Research Radar — 2026-06-12
Methods & AI
Computational
Large-scale, spatially resolved panoramic CRISPR screening in native tissue environments using Perturb-DBiT
Nature Biotechnology Published 2026-06-11 research article DOI: 10.1038/s41587-026-03127-y
spatial CRISPR screen in vivo perturbation total RNA sequencing tumour microenvironment non-coding RNA clonal dynamics Perturb-DBiT functional genomics
Summary: Presents Perturb-DBiT, a method for co-sequencing spatial total RNA whole transcriptomes and single guide RNAs (sgRNAs) on the same tissue section in situ, enabling large-scale spatially resolved CRISPR screening in native tissue environments. Previous in vivo spatial CRISPR screening methods have been limited to small perturbation panels and subsets of protein-coding RNAs, severely restricting the biological questions that could be asked. Perturb-DBiT overcomes these limitations by simultaneously capturing the full transcriptome including mRNAs, long non-coding RNAs, microRNAs, and tRNAs alongside sgRNA identities at each spatial coordinate. The authors demonstrate the method power in two complementary systems. In a human cancer metastatic colonization model, they applied large (80,000+) sgRNA panels across tumour colonies, linking perturbations affecting lncRNA covariation, miRNA-mRNA interactions, and amino acid-specific tRNA alterations to tumour migration and growth. Integration with transcriptional pseudotime trajectories further revealed how perturbations influence clonal dynamics and cooperation. In an immune-competent syngeneic mouse model, Perturb-DBiT uncovered distinct, synergistic perturbation effects on immune infiltration and suppression within the tumour microenvironment. This represents a step-change in functional genomics, moving from what does a gene do to a cell to what does a gene do to a tissue, and to every other gene in that tissue.
Why it matters: This method represents a convergence of three transformative technologies: CRISPR screening, spatial transcriptomics, and total RNA sequencing into a single platform. The key advance is scale and comprehensiveness: previous spatial CRISPR screens could interrogate tens of perturbations and only protein-coding genes; Perturb-DBiT handles tens of thousands of perturbations and captures the full RNA landscape including non-coding species whose roles in tissue biology are poorly understood. The ability to observe how a perturbation reshapes non-coding RNA networks, clonal dynamics, and immune infiltration simultaneously in space is unprecedented. This will be transformative for understanding multicellular processes where gene function cannot be reduced to isolated cell-autonomous effects.
Why for Yiru: Perturb-DBiT is directly applicable to TME functional genomics. One could systematically perturb immune checkpoint genes, chemokine receptors, metabolic enzymes, or epigenetic modifiers in tumour-immune co-cultures or in vivo models and map how each perturbation reshapes the spatial organization and communication networks of the TME. The total RNA capability is particularly valuable for the TME because non-coding RNAs (lncRNAs, miRNAs) are increasingly recognized as regulators of immune cell function and tumour immune evasion, yet their spatial roles are almost entirely unexplored. Computationally, Perturb-DBiT data present rich opportunities for method development: integrating spatial perturbation effects with ligand-receptor inference, modeling how perturbations propagate through tissue via cell-cell communication, and identifying synergistic perturbation combinations that reprogramme the TME toward anti-tumour states.
Bridging three-dimensional molecular structures and artificial intelligence with a conformation description language
Nature Machine Intelligence Published 2026-06-11 research article DOI: 10.1038/s42256-026-01250-8
molecular conformation language model 3D molecular generation conformer prediction drug design ConfSeq representation learning computational chemistry
Summary: Introduces ConfSeq, a molecular conformation description language that translates three-dimensional molecular structures into a tokenized sequence representation, enabling language models to perform 3D molecular modeling tasks with strong performance. A fundamental challenge in AI-driven drug discovery is that molecular structures are inherently three-dimensional: atoms occupy specific positions in space, and molecular function depends on the 3D arrangement (conformation) of these atoms. However, most language-based molecular AI models (such as SMILES-based or graph-based models) operate on 1D string or 2D graph representations that discard 3D information. ConfSeq bridges this gap by designing a language for describing molecular conformations: it tokenizes the 3D coordinates, bond lengths, angles, and torsional information of a molecule into a sequence that preserves spatial relationships while being processable by transformer architectures. The authors demonstrate that language models trained on ConfSeq representations achieve strong performance on key 3D tasks: conformer prediction, 3D molecular generation, and 3D molecular representation learning. The approach is notable for its conceptual simplicity: rather than designing specialized 3D-aware neural architectures, it makes 3D information accessible to the mature ecosystem of language model training and inference tools.
Why it matters: The integration of 3D structural information into molecular AI has been a persistent challenge. 3D-aware models (equivariant neural networks, SE(3) transformers) require specialized architectures that are harder to train and scale than standard transformers. ConfSeq key insight, that 3D information can be serialized into a language that standard transformers understand, is elegant and pragmatic. This could democratize 3D molecular AI by allowing labs to leverage the mature infrastructure for training and deploying language models, rather than requiring specialized 3D deep learning expertise. For drug discovery, better 3D molecular representations directly impact virtual screening, de novo drug design, and binding affinity prediction: all tasks where 3D shape complementarity between drug and target is critical.
Why for Yiru: While primarily a drug design tool, the ConfSeq concept of translating complex 3D biological structures into sequence representations for language models could inspire analogous approaches for TME-relevant molecular modeling. For example, one could imagine a conformation description language for protein-protein interfaces (such as checkpoint ligand-receptor complexes or TCR-pMHC interactions) that enables language models to predict how mutations affect binding. The principle of making specialized 3D structural information accessible to general-purpose language models could also be applied to modeling the 3D organization of receptor-ligand signaling complexes in the TME.
Immune BioGraphy: A tale of graphical approaches in systems and virtual immunology
Cell Systems Published 2026-06-05 review DOI:
graph machine learning systems immunology virtual immunology knowledge graph multimodal integration immune system modeling interpretable AI network biology
Summary: Presents a comprehensive review and vision for how graph-based machine learning can serve as a unifying framework to model the immune system multiscale complexity, from molecular interactions to cellular networks to organism-level responses. The immune system is inherently graph-structured: molecules interact in signaling networks, cells communicate in tissue niches, and immunological memory forms through clonal lineage trees. Graph ML naturally captures these relational structures in ways that traditional tabular or grid-based methods cannot. The authors articulate how graph neural networks, knowledge graphs, and graph-enhanced language models can integrate multimodal immunological data (transcriptomics, proteomics, spatial data, clinical metadata) while embedding biological priors about known pathways, cell-cell interactions, and disease mechanisms. A key contribution is the concept of mechanistic tracing: using graph ML to propagate perturbation effects through the immune network from molecular events to systemic outcomes, enabling in silico prediction of how a specific drug or genetic alteration will affect immune function. The review also addresses practical challenges including interpretability, data integration across scales and modalities, and translational relevance for vaccine design, immunotherapy, and autoimmune disease.
