EBioMedicine最新文献

Background: Re-irradiation of recurrent head and neck cancer (HNC) is often limited by tumour adherence to critical structures and/or radiation tolerance of critical normal tissues. Iopofosine I 131 (CLR 131) is a targeted small molecular phospholipid ether (PLE) drug conjugate that delivers iodine-131 selectively to tumour cells. We conducted a phase 1, single-centre, open-label study to determine whether CLR 131 given with reduced dose of external beam radiation therapy (EBRT) would be tolerable and feasible.

Methods: All participants received previous curative intent treatment with radiotherapy as primary or adjuvant treatment. Eligible participants demonstrated uptake of CLR 131 as indicated via single photon emission CT/CT (SPECT/CT) imaging following CLR 131 test dose. Participants received two therapeutic doses of CLR 131 (days 1 and 8) with SPECT/CT imaging performed to quantitate the biodistribution of CLR 131. Participants subsequently received EBRT to achieve the designated radiation dose (60-70 Gy). The primary endpoint was safety. This trial was registered with ClinicalTrials.gov, NCT04105543, and enrolment and follow-up are complete.

Findings: Twelve participants completed treatment with CLR 131 and EBRT. Eight participants experienced grade 4 non-DLT haematologic toxicities (2 anaemia, 8 leukopenia, 5 thrombocytopenia) at least probably attributed to CLR 131, consistent with the expected toxicity profile. Haematologic toxicities occurred during weeks 6-8 from the first dose of CLR 131 and resolved within three weeks without sequelae. There were no treatment-related grade 3-4 non-haematologic toxicities.

Interpretation: CLR 131 in combination with EBRT did not confer any safety concerns, and was tolerable in participants with recurrent/metastatic HNC. Myelosuppression was consistent with the known toxicity profile of CLR 131.

Funding: National Institutes of HealthP50 DE026787, National Cancer InstituteP30 CA014520, National Institutes of Health1UL1TR002373, Cellectar, NCT04105543.

Background: With many rare tumour types, acquiring the correct diagnosis is a challenging but crucial process in paediatric oncology. Historically, this is done based on histology and morphology of the disease. However, advances in genome wide profiling techniques such as RNA sequencing now allow the development of molecular classification tools.

Methods: Here, we present M&M, a pan-paediatric cancer ensemble-based machine learning algorithm tailored towards inclusion of rare tumour types.

Findings: The RNA-seq based algorithm can classify 52 different tumour types (precision ∼99%, recall ∼80%), plus the underlying 96 tumour subtypes (precision ∼96%, recall ∼70%). For low-confidence classifications, a comparable precision is achieved when including the three highest-scoring labels. We then validated M&M on an internal dataset (precision 99%, recall 76%) and an external dataset from the KidsFirst initiative (precision 98%, recall 77%). Finally, we show that M&M has similar performance as existing disease or domain specific classification algorithms based on RNA sequencing or methylation data.

Interpretation: M&M's pan-cancer setup allows for easy clinical implementation, requiring only one classifier for all incoming diagnostic samples, including samples from different tumour stages and treatment statuses. Simultaneously, its performance is comparable to existing tumour- and tissue-specific classifiers. The introduction of an extensive pan-cancer classifier in diagnostics has the potential to increase diagnostic accuracy for many paediatric cancer cases, thereby contributing towards optimal patient survival and quality of life.

Funding: Financial support was provided by the Foundation Children Cancer Free (KiKa core funding) and Adessium Foundation.

Background: Clinical decision-making is increasingly shifting towards data-driven approaches and requires large databases to develop state-of-the-art algorithms for diagnosing, detecting and predicting diseases. The intensive care unit (ICU), a data-rich setting, faces challenges with high-frequency, unstructured monitor data. Here, we showcase a successful example of a data pipeline to efficiently move patient data to the cloud environment for structured storage. This supports individual patient analysis, enables largescale retrospective research, and the development of data-driven algorithms.

