAs are present in which cells at which spatial coordinates, together with the transcriptomic state of each cell and its neighbors. The companion tool TARDIS analyzes these data to identify cell-autonomous effects, cell-non-autonomous effects (where perturbing cell A changes gene expression in neighboring cell B), and spatial determinants of perturbation response. Applied to immune-tumour co-cultures, SPAC-seq reveals that certain tumour-cell perturbations alter the activation state of neighboring T cells in a contact-dependent manner — effects invisible to standard pooled screens.
Why it matters: Spatial context is essential for understanding gene function in tissues — a gene knockout that is lethal in monoculture may have no effect in tissue context, and vice versa. SPAC-seq bridges the gap between the scale of pooled CRISPR screens and the spatial resolution of imaging-based approaches, enabling systematic discovery of how genetic perturbations propagate through tissue neighborhoods. This is particularly relevant for the TME, where cell-cell communication determines whether perturbations enhance or suppress anti-tumour immunity.
Why for Yiru: SPAC-seq directly addresses a central challenge in TME functional genomics: understanding how genetic perturbations in tumour cells affect the behavior of neighboring immune and stromal cells. The ability to map cell-non-autonomous perturbation effects in spatial context could identify tumour-intrinsic genes that regulate immune cell recruitment, activation, or exclusion — providing a new class of immunotherapy targets that act by remodeling the TME rather than by directly killing tumour cells.
Immunotherapy Drug Target Identification Using Machine Learning and Patient-Derived Tumour Explant Validation
Nature Machine Intelligence Published 2026-05-18 research article DOI: 10.1038/s42256-026-01201-3
immunotherapy drug target machine learning graph neural network patient-derived explant multi-omic computational method
Summary: Presents a multimodal graph neural network framework that identifies cancer immunotherapy targets by integrating multi-omic data from tumour samples and validating predictions in patient-derived tumour explant models. Identifying immunotherapy targets is challenging because effective targets must meet multiple criteria: tumour-specific expression, functional relevance to immune evasion, druggability, and validation in physiologically relevant models. The framework addresses this by constructing a knowledge graph that connects genes, proteins, pathways, cell types, and clinical outcomes, then training a graph neural network to prioritize targets based on their multi-omic features and network context. The predictions are experimentally validated in patient-derived tumour explants — short-term cultures that preserve TME architecture and immune cell composition — enabling assessment of whether target modulation actually enhances anti-tumour immune responses in a clinically relevant context. The approach identifies both known immunotherapy targets (validating the method) and novel targets that were not previously linked to immune evasion, demonstrating the value of machine learning for target discovery beyond literature-based approaches.
Why it matters: Immunotherapy target discovery has been largely hypothesis-driven, focusing on well-characterized immune checkpoints. A systematic, ML-driven approach that integrates diverse data types and validates in patient-derived models could expand the immunotherapy target landscape — particularly for tumour types that are currently unresponsive to existing immunotherapies. The use of patient-derived explants for validation is notable because it preserves the native TME, providing more clinically relevant readouts than cell-line-based assays.
Why for Yiru: Computational target identification for immunotherapy directly aligns with TME research goals — understanding which molecular targets, when modulated, will enhance anti-tumour immunity in the complex cellular context of patient tumours. The graph neural network approach is particularly relevant because it captures the network-level effects that are central to TME biology, where targets act not in isolation but through cascading effects on multiple cell types.
