CoChat
Sign Up
Cited by 50Genetic and Clinical Aspects of Sex Determination and Chromosomal AbnormalitiesCancer Immunotherapy and BiomarkersCancer Genomics and Diagnostics

Concurrent loss of the Y chromosome in cancer and T cells impacts outcome

Open access
Chen, Xingyu, Shen, Yiling, Choi, Suhyeon, Abdel-Hafiz, Hany A., Basu, Mukta, Hoelzen, Lena, Tufano, Martina, Kailasam Mani, Saravana Kumar +7 more·2025·Nature
DOI 10.1038/s41586-025-09071-2PMC PMC12221978
Open this paper in CoChat — free account

Abstract

Loss of the Y chromosome (LOY) in peripheral blood mononuclear cells (PBMCs) is the most common somatic alteration in men and is associated with higher mortality from epithelial cancers1–3. In tumours, epithelial LOY is also associated with poor survival4–7. This raises several fundamental questions, such as why LOY in PBMCs drives cancer mortality and whether there is a relationship between LOY in PBMCs, PBMC-derived immune cells and cancer cells (and, if so, what its consequences are). We sought to answer these questions through a comprehensive pan-cancer analysis of bulk and single-cell RNA sequencing data from 29 human tumour types, along with autochthonous and syngeneic mouse models. In human and mouse tumours, malignant epithelial cells had the highest LOY prevalence, yet LOY was also present in tumour stromal and immune cells, with LOY in malignant epithelial cells predicting LOY in benign cells. LOY also correlated between paired tumour and PBMC samples from patients. Among benign cells, LOY induced the strongest shift in CD4+ and CD8+ T cells, with both showing transcriptomic signatures of immunosuppression. Furthermore, the magnitude of LOY in epithelial cells, CD4+ T cells and CD8+ T cells independently predicts survival, with tumours exhibiting concurrent epithelial and T cell LOY having the worst outcomes. Here we establish a model that links LOY in immune cells to LOY in malignant cells, which may explain in part why LOY in PBMCs is associated with increased cancer mortality. Comprehensive pan-cancer analysis of loss of the Y chromosome (LOY) in benign and malignant cells establishes a new model linking LOY in circulating and tumour-infiltrating immune cells to LOY in malignant cells.

The claims and statistics of this paper haven’t been checked yet.

Run the check — free, no account needed. It reads the open-access text and checks the sources it cites.

A few free checks a day · papers already verified open instantly · results are public

Integrity signals

Registry

Crossref · 10.1038/s41586-025-09071-2

Cites

5 of 5 references verified0 citation errors · 0 not checkable

Statistics

Claims the paper makes

The claims and statistics of this paper haven’t been checked yet.

Run the check — free, no account needed. It reads the open-access text and checks the sources it cites.

A few free checks a day · papers already verified open instantly · results are public

Claims other papers make about this one

Each is checked against this paper’s own text: does it say what the citing paper says it does?

  • The redundancy among sex markers helps address Amelogenin dropout caused by Y-chromosome deletions in certain leukemias and other tumor cells.

    “The redundancy among sex markers helps to address the rare but documented issue of Amelogenin dropout, which can be caused by Y-chromosome deletions in certain leukemias and other tumor cells [40].”

    From Genetic Insights into the Economic Toll of Cell Line Misidentification: A Comprehensive Review (2026) · cites this paper as [40]

    The 'Tumour LOY and cohort characteristics' section examines Y-chromosome loss across cancers and includes acute myeloid leukemia among the cancer types studied (page unavailable). The paper does not establish that this loss causes Amelogenin dropout or that redundant sex markers address it. (LLM, conf=0.82)

    Experimental — accuracy not yet measured

Self-check

Percentages, sums and reported differences re-computed from the paper’s own numbers.

The self-check needs the paper’s full text.

