Communications medicine最新文献

Background: In tumors, reciprocal spatial interactions between immune cells, their mediators, the extracellular matrix, and mutated neoplastic cells impact all aspects of treatment resistance. The operational mechanisms of these interactions are foundational for developing insights and targets for cancer therapy and prevention. Spatial quantification of the tumor microenvironment system from image data has untapped potential for patient stratification.

Methods: Here, we present SparTile, a powerful computational approach for the analysis of multiplex proteomics images to reveal clinically relevant structural organization in the tumor microenvironment. SparTile enables robust and unbiased identification and characterization of tumor microenvironments based on spatial relationships among protein markers without the need for cell segmentation or classification.

Results: Applied to tissues of patients with triple-negative breast cancer (TNBC), an aggressive subtype of breast cancer, SparTile identifies repeatable microenvironments with specific cellular relationships. Several microenvironments are characterized by risk markers such as Ki67+ (p-value = 0.052) and vimentin+ (p-value < 0.01) tumor cells, which correlate with poor survival. Furthermore, myeloid markers in an MX1-positive tumor environment correlate with shorter survival (p-value = 0.04). Finally, the relative distance between tumor and myeloid cell markers is a strong prognostic risk factor in multivariate Cox models (p-value < 0.01). This distance metric is externally validated on two datasets of breast cancer multiplex images (p-values < 0.01).

Conclusions: Our results show that unbiased protein-based and segmentation-free spatial analysis is effective for identifying clinically relevant biomarkers from multiplex tumor images and identifying predictive biology.

Background: Idiopathic Multicentric Castleman Disease, Thrombocytopenia, Anasarca, Fever, Reticulin Fibrosis, Organomegaly (iMCD-TAFRO) is a rare cytokine storm syndrome with high mortality. Pathogenesis of Castleman disease (CD) remain largely unknown. We aim to unravel the role of Mediterranean fever gene MEFV variants, the key gene variants implicated in familial Mediterranean fever, in CD.

Methods: Clinical data from a retrospective cohort of 37 patients with CD were collected. Blood and/or lymph node biopsy specimens were obtained for whole-exome sequencing. In vitro lipopolysaccharide stimulation experiment and single-cell RNA-sequencing (scRNA-seq) were performed to characterize the immune signature using peripheral blood mononuclear cells from an adolescent TAFRO patient, his asymptomatic parents, and a healthy control. The relative gene expression were examined by quantitative PCR. Cytokine levels were assessed using Luminex. Statistics were performed by SPSS and GraphPad Prism.

Results: Here we show an adolescent TAFRO patient with familial MEFV mutations demonstrates responsiveness to anti-IL-6 containing therapy. Through comprehensive analysis of a cohort of 37 CD patients, we observe a high prevalence of MEFV mutations (76%, 28/37). Notably, the MEFV E148Q-P369S-R408Q variant is present in 19% (7/37) of all patients and 50% (2/4) of the TAFRO subtype, with variant carriers exhibiting more severe disease course. Inflammation responses experiments and scRNA landscape reveal that MEFV expression is dominant in CD16⁺ monocytes and correlated with IL-6 pathway activation likely via the interaction with naïve B/memory B cells in the TAFRO.

Conclusions: This study presents one of the largest cohorts demonstrating the high prevalence of MEFV variants in CD, providing important insights for understanding and treating CD, particularly TAFRO.

Background: Relatively few studies have investigated HIV-1 persistence in tissues, especially in healthy people-living-with-HIV-1 (PLWH) on a successful antiretroviral regimen containing second generation integrase inhibitors.

Methods: In the ANRS EP64 DOLUVOIR, we explore HIV-1 persistence in five accessible anatomical sites in 20 PLWH on an efficient first-line ART regimen containing dolutegravir with virological load <50 copies/mL: PBMCs, rectum, adipose tissue, lymph node and sperm. We quantify total HIV-DNA and cell-associated HIV-1 RNA in different compartments. We sequence HIV-1 DNA for searching drug resistance mutations (DRM) (in RT and INT) and for studying HIV diversity within tissues (ENV). Intact proviral DNA is estimated in PBMCs with an adapted IPDA assay.

Results: Broad ranges of total HIV-DNA and transcripts levels are detected in lymph nodes, PBMCs, adipose tissue and rectum with the highest levels being found in lymph nodes (2.77 log copies HIV-1-DNA/10⁶ cells and 1.50 log copies of HIV-1 cell-associated-RNA/µg RNA). HIV-1 DNA is undetected in all sperm samples (n = 19) except for one (1.52 log copies HIV-1-DNA/106 cells). No difference is noted between the diversity in the four compartments. DRMs to the current regimen are found archived in compartments of six participants. Only two major DRMs to dolutegravir (G118R and R263K) are found archived in two participants. They are the results of APOBEC hypermutations.

Conclusions: Despite ongoing transcriptional activity, persistence of HIV-1 in deep tissues is not associated with the selection of DRMs to dolutegravir on intact proviruses. Our results suggest that the detectable transcriptional activity stems predominantly from defective proviral DNA.