Biomedical discoveries
Biomedicine
Dietary Sulfur Amino Acids Enhance Anti-Tumor Immunity in Colon Cancer via an NKT Cell-XCL1-cDC1 Circuit
Immunity Published 2026-05-26 research article DOI: 10.1016/j.immuni.2026.05.001
diet immunotherapy colon cancer NKT cell dendritic cell microbiome sulfur amino acid tumour microenvironment immunometabolism
Summary: Demonstrates that dietary sulfur amino acids (SAA) — methionine and cysteine — enhance anti-tumour immunity in colon cancer through a microbiome-dependent mechanism involving NKT cells and type 1 conventional dendritic cells (cDC1s). A high-SAA diet expands the commensal bacterium Mucispirillum schaedleri in the gut, which in turn activates NKT cells through lipid antigen presentation via CD1d. Activated NKT cells produce the chemokine XCL1, which recruits XCR1-expressing cDC1s into the tumour microenvironment. These cDC1s are essential for priming CD8+ T cell responses against tumour antigens, and their recruitment by NKT-derived XCL1 creates a positive feedback loop that amplifies anti-tumour immunity. In mouse models of colon cancer, a high-SAA diet significantly reduces tumour burden, and this effect depends on each component of the identified circuit: Mucispirillum colonization, NKT cell activation, XCL1 production, and cDC1 recruitment. The study establishes a mechanistic chain connecting dietary intake → gut microbiome composition → innate-like lymphocyte activation → dendritic cell recruitment → adaptive anti-tumour immunity — a complete pathway from nutrition to tumour control.
Why it matters: Diet is increasingly recognized as a modifier of cancer risk and immunotherapy response, yet the mechanisms connecting specific dietary components to anti-tumour immunity have been largely correlative. This study provides a remarkably complete mechanistic pathway — from specific dietary amino acids to a specific bacterial species to a specific immune cell circuit — that enhances anti-tumour immunity. The identification of the NKT-XCL1-cDC1 axis provides specific molecular targets for dietary or pharmacological interventions to boost immunotherapy efficacy. The finding that sulfur amino acids, which are abundant in protein-rich foods, can enhance anti-tumour immunity also has implications for dietary recommendations in cancer patients, who are often counseled on nutrition without evidence-based guidance on how specific nutrients affect treatment outcomes.
Why for Yiru: The diet-microbiome-immune axis is an emerging dimension of TME biology that is often neglected in computational analyses focused on tumour-intrinsic features. This study provides concrete molecular mechanisms that could be incorporated into TME models — for instance, analyzing whether XCL1-XCR1 signaling or cDC1 abundance in spatial transcriptomic data correlates with microbiome composition or dietary metadata. The NKT-cDC1 circuit also represents a therapeutically actionable axis that could be modulated independently of checkpoint blockade.
Tumor-Draining Lymph Nodes in Ovarian Cancer Lack Germinal Centers but Harbor Tumor-Reactive Memory B Cells Clonally Linked to Intra-Tumoral B Cells
Immunity Published 2026-05-26 research article DOI: 10.1016/j.immuni.2026.04.017
tumour-draining lymph node ovarian cancer B cell memory B cell germinal center tumour immunity humoral immunity TME
Summary: Reveals unexpected B cell biology in the tumour-draining lymph nodes (TDLNs) of high-grade serous ovarian cancer (HGSOC) patients. B cell infiltration in HGSOC is associated with favourable prognosis and correlates with tertiary lymphoid structure (TLS) formation, suggesting that anti-tumour humoral immunity is clinically important. However, the nature of the B cell response in HGSOC TDLNs — the lymph nodes that directly drain the tumour and are the primary site where anti-tumour B cell responses are expected to develop — has been unknown. The authors show that HGSOC TDLNs paradoxically lack active germinal centers (GCs), the specialized microanatomical structures where B cells undergo affinity maturation and class switching. Despite this absence, the TDLNs harbor clonally expanded tumour-reactive memory B cells that are clonally related to B cells found within the tumour itself. This suggests that anti-tumour B cell responses are initiated — potentially at earlier disease stages or in alternative sites — but that ongoing GC reactions are suppressed in TDLNs by the time of surgical resection. The TDLN memory B cells recognize tumour-associated antigens and can produce tumour-binding antibodies when stimulated ex vivo, demonstrating their functional competence despite the absence of active GCs.
