Abstract
Loss of the Y chromosome (LOY) is observed in multiple cancer types, including 10–40% of bladder cancers1,2,3,4,5,6, but its clinical and biological significance is unknown. Here, using genomic and transcriptomic studies, we report that LOY correlates with poor prognoses in patients with bladder cancer. We performed in-depth studies of naturally occurring LOY mutant bladder cancer cells as well as those with targeted deletion of Y chromosome by CRISPR–Cas9. Y-positive (Y+) and Y-negative (Y–) tumours grew similarly in vitro, whereas Y− tumours were more aggressive than Y+ tumours in immune-competent hosts in a T cell-dependent manner. High-dimensional flow cytometric analyses demonstrated that Y− tumours promote striking dysfunction or exhaustion of CD8+ T cells in the tumour microenvironment. These findings were validated using single-nuclei RNA sequencing and spatial proteomic evaluation of human bladder cancers. Of note, compared with Y+ tumours, Y− tumours exhibited an increased response to anti-PD-1 immune checkpoint blockade therapy in both mice and patients with cancer. Together, these results demonstrate that cancer cells with LOY mutations alter T cell function, promoting T cell exhaustion and sensitizing them to PD-1-targeted immunotherapy. This work provides insights into the basic biology of LOY mutation and potential biomarkers for improving cancer immunotherapy.
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Data availability
The data supporting the findings of this study are available within the article, extended data and supplementary information files. RNA-seq and WES data are available at the Gene Expression Omnibus with accession reference GSE229233 and GSE230820. Source data are provided with this paper.
Change history
29 January 2024
A Correction to this paper has been published: https://doi.org/10.1038/s41586-024-07104-w
References
Caceres, A., Jene, A., Esko, T., Perez-Jurado, L. A. & Gonzalez, J. R. Extreme downregulation of chromosome Y and cancer risk in men. J. Natl Cancer Inst. 112, 913–920 (2020).
Kido, T. & Lau, Y. F. Roles of the Y chromosome genes in human cancers. Asian J. Androl. 17, 373–380 (2015).
Brown, D. W. & Machiela, M. J. Why Y? Downregulation of chromosome Y genes potentially contributes to elevated cancer risk. J. Natl Cancer Inst. 112, 871–872 (2020).
Panani, A. D. & Roussos, C. Sex chromosome abnormalities in bladder cancer: Y polysomies are linked to PT1-grade III transitional cell carcinoma. Anticancer Res. 26, 319–323 (2006).
Sauter, G. et al. Y chromosome loss detected by FISH in bladder cancer. Cancer Genet. Cytogenet. 82, 163–169 (1995).
Powell, I., Tyrkus, M. & Kleer, E. Apparent correlation of sex chromosome loss and disease course in urothelial cancer. Cancer Genet. Cytogenet. 50, 97–101 (1990).
Maan, A. A. et al. The Y chromosome: a blueprint for men’s health? Eur. J. Hum. Genet. 25, 1181–1188 (2017).
Adikusuma, F., Williams, N., Grutzner, F., Hughes, J. & Thomas, P. Targeted deletion of an entire chromosome using CRISPR/Cas9. Mol. Ther. 25, 1736–1738 (2017).
Sano, S. et al. Hematopoietic loss of Y chromosome leads to cardiac fibrosis and heart failure mortality. Science 377, 292–297 (2022).
Forsberg, L. A. et al. Mosaic loss of chromosome Y in peripheral blood is associated with shorter survival and higher risk of cancer. Nat. Genet. 46, 624–628 (2014).
Fadl-Elmula, I. et al. Karyotypic characterization of urinary bladder transitional cell carcinomas. Genes Chromosomes Cancer 29, 256–265 (2000).
Sauter, G., Moch, H., Mihatsch, M. J. & Gasser, T. C. Molecular cytogenetics of bladder cancer progression. Eur. Urol. 33, 9–10 (1998).
Smeets, W., Pauwels, R., Laarakkers, L., Debruyne, F. & Geraedts, J. Chromosomal analysis of bladder cancer. III. Nonrandom alterations. Cancer Genet. Cytogenet. 29, 29–41 (1987).
