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Landscape of B cell immunity and related immune evasion in human cancers

Nature Genetics volume 51, pages 560–567 (2019)Cite this article

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A Publisher Correction to this article was published on 17 May 2019

This article has been updated

Abstract

Tumor-infiltrating B cells are an important component in the microenvironment but have unclear anti-tumor effects. We enhanced our previous computational algorithm TRUST to extract the B cell immunoglobulin hypervariable regions from bulk tumor RNA-sequencing data. TRUST assembled more than 30 million complementarity-determining region 3 sequences of the B cell heavy chain (IgH) from The Cancer Genome Atlas. Widespread B cell clonal expansions and immunoglobulin subclass switch events were observed in diverse human cancers. Prevalent somatic copy number alterations in the MICA and MICB genes related to antibody-dependent cell-mediated cytotoxicity were identified in tumors with elevated B cell activity. The IgG3–1 subclass switch interacts with B cell–receptor affinity maturation and defects in the antibody-dependent cell-mediated cytotoxicity pathway. Comprehensive pancancer analyses of tumor-infiltrating B cell–receptor repertoires identified novel tumor immune evasion mechanisms through genetic alterations. The IgH sequences identified here are potentially useful resources for future development of immunotherapies.

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Fig. 1: TRUST performance on tumor samples with matched BCR-seq data.
Fig. 2: Immunoglobulin isotypes and SHMs of the tumor-infiltrating B cell IgH repertoire.
Fig. 3: Immunoglobulin isotype subclass switches are associated with B cell and T cell activation.
Fig. 4: Features of tumor-infiltrating B cell repertoire compared between tumor and adjacent normal tissues.
Fig. 5: Interaction between the IgG3–1 switch and SHMs.
Fig. 6: Potential immune evasion of ADCC through MIC shedding.

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Code availability

Modified TRUST applicable to BCR IgH CDR3 assemblies and supporting files are available at https://bitbucket.org/liulab/trust/. Code for performance evaluation has been deposited at https://bitbucket.org/liulab/ng-bcr-validate/.

Data availability

The data sets generated during the study are available from FireCloud and with dbGap permission to retrieve restricted TCGA data. The RNA-seq data set generated for validating TRUST performance is available in the SRA repository (PRJNA492301), and the matched iRepertoire data are available at https://bitbucket.org/liulab/ng-bcr-validate/src/master/iRep/.

Change history

  • 17 May 2019

    An amendment to this paper has been published and can be accessed via a link at the top of the paper.

References

  1. Thorsson, V. et al. The immune landscape of cancer. Immunity 48, 812–830.e14 (2018).

    Article  CAS  Google Scholar 

  2. Nutt, S. L., Hodgkin, P. D., Tarlinton, D. M. & Corcoran, L. M. The generation of antibody-secreting plasma cells. Nat. Rev. Immunol. 15, 160–171 (2015).

    Article  CAS  Google Scholar 

  3. Raposo, G. et al. B lymphocytes secrete antigen-presenting vesicles. J. Exp. Med. 183, 1161–1172 (1996).

    Article  CAS  Google Scholar 

  4. Hagn, M. et al. Human B cells differentiate into granzyme B-secreting cytotoxic B lymphocytes upon incomplete T-cell help. Immunol. Cell Biol. 90, 457–467 (2012).

    Article  CAS  Google Scholar 

  5. Nelson, B. H. CD20+ B cells: the other tumor-infiltrating lymphocytes. J. Immunol. 185, 4977–4982 (2010).

    Article  CAS  Google Scholar 

  6. Linnebacher, M. & Maletzki, C. Tumor-infiltrating B cells: the ignored players in tumor immunology. Oncoimmunology 1, 1186–1188 (2012).

    Article  Google Scholar 

  7. Nielsen, J. S. & Nelson, B. H. Tumor-infiltrating B cells and T cells: working together to promote patient survival. Oncoimmunology 1, 1623–1625 (2012).