Why it matters: The role of B cells in anti-tumour immunity is understudied compared to T cells, yet B cells are the second most abundant lymphocyte population in many solid tumours and their presence correlates with immunotherapy response. The finding that TDLNs in ovarian cancer lack GCs but retain functional memory B cells challenges the assumption that TDLNs are sites of active anti-tumour B cell responses and suggests that the tumour suppresses GC formation as an immune evasion strategy. Understanding whether — and when — TDLN GCs are functional could inform the timing of surgical resection (to preserve active immune responses) and identify therapeutic strategies to restore GC reactions in TDLNs.
Why for Yiru: B cells and tertiary lymphoid structures are increasingly recognized as important components of the TME that influence immunotherapy outcomes. This study provides detailed characterization of B cell responses in TDLNs — a compartment that is often inaccessible in computational TME studies that focus on the tumour itself. The finding that TDLN B cell memory exists despite GC suppression suggests that spatial and temporal analysis of B cell dynamics across tumour, TDLN, and peripheral blood compartments could reveal windows of opportunity for immunotherapy that are invisible when analyzing the tumour alone.
Endocytic Evasion Confers Resistance to Antibody-Drug Conjugates Therapy in Cancer
Cancer Cell Published 2026-05-21 research article DOI: 10.1016/j.ccell.2026.04.010
antibody-drug conjugate ADC drug resistance endocytosis urothelial cancer NECTIN4 enfortumab vedotin cancer therapy
Summary: Identifies endocytic trafficking defects as a clinically relevant mechanism of resistance to enfortumab vedotin (EV), a NECTIN4-targeting antibody-drug conjugate (ADC) used in urothelial cancer. ADCs deliver cytotoxic payloads to cancer cells by binding to a surface antigen, undergoing receptor-mediated endocytosis, and releasing the payload upon lysosomal processing. Resistance has been primarily attributed to target antigen loss or drug efflux — but this study reveals that cells can resist ADCs while maintaining normal NECTIN4 surface expression through defects in endocytic trafficking. Using patient specimens and preclinical models, the authors show that resistant tumours exhibit impaired internalization of the NECTIN4-EV complex, with the receptor-ligand complex stalling at the cell surface rather than progressing through the endosomal-lysosomal pathway. The trafficking defect is associated with altered expression of endocytic regulators including specific Rab GTPases and can be overcome by combining EV with agents that promote endocytosis or by using alternative ADC formats with different internalization requirements. Analysis of clinical samples from EV-treated patients confirms that endocytic gene expression signatures correlate with treatment response, establishing this as a clinically relevant resistance mechanism.
Why it matters: ADCs represent one of the fastest-growing classes of cancer therapeutics, with multiple approvals across solid tumour types. Understanding resistance mechanisms is critical for optimizing ADC use and developing next-generation conjugates. Endocytic evasion represents a new category of resistance — distinct from target loss or payload efflux — that cannot be detected by simply measuring surface antigen levels. This has immediate clinical implications: patients progressing on ADCs should be assessed for endocytic competence, and combination strategies that enhance internalization could rescue ADC sensitivity.
Why for Yiru: ADC resistance through endocytic evasion highlights the importance of understanding cellular trafficking dynamics in the TME — tumour cells may exploit trafficking pathways not just for drug resistance but also for modulating surface receptor landscapes that affect immune recognition. Spatial and single-cell analyses that capture endocytic gene expression signatures could identify subclones within the TME that are primed for or resistant to ADCs and other receptor-targeted therapies.