Why it matters: Immunology is drowning in data but starving for integration. Single-cell atlases, spatial omics, CRISPR screens, and clinical trials each provide partial views of the immune system, but connecting these views into a coherent predictive model remains a grand challenge. Graph ML offers a natural solution because the immune system is itself a graph: nodes are cells and molecules, edges are interactions and communication. This review provides both a roadmap for computational immunologists and a compelling argument that graph-based approaches are not just one option among many, but the most natural framework for modeling immune system complexity. The vision of virtual immunology, where experiments can be simulated in silico before being performed in the lab, is ambitious but increasingly tractable.
Why for Yiru: The TME is fundamentally a graph-structured system: tumour cells, immune cells, and stromal cells are nodes; ligand-receptor interactions, cytokine gradients, and metabolic exchanges are edges. Graph ML approaches are directly applicable to modeling how this network reorganizes during tumorigenesis, metastasis, and therapy. The concept of mechanistic tracing, propagating perturbation effects through immune networks, is exactly what is needed to predict how specific immunotherapies will reshape the TME. Knowledge graphs that encode known TME biology (checkpoint interactions, chemokine signaling, metabolic pathways) could be combined with graph neural networks trained on spatial omics data to build predictive TME models.
Structural motif search across the protein universe with Folddisco
Nature Biotechnology Published 2026-06-05 research article DOI: 10.1038/s41587-026-03162-9
protein structure structural motif Foldseek database search Folddisco structural bioinformatics protein function remote homology
Summary: Introduces Folddisco, a computational tool that enables rapid structural motif search across million-scale protein structure databases, built on the Foldseek framework for fast structural alignment. Protein function is often mediated by small structural motifs: specific three-dimensional arrangements of a few amino acids that form catalytic sites, binding pockets, or metal coordination centers. Identifying all occurrences of a given motif across the protein universe can reveal functional relationships between otherwise dissimilar proteins, identify convergent evolution, and annotate proteins of unknown function. However, searching for structural motifs at scale has been computationally prohibitive because traditional structural alignment methods are too slow for million-scale databases. Folddisco addresses this by extending the Foldseek approach, which represents protein structures as sequences over a 3D interaction alphabet for ultrafast alignment, to the motif search problem. Users can define a motif as a set of residues with specified geometric relationships, and Folddisco finds all occurrences across databases like the AlphaFold Protein Structure Database (hundreds of millions of structures) in minutes. The authors demonstrate Folddisco power by identifying known and novel occurrences of catalytic motifs, metal-binding sites, and ligand-binding pockets across the protein universe, including in proteins annotated as having unknown function.
Why it matters: The explosion of predicted protein structures from AlphaFold and related methods has created an urgent need for tools that can efficiently mine this structural data for functional insight. Folddisco fills a critical gap: while Foldseek enables fast whole-structure similarity searches, Folddisco enables searches at the sub-structure motif level, which is often more biologically meaningful. A protein may share no global structural similarity with another but use the same catalytic motif: Folddisco can find these connections. This has immediate applications in functional annotation, enzyme discovery, and drug target identification.
Why for Yiru: While not directly a TME tool, Folddisco represents an important capability for the broader structural biology ecosystem that underlies drug discovery and target identification, both central to cancer immunotherapy. The ability to rapidly search for structural motifs could be applied to identify all proteins in the proteome that contain immune checkpoint-like binding interfaces, or to find structural homologs of immunotherapy targets in pathogens or the microbiome that might influence immune responses. The Foldseek/Folddisco ecosystem also exemplifies the kind of algorithmic innovation, representing complex 3D data as sequences for ultrafast processing, that could inspire analogous approaches for spatial transcriptomics data or TME cellular neighborhood analysis.
MIXPRS enables multi-population and multi-method polygenic risk scores using summary statistics
Nature Genetics Published 2026-06-09 research article DOI: 10.1038/s41588-026-02637-4
polygenic risk score GWAS multi-population summary statistics statistical genetics PRS cross-ancestry MIXPRS
Summary: Presents MIXPRS, a statistical framework for combining polygenic risk scores (PRS) from multiple populations and multiple PRS methods using only GWAS summary statistics, enabling risk prediction that is effective across diverse ancestries. A major limitation of current PRS is that they perform poorly when applied to populations different from the one in which they were developed, typically European-ancestry cohorts, exacerbating health disparities. MIXPRS addresses this by providing a principled framework for integrating PRS computed from multiple GWAS (e.g., European, East Asian, African) and multiple PRS methods (e.g., pruning and thresholding, LDpred, PRS-CS) into a single optimized score. The method operates on summary statistics, making it applicable even when individual-level data cannot be shared across studies. The key innovation is a mixture-of-experts approach that learns optimal weights for each population-method combination, leveraging the fact that different PRS methods capture different aspects of genetic architecture and different populations contribute complementary information about variant effect sizes and linkage disequilibrium patterns. The authors demonstrate that MIXPRS-derived scores consistently outperform single-population, single-method PRS across diverse ancestry groups in both simulations and real data analyses of multiple complex traits.
Why it matters: The poor cross-population portability of PRS is one of the most pressing challenges in translating genomic risk prediction to clinical practice. MIXPRS provides a practical solution that does not require access to individual-level data from multiple cohorts, a major bottleneck. By operating on summary statistics, the method can leverage the growing number of GWAS from diverse populations without the administrative and privacy barriers of data sharing. As PRS move toward clinical implementation for cancer risk, cardiovascular disease, and other common conditions, methods like MIXPRS will be essential to ensure these tools benefit all populations equitably.
Why for Yiru: While Yiru primary focus is TME biology rather than germline genetics, polygenic risk scores are increasingly relevant to cancer immunotherapy. Germline genetic variants influence immune function, and PRS for immune-related traits could potentially predict immunotherapy response or toxicity risk. MIXPRS could be applied to build cross-population PRS for immune phenotypes (cytokine levels, immune cell counts, autoimmune risk) that may influence TME composition and immunotherapy outcomes. More broadly, the mixture-of-experts framework for integrating predictions from multiple sources could inspire analogous approaches for integrating predictions from multiple TME models or multiple data modalities.
