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Received — 10 September 2026 ⏭

Targeting of the oncogenic fusion EWSR1-FLI1 in Ewing Sarcoma by CRISPR/dCas9 silencers

✇Molecular Therapy
By: Shahama Taifour · Chee-Wai Wong · Christopher Wallis · Edina Wang · Ash W.J. Tie · Eleanor Woodward · Charlene Waryah · Mahnaz Hosseinpour · Dilys Lam · Ryan Lister · Larissa Dymond · Andrew Woo · Peter J. Houghton · K.Swaminathan Iyer · Marck Norret · Cameron W. Evans · Louise Winteringham · Silvana Gaudieri · Pilar Blancafort
31 August 2026 at 08:00
Blancafort and colleagues describe a non-viral polymeric system for the delivery of dCas9-KRAB silencers as ribonucleoprotein (RNP) payloads for EWSR1-FLI1 repression. They demonstrate highly efficient RNP delivery and robust silencing of EWSR1-FLI1 in both cell line and patient-derived xenografts of Ewing sarcoma, accompanied by potent anti-tumor effects.

ConvMem: Convolutional Memory for Long-Context Reasoning

✇cs.AI, q-bio.NC updates on arXiv.org
By: Hongming Zhang · Zhaozhen Gu · Fengshuo Bai · Ming Hao · Qingyang Zhang · Yuanyuan Wang · Shiyang Tang · Yanna Wang · Bo Xu
10 September 2026 at 12:00
arXiv:2609.10441v1 Announce Type: new Abstract: While Large Language Models (LLMs) have demonstrated impressive capabilities, they often struggle with extremely long contexts due to fixed context limits. To address this, sequential approaches like MemAgent extend the effective context by reading text in segments and iteratively updating a fixed-size memory. However, this sequential paradigm suffers from high latency and requires costly reinforcement learning (RL) training, which can lead to overfitting on specific datasets. To overcome these limitations, we propose ConvMem, a training-free, highly parallelizable framework that reformulates long-context reasoning as a hierarchical convolution. Inspired by CNNs, ConvMem treats an LLM prompted with a specific query as a convolutional kernel. This kernel summarizes text segments hierarchically, shortening the reasoning path from a linear chain into a logarithmic tree. Specifically, ConvMem integrates \textit{Configurable Strides} and \textit{Skip Connections} to ensure robust evidence capture and propagation, while employing \textit{Multi-Kernel Convolution} to decompose complex queries into disentangled semantic channels. This design not only mitigates error accumulation but also enables massive parallelization across both text segments and reasoning threads. Experiments on RULER-HotpotQA and RULER-2WikiMultiHopQA demonstrate that ConvMem outperforms training-free baselines and avoids the risk of overfitting to parametric priors often observed in RL-trained models on out-of-distribution tasks.

CAFs shape the immunosuppressive microenvironment of pancreatic cancer through the Lin28b-STING Axis

✇(Multiomics OR Omics) AND (Pancreatic)
By: Minghe Fan · Ziyang Zhang · Wei Xu · Yuwei Liu · Ruoxuan Wang · Wenqin Hao · Sihan Huo · Yuyao Guo · Lina Wang · Ang Lv · Xiangyu Liu · Wei Wang · Chuanhui Han · Ying Zhao
3 September 2026 at 18:00

Nat Commun. 2026 Aug 7;17(1):9491. doi: 10.1038/s41467-026-76495-3.

ABSTRACT

Cancer-associated fibroblasts comprise diverse functionally distinct cellular subsets, with certain subpopulations exerting pivotal influence in shaping the pancreatic cancer immune microenvironment. Here we show that Lin28b+ cancer-associated fibroblasts contribute to establishing an immunologically cold tumor microenvironment in pancreatic ductal adenocarcinoma. Mechanistically, Lin28b directly binds to STING mRNA and promotes its degradation, thereby suppressing STING expression and downstream type I interferon signaling. Loss of Lin28b in cancer-associated fibroblasts activates the cGAS-STING-interferon signaling cascade, enhancing dendritic cell antigen presentation and CD8+ T cell cytotoxic function. Importantly, genetic inhibition of Lin28b in cancer-associated fibroblasts enhances sensitivity to anti-PD-L1 immune checkpoint blockade therapy. These findings reveal that targeting the Lin28b-STING axis represents a promising therapeutic strategy for overcoming the intrinsic resistance of pancreatic ductal adenocarcinoma to immunotherapy.