    Article  Google Scholar 

  8. Al-Shibli, K. I. et al. Prognostic effect of epithelial and stromal lymphocyte infiltration in non-small cell lung cancer. Clin. Cancer Res. 14, 5220–5227 (2008).

    Article  CAS  Google Scholar 

  9. Coronella-Wood, J. A. & Hersh, E. M. Naturally occurring B-cell responses to breast cancer. Cancer Immunol. Immunother. 52, 715–738 (2003).

    Article  Google Scholar 

  10. Milne, K. et al. Systematic analysis of immune infiltrates in high-grade serous ovarian cancer reveals CD20, FoxP3 and TIA-1 as positive prognostic factors. PLoS ONE 4, e6412 (2009).

    Article  Google Scholar 

  11. Liu, X. S. & Mardis, E. R. Applications of immunogenomics to cancer. Cell 168, 600–612 (2017).

    Article  CAS  Google Scholar 

  12. Xu, J. L. & Davis, M. M. Diversity in the CDR3 region of V(H) is sufficient for most antibody specificities. Immunity 13, 37–45 (2000).

    Article  CAS  Google Scholar 

  13. Stavnezer, J., Guikema, J. E. & Schrader, C. E. Mechanism and regulation of class switch recombination. Annu. Rev. Immunol. 26, 261–292 (2008).

    Article  CAS  Google Scholar 

  14. Li, Z., Woo, C. J., Iglesias-Ussel, M. D., Ronai, D. & Scharff, M. D. The generation of antibody diversity through somatic hypermutation and class switch recombination. Genes Dev. 18, 1–11 (2004).

    Article  Google Scholar 

  15. Gadala-Maria, D., Yaari, G., Uduman, M. & Kleinstein, S. H. Automated analysis of high-throughput B-cell sequencing data reveals a high frequency of novel immunoglobulin V gene segment alleles. Proc. Natl Acad. Sci. USA 112, E862–E870 (2015).

    Article  CAS  Google Scholar 

  16. Lin, S. G. et al. Highly sensitive and unbiased approach for elucidating antibody repertoires. Proc. Natl Acad. Sci. USA 113, 7846–7851 (2016).

    Article  CAS  Google Scholar 

  17. Yaari, G. & Kleinstein, S. H. Practical guidelines for B-cell receptor repertoire sequencing analysis. Genome Med. 7, 121 (2015).

    Article  Google Scholar 

  18. Blachly, J. S. et al. Immunoglobulin transcript sequence and somatic hypermutation computation from unselected RNA-seq reads in chronic lymphocytic leukemia. Proc. Natl Acad. Sci. USA 112, 4322–4327 (2015).

    Article  CAS  Google Scholar 

  19. Mose, L. E. et al. Assembly-based inference of B-cell receptor repertoires from short read RNA sequencing data with V’DJer. Bioinformatics 32, 3729–3734 (2016).

    Article  CAS  Google Scholar 

  20. Liu, S. et al. Direct measurement of B-cell receptor repertoire’s composition and variation in systemic lupus erythematosus. Genes Immun. 18, 22–27 (2017).

    Article  CAS  Google Scholar 

  21. Kurtz, D. M. et al. Noninvasive monitoring of diffuse large B-cell lymphoma by immunoglobulin high-throughput sequencing. Blood 125, 3679–3687 (2015).

    Article  CAS  Google Scholar 

  22. Katoh, H. et al. Immunogenetic profiling for gastric cancers identifies sulfated glycosaminoglycans as major and functional B cell antigens in human malignancies. Cell Rep. 20, 1073–1087 (2017).

    Article  CAS  Google Scholar 

  23. Li, B. et al. Landscape of tumor-infiltrating T cell repertoire of human cancers. Nat. Genet. 48, 725–732 (2016).

    Article  CAS  Google Scholar 

  24. Li, B. et al. Ultrasensitive detection of TCR hypervariable region in solid-tissue RNA-seq data. Nat. Genet. 49, 482–483 (2017).