Biomedical discoveries
Biomedicine
Dynamic transitioning between MAPK-driven and WNT-driven cell states drives intestinal cancer and shapes therapy response
Nature Genetics Published 2026-06-11 research article DOI: 10.1038/s41588-026-02611-0
KRAS colorectal cancer MAPK signaling WNT signaling cancer plasticity drug resistance targeted therapy tumour heterogeneity
Summary: Reveals that KRASG12D-driven intestinal tumours do not exist in a single fixed cell state but dynamically transition between MAPK-driven regenerative states and WNT-dependent stem-like states, and that both signaling pathways must be blocked for sustained tumour control. KRAS is the most frequently mutated oncogene in human cancer, and while KRASG12C inhibitors have recently entered the clinic for lung cancer, targeting KRAS in colorectal cancer has proven more challenging. Using genetically engineered mouse models of KRASG12D-driven intestinal cancer, the authors from Genentech discover that tumours are not homogeneous: tumour cells continuously toggle between a MAPK-high regenerative state (characterized by active KRAS signaling and rapid proliferation) and a WNT-high stem-like state (characterized by LGR5 expression and stem cell programs). When MAPK signaling is blocked with a KRASG12D inhibitor, tumour cells shift to the WNT-dependent state, evading the therapy. Conversely, blocking WNT signaling drives cells into the MAPK-dependent state. Critically, only combined inhibition of both MAPK and WNT pathways, achieved by co-targeting KRASG12D and the WNT co-receptor LGR5, produces sustained tumour regression and prevents the emergence of drug-tolerant persister cells. The study provides a mechanistic framework for understanding why KRAS inhibitors alone are insufficient in colorectal cancer and identifies a specific combinatorial strategy for durable responses.
Why it matters: This study fundamentally reframes our understanding of KRAS-driven cancer. Rather than being a problem of genetic resistance (new mutations that bypass the drug), KRAS inhibitor failure in colorectal cancer appears to be driven by non-genetic plasticity: tumour cells switch to an alternative signaling state that is pre-existing in the tumour population, not newly acquired. This state-switching model of drug tolerance has profound therapeutic implications: it explains why monotherapy fails, it identifies the specific alternative pathway that must be co-targeted (WNT/LGR5), and it suggests that both pathways should be blocked simultaneously to prevent state transitions. The identification of LGR5 as a co-target is particularly exciting because LGR5 is already being pursued as a therapeutic target in oncology, and LGR5-directed antibody-drug conjugates are in clinical development.
Why for Yiru: The concept of dynamic cell state transitions driving drug tolerance is highly relevant to TME biology and immunotherapy resistance. In the TME, immune cells also exhibit state plasticity: T cells toggle between functional and exhausted states, macrophages switch between pro-inflammatory and immunosuppressive phenotypes, and CAFs transition between inflammatory and myofibroblastic states. The framework established here, that blocking one state drives cells into an alternative state and that both states must be targeted simultaneously, may apply directly to immunotherapy combinations. For example, PD-1 blockade may push exhausted T cells toward a different dysfunctional state rather than true reinvigoration; understanding the alternative state and co-targeting it could produce more durable responses. Computationally, methods for detecting dynamic state transitions in single-cell data and predicting which pathways drive these transitions are directly relevant.
Post-adjuvant chemotherapy in ctDNA-positive patients with resected colorectal cancer: a randomized phase 3 trial
Nature Medicine Published 2026-06-08 clinical trial (phase 3) DOI: 10.1038/s41591-026-04428-0
colorectal cancer ctDNA adjuvant chemotherapy phase 3 trial minimal residual disease liquid biopsy trifluridine/tipiracil ALTAIR
Summary: Reports results from the randomized, double-blind phase 3 ALTAIR trial, which evaluated whether additional chemotherapy with trifluridine/tipiracil (FTD/TPI) could improve disease-free survival in patients with resected colorectal cancer who became positive for circulating tumour DNA (ctDNA) during post-adjuvant surveillance. ctDNA detection after curative-intent surgery is a powerful predictor of recurrence, identifying patients with molecular residual disease who are at very high risk of relapse. The ALTAIR trial tested the hypothesis that intervening at the moment of ctDNA conversion, before clinical recurrence, could alter the disease trajectory. Patients with resected stage II-IV colorectal cancer who completed standard adjuvant chemotherapy and subsequently became ctDNA-positive during surveillance were randomized to receive FTD/TPI or placebo. The trial did not meet its primary endpoint: FTD/TPI did not significantly prolong disease-free survival compared with placebo. This negative result is important because it addresses a critical clinical question: now that we can detect molecular recurrence early via ctDNA, does treating early improve outcomes? The answer from ALTAIR, at least with FTD/TPI, is no. However, the trial provides valuable insights on ctDNA kinetics, the lead time between ctDNA detection and clinical recurrence, and the performance of ctDNA as a surveillance tool in a prospective randomized setting. These data will inform the design of future ctDNA-guided intervention trials with potentially more effective therapeutic regimens.
Why it matters: ALTAIR is one of the first randomized phase 3 trials to test a ctDNA-guided intervention strategy, and its negative result is as informative as a positive one would have been. The trial establishes that simply detecting molecular recurrence earlier and treating with a modestly active agent is insufficient: the intervention must be potent enough to eradicate residual disease. This has immediate implications for the dozens of ongoing and planned ctDNA-guided trials: the choice of therapeutic agent is critical, and more effective regimens (such as immunotherapy for MSI-H patients or novel targeted combinations) should be prioritized. The trial also validates the feasibility of ctDNA-guided trial designs, demonstrating that prospective ctDNA surveillance and rapid randomization upon conversion is logistically achievable in a multi-center international setting.
Why for Yiru: ctDNA is increasingly relevant to immuno-oncology and TME research. ctDNA can detect not just tumour-derived mutations but also fragmentation patterns and methylation signatures that reflect TME biology: for example, immune infiltration affects chromatin accessibility and thus ctDNA fragmentation. ALTAIR demonstrates the clinical feasibility of ctDNA-guided intervention, a paradigm that could be extended to immunotherapy: ctDNA monitoring during checkpoint blockade could identify molecular progression before clinical progression, potentially enabling early switching to alternative immunotherapies. Integrating ctDNA dynamics with TME information from paired tissue biopsies could reveal how TME changes drive or reflect ctDNA changes, creating a more complete picture of therapeutic response and resistance.
