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Breast Cancer Research and Treatment

, Volume 173, Issue 1, pp 167–177 | Cite as

Immune receptor recombinations from breast cancer exome files, independently and in combination with specific HLA alleles, correlate with better survival rates

  • Wei Lue Tong
  • Blake M. Callahan
  • Yaping N. Tu
  • Saif Zaman
  • Boris I. Chobrutskiy
  • George BlanckEmail author
Epidemiology

Abstract

Purpose

Immune characterizations of cancers, including breast cancer, have led to information useful for prognoses and are considered to be important in the future of refining the use of immunotherapies, including immune checkpoint inhibitor therapies. In this study, we sought to extend these characterizations with genomics approaches, particularly with cost-effective employment of exome files.

Methods

By recovery of immune receptor recombination reads from the cancer genome atlas (TCGA) breast cancer dataset, we observed associations of these recombinations with T-cell and B-cell biomarkers and with distinct survival rates.

Results

Recovery of TRD or IGH recombination reads was associated with an improved disease-free survival (p = 0.047 and 0.045, respectively). Determination of the HLA types using the exome files allowed matching of T-cell receptor V- and J-gene segment usage with specific HLA alleles, in turn allowing a refinement of the association of immune receptor recombination read recoveries with survival. For example, the TRBV7, HLA-C*07:01 combination represented a significantly worse, disease-free outcome (p = 0.014) compared to all other breast cancer samples. By direct comparisons of distinct TRB gene segment usage, HLA allele combinations revealed breast cancer subgroups, within the entire TCGA breast cancer dataset with even more dramatic survival distinctions.

Conclusions

In sum, the use of exome files for recovery of adaptive immune receptor recombination reads, and the simultaneous determination of HLA types, has the potential of advancing the use of immunogenomics for immune characterization of breast tumor samples.

Keywords

Breast cancer Immune receptor recombinations V- and J-gene segment usage HLA alleles 

Abbreviations

BCR

B-cell receptor

BRCA

Breast cancer

DFS

Disease-free survival

ER

Estrogen receptor

GDC

Genomic data commons

HLA

human leukocyte antigen

IGH

Immunoglobulin heavy gene

IGK

Immunoglobulin kappa gene

IGL

immunoglobulin lambda gene

IMGT

ImmunoGeneTics organization

KM

Kaplan–Meier

OS

Overall survival

PR

Progesterone receptor

TCGA

The cancer genome atlas

TIL

Tumor-infiltrating lymphocyte

TCR

T-cell receptor

TNBC

Triple-negative breast cancer (negative for ER, PR, and HER2)

TRA

T-cell receptor alpha gene

TRB

T-cell receptor beta gene

TRG

T-cell receptor gamma gene

TRD

T-cell receptor delta gene

WXS

Whole exome file

Notes

Acknowledgements

Authors gratefully acknowledge the support of USF research computing and the taxpayers of the State of Florida. This study is dedicated to Frances.

Compliance with ethical standards

Conflict of interest

Authors have nothing to declare.

Research involving human participants and/or animals

Not applicable. (non-human subjects research).

Informed consent

Not applicable.

Supplementary material

10549_2018_4961_MOESM1_ESM.pdf (2.3 mb)
Supplementary material 1 (PDF 2396 KB)