Methods: Since June 2021, ICU data of the Amsterdam UMC have been collected and stored in a third-party cloud environment which is hosted on large virtual servers. The feasibility of the pipeline will be demonstrated with the available data through research and clinical use cases. Furthermore, privacy, safety, data quality, and environmental impact are carefully considered in the cloud storage transition.

Findings: Over two years, data from over 9000 patients have been stored in the cloud. The availability, agility, computational power, high uptime, and streaming data pipelines allow for large retrospective analyses as well as the opportunity to implement real-time prediction of critical events with machine learning algorithms. Critical events can be accessed by applying keyword search in the natural language data, annotated by the treating team. Besides, the cloud environment offers storage of institutional data enabling evaluation of healthcare.

Interpretation: The combined data and features of cloud environments offer support for predictive algorithm development and implementation, healthcare evaluation, and improved individual patient care.

Funding: University of Amsterdam Research Priority Agenda Program AI for Heath Decision-Making.

Epigenetic modifications have been identified as critical molecular determinants influencing macrophage plasticity and heterogeneity. Among these, histone lactylation is a recently discovered epigenetic modification. Research examining the effects of histone lactylation on macrophage activation and polarization has grown substantially in recent years. Evidence increasingly suggests that lactate-mediated changes in histone lactylation levels within macrophages can modulate gene transcription, thereby contributing to the pathogenesis of various diseases. This review provides a comprehensive analysis of the role of histone lactylation in macrophage activation, exploring its discovery, effects, and association with macrophage diversity and phenotypic variability. Moreover, it highlights the impact of alterations in macrophage histone lactylation in diverse pathological contexts, such as inflammation, tumorigenesis, neurological disorders, and other complex conditions, and demonstrates the therapeutic potential of drugs targeting these epigenetic modifications. This mechanistic understanding provides insights into the underlying disease mechanisms and opens new avenues for therapeutic intervention.

Background: Neovascularisation of carotid plaques contributes to their vulnerability. Current imaging methods such as contrast-enhanced ultrasound (CEUS) usually lack the required spatial resolution and quantification capability for precise neovessels identification. We aimed at quantifying plaque vascularisation with ultrasound localization microscopy (ULM) and compared the results to histological analysis.

Methods: We conducted a prospective, monocentric, study involving patients who were undergoing carotid endarterectomy (CEA) for carotid artery stenosis. The day before CEA ultrasound examination coupled with the injection of microbubbles (MB) as a contrast agent (CEUS) to image the MB circulating within and around the carotid plaque was performed. CEUS images analysis classified patients into 2 groups: absence of neovascularisation (group A) or presence of neovascularisation (group B). ULM was performed by localising and tracking individual MB centres to reconstruct the neovessels structure with a resolution of around 60 μm. Plaques were manually segmented on the images to quantify the number of neovessels and various haemodynamic metrics inside the plaques. Histological analysis of the excised carotid plaque specimens classified patients into 2 groups: absence of neovascularisation (group I) or presence of neovascularisation (group II).

Findings: Among the 26 patients included, classification was as follows: group I: n = 8 and group II: n = 18, 18 patients had analysable CEUS images and were classified as follows: group A: n = 10, group B: n = 8. The median (Q1-Q3) number of MB tracked per second inside the plaque was 0.03 (0-0.37) for patients in group I and 0.51 (0-3) for patients in group A versus (vs.) 3.55 (1.26-17.68) for patients in group II and 9.69 (5.83-34.68) for patients in group B (p = 0.00049; p = 0.010 respectively). The length of the MB tracks was 0.02 mm (0-0.16) in group I vs. 0.29 mm (0.22-0.45) in group II (p = 0.0069). The study also showed that flow in the neovessels was greater during systole than during diastole period: 9.38 (1.67-19.17) MB tracked per second vs. 1.35 (0.28-6.56) (p = 0.021).

