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Integrative proteogenomics maps multifactorial aetiology, progression and therapeutic vulnerabilities in gastric cancer

Gut. 2026 Jan 30:gutjnl-2025-337247. doi: 10.1136/gutjnl-2025-337247. Online ahead of print.

ABSTRACT

BACKGROUND: Gastric cancer, with disproportionately higher incidence in East Asia, arises from complex host-microbiome-environment interactions beyond Helicobacter pylori (HP) infection. However, the molecular architecture linking environmental carcinogens, microbial succession and host response remains unclear.

OBJECTIVE: To delineate multifactorial aetiologies and clinically actionable subtypes/biomarkers of gastric cancer through integrative proteogenomic, microbial and environmental exposure profiling.

DESIGN: We established a multiomics atlas of paired tumour, adjacent mucosa tissues and blood from 154 treatment-naïve Taiwanese patients, integrating whole-exome sequencing, RNA-seq, proteome and phosphoproteome profiling with carcinogen signatures, HP status, microbiome composition and refined anatomical mapping. Cell-based functional assays tested carcinogen effects. Microbial subtype was assessed in an independent cohort.

RESULTS: A polycyclic-aromatic-hydrocarbon signature, dibenz[a,h]acridine, emerged as a high-risk exposure promoting invasion, immune suppression and poor survival, significantly exceeding nitrosamine-linked risk in this cohort. Multilayer integration defined three initiation ecologies: HP-driven inflammatory, non-HP microbiome-enriched immune-silent and HP-free microbially depleted states. Among HP-negative tumours, a Streptococcus-enriched subtype associated with tight-junction (CLDN18.2/ZO-1/OCLN) disruption and epithelial-mesenchymal transition, whereas a subset of clinically aggressive cases retained CLDN18.2-high epithelial-stable subtype for therapeutic accessibility. An independent cohort revealed gastric juice-derived Streptococcus anginosus abundance inversely correlated with tight-junction proteins. Anatomical mapping reveals location-specific, sex-specific, subtype-specific oncogenic networks and kinase activity, including CDK4 activation in clinical biomarker-negative tumours. Decision-tree models combining exposure and proteome-immune states refined recurrence and survival prediction beyond stage.

CONCLUSION: This proteogenomic framework defines exposure-informed and microbiome-informed gastric cancer subtypes, providing a molecular schema for patient stratification, prevention and actionable therapeutic vulnerabilities.

PMID:41617485 | DOI:10.1136/gutjnl-2025-337247

The Alignment Paradox of Medical Large Language Models in Infertility Care: Decoupling Algorithmic Improvement from Clinical Decision-making Quality

arXiv:2511.18084v1 Announce Type: cross Abstract: Large language models (LLMs) are increasingly adopted in clinical decision support, yet aligning them with the multifaceted reasoning pathways of real-world medicine remains a major challenge. Using more than 8,000 infertility treatment records, we systematically evaluate four alignment strategies: Supervised Fine-Tuning (SFT), Direct Preference Optimization (DPO), Group Relative Policy Optimization (GRPO), and In-Context Learning (ICL) through a dual-layer framework combining automatic benchmarks with blinded doctor-in-the-loop assessments. GRPO achieves the highest algorithmic accuracy across multiple decision layers, confirming the value of reinforcement-based optimization for structured prediction tasks. However, clinicians consistently prefer the SFT model, citing clearer reasoning processes (p = 0.035) and higher therapeutic feasibility (p = 0.019). In blinded pairwise comparisons, SFT attains the highest winning rate (51.2%), outperforming both GRPO (26.2%) and even physicians' original decisions (22.7%). These results reveal an alignment paradox: algorithmic improvements do not necessarily translate into higher clinical trust, and may diverge from human-centered preferences. Our findings highlight the need for alignment strategies that prioritize clinically interpretable and practically feasible reasoning, rather than solely optimizing decision-level accuracy.

Stereo-seq V2: Spatial mapping of total RNA on FFPE sections with high resolution

Stereo-seq V2 facilitates single-cell-resolution spatial RNA mapping in FFPE samples through random primer capture, uncovering ncRNAs, host-pathogen transcriptome profiling, and spatial immune repertoires in situ.

