Single-cell profiling of breast cancer T cells reveals a tissue-resident memory subset associated with improved prognosis (original) (raw)

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• 22 August 2018

In the version of this article originally published, the institution in affiliation 10 was missing. Affiliation 10 was originally listed as Department of Surgery, Royal Melbourne Hospital and Royal Womens’ Hospital, Melbourne, Victoria, Australia. It should have been Department of Surgery, Royal Melbourne Hospital and Royal Womens’ Hospital, University of Melbourne, Melbourne, Victoria, Australia. The error has been corrected in the HTML and PDF versions of this article.

References

1. Savas, P. et al. Clinical relevance of host immunity in breast cancer: from TILs to the clinic. Nat. Rev. Clin. Oncol. 13, 228–241 (2016).
   Article PubMed CAS Google Scholar
2. Ruffell, B. et al. Leukocyte composition of human breast cancer. Proc. Natl Acad. Sci. USA 109, 2796–2801 (2012).
   Article PubMed Google Scholar
3. Adams, S. et al. Phase 2 study of pembrolizumab (pembro) monotherapy for previously treated metastatic triple-negative breast cancer (mTNBC): KEYNOTE-086 cohort A. J. Clin. Oncol. 35 no. 15_suppl, 1008 (2017).
4. Schmid, P. et al. Atezolizumab in metastatic TNBC (mTNBC): long-term clinical outcomes and biomarker analyses. Cancer Res. 77, 2986 (2017).
   Article Google Scholar
5. Loi, S. et al. Relationship between tumor infiltrating lymphocyte (TIL) levels and response to pembrolizumab (pembro) in metastatic triple-negative breast cancer (mTNBC): results from KEYNOTE-086. Ann. Oncol. 28, LBA13 (2017).
   Article Google Scholar
6. Loi, S.et al. Pooled individual patient data analysis of stromal tumor infiltrating lymphocytes in primary triple negative breast cancer treated with anthracycline-based chemotherapy. Cancer Res. 76, abstr. S1–03 (2015).
7. Nolan, E. et al. Combined immune checkpoint blockade as a therapeutic strategy for _BRCA1_-mutated breast cancer. Sci. Transl. Med. 9, eaal4922 (2017).
   Article PubMed PubMed Central CAS Google Scholar
8. Gu-Trantien, C. et al. CD4+ follicular helper T cell infiltration predicts breast cancer survival. J. Clin. Invest. 123, 2873–2892 (2013).
   Article PubMed PubMed Central CAS Google Scholar
9. Plitas, G. et al. Regulatory T cells exhibit distinct features in human breast cancer. Immunity 45, 1122–1134 (2016).
   Article PubMed PubMed Central CAS Google Scholar
10. Hombrink, P. et al. Programs for the persistence, vigilance and control of human CD8+ lung-resident memory T cells. Nat. Immunol. 17, 1467–1478 (2016).
    Article PubMed CAS Google Scholar
11. Kumar, B. V. et al. Human tissue-resident memory t cells are defined by core transcriptional and functional signatures in lymphoid and mucosal sites. Cell Reports 20, 2921–2934 (2017).
    Article PubMed PubMed Central CAS Google Scholar
12. Mackay, L. K. et al. The developmental pathway for CD103(+)CD8+ tissue-resident memory T cells of skin. Nat. Immunol. 14, 1294–1301 (2016).
    Article CAS Google Scholar
13. Schenkel, J. M. & Masopust, D. Tissue-resident memory T cells. Immunity 41, 886–897 (2014).
    Article PubMed PubMed Central CAS Google Scholar
14. Trapnell, C. et al. The dynamics and regulators of cell fate decisions are revealed by pseudotemporal ordering of single cells. Nat. Biotechnol. 32, 381–386 (2014).
    Article PubMed PubMed Central CAS Google Scholar
15. Mueller, S. N. & Mackay, L. K. Tissue-resident memory T cells: local specialists in immune defence. Nat. Rev. Immunol. 16, 79–89 (2016).
    Article PubMed CAS Google Scholar
16. Malik, B. T. et al. Resident memory T cells in the skin mediate durable immunity to melanoma. Sci. Immunol. 2, eaam6346 (2017).
    Article PubMed PubMed Central Google Scholar
17. Nizard, M. et al. Induction of resident memory T cells enhances the efficacy of cancer vaccine. Nat. Commun. 8, 15221 (2017).
    Article PubMed PubMed Central CAS Google Scholar
18. Ganesan, A. P. et al. Tissue-resident memory features are linked to the magnitude of cytotoxic T cell responses in human lung cancer. Nat. Immunol. 18, 940–950 (2017).