Concurrent Genetic and Non-Genetic Resistance Mechanisms to KRAS Inhibition in Colorectal Cancer
Cancer Cell Published 2026-05-21 research article DOI: 10.1016/j.ccell.2026.04.009
KRAS colorectal cancer drug resistance genetic resistance non-genetic resistance spatial transcriptomics tumour heterogeneity
Summary: Combines exome sequencing and spatial transcriptomic profiling of patient-matched colorectal cancer (CRC) biopsies to demonstrate that genetic and non-genetic resistance mechanisms coexist within individual tumours during KRAS/EGFR inhibitor treatment. KRAS mutations are the most common oncogenic drivers in CRC, and while KRAS G12C inhibitors have shown clinical activity, responses are typically short-lived due to acquired resistance. Current models of resistance assume either genetic mechanisms (new mutations that reactivate downstream pathways) or non-genetic mechanisms (transcriptional reprogramming), with most studies focusing on one or the other. This study shows that both types co-occur — sometimes in the same tumour region. Genetic resistance mechanisms include acquired mutations in KRAS itself (secondary mutations that prevent drug binding) and in downstream effectors (BRAF, MAP2K1). Non-genetic resistance involves transcriptional reprogramming toward a more mesenchymal, invasive state that is less dependent on KRAS signaling. Critically, spatial transcriptomics reveals that genetically resistant and non-genetically resistant clones can occupy distinct tumour regions, and that the non-genetic resistant state is associated with specific spatial niches characterized by particular stromal and immune cell compositions.
Why it matters: The co-occurrence of genetic and non-genetic resistance within single tumours has profound implications for treatment strategy — targeting only one resistance mechanism will select for clones using the other. This explains why sequential single-agent approaches often fail and supports the need for combination therapies that address both genetic and non-genetic resistance simultaneously. The spatial organization of resistance mechanisms also suggests that tumour sampling bias — taking a single biopsy from one region — may miss coexisting resistance mechanisms, leading to ineffective treatment decisions.
Why for Yiru: Spatial heterogeneity of resistance mechanisms is a frontier problem in TME research that requires computational methods capable of integrating genetic and transcriptomic data in spatial context. This study provides a template for how to approach this problem — combining exome sequencing for genetic resistance and spatial transcriptomics for non-genetic resistance and spatial niche analysis. The tools and analytical frameworks used here could be adapted to study resistance heterogeneity in other targeted therapy contexts relevant to the TME.
Fibroblast Growth Factor Receptor Inhibition for Succinate Dehydrogenase-Deficient Gastrointestinal Stromal Tumors: A Phase 2 Trial
Nature Medicine Published 2026-05-26 research article DOI: 10.1038/s41591-026-04376-9
gastrointestinal stromal tumour GIST FGFR SDH-deficiency targeted therapy phase 2 trial rogratinib sarcoma
Summary: Reports results of a multicenter phase 2 trial testing the FGFR inhibitor rogaratinib in patients with succinate dehydrogenase (SDH)-deficient gastrointestinal stromal tumours (GIST). SDH-deficient GIST represents a distinct molecular subtype that typically occurs in young patients, is resistant to standard GIST therapies (imatinib, which targets KIT/PDGFRA mutations that SDH-deficient GISTs lack), and has limited treatment options. The study was based on preclinical evidence that SDH deficiency leads to accumulation of succinate, which inhibits α-ketoglutarate-dependent dioxygenases, resulting in epigenetic dysregulation and activation of FGFR signaling as a compensatory survival pathway. In the phase 2 trial, rogaratinib showed encouraging clinical activity with a meaningful proportion of patients achieving durable disease control, and the treatment was generally well tolerated. Correlative analyses confirmed FGFR pathway activation in tumour samples and demonstrated on-target effects of rogaratinib. This represents one of the first targeted therapy trials specifically designed for the SDH-deficient GIST population.
Why it matters: SDH-deficient GIST is a rare but clinically challenging sarcoma subtype with no effective targeted therapies. The positive phase 2 results establish FGFR inhibition as a new therapeutic strategy for this molecularly defined patient population and validate the mechanistic rationale linking SDH loss to FGFR dependency. More broadly, the study demonstrates the power of molecularly-guided clinical trials for rare cancer subtypes — defining the patient population by the molecular driver rather than by histology alone.
Why for Yiru: This study exemplifies the paradigm of targeting metabolic-epigenetic vulnerabilities in cancer — SDH loss creates a metabolic state (succinate accumulation) that drives epigenetic reprogramming and creates collateral dependencies (FGFR activation). This framework — identifying metabolic perturbations and the signaling pathways they activate — is applicable to other TME contexts where metabolic stress (hypoxia, nutrient deprivation) may create similar epigenetic and signaling vulnerabilities.