Mapping cell type-resolved transcriptomic profiles to patient survival in pancreatic cancer
Cancer Cell Published 2026-06-11 research article DOI:
pancreatic cancer single-nucleus RNA-seq prognosis cell type survival analysis tumour microenvironment PDAC precision oncology
Summary: Establishes a cell-type-resolved prognostic atlas for pancreatic ductal adenocarcinoma (PDAC) by integrating single-nucleus transcriptomes with long-term survival data from 152 patients. PDAC is one of the most lethal cancers with a 5-year survival rate below 12%, and prognostic models based on bulk tumour transcriptomics have had limited clinical utility because they obscure the cellular complexity of the tumour. This study takes a fundamentally different approach: rather than asking what is the overall gene expression signature of poor prognosis, it asks which specific cell types and cell states are associated with patient survival. By performing single-nucleus RNA-seq on 152 PDAC tumours with linked long-term clinical follow-up, the authors identify cell-type-specific transcriptional programs that independently predict survival. Key findings include the identification of a specific cancer cell subpopulation whose gene expression profile strongly correlates with poor outcomes, as well as stromal and immune cell states that are associated with either favourable or unfavourable prognosis. The atlas provides a framework for moving from bulk-level prognostic signatures, which conflate signals from multiple cell types, to cell-type-resolved biomarkers that are more mechanistically interpretable and potentially more clinically actionable. The data also serve as a reference for understanding which aspects of TME composition and function are most strongly linked to patient outcomes in pancreatic cancer.
Why it matters: This study represents a paradigm shift in cancer prognostics: from what genes predict survival to which cells predict survival. The distinction is crucial because bulk transcriptomic signatures can be confounded by changes in cell-type composition: a signature that appears to predict poor survival might simply reflect a higher proportion of fibroblasts in the tumour, not a true cancer-cell-intrinsic program. By resolving prognostic signals at cell-type resolution, this atlas provides more interpretable and potentially more robust biomarkers. For pancreatic cancer specifically, where effective therapies are desperately needed, identifying the specific cell states that drive lethality could reveal new therapeutic targets. The framework is also generalizable to any cancer type with available single-cell and survival data.
Why for Yiru: This study is a template for TME-focused prognostic research. The approach of linking single-cell-resolved TME composition to patient outcomes is directly applicable to other cancers and other clinical endpoints (immunotherapy response, recurrence, metastasis). For Yiru work on TME analysis, this study demonstrates the clinical value of moving beyond cell-type proportions to cell-state-specific transcriptional programs that predict outcomes. The dataset itself, 152 PDAC tumours with single-nucleus data and survival, is a valuable resource for computational method development, including methods for identifying which TME cell states and cellular interactions are most prognostic.
An epigenetic four-lane route to camouflage for cancer cells
Cancer Cell Published 2026-06-11 preview DOI:
immune evasion whole-genome doubling antigen presentation epigenetics CD8 T cell cancer immunology MHC-I tumour immune escape
Summary: Highlights and contextualizes a study by Foidart et al. demonstrating that cancer cells undergoing whole-genome doubling (WGD) acquire epigenetic changes that suppress antigen presentation, effectively hiding from CD8+ T cell recognition. WGD is a common event in cancer evolution (approximately 30% of human tumours are tetraploid) and is associated with poor prognosis and immune evasion, but the mechanistic link has been unclear. The featured study reveals that WGD triggers a specific epigenetic program, mediated by DNA methylation and histone modifications, that silences multiple components of the MHC class I antigen presentation pathway. This effectively camouflages the cancer cells from CD8+ T cells because they can no longer display tumour antigens on their surface. The preview discusses how this epigenetic immune evasion mechanism is distinct from genetic mechanisms (such as B2M mutations that permanently disable MHC-I): it is potentially reversible with epigenetic therapies, suggesting a therapeutic opportunity to restore immune visibility of WGD tumours. The four-lane route in the title refers to the multiple parallel epigenetic mechanisms that WGD cells deploy to suppress antigen presentation.
Why it matters: The link between whole-genome doubling and immune evasion is clinically important because WGD tumours represent a large fraction of human cancers and are often resistant to immunotherapy. Understanding the mechanism, in this case epigenetic silencing of antigen presentation, opens therapeutic avenues: epigenetic drugs (such as DNA methyltransferase inhibitors or HDAC inhibitors) could potentially reverse the camouflage and re-sensitize WGD tumours to immunotherapy. This is a different and potentially more tractable mechanism than genetic loss of MHC-I, which cannot be pharmacologically reversed.
Why for Yiru: The intersection of tumour genomics (WGD, chromosomal instability), epigenetics, and immune evasion is directly relevant to TME research. In the TME, tumour cells with different ploidy states may coexist and differentially interact with immune cells: WGD cells may be invisible to CD8+ T cells while diploid cells are recognized and eliminated, creating a selective pressure favouring WGD clones during immunotherapy. Computational analyses of single-cell and spatial data from tumours could identify WGD cells and correlate their presence with immune infiltration patterns and immunotherapy outcomes. The epigenetic reversibility of this immune evasion mechanism also opens possibilities for computational prediction of which tumours would benefit from epigenetic therapy plus immunotherapy combinations.
Prion-based protein self-assembly tunes mutagenesis to enable rapid adaptation
Cell Published 2026-06-09 research article DOI:
prion protein self-assembly mutagenesis drug resistance adaptation genome maintenance evolution cancer
Summary: Reveals that prion-based protein self-assembly enables reversible switching of genome-maintenance pathways during rapid adaptation and the emergence of drug resistance. Prions are proteins that can adopt alternative, self-perpetuating conformations, and while they are best known for their role in neurodegenerative diseases, a growing body of work has shown that prion-like protein switches can serve adaptive functions in cells. This study demonstrates that a yeast prion protein can toggle between two functional states, one that promotes high-fidelity DNA repair and another that increases mutagenesis, enabling the population to rapidly generate genetic diversity when facing environmental stress such as antifungal drugs. The prion switch is tunable: the level of mutagenesis can be adjusted by the proportion of cells in the prion state, creating a bet-hedging strategy where some cells maintain genomic integrity while others explore the fitness landscape through elevated mutation rates. The authors show that this mechanism directly contributes to the emergence of drug resistance, and that blocking prion propagation abrogates the adaptive mutagenesis. While demonstrated in yeast, the principles may extend to human cells where prion-like protein aggregation has been implicated in cancer-associated signaling and stress responses.
Why it matters: This study provides a mechanistic basis for a provocative idea: that cells can actively tune their mutation rate through protein conformational switches in response to stress. This challenges the traditional view of mutation as a passive, stochastic process and suggests that mutagenesis can be regulated. If similar mechanisms operate in human cells or tumours, this could explain how cancer cells rapidly evolve drug resistance: rather than waiting for rare random mutations, cells under therapeutic pressure might actively increase their mutation rate through prion-like switches. Targeting such switches could represent a novel strategy to prevent or delay the emergence of drug resistance.
