Clinical and Translational Medicine最新文献

{"title":"Breakthroughs in deep tumour penetrating nano-phototheranostics for tumour ablation","authors":"Min-Jun Baek, Sang Min Lee, Dae-Duk Kim, Jae-Young Lee","doi":"10.1002/ctm2.70188","DOIUrl":"10.1002/ctm2.70188","url":null,"abstract":"<p>Photodynamic therapy (PDT), which leverages reactive oxygen species to eliminate cancer cells, offers a promising alternative to conventional cancer treatments. By utilising light-activated photosensitizers (PSs), PDT achieves precise tumour targeting while minimising damage to surrounding healthy tissues. This targeted approach positions PDT as a potential replacement for surgery and radiation therapy in selected cases. However, the clinical utility of PDT in managing solid tumours remains constrained by several critical challenges, including suboptimal tumour accumulation and limited penetration of PSs into tumour tissues.<span><sup>1</sup></span> These barriers often lead to incomplete tumour remission, rendering PDT less effective compared to traditional therapies. Addressing these limitations requires innovative PS delivery systems to enhance the performance of PDT.</p><p>Nanoparticle (NP)-based delivery systems have emerged as a promising approach to overcome these obstacles. By leveraging their unique properties, NPs can improve the solubility, stability and tumour selectivity of PSs. However, NP-based approaches often fail to achieve satisfactory outcomes due to poor penetration of NPs into tumour tissues.<span><sup>2</sup></span> Overcoming these challenges requires innovative tumour-targeted delivery systems that enhance both the specificity and penetrability of NPs.</p><p>Our recent study introduced photobleaching-mediated charge-convertible zwitterionic near-infrared NPs (P-ZWNIR NPs), representing a transformative innovation in nano-phototheranostics.<span><sup>3</sup></span> These multifunctional NPs address critical limitations of PDT and nanotherapeutics by integrating advanced design principles to enhance targeting and penetration in solid tumours. P-ZWNIR NPs feature a photobleaching-mediated charge conversion mechanism. Initially, the NPs are designed to have zwitterionic surface charge to ensure colloidal stability, reduce off-target adsorptions and facilitate tumour-selective accumulation upon intravenous injection. The outer zwitterionic near-infrared (NIR) fluorophore component of the NPs undergoes photobleaching upon exposure to an 808 nm laser, which induces charge conversion to a cationic charge (Figure 1A).</p><p>A key innovation of P-ZWNIR NPs is rapid and efficient charge conversion within tumour tissue, which further facilitates deep tumour penetration. Upon exposure to 808 nm laser, the zwitterionic surface transitions to a cationic state via photooxidative cleavage of the NIR fluorophore component in the NPs. The resulting cationic charge facilitates transcytosis of NPs, enabling them to cross multiple layers of cells in tumour tissue. By promoting active penetration, P-ZWNIR NPs achieved homogeneous distribution of PSs throughout the tumour tissue (Figure 1B).</p><p>In orthotopic rectal tumour-bearing mouse models, intravenous administration of P-ZWNIR NPs resulted in a tumour-to-background ratio as high as 10 ","PeriodicalId":10189,"journal":{"name":"Clinical and Translational Medicine","volume":"15 1","pages":""},"PeriodicalIF":7.9,"publicationDate":"2025-01-12","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11726636/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142968842","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Lentivirus-mediated gene therapy for Fabry disease: 5-year End-of-Study results from the Canadian FACTs trial","authors":"Aneal Khan, Dwayne L. Barber, William M. McKillop, C. Anthony Rupar, Christiane Auray-Blais, Graeme Fraser, Daniel H. Fowler, Alexandra Berger, Ronan Foley, Armand Keating, Michael L. West, Jeffrey A. Medin","doi":"10.1002/ctm2.70073","DOIUrl":"10.1002/ctm2.70073","url":null,"abstract":"<div>\u0000 \u0000 \u0000 <section>\u0000 \u0000 <h3> Background</h3>\u0000 \u0000 <p>Fabry disease is an X-linked lysosomal storage disorder due to a deficiency of α-galactosidase A (α-gal A) activity. Our goal was to correct the enzyme deficiency in Fabry patients by transferring the cDNA for α-gal A into their CD34+ hematopoietic stem/progenitor cells (HSPCs). Overexpression of α-gal A leads to secretion of the hydrolase; which can be taken up and used by uncorrected bystander cells. Gene-augmented HSPCs can circulate and thus provide sustained systemic correction. Interim results from this ‘first-in-the-world’ Canadian FACTs (Fabry Disease Clinical Research and Therapeutics) trial were published in 2021. Herein we report 5-year ‘End-of-Study’ results.</p>\u0000 </section>\u0000 \u0000 <section>\u0000 \u0000 <h3> Methods</h3>\u0000 \u0000 <p>Five males with classical Fabry disease were treated. Their HSPCs were mobilized, enriched, and transduced with a recombinant lentivirus engineering expression of α-gal A. Autologous transduced cells were infused after conditioning with a nonmyeloablative, reduced dose, melphalan regimen. Safety monitoring was performed. α-Gal A activity was measured in plasma and peripheral blood (PB) leucocytes. Globotriaosylceramide (Gb3) and lyso-Gb3 levels in urine and plasma were assessed by mass spectrometry. qPCR assays measured vector copy number in PB leucocytes. Antibody titers were measured by ELISA. Body weight, blood pressure, urinary protein levels, eGFR, troponin levels, and LVMI were tracked.</p>\u0000 </section>\u0000 \u0000 <section>\u0000 \u0000 <h3> Results</h3>\u0000 \u0000 <p>Four out of 5 patients went home the same day as their infusions; one was kept overnight for observation. Circulating α-gal A activity was observed at Day 6–8 in each patient following infusion and has remained durable for 5+ years. LV marking of peripheral blood cells has remained durable and polyclonal. All 5 patients were eligible to come off biweekly enzyme therapy; 3 patients did so. Plasma lyso-Gb3 was significantly lower in 4 of 5 patients. There was no sustained elevation of anti-α-gal A antibodies. Patient weight was stable in 4 of the 5 patients. All blood pressures were in the normal range. Kidney symptoms were stabilized in all patients.</p>\u0000 </section>\u0000 \u0000 <section>\u0000 \u0000 <h3> Conclusions</h3>\u0000 \u0000 <p>This treatment was well tolerated as only two SAEs occurred (during the treatment phase) and only two AEs were reported since 2021. We demonstrate that this therapeutic approach has merit, is durable, and should be explored in a larger clinical trial.</p>\u0000 </section>\u0000 \u0000 <section>\u0000 \u0000 <h3> Highlights</h3>\u0000 ","PeriodicalId":10189,"journal":{"name":"Clinical and Translational Medicine","volume":"15 1","pages":""},"PeriodicalIF":7.9,"publicationDate":"2025-01-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11726700/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143055968","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Lower respiratory tract microbiome dysbiosis impairs clinical responses to immune checkpoint blockade in advanced non-small-cell lung cancer","authors":"Yong Zhang, Xiang-Xiang Chen, Ruo Chen, Ling Li, Qing Ju, Dan Qiu, Yuan Wang, Peng-Yu Jing, Ning Chang, Min Wang, Jian Zhang, Zhi-Nan Chen, Ke Wang","doi":"10.1002/ctm2.70170","DOIUrl":"10.1002/ctm2.70170","url":null,"abstract":"<div>\u0000 \u0000 \u0000 <section>\u0000 \u0000 <h3> Background</h3>\u0000 \u0000 <p>Gut microbiome on predicting clinical responses to immune checkpoint inhibitors (ICIs) has been discussed in detail for decades, while microecological features of the lower respiratory tract within advanced non-small-cell lung cancer (NSCLC) are still relatively vague.