Sauter, G. et al. DNA aberrations in urinary bladder cancer detected by flow cytometry and FISH. Urol. Res. 25, S37–S43 (1997).
Neuhaus, M. et al. Polysomies but not Y chromosome losses have prognostic significance in pTa/pT1 urinary bladder cancer. Hum. Pathol. 30, 81–86 (1999).
Siegel, R. L., Miller, K. D., Fuchs, H. E. & Jemal, A. Cancer statistics, 2021. CA Cancer J. Clin. 71, 7–33 (2021).
Johansson, S. L. & Cohen, S. M. Epidemiology and etiology of bladder cancer. Semin. Surg. Oncol. 13, 291–298 (1997).
Dumanski, J. P. et al. Smoking is associated with mosaic loss of chromosome Y. Science 347, 81–83 (2015).
Tabayoyong, W. & Gao, J. The emerging role of immunotherapy in advanced urothelial cancers. Curr. Opin. Oncol. 30, 172–180 (2018).
Rouanne, M. et al. Development of immunotherapy in bladder cancer: present and future on targeting PD(L)1 and CTLA-4 pathways. World J. Urol. 36, 1727–1740 (2018).
Prokop, J. W. & Deschepper, C. F. Chromosome Y genetic variants: impact in animal models and on human disease. Physiol. Genomics 47, 525–537 (2015).
Robertson, A. G. et al. Comprehensive molecular characterization of muscle-invasive bladder cancer. Cell 171, 540–556.e525 (2017).
Lindskrog, S. V. et al. An integrated multi-omics analysis identifies prognostic molecular subtypes of non-muscle-invasive bladder cancer. Nat. Commun. 12, 2301 (2021).
Gonzalez, J. R. et al. MADloy: robust detection of mosaic loss of chromosome Y from genotype-array-intensity data. BMC Bioinformatics 21, 533 (2020).
Summerhayes, I. C. & Franks, L. M. Effects of donor age on neoplastic transformation of adult mouse bladder epithelium in vitro. J. Natl Cancer Inst. 62, 1017–1023 (1979).
Chan, E., Patel, A., Heston, W. & Larchian, W. Mouse orthotopic models for bladder cancer research. BJU Int. 104, 1286–1291 (2009).
White-Gilbertson, S., Davis, M., Voelkel-Johnson, C. & Kasman, L. M. Sex differences in the MB49 syngeneic, murine model of bladder cancer. Bladder 3, e22 (2016).
Tu, M. M. et al. Targeting DDR2 enhances tumor response to anti-PD-1 immunotherapy. Sci. Adv. 5, eaav2437 (2019).
Sugiura, K. & Stock, C. C. The effect of 2,4,6-triethylenimino-s-triazine on the growth of a variety of mouse and rat tumors. Cancer 5, 979–991 (1952).
Gouin, K. H. 3rd et al. An N-cadherin 2 expressing epithelial cell subpopulation predicts response to surgery, chemotherapy and immunotherapy in bladder cancer. Nat. Commun. 12, 4906 (2021).
Becht, E. et al. Dimensionality reduction for visualizing single-cell data using UMAP. Nat. Biotechnol. 37, 38–44 (2019).
Hashimoto, M. et al. CD8 T cell exhaustion in chronic infection and cancer: opportunities for interventions. Annu. Rev. Med. 69, 301–318 (2018).
Kwon, H. et al. Androgen conspires with the CD8+ T cell exhaustion program and contributes to sex bias in cancer. Sci. Immunol. 7, eabq2630 (2022).
Mariathasan, S. et al. TGFβ attenuates tumour response to PD-L1 blockade by contributing to exclusion of T cells. Nature 554, 544–548 (2018).
Zhang, Q. et al. Mosaic loss of chromosome Y promotes leukemogenesis and clonal hematopoiesis. JCI Insight 7, e153768 (2022).
Minner, S. et al. Y chromosome loss is a frequent early event in urothelial bladder cancer. Pathology 42, 356–359 (2010).