Nociceptive Innervation Limits Tertiary Lymphoid Structures to Promote Lung Cancer
Cell Published 2026-05-19 research article DOI: 10.1016/j.cell.2026.04.038
lung cancer nociceptive neuron tertiary lymphoid structure TLS CGRP neuro-immune tumour microenvironment sensory innervation
Summary: Discovers that nociceptive sensory neurons are engaged during lung adenocarcinoma tumorigenesis and actively suppress tertiary lymphoid structure (TLS) formation, thereby blunting anti-tumour immunity. TLS are organized aggregates of immune cells that form in non-lymphoid tissues during chronic inflammation and cancer, and their presence in tumours is strongly associated with favourable prognosis and response to immunotherapy. How TLS formation is regulated in the TME has been poorly understood. Using mouse models of lung adenocarcinoma and human tumour samples, the authors show that tumour development recruits and activates nociceptive sensory neurons that innervate the tumour bed. These neurons release calcitonin gene-related peptide (CGRP), which acts on immune cells to suppress the local inflammatory signals required for TLS initiation and maturation. Genetic or pharmacological ablation of nociceptive neurons, or blockade of CGRP signaling, restores TLS formation and enhances anti-tumour T cell responses, leading to reduced tumour growth. Analysis of human lung adenocarcinoma specimens confirms that increased neuronal density correlates with reduced TLS presence and worse clinical outcomes, establishing the clinical relevance of neuro-immune suppression in cancer.
Why it matters: The emerging field of cancer neuroscience is revealing that tumours are innervated and that neural signaling actively regulates tumour biology and anti-tumour immunity. This study adds a critical new dimension — neural suppression of TLS formation — and identifies a specific molecular pathway (CGRP) that can be targeted to restore TLS-dependent anti-tumour immunity. CGRP inhibitors are already clinically available for migraine treatment, providing an immediate path to repurposing these drugs as potential cancer immunotherapies.
Why for Yiru: Neuro-immune interactions in the TME represent a frontier that is largely invisible to standard transcriptomic and spatial analyses. This study suggests that TLS formation — a key determinant of immunotherapy response — is actively suppressed by neural signals, adding a new regulatory layer to TME organization. Computational methods that integrate neural, immune, and tumour spatial data could reveal neuro-immune axes that determine TLS presence and immunotherapy outcomes across cancer types.
Cross-disciplinary watchlist
Other Fields
Exome-Wide Association Study of Blood Lipids in 1,158,017 Individuals from Diverse Populations
Nature Genetics Published 2026-05-25 research article DOI: 10.1038/s41588-026-02613-y
exome-wide association blood lipids rare variants multi-ancestry UK Biobank Million Veteran Program All of Us genomics
Summary: Reports the largest exome-wide association study of blood lipid traits to date, analyzing 1,158,017 individuals across the Million Veteran Program, UK Biobank, and All of Us cohorts — representing unprecedented scale and diversity for rare coding variant discovery. The study identifies hundreds of rare coding variants associated with LDL cholesterol, HDL cholesterol, triglycerides, and total cholesterol, many in genes not previously linked to lipid metabolism. By leveraging the multi-ancestry design, the authors identify population-specific variants and demonstrate that effect sizes for lipid-associated variants are generally consistent across ancestry groups, supporting the transferability of genetic findings. Several identified genes represent promising therapeutic targets for dyslipidemia and cardiovascular disease, with naturally occurring loss-of-function variants providing human genetic validation analogous to the PCSK9 story that led to successful LDL-lowering therapies. The study also provides a comprehensive catalog of rare variant associations that can be used for polygenic risk score refinement and for identifying individuals with extreme genetic risk for lipid disorders.
Why it matters: Large-scale exome sequencing studies are essential for identifying rare coding variants with large effects on disease-relevant traits — these variants provide the strongest human genetic evidence for therapeutic target validation. The unprecedented sample size (>1M) and multi-ancestry design make this the definitive resource for understanding the genetic architecture of blood lipids, with immediate implications for cardiovascular drug development. The catalog of validated targets with human genetic support will accelerate the translation of genomic findings into lipid-lowering therapies.