References

  1. 1.
    Whiteside TL, Ferrone S (2012) For breast cancer prognosis, immunoglobulin kappa chain surfaces to the top. Clinical Cancer Res 18:2417–2419Google Scholar
  2. 2.
    Kato T, Park JH, Kiyotani K, Ikeda Y, Miyoshi Y, Nakamura Y (2017) Integrated analysis of somatic mutations and immune microenvironment of multiple regions in breast cancers. Oncotarget 8:62029–62038Google Scholar
  3. 3.
    Schmidt M, Hellwig B, Hammad S, Othman A, Lohr M, Chen Z, Boehm D, Gebhard S, Petry I, Lebrecht A, Cadenas C, Marchan R, Stewart JD, Solbach C, Holmberg L, Edlund K, Kultima HG, Rody A, Berglund A, Lambe M, Isaksson A, Botling J, Karn T, Muller V, Gerhold-Ay A, Cotarelo C, Sebastian M, Kronenwett R, Bojar H, Lehr HA, Sahin U, Koelbl H, Gehrmann M, Micke P, Rahnenfuhrer J, Hengstler JG (2012) A comprehensive analysis of human gene expression profiles identifies stromal immunoglobulin kappa C as a compatible prognostic marker in human solid tumors, Clinical Cancer Res 18: 2695–2703Google Scholar
  4. 4.
    Fournie JJ, Sicard H, Poupot M, Bezombes C, Blanc A, Romagne F, Ysebaert L, Laurent G (2013) What lessons can be learned from gammadelta T cell-based cancer immunotherapy trials? Cell Mol Immunol 10:35–41Google Scholar
  5. 5.
    Tu YN, Tong WL, Yavorski JM, Blanck G (2018) Immunogenomics: a negative prostate cancer outcome associated with TcR-gamma/delta recombinations. Cancer Microenviron 11(1):41–49Google Scholar
  6. 6.
    Callahan BM, Yavorski JM, Tu YN, Tong WL, Kinskey JC, Clark KR, Fawcett TJ, Blanck G (2018) T-cell receptor-beta V and J usage, in combination with particular HLA class I and class II alleles, correlates with cancer survival patterns. Cancer Immunol Immunother 67(6):885–892Google Scholar
  7. 7.
    Callahan BM, Tong WL, Blanck G (2018) T cell receptor-beta J usage, in combination with particular HLA class II alleles, correlates with better cancer survival rates. Immunol Res 66:219–223Google Scholar
  8. 8.
    Malaspinas AS, Tange O, Moreno-Mayar JV, Rasmussen M, DeGiorgio M, Wang Y, Valdiosera CE, Politis G, Willerslev E, Nielsen R. (2014) bammds: a tool for assessing the ancestry of low-depth whole-genome data using multidimensional scaling (MDS), Bioinformatics, 30 2962–2964Google Scholar
  9. 9.
    Tange O (2011) Gnu parallel-the command-line power tool. USENIX Mag 36:42–47Google Scholar
  10. 10.
    Govindan S, Choi J, Urgaonkar B, Sivasubramaniam A, Baldini A (2009) Statistical profiling-based techniques for effective power provisioning in data centers. Proceedings of the 4th ACM European conference on Computer systems, ACM, pp. 317–330Google Scholar
  11. 11.
    Ping Z, Siegal GP, Almeida JS, Schnitt SJ, Shen D (2014) Mining genome sequencing data to identify the genomic features linked to breast cancer histopathology. J Pathol Inf 5:3Google Scholar
  12. 12.
    Gao J, Aksoy BA, Dogrusoz U, Dresdner G, Gross B, Sumer SO, Sun Y, Jacobsen A, Sinha R, Larsson E, Cerami E, Sander C, Schultz N (2013) Integrative analysis of complex cancer genomics and clinical profiles using the cBioPortal. Sci Signal 6:pl1Google Scholar
  13. 13.
    Cerami E, Gao J, Dogrusoz U, Gross BE, Sumer SO, Aksoy BA, Jacobsen A, Byrne CJ, Heuer ML, Larsson E, Antipin Y, Reva B, Goldberg AP, Sander C, Schultz N (2012) The cBio cancer genomics portal: an open platform for exploring multidimensional cancer genomics data. Cancer Discov 2:401–404Google Scholar
  14. 14.
    Xie C, Yeo ZX, Wong M, Piper J, Long T, Kirkness EF, Biggs WH, Bloom K, Spellman S, Vierra-Green C, Brady C, Scheuermann RH, Telenti A, Howard S, Brewerton S, Turpaz Y, Venter JC (2017) Fast and accurate HLA typing from short-read next-generation sequence data with xHLA. Proc Natl Acad Sci USA 114:8059–8064Google Scholar
  15. 15.