    Article  CAS  Google Scholar 

  25. Shi, B. et al. Comparative analysis of human and mouse immunoglobulin variable heavy regions from IMGT/LIGM-DB with IMGT/HighV-QUEST. Theor. Biol. Med. Model. 11, 30 (2014).

    Article  Google Scholar 

  26. Lefranc, M. P. et al. IMGT(R), the international ImMunoGeneTics information system(R) 25 years on. Nucleic Acids Res. 43, D413–D422 (2015).

    Article  CAS  Google Scholar 

  27. Schroeder, H. W. Jr & Cavacini, L. Structure and function of immunoglobulins. J. Allergy Clin. Immunol. 125, S41–S52 (2010).

    Article  Google Scholar 

  28. Di Noia, J. M. & Neuberger, M. S. Molecular mechanisms of antibody somatic hypermutation. Annu. Rev. Biochem. 76, 1–22 (2007).

    Article  Google Scholar 

  29. Rogozin, I. B. & Kolchanov, N. A. Somatic hypermutagenesis in immunoglobulin genes. II. Influence of neighbouring base sequences on mutagenesis. Biochim. Biophys. Acta 1171, 11–18 (1992).

    Article  CAS  Google Scholar 

  30. Keim, C., Kazadi, D., Rothschild, G. & Basu, U. Regulation of AID, the B-cell genome mutator. Genes Dev. 27, 1–17 (2013).

    Article  CAS  Google Scholar 

  31. Krishnamurty, A. T. et al. Somatically hypermutated Plasmodium-specific IgM(+) memory B cells are rapid, plastic, early responders upon malaria rechallenge. Immunity 45, 402–414 (2016).

    Article  CAS  Google Scholar 

  32. Kitaura, K. et al. Different somatic hypermutation levels among antibody subclasses disclosed by a new next-generation sequencing-based antibody repertoire analysis. Front. Immunol. 8, 389 (2017).

    Article  Google Scholar 

  33. Odegard, V. H. & Schatz, D. G. Targeting of somatic hypermutation. Nat. Rev. Immunol. 6, 573–583 (2006).

    Article  CAS  Google Scholar 

  34. LeBien, T. W. & Tedder, T. F. B lymphocytes: how they develop and function. Blood 112, 1570–1580 (2008).

    Article  CAS  Google Scholar 

  35. Jackson, K. J., Wang, Y. & Collins, A. M. Human immunoglobulin classes and subclasses show variability in VDJ gene mutation levels. Immunol. Cell Biol. 92, 729–733 (2014).

    Article  CAS  Google Scholar 

  36. Li, B. et al. Comprehensive analyses of tumor immunity: implications for cancer immunotherapy. Genome Biol. 17, 174 (2016).

    Article  Google Scholar 

  37. Geissler, K. et al. Immune signature of tumor infiltrating immune cells in renal cancer. Oncoimmunology 4, e985082 (2015).

    Article  Google Scholar 

  38. William, J., Euler, C., Christensen, S. & Shlomchik, M. J. Evolution of autoantibody responses via somatic hypermutation outside of germinal centers. Science 297, 2066–2070 (2002).

    Article  CAS  Google Scholar 

  39. Hwang, J. K., Alt, F. W. & Yeap, L. S. Related mechanisms of antibody somatic hypermutation and class switch recombination. Microbiol. Spectr. 3, MDNA3-0037-2014 (2015).

    PubMed  Google Scholar 

  40. Schreiber, R. D., Old, L. J. & Smyth, M. J. Cancer immunoediting: integrating immunity’s roles in cancer suppression and promotion. Science 331, 1565–1570 (2011).

    Article  CAS  Google Scholar 

  41. Shukla, S. A. et al. Comprehensive analysis of cancer-associated somatic mutations in class I HLA genes. Nat. Biotechnol. 33, 1152–1158 (2015).