Why for Yiru: Drug resistance is a fundamental challenge in cancer therapy, including immunotherapy. Tumours can evolve resistance to checkpoint blockade through multiple mechanisms (antigen loss, immune checkpoint upregulation, T cell exclusion), and understanding whether tumours can actively accelerate this evolutionary process is critical. If prion-like or other protein-conformation-based switches regulate mutation rates or phenotypic plasticity in cancer cells, they could represent novel therapeutic targets. The concept of tunable mutagenesis also has implications for understanding TME heterogeneity: different regions of a tumour experiencing different microenvironmental stresses (hypoxia, immune attack, nutrient deprivation) might have different mutation rates, contributing to spatial heterogeneity in drug sensitivity. Computationally, methods for detecting signatures of regulated mutagenesis from sequencing data could help identify tumours employing this strategy.
Cross-disciplinary watchlist
Other Fields
Replaying germinal center evolution on a quantified affinity landscape
Cell Published 2026-06-05 research article DOI:
germinal center B cell antibody affinity maturation evolution somatic hypermutation immunology replay experiment
Summary: Reports a landmark parallel replay experiment that quantitatively maps the evolutionary forces driving antibody affinity maturation in germinal centers. During an immune response, B cells enter germinal centers where they undergo somatic hypermutation and selection for improved antigen binding, a process of rapid Darwinian evolution occurring over days to weeks. Despite decades of study, fundamental questions remain: how predictable is the evolutionary trajectory? Does selection act primarily on affinity or also on other B cell properties? What fraction of B cell clones are doomed from the start versus capable of achieving high affinity? To answer these questions, the authors developed a system where they could replay germinal center evolution multiple times from the same starting population of B cells. By quantifying the affinity landscape (measuring how each mutation affects antibody binding) and tracking clonal dynamics across parallel germinal center reactions, they could directly measure the strength and mode of selection. Key findings include: (1) affinity maturation is highly predictable, with the same B cell clones reproducibly dominating across independent germinal center reactions; (2) selection acts strongly on affinity, with high-affinity clones having a substantial fitness advantage; (3) the affinity landscape is smooth enough that B cells can evolve to high affinity through sequential single mutations without requiring rare multi-mutation events; and (4) the predictability of the outcome depends on the initial clonal composition of the germinal center. This study provides the most quantitative picture yet of how antibody evolution works at the level of individual B cell clones.
Why it matters: This study is a tour de force at the intersection of immunology and evolutionary biology. The parallel replay experimental design, running evolution multiple times from the same starting conditions, is a powerful approach borrowed from experimental evolution in microorganisms and applied here to the complex vertebrate immune system. The finding that affinity maturation is highly predictable has practical implications for vaccine design: it suggests that immunogens can be rationally designed to guide B cell evolution toward desired antibody specificities, and that the outcome of vaccination may be more controllable than previously thought. Understanding the rules of germinal center evolution also has implications for understanding how B cell lymphomas arise from germinal center B cells that acquire mutations dysregulating the same evolutionary process.
Why for Yiru: While Yiru focus is primarily on T cells and the TME, B cells and tertiary lymphoid structures (TLS) are increasingly recognized as important components of the TME that influence immunotherapy responses. The principles of clonal evolution and selection quantified in this study, how cell-intrinsic properties (affinity) interact with cell-extrinsic selection to determine clonal outcomes, have direct parallels in T cell evolution within tumours. T cell clones competing for antigen and survival signals in the TME undergo analogous evolutionary dynamics. The replay experimental framework could also inspire computational approaches: one could computationally replay T cell clonal dynamics from single-cell TCR sequencing data to infer the selection pressures operating in different TME contexts. More broadly, the quantitative evolutionary biology framework in this study provides a conceptual toolkit for thinking about any clonal population under selection.
Dual-target gene therapy in Parkinson disease: a multicenter phase 1 trial
Nature Medicine Published 2026-06-10 clinical trial (phase 1) DOI: 10.1038/s41591-026-04436-0
Parkinson disease gene therapy AAV dual-target neurodegeneration phase 1 trial dopamine neurotrophic factor
Summary: Reports results from a multicenter phase 1 trial of a dual-target AAV gene therapy for Parkinson disease, designed to simultaneously address dopamine deficiency and provide neurotrophic support to degenerating neurons. Parkinson disease is characterized by progressive loss of dopaminergic neurons in the substantia nigra, and current treatments (L-DOPA, deep brain stimulation) manage symptoms but do not slow disease progression. Gene therapy offers the potential for disease modification by delivering therapeutic genes directly to affected brain regions. This trial uses an adeno-associated virus (AAV) vector that delivers two therapeutic genes: one encoding aromatic L-amino acid decarboxylase (AADC) to enhance dopamine production from L-DOPA, and another encoding a neurotrophic factor to promote neuronal survival. The dual-target approach addresses both the symptomatic (dopamine deficiency) and degenerative (neuronal loss) aspects of the disease in a single intervention. The phase 1 trial, conducted across multiple centers, demonstrates a favorable safety profile with no serious adverse events attributed to the therapy. Early efficacy signals include improvements in motor function as measured by the Unified Parkinson Disease Rating Scale (UPDRS) and evidence of sustained transgene expression on PET imaging. These results support advancing this dual-target gene therapy to later-phase trials.
Why it matters: Parkinson disease affects over 10 million people worldwide, and no currently approved therapy slows disease progression. Gene therapy for neurodegenerative diseases has had a challenging history, with several high-profile failures in earlier trials. The positive safety and early efficacy signals from this trial are therefore encouraging: they suggest that AAV-mediated gene delivery to the brain can be safe and that targeting both symptomatic and disease-modifying mechanisms may be a winning strategy. The dual-target approach could also serve as a template for gene therapies in other neurodegenerative diseases (Alzheimer, Huntington, ALS) where multiple pathological mechanisms contribute to disease.
Why for Yiru: While not directly TME-related, this trial demonstrates several principles relevant to therapeutic development in general. The dual-target strategy, addressing both symptoms and underlying pathology, is analogous to combination immunotherapy approaches that aim to both activate anti-tumour immunity (symptomatic) and reprogramme the TME to prevent immune escape (disease-modifying). The use of imaging biomarkers to confirm target engagement is a principle that could be applied to TME-targeted therapies: confirming that an immunotherapy is actually reaching the tumour and engaging its target. The trial also illustrates how AAV vectors can be used for local delivery of therapeutic payloads, a technology that could potentially be adapted for intratumoural delivery of immunomodulatory genes.