Cites

Flagged first, then by citations

Research works printed but not linked: 87

  1. [1]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). 24777449 PMC5536222 · DOI
  2. [2]Danielsson, M. et al. Longitudinal changes in the frequency of mosaic chromosome Y loss in peripheral blood cells of aging men varies profoundly between individuals. Eur. J. Hum. Genet. 28, 349–357 (2020). 31654039 PMC7028735 · DOI
  3. [3]Duan, Q. et al. Mosaic loss of chromosome Y in peripheral blood cells is associated with age-related macular degeneration in men. Cell Biosci 12, 73 (2022). 35642040 PMC9153148 · DOI
  4. [4]Kido, T. & Lau, Y. F. Roles of the Y chromosome genes in human cancers. Asian J. Androl. 17, 373–380 (2015). 25814157 PMC4430935 · DOI
  5. [5]Qi, M., Pang, J., Mitsiades, I., Lane, A. A. & Rheinbay, E. Loss of chromosome Y in primary tumors. Cell 10.1016/j.cell.2023.06.006 (2023). 37385248 · DOI
  6. [6]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). 31945785 PMC7492761 · DOI
  7. [7]Abdel-Hafiz, H. A. et al. Y chromosome loss in cancer drives growth by evasion of adaptive immunity. Nature 619, 624–631 (2023). 37344596 PMC10975863 · DOI
  8. [8]Noveski, P. et al. Loss of Y chromosome in peripheral blood of colorectal and prostate cancer patients. PLoS ONE 11, e0146264 (2016). 26745889 PMC4706411 · DOI
  9. [9]Mattisson, J. et al. Loss of chromosome Y in regulatory T cells. BMC Genomics 25, 243 (2024). 38443832 PMC10913415 · DOI
  10. [10]Sonugur, F. G. & Akbulut, H. The role of tumor microenvironment in genomic instability of malignant tumors. Front. Genet. 10, 1063 (2019). 31737046 PMC6828977 · DOI
  11. [11]Prokop, J. W. & Deschepper, C. F. Chromosome Y genetic variants: impact in animal models and on human disease. Physiol. Genomics 47, 525–537 (2015). 26286457 PMC4629007 · DOI
  12. [12]Scanlan, M. J., Simpson, A. J. & Old, L. J. The cancer/testis genes: review, standardization, and commentary. Cancer Immun. 4, 1 (2004). 14738373
  13. [13]Yuan, J. et al. Integrated analysis of genetic ancestry and genomic alterations across cancers. Cancer Cell 34, 549–560 e549 (2018). 30300578 PMC6348897 · DOI
  14. [14]Carrot-Zhang, J. et al. Comprehensive analysis of genetic ancestry and its molecular correlates in cancer. Cancer Cell 37, 639–654 e636 (2020). 32396860 PMC7328015 · DOI
  15. [15]McGrail, D. J. et al. High tumor mutation burden fails to predict immune checkpoint blockade response across all cancer types. Ann. Oncol. 32, 661–672 (2021). 33736924 PMC8053682 · DOI
  16. [16]Rizvi, N. A. et al. Mutational landscape determines sensitivity to PD-1 blockade in non-small cell lung cancer. Science 348, 124–128 (2015). 25765070 PMC4993154 · DOI · Paper page
  17. [17]Yarchoan, M., Johnson, B. A., Lutz, E. R., Laheru, D. A. & Jaffee, E. M. Targeting neoantigens to augment antitumour immunity. Nat. Rev. Cancer 17, 209–222 (2017). 28233802 PMC5575801 · DOI · Paper page
  18. [18]Lauss, M. et al. Mutational and putative neoantigen load predict clinical benefit of adoptive T cell therapy in melanoma. Nat. Commun. 8, 1738 (2017). 29170503 PMC5701046 · DOI
  19. [19]Telli, M. L. et al. Homologous recombination deficiency (HRD) score predicts response to platinum-containing neoadjuvant chemotherapy in patients with triple-negative breast cancer. Clin. Cancer Res. 22, 3764–3773 (2016). 26957554 PMC6773427 · DOI · Paper page
  20. [20]Abkevich, V. et al. Patterns of genomic loss of heterozygosity predict homologous recombination repair defects in epithelial ovarian cancer. Br. J. Cancer 107, 1776–1782 (2012). 23047548 PMC3493866 · DOI
  21. [21]Popova, T. et al. Ploidy and large-scale genomic instability consistently identify basal-like breast carcinomas with BRCA1/2 inactivation. Cancer Res. 72, 5454–5462 (2012). 22933060 · DOI
  22. [22]Birkbak, N. J. et al. Telomeric allelic imbalance indicates defective DNA repair and sensitivity to DNA-damaging agents. Cancer Discov. 2, 366–375 (2012). 22576213 PMC3806629 · DOI