</p>\u0000 </section>\u0000 \u0000 <section>\u0000 \u0000 <h3> Methods</h3>\u0000 \u0000 <p>During this study, 26 bronchoalveolar lavage fluids (BALF) from advanced NSCLC participants who received immune checkpoint inhibitor monotherapy were performed 16S rRNA sequencing and untargeted metabolome sequencing to identify differentially abundant microbes and metabolic characteristics. Additionally, inflammatory cytokines and chemokines were also launched in paired BALF and serum samples by immunoassays to uncover their underlying correlations. The omics data were separately analyzed and integrated by using multiple correlation coefficients. Multiplex immunohistochemical staining was then used to assess the immune cell infiltration after immune checkpoint blockade therapy.</p>\u0000 </section>\u0000 \u0000 <section>\u0000 \u0000 <h3> Results</h3>\u0000 \u0000 <p>Lower respiratory tract microbiome diversity favoured preferred responses to ICIs. Microbial markers demonstrated microbial diversity overweight a single strain in favoured response to ICI therapy, where Bacillus matters. <i>Sphingomonas</i> and <i>Sediminibacterium</i> were liable to remodulate lipid and essential amino acid degradations to embrace progression after immunotherapies. Microbiome-derived metabolites reshaped the immune microenvironment in the lower respiratory tract by releasing inflammatory cytokines and chemokines, which was partially achieved by metabolite-mediated tumoral inflammatory products and reduction of CD8<sup>+</sup> effective T cells and M1 phenotypes macrophages in malignant lesions.</p>\u0000 </section>\u0000 \u0000 <section>\u0000 \u0000 <h3> Conclusions</h3>\u0000 \u0000 <p>This study provided a microecological landscape of the lower respiratory tract with advanced NSCLC to ICI interventions and presented a multidimensional perspective with favoured outcomes that may improve the predictive capacity of the localized microbiome in clinical practices.</p>\u0000 </section>\u0000 \u0000 <section>\u0000 \u0000 <h3> Highlights</h3>\u0000 \u0000 <div>\u0000 <ul>\u0000 \u0000 <li>Alterations of the lower respiratory tract microbiome indicate different clinical responses to ICB within advanced NSCLC.</li>\u0000 \u0000 <li>Reduced microbial diversity of lower respiratory trac","PeriodicalId":10189,"journal":{"name":"Clinical and Translational Medicine","volume":"15 1","pages":""},"PeriodicalIF":7.9,"publicationDate":"2025-01-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11726686/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142964143","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"CLINICAL AND TRANSLATIONAL MEDICINE","authors":"","doi":"10.1002/ctm2.70194","DOIUrl":"https://doi.org/10.1002/ctm2.70194","url":null,"abstract":"","PeriodicalId":10189,"journal":{"name":"Clinical and Translational Medicine","volume":"15 1","pages":""},"PeriodicalIF":7.9,"publicationDate":"2025-01-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://onlinelibrary.wiley.com/doi/epdf/10.1002/ctm2.70194","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143113748","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"LcProt: Proteomics-based identification of plasma biomarkers for lung cancer multievent, a multicentre study","authors":"Hengrui Liang, Runchen Wang, Ran Cheng, Zhiming Ye, Na Zhao, Xiaohong Zhao, Ying Huang, Zhanpeng Jiang, Wangzhong Li, Jianqi Zheng, Hongsheng Deng, Yu Jiang, Yuechun Lin, Yun Yan, Lei Song, Jie Li, Xin Xu, Wenhua Liang, Jun Liu, Jianxing He","doi":"10.1002/ctm2.70160","DOIUrl":"10.1002/ctm2.70160","url":null,"abstract":"<div>\u0000 \u0000 \u0000 <section>\u0000 \u0000 <h3> Background</h3>\u0000 \u0000 <p>Plasma protein has gained prominence in the non-invasive predicting of lung cancer. We utilised Zeolite Zotero NaY-based plasma proteomics to investigate its potential for multiple event predicting, including lung cancer diagnosis (task #1), lymph node metastasis detection (task #2) and tumour‒node‒metastasis (TNM) staging (task #3).