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
<div> <section> <h3> Background</h3> <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> </section> <section> <h3> Methods</h3> <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> </section> <section> <h3> Results</h3> <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> </section> <section> <h3> Conclusions</h3> <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> </section> <section> <h3> Highlights</h3>
{"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}
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
<div> <section> <h3> Background</h3> <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> </section> <section> <h3> Methods</h3> <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> </section> <section> <h3> Results</h3> <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> </section> <section> <h3> Conclusions</h3> <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> </section> <section> <h3> Highlights</h3> <div> <ul> <li>Alterations of the lower respiratory tract microbiome indicate different clinical responses to ICB within advanced NSCLC.</li> <li>Reduced microbial diversity of lower respiratory trac
{"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}
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
<div> <section> <h3> Background</h3> <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> </section> <section> <h3> Methods</h3> <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> </section> <section> <h3> Results</h3> <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> </section> <section> <h3> Conclusions</h3> <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> </section> <section> <h3> Key points</h3> <div> <ul> <li> <p>Our study developed an innovative nanomaterial, Zeolite NaY, which addressed the masking effect and improved the depth of the proteome.</p> </li> <li> <p>The performance of NaY-based plasma proteomics as a preclinical diagnostic tool was validated through both internal and external cohort.</p> </li> <li> <p>Furthermore, we explore
{"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}
Sandy Chevrier, Corentin Richard, Marie Mille, Denis Bertrand, Romain Boidot
Background
Molecular diagnosis has become highly significant for patient management in oncology.
Methods
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.
Results
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 BRCA1 (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 BRCA1 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 >10× and quality score >14) could be considered as real variants.
Conclusions
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.
Key points
Adaptive sampling is suitable for the analysis of germline alterations.
Improves the characterization of Large Scale Rearrangement and detects SNV at a minimum coverage of 10x.
{"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, Corentin Richard, Marie Mille, Denis Bertrand, 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 >10× and quality score >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}
<p>Dear Editor,</p><p>The global prevalence of chronic kidney disease (CKD) is about 10%.<span><sup>1</sup></span> Diabetic kidney disease (DKD) has emerged as the leading cause of end-stage renal failure.<span><sup>2</sup></span> 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.<span><sup>3</sup></span> 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.</p><p>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.</p><p>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).</p><p>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
{"title":"Machine learning-based multi-omics models for diagnostic classification and risk stratification in diabetic kidney disease","authors":"Xian Shao, Suhua Gao, Pufei Bai, Qian Yang, Yao Lin, Mingzhen Pang, Weixi Wu, Lihua Wang, Ying Li, Saijun Zhou, Hongyan Liu, Pei Yu","doi":"10.1002/ctm2.70133","DOIUrl":"10.1002/ctm2.70133","url":null,"abstract":"<p>Dear Editor,</p><p>The global prevalence of chronic kidney disease (CKD) is about 10%.<span><sup>1</sup></span> Diabetic kidney disease (DKD) has emerged as the leading cause of end-stage renal failure.<span><sup>2</sup></span> 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.<span><sup>3</sup></span> 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.</p><p>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.</p><p>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).</p><p>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}
Xinhui Li, Yicong Xu, Weixing Zhang, Zihao Chen, Dongjie Peng, Wenxu Ren, Zhongjie Tang, Huilu Li, Jin Xu, Yaqing Shu