  23. [24]Liu, X. et al. Th17 cells secrete TWEAK to trigger epithelial-mesenchymal transition and promote colorectal cancer liver metastasis. Cancer Res. 84, 1352–1371 (2024). 38335276 · DOI
  24. [25]Dumanski, J. P. et al. Smoking is associated with mosaic loss of chromosome Y. Science 347, 81–83 (2015). 25477213 PMC4356728 · DOI
  25. [26]Bonnet, S. et al. A mitochondria-K channel axis is suppressed in cancer and its normalization promotes apoptosis and inhibits cancer growth. Cancer Cell 11, 37–51 (2007). 17222789 · DOI
  26. [27]Pishesha, N., Harmand, T. J. & Ploegh, H. L. A guide to antigen processing and presentation. Nat. Rev. Immunol. 22, 751–764 (2022). 35418563 · DOI · Paper page
  27. [29]Fantini, D. et al. A carcinogen-induced mouse model recapitulates the molecular alterations of human muscle invasive bladder cancer. Oncogene 37, 1911–1925 (2018). 29367767 PMC5886988 · DOI
  28. [30]Williams, P. D., Lee, J. K. & Theodorescu, D. Molecular credentialing of rodent bladder carcinogenesis models. Neoplasia 10, 838–846 (2008). 18670642 PMC2481571 · DOI
  29. [31]Abdel-Hafiz, H. A. et al. Single-cell profiling of murine bladder cancer identifies sex-specific transcriptional signatures with prognostic relevance. iScience 26, 107703 (2023). 37701814 PMC10494466 · DOI
  30. [32]Puram, S. V. et al. Cellular states are coupled to genomic and viral heterogeneity in HPV-related oropharyngeal carcinoma. Nat. Genet. 55, 640–650 (2023). 37012457 PMC10191634 · DOI
  31. [33]Alvisi, G. et al. Multimodal single-cell profiling of intrahepatic cholangiocarcinoma defines hyperactivated Tregs as a potential therapeutic target. J. Hepatol. 77, 1359–1372 (2022). 35738508 · DOI
  32. [34]Yu, H. et al. Tumor-infiltrating myeloid cells confer de novo resistance to PD-L1 blockade through EMT-stromal and Tgfβ-dependent mechanisms. Mol. Cancer Ther. 21, 1729–1741 (2022). 36129800 PMC9706595 · DOI
  33. [35]Hosohama, L. et al. Colorectal cancer stem cell subtypes orchestrate distinct tumor microenvironments. Preprint at bioRxiv10.1101/2024.04.25.591144 (2024).
  34. [36]Li, R. et al. Mapping single-cell transcriptomes in the intra-tumoral and associated territories of kidney cancer. Cancer Cell 40, 1583–1599 e1510 (2022). 36423636 PMC9767677 · DOI
  35. [37]Cillo, A. R. et al. Immune landscape of viral- and carcinogen-driven head and neck cancer. Immunity 52, 183–199 e189 (2020). 31924475 PMC7201194 · DOI
  36. [38]Ruffin, A. T. et al. B cell signatures and tertiary lymphoid structures contribute to outcome in head and neck squamous cell carcinoma. Nat. Commun. 12, 3349 (2021). 34099645 PMC8184766 · DOI
  37. [40]Xue, R. et al. Liver tumour immune microenvironment subtypes and neutrophil heterogeneity. Nature 612, 141–147 (2022). 36352227 · DOI
  38. [41]Maluf, S. W., Martinez-Lopez, W. & da Silva, J. DNA damage: health and longevity. Oxid. Med. Cell Longev. 2018, 9701647 (2018). 30302175 PMC6158954 · DOI
  39. [42]Ten, A., Kumeiko, V., Farniev, V., Gao, H. L. & Shevtsov, M. Tumor microenvironment modulation by cancer-derived extracellular vesicles. Cells 13, 682 (2024). 38667297 PMC11049026 · DOI
  40. [43]Sakaguchi, S., Yamaguchi, T., Nomura, T. & Ono, M. Regulatory T cells and immune tolerance. Cell 133, 775–787 (2008). 18510923 · DOI
  41. [44]Kverneland, A. H. et al. Adoptive cell therapy with tumor-infiltrating lymphocytes supported by checkpoint inhibition across multiple solid cancer types. J. Immunother. Cancer 9, e003499 (2021). 34607899 PMC8491427 · DOI
  42. [45]Ratto, G. B. et al. A randomized trial of adoptive immunotherapy with tumor-infiltrating lymphocytes and interleukin-2 versus standard therapy in the postoperative treatment of resected nonsmall cell lung carcinoma. Cancer 78, 244–251 (1996). 8673999 · DOI
  43. [46]Liu, J. et al. An integrated TCGA pan-cancer clinical data resource to drive high-quality survival outcome analytics. Cell 173, 400–416.e411 (2018). 29625055 PMC6066282 · DOI · Paper page
  44. [47]Diboun, I., Wernisch, L., Orengo, C. A. & Koltzenburg, M. Microarray analysis after RNA amplification can detect pronounced differences in gene expression using limma. BMC Genomics 7, 252 (2006). 17029630 PMC1618401 · DOI