</p>\u0000 </section>\u0000 \u0000 <section>\u0000 \u0000 <h3> Methods</h3>\u0000 \u0000 <p>A total of 4703 plasma proteins were quantified from 241 participants based on a prospective cohort of 2757 participants. An additional 46 participants from external prospective cohort of 735 participants were used for validation. Feature selection was performed using differential expressed protein analysis, area under curve (AUC) evaluation and least absolute shrinkage and selection operator (LASSO) regression. Random forest was used for multitask model construction based on the key proteins. Feature importance was interpreted using Shapley additive explanations (SHAP) algorithm.</p>\u0000 </section>\u0000 \u0000 <section>\u0000 \u0000 <h3> Results</h3>\u0000 \u0000 <p>For task #1, 10 proteins panel showed an AUC of .87 (.77‒.97) in the external validation. After integrating clinical factors, a significant increase diagnostic accuracy was observed with AUC of .91 (.85‒.98). For task #2, nine proteins panel achieved an AUC of .88 (.80‒.96), integration model showed an increase diagnostic accuracy with AUC of .90 (.85‒.97). For task #3, 10 proteins panel showed an AUC of .88 (.74‒.96) for stage I, .92 (.84‒.97) for stage II, .88 (.76‒.96) for stage III and .99 (.98‒.99) for stage IV in the integration model.</p>\u0000 </section>\u0000 \u0000 <section>\u0000 \u0000 <h3> Conclusions</h3>\u0000 \u0000 <p>This study comprehensively profiled the NaY-based plasma proteome biomarker, laying the foundation for a high-performance blood test for predicting multiple events in lung cancer.</p>\u0000 </section>\u0000 \u0000 <section>\u0000 \u0000 <h3> Key points</h3>\u0000 \u0000 <div>\u0000 <ul>\u0000 \u0000 <li>\u0000 <p>Our study developed an innovative nanomaterial, Zeolite NaY, which addressed the masking effect and improved the depth of the proteome.</p>\u0000 </li>\u0000 \u0000 <li>\u0000 <p>The performance of NaY-based plasma proteomics as a preclinical diagnostic tool was validated through both internal and external cohort.</p>\u0000 </li>\u0000 \u0000 <li>\u0000 <p>Furthermore, we explore","PeriodicalId":10189,"journal":{"name":"Clinical and Translational Medicine","volume":"15 1","pages":""},"PeriodicalIF":7.9,"publicationDate":"2025-01-09","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11714244/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142945530","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Nanopore adaptive sampling accurately detects nucleotide variants and improves the characterization of large-scale rearrangement for the diagnosis of cancer predisposition","authors":"Sandy Chevrier,&nbsp;Corentin Richard,&nbsp;Marie Mille,&nbsp;Denis Bertrand,&nbsp;Romain Boidot","doi":"10.1002/ctm2.70138","DOIUrl":"10.1002/ctm2.70138","url":null,"abstract":"<div>\u0000 \u0000 \u0000 <section>\u0000 \u0000 <h3> Background</h3>\u0000 \u0000 <p>Molecular diagnosis has become highly significant for patient management in oncology.</p>\u0000 </section>\u0000 \u0000 <section>\u0000 \u0000 <h3> Methods</h3>\u0000 \u0000 <p>Here, 30 well-characterized clinical germline samples were studied with adaptive sampling to enrich the full sequence of 152 cancer predisposition genes. Sequencing was performed on Oxford Nanopore (ONT) R10.4.1 MinION flowcells with the Q20+ chemistry.</p>\u0000 </section>\u0000 \u0000 <section>\u0000 \u0000 <h3> Results</h3>\u0000 \u0000 <p>In our cohort, 11 samples had large-scale rearrangements (LSR), which were all detected with ONT sequencing. In addition to perfectly detecting the locus of the LSR, we found a known MLPA amplification of exon 13 in the <i>BRCA1</i> (NM_7294) gene corresponded to a duplication in tandem of both exons 12 and 13 of the reference NM_7300. Similarly, in another sample with a known total deletion of the <i>BRCA1</i> gene, ONT sequencing highlighted this complete deletion was the consequence of a large deletion of almost 140 000 bp carrying over five different genes. ONT sequencing was also able to detect all pathogenic nucleotide variants present in 16 samples at low coverage. As we analyzed complete genes and more genes than with short-read sequencing, we detected novel unknown variants. We randomly selected six new variants with a coverage larger than 10× and an average quality higher than 14, and confirmed all of them by Sanger sequencing, suggesting that variants detected with ONT (coverage &gt;10× and quality score &gt;14) could be considered as real variants.