Cell Reports Medicine最新文献

Immune checkpoint blockade (ICB) therapy exerts anti-tumor efficacy mainly by activating intratumoral CD8⁺ T cells but fails to re-activate terminally exhausted CD8⁺ T cells. Interleukin-10 (IL-10) has been shown to directly expand and activate these cells and to exert a synergistic effect when combined with ICB. Nevertheless, the clinical application of IL-10 for cancer immunotherapy is restricted by severe hematological toxicity. Here, we design FP008 (anti-PD-1×IL-10M), a clinical-stage fusion protein composed of an anti-PD-1 antibody and an attenuated IL-10 monomer (IL-10M). Mechanistically, the activity and toxicity of IL-10M are significantly reduced, while its therapeutic benefits are enhanced through anti-PD-1-targeted enrichment and cis-activation. Anti-PD-1×IL-10M therapy displays robust anti-tumor activity in various mouse models, including those resistant to anti-PD-1 therapy, and exhibits promising safety in GLP toxicology studies in cynomolgus monkeys. Altogether, reinvigorating exhausted CD8⁺ T cells in the tumor microenvironment through anti-PD-1×IL-10M represents a promising therapeutic strategy for overcoming anti-PD-1/L1-refractory solid tumors.

Cytokine release syndrome (CRS) is a potentially life-threatening inflammatory condition. However, the defining features that distinguish it from self-resolving inflammation remain poorly understood. In this study, we identified monocyte/macrophage hyper-translation as a hallmark of CRS pathogenesis in patient samples. To uncover the molecular drivers of this phenomenon, a CRISPR screen followed by genetic validation pinpointed BCAP as a critical regulator of hyper-translation. Mechanistically, BCAP activated the RSK-EIF4B axis, fueling hyperactive translation in macrophages. Genetic ablation of RSK attenuated CRS-associated inflammation, and pharmacological inhibition of RSK alleviated CRS symptoms in a humanized mouse model. These findings establish hyper-translation as a key pathogenic feature of CRS and highlight protein translation as a druggable pathway, opening venues for therapeutic interventions of CRS and other inflammatory diseases.

Microsatellite stable (MSS) colorectal cancer (CRC) is often considered a "cold" tumor with limited response to programmed death-1 (PD-1) antibody monotherapy. The mechanisms underlying its intrinsic resistance to immunotherapy remain unclear. Here, we show that cathepsin D (CTSD) is highly expressed in MSS CRC and contributes significantly to immunotherapy resistance. Mechanistically, CTSD, acting as a protease, interacts with the α2 domain of the major histocompatibility complex (MHC) class I via the light chain of its catalytic domain, promoting MHC class I degradation through lysosomal pathways and impairing its recycling to the cell surface. This mechanism shields tumor cells from cytotoxic T-cell-mediated killing and facilitates immune evasion. Notably, genetic deletion or pharmacological inhibition of CTSD using pepstatin A prevents immune escape and enhances anti-PD-1 efficacy. These findings identify CTSD as a key mediator of immune evasion in MSS CRC and support the development of a combination therapy comprising CTSD inhibition and anti-PD-1 immunotherapy.

The immunosuppressive tumor microenvironment (TME) fosters cancer progression, yet overarching determinants of cancer-borne immunoinstruction remain ill-defined. By multimodal integration of single-nucleus and bulk transcriptomics, proteomics, functional approaches, and clinical parameters, we discover a cancer-immunoinstructive secretory signature (CISS) across multiple human cancers-a set of inflammatory proteins correlated with poor prognosis and pro-tumorigenic TMEs. In pancreatic cancer (PC), CISS arises in pre-malignant epithelium, intensifies along transformation toward most malignant basal-like PC, and particularly correlates with suppressed natural killer (NK) cell activity. The CISS is quantitatively dominated by tissue inhibitor of metalloproteinases (TIMP)-1, most prevalent in TIMP-1^hi/CISS^hi basal-like PC, and causal for PC-cell-mediated NK cell suppression, reflected by impaired cytotoxicity, interleukin-2 (IL-2) responses, and mammalian target of rapamycin (mTOR) signaling. In pre-clinical PC, TIMP-1/CISS proves targetable through combined inhibition of upstream kinases with clinically approved drugs trametinib and nintedanib. Collectively, CISS represents a ubiquitous signature of pro-tumor immunoinstruction with actionable diagnostic and therapeutic potential across human cancers.

Medical reasoning is fundamental to clinical decision-making, underpinning tasks such as patient communication, diagnosis, and treatment planning. Inspired by psychological findings that peer interaction promotes self-correction, we introduce model confrontation and collaboration (MCC), a debate intelligence framework that transcends static ensemble methods by integrating critique and self-reflection to iteratively refine reasoning through structured, multi-round confrontation and collaboration among diverse large language models (LLMs). In multiple-choice benchmarks, MCC achieved mean accuracy on MedQA (92.6%) and PubMedQA (84.8%) and demonstrated strong performance on medical subsets of MMLU. In long-form medical question answering, MCC outperformed all individual LLMs and the domain-specific LLM Med-PaLM 2 in both physician and layperson evaluations. In diagnostic dialog tasks, MCC further excelled in both history-taking and diagnostic accuracy, reaching a top-1 diagnosis rate of 80%. These results position MCC as a scalable, model-agnostic framework that advances medical reasoning through collaborative deliberation.