    Article PubMed PubMed Central CAS Google Scholar
19. Thompson, E. D., Enriquez, H. L., Fu, Y. X. & Engelhard, V. H. Tumor masses support naive T cell infiltration, activation, and differentiation into effectors. J. Exp. Med. 207, 1791–1804 (2010).
    Article PubMed PubMed Central CAS Google Scholar
20. Riaz, N. et al. Tumor and microenvironment evolution during immunotherapy with nivolumab. Cell 171, 934–949.e915 (2017).
    Article PubMed CAS Google Scholar
21. Tumeh, P. C. et al. PD-1 blockade induces responses by inhibiting adaptive immune resistance. Nature 515, 568–571 (2014).
    Article PubMed PubMed Central CAS Google Scholar
22. Desmedt, C. et al. Biological processes associated with breast cancer clinical outcome depend on the molecular subtypes. Clinical Cancer Res. 14, 5158–5165 (2008).
    Article CAS Google Scholar
23. Teschendorff, A. E., Miremadi, A., Pinder, S. E., Ellis, I. O. & Caldas, C. An immune response gene expression module identifies a good prognosis subtype in estrogen receptor negative breast cancer. Genome Biol. 8, R157 (2007).
    Article PubMed PubMed Central CAS Google Scholar
24. Denkert, C. et al. Tumor-associated lymphocytes as an independent predictor of response to neoadjuvant chemotherapy in breast cancer. J. Clin. Oncol. 28, 105–113 (2010).
    Article PubMed CAS Google Scholar
25. Loi, S. et al. Tumor infiltrating lymphocytes (TILs) indicate trastuzumab benefit in early-stage HER2-positive breast cancer (HER2+ BC). Cancer Res. 73, abstr. S1–05 (2013).
    Google Scholar
26. Loi, S. et al. Tumor infiltrating lymphocytes are prognostic in triple negative breast cancer and predictive for trastuzumab benefit in early breast cancer: results from the FinHER trial. Ann. Oncol. 25, 1544–1550 (2014).
    Article PubMed CAS Google Scholar
27. Luen, S. J. et al. Tumour-infiltrating lymphocytes in advanced HER2-positive breast cancer treated with pertuzumab or placebo in addition to trastuzumab and docetaxel: a retrospective analysis of the CLEOPATRA study. Lancet Oncol. 18, 52–62 (2017).
    Article PubMed CAS Google Scholar
28. Salgado, R. et al. Tumor-infiltrating lymphocytes and associations with pathological complete response and event-free survival in HER2-positive early-stage breast cancer treated with lapatinib and trastuzumab: a secondary analysis of the NeoALTTO trial. JAMA Oncol 1, 448–454 (2015).
    Article PubMed PubMed Central Google Scholar
29. Ali, H. R. et al. Association between CD8+ T-cell infiltration and breast cancer survival in 12,439 patients. Ann. Oncol 25, 1536–1543 (2014).
    Article PubMed CAS Google Scholar
30. DeNardo, D. G. & Coussens, L. M. Inflammation and breast cancer. Balancing immune response: crosstalk between adaptive and innate immune cells during breast cancer progression. BCR 9, 212 (2007).
    Article PubMed CAS Google Scholar
31. Topalian, S. L., Muul, L. M., Solomon, D. & Rosenberg, S. A. Expansion of human tumor infiltrating lymphocytes for use in immunotherapy trials. J. Immunol. Methods 102, 127–141 (1987).
    Article PubMed CAS Google Scholar
32. Stack, E. C., Wang, C., Roman, K. A. & Hoyt, C. C. Multiplexed immunohistochemistry, imaging, and quantitation: a review, with an assessment of Tyramide signal amplification, multispectral imaging and multiplex analysis. Methods 70, 46–58 (2014).
    Article PubMed CAS Google Scholar
33. Jiang, H., Lei, R., Ding, S. W. & Zhu, S. Skewer: a fast and accurate adapter trimmer for next-generation sequencing paired-end reads. BMC Bioinformatics 15, 182 (2014).
    Article PubMed PubMed Central Google Scholar
34. Bray, N. L., Pimentel, H., Melsted, P. & Pachter, L. Near-optimal probabilistic RNA-seq quantification. Nat. Biotechnol. 34, 525–527 (2016).
    Article PubMed CAS Google Scholar
35. Soneson, C., Love, M. I. & Robinson, M. D. Differential analyses for RNA-seq: transcript-level estimates improve gene-level inferences. F1000Res 4, 1521 (2015).
    Article PubMed CAS Google Scholar
36. Ritchie, M. E. et al. limma powers differential expression analyses for RNA-sequencing and microarray studies. Nucleic Acids Res. 43, e47 (2015).
