- Article
- Published:
Y chromosome loss in cancer drives growth by evasion of adaptive immunity
- Hany A. Abdel-Hafiz ORCID:orcid.org/0000-0002-5402-55401 na1,
- Johanna M. Schafer ORCID:orcid.org/0000-0001-7834-56212 na1 nAff5,
- Xingyu Chen1 na1,
- Tong Xiao2,
- Timothy D. Gauntner ORCID:orcid.org/0000-0002-4745-21302,
- Zihai Li ORCID:orcid.org/0000-0003-4603-927X2 na2 &
- …
- Dan Theodorescu ORCID:orcid.org/0000-0002-8708-82061,3,4 na2
Naturevolume 619, pages624–631 (2023)Cite this article
29kAccesses
1622Altmetric
AnAuthor Correction to this article was published on 29 January 2024
This article has beenupdated
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.
This is a preview of subscription content,access via your institution
Access options
Access Nature and 54 other Nature Portfolio journals
Get Nature+, our best-value online-access subscription
9,800 Yen / 30 days
cancel any time
Subscription info for Japanese customers
We have a dedicated website for our Japanese customers. Please go tonatureasia.com to subscribe to this journal.
Prices may be subject to local taxes which are calculated during checkout
Similar content being viewed by others
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 referenceGSE229233 andGSE230820. 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.Science377, 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 Cancer29, 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.Science347, 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. Genomics47, 525–537 (2015).
Robertson, A. G. et al. Comprehensive molecular characterization of muscle-invasive bladder cancer.Cell171, 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 Bioinformatics21, 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.Bladder3, 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.Cancer5, 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.Nature554, 544–548 (2018).
Zhang, Q. et al. Mosaic loss of chromosome Y promotes leukemogenesis and clonal hematopoiesis.JCI Insight7, e153768 (2022).
Minner, S. et al. Y chromosome loss is a frequent early event in urothelial bladder cancer.Pathology42, 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-specificN-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. USA116, 12410–12415 (2019).
Khan, O. et al. TOX transcriptionally and epigenetically programs CD8+ T cell exhaustion.Nature571, 211–218 (2019).
Thompson, D. J. et al. Genetic predisposition to mosaic Y chromosome loss in blood.Nature575, 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.Oncotarget9, 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.Bioinformatics29, 15–21 (2013).
Li, B. & Dewey, C. N. RSEM: accurate transcript quantification from RNA-Seq data with or without a reference genome.BMC Bioinformatics12, 323 (2011).
Ewels, P., Magnusson, M., Lundin, S. & Kaller, M. MultiQC: summarize analysis results for multiple tools and samples in a single report.Bioinformatics32, 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 Bioinformatics14, 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.Lancet387, 1909–1920 (2016).
Hedegaard, J. et al. Comprehensive transcriptional analysis of early-stage urothelial carcinoma.Cancer Cell30, 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.Immunity50, 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).
Author information
Johanna M. Schafer
Present address: Roche Diagnostics Solutions, Oro Valley, AZ, USA
These authors contributed equally: Hany A. Abdel-Hafiz, Johanna M. Schafer, Xingyu Chen
These authors jointly supervised this work: Zihai Li, Dan Theodorescu
Authors and Affiliations
Department of Urology, Cedars–Sinai Medical Center, Los Angeles, CA, USA
Hany A. Abdel-Hafiz, Xingyu Chen & Dan Theodorescu
Pelotonia Institute for Immuno-Oncology, The Ohio State University Comprehensive Cancer Center–The James, Columbus, OH, USA
Johanna M. Schafer, Tong Xiao, Timothy D. Gauntner & Zihai Li
Department of Pathology and Laboratory Medicine, Cedars-Sinai Medical Center, Los Angeles, CA, USA
Dan Theodorescu
Cedars-Sinai Cancer Center, Los Angeles, CA, USA
Dan Theodorescu
- Hany A. Abdel-Hafiz
You can also search for this author inPubMed Google Scholar
- Johanna M. Schafer
You can also search for this author inPubMed Google Scholar
- Xingyu Chen
You can also search for this author inPubMed Google Scholar
- Tong Xiao
You can also search for this author inPubMed Google Scholar
- Timothy D. Gauntner
You can also search for this author inPubMed Google Scholar
- Zihai Li
You can also search for this author inPubMed Google Scholar
- Dan Theodorescu
You can also search for this author inPubMed Google Scholar
Contributions
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.
Corresponding author
Correspondence toDan Theodorescu.
Ethics declarations
Competing interests
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.
Peer review
Peer review information
Nature thanks Mitchell Machiela, Soichi Sano and the other, anonymous, reviewer(s) for their contribution to the peer review of this work.
Additional information
Publisher’s note Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.
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, Logrankp-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 panelc. NE, not expressed.c, Kaplan-Meier plots of OS from TCGA data for males with MIBC and either high or lowKDM5D,TBL1Y,UTY (KDM6C), orZFY expression.d, Kaplan-Meier survival curves stratified by the Y signature score or expression levels forUTY andKDM5D in NMIBC from theE-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 ofUty,Kdm5d,Eifs3y, andDdx3y 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 ofUty and Kdm5d expression in the pooled Y+ and Y- sublines described ina. 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 unpairedt-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 ofUty 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 ina.c, Gene ontology (GO) pathway enrichment score plots of statistically significant gene set enrichment analyses (GSEA) using DEGs froma. 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 ina.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 ine. Statistics were determined using two-sided unpairedt-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 ina. Scale bar, 125 µM.c, Quantification (log2 fold change andP-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 unpairedt-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 ind. See Fig.5 for additional method details. Statistical significance was determined by two-sided unpairedt-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, andKdm5d 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 unpairedt-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.
Rights and permissions
Springer Nature or its licensor (e.g. a society or other partner) holds exclusive rights to this article under a publishing agreement with the author(s) or other rightsholder(s); author self-archiving of the accepted manuscript version of this article is solely governed by the terms of such publishing agreement and applicable law.
About this article
Cite this article
Abdel-Hafiz, H.A., Schafer, J.M., Chen, X.et al. Y chromosome loss in cancer drives growth by evasion of adaptive immunity.Nature619, 624–631 (2023). https://doi.org/10.1038/s41586-023-06234-x
Received:
Accepted:
Published:
Issue Date:
Share this article
Anyone you share the following link with will be able to read this content:
Sorry, a shareable link is not currently available for this article.
Provided by the Springer Nature SharedIt content-sharing initiative
This article is cited by
CENP-A/CENP-B uncoupling in the evolutionary reshuffling of centromeres in equids
- Eleonora Cappelletti
- Francesca M. Piras
- Elena Giulotto
Genome Biology (2025)
The effects of loss of Y chromosome on male health
- Bozena Bruhn-Olszewska
- Ellen Markljung
- Jan P. Dumanski
Nature Reviews Genetics (2025)
KDM5C and KDM5D mutations have different consequences in clear cell renal cell carcinoma cells
- Marvin Müller
- Kyra Zodel
- Ian J. Frew
Communications Biology (2025)
Sex differences in immunotherapy outcomes and tumor-infiltrating immune cell profiles in patients with advanced renal cell carcinoma
- Hiroki Ishihara
- Hironori Fukuda
- Toshio Takagi
Cancer Immunology, Immunotherapy (2025)
DNA methylation patterns contribute to changes of cellular differentiation pathways in leukocytes with LOY from patients with Alzheimer´s disease
- Marcin Jąkalski
- Bożena Bruhn-Olszewska
- Jan P. Dumanski
Cellular and Molecular Life Sciences (2025)