PMID:42693143 | PMC:PMC13542369 | DOI:10.1038/s41467-026-76495-3

Received — 27 May 2026 ⏭

From Variant to Biomarker in NSCLC Immunotherapy Resistance: Multiomics Evidence Chains and Accountable AI Integration

✇Omics In Lung
By: Yiqing Jiang · Na Wang · Qin Zeng · Jun Liu · Xiaoqin Liu · Guili Cao
25 May 2026 at 18:00

Hum Mutat. 2026 May 21;2026:6434376. doi: 10.1155/humu/6434376. eCollection 2026.

ABSTRACT

Immune checkpoint inhibitors have become integral to the management of non-small cell lung cancer (NSCLC), yet both primary and acquired resistance remain frequent and are only partially captured by routine biomarkers such as programmed death-ligand 1 (PD-L1) immunohistochemistry and tumor mutational burden (TMB). Resistance is increasingly viewed as a multiaxis functional phenotype shaped by antigenicity and neoantigen quality, antigen processing and presentation competence, interferon signaling and adaptive resistance programs, tumor-immune spatial organization, suppressive myeloid/stromal ecosystems, and metabolic constraints that limit effector function. Multiomics profiling provides a practical route to translate genomic event anchors into reproducible, mechanistically interpretable biomarker outputs by assembling coherent evidence chains across genomics, transcriptomics, epigenomics, proteomics, and metabolomics, complemented by spatial assays, digital pathology, and imaging-derived surrogates.

PMID:42181742 | PMC:PMC13191777 | DOI:10.1155/humu/6434376

Spatial transcriptomic-metabolic features of tumor foci and tumor capsule in microvascular invasion with hepatocellular carcinoma: A spatial multi-omics study

✇Omics in Hepatocellular
By: Zhi-Hui Luo · Na Wang · Jingwei Zhao · Fei Long · Si Wu · Wei Zhong · Wei-Ming Chen · Bicheng Wang · Kun Wang · Yufeng Yuan · Jingjiao Zhou · Chunhui Yuan · Fubing Wang
15 May 2026 at 18:00

PLoS Med. 2026 May 15;23(5):e1004703. doi: 10.1371/journal.pmed.1004703. eCollection 2026 May.

ABSTRACT

BACKGROUND: Microvascular invasion (MVI) is closely related to the recurrence and metastasis of hepatocellular carcinoma (HCC), but the underlying cellular mechanism remains largely elusive. This study aims to elucidate the regional cellular discrepancy between MVI-positive (MVI+) and MVI-negative (MVI-) HCC by integrating Spatial transcriptomics (ST) and spatial metabolomics (SM).