</p>\u0000 </section>\u0000 \u0000 <section>\u0000 \u0000 <h3> Conclusions</h3>\u0000 \u0000 <p>We showed that ONT adaptive sampling sequencing is suitable for the analysis of germline alterations, improves characterization of LSR, and detects single nucleotide variations even at low coverage.</p>\u0000 </section>\u0000 \u0000 <section>\u0000 \u0000 <h3> Key points</h3>\u0000 \u0000 <div>\u0000 <ul>\u0000 \u0000 <li>Adaptive sampling is suitable for the analysis of germline alterations.</li>\u0000 \u0000 <li>Improves the characterization of Large Scale Rearrangement and detects SNV at a minimum coverage of 10x.</li>\u0000 \u0000 <li>Allows flexibility of sequencing.</li>\u0000 </ul>\u0000 </div>\u0000 </section>\u0000 </div>","PeriodicalId":10189,"journal":{"name":"Clinical and Translational Medicine","volume":"15 1","pages":""},"PeriodicalIF":7.9,"publicationDate":"2025-01-09","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11714230/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142945539","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Machine learning-based multi-omics models for diagnostic classification and risk stratification in diabetic kidney disease","authors":"Xian Shao,&nbsp;Suhua Gao,&nbsp;Pufei Bai,&nbsp;Qian Yang,&nbsp;Yao Lin,&nbsp;Mingzhen Pang,&nbsp;Weixi Wu,&nbsp;Lihua Wang,&nbsp;Ying Li,&nbsp;Saijun Zhou,&nbsp;Hongyan Liu,&nbsp;Pei Yu","doi":"10.1002/ctm2.70133","DOIUrl":"10.1002/ctm2.70133","url":null,"abstract":"&lt;p&gt;Dear Editor,&lt;/p&gt;&lt;p&gt;The global prevalence of chronic kidney disease (CKD) is about 10%.&lt;span&gt;&lt;sup&gt;1&lt;/sup&gt;&lt;/span&gt; Diabetic kidney disease (DKD) has emerged as the leading cause of end-stage renal failure.&lt;span&gt;&lt;sup&gt;2&lt;/sup&gt;&lt;/span&gt; Early identification of DKD is important for improving the survival rate and improving the quality of life. However, the preclinical stages of DKD may lack obvious symptoms and non-invasive biomarkers.&lt;span&gt;&lt;sup&gt;3&lt;/sup&gt;&lt;/span&gt; Through blood lipidomics, urine proteomics and metabolomics technologies, potential DKD markers are identified to establish an accurate early warning model for DKD. We aim to provide effective tools for the individualised prevention of DKD, and help to explain the associations between different molecules and their risk of DKD from multiple perspectives. The methods of study are shown in Appendix 1.&lt;/p&gt;&lt;p&gt;Figure 1 illustrates the overview of the common and unique changes in proteomics pathways observed at various stages of DKD. The pathways reflected the active biological processes closely related to multi-omics during the development of DKD. Potential proteomic biomarkers were identified through a multi-level screening process, with a comprehensive score used to assess their significance (Appendix 1). Finally, CD300LF, CST4, MMRN2, SERPINA14, L-glutamic acid dimethyl ester (DLG) and phosphatidylcholine (PC) were selected. The results of study are provided in Appendix 2.&lt;/p&gt;&lt;p&gt;The cross-sectional study included a total of 1500 patients (Figure S2 and Appendix 1). Patients were categorised into four groups: healthy control (HC, 30), type 2 diabetes mellitus (T2DM, 361), high-risk DKD (HR-DKD, 555) and DKD group (554). Baseline patient information is detailed in Appendix 2. The patients were categorised into two groups: a training and a test set (3:1). A total of seven prediction models for diagnosis classification were established, with the included indicators provided in Table S17 and Appendix 2. The integration of clinical indicators with multi-omics indicators resulted in a substantial accuracy improvement (Accuracy = .923 [.893, .947]; Figure 2A–G). This integrated model was the most effective, with improved performance across all metrics, including area under the curve (AUC), sensitivity, specificity and accuracy. Additionally, the study utilised a total of 12 machine learning algorithms, all of which achieved AUC values above .940 (Figure 2H).