Background: Gait disturbances are the clinical hallmark of ataxia. Their severity is assessed within a well-established clinical scale, which only allows coarse scoring and does not reflect the complexity of individual gait deterioration. We investigated whether sensor-free motion capture enables to replicate clinical scoring and improve the assessment of gait disturbances.

Methods: The normal walking task during clinical assessment was videotaped in 91 ataxia patients and 28 healthy controls. A full-body pose estimation model (AlphaPose) was used to extract positions, distances, and angles over time while walking. The resulting time series were analyzed with four machine learning (ML) models, which were combinations of feature extraction (tsfresh, ROCKET) and prediction methods (XGBoost, Ridge). First, in a regression and classification approach, we trained the ML models on reconstructing the clinical score. Second, we used explainable AI (SHAP) to identify the most important time series. Third, we investigated time series features to study longitudinal changes.

Results: Gait disturbances are assessed with high accuracy by ML models, slightly improving human rating (i) in the categorial prediction of the clinical score (F1-score best model: 63.99%, human: 60.57% F1-score), (ii) in the detection of subtle changes (pre-symptomatic patients, clinically rated unimpaired are differentiated from HC with a F1-score of 75.96%) and (iii) in the detection of longitudinal changes over time (Pearson's correlation coefficient model: -0.626, p < 0.01; human: -0.060, not significant).

Conclusions: ML-based analysis shows improved sensitivity in assessing gait disturbances in ataxia. Subtle and longitudinal changes can be captured within this study. These findings suggest that such approaches may hold promise as potential outcome parameters for early interventions, therapy monitoring, and home-based assessments.

Background: The interrelationships between atrial fibrillation (AF), brain lesions and cognitive function are poorly understood. We aimed to investigate the relationship of AF with brain lesions and cognition.

Methods: We enrolled 1,480 patients with and 959 without AF in a multicenter prospective study (Swiss-AF; NCT02105844). We assessed brain structure, and cognition using the Montreal Cognitive Assessment (MoCA). Brain magnetic resonance imaging (MRI) was performed to assess large non-cortical and cortical infarcts (LNCCI), small non-cortical infarcts (SNCI), white matter hyperintensities (WMH), and microbleeds. Using causal mediation analyses, we investigated the direct (lesion-independent) and indirect (lesion-mediated) effects of AF on cognition.

Results: Mean age in AF patients is 75.0 vs. 74.2 years in no-AF patients, 28.6% vs. 36.9% are female, and comorbidities are comparable. The prevalence of MRI-detected brain infarcts (LNCCI and/or SNCI) is 40.1% in AF patients vs. 24.0% in no-AF patients, adjusted OR (95% CI): 1.78 (1.30; 2.44), p = 0.0003. WMH (Fazekas ≥2) are more prevalent in AF patients (59.2% vs 44.4%), adjusted OR (95% CI): 2.03 (1.50; 2.77), p = 4.6e-06. The mean MoCA score is 25.3 in AF patients and 26.4 in no-AF patients. In mediation analysis, the total effect of AF on cognition is -1.05 MoCA points, decomposed into a direct effect of -0.99 and an indirect, lesion-mediated, effect of -0.06 points.

Conclusions: The prevalence of ischemic brain infarcts and WMH is higher in patients with AF than without AF despite comparable comorbidities. AF is associated with lower cognitive function, primarily through a direct effect rather than mediated by brain lesions.

Background: Emotional distress (ED) has been demonstrated to compromise immune responses against tumors; however, few clinical studies have explored its influence on the efficacy of immune checkpoint inhibitors (ICIs) in cancer patients, especially those with gastroesophageal cancer (GEC). Additionally, reliable biomarkers for predicting the response to immunotherapy remain elusive. This study was aimed at investigating whether ED affects the outcomes of immunotherapy in advanced GEC patients and identifying potential biomarkers predictive of immunotherapy efficacy.

Methods: This prospective observational cohort study enrolled 84 patients with advanced, treatment-naïve, and inoperable GEC. ED was evaluated at baseline using the Patient Health Questionnaire-9 and the Generalized Anxiety Disorder 7-item Scale. The primary endpoint was Progression-Free Survival (PFS), while the secondary endpoint was Disease Control Rate (DCR).

Results: Patients with baseline ED exhibit significantly shorter median PFS (7.8 months vs. 14.0 months, HR = 2.59, 95% CI: 1.35-4.97, P = 0.004) and a lower DCR (39.5% vs. 68.3%, OR = 3.21, 95% CI: 1.29-7.98, P = 0.012) compared to those without ED. Exploratory analyses further demonstrate that both pre- and post-treatment peripheral inflammatory markers (PIMs) are independently and jointly associated with survival outcomes in combination with ED.

Conclusions: This prospective study demonstrates that ED and elevated PIMs significantly impair ICI efficacy in advanced GEC. The synergistic interaction between ED and PIMs suggests underlying psycho-inflammatory mechanisms affecting treatment outcomes. These findings establish the clinical importance of integrating routine psychological assessment and PIMs monitoring in cancer patients receiving immunotherapy.