    Article PubMed PubMed Central CAS Google Scholar
37. Law, C. W., Chen, Y., Shi, W. & Smyth, G. K. voom: Precision weights unlock linear model analysis tools for RNA-seq read counts. Genome Biol. 15, R29 (2014).
    Article PubMed PubMed Central CAS Google Scholar
38. Liu, R. et al. Why weight? Modelling sample and observational level variability improves power in RNA-seq analyses. Nucleic Acids Res. 43, e97 (2015).
    Article PubMed PubMed Central CAS Google Scholar
39. Bolotin, D. A. et al. MiXCR: software for comprehensive adaptive immunity profiling. Nat. Methods 12, 380–381 (2015).
    Article PubMed CAS Google Scholar
40. Bolotin, D. A. et al. Antigen receptor repertoire profiling from RNA-seq data. Nat. Biotechnol. 35, 908–911 (2017).
    Article PubMed CAS PubMed Central Google Scholar
41. Macosko, E. Z. et al. Highly parallel genome-wide expression profiling of individual cells using nanoliter droplets. Cell 161, 1202–1214 (2015).
    Article PubMed PubMed Central CAS Google Scholar
42. Risso, D., Perraudeau, F., Gribkova, S., Dudoit, S. & Vert, J.-P. ZINB-WaVE: a general and flexible method for signal extraction from single-cell RNA-seq data. Preprint at https://doi.org/10.1101/125112 (2017).
43. Huang, M. et al. SAVER: Gene expression recovery for UMI-based single cell RNA sequencing. Preprint at https://doi.org/10.1101/138677 (2017).
44. Ye, C., Speed, T.P. & Salim, A. DECENT: differential expression with capture efficiency adjustment for single-cell RNA-seq data. Preprint at https://doi.org/10.1101/225177 (2017).
45. Wu, D. & Smyth, G. K. Camera: a competitive gene set test accounting for inter-gene correlation. Nucleic Acids Res. 40, e133 (2012).
    Article PubMed PubMed Central CAS Google Scholar
46. Scialdone, A. et al. Computational assignment of cell-cycle stage from single-cell transcriptome data. Methods 85, 54–61 (2015).
    Article PubMed CAS Google Scholar
47. Lun, A. T., McCarthy, D. J. & Marioni, J. C. A step-by-step workflow for low-level analysis of single-cell RNA-seq data with Bioconductor. F1000Res 5, 2122 (2016).
    PubMed PubMed Central Google Scholar
48. Qiu, X. et al. Reversed graph embedding resolves complex single-cell trajectories. Nat. Methods 14, 979–982 (2017).
    Article PubMed PubMed Central CAS Google Scholar
49. Curtis, C. et al. The genomic and transcriptomic architecture of 2,000 breast tumours reveals novel subgroups. Nature 486, 346–352 (2012).
    Article PubMed PubMed Central CAS Google Scholar
50. Gao, J. et al. Integrative analysis of complex cancer genomics and clinical profiles using the cBioPortal. Sci. Signal. 6, pl1 (2013).
    Article PubMed PubMed Central CAS Google Scholar
51. Perou, C. M. et al. Molecular portraits of human breast tumours. Nature 406, 747–752 (2000).
    Article PubMed CAS Google Scholar
52. McCarthy, D. J., Chen, Y. & Smyth, G. K. Differential expression analysis of multifactor RNA-Seq experiments with respect to biological variation. Nucleic Acids Res 40, 4288–4297 (2012).
    Article PubMed PubMed Central CAS Google Scholar
53. Robinson, M. D., McCarthy, D. J. & Smyth, G. K. edgeR: a Bioconductor package for differential expression analysis of digital gene expression data. Bioinformatics 26, 139–140 (2010).
    Article PubMed CAS Google Scholar
54. Smyth, G. K., Michaud, J. & Scott, H. S. Use of within-array replicate spots for assessing differential expression in microarray experiments. Bioinformatics 21, 2067–2075 (2005).
    Article PubMed CAS Google Scholar
55. Wu, D. et al. ROAST: rotation gene set tests for complex microarray experiments. Bioinformatics 26, 2176–2182 (2010).
    Article PubMed PubMed Central CAS Google Scholar
56. Bonett, D. G. & Wright, T. A. Sample size requirements for estimating pearson, kendall and spearman correlations. Psychometrika 65, 23–28 (2000).
    Article Google Scholar
57. Bishara, A. J. & Hittner, J. B. Confidence intervals for correlations when data are not normal. Behav. Res. Methods 49, 294–309 (2017).
    Article PubMed Google Scholar