&lt;/p&gt;&lt;p&gt;The prospective cohort study involved 919 patients, with a median follow-up duration of 1.07 years. Based on the clinical and multi-omics indicators, three risk-prognostic prediction models were developed: the biomarker model (Model 1), clinical indicators model (Model 2) and integrated model (Model 3). The specific indicators used are detailed in Table S22 and Appendix 2. Figure 2I displays the AUC curves of these models, with Model 3 achieving the highest AUC of .813. A risk score was calculated using Model 3 (score cut-off = 1.06; Figure 2J). In","PeriodicalId":10189,"journal":{"name":"Clinical and Translational Medicine","volume":"15 1","pages":""},"PeriodicalIF":7.9,"publicationDate":"2025-01-08","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11707431/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142945533","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Immunoregulatory programs in anti-N-methyl-D-aspartate receptor encephalitis identified by single-cell multi-omics analysis","authors":"Xinhui Li,&nbsp;Yicong Xu,&nbsp;Weixing Zhang,&nbsp;Zihao Chen,&nbsp;Dongjie Peng,&nbsp;Wenxu Ren,&nbsp;Zhongjie Tang,&nbsp;Huilu Li,&nbsp;Jin Xu,&nbsp;Yaqing Shu","doi":"10.1002/ctm2.70173","DOIUrl":"10.1002/ctm2.70173","url":null,"abstract":"&lt;div&gt;\u0000 \u0000 \u0000 &lt;section&gt;\u0000 \u0000 &lt;h3&gt; Background&lt;/h3&gt;\u0000 \u0000 &lt;p&gt;Anti-&lt;i&gt;N&lt;/i&gt;-methyl-D-aspartate receptor encephalitis (anti-NMDARE) is a prevalent type of autoimmune encephalitis caused by antibodies targeting the NMDAR's GluN1 subunit. While significant progress has been made in elucidating the pathophysiology of autoimmune diseases, the immunological mechanisms underlying anti-NMDARE remain elusive. This study aimed to characterize immune cell interactions and dysregulation in anti-NMDARE by leveraging single-cell multi-omics sequencing technologies.&lt;/p&gt;\u0000 &lt;/section&gt;\u0000 \u0000 &lt;section&gt;\u0000 \u0000 &lt;h3&gt; Methods&lt;/h3&gt;\u0000 \u0000 &lt;p&gt;Peripheral blood mononuclear cells (PBMCs) from patients in the acute phase of anti-NMDARE and healthy controls were sequenced using single-cell joint profiling of transcriptome and chromatin accessibility. Differential gene expression analysis, transcription factor activity profiling, and cell-cell communication modeling were performed to elucidate the immune mechanisms underlying the disease. In parallel, single-cell B cell receptor sequencing (scBCR-seq) and repertoire analysis were conducted to assess antigen-driven clonal expansion within the B cell population.&lt;/p&gt;\u0000 &lt;/section&gt;\u0000 \u0000 &lt;section&gt;\u0000 \u0000 &lt;h3&gt; Results&lt;/h3&gt;\u0000 &lt;p&gt;The study revealed a significant clonal expansion of B cells, particularly plasma cells, in anti-NMDARE patients. The novel finding of type I interferon (IFN-I) pathway activation suggests a regulatory mechanism that may drive this expansion and enhance antibody secretion. Additionally, activation of Toll-like receptor 2 (TLR2) in myeloid cells was noted, which may connect to tumor necrosis factor-alpha (TNF-α) secretion. This cytokine may contribute to the activation of B and T cells, thereby perpetuating immune dysregulation.&lt;/p&gt;\u0000 &lt;/section&gt;\u0000 \u0000 &lt;section&gt;\u0000 \u0000 &lt;h3&gt; Conclusions&lt;/h3&gt;\u0000 \u0000 &lt;p&gt;This study presents a comprehensive single-cell multi-omics characterization of immune dysregulation in anti-NMDARE, highlighting the expansion of B cell and the activation of the IFN-I and TLR2 pathways. These findings provide deeper insights into the molecular mechanism driving the pathogenesis of anti-NMDARE and offer promising targets for future therapeutic intervention.&lt;/p&gt;\u0000 &lt;/section&gt;\u0000 \u0000 &lt;section&gt;\u0000 \u0000 &lt;h3&gt; Key points&lt;/h3&gt;\u0000 \u0000 &lt;div&gt;\u0000 &lt;ul&gt;\u0000 \u0000 &lt;li&gt;Significant B cell clonal expansion, particularly in plasma cells, driven by antigen recognition.