Fabris, V. T. et al. Cytogenetic characterization of the murine bladder cancer model MB49 and the derived invasive line MB49-I. Cancer Genet. 205, 168–176 (2012).
Ler, L. D. et al. Loss of tumor suppressor KDM6A amplifies PRC2-regulated transcriptional repression in bladder cancer and can be targeted through inhibition of EZH2. Sci. Transl. Med. 9, eaai8312 (2017).
Walport, L. J. et al. Human UTY(KDM6C) is a male-specific N-methyl lysyl demethylase. J. Biol. Chem. 289, 18302–18313 (2014).
Li, N. et al. JARID1D is a suppressor and prognostic marker of prostate cancer invasion and metastasis. Cancer Res. 76, 831–843 (2016).
Seo, H. et al. TOX and TOX2 transcription factors cooperate with NR4A transcription factors to impose CD8+ T cell exhaustion. Proc. Natl Acad. Sci. USA 116, 12410–12415 (2019).
Khan, O. et al. TOX transcriptionally and epigenetically programs CD8+ T cell exhaustion. Nature 571, 211–218 (2019).
Thompson, D. J. et al. Genetic predisposition to mosaic Y chromosome loss in blood. Nature 575, 652–657 (2019).
Lattime, E. C., Gomella, L. G. & McCue, P. A. Murine bladder carcinoma cells present antigen to BCG-specific CD4+ T-cells. Cancer Res. 52, 4286–4290 (1992).
Tu, M. M. et al. Inhibition of the CCL2 receptor, CCR2, enhances tumor response to immune checkpoint therapy. Commun. Biol. 3, 720 (2020).
Song, N. J. et al. Treatment with soluble CD24 attenuates COVID-19-associated systemic immunopathology. J. Hematol. Oncol. 15, 5 (2022).
Richmond, C. S. et al. Glycogen debranching enzyme (AGL) is a novel regulator of non-small cell lung cancer growth. Oncotarget 9, 16718–16730 (2018).
Martin, M. Cutadapt removes adapter sequences from high-throughput sequencing reads. EMBnet J. 17, 10–12 (2011).
Dobin, A. et al. STAR: ultrafast universal RNA-seq aligner. Bioinformatics 29, 15–21 (2013).
Li, B. & Dewey, C. N. RSEM: accurate transcript quantification from RNA-Seq data with or without a reference genome. BMC Bioinformatics 12, 323 (2011).
Ewels, P., Magnusson, M., Lundin, S. & Kaller, M. MultiQC: summarize analysis results for multiple tools and samples in a single report. Bioinformatics 32, 3047–3048 (2016).
Love, M. I., Huber, W. & Anders, S. Moderated estimation of fold change and dispersion for RNA-seq data with DESeq2. Genome Biol. 15, 550 (2014).
DePristo, M. A. et al. A framework for variation discovery and genotyping using next-generation DNA sequencing data. Nat. Genet. 43, 491–498 (2011).
Hernandez, S. et al. Challenges and opportunities for immunoprofiling using a spatial high-plex technology: the NanoString GeoMx((R)) digital spatial profiler. Front. Oncol. 12, 890410 (2022).
Hänzelmann, S., Castelo, R. & Guinney, J. GSVA: gene set variation analysis for microarray and RNA-seq data. BMC Bioinformatics 14, 7 (2013).
Wolf, F. A., Angerer, P. & Theis, F. J. SCANPY: large-scale single-cell gene expression data analysis. Genome Biol. 19, 15 (2018).
Rosenberg, J. E. et al. Atezolizumab in patients with locally advanced and metastatic urothelial carcinoma who have progressed following treatment with platinum-based chemotherapy: a single-arm, multicentre, phase 2 trial. Lancet 387, 1909–1920 (2016).
Hedegaard, J. et al. Comprehensive transcriptional analysis of early-stage urothelial carcinoma. Cancer Cell 30, 27–42 (2016).
Becht, E. et al. Estimating the population abundance of tissue-infiltrating immune and stromal cell populations using gene expression. Genome Biol. 17, 218 (2016).
Andreatta, M. et al. Interpretation of T cell states from single-cell transcriptomics data using reference atlases. Nat. Commun. 12, 2965 (2021).