<div> <section> <h3> Background</h3> <p>Anti-<i>N</i>-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.</p> </section> <section> <h3> Methods</h3> <p>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.</p> </section> <section> <h3> Results</h3> <p>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.</p> </section> <section> <h3> Conclusions</h3> <p>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.</p> </section> <section> <h3> Key points</h3> <div> <ul> <li>Significant B cell clonal expansion, particularly in plasma cells, driven by antigen recognition.</li> <li>IFN-I pathway activation in plasma cells boosts their antibody production
背景：抗n -甲基- d -天冬氨酸受体脑炎（anti-NMDARE）是一种常见的自身免疫性脑炎，由靶向NMDAR的GluN1亚基的抗体引起。虽然在阐明自身免疫性疾病的病理生理方面取得了重大进展，但抗nmdare的免疫学机制仍然难以捉摸。本研究旨在利用单细胞多组学测序技术表征抗nmdare免疫细胞相互作用和失调。方法：采用单细胞联合转录组和染色质可及性分析对抗nmdare急性期患者和健康对照的外周血单个核细胞（PBMCs）进行测序。通过差异基因表达分析、转录因子活性分析和细胞间通讯模型来阐明该疾病的免疫机制。同时，进行了单细胞B细胞受体测序（scBCR-seq）和库分析，以评估B细胞群体中抗原驱动的克隆扩增。结果：研究显示抗nmdare患者的B细胞，特别是浆细胞有显著的克隆扩增。I型干扰素（IFN-I）通路激活的新发现提示了一种可能驱动这种扩展并增强抗体分泌的调节机制。此外，髓细胞中toll样受体2 （TLR2）的激活可能与肿瘤坏死因子α (TNF-α)的分泌有关。这种细胞因子可能有助于B细胞和T细胞的激活，从而使免疫失调永久化。结论：本研究对抗nmdare免疫失调进行了全面的单细胞多组学表征，强调了B细胞的扩增和IFN-I和TLR2通路的激活。这些发现为探究抗nmdare的分子机制提供了更深入的见解，并为未来的治疗干预提供了有希望的靶点。重点：在抗原识别驱动下，显著的B细胞克隆扩增，特别是在浆细胞中。血浆细胞中IFN-I通路的激活促进了它们的抗体产生，并可能加剧免疫失调。髓细胞中TLR2通路的激活有助于TNF-α的分泌，并可能影响适应性免疫反应。
{"title":"Immunoregulatory programs in anti-N-methyl-D-aspartate receptor encephalitis identified by single-cell multi-omics analysis","authors":"Xinhui Li, Yicong Xu, Weixing Zhang, Zihao Chen, Dongjie Peng, Wenxu Ren, Zhongjie Tang, Huilu Li, Jin Xu, Yaqing Shu","doi":"10.1002/ctm2.70173","DOIUrl":"10.1002/ctm2.70173","url":null,"abstract":"<div>\u0000 \u0000 \u0000 <section>\u0000 \u0000 <h3> Background</h3>\u0000 \u0000 <p>Anti-<i>N</i>-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.</p>\u0000 </section>\u0000 \u0000 <section>\u0000 \u0000 <h3> Methods</h3>\u0000 \u0000 <p>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.</p>\u0000 </section>\u0000 \u0000 <section>\u0000 \u0000 <h3> Results</h3>\u0000 <p>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.</p>\u0000 </section>\u0000 \u0000 <section>\u0000 \u0000 <h3> Conclusions</h3>\u0000 \u0000 <p>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.</p>\u0000 </section>\u0000 \u0000 <section>\u0000 \u0000 <h3> Key points</h3>\u0000 \u0000 <div>\u0000 <ul>\u0000 \u0000 <li>Significant B cell clonal expansion, particularly in plasma cells, driven by antigen recognition.</li>\u0000 \u0000 <li>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}
<div> <section> <h3> Background</h3> <p>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.</p> </section> <section> <h3> Methods</h3> <p>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 <i>CENPM</i> 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.</p> </section> <section> <h3> Results</h3> <p>Among the 12 035 analyzed genes, 363 genes were related to ACC metastasis and <i>CENPM</i> was identified as the hub gene. CENPM was upregulated in ACC samples and associated with metastasis and poor prognosis. Knockdown of <i>CENPM</i> 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 <i>CENPM</i> was knocked down. FGL1, important components of ECM signalling and immune checkpoint ligand of LAG3, were downregulated following <i>CENPM</i> silence, overexpressed in human advanced ACC samples, and colocalized with CENPM. Physical interaction between CENPM and FGL1 was identified. Overexpression of <i>FGL1</i> rescued migration and invasion of <i>CENPM</i> knockdown ACC cells.</p> </section> <section> <h3> Conclusions</h3> <p><i>CENPM</i> 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.</p> </section> <section> <h3> Highlights</h3> <div> <ul> <li><i>CENPM</i> is the key gene that drives ACC metastasis, and a robust biomarker for ACC prognosis.</li> <li>Silencing <i>CENPM</i> impedes ACC metastasis in vitro and in vivo by physical interaction with immune checkpoint ligand FGL1.</li>
{"title":"Identification of CENPM as a key gene driving adrenocortical carcinoma metastasis via physical interaction with immune checkpoint ligand FGL1","authors":"Cunru Zou, Yu Zhang, Chengyue Liu, Yaxin Li, Congjie Lin, Hao Chen, Jiangping Hou, Guojun Gao, Zheng Liu, Qiupeng Yan, Wenxia Su","doi":"10.1002/ctm2.70182","DOIUrl":"10.1002/ctm2.70182","url":null,"abstract":"<div>\u0000 \u0000 \u0000 <section>\u0000 \u0000 <h3> Background</h3>\u0000 \u0000 <p>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.</p>\u0000 </section>\u0000 \u0000 <section>\u0000 \u0000 <h3> Methods</h3>\u0000 \u0000 <p>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 <i>CENPM</i> 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.