&lt;/li&gt;\u0000 \u0000 &lt;li&gt;IFN-I pathway activation in plasma cells boosts their antibody production","PeriodicalId":10189,"journal":{"name":"Clinical and Translational Medicine","volume":"15 1","pages":""},"PeriodicalIF":7.9,"publicationDate":"2025-01-08","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11710936/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142945241","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Identification of CENPM as a key gene driving adrenocortical carcinoma metastasis via physical interaction with immune checkpoint ligand FGL1","authors":"Cunru Zou,&nbsp;Yu Zhang,&nbsp;Chengyue Liu,&nbsp;Yaxin Li,&nbsp;Congjie Lin,&nbsp;Hao Chen,&nbsp;Jiangping Hou,&nbsp;Guojun Gao,&nbsp;Zheng Liu,&nbsp;Qiupeng Yan,&nbsp;Wenxia Su","doi":"10.1002/ctm2.70182","DOIUrl":"10.1002/ctm2.70182","url":null,"abstract":"&lt;div&gt;\u0000 \u0000 \u0000 &lt;section&gt;\u0000 \u0000 &lt;h3&gt; Background&lt;/h3&gt;\u0000 \u0000 &lt;p&gt;Distant metastasis occurs in the majority of adrenocortical carcinoma (ACC), leading to an extremely poor prognosis. However, the key genes driving ACC metastasis remain unclear.&lt;/p&gt;\u0000 &lt;/section&gt;\u0000 \u0000 &lt;section&gt;\u0000 \u0000 &lt;h3&gt; Methods&lt;/h3&gt;\u0000 \u0000 &lt;p&gt;Weighted gene co-expression network analysis (WGCNA) and functional enrichment analysis were conducted to identify ACC metastasis-related genes. Data from RNA-seq and microarray were analyzed to reveal correlations of the &lt;i&gt;CENPM&lt;/i&gt; gene with cancer, metastasis, and survival in ACC. Immunohistochemistry was used to assess CENPM protein expression. The impact of CENPM on metastasis behaviour was verified in ACC (H295R and SW-13) cells and xenograft NPG mice. DIA quantitative proteomics analysis, western blot, immunofluorescence, and co-immunoprecipitation assay were performed to identify the downstream target of CENPM.&lt;/p&gt;\u0000 &lt;/section&gt;\u0000 \u0000 &lt;section&gt;\u0000 \u0000 &lt;h3&gt; Results&lt;/h3&gt;\u0000 \u0000 &lt;p&gt;Among the 12 035 analyzed genes, 363 genes were related to ACC metastasis and &lt;i&gt;CENPM&lt;/i&gt; was identified as the hub gene. CENPM was upregulated in ACC samples and associated with metastasis and poor prognosis. Knockdown of &lt;i&gt;CENPM&lt;/i&gt; inhibited proliferation, invasion, and migration of ACC cells and suppressed liver metastasis in xenograft NPG mice. Collagen-containing extracellular matrix signalling was primarily downregulated when &lt;i&gt;CENPM&lt;/i&gt; was knocked down. FGL1, important components of ECM signalling and immune checkpoint ligand of LAG3, were downregulated following &lt;i&gt;CENPM&lt;/i&gt; silence, overexpressed in human advanced ACC samples, and colocalized with CENPM. Physical interaction between CENPM and FGL1 was identified. Overexpression of &lt;i&gt;FGL1&lt;/i&gt; rescued migration and invasion of &lt;i&gt;CENPM&lt;/i&gt; knockdown ACC cells.&lt;/p&gt;\u0000 &lt;/section&gt;\u0000 \u0000 &lt;section&gt;\u0000 \u0000 &lt;h3&gt; Conclusions&lt;/h3&gt;\u0000 \u0000 &lt;p&gt;&lt;i&gt;CENPM&lt;/i&gt; is a key gene in driving ACC metastasis. CENPM promotes ACC metastasis through physical interaction with the immune checkpoint ligand FGL1. CENPM can be used as a new prognostic biomarker and therapeutic target for metastatic ACC.&lt;/p&gt;\u0000 &lt;/section&gt;\u0000 \u0000 &lt;section&gt;\u0000 \u0000 &lt;h3&gt; Highlights&lt;/h3&gt;\u0000 \u0000 &lt;div&gt;\u0000 &lt;ul&gt;\u0000 \u0000 &lt;li&gt;&lt;i&gt;CENPM&lt;/i&gt; is the key gene that drives ACC metastasis, and a robust biomarker for ACC prognosis.&lt;/li&gt;\u0000 \u0000 &lt;li&gt;Silencing &lt;i&gt;CENPM&lt;/i&gt; impedes ACC metastasis in vitro and in vivo by physical interaction with immune checkpoint ligand FGL1.