Interpretation: ULM allows the detection of neovessels within the carotid atherosclerotic plaque. Thus, ULM provides a precise picture of plaque neovascularisation in patients and could be used as a non-invasive imaging technique to assess carotid plaque vulnerability.

Funding: The study was sponsored and funded by Assistance Publique-Hôpitaux de Paris (CRC 1806 APHP INNOVATION 2018). Co-funding by ART (Technological Research Accelerator) biomedical ultrasound program of INSERM, France.

Background: Metrics evaluating the functional quality of red blood cells (RBCs) must consider their role in oxygen delivery. Whereas oxygen-carrying capacity is routinely reported using haemoglobin assays, the rate of oxygen exchange is not measured, yet also important for tissue oxygenation. Since oxygen-unloading depends on the diffusion pathlength inside RBCs, cell geometry offers a plausible surrogate.

Methods: We related the time-constant of oxygen-unloading (τ), measured using single-cell oxygen saturation imaging, with flow-cytometric variables recorded on a haematology analyser. Experiments compared freshly-drawn RBCs with stored RBCs, wherein metabolic run-down and spherical remodelling hinder oxygen unloading.

Findings: Multivariable regression related τ to a ratio of side- and forward-scatter, referred to herein as FlowScore. FlowScore was able to distinguish, with sensitivity and specificity >80%, freshly drawn blood from blood that underwent storage-related kinetic attrition in O₂-handling. Moreover, FlowScore predicted τ restoration upon biochemical rejuvenation of stored blood. Since RBC geometry and metabolic state are related, variants of FlowScore estimated [ATP] and [2,3-diphosphoglycerate]. The veracity of FlowScore was confirmed by four blood-banking systems (Australia, Canada, England, Spain). Applying FlowScore to data from the COMPARE study revealed a positive association with the time-delay from sample collection to measurement, which was verified experimentally. The LifeLines dataset revealed age, sex, and smoking among factors affecting FlowScore.

Interpretation: We establish FlowScore as a widely-accessible and cost-effective surrogate of RBC oxygen-unloading kinetics. As a metric of a cellular process that is sensitive to storage and disease, we propose FlowScore as an RBC quality marker for blood-banking and haematology.

Funding: See Acknowledgements.

Background: Previous prediction models for adiposity gain have not yet achieved sufficient predictive ability for clinical relevance. We investigated whether traditional and genetic factors accurately predict adiposity gain.

Methods: A 5-year gain of ≥5% in body mass index (BMI) and waist-to-hip ratio (WHR) from baseline were predicted in mid-late adulthood individuals (median of 55 years old at baseline). Proportional hazards models were fitted in 245,699 participants from the European Prospective Investigation into Cancer and Nutrition (EPIC) cohort to identify robust environmental predictors. Polygenic risk scores (PRS) of 5 proxies of adiposity [BMI, WHR, and three body shape phenotypes (PCs)] were computed using genetic weights from an independent cohort (UK Biobank). Environmental and genetic models were validated in 29,953 EPIC participants.

Findings: Environmental models presented a remarkable predictive ability (AUC_BMI: 0.69, 95% CI: 0.68-0.70; AUC_WHR: 0.75, 95% CI: 0.74-0.77). The genetic geographic distribution for WHR and PC1 (overall adiposity) showed higher predisposition in North than South Europe. Predictive ability of PRSs was null (AUC: ∼0.52) and did not improve when combined with environmental models. However, PRSs of BMI and PC1 showed some prediction ability for BMI gain from self-reported BMI at 20 years old to baseline observation (early adulthood) (AUC: 0.60-0.62).

Interpretation: Our study indicates that environmental models to discriminate European individuals at higher risk of adiposity gain can be integrated in standard prevention protocols. PRSs may play a robust role in predicting adiposity gain at early rather than mid-late adulthood suggesting a more important role of genetic factors in this life period.

Funding: French National Cancer Institute (INCA_N°2019-176) 1220, German Research Foundation (BA 5459/2-1), Instituto de Salud Carlos III (Miguel Servet Program CP21/00058).