Cervical abnormality screening is pivotal for prevention and treatment. However, the substantial size of whole slide images (WSIs) makes examination labor-intensive and time-consuming. Current deep learning-based approaches struggle with the morphological diversity of cervical cytology and require specialized models for distinct diagnostic tasks, leading to fragmented workflows. Here, we present UniCAS, a cytology foundation model pre-trained on 48,532 cervical WSIs encompassing diverse patient demographics and pathological conditions. UniCAS enables various clinical analysis tasks, achieving state-of-the-art performance in slide-level diagnosis, region-level analysis, and pixel-level image enhancement. In particular, by integrating a multi-task aggregator for slide-level diagnosis, UniCAS achieves area under the curve (AUC) values of 92.60%, 92.58%, and 98.39% for cancer screening, candidiasis testing, and clue cell diagnosis, respectively, while reducing diagnostic time by 70% compared with conventional approaches. This work establishes a paradigm for efficient multi-scale analysis in automated cervical cytology, bridging the gap between computational pathology and clinical diagnostic workflows.

Small cell lung cancer (SCLC), a highly lethal disease, limits T cell responses by downregulating major histocompatibility (MHC) class I molecules. Because chimeric antigen receptor (CAR) T cells are not MHC restricted, they may provide a powerful strategy against SCLC. However, few CAR targets for SCLC are known. Here, we show that B7-H3/CD276 is expressed in SCLC and thoracic SMARCA4-deficient undifferentiated tumors (UTs) that can clinicopathologically mimic SCLC. Thoracic SMARCA4-deficient UTs limit killing by B7-H3 CAR T cells via secretion of transforming growth factor β1 (TGF-β1). To overcome tumor-driven CAR T cell suppression, we knock in c-JUN alongside a B7-H3 CAR into the TRAC locus of primary human T cells utilizing CRISPR-Cas9. Non-viral c-JUN+B7-H3 CAR T cells show enhanced killing of both SCLC cells with low antigen density and thoracic SMARCA4-deficient UTs, providing a platform to address these highly aggressive entities. We also provide evidence that good manufacturing practice (GMP) clinical-scale manufacturing is feasible for c-JUN+B7-H3 CAR T cells.

Emerging evidence links the gut microbiome to autism spectrum disorder (ASD), yet the role of microbial genomic variation remains underexplored. We generated a large-scale metagenomic and metabolomic dataset from over 1,100 children, integrating public datasets, to characterize ASD-associated microbial changes. We identified 35 species, 213 genes, 28 pathways, and 99 metabolites, alongside 1,369 single-nucleotide variants, 233 insertions/deletions, and 195 structural variants with differential abundance. Profiling of microbial genomic variation revealed 33 species and 196 enzymes lacking abundance differences, yet exhibiting significant sequence variation. Integrated analysis of microbial variants and metabolites uncovered 357 neurological associations, with mediation analysis showing that several metabolites link microbial variants to the ASD phenotype. Importantly, diagnostic models incorporating microbial variant and/or metabolite features achieved superior performance and generalizability. Our findings highlight microbial genomic variation as a critical, previously overlooked dimension of ASD-associated dysbiosis, offering valuable insights for diagnosis and mechanistic studies.

In ovarian cancer, 80% of patients relapse after first-line therapy. In recurrent cases, oncologists lack reliable tests to guide chemotherapy choices, creating an unmet clinical need. Here, we develop the ovarian cancer zebrafish Avatar-test, a functional in vivo model using patient tumor cells implanted in zebrafish embryos to predict treatment responses. We present the largest observational study (32 patients), where the zAvatar-test achieves 91% accuracy in predicting patient outcomes. Patients with a zAvatar-sensitive-test correlate with longer progression-free survival (17 vs. 6 months). Tumors in zAvatars are dynamic, with human-host cell interactions, and higher metastatic potential in poor-prognosis cases. Finally, as a proof of concept, we demonstrate that venetoclax has the potential to sensitize multidrug-resistant tumors. Altogether, this clinical study demonstrates that the zAvatar-test may help clinicians personalize treatments for ovarian cancer patients. We are now conducting a multicentric randomized clinical trial to evaluate the zAvatar-test as a companion tool in clinical oncology.

The NeoPAL trial compares neoadjuvant letrozole-palbociclib (LP) with chemotherapy (CT) in 103 patients with high-risk, early luminal breast cancer. At surgery, the NanoString BC360 proliferation score and Ki67 expression are reduced in both arms, together with upregulation of immune-related signatures. Overall, there is very little difference in the changes observed with LP as compared to CT, even in signatures related to response to estrogen and CDK4/6 manipulation. Deconvolution of bulk RNA sequencing (RNA-seq) data reveals high content at baseline in cancer cells, immunosuppressive cancer-associated fibroblasts, FOXP3+ CD4⁺ regulatory T lymphocytes, and TREM2+ macrophages. In contrast, myoepithelial cells, normal-like fibroblasts, FOLR2+ macrophages, and SELL+ CD4⁺ T lymphocytes accumulate after treatment in both arms. A low ROR score is observed at surgery in 63.3% and 43.5% of patients in the LP and CT arms, respectively. No 3-year breast cancer-specific survival events are observed in these patients. These data provide a rationale for CT-sparing trials in this setting.