  45. [48]Barbie, D. A. et al. Systematic RNA interference reveals that oncogenic-driven cancers require TBK1. Nature 462, 108–112 (2009). 19847166 PMC2783335 · DOI
  46. [49]Hänzelmann, S., Castelo, R. & Guinney, J. GSVA: gene set variation analysis for microarray and RNA-seq data. BMC Bioinf. 14, 7 (2013). PMC3618321 23323831 · DOI · Paper page
  47. [50]Taylor, A. M. et al. Genomic and functional approaches to understanding cancer aneuploidy. Cancer Cell 33, 676–689.e3 (2018). 29622463 PMC6028190 · DOI
  48. [51]Mermel, C. H. et al. GISTIC2.0 facilitates sensitive and confident localization of the targets of focal somatic copy-number alteration in human cancers. Genome Biol. 12, R41 (2011). 21527027 PMC3218867 · DOI
  49. [52]Carter, S. L. et al. Absolute quantification of somatic DNA alterations in human cancer. Nat. Biotechnol. 30, 413–421 (2012). 22544022 PMC4383288 · DOI
  50. [53]Knijnenburg, T. A. et al. Genomic and molecular landscape of DNA damage repair deficiency across The Cancer Genome Atlas. Cell Rep. 23, 239–254.e6 (2018). 29617664 PMC5961503 · DOI
  51. [54]Timms, K. M. et al. Association of defects with genomic scores predictive of DNA damage repair deficiency among breast cancer subtypes. Breast Cancer Res. 16, 475 (2014). 25475740 PMC4308910 · DOI
  52. [55]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). 27029192 PMC4812631 · DOI
  53. [56]Hundal, J. et al. pVAC-Seq: a genome-guided in silico approach to identify tumor neoantigens. Genome Med. 8, 11. PMC4733280 26825632
  54. [57]Cristescu, R. et al. Molecular analysis of gastric cancer identifies subtypes associated with distinct clinical outcomes. Nat. Med. 21, 449–456 (2015). 25894828 · DOI · Paper page
  55. [58]Li, Y. et al. Pan-cancer characterization of immune-related lncRNAs identifies potential oncogenic biomarkers. Nat. Commun. 11, 1000 (2020). 32081859 PMC7035327 · DOI
  56. [59]Zeng, D. Q. et al. IOBR: multi-omics immuno-oncology biological research to decode tumor microenvironment and signatures. Front. Immunol. 12, 687975 (2021). 34276676 PMC8283787 · DOI
  57. [60]Chen, X. Y. et al. Turning up the heat on non-immunoreactive tumours: pyroptosis influences the tumor immune microenvironment in bladder cancer. Oncogene 40, 6381–6393 (2021). 34588621 · DOI
  58. [61]Chen, X. Y. et al. CD8 T effector and immune checkpoint signatures predict prognosis and responsiveness to immunotherapy in bladder cancer. Oncogene 40, 6223–6234 (2021). 34552192 · DOI
  59. [62]Mariathasan, S. et al. TGFβ attenuates tumour response to PD-L1 blockade by contributing to exclusion of T cells. Nature 554, 544–548 (2018). 29443960 PMC6028240 · DOI · Paper page
  60. [63]Li, R. Y. et al. Mapping single-cell transcriptomes in the intra-tumoral and associated territories of kidney cancer. Cancer Cell 40, 1583–1599.e10 (2022). 36423636 PMC9767677 · DOI
  61. [64]Zhang, Y. P. et al. Single-cell analyses of renal cell cancers reveal insights into tumor microenvironment, cell of origin, and therapy response. Proc. Natl Acad. Sci. USA 118, e2103240118 (2021). 34099557 PMC8214680 · DOI
  62. [65]Werba, G. et al. Single-cell RNA sequencing reveals the effects of chemotherapy on human pancreatic adenocarcinoma and its tumor microenvironment. Nat. Commun. 14, 797 (2023). 36781852 PMC9925748 · DOI
  63. [66]Ma, L. C. et al. Tumor cell biodiversity drives microenvironmental reprogramming in liver cancer. Cancer Cell 36, 418–430.e6 (2019). 31588021 PMC6801104 · DOI
  64. [67]Regner, M. J. et al. A multi-omic single-cell landscape of human gynecologic malignancies. Mol. Cell 81, 4924–4941.e10 (2021). 34739872 PMC8642316 · DOI
  65. [68]Chen, Y. P. et al. Single-cell transcriptomics reveals regulators underlying immune cell diversity and immune subtypes associated with prognosis in nasopharyngeal carcinoma. Cell Res. 30, 1024–1042 (2020). 32686767 PMC7784929 · DOI
  66. [69]Xu, J. F. et al. Single-cell RNA sequencing reveals the tissue architecture in human high-grade serous ovarian cancer. Clin. Cancer Res. 28, 3590–3602 (2022). 35675036 PMC9662915 · DOI