Microglia at a key inflection point in Alzheimer disease
Nature Medicine Published 2026-06-11 editorial DOI: 10.1038/s41591-026-04409-3
Alzheimer disease microglia neuroinflammation neurodegeneration TREM2 immunology therapeutic target dementia
Summary: Discusses the emerging recognition of microglia as central players in Alzheimer disease pathogenesis, marking a shift from the long-dominant amyloid-centric view toward a more integrated neuroimmune perspective. For decades, Alzheimer research focused almost exclusively on amyloid-beta plaques and tau tangles as the primary drivers of neurodegeneration. However, the repeated failure of amyloid-targeting therapies to produce meaningful clinical benefit, and the modest effects of recently approved anti-amyloid antibodies, has prompted a broader view. Genome-wide association studies have consistently identified microglial genes (TREM2, CD33, ABI3) as Alzheimer risk factors, and functional studies now show that microglia play dual roles: they can clear amyloid and protect synapses, but they can also drive neuroinflammation and synaptic pruning when chronically activated. This editorial frames the current moment as an inflection point where microglia are transitioning from being viewed as passive responders to amyloid pathology to being recognized as active drivers of disease progression. Key open questions include: can we therapeutically modulate microglial states to promote their protective functions while suppressing their harmful ones? How do microglial responses differ across Alzheimer subtypes and stages? And can microglial imaging biomarkers serve as earlier and more dynamic readouts of disease progression than traditional amyloid and tau PET?
Why it matters: The shift toward a neuroimmune view of Alzheimer disease has profound implications for therapeutic development. The amyloid hypothesis has dominated Alzheimer research and drug development for over 30 years, with limited clinical success. A microglia-centered framework opens entirely new therapeutic strategies: TREM2 agonists, microglial checkpoints, and treatments that shift microglia from neurotoxic to neuroprotective states. The concept also connects Alzheimer to broader themes in immunology and immunotherapy: microglia are the brain resident macrophages, and lessons learned from targeting tumour-associated macrophages in cancer may be transferable to targeting microglia in neurodegeneration. For the millions of patients and families affected by Alzheimer, this expanded therapeutic horizon is genuinely hopeful.
Why for Yiru: There are deep conceptual parallels between the TME and the neurodegenerative microenvironment. Both involve tissue-resident myeloid cells (microglia in the brain, macrophages in the TME) that can adopt either protective or pathogenic phenotypes. The therapeutic challenge is the same: how to shift these cells from a disease-promoting to a disease-fighting state. Methodologies developed for analyzing TME macrophage heterogeneity, single-cell and spatial transcriptomics, ligand-receptor inference, cell state trajectory analysis, are directly applicable to studying microglial heterogeneity in Alzheimer. Computational tools for identifying the molecular drivers of macrophage polarization in the TME could be repurposed for microglia. The cross-pollination between cancer immunology and neuroimmunology is a growing area where Yiru computational expertise could contribute.
Adjuvanted inactivated rabies virus-vectored Lassa virus vaccine in healthy adults: a phase 1 trial
Nature Medicine Published 2026-06-09 clinical trial (phase 1) DOI: 10.1038/s41591-026-04429-z
Lassa virus vaccine phase 1 trial rabies vector emerging infectious disease global health adjuvant immunogenicity
Summary: Reports results from a phase 1 trial of a novel Lassa virus vaccine that uses an inactivated rabies virus as a delivery vector with an adjuvant to enhance immunogenicity. Lassa fever is a severe viral hemorrhagic fever endemic to West Africa, causing an estimated 100,000-300,000 infections and 5,000 deaths annually. Despite being classified as a WHO priority pathogen, no licensed vaccine exists for Lassa virus. This vaccine candidate takes a pragmatic approach: it repurposes the well-established rabies vaccine platform, an inactivated virus with decades of safety data, and engineers it to also express Lassa virus glycoprotein, creating a bivalent vaccine against both rabies and Lassa. The addition of a clinical-grade adjuvant boosts the immune response to the Lassa component. The phase 1 trial in healthy adults demonstrates that the vaccine is safe and well-tolerated, with no serious adverse events. Immunogenicity analyses show that the vaccine induces both binding and neutralizing antibodies against Lassa virus, as well as T cell responses. The antibody responses were durable over the follow-up period, and the adjuvant significantly enhanced the magnitude and quality of the immune response. These results support advancing the vaccine to phase 2 trials in Lassa-endemic regions.
Why it matters: Lassa fever is a neglected tropical disease with epidemic potential, and the absence of a licensed vaccine is a major global health gap. The WHO R&D Blueprint lists Lassa as a priority pathogen for vaccine development. This trial is significant because it uses a proven, low-cost platform (inactivated rabies virus) that is already manufactured at scale in multiple countries, meaning the path to large-scale production is clearer than for novel vaccine platforms. The addition of an adjuvant to boost immunogenicity is a practical innovation. If successful, this vaccine could simultaneously protect against rabies, which kills 59,000 people annually, and Lassa, increasing its public health value and cost-effectiveness.
Why for Yiru: While not directly TME-related, vaccine technologies are increasingly relevant to cancer immunotherapy. The rabies virus vector platform used here could potentially be adapted for cancer vaccines, delivering tumour antigens rather than viral antigens. Inactivated viral vectors have favorable safety profiles compared to replication-competent vectors, making them attractive for cancer vaccine development where the target population may be immunocompromised. The adjuvant strategy, using a clinically approved adjuvant to boost responses to a vectored vaccine, could also inform cancer vaccine design, where weak immunogenicity has been a major limitation. Understanding how different vaccine platforms and adjuvants shape the quality of T cell and antibody responses is broadly relevant to immunotherapy.
Hybrid solid-liquid optics enable scalable, high-resolution light-sheet microscopy
Nature Biotechnology Published 2026-06-08 research highlight DOI: 10.1038/s41587-026-03172-7
light-sheet microscopy optics imaging spatial biology resolution scalability microscopy technology
Summary: Highlights a technical advance in light-sheet microscopy that uses hybrid solid-liquid optical elements to achieve high-resolution imaging across large tissue volumes with scalable, cost-effective optics. Light-sheet microscopy has become an essential tool for spatial biology because it enables rapid 3D imaging of intact tissues with minimal phototoxicity. However, conventional light-sheet microscopes face a fundamental trade-off: high-resolution objectives have short working distances and small fields of view, while objectives that can image large samples compromise on resolution. The hybrid solid-liquid optics approach addresses this by using tunable liquid lenses combined with solid glass optics to dynamically adjust the focal plane and correct optical aberrations as the light sheet scans through tissue. This enables the microscope to maintain diffraction-limited resolution across large imaging volumes while adapting to different immersion media, important because cleared tissues have different refractive indices than live samples. The result is a scalable platform that can be built from commercially available components, making high-performance light-sheet microscopy more accessible to labs without dedicated optics expertise or million-dollar budgets.