Background: Escherichia coli clonal complex 38 (CC38) is a genetically diverse lineage increasingly linked to antimicrobial resistance and extraintestinal infections in humans. Despite its clinical and epidemiological relevance, its population structure, zoonotic potential, and ecological associations remain poorly understood.

Methods: We analyzed 242 human E. coli CC38 bloodstream isolates collected through Danish national surveillance, 83 isolates from food and production animals, and 2313 international genomes to investigate host associations and transmission dynamics. Phylogenetic reconstruction, Bayesian host prediction based on mobile genetic elements, and statistical testing of plasmid-host associations were used to delineate population structure and identify potential host-associated markers.

Results: Here we show that Danish CC38 isolates belong to multiple sub-lineages, with no evidence of foodborne outbreaks and limited hospital transmission. Bayesian host prediction supports a poultry origin for several distinct human sub-lineages. Global analyses of 2638 genomes reveal two major clusters: a poultry-associated Cluster I and a predominantly human-associated Cluster II, which subdivides into eight sub-lineages with distinct host, resistance, and virulence profiles. Two small plasmids, ColRNAI and Col(MG828), are strongly enriched in poultry and livestock isolates but largely absent from human-associated sub-clusters, indicating their value as host-associated genetic markers.

Conclusions: Our findings refine the phylogenetic structure of E. coli CC38 and identify plasmid markers that may enhance genomic surveillance of zoonotic transmission. These results highlight the importance of a One Health approach to monitor antimicrobial resistance across human, food, and animal reservoirs. Together, these insights support data-driven One Health surveillance and intervention strategies.

Background: Acute health effects of temperature extremes and variability in temperate zones has been rarely quantified. We examine the associations of ambient temperature and temperature change between neighbouring days with all-cause and cause-specific hospitalizations.

Methods: Daily hospital admission data were identified through hospital record linkage with UK Biobank, a cohort of half-a-million participants during 2006-2022. Temperature exposure was measured at 1×1 Km² spatial resolution based on participants' residential addresses. We used a time-stratified case-crossover design to examine short-term associations of ambient temperature and change in temperature between neighbouring days with all-cause and cause-specific hospitalizations.

Results: We identify 709,052 warm-season hospitalizations and 676,686 cold-season hospitalizations. During warm season, high temperature cumulated over lag 0-3 days is associated with 9% [odds ratio (OR) = 1.09, 95% confidence interval (CI) = 1.02, 1.16] and 18% (OR = 1.18, 95% CI = 1.05, 1.34) higher odds of hospitalizations for renal disease and heat-related illness, respectively. During cold season, high temperature is associated with 4% (OR = 1.04, 95% CI = 1.01, 1.06) higher odds of overall hospitalizations from any cause, and also for cardiovascular disease (OR = 1.06, 95% CI = 1.02, 1.09), respiratory disease (OR = 1.05, 95% CI = 1.00, 1.11), mental disorders (OR = 1.08, 95% CI = 1.00, 1.16) and heat-related illness (OR = 1.25, 95% CI = 1.05, 1.48). We observe more pronounced associations between ambient temperature and overall hospitalization among subgroups residing in the most deprived neighbourhoods and with the least greenspace coverage during both warm and cold seasons.

Conclusions: Our findings suggest the need for multilevel mitigation and adaptation strategies for strengthening individual and urban resilience to minimize adverse health effects attributable to temperature extremes.

Background: Next-generation sequencing (NGS) tests are integral to oncology care. To address the need for clinical and NGS data management, interpretation, and reporting, we developed iCatalog for the multi-institutional Individualized Cancer Therapy 2/Genomic Assessment Informs Novel Therapy Consortium (GAIN) pediatric precision oncology (PO) study.

Methods: We designed iCatalog as a secure, web-based clinical decision support application that stores and integrates clinical, specimen, and molecular data from multiple sources at the patient level. The knowledge base (KB) and centralized patient/test database are intended to manage information for the 825 patients expected to enroll in the GAIN study. User permissions and access are controlled. Gene- and variant-level interpretation is facilitated through linked external resources and an internal KB that can be updated during application use. iCatalog generates editable, study-specific patient reports for each molecular test.

Results: Launched to support the GAIN study, iCatalog integrates genomic data from eight NGS platforms, generates 1002 clinical interpretation reports, and stores data for 1194 tests involving 777 patients with pediatric solid tumors across 133 diagnoses. The KB contains pediatric cancer-specific curations, authored by the research team, spanning 581 genes and 2659 variants (including 2146 single-nucleotide variants and insertions-deletions, 235 copy-number variants, 278 structural variants).

Conclusions: iCatalog is a robust tool designed and proven to support a PO study. It integrates clinical and genomic data to facilitate the clinical interpretation and reporting of variants identified through NGS testing while maintaining a pediatric-specific KB generated during the study. As a scalable, modular platform, iCatalog can accelerate clinical decision-making and elevate PO insights across studies.