    Article  CAS  Google Scholar 

  42. Zaretsky, J. M. et al. Mutations associated with acquired resistance to PD-1 blockade in melanoma. N. Engl. J. Med. 375, 819–829 (2016).

    Article  CAS  Google Scholar 

  43. Iannello, A. & Ahmad, A. Role of antibody-dependent cell-mediated cytotoxicity in the efficacy of therapeutic anti-cancer monoclonal antibodies. Cancer Metastasis Rev. 24, 487–499 (2005).

    Article  CAS  Google Scholar 

  44. Nimmerjahn, F. & Ravetch, J. V. Fcgamma receptors as regulators of immune responses. Nat. Rev. Immunol. 8, 34–47 (2008).

    Article  CAS  Google Scholar 

  45. Waldhauer, I. & Steinle, A. NK cells and cancer immunosurveillance. Oncogene 27, 5932–5943 (2008).

    Article  CAS  Google Scholar 

  46. Smyth, M. J. et al. Activation of NK cell cytotoxicity. Mol. Immunol. 42, 501–510 (2005).

    Article  CAS  Google Scholar 

  47. Bruhns, P. et al. Specificity and affinity of human Fcgamma receptors and their polymorphic variants for human IgG subclasses. Blood 113, 3716–3725 (2009).

    Article  CAS  Google Scholar 

  48. Waldhauer, I. et al. Tumor-associated MICA is shed by ADAM proteases. Cancer Res. 68, 6368–6376 (2008).

    Article  CAS  Google Scholar 

  49. Raulet, D. H. Roles of the NKG2D immunoreceptor and its ligands. Nat. Rev. Immunol. 3, 781–790 (2003).

    Article  CAS  Google Scholar 

  50. Doubrovina, E. S. et al. Evasion from NK cell immunity by MHC class I chain-related molecules expressing colon adenocarcinoma. J. Immunol. 171, 6891–6899 (2003).

    Article  CAS  Google Scholar 

  51. Liu, G., Atteridge, C. L., Wang, X., Lundgren, A. D. & Wu, J. D. The membrane type matrix metalloproteinase MMP14 mediates constitutive shedding of MHC class I chain-related molecule A independent of A disintegrin and metalloproteinases. J. Immunol. 184, 3346–3350 (2010).

    Article  CAS  Google Scholar 

  52. Zhang, J., Basher, F. & Wu, J. D. NKG2D ligands in tumor immunity: two sides of a coin. Front. Immunol. 6, 97 (2015).

    CAS  PubMed  PubMed Central  Google Scholar 

  53. Jinushi, M., Hodi, F. S. & Dranoff, G. Therapy-induced antibodies to MHC class I chain-related protein A antagonize immune suppression and stimulate antitumor cytotoxicity. Proc. Natl Acad. Sci. USA 103, 9190–9195 (2006).

    Article  CAS  Google Scholar 

  54. Tabuchi, Y. et al. Protective effect of naturally occurring anti-HER2 autoantibodies on breast cancer. Breast Cancer Res. Treat. 157, 55–63 (2016).

    Article  CAS  Google Scholar 

  55. Kim, D. et al. TopHat2: accurate alignment of transcriptomes in the presence of insertions, deletions and gene fusions. Genome Biol. 14, R36 (2013).

    Article  Google Scholar 

  56. Wang, K. et al. MapSplice: accurate mapping of RNA-seq reads for splice junction discovery. Nucleic Acids Res. 38, e178 (2010).

    Article  Google Scholar 

  57. 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).

    Article  Google Scholar 

  58. DeWitt, W. S. et al. A public database of memory and naive B-cell receptor sequences. PLoS ONE 11, e0160853 (2016).

    Article  Google Scholar 

  59. Thompson, J. D., Higgins, D. G. & Gibson, T. J. CLUSTAL W: improving the sensitivity of progressive multiple sequence alignment through sequence weighting, position-specific gap penalties and weight matrix choice. Nucleic Acids Res. 22, 4673–4680 (1994).