Why for Yiru: Lipid metabolism is increasingly recognized as relevant to cancer biology — cholesterol and lipid pathways influence membrane composition, signaling platform formation, and immune cell function in the TME. The scale and diversity of this study provide a reference for analyzing whether lipid-associated genetic variants also influence cancer risk, progression, or treatment response, particularly for cancer types where metabolic dysregulation is a hallmark.
Genome Instability Triggers Intercellular DNA Transfer Between Human Cells
Cell Published 2026-05-19 research article DOI: 10.1016/j.cell.2026.04.041
genome instability DNA transfer intercellular nanotube chromothripsis horizontal gene transfer genomic cell biology
Summary: Discovers that mammalian cells with genome instability can transfer damaged DNA fragments directly to neighboring cells through contact-dependent, nanotube-like connections — and that these transferred fragments persist and are functional in recipient cells. Horizontal gene transfer — the movement of genetic material between cells outside of cell division — is well established in bacteria but historically considered rare or absent in mammalian somatic cells. This study challenges that assumption by showing that cells experiencing replication stress or DNA damage (including cancer cells) form thin membrane nanotubes that transport double-stranded DNA fragments to adjacent cells. The transferred DNA includes complex rearrangements characteristic of chromothripsis (shattered chromosomes) and can contain full genes with regulatory sequences. In recipient cells, the transferred DNA is transcribed and can produce functional proteins, demonstrating that horizontal DNA transfer can alter the phenotype of cells that never experienced the original genomic insult. The process depends on actin polymerization and specific proteins involved in nanotube formation, identifying potential targets to block or exploit this phenomenon.
Why it matters: Intercellular DNA transfer in mammalian cells fundamentally challenges the assumption that the genome of somatic cells is vertically inherited and cell-autonomous. If DNA — including cancer-associated mutations and rearrangements — can move between cells, this has profound implications for understanding tumour heterogeneity (genetic diversity may arise not only from clonal evolution but from horizontal transfer), therapy resistance (resistance mutations could spread between cells), and potentially for normal physiology. The discovery also raises questions about whether transferred DNA contributes to the genetic diversity observed in single-cell sequencing studies that is currently attributed entirely to cell division.
Why for Yiru: Tumour heterogeneity and clonal evolution are central topics in TME research, and the discovery of horizontal DNA transfer adds a new dimension — genetic variation may propagate not only vertically through cell division but horizontally between cells within the TME. This could explain observations of shared complex mutations across spatially distant tumour regions and suggests that blocking intercellular DNA transfer could limit the spread of resistance mutations.
De Novo Design of Miniproteins Targeting GPCRs
Nature Published 2026-05-21 research article DOI: 10.1038/s41586-026-10656-8
protein design GPCR miniprotein de novo design computational biology Rosetta deep learning drug discovery
Summary: Reports the computational de novo design of miniproteins that bind with high affinity and specificity to G protein-coupled receptors (GPCRs), the largest family of drug targets in human biology. GPCRs are transmembrane proteins that mediate cellular responses to hormones, neurotransmitters, and sensory stimuli, and approximately one-third of all FDA-approved drugs target GPCRs. However, developing selective GPCR modulators remains challenging because many GPCRs share conserved binding pockets, making it difficult to achieve specificity. The authors use computational protein design — combining Rosetta energy calculations with deep learning-based structure prediction — to design miniproteins (small, stable protein scaffolds of 40-60 amino acids) that bind to specific extracellular surfaces of target GPCRs. Unlike small molecules that typically bind in the conserved orthosteric pocket, these miniproteins target unique surface epitopes, achieving high selectivity even among closely related GPCR subtypes. The designed miniproteins are experimentally validated by cryo-EM structures confirming the designed binding modes, and functional assays demonstrate that they can act as agonists, antagonists, or allosteric modulators depending on the binding site. The approach is demonstrated on multiple GPCRs, showing generality.