  67. [70]Li, C. B. et al. Single-cell transcriptomics reveals cellular heterogeneity and molecular stratification of cervical cancer. Commun. Biol. 5, 1208 (2022). 36357663 PMC9649750 · DOI
  68. [71]Luo, H. et al. Pan-cancer single-cell analysis reveals the heterogeneity and plasticity of cancer-associated fibroblasts in the tumor microenvironment. Nat. Commun. 13, 6619 (2022). 36333338 PMC9636408 · DOI
  69. [72]Sathe, A. et al. Single-cell genomic characterization reveals the cellular reprogramming of the gastric tumor microenvironment. Clin. Cancer Res. 26, 2640–2653 (2020). 32060101 PMC7269843 · DOI
  70. [73]Kim, N. et al. Single-cell RNA sequencing demonstrates the molecular and cellular reprogramming of metastatic lung adenocarcinoma. Nat. Commun. 11, 2285 (2020). 32385277 PMC7210975 · DOI
  71. [74]Lambrechts, D. et al. Phenotype molding of stromal cells in the lung tumor microenvironment. Nat. Med. 24, 1277–1289 (2018). 29988129 · DOI · Paper page
  72. [75]Chen, S. J. et al. Single-cell analysis reveals transcriptomic remodellings in distinct cell types that contribute to human prostate cancer progression. Nat. Cell Biol. 23, 87–98 (2021). 33420488 · DOI
  73. [76]Ma, X. S. et al. Identification of a distinct luminal subgroup diagnosing and stratifying early stage prostate cancer by tissue-based single-cell RNA sequencing. Mol. Cancer 19, 147 (2020). 33032611 PMC7545561 · DOI
  74. [77]Luo, H. et al. Characterizing dedifferentiation of thyroid cancer by integrated analysis. Sci. Adv. 7, eabf3657 (2021). 34321197 PMC8318367 · DOI
  75. [78]Chen, Z. H. et al. Single-cell RNA sequencing highlights the role of inflammatory cancer-associated fibroblasts in bladder urothelial carcinoma. Nat. Commun. 11, 5077 (2020). 33033240 PMC7545162 · DOI · Paper page
  76. [79]Qian, J. B. et al. A pan-cancer blueprint of the heterogeneous tumor microenvironment revealed by single-cell profiling. Cell Res. 30, 745–762 (2020). 32561858 PMC7608385 · DOI
  77. [80]Zhang, M. et al. Single-cell transcriptomic architecture and intercellular crosstalk of human intrahepatic cholangiocarcinoma. J. Hepatol. 73, 1118–1130 (2020). 32505533 · DOI
  78. [81]Peng, J. Y. et al. Single-cell RNA-seq highlights intra-tumoral heterogeneity and malignant progression in pancreatic ductal adenocarcinoma. Cell Res. 29, 725–738 (2019). 31273297 PMC6796938 · DOI · Paper page
  79. [82]Lin, W. et al. Single-cell transcriptome analysis of tumor and stromal compartments of pancreatic ductal adenocarcinoma primary tumors and metastatic lesions. Genome Med. 12, 80 (2020). 32988401 PMC7523332 · DOI
  80. [84]Conde, C. D. et al. Cross-tissue immune cell analysis reveals tissue-specific features in humans. Science 376, eabl5197 (2022). 35549406 PMC7612735 · DOI
  81. [85]Zhao, Q. et al. Single-cell transcriptome analyses reveal endothelial cell heterogeneity in tumors and changes following antiangiogenic treatment. Cancer Res. 78, 2370–2382 (2018). 29449267 · DOI
  82. [87]Weglarz, T. C., Degen, J. L. & Sandgren, E. P. Hepatocyte transplantation into diseased mouse liver. Kinetics of parenchymal repopulation and identification of the proliferative capacity of tetraploid and octaploid hepatocytes. Am. J. Pathol. 157, 1963–1974 (2000). 11106569 PMC1885759 · DOI
  83. [88]Thorsson, V. et al. The immune landscape of cancer. Immunity 48, 812–830.e814 (2018). 29628290 PMC5982584 · DOI · Paper page
  84. [89]Chu, Y. S. et al. Pan-cancer T cell atlas links a cellular stress response state to immunotherapy resistance. Nat. Med. 29, 1550–1562 (2023). 37248301 PMC11421770 · DOI
  85. [90]Ho, A. S. et al. Comparative proteomic analysis of HPV(+) oropharyngeal squamous cell carcinoma recurrence. J. Proteome Res. 21, 200–208 (2022). 34846153 · DOI
  86. [91]Lausen, B. & Schumacher, M. Maximally selected rank statistics. Biometrics 48, 73–85 (1992). · DOI
  87. [92]Grambsch, P. M. & Therneau, T. M. Proportional hazards tests and diagnostics based on weighted residuals. Biometrika 81, 515–526 (1994). · DOI · Paper page