</p>\u0000 </section>\u0000 \u0000 <section>\u0000 \u0000 <h3> Results</h3>\u0000 \u0000 <p>Among the 12 035 analyzed genes, 363 genes were related to ACC metastasis and <i>CENPM</i> was identified as the hub gene. CENPM was upregulated in ACC samples and associated with metastasis and poor prognosis. Knockdown of <i>CENPM</i> 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 <i>CENPM</i> was knocked down. FGL1, important components of ECM signalling and immune checkpoint ligand of LAG3, were downregulated following <i>CENPM</i> silence, overexpressed in human advanced ACC samples, and colocalized with CENPM. Physical interaction between CENPM and FGL1 was identified. Overexpression of <i>FGL1</i> rescued migration and invasion of <i>CENPM</i> knockdown ACC cells.</p>\u0000 </section>\u0000 \u0000 <section>\u0000 \u0000 <h3> Conclusions</h3>\u0000 \u0000 <p><i>CENPM</i> 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.</p>\u0000 </section>\u0000 \u0000 <section>\u0000 \u0000 <h3> Highlights</h3>\u0000 \u0000 <div>\u0000 <ul>\u0000 \u0000 <li><i>CENPM</i> is the key gene that drives ACC metastasis, and a robust biomarker for ACC prognosis.</li>\u0000 \u0000 <li>Silencing <i>CENPM</i> impedes ACC metastasis in vitro and in vivo by physical interaction with immune checkpoint ligand FGL1.</li>\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}
<p>Dear Editor,</p><p>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,<span><sup>1</sup></span> may be indicative of high tumour mutation burden, a good predictor used to guide immunotherapy decisions in clinical,<span><sup>2</sup></span> 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.</p><p>Melanoma is a highly aggressive skin cancer originating from melanocyte transformation, and its incidence has been increasing globally in recent years.<span><sup>3</sup></span> 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,<span><sup>4-6</sup></span> 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.<span><sup>7-9</sup></span> 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.</p><p>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 <i>E</i>, resulting in an <i>S</i> matr
{"title":"iDICss robustly predicts melanoma immunotherapy response by synergizing genomic and transcriptomic knowledge via independent component analysis","authors":"Jiayue Qiu, Nana Jin, Lixin Cheng, Chen Huang","doi":"10.1002/ctm2.70183","DOIUrl":"10.1002/ctm2.70183","url":null,"abstract":"<p>Dear Editor,</p><p>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,<span><sup>1</sup></span> may be indicative of high tumour mutation burden, a good predictor used to guide immunotherapy decisions in clinical,<span><sup>2</sup></span> 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.</p><p>Melanoma is a highly aggressive skin cancer originating from melanocyte transformation, and its incidence has been increasing globally in recent years.<span><sup>3</sup></span> 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,<span><sup>4-6</sup></span> 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.<span><sup>7-9</sup></span> 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.</p><p>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 <i>E</i>, resulting in an <i>S</i> 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}
Heng Xu, Lu Wen, Yi Luo, Jiaying Zhou, Sheng Yao, Wei Ding, Jing Feng
<p>Dear Editor:</p><p>Here, we demonstrated that cannabinoid receptor 2 (CB2) plays a pivotal role in Schwann cells (SCs) by promoting the remyelination process following peripheral nerve injury (PNI). Selective activation of CB2 shows potential as a therapeutic approach to enhance nerve repair in these injuries.</p><p>Schwann cells form Büngner bands to guide axonal regeneration during PNI. However, the slow pace of axonal growth (∼1–3 mm/day) often leads to the degradation of these structures within 8 weeks, thereby disrupting the regenerative process.<span><sup>1, 2</sup></span> CB2 activation has been shown to promote remyelination and enhance nerve regeneration by modulating inflammatory responses and supporting cellular processes essential for nerve repair.<span><sup>3, 4</sup></span> However, the cellular mechanisms of CB2 function in remyelination remain unclear. This prompted the hypothesis that selective activation of CB2 could maintain Schwann cell function and create a more conducive environment for axonal regeneration.