Why it matters: De novo protein design has advanced rapidly — from designing static protein structures to creating functional binding proteins against therapeutically important targets. Designing binders against GPCRs is particularly challenging because these are membrane proteins with complex conformational dynamics. The success demonstrates that computational design can now tackle membrane protein targets, dramatically expanding the scope of designed protein therapeutics. Miniproteins occupy a middle ground between small molecules and antibodies — they are large enough to achieve high specificity through extensive surface contacts, yet small enough to be chemically synthesized and engineered for favorable pharmacokinetics.
Why for Yiru: GPCRs are increasingly recognized as important modulators of immune cell function and TME biology — chemokine receptors direct immune cell trafficking into tumours, and specific GPCRs regulate T cell activation, macrophage polarization, and tumour cell survival. The ability to computationally design selective GPCR modulators could enable precision manipulation of these pathways in the TME — for instance, designing miniproteins that enhance T cell trafficking into tumours or block immunosuppressive GPCR signaling.
Genetic Analysis of Circulating Metabolic Traits in 619,372 Individuals
Nature Published 2026-05-20 research article DOI: 10.1038/s41586-026-10532-5
GWAS metabolomics metabolic traits Estonian Biobank UK Biobank rare variant genomics population genetics
Summary: Presents a large-scale genome-wide association study of circulating metabolic traits combining data from the Estonian Biobank and UK Biobank, totaling 619,372 individuals with metabolomic profiling. The study goes beyond standard GWAS by integrating common and low-frequency genetic variants with quantitative measurements of hundreds of circulating metabolites, enabling systematic discovery of genetic influences on the human metabolome. The authors identify thousands of genetic associations, many involving low-frequency coding variants with large effects on specific metabolite levels — providing insight into which enzymes and transporters control metabolite abundance in human blood. The integration of metabolomic and genomic data at this scale enables the construction of genetic instruments for metabolite levels that can be used in Mendelian randomization studies to assess causal relationships between metabolites and disease. Several identified gene-metabolite associations point to new biology — including unexpected regulatory relationships between seemingly unrelated metabolic pathways — and nominate genes as potential therapeutic targets for metabolic disorders.
Why it matters: Circulating metabolites integrate genetic, dietary, microbiome, and environmental influences, and are increasingly used as biomarkers for disease risk, diagnosis, and treatment monitoring. A comprehensive genetic map of the human metabolome provides the foundation for understanding how genetic variation shapes individual metabolic profiles and for using metabolomic data in precision medicine. The identification of low-frequency variants with large effects is particularly valuable because these mimic the effect size of pharmacological interventions, providing human genetic validation for drug targets.
Why for Yiru: Metabolic reprogramming is a hallmark of cancer, and circulating metabolites can reflect tumour metabolism, host systemic responses, and immune-metabolic interactions. The genetic instruments developed in this study could be used to test whether specific metabolites causally influence cancer risk or treatment response — relevant to understanding how systemic metabolism shapes the TME and whether metabolic interventions could enhance immunotherapy.
Subcellular Chemical Mapping Using Correlated Cryogenic Electron and Mass Spectrometry Imaging
Nature Methods Published 2026-05-25 research article DOI: 10.1038/s41592-026-03109-7
cryo-electron microscopy mass spectrometry imaging correlative microscopy chemical mapping FIB-SIMS cryo-EM subcellular imaging method
Summary: Introduces a correlative workflow combining cryo-electron microscopy (cryo-EM) and cryo-electron tomography (cryo-ET) with focused ion beam secondary ion mass spectrometry (FIB-SIMS) to achieve subcellular chemical mapping — determining not just what cellular structures look like, but what molecules they contain and where those molecules are distributed. Cryo-EM/ET provides exquisite structural detail of cellular architecture but cannot identify the chemical composition of observed structures beyond what can be inferred from morphology. Mass spectrometry imaging provides chemical information but traditionally lacks the spatial resolution to resolve subcellular features. This workflow bridges the gap by performing FIB-SIMS on the same cryo-preserved sample that was imaged by cryo-EM/ET, enabling direct correlation of structural features with their chemical composition at subcellular resolution. The authors demonstrate the approach on multiple biological systems, mapping the distribution of specific lipids, metabolites, and elements within organelles and macromolecular assemblies. The chemical identity of previously unannotated densities in cryo-ET reconstructions can be determined, transforming unknown structural features into biochemically characterized entities.