    Article  CAS  Google Scholar 

  60. Bodenhofer, U., Bonatesta, E., Horejs-Kainrath, C. & Hochreiter, S. msa: an R package for multiple sequence alignment. Bioinformatics 31, 3997–3999 (2015).

    CAS  PubMed  Google Scholar 

  61. Paradis, E., Claude, J. & Strimmer, K. APE: analyses of phylogenetics and evolution in R language. Bioinformatics 20, 289–290 (2004).

    Article  CAS  Google Scholar 

  62. Kim, J. et al. Somatic ERCC2 mutations are associated with a distinct genomic signature in urothelial tumors. Nat. Genet. 48, 600–606 (2016).

    Article  CAS  Google Scholar 

  63. R v. 3.5.1 (The R Project for Statistical Computing, 2018).

Download references

Acknowledgements

We thank F. Alt, M. Shipp, and G. Freeman for helpful discussions during manuscript preparation, and T. Wu for suggesting the Single Instruction Multiple Data (SIMD) acceleration. We also acknowledge the following funding sources for supporting this work: NCI, grants U01 CA226196 (X.S.L.) and U24 CA224316 (X.S.L.), Chinese Scholarship Council Funding (Jian Zhang), CPRIT RR170079 (B.L.), the Breast Cancer Research Foundation (X.S.L.), and the National Natural Science Foundation of China, 81702701 (T.L.).

Author information

Author notes

  1. These authors contributed equally: Xihao Hu, Jian Zhang.

Authors and Affiliations

  1. Department of Data Sciences, Dana-Farber Cancer Institute and Harvard T.H. Chan School of Public Health, Boston, MA, USA

    Xihao Hu, Jin Wang, Jingxin Fu, Binbin Wang, Shengqing Gu, Peng Jiang, Jingyu Fan & X. Shirley Liu

  2. Center for Computational Biology, Beijing Institute of Basic Medical Sciences, Beijing, China

    Jian Zhang & Xiaomin Ying

  3. Shanghai Key Laboratory of Tuberculosis, Clinical Translational Research Center, Shanghai Pulmonary Hospital, School of Life Sciences and Technology, Tongji University, Shanghai, China

    Jin Wang, Jingxin Fu, Binbin Wang, Jingyu Fan, Jing Zhang, Fan Zhang, Peng Zhang & X. Shirley Liu

  4. State Key Laboratory of Oral Diseases, West China Hospital of Stomatology, Sichuan University, Chengdu, China

    Taiwen Li

  5. Department of Mathematics, Shanghai Normal University, Shanghai, China

    Xiaoqi Zheng

  6. Program in Cellular and Molecular Medicine, Boston Children’s Hospital, Boston, MA, USA

    Michael C. Carroll

  7. Department of Cancer Immunology and Virology, Dana-Farber Cancer Institute, Boston, MA, USA

    Kai W. Wucherpfennig

  8. Massachusetts General Hospital Cancer Center, Harvard Medical School, Boston, MA, USA

    Nir Hacohen

  9. Department of Thoracic Surgery, Shanghai Pulmonary Hospital, Tongji University School of Medicine, Shanghai, China

    Fan Zhang & Peng Zhang

  10. Department of Statistics, Harvard University, Cambridge, MA, USA

    Jun S. Liu & X. Shirley Liu

  11. Lyda Hill Department of Bioinformatics, UT Southwestern Medical Center, Dallas, TX, USA

    Bo Li

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Contributions

B.L. conceived this project, modified TRUST codes to assemble B cell CDR3 sequences, and analyzed TCGA data. X.H. and Jian Zhang accelerated the TRUST codes, performed the validation analyses, and tested the clustering method. J.W., J. Fu, and T.L. helped with figure generation and data analyses, and B.W. and J. Fan helped to generate intermediate data. P.Z. provided tumor samples, and S.G., F.Z., and X.Z. performed the BCR-seq and RNA-seq validation experiments. P.J., X.Y., Jing Zhang, M.C.C., K.W.W., and N.H. provided expertise in immune evasive mechanisms and data analysis. J.S.L. and X.S.L. supervised the study and wrote the manuscript with X.H. and B.L. All coauthors contributed to manuscript preparation and research progress discussion.