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Acknowledgements

We wish to thank H. Thorne, E. Niedermayr, all the kConFab research nurses and staff, the heads and staff of the Family Cancer Clinics and the Clinical Follow Up Study (which has received funding from the National Health and Medical Research Council of Australia (NHMRC), the National Breast Cancer Foundation (NBCF), Cancer Australia and the National Institute of Health (United States) for their contributions to this research, and the many families who contribute to kConFab. We wish to thank the FACS core facility staff R. Rossi, V. Milovac and S. Curcio, and T. Tan and P. Petrone for additional FACS assistance. We also thank S. Ellis for assistance with confocal imaging, G. M. Arnau for facilitating RNA-seq, and the Anatomical Pathology staff at the Peter MacCallum Cancer Centre. Special thanks also to J. Jabbari and the Australian Genome Research Facility for making the single-cell sequencing possible.

This study was funded by the Breast Cancer Research Foundation (BCRF), NY. S.L. is supported by the Cancer Council Victoria John Colebatch Fellowship and the National Breast Cancer Foundation. P.S. is supported by the NHMRC and the NBCF (Post Graduate Scholarship 1094388), the Cancer Therapeutics CRC and the Peter Mac Foundation. Z.L.T. is supported by the NHMRC (Early Career Fellowship 1106967). D.G. is supported by the Peter Mac Foundation. P.A.B. is supported by the NHMRC (Early Career Fellowship 17-005). S.J.L is supported by the University of Melbourne. S.B.F. is supported by the NHMRC (Practitioner Fellowship 1079329). kConFab is supported by a grant from NBCF, and previously NHMRC, the Queensland Cancer Fund, the Cancer Councils of New South Wales, Victoria, Tasmania and South Australia, and the Cancer Foundation of Western Australia. P.K.D is supported by the NHMRC (Senior Research Fellowship 1136680 and Program Grant 1132373). T.S. is supported by the NHMRC (Program Grant 1054618). P.J.N. is supported by the NHMRC (Program Grant 1132373).

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Author notes

1. These authors contributed equally: Peter Savas, Balaji Virassamy.
2. These authors jointly directed this work: Laura K. Mackay, Paul J. Neeson, Sherene Loi.
3. A full list of members and affiliations appears in the Supplementary Note.

Authors and Affiliations

1. Division of Research, Peter MacCallum Cancer Centre, University of Melbourne, Melbourne, Victoria, Australia
   Peter Savas, Balaji Virassamy, Christopher P. Mintoff, Franco Caramia, Roberto Salgado, Zhi L. Teo, Sathana Dushyanthen, Ann Byrne, Lironne Wein, Stephen J. Luen, Paul A. Beavis, Phillip K. Darcy, Paul J. Neeson & Sherene Loi
2. Sir Peter MacCallum Department of Oncology, University of Melbourne, Melbourne, Victoria, Australia
   Peter Savas, Zhi L. Teo, Paul A. Beavis, Stephen B. Fox, Phillip K. Darcy, Paul J. Neeson & Sherene Loi
3. Bioinformatics Division, Walter & Eliza Hall Institute of Medical Research, Melbourne, Victoria, Australia
   Chengzhong Ye, Agus Salim & Terence P. Speed
4. Department of Medical Biology, University of Melbourne, Melbourne, Victoria, Australia
   Chengzhong Ye
5. School of Medicine, Tsinghua University, Beijing, China
   Chengzhong Ye
6. Department of Mathematics and Statistics, La Trobe University, Melbourne, Victoria, Australia
   Agus Salim
7. Department of Pathology, GZA Ziekenhuizen, Antwerp, Belgium
   Roberto Salgado
8. Department of Pathology, Peter MacCallum Cancer Centre, Melbourne, Victoria, Australia
   David J. Byrne & Stephen B. Fox
9. Division of Cancer Surgery, Peter MacCallum Cancer Centre, Melbourne, Victoria, Australia
   Catherine Poliness, Sophie S. Nightingale, Anita S. Skandarajah, David E. Gyorki & Chantel M. Thornton
10. Department of Surgery Royal Melbourne Hospital and Royal Womens’ Hospital, University of Melbourne, Melbourne, Victoria, Australia
    Anita S. Skandarajah
11. Department of Mathematics and Statistics, University of Melbourne, Melbourne, Victoria, Australia
    Terence P. Speed
12. Department of Microbiology and Immunology, Peter Doherty Institute for Infection and Immunity, University of Melbourne, Melbourne, Victoria, Australia
    Laura K. Mackay