Bassez, A. et al. A single-cell map of intratumoral changes during anti-PD1 treatment of patients with breast cancer. Nat. Med. 27, 820–832 (2021).
Daud, A. I. et al. Tumor immune profiling predicts response to anti-PD-1 therapy in human melanoma. J. Clin. Invest. 126, 3447–3452 (2016).
Gros, A. et al. PD-1 identifies the patient-specific CD8+ tumor-reactive repertoire infiltrating human tumors. J. Clin. Invest. 124, 2246–2259 (2014).
Miller, B. C. et al. Subsets of exhausted CD8+ T cells differentially mediate tumor control and respond to checkpoint blockade. Nat. Immunol. 20, 326–336 (2019).
Siddiqui, I. et al. Intratumoral Tcf1+PD-1+CD8+ T cells with stem-like properties promote tumor control in response to vaccination and checkpoint blockade immunotherapy. Immunity 50, 195–211.e110 (2019).
Thommen, D. S. et al. A transcriptionally and functionally distinct PD-1+CD8+ T cell pool with predictive potential in non-small-cell lung cancer treated with PD-1 blockade. Nat. Med. 24, 994–1004 (2018).
Kumagai, S. et al. The PD-1 expression balance between effector and regulatory T cells predicts the clinical efficacy of PD-1 blockade therapies. Nat. Immunol. 21, 1346–1358 (2020).
Nielsen, M. & Andreatta, M. NetMHCpan-3.0; improved prediction of binding to MHC class I molecules integrating information from multiple receptor and peptide length datasets. Genome Med. 8, 33 (2016).
Acknowledgements
This work was supported in part by NIH P01CA278732 and R01CA143971 to D.T. and NIH R01CA262069, R01CA262388 and R01AI077283 to Z.L. J.M.S. and T.D.G. were supported by the Ohio State University Comprehensive Cancer Center’s Tumor Immunology T32 (2T32CA09223-16A1) post-doctoral fellowship award. We thank K. Walsh for providing the LOY gRNA plasmids, and N.-J. Song and B. Riesenberg for development of the ‘all immune phenotyping’ flow antibody panel. We acknowledge resources from the Immune Monitoring and Discovery Platform and the Pelotonia Institute for Immuno-Oncology at OSU Comprehensive Cancer Center (P30CA016058).
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The study was initiated by H.A.A.-H. and D.T. H.A.A.-H., J.M.S., X.C., T.X. and T.D.G. developed methodology. H.A.A.-H., J.M.S., X.C., T.X. and T.D.G. acquired data. H.A.A.-H. generated the MB49 cell line models and conducted the wild-type versus immune-compromised mouse model experiments. J.M.S. conducted the Y+ versus Y− mouse experiments and associated flow cytometry studies. T.X. and T.D.G. conducted the CRISPR Y-Scr and CRISPR Y-KO mouse experiments and associated flow cytometry studies. X.C. performed the human sample biostatistical analyses and associated graph generations. H.A.A.-H., J.M.S., X.C., T.X., T.D.G., Z.L. and D.T. analysed and interpreted data. H.A.A.-H., J.M.S., Z.L. and D.T. wrote the manuscript. D.T. and Z.L. supervised the study. All authors reviewed and approved the final manuscript.
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Z.L. reports personal consultation fees from Alphamab, HanchorBio, Henlius, Heat Biologic and Ikonisys outside the submitted work. All other authors declare no competing interests.
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Extended data figures and tables
Extended Data Fig. 1 LOY is associated with a worse clinical outcome for patients with MIBC and NMIBC.
a, Y chromosome genes expressed in normal bladder urothelium that were used to create a Y chromosome gene expression signature. b, Logrank p-values based on stratification by Y chromosome gene expression (normalized FPKM) on TCGA MIBC patient overall survival (OS). Genes resulting in statistically significant OS are plotted in panel c. NE, not expressed. c, Kaplan-Meier plots of OS from TCGA data for males with MIBC and either high or low KDM5D, TBL1Y, UTY (KDM6C), or ZFY expression. d, Kaplan-Meier survival curves stratified by the Y signature score or expression levels for UTY and KDM5D in NMIBC from the E-MTAB-4321 cohort. Survival differences are based on Logrank statistics. e, ChrY gene expression signature scores of TCGA data plotted with respect to extreme downregulation of chromosome Y (EDY, left panel) and Mosaic Alteration Detection for LOY (mLOY, right panel) levels. Statistical significance was determined by Wilcoxon rank-sum test (NoLOY n = 151, LOY n = 90, NoEDY n = 165, EDY n = 76). Boxplots represent the mean with first and third quartile data. Minimum and maximum datapoints are included.