Why it matters: Spatial biology, the study of where molecules and cells are located in tissues, is driving discoveries across immunology, neuroscience, developmental biology, and cancer research. The bottleneck has shifted from data generation (we can now measure thousands of genes in situ) to the quality and throughput of imaging. Advances in microscopy optics directly impact the resolution, speed, and accessibility of spatial omics workflows. This hybrid optics approach is notable because it addresses a real and persistent limitation (the resolution vs. field-of-view trade-off) with a practical, scalable solution. Making high-quality light-sheet microscopy more accessible could accelerate spatial biology research globally.
Why for Yiru: Spatial transcriptomics and spatial proteomics of the TME rely on high-quality microscopy to image tissues at single-cell or subcellular resolution. The ability to image large TME regions, entire tumour sections or even whole tumours in 3D, while maintaining the resolution needed to distinguish individual cells and their molecular profiles is directly relevant to TME research. Improved light-sheet microscopy could enable whole-tumour spatial transcriptomics, revealing how cellular neighbourhoods and communication networks are organized across entire tumours rather than in small representative regions. This could uncover spatial patterns of immune infiltration, tumour heterogeneity, and treatment response that are invisible in 2D sections or small fields of view.
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Novartis doubles down on molecular glue strategy
STAT Published 2026-06-11 biotech news DOI:
molecular glue targeted protein degradation Novartis drug discovery biotech PROTAC protein degradation
Summary: Reports that Novartis is significantly expanding its investment in molecular glue degraders, small molecules that induce proximity between a target protein and an E3 ubiquitin ligase, leading to ubiquitination and proteasomal degradation of the target. Unlike PROTACs, which are typically larger heterobifunctional molecules, molecular glues are smaller, more drug-like, and work by stabilizing protein-protein interactions rather than bridging them with a linker. Novartis expanded strategy includes internal R&D programs, external partnerships, and a dedicated molecular glue platform. This move signals growing industry confidence that molecular glues can address previously undruggable targets, and positions targeted protein degradation alongside traditional small-molecule inhibition as a mainstream therapeutic modality. The article also covers other biotech news including rising seniors drug costs and Enliven Therapeutics promising leukemia drug data.
Why it matters: Targeted protein degradation is arguably the most important new therapeutic modality since monoclonal antibodies. Molecular glues offer advantages over PROTACs including better oral bioavailability, lower molecular weight, and the ability to degrade proteins that lack deep binding pockets for traditional inhibitors. Novartis commitment signals that large pharma sees molecular glues as a platform technology, not a niche approach. For cancer patients, this could mean new therapies targeting oncogenic transcription factors, scaffold proteins, and other high-value targets that have resisted decades of traditional drug discovery efforts.
Why for Yiru: Molecular glues and targeted protein degradation are highly relevant to cancer immunotherapy and TME biology. Many key TME regulators, including transcription factors controlling T cell exhaustion, scaffold proteins organizing immune synapses, and cytokines mediating intercellular communication, are undruggable by conventional small molecules. Molecular glues could provide a path to pharmacologically targeting these proteins. For example, degrading the transcription factor TOX (which drives T cell exhaustion) or C/EBP-beta (which drives immunosuppressive macrophage polarization) could represent novel immunotherapy strategies. The concept of induced proximity, using a small molecule to bring two proteins together for a functional outcome, could also be adapted for TME applications beyond degradation, such as forcing immune synapses or blocking inhibitory receptor signaling.
Synthetic lethality could trigger another round of biotech M&A
STAT Published 2026-06-11 biotech news DOI:
synthetic lethality cancer therapy M&A biotech drug discovery PARP precision oncology
Summary: Analyzes how the emerging success of synthetic lethality approaches in oncology, where two genes are synthetically lethal if mutation in either alone is tolerated but mutation in both kills the cell, is driving a new wave of biotech mergers and acquisitions. The concept was validated by PARP inhibitors, which exploit synthetic lethality with BRCA mutations and have become blockbuster drugs. Now, a second generation of synthetic lethality targets is maturing, including ATR, WEE1, PRMT5, MAT2A, and USP1. Companies with clinical-stage synthetic lethality programs are becoming attractive acquisition targets for large pharma seeking to expand their oncology pipelines. The article discusses how the convergence of better biomarker strategies, an expanded target landscape, and clinical validation of new synthetic lethal pairs could trigger deal-making similar to the ADC acquisition wave of 2023-2025. Specific companies and programs mentioned include Tango Therapeutics, Repare Therapeutics, and IDEAYA Biosciences.
Why it matters: Synthetic lethality is one of the most rational approaches to cancer therapy: it exploits the specific genetic vulnerabilities created by tumour mutations while sparing normal cells. The PARP inhibitor story demonstrated that this concept can translate into drugs that meaningfully improve patient outcomes. A second generation of synthetic lethality drugs targeting a wider range of mutations could dramatically expand precision oncology, particularly for tumour suppressor gene mutations (TP53, PTEN, ARID1A) that have been undruggable because you cannot inhibit a protein that is already lost. The M&A angle reflects pharma confidence that synthetic lethality is not a one-hit wonder (PARP) but a generalizable strategy.
Why for Yiru: Synthetic lethality is directly relevant to TME and immunotherapy research. Tumour cells in the TME are under unique metabolic, oxidative, and immune stress that may create TME-specific synthetic lethal vulnerabilities not present in normal tissue or even in tumour cells grown in vitro. For example, genes required for survival under immune attack (such as those involved in antigen presentation or stress response) may be synthetically lethal with immune checkpoint expression, creating opportunities for combination therapies. Computational approaches for identifying synthetic lethal interactions, particularly those that integrate multi-omics data from TME contexts rather than cell line screens, could uncover novel therapeutic strategies.