&lt;/li&gt;\u0000 ","PeriodicalId":10189,"journal":{"name":"Clinical and Translational Medicine","volume":"15 1","pages":""},"PeriodicalIF":7.9,"publicationDate":"2025-01-08","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11707433/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142945719","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"iDICss robustly predicts melanoma immunotherapy response by synergizing genomic and transcriptomic knowledge via independent component analysis","authors":"Jiayue Qiu,&nbsp;Nana Jin,&nbsp;Lixin Cheng,&nbsp;Chen Huang","doi":"10.1002/ctm2.70183","DOIUrl":"10.1002/ctm2.70183","url":null,"abstract":"&lt;p&gt;Dear Editor,&lt;/p&gt;&lt;p&gt;Here, we present a tool named iDIC-based scoring system (iDICss) that is useful for predicting immunotherapy response and prognostic outcomes in melanoma patients. The core principle of the tool is that specific driver alterations affecting the immuno-related gene expression and functions,&lt;span&gt;&lt;sup&gt;1&lt;/sup&gt;&lt;/span&gt; may be indicative of high tumour mutation burden, a good predictor used to guide immunotherapy decisions in clinical,&lt;span&gt;&lt;sup&gt;2&lt;/sup&gt;&lt;/span&gt; and analysis of such interplays may provide novel strategies to improve prediction of immunotherapy response. This tool thereby builds on an immune driver independent components (iDICs) profile, which innovatively integrates the immunogenic properties into transcriptome using the independent component analysis (ICA), a popular matrix decomposition method. Optimized by comparison of multiple machine-learning models, an iDICss was established, which exhibits a superior performance of prognostic and immune response prediction compared with other published state-of-art biomarkers. Our study provides a novel strategy to improve the prediction of immunotherapy response for melanoma, which could be adaptable in numerous clinical prediction situations.&lt;/p&gt;&lt;p&gt;Melanoma is a highly aggressive skin cancer originating from melanocyte transformation, and its incidence has been increasing globally in recent years.&lt;span&gt;&lt;sup&gt;3&lt;/sup&gt;&lt;/span&gt; Immune checkpoint blocking immunotherapy is one of the most advanced treatment strategies and significantly improves the survival outcomes for melanoma sufferers. However, high genetic heterogeneity of melanoma results in immune responses occurring in only a small proportion of patients,&lt;span&gt;&lt;sup&gt;4-6&lt;/sup&gt;&lt;/span&gt; which motivates us to explore a robust biomarker to predict patients’ immunotherapy response and guide treatment decision. Accumulated studies demonstrate that oncogenic driver mutations shape tumor immune microenvironment (TIME), and cause impediments to immunotherapy.&lt;span&gt;&lt;sup&gt;7-9&lt;/sup&gt;&lt;/span&gt; Hence, the crosstalk between oncogene driver mutations and TIME-related gene expression alterations may reflect if a patient will respond to immunotherapy. Herein we started by integrating driver gene mutation and expression information via ICA by which we successfully figured out seven key TIME-driver iDICs, and then established an iDIC-based scoring system (iDICss) by a comparative analysis of multiple machine-learning methods. The main pipeline proceeded as follows: (1) independent component analysis, (2) independent component (IC) selection, (3) TIME-driver IC profile calculation, and (4) iDICss construction (Figure 1). The datasets involved in the study were summarized in Table S1.&lt;/p&gt;&lt;p&gt;Briefly, we collected the multi-omics data of 450 melanoma patients from the TCGA database, including gene expression, mutation as well as clinical information. ICA analysis was initially applied to the gene expression matrix &lt;i&gt;E&lt;/i&gt;, resulting in an &lt;i&gt;S&lt;/i&gt; matr","PeriodicalId":10189,"journal":{"name":"Clinical and Translational Medicine","volume":"15 1","pages":""},"PeriodicalIF":7.9,"publicationDate":"2025-01-08","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11707425/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142944908","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}