METHODS AND FINDINGS: ST and SM were performed on six tissue samples from four patients (including 2 MVI+, 2 MVI-, and 2 paratumor tissues), with the integration of 79 public single-cell RNA sequencing datasets of HCC. Patient identity was used as a covariate in the linear equation for regional differentially expressed gene analysis with the ST data. Clinical validation was conducted through multiplex immunofluorescence staining in 79 patients, together with external validation in the cancer genome atlas (TCGA)-liver hepatocellular carcinoma (LIHC) cohort (n = 299) and an independent microarray dataset (n = 62). For cell-type-specific metabolic profiling, spatial transcriptomic-metabolic registration was performed. The functional roles of key metabolites were further validated in vitro using inflammatory cancer-associated fibroblasts (iCAFs) derived from hepatic stellate cells (HSCs) and primary CAFs through co-culture models and various functional assays assessing cell proliferation, migration, and invasion. In the tumor lesion, a malignant STMN1+HMGN2+GPC3+ cell subtype enriched in MVI+ HCC was identified, which exhibited enhanced proliferative activity and was associated with poor prognosis. This finding was further confirmed in a local cohort of 79 patients, where multiplex immunofluorescence staining for the three genes (STMN1, HMGN2, and GPC3) showed significantly higher expression in the MVI+ group than in the MVI- group (p = 0.046). Integrated SM analysis further revealed that this cell population underwent metabolic reprogramming characterized by suppressed glycerolipid metabolism. In the tumor capsule, iCAFs-related genes were downregulated in MVI+ cases, and iCAFs were located distally from the tumor boundary. Spatial metabolite mapping showed a strong correlation between taurine and iCAFs, and functional assays demonstrated that taurine promotes HCC proliferation and migration by suppressing iCAF activity. One limitation of this study is the small sample size of spatial omics data, which hinders a more complete molecular functional analysis of the STMN1+HMGN2+GPC3+ cell subtype and iCAFs in MVI+ HCC. Larger-scale ST cohorts are required to further validate and expand the findings of this study.

CONCLUSIONS: This integrative spatial atlas proposes a hypothesis that there exists a highly proliferative and metabolically reprogrammed malignant cell subtype in the tumor lesion of MVI+ HCC, and that taurine in the tumor capsule modulates iCAF activity to influence tumor progression. The exploratory results provide mechanistic insights into MVI-related HCC progression and offer potential avenues for targeted therapeutic intervention of MVI+ HCC.

PMID:42139279 | PMC:PMC13178920 | DOI:10.1371/journal.pmed.1004703

Received — 8 April 2026 ⏭

GENFIG1: Visual Summaries of Scholarly Work as a Challenge for Vision-Language Models

✇cs.AI, q-bio.NC updates on arXiv.org
By: Yaohan Guan · Pristina Wang · Najim Dehak · Alan Yuille · Jieneng Chen · Daniel Khashabi
7 April 2026 at 12:00
arXiv:2604.04172v1 Announce Type: cross Abstract: In many science papers, "Figure 1" serves as the primary visual summary of the core research idea. These figures are visually simple yet conceptually rich, often requiring significant effort and iteration by human authors to get right, highlighting the difficulty of science visual communication. With this intuition, we introduce GENFIG1, a benchmark for generative AI models (e.g., Vision-Language Models). GENFIG1 evaluates models for their ability to produce figures that clearly express and motivate the central idea of a paper (title, abstract, introduction, and figure caption) as input. Solving GENFIG1 requires more than producing visually appealing graphics: the task entails reasoning for text-to-image generation that couples scientific understanding with visual synthesis. Specifically, models must (i) comprehend and grasp the technical concepts of the paper, (ii) identify the most salient ones, and (iii) design a coherent and aesthetically effective graphic that conveys those concepts visually and is faithful to the input. We curate the benchmark from papers published at top deep-learning conferences, apply stringent quality control, and introduce an automatic evaluation metric that correlates well with expert human judgments. We evaluate a suite of representative models on GENFIG1 and demonstrate that the task presents significant challenges, even for the best-performing systems. We hope this benchmark serves as a foundation for future progress in multimodal AI.
Received — 26 March 2026 ⏭
Received — 11 March 2026 ⏭

Lysophosphatidylcholine acyltransferase 1 promotes head and neck squamous cell carcinoma progression by enhancing COX17-dependent oxidative phosphorylation

✇Cell Death Discovery nature.com science feeds
By: Yuanyang Zhao · Yun Li · Yanshi Li · Zhihai Wang · Chuan Liu · Lin Chen · Min Wang · Mengna Wang · Zhaobo Cheng · Guohua Hu · Min Pan
6 March 2026 at 08:00

Cell Death Discovery, Published online: 06 March 2026; doi:10.1038/s41420-026-02994-3

Lysophosphatidylcholine acyltransferase 1 promotes head and neck squamous cell carcinoma progression by enhancing COX17-dependent oxidative phosphorylation
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