Authors

1. Peter Savas
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2. Balaji Virassamy
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3. Chengzhong Ye
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4. Agus Salim
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5. Christopher P. Mintoff
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6. Franco Caramia
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7. Roberto Salgado
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8. David J. Byrne
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9. Zhi L. Teo
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10. Sathana Dushyanthen
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11. Ann Byrne
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12. Lironne Wein
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13. Stephen J. Luen
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14. Catherine Poliness
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15. Sophie S. Nightingale
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16. Anita S. Skandarajah
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17. David E. Gyorki
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18. Chantel M. Thornton
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19. Paul A. Beavis
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20. Stephen B. Fox
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21. Phillip K. Darcy
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22. Terence P. Speed
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23. Laura K. Mackay
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24. Paul J. Neeson
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25. Sherene Loi
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Consortia

Kathleen Cuningham Foundation Consortium for Research into Familial Breast Cancer (kConFab)

Contributions

P.S. conceived and designed the study, provided and collected study materials and samples and patient data, performed experiments, analyzed data and wrote the manuscript. B.V. designed the study, provided and collected study materials and samples and patient data, performed experiments, analyzed data and wrote the manuscript. C.Y. developed analysis methods and software, analyzed data and wrote the manuscript. A.S. developed analysis methods and software, analyzed single-cell sequencing data and wrote the manuscript. C.P.M performed experiments, analyzed data and wrote the manuscript. F.C. analyzed data and wrote the manuscript. R.S. analyzed data and wrote the manuscript. D.J.B performed experiments and wrote the manuscript. Z.L.T. provided and collected study materials and samples, analyzed data and wrote the manuscript. S.D. performed experiments, analyzed data and wrote the manuscript. A.B. performed experiments, analyzed data and wrote the manuscript. L.W. provided and collected study materials and samples and patient data and wrote the manuscript. S.J.L. provided and collected study materials and samples and patient data and wrote the manuscript. C.P. provided and collected study materials and samples and patient data. S.S.N. provided and collected study materials and samples and patient data. A.S.S. provided and collected study materials and samples and patient data. D.E.G. provided and collected study materials and samples and patient data. C.M.T. provided and collected study materials and samples and patient data. P.A.B. analyzed data and wrote the manuscript. S.B.F provided and collected study materials and samples and patient data, analyzed data and wrote the manuscript. kConFab provided and collected study materials and samples. P.K.D. designed the study, analyzed data and wrote the manuscript. T.P.S. developed analysis methods and software, designed the study and wrote the manuscript. L.K.M. designed the study and wrote the manuscript. P.J.N. designed the study and wrote the manuscript. S.L. conceived and designed the study, provided and collected study materials and samples and patient data and wrote the manuscript.

Corresponding authors

Correspondence toPaul J. Neeson or Sherene Loi.

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Savas, P., Virassamy, B., Ye, C. et al. Single-cell profiling of breast cancer T cells reveals a tissue-resident memory subset associated with improved prognosis.Nat Med 24, 986–993 (2018). https://doi.org/10.1038/s41591-018-0078-7

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• Received: 28 November 2017
• Accepted: 25 April 2018
• Published: 25 June 2018
• Issue Date: July 2018
• DOI: https://doi.org/10.1038/s41591-018-0078-7