Extended Data Fig. 2 Generation of Y+ and Y- BC models.
a, Histogram representation of deferentially regulated genes (DEG) from Y+ vs. Y- MB49 RNAseq data per mouse chromosome. b, qRT-PCR analysis of Uty, Kdm5d, Eifs3y, and Ddx3y expression in MB49 clones isolated from the parental MB49 compared to female murine breast cancer (E0771) and bladder cancer cells (NA13), and testis tissue. Curly brackets indicate the clonal lines used to generate the pooled Y+ and Y- MB49 sublines. c, qRT-PCR analysis of Uty and Kdm5d expression in the pooled Y+ and Y- sublines described in a. n = 3 biological replicates. Data are mean ± s.e.m. d, Bar graph of sequencing depth for each chromosome after performing whole exome sequencing (WES) on DNA from the Parental, Y-, and Y+ MB49 cell lines.
Extended Data Fig. 3 LOY has no effect on colony forming ability of BC in vitro.
a, MB49 Y+ and Y- cells were grown in 0.4% agar for two weeks. Colonies were stained with Nitro-BT and quantified using ImageJ. Average colony number and area were determined from those with a diameter that exceeded 100 µm (n = 4 biological replicates). Data representative of three independent experiments. Statistical significance was determined by two-sided unpaired t-test, P-value = 0.722. Data are mean ± s.e.m. b, In vitro cell proliferation (MTT cell viability) over a 6–8-day time course using three sets of genetically engineered MB49 cells: Y+, Y+ Kdm5d KO, Y+ Uty KO (left panel), Y-, Y- Kdm5d OE, Y- Uty OE (middle panel), and CRISPR-Y-Scr vs. CRISPR-Y-KO (right panel). n = 3 biological replicates. Data are mean ± s.e.m. c, qRT-PCR analysis of Uty expression in MB49 clones isolated from the CRISPR-generated Y-KO and Y-Scr MB49 cell lines. Curly brackets indicate the clonal lines used to generate the pooled Y+ Control and Y- KO MB49 sublines. Representative immunofluorescence images of the CRISPR-generated Y-KO and Y-Scr MB49 cell lines. Scale bar, 150 µM.
Extended Data Fig. 4 Increased lymphocyte activation in Y+ tumors.
a, Volcano plot of DEGs from bulk RNA isolated from Y+ and Y- MB49 tumors grown in male WT mice. Blue (Y+ tumors) and red (Y- tumors) genes correspond to statistically significant (Benjamini-Hochberg method, P < 0.05) genes that have a | >1 log2 | fold-change in expression. b, PCA of DEGs described in a. c, Gene ontology (GO) pathway enrichment score plots of statistically significant gene set enrichment analyses (GSEA) using DEGs from a. NES, normalized enrichment score.
Extended Data Fig. 5 Comprehensive immune phenotyping of tumor-infiltrating leukocytes (TILs) in Y+ and Y- MB49 tumors.
a, UMAPs demonstrating individual spectral flow cytometry analysis of protein marker expression in CD45+ immune cells isolated from Y+ and Y- MB49 tumors grown in WT male mice. b, Heatmap of relative protein expression from immune cells described in a. c, Violin plots of each tumor sample across each cluster from the CD45+ immune cell UMAP (see Fig. 3b). d, Violin plot of PD-1 and PD-L1 mean fluorescence intensity in CD45+ immune cells from Y+ and Y- MB49 tumors. e, Representative dot plots and percentages of CD8+ and CD4+ T cells gated on total CD3+ T cells from CRISPR-Y-Scr (n = 8) and CRISPR-Y-KO (n = 9), MB49 tumors grown for 22 or 17 days, respectively, in male WT mice (left panels). Percentage of CD8+ T cells of total CD3+ T cells per tumor sample (right panel). f, Percentage of CD206+PDL1+ macrophages among total CD11b+F4/80+ macrophages from Control and Y KO MB49 tumors described in e. Statistics were determined using two-sided unpaired t-tests.
