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).
Google Scholar
Kido, T. & Lau, Y. F. Roles of the Y chromosome genes in human cancers. Asian J. Androl. 17, 373–380 (2015).
Google Scholar
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).
Google Scholar
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).
Google Scholar
Sauter, G. et al. Y chromosome loss detected by FISH in bladder cancer. Cancer Genet. Cytogenet. 82, 163–169 (1995).
Google Scholar
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).
Google Scholar
Maan, A. A. et al. The Y chromosome: a blueprint for men’s health? Eur. J. Hum. Genet. 25, 1181–1188 (2017).
Google Scholar
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).
Google Scholar
Sano, S. et al. Hematopoietic loss of Y chromosome leads to cardiac fibrosis and heart failure mortality. Science 377, 292–297 (2022).
Google Scholar
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).
Google Scholar
Fadl-Elmula, I. et al. Karyotypic characterization of urinary bladder transitional cell carcinomas. Genes Chromosomes Cancer 29, 256–265 (2000).
Google Scholar
Sauter, G., Moch, H., Mihatsch, M. J. & Gasser, T. C. Molecular cytogenetics of bladder cancer progression. Eur. Urol. 33, 9–10 (1998).
Google Scholar
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).
Google Scholar
Sauter, G. et al. DNA aberrations in urinary bladder cancer detected by flow cytometry and FISH. Urol. Res. 25, S37–S43 (1997).
Google Scholar
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).
Google Scholar
Siegel, R. L., Miller, K. D., Fuchs, H. E. & Jemal, A. Cancer statistics, 2021. CA Cancer J. Clin. 71, 7–33 (2021).
Google Scholar
Johansson, S. L. & Cohen, S. M. Epidemiology and etiology of bladder cancer. Semin. Surg. Oncol. 13, 291–298 (1997).
Google Scholar
Dumanski, J. P. et al. Smoking is associated with mosaic loss of chromosome Y. Science 347, 81–83 (2015).
Google Scholar
Tabayoyong, W. & Gao, J. The emerging role of immunotherapy in advanced urothelial cancers. Curr. Opin. Oncol. 30, 172–180 (2018).
Google Scholar
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).
Google Scholar
Prokop, J. W. & Deschepper, C. F. Chromosome Y genetic variants: impact in animal models and on human disease. Physiol. Genomics 47, 525–537 (2015).
Google Scholar
Robertson, A. G. et al. Comprehensive molecular characterization of muscle-invasive bladder cancer. Cell 171, 540–556.e525 (2017).
Google Scholar
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).
Google Scholar
Gonzalez, J. R. et al. MADloy: robust detection of mosaic loss of chromosome Y from genotype-array-intensity data. BMC Bioinformatics 21, 533 (2020).
Google Scholar
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).
Google Scholar
Chan, E., Patel, A., Heston, W. & Larchian, W. Mouse orthotopic models for bladder cancer research. BJU Int. 104, 1286–1291 (2009).
Google Scholar
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).
Google Scholar
Tu, M. M. et al. Targeting DDR2 enhances tumor response to anti-PD-1 immunotherapy. Sci. Adv. 5, eaav2437 (2019).
Google Scholar
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).
Google Scholar
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).
Google Scholar
Becht, E. et al. Dimensionality reduction for visualizing single-cell data using UMAP. Nat. Biotechnol. 37, 38–44 (2019).
Google Scholar
Hashimoto, M. et al. CD8 T cell exhaustion in chronic infection and cancer: opportunities for interventions. Annu. Rev. Med. 69, 301–318 (2018).
Google Scholar
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).
Google Scholar
Mariathasan, S. et al. TGFβ attenuates tumour response to PD-L1 blockade by contributing to exclusion of T cells. Nature 554, 544–548 (2018).
Google Scholar
Zhang, Q. et al. Mosaic loss of chromosome Y promotes leukemogenesis and clonal hematopoiesis. JCI Insight 7, e153768 (2022).
Google Scholar
Minner, S. et al. Y chromosome loss is a frequent early event in urothelial bladder cancer. Pathology 42, 356–359 (2010).
Google Scholar
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).
Google Scholar
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).
Google Scholar
Walport, L. J. et al. Human UTY(KDM6C) is a male-specific N-methyl lysyl demethylase. J. Biol. Chem. 289, 18302–18313 (2014).
Google Scholar
Li, N. et al. JARID1D is a suppressor and prognostic marker of prostate cancer invasion and metastasis. Cancer Res. 76, 831–843 (2016).
Google Scholar
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).
Google Scholar
Khan, O. et al. TOX transcriptionally and epigenetically programs CD8+ T cell exhaustion. Nature 571, 211–218 (2019).
Google Scholar
Thompson, D. J. et al. Genetic predisposition to mosaic Y chromosome loss in blood. Nature 575, 652–657 (2019).
Google Scholar
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).
Google Scholar
Tu, M. M. et al. Inhibition of the CCL2 receptor, CCR2, enhances tumor response to immune checkpoint therapy. Commun. Biol. 3, 720 (2020).
Google Scholar
Song, N. J. et al. Treatment with soluble CD24 attenuates COVID-19-associated systemic immunopathology. J. Hematol. Oncol. 15, 5 (2022).
Google Scholar
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).
Google Scholar
Martin, M. Cutadapt removes adapter sequences from high-throughput sequencing reads. EMBnet J. 17, 10–12 (2011).
Google Scholar
Dobin, A. et al. STAR: ultrafast universal RNA-seq aligner. Bioinformatics 29, 15–21 (2013).
Google Scholar
Li, B. & Dewey, C. N. RSEM: accurate transcript quantification from RNA-Seq data with or without a reference genome. BMC Bioinformatics 12, 323 (2011).
Google Scholar
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).
Google Scholar
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).
Google Scholar
DePristo, M. A. et al. A framework for variation discovery and genotyping using next-generation DNA sequencing data. Nat. Genet. 43, 491–498 (2011).
Google Scholar
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).
Google Scholar
Hänzelmann, S., Castelo, R. & Guinney, J. GSVA: gene set variation analysis for microarray and RNA-seq data. BMC Bioinformatics 14, 7 (2013).
Google Scholar
Wolf, F. A., Angerer, P. & Theis, F. J. SCANPY: large-scale single-cell gene expression data analysis. Genome Biol. 19, 15 (2018).
Google Scholar
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).
Google Scholar
Hedegaard, J. et al. Comprehensive transcriptional analysis of early-stage urothelial carcinoma. Cancer Cell 30, 27–42 (2016).
Google Scholar
Becht, E. et al. Estimating the population abundance of tissue-infiltrating immune and stromal cell populations using gene expression. Genome Biol. 17, 218 (2016).
Google Scholar
Andreatta, M. et al. Interpretation of T cell states from single-cell transcriptomics data using reference atlases. Nat. Commun. 12, 2965 (2021).
Google Scholar
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).
Google Scholar
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).
Google Scholar
Gros, A. et al. PD-1 identifies the patient-specific CD8+ tumor-reactive repertoire infiltrating human tumors. J. Clin. Invest. 124, 2246–2259 (2014).
Google Scholar
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).
Google Scholar
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).
Google Scholar
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).
Google Scholar
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).
Google Scholar
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).
Google Scholar