RESUMEN
Photothermal therapy is an alternative cancer therapy that uses a photothermal agent with light irradiation to induce fatal hyperthermia in cancer cells. In a previous study, we found that ex vivo photothermal (PT) treatment induced expression of heat shock proteins (HSPs), such as HSP70, HSP27, and HSP90, in cancer cells; moreover, immunization with lysates from PT-treated tumor cells resulted in significant tumor growth inhibition in tumor-bearing mice. In this study, we hypothesized that sublethal PT treatment of antigen-presenting cells regulates their immunogenicity. We observed the upregulation of expression of intracellular HSP70 and surface activation markers, such as CD40, CD80, CD86, and MHC class II, in sublethal PT-treated cells. The protumoral activity of myeloid-derived suppressor cells (MDSCs) was reduced by sublethal hyperthermia. Furthermore, poorly immunogenic MDSCs were converted into immunogenic antigen-presenting cells by PT treatment. The differences in immunogenicity between MDSCs untreated or treated with the PT technique were evaluated using the Student's t-test or Mann-Whitney rank sum test. Collectively, direct hyperthermic treatment resulted in phenotypic changes and the functional regulation of immune cells.
Asunto(s)
Respuesta al Choque Térmico , Células Supresoras de Origen Mieloide , Terapia Fototérmica , Células Supresoras de Origen Mieloide/metabolismo , Células Supresoras de Origen Mieloide/inmunología , Animales , Ratones , Terapia Fototérmica/métodos , Línea Celular Tumoral , Células Presentadoras de Antígenos/inmunología , Células Presentadoras de Antígenos/metabolismo , Proteínas HSP70 de Choque Térmico/metabolismo , Hipertermia Inducida/métodos , Ratones Endogámicos C57BL , Femenino , HumanosRESUMEN
OBJECTIVES: In general, fetal cfDNA is shorter than maternal cfDNA, and accuracy of noninvasive prenatal testing (NIPT) results can be improved by selecting shorter cfDNA fragments to enrich fetal-derived cfDNA. This study investigated potential improvements in the accuracy of NIPT by performing classification and analysis based on differences in cfDNA size. METHODS: We performed paired-end sequencing to identify size ranges of fetal and maternal cfDNA from 62,374 pregnant women. We then developed a size-selection method to isolate and analyze both fetal and maternal cfDNA, defining fetal-derived cfDNA as less than 150 bp and maternal-derived cfDNA as greater than 180 bp. RESULTS: By implementing size-selection method, the accuracy of NIPT was improved, resulting in an increase in the overall positive predictive value for all aneuploidies from 89.57% to 97.1%. This was achieved by enriching both fetal and maternal-derived cfDNA, which increased fetal DNA fraction while the number of false positives for all aneuploidies was reduced by more than 70%. CONCLUSIONS: We identified the differences in read length between fetal and maternal-derived cfDNA, and selectively enriched both shorter and longer cfDNA fragments for subsequent analysis. Our approach can increase the detection accuracy of NIPT for detecting fetal aneuploidies and reduce the number of false positives caused by maternal chromosomal abnormalities.
Asunto(s)
Ácidos Nucleicos Libres de Células , Pruebas Prenatales no Invasivas , Embarazo , Femenino , Humanos , Diagnóstico Prenatal/métodos , Aneuploidia , Aberraciones CromosómicasRESUMEN
Early detection of lung cancer is crucial for patient survival and treatment. Recent advancements in next-generation sequencing (NGS) analysis enable cell-free DNA (cfDNA) liquid biopsy to detect changes, like chromosomal rearrangements, somatic mutations, and copy number variations (CNVs), in cancer. Machine learning (ML) analysis using cancer markers is a highly promising tool for identifying patterns and anomalies in cancers, making the development of ML-based analysis methods essential. We collected blood samples from 92 lung cancer patients and 80 healthy individuals to analyze the distinction between them. The detection of lung cancer markers Cyfra21 and carcinoembryonic antigen (CEA) in blood revealed significant differences between patients and controls. We performed machine learning analysis to obtain AUC values via Adaptive Boosting (AdaBoost), Multi-Layer Perceptron (MLP), and Logistic Regression (LR) using cancer markers, cfDNA concentrations, and CNV screening. Furthermore, combining the analysis of all multi-omics data for ML showed higher AUC values compared with analyzing each element separately, suggesting the potential for a highly accurate diagnosis of cancer. Overall, our results from ML analysis using multi-omics data obtained from blood demonstrate a remarkable ability of the model to distinguish between lung cancer and healthy individuals, highlighting the potential for a diagnostic model against lung cancer.
RESUMEN
Copy number variation (CNV) is a primary source of structural variation in the human genome, leading to several disorders. Therefore, analyzing neonatal CNVs is crucial for managing CNV-related chromosomal disabilities. However, genomic waves can hinder accurate CNV analysis. To mitigate the influences of the waves, we adopted a machine learning approach and developed a new method that uses a modified log R ratio instead of the commonly used log R ratio. Validation results using samples with known CNVs demonstrated the superior performance of our method. We analyzed a total of 16,046 Korean newborn samples using the new method and identified CNVs related to 39 genetic disorders were identified in 342 cases. The most frequently detected CNV-related disorder was Joubert syndrome 4. The accuracy of our method was further confirmed by analyzing a subset of the detected results using NGS and comparing them with our results. The utilization of a genome-wide single nucleotide polymorphism array with wave offset was shown to be a powerful method for identifying CNVs in neonatal cases. The accurate screening and the ability to identify various disease susceptibilities offered by our new method could facilitate the identification of CNV-associated chromosomal disease etiologies.
RESUMEN
Human lung organoids (hLOs) are useful for disease modelling and drug screening. However, a lack of immune cells in hLOs limits the recapitulation of in vivo cellular physiology. Here, we generated hLOs containing alveolar macrophage (AMφ)-like cells derived from pluripotent stem cells (PSC). To bridge hLOs with advanced human lung high-resolution X-ray computed tomography (CT), we acquired quantitative micro-CT images. Three hLO types were observed during differentiation. Among them, alveolar hLOs highly expressed not only lung epithelial cell markers but also AMφ-specific markers. Furthermore, CD68+ AMφ-like cells were spatially organized on the luminal epithelial surface of alveolar hLOs. Bleomycin-treated alveolar hLOs showed upregulated expression of fibrosis-related markers and extracellular matrix deposits in the alveolar sacs. Alveolar hLOs also showed structural alterations such as excessive tissue fraction under bleomycin treatment. Therefore, we suggest that micro-CT analyzable PSC-derived alveolar hLOs are a promising in vitro model to predict lung toxicity manifestations, including fibrosis.