Extended Data Fig. 6 GeoMX histological evaluation of infiltrating immune cells in Y- and Y+ MB49 tumors.
a, Table of markers that are functionally categorized for GeoMX evaluation of Y+ and Y- MB49 tumors. b, Representative H&E image (left), immunofluorescence detection of nuclei (blue), cytokeratin (green), CD45+ immune cells (red) (middle), and associated computational digital profiling (right) to quantify markers shown in a. Scale bar, 125 µM. c, Quantification (log2 fold change and P-value) of the markers listed in Y+ versus Y- MB49 tumors (n = 10 tumors per group and three TMA cores per tumor). Data representative of two independent experiments. Statistical significance was determined by two-sided unpaired t-test.
Extended Data Fig. 7 Characterization of tumor-infiltrating CD8+ T cells after PD-1 pathway blockade.
a–c, Relative spectral flow protein expression (a), sample-level violin plots per cluster (b), and heatmap of individual targets per cluster (c) after 200 μg anti-PD-1 or isotype control IgG treatments for 7 days using CD8+ T cells from Y+ and Y- MB49 tumors. d, Representative dot plots and percentages of TOX and/or GZMB-expressing CD8+ T cells from CRISPR-Y-Scr and CRISPR-Y-KO MB49 tumors grown in male WT mice after 200 μg anti-PD-1 or isotype control IgG treatments for 7 days. e–f, Percentage of PD1+TOX+ CD8+ T cells (e) and TOX−CD44+ (top panel) or TOX−ICOS+ (bottom panel) CD8+ T cells (f) from tumor samples described in d. See Fig. 5 for additional method details. Statistical significance was determined by two-sided unpaired t-test. Tests were conducted between isotype controls or between isotype controls and anti-PD1 treatment groups.
Extended Data Fig. 8 DDR-related pathways in TCGA Ylow vs. Yhigh BC.
a, Heatmap of the indicated pathways and metadata from BC TCGA data. b–c, box plot of tumor neoantigen burden (TNB) per megabase (P = 0.700) (b), and associated pathway enrichment levels (c) from Yhigh and Ylow tumors described in Fig. 1a. Statistical significance was determined by Wilcoxon test (Ylow n = 118 and Yhigh n = 182). Boxplots represent the mean with first and third quartile data. Minimum and maximum datapoints are included.
Extended Data Fig. 9 Defective DDR pathway activation in Y- MB49 cells.
Normalized enrichment scores of statistically significant GSEA GO pathways using DEGs from Y- vs. Y+ MB49 cell cultures. Purple color denotes DNA repair-related pathways enriched in Y- cells.
Extended Data Fig. 10 Elevated genomic instability in LOY, Uty KO, and Kdm5d KO MB49 lines.
Genome instability pathway enrichment scores using RNA-seq data from control and genetically modified MB49 cell lines (Y+ and Y- cells, n = 5 technical replicates. n = 3 for all other cell lines). Two-sided unpaired t-test. Boxplots represent the mean with first and third quartile data. Minimum and maximum datapoints are included.
Supplementary information
Supplementary Fig. 1
Spectral flow gating strategies.
Supplementary Table 1
Y chromosome gene expression in MB49 clones.
Supplementary Table 2
Immune Phenotyping spectral flow cytometry antibody panel.
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Abdel-Hafiz, H.A., Schafer, J.M., Chen, X. et al. Y chromosome loss in cancer drives growth by evasion of adaptive immunity. Nature 619, 624–631 (2023). https://doi.org/10.1038/s41586-023-06234-x
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DOI: https://doi.org/10.1038/s41586-023-06234-x
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