Why it matters: Cryo-EM/CT produces increasingly detailed views of cellular interiors, yet the chemical identity of observed structures often remains ambiguous — is that density a protein complex, a lipid droplet, or a metabolite condensate? Adding chemical mapping to structural imaging resolves this ambiguity, transforming descriptive structural biology into a chemically informed discipline. The ability to map specific molecules within their native structural context has broad applications — from determining drug distribution within cells to understanding how metabolites are organized at organelle contact sites.
Why for Yiru: Spatial metabolomics is an emerging dimension of TME analysis that could reveal how metabolite distributions within and between cells influence immune function, tumour metabolism, and drug response. While the cryo-correlative approach described here requires specialized instrumentation, the conceptual framework — combining structural and chemical imaging — points toward future TME analysis platforms that integrate spatial transcriptomics, proteomics, and metabolomics to achieve comprehensive molecular mapping of the tumour ecosystem.
Mitochondrial L-2-Hydroxyglutarate Is a Physiological Signalling Metabolite
Nature Published 2026-05-20 research article DOI: 10.1038/s41586-026-10564-x
metabolism L-2-hydroxyglutarate mitochondria signalling metabolite epigenetics oncometabolite physiology
Summary: Establishes L-2-hydroxyglutarate (L-2HG) as a bona fide physiological signalling metabolite under normal cellular conditions, overturning the prevailing view that L-2HG is merely a pathological oncometabolite produced by mutant isocitrate dehydrogenase (IDH) in cancer. L-2HG is structurally similar to α-ketoglutarate (α-KG) and competitively inhibits α-KG-dependent dioxygenases, including DNA and histone demethylases — making it a potent epigenetic regulator. While L-2HG accumulation in IDH-mutant cancers is well characterized as driving DNA hypermethylation and blocking differentiation, whether L-2HG has normal physiological roles has been unclear. The authors show that L-2HG is produced at low levels in normal cells by the mitochondrial enzyme L-2-hydroxyglutarate dehydrogenase (L2HGDH) acting in reverse under specific metabolic conditions, and that this production is tightly regulated by mitochondrial redox state and substrate availability. Physiological L-2HG levels modulate the activity of specific dioxygenases to regulate gene expression programs involved in cellular adaptation to metabolic stress. Control of L-2HG levels is essential for normal mitochondrial function and cell differentiation, and dysregulation of this pathway — even without IDH mutations — contributes to disease states including metabolic disorders and certain cancers.
Why it matters: Oncometabolites — metabolites that accumulate in cancer and drive tumorigenesis — have been viewed as pathological aberrations. The discovery that L-2HG has physiological signaling functions at low concentrations reframes our understanding: the pathology arises from deregulation of a normal metabolic signaling pathway, not from an entirely foreign biochemical entity. This has therapeutic implications — targeting L-2HG production in IDH-mutant cancers may need to spare physiological L-2HG signaling, and L-2HG-modulating therapies could be relevant beyond IDH-mutant cancers to any condition where L-2HG homeostasis is disrupted.
Why for Yiru: Metabolic signaling in the TME extends beyond the well-known Warburg effect — metabolites like L-2HG that directly regulate the epigenome create a direct link between metabolic state and gene expression in tumour, immune, and stromal cells. Understanding how physiological L-2HG signaling is altered in the TME — in hypoxic regions, in specific immune cell types, or in therapy-exposed cells — could reveal metabolic-epigenetic vulnerabilities that complement existing immunotherapy approaches.