Prevalence of Dropout and Influencing Factors in Digital Psychosocial Intervention Trials for Adult Illicit Substance Users: Systematic Review and Meta-Analysis

Background: Globally, the number of illegal drug users is rising, posing mental and physical health challenges and increasing societal burdens. Despite a significant need for treatment, only about 10% of these individuals receive it worldwide, often with poor adherence. Traditional treatments, while effective, suffer from high dropout rates due to limitations. The COVID-19 pandemic has spurred the growth of digital interventions like apps and online platforms, offering flexibility and cost-effectiveness that better meet patient needs and improve engagement. However, addressing the persistently high dropout rates in these online treatments is crucial and necessitates further research. Objective: This study aimed to estimate dropout rates among adults with illicit drug use participating in digital psychosocial intervention trials, and to identify factors associated with attrition. Methods: We conducted a systematic search of five major databases for English-language randomized trials published up to January 27, 2025. A total of 40 studies (80 arms; 9,563 participants) reporting 46 dropout rate estimates were included. A random-effects model was used to calculate pooled dropout rates, with meta-regression and subgroup analyses exploring potential moderators. The study was registered on PROSPERO (CRD42024534389). Results: At post-test, the pooled dropout rate in the intervention group across 17 studies was 22.4% (95% CI: 12.4%–37.2%). Dropout was significantly associated with education level, employment status, baseline clinical diagnosis, intervention frequency, and initial medication use. During the longest follow-up (29 studies), the dropout rate was 27.9% (95% CI: 18.8%–39.3%), with marital status, recruitment source, medication frequency, and intervention modality as significant predictors. Control group dropout rates were 25.9% and 28.3%, both higher than those in the intervention group. Conclusions: This meta-analysis revealed substantial dropout among adults with illicit drug use receiving digital psychosocial interventions. Targeted modifications to intervention design may improve engagement and long-term retention. Clinical Trial: The study was registered on PROSPERO (CRD42024534389).

Stereo-seq V2: Spatial mapping of total RNA on FFPE sections with high resolution

Cell. 2025 Aug 22:S0092-8674(25)00922-5. doi: 10.1016/j.cell.2025.08.008. Online ahead of print.

ABSTRACT

Performing total RNA profiling on formalin-fixed, paraffin-embedded (FFPE) samples, the predominant sample conservation method in clinical practice, remains challenging for current spatial transcriptomics techniques. Here, we introduce Stereo-seq V2, which employs random primers to capture and sequence RNAs in situ on FFPE sections and provides single-cell resolution. The random-priming-based strategy offers unbiased transcript capturing and uniform gene body coverage, which increase the sensitivity to marker genes, the efficiency of non-polyadenylation (poly(A)) RNA profiling, and immune repertoire coverage. We demonstrated the robust performance of Stereo-seq V2 on clinical FFPE samples using triple-negative breast cancer (TNBC) sections and identified tumor-specific alternative splicing events. In a Mycobacterium tuberculosis (Mtb)-infected mouse model, we monitored gene expression dynamics of host and pathogen transcriptomes simultaneously by utilizing Stereo-seq V2. We also assembled immune repertoires and identified Mtb-specific BCR clones, which could also be observed in human tuberculous lung samples. These results highlight Stereo-seq V2's potential in biomedical research and personalized medicine.

PMID:40882628 | DOI:10.1016/j.cell.2025.08.008

Integrative single-cell multi-omics profiling of human pancreatic islets identifies T1D-associated genes and regulatory signals

Cell Rep. 2025 Jul 29;44(8):116065. doi: 10.1016/j.celrep.2025.116065. Online ahead of print.

ABSTRACT

Genome-wide association studies (GWASs) have identified over 100 signals associated with type 1 diabetes (T1D). However, it has been challenging to translate any given T1D GWAS signal into mechanistic insights, such as causal variants, their target genes, and the specific cell types involved. Here, we present a comprehensive multi-omic integrative analysis of single-cell/nucleus resolution profiles of gene expression and chromatin accessibility in human pancreatic islets under baseline and T1D-stimulating conditions. We nominate effector cell types for all T1D GWAS signals and the regulatory elements and genes for three independent T1D signals acting through β cells at the DLK1/MEG3, RASGRP1, and TOX loci. Subsequently, we validated the functional impact of these genes and regulatory regions using isogenic human embryonic stem cells (hESCs). We found that loss of RASGRP1 or DLK1, as well as disruption of their corresponding regulatory regions, led to increased β cell apoptosis. Furthermore, β cells derived from isogenic hESCs carrying the T1D risk allele of rs3783355 associated with DLK1 showed elevated β cell death. Through additional RNA sequencing (RNA-seq) and assay for transposase-accessible chromatin using sequencing (ATAC-seq) analyses, we identified five genes upregulated in both RASGRP1-/- and DLK1-/- β-like cells, four of which are near T1D GWAS signals. This integrative approach combining single-cell multi-omics, GWASs, and isogenic human pluripotent stem cell (hPSC)-derived β-like cells illuminates cell type context, genes, single nucleotide polymorphisms (SNPs), and regulatory elements underlying T1D-associated signals, providing insights into the biological functions and molecular mechanisms involved.

PMID:40737125 | DOI:10.1016/j.celrep.2025.116065

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