Corresponding authors

Correspondence to Jun S. Liu, Bo Li or X. Shirley Liu.

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Competing interests

X.S.L. is a cofounder and board member of GV20 Oncotherapy, SAB of 3DMed Care, consultant for Genentech, and stock holder of BMY, TMO, WBA, ABT, ABBV, and JNJ. J.S.L. is a cofounder and board member of Beijing Neoantigen Biotechnology Co. Ltd, and shareholder of BGNE, DVAX, CELG, NKTR, CRSP, and EDIT. K.W.W. serves on the SAB of Nextech, TCR2, and T-scan. He is also a consultant for Novartis. His laboratory has sponsored research agreements with Novartis, BMS, and Astellas. N.H. is a founder and SAB member of Neon Therapeutics and SAB member of IFM Therapeutics.

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Integrated supplementary information

Supplementary Figure 1 Performance evaluation of modified TRUST.

a, Precision and sensitivity of modified TRUST to call B cell IgH CDR3 sequences with 20 replicates for different coverages (see Methods). Box plots show the lower, median, and upper quartiles of values, and lines extend to 1.5 times the interquartile range. b,c, Clonal frequency and overlap of IgH CDR3s from BCR-seq and TRUST. Six lung tumor samples were sequenced by both BCR-seq and RNA-seq. b, Pearson’s correlations of clonal frequencies from TRUST and BCR-seq. The sample sizes in the 6 cases were 441, 810, 738, 452, 631, and 398, respectively. c, Jaccard index of overlapped CDR3s in B cell repertoires. B cell repertoires from BCR-seq and TRUST have much higher overlaps for matched samples than unmatched samples.

Supplementary Figure 2 Overview of IgH CDR3 and SHMs.

a, IgH CDR3 length distributions and sequence motifs. Histograms show the distributions of CDR3 amino acid sequence length by each immunoglobulin isotype. The sequence logos are for all of the CDR3s with the highest frequency in length (14 aa) in TCGA samples. b, Ratio of synonymous and missense mutations in CDR3s. We summarized all of the SHMs within the CDR3 region and classified them by the effect on amino acid sequences. Synonymous mutations are the nucleotide mutations with no effect on the protein sequence. Missense mutations are the ones that change the amino acid in the protein sequence. c, Correlation between the SHM rate and AID expression. Patient samples (n = 4,708) were grouped by the SHM rates and displayed the distribution of AID expression levels. The boxes in violin plots show the lower, median, and upper quartiles of values, and the curves show the density of values.

Supplementary Figure 3 Grid search on the optimal parameters for clustering B cell CDR3s and the differences among clones.

a–c, Heatmaps show the values of FDR (a), cluster ratio (b), and final score (c) for ranking the clustering results of 500 randomly sampled pairs. The best solution with the highest final score was marked in red boxes. d, CDR3 amino acid length comparison for expanded (n = 2,687,374) and non-expanded (n = 32,631) B cell clones. Expanded clones are complete IgH CDR3s found in any B cell cluster (see Methods), and the remaining complete CDR3s are non-expanded ones. Statistical significance was evaluated by Student’s t-test. The red dots and the numbers show the mean values and the bars extend to 2 s.d. on both sides. The curves in the violin plots show the value distribution. e, Overlap of B cell clonotypes for tumor samples and adjacent normal tissues. We chose matched TCGA tumor and adjacent normal tissue samples with at least 500 clonotypes called by TRUST. As a comparison, we downloaded the top 100 B cell clones of paired normal and tumor samples published by Katoh et al.22. We observed a similar Jaccard index of tumor–normal clonotype overlap between data from TCGA (n = 26) and from Katoh et al.22 (n = 30). The two-sided P value was from Wilcoxon signed-rank test.