</p><p>To investigate this, we employed a mouse model of crush injury and treated the mice with delta-9-tetrahydrocannabinol (Δ<sup>9</sup>-THC, a partial agonist of both CB1 and CB2 receptors, for 16 consecutive days; Figure S1A). Interestingly, Δ<sup>9</sup>-THC treatment resulted in significantly increased growth rate of injured axons and improved behaviour in mice, including reflexive mechanical allodynia and walking parameters (Figure S1B–K). To exclude the possibility that Δ<sup>9</sup>-THC exerts its effect via CB1 activation, we used CB2-specific agonists. Although the agonist (GW842166X, EC50: ∼63 nM) did not improve mechanical pain (Figure S2A), the more efficient agonist, AM1241 (EC50: ∼3.4 nM), showed therapeutic efficacy. As expected, compared with vehicle treatment, daily administration of AM1241 significantly improved mechanical allodynia, gait abnormalities and motor function in mice. These improvements were further abolished when AM1241 was co-administered with the CB2 antagonist AM630 (Figure 1 and Figure S2B). Consistent with these behavioural data, immunofluorescence staining showed that while nerves in the control group began to extend along their original growth direction, this change appeared to be more pronounced after AM1241 treatment (Figure S3A–C). We also examined the mRNA expression of genes related to myelination in the sciatic nerve tissues using real-time quantitative PCR (RT-qPCR). In the presence of AM1241, pro-myelination-related genes including Sox10, Egr2 and Tprv4 were significantly upregulated, whereas AM630 blocked CB2-dependent gene upregulation. Trpv4 has been proved to delay the re-myelination after nerve injury.<span><sup>5, 6</sup></span> Interestingly, the expression of the SCs dedifferentiation-related genes Sox2 and c-Jun was not affected (Figure S3D). Moreover, Aniline Blue staining and electron microscopy demonstrated that CB2 stimulation significantly increased myelin t
{"title":"Cannabinoid receptor 2 facilitates the Schwann cells-dependent peripheral nerve regeneration","authors":"Heng Xu, Lu Wen, Yi Luo, Jiaying Zhou, Sheng Yao, Wei Ding, Jing Feng","doi":"10.1002/ctm2.70184","DOIUrl":"10.1002/ctm2.70184","url":null,"abstract":"<p>Dear Editor:</p><p>Here, we demonstrated that cannabinoid receptor 2 (CB2) plays a pivotal role in Schwann cells (SCs) by promoting the remyelination process following peripheral nerve injury (PNI). Selective activation of CB2 shows potential as a therapeutic approach to enhance nerve repair in these injuries.</p><p>Schwann cells form Büngner bands to guide axonal regeneration during PNI. However, the slow pace of axonal growth (∼1–3 mm/day) often leads to the degradation of these structures within 8 weeks, thereby disrupting the regenerative process.<span><sup>1, 2</sup></span> CB2 activation has been shown to promote remyelination and enhance nerve regeneration by modulating inflammatory responses and supporting cellular processes essential for nerve repair.<span><sup>3, 4</sup></span> However, the cellular mechanisms of CB2 function in remyelination remain unclear. This prompted the hypothesis that selective activation of CB2 could maintain Schwann cell function and create a more conducive environment for axonal regeneration.</p><p>To investigate this, we employed a mouse model of crush injury and treated the mice with delta-9-tetrahydrocannabinol (Δ<sup>9</sup>-THC, a partial agonist of both CB1 and CB2 receptors, for 16 consecutive days; Figure S1A). Interestingly, Δ<sup>9</sup>-THC treatment resulted in significantly increased growth rate of injured axons and improved behaviour in mice, including reflexive mechanical allodynia and walking parameters (Figure S1B–K). To exclude the possibility that Δ<sup>9</sup>-THC exerts its effect via CB1 activation, we used CB2-specific agonists. Although the agonist (GW842166X, EC50: ∼63 nM) did not improve mechanical pain (Figure S2A), the more efficient agonist, AM1241 (EC50: ∼3.4 nM), showed therapeutic efficacy. As expected, compared with vehicle treatment, daily administration of AM1241 significantly improved mechanical allodynia, gait abnormalities and motor function in mice. These improvements were further abolished when AM1241 was co-administered with the CB2 antagonist AM630 (Figure 1 and Figure S2B). Consistent with these behavioural data, immunofluorescence staining showed that while nerves in the control group began to extend along their original growth direction, this change appeared to be more pronounced after AM1241 treatment (Figure S3A–C). We also examined the mRNA expression of genes related to myelination in the sciatic nerve tissues using real-time quantitative PCR (RT-qPCR). In the presence of AM1241, pro-myelination-related genes including Sox10, Egr2 and Tprv4 were significantly upregulated, whereas AM630 blocked CB2-dependent gene upregulation. Trpv4 has been proved to delay the re-myelination after nerve injury.<span><sup>5, 6</sup></span> Interestingly, the expression of the SCs dedifferentiation-related genes Sox2 and c-Jun was not affected (Figure S3D). Moreover, Aniline Blue staining and electron microscopy demonstrated that CB2 stimulation significantly increased myelin t","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/PMC11707426/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142945714","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}
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