Enliven Therapeutics leukemia drug shows promise in new study
STAT Published 2026-06-11 biotech news DOI:
leukemia targeted therapy Enliven Therapeutics clinical data hematologic malignancy precision oncology kinase inhibitor
Summary: Reports new clinical data from Enliven Therapeutics showing that their investigational targeted drug induced molecular responses in nearly half of patients with advanced leukemia. The drug targets a specific kinase mutation that drives proliferation in certain leukemias, and the new data come from an ongoing phase 1/2 trial. Molecular response, reduction in the level of the mutated gene transcript below a defined threshold, is a well-established surrogate endpoint in leukemia that strongly predicts long-term outcomes including progression-free and overall survival. Achieving molecular responses in nearly 50% of heavily pretreated patients represents a clinically meaningful result that supports advancing the drug to pivotal trials. The safety profile appears manageable, with mostly low-grade adverse events. Enliven approach exemplifies the precision medicine paradigm in hematologic malignancies, where the genetic driver is well-defined and can be directly targeted with a selective inhibitor.
Why it matters: Targeted therapies have transformed the treatment of chronic myeloid leukemia (CML) and certain forms of acute leukemia, but many patients still develop resistance or relapse. New targeted agents with activity in resistant populations address a genuine unmet need. The molecular response rate of nearly 50% in a heavily pretreated population is noteworthy and suggests this drug could become a meaningful new option. From a biotech industry perspective, these data also validate Enliven drug discovery platform and could position the company for partnership discussions or an expanded pivotal program.
Why for Yiru: While Yiru research focuses on solid tumours and the TME, hematologic malignancies have historically led the way in precision oncology: targeted therapies, MRD monitoring, and immunotherapy (CAR-T) all achieved their first successes in blood cancers before being adapted for solid tumours. The principles of molecular response monitoring, targeted inhibitor development, and resistance mechanism analysis developed in leukemia are now being applied to solid tumours through ctDNA monitoring (as in the ALTAIR trial above) and targeted therapy combinations. Lessons from hematology, particularly around how to use molecular biomarkers to guide therapy, are directly applicable to the emerging field of TME-guided precision immunotherapy.
Nonprofit buys experimental cancer drug to maintain patient access
STAT Published 2026-06-11 biotech news DOI:
patient access compassionate use cancer drug nonprofit biotech drug development expanded access
Summary: Reports on an unusual transaction: the nonprofit organization Blood Cancer United has purchased the remaining supplies of a discontinued investigational cancer drug (luvelta) to maintain access for patients currently receiving it through compassionate use programs. When a biotech company discontinues development of a drug, whether due to strategic reprioritization, financial constraints, or disappointing data, patients enrolled in compassionate use or expanded access programs can suddenly lose access to a therapy they depend on. This nonprofit intervention represents a novel model for addressing this gap: rather than relying on the original manufacturer to maintain supply, a patient advocacy organization steps in to purchase the remaining drug supply and manage its distribution. The article discusses the ethical and logistical complexities of this model, including who decides which patients receive the limited remaining doses, how drug quality and safety are maintained outside the original manufacturer control, and whether this model is scalable to other discontinued drugs. While the number of patients affected is small, the precedent is significant: it establishes that there are alternative pathways to maintain patient access when commercial development stops.
Why it matters: The discontinuation of investigational drugs is an underappreciated problem in drug development. When a company stops developing a drug, patients who were benefiting often have no recourse: the drug simply disappears. This nonprofit purchase model, while clearly a stopgap rather than a systemic solution, highlights the need for better mechanisms to protect patients during drug development transitions. It also raises important questions about the responsibilities of drug developers to patients enrolled in their trials. As personalized medicine leads to more niche drug development programs with smaller patient populations, this issue may become more common: smaller programs are more likely to be discontinued for strategic or financial reasons.
Why for Yiru: This story touches on important themes in cancer drug development that intersect with TME research. Many investigational immunotherapies and TME-targeted agents are developed by small biotech companies, and discontinuation can leave patients without options. The development of better biomarkers, including TME-based biomarkers, could help identify which patients are most likely to benefit from a given therapy, potentially preventing discontinuation of drugs that work well in a subset of patients. More broadly, as TME research leads to increasingly personalized combination immunotherapy strategies, ensuring patient access to these therapies, particularly when they are developed by small companies with limited resources, will become an increasingly important translational challenge.
An obesity drug deep-dive, and peptides move mainstream
STAT Published 2026-06-11 biotech news DOI:
obesity GLP-1 drug development peptides biotech metabolic disease weight loss
Summary: Provides an overview of the increasingly crowded obesity drug landscape, examining whether any of the numerous GLP-1 follow-on and next-generation agents in development can differentiate themselves in a market dominated by semaglutide (Wegovy) and tirzepatide (Zepbound). Key themes include: the pursuit of oral formulations to improve convenience; combination approaches targeting multiple incretin receptors (GIP, GLP-1, glucagon, amylin) to improve weight loss efficacy and metabolic benefits; efforts to preserve muscle mass during weight loss; and the broader trend of peptide therapeutics moving from niche applications to mainstream primary care. The article also covers a record-breaking biotech IPO in the metabolic disease space. The context is that obesity drugs have become the pharmaceutical industry biggest growth driver, with the market projected to exceed 100 billion dollars annually, driving massive R&D investment and intense competition.
Why it matters: The obesity drug market is reshaping the pharmaceutical industry. After decades of failed weight-loss drugs with marginal efficacy and safety concerns, the GLP-1 receptor agonists have demonstrated that pharmacologically meaningful, sustained weight loss is achievable. This has profound public health implications given that over 40% of US adults are obese and obesity drives risk for diabetes, cardiovascular disease, cancer, and many other conditions. The competition to develop better obesity drugs, more effective, more convenient (oral), better tolerated, and preserving muscle mass, will benefit patients through expanded options and potentially lower costs. The rise of peptide therapeutics more broadly also signals a shift in what kinds of molecules can be developed as drugs.
Why for Yiru: Obesity is a major risk factor for at least 13 types of cancer, and the relationship between obesity and cancer is mediated in part through the TME. Adipose tissue in obesity is characterized by chronic low-grade inflammation, altered adipokine secretion, and immune cell infiltration: features that overlap with TME biology. Understanding how obesity-driven systemic metabolic and inflammatory changes affect the TME is an active area of research. The GLP-1 drug class may have direct effects on tumour biology beyond weight loss: GLP-1 receptors are expressed on immune cells, and GLP-1 agonism has been shown to modulate inflammation. As GLP-1 drugs become widely used, understanding their effects on cancer risk, tumour biology, and immunotherapy efficacy will be important. The peptide therapeutics trend is also relevant: peptide-based cancer vaccines and peptide-MHC-targeted therapies are an area of active development in immunotherapy.