Cited by

Sorted by citations

Authors
Chen, X., Shen, Y., Choi, S., Abdel-Hafiz, H. A., Basu, M., Hoelzen, L., Tufano, M., Kailasam Mani, S. K., Ranjpour, M., Zhu, J., Ramanujan, V. K., Koltsova, E. K., Calsavara, V. F., Knott, S. R. V., & Theodorescu, D.
Affiliations
Cedars-Sinai Medical Center (US)
Corresponding author
  • Knott, Simon R. V.
  • Theodorescu, Dan
ORCID
Venue
Nature
Publisher
Springer Science and Business Media LLC
Published
2025-06-04
Volume
642
Issue
8069
Pages
1041-1050
ISSN
  • 0028-0836 (ISSN-L)
  • 1476-4687
DOI
10.1038/s41586-025-09071-2
PMC
PMC12221978
OpenAlex
W4411022810
Cited by
50
References
5
Citations by year
2026: 39 · 2025: 11
FWCI
29.39
Type
journal article
Language
en
Open access
Licence
Locations
  • Nature (publishedVersion, cc-by-nc-nd)
  • PubMed Central (publishedVersion, cc-by-nc-nd)
  • PubMed (publishedVersion)
Funders
  • National Institutes of Health (UL1TR001881)
  • National Cancer Institute (R01CA273925, P01CA278732)
  • National Center for Advancing Translational Sciences
Topics
  • Life Sciences › Biochemistry, Genetics and Molecular Biology › Genetics › Genetic and Clinical Aspects of Sex Determination and Chromosomal Abnormalities
  • Health Sciences › Medicine › Oncology › Cancer Immunotherapy and Biomarkers
  • Life Sciences › Biochemistry, Genetics and Molecular Biology › Cancer Research › Cancer Genomics and Diagnostics
Keywords
tumor immunosuppression, CD4+ T cells, CD8+ T cells, tumor-infiltrating immune cells, tumor stroma, epithelial cancers, cancer survival, pan-cancer analysis, tumor microenvironment, mouse tumor model
MeSH terms
Animals, Epithelial Cells, Female, Humans, Leukocytes, Mononuclear, Male, Neoplasms, T-Lymphocytes, CD4-Positive T-Lymphocytes, CD8-Positive T-Lymphocytes, Chromosomes, Human, Y, Mice, Single-Cell Analysis

About these checks

Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.

Statistics and arithmetic are recomputed, not judged.

Confidence bands are shown only for fields calibrated on our annotated test set. None is calibrated yet, so no band is shown.

What counts as a claim ›How we measure confidence ›How we check this ›

Are you an author, or do you see a problem with this page? Request a review