Supplementary Figure 4 Survival analysis for samples with high or low IgG3–1 switches.

a, IgG3–1 switch level is the number of B cell clusters with both IgG1 and IgG3 divided by the total number of unique CDR3s. The group of top 25% samples with high IgG3–1 switch was compared with the bottom 25% samples in terms of the overall survival (see Supplementary Table 3 for sample sizes). Hazard ratios and statistical significance from Wald test were evaluated using Cox proportional hazard regressions corrected for tumor purity and patient age. FDR correction was performed using the Benjamini–Hochberg procedure for testing on multiple cancer types. b, Kaplan–Meier curves for significant tumor types were shown with raw P values at the bottom.

Supplementary Figure 5 Survival analysis for the interaction between SHM and IgG3–1 switches.

a, SHM high and low were split by the median SHM rate in all patient samples. IgG3–1 switch high or low was split by the median IgG3–1 switch level among patient samples (see Supplementary Table 3 for sample sizes). Hazard ratios and statistical significance from Wald test were evaluated using Cox proportional hazard regressions corrected for tumor purity and patient age. FDR correction was performed using Benjamini–Hochberg procedure for testing on multiple cancer types. b, Kaplan–Meier curves for significant tumor types were shown with raw P values at the bottom.

Supplementary Figure 6 MICA amplification in TCGA samples and the associations with gene expression, IgG1/3 level, and patient survival.

a, MICA amplification in paired normal and tumor samples. The table summarizes the number of patients with different MICA amplification status in paired tumor and adjacent normal tissues. MICA AMP means amplification in the MICA/MICB genes, and MICA WT means no amplification. b, MICA amplification correlated with gene expression differences (n = 9,846). Box plots show the differential expression of MICA, ADAM17, and MMP14 in tumors with or without MICA amplifications, where the boxes show the lower, median, and upper quartiles of values, and lines extend to 1.5 times the interquartile range. Statistical significance was evaluated using Wilcoxon rank sum test. c, IgG1/3 level in tumor samples with or without MICA amplification (n = 5,104). The boxplot shows the distribution of the IgG1/3 switch level in tumor samples with or without MICA amplification. Statistical significance was evaluated using Wilcoxon rank sum test (two-sided P value). Box plots show the lower, median, and upper quartiles of values, and lines extend to 1.5 times the interquartile range. d, Kaplan–Meier curve shows the survival differences in all TCGA patients with or without MICA amplification (n = 7,147). e, For each tumor type, hazard ratios and statistical significance from Wald test were evaluated using Cox proportional hazard regressions corrected for tumor purity and patient age. f, High- or low-IgG1/3 groups were split by the median value of IgG1/3 levels in each MICA group (see Supplementary Table 3 for sample sizes). The IgG1/3 level was the number of IgG1 or IgG3 B cell clusters normalized by the total number of unique CDR3s. Hazard ratios and statistical significance were evaluated using Cox proportional hazard regressions corrected for tumor purity and patient age. FDR correction was performed using the Benjamini–Hochberg procedure for testing on multiple cancer types.

Supplementary information

Supplementary Text and Figures

Supplementary Figures 1–6

Reporting Summary

Supplementary Table 1

Summary of all TCGA RNA-seq samples involved in this study by disease types

Supplementary Table 2

Summary information for patients involved in this study, including survival, disease type, number of B cell clusters, IgG subclass switch events, somatic hypermutation rate, copy number alterations in MICA, and tumor purity

Supplementary Table 3

Sample sizes and P values

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Hu, X., Zhang, J., Wang, J. et al. Landscape of B cell immunity and related immune evasion in human cancers. Nat Genet 51, 560–567 (2019). https://doi.org/10.1038/s41588-018-0339-x

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