    Callahan BM, Patel JS, Fawcett TJ, Blanck G (2018) Cytoskeleton and ECM tumor mutant peptides: increased protease sensitivities and potential consequences for the HLA class I mutant epitope reservoir. Int J Cancer 142:988–998Google Scholar
  16. 16.
    Gill TR, Samy MD, Butler SN, Mauro JA, Sexton WJ, Blanck G (2016) Detection of productively rearranged TcR-alpha V-J sequences in TCGA exome files: implications for tumor immunoscoring and recovery of antitumor T-cells. Cancer Inform 15:23–28Google Scholar
  17. 17.
    Mai AT, Tong WL, Tu YN, Blanck G (2018) T-cell receptor-alpha recombinations in renal cell carcinoma exome files correlate with an intermediate level of T-cell exhaustion biomarkers. Int immunol 30:35–40Google Scholar
  18. 18.
    Tong WL, Tu YN, Samy MD, Sexton WJ, Blanck G (2017) Identification of immunoglobulin V(D)J recombinations in solid tumor specimen exome files: evidence for high level B-cell infiltrates in breast cancer. Human Vaccin Immunother 13:501–506Google Scholar
  19. 19.
    Christiansson L, Soderlund S, Mangsbo S, Hjorth-Hansen H, Hoglund M, Markevarn B, Richter J, Stenke L, Mustjoki S, Loskog A, Olsson-Stromberg U (2015) The tyrosine kinase inhibitors imatinib and dasatinib reduce myeloid suppressor cells and release effector lymphocyte responses. Mol Cancer Ther 14:1181–1191Google Scholar
  20. 20.
    Steimle V, Otten LA, Zufferey M, Mach B (1993) Complementation cloning of an MHC class II transactivator mutated in hereditary MHC class II deficiency (or bare lymphocyte syndrome), Cell 75:135–146Google Scholar
  21. 21.
    Chae YK, Arya A, Iams W, Cruz MR, Chandra S, Choi J, Giles F (2018) Current landscape and future of dual anti-CTLA4 and PD-1/PD-L1 blockade immunotherapy in cancer; lessons learned from clinical trials with melanoma and non-small cell lung cancer (NSCLC). J Immunother Cancer 6:39Google Scholar
  22. 22.
    Geller LT, Barzily-Rokni M, Danino T, Jonas OH, Shental N, Nejman D, Gavert N, Zwang Y, Cooper ZA, Shee K, Thaiss CA, Reuben A, Livny J, Avraham R, Frederick DT, Ligorio M, Chatman K, Johnston SE, Mosher CM, Brandis A, Fuks G, Gurbatri C, Gopalakrishnan V, Kim M, Hurd MW, Katz M, Fleming J, Maitra A, Smith DA, Skalak M, Bu J, Michaud M, Trauger SA, Barshack I, Golan T, Sandbank J, Flaherty KT, Mandinova A, Garrett WS, Thayer SP, Ferrone CR, Huttenhower C, Bhatia SN, Gevers D, Wargo JA, Golub TR, Straussman R (2017) Potential role of intratumor bacteria in mediating tumor resistance to the chemotherapeutic drug gemcitabine. Science 357:1156–1160Google Scholar
  23. 23.
    Kinskey JC, Tu YN, Tong WL, Yavorski JM, Blanck G (2018) Recovery of immunoglobulin VJ recombinations from pancreatic cancer exome files strongly correlates with reduced survival, Cancer Microenviron 11(1):51–59Google Scholar
  24. 24.
    Li B, Li T, Pignon JC, Wang B, Wang J, Shukla SA, Dou R, Chen Q, Hodi FS, Choueiri TK, Wu C, Hacohen N, Signoretti S, Liu JS, Liu XS (2016) Landscape of tumor-infiltrating T cell repertoire of human cancers. Nat Genet 48:725–732Google Scholar
  25. 25.
    Levy E, Marty R, Garate Calderon V, Woo B, Dow M, Armisen R, Carter H, Harismendy O (2016) Immune DNA signature of T-cell infiltration in breast tumor exomes. Sci Rep 6:30064Google Scholar
  26. 26.
    Iglesia MD, Parker JS, Hoadley KA, Serody JS, Perou CM, Vincent BG (2016) Genomic analysis of immune cell infiltrates across 11 tumor types, J Natl Cancer Inst,  https://doi.org/10.1093/jnci/djw144 Google Scholar
  27. 27.
    Brown SD, Raeburn LA, Holt RA (2015) Profiling tissue-resident T cell repertoires by RNA sequencing. Genome Med 7:125Google Scholar
  28. 28.
    Samy MD, Tong WL, Yavorski JM, Sexton WJ, Blanck G (2017) T cell receptor gene recombinations in human tumor specimen exome files: detection of T cell receptor-beta VDJ recombinations associates with a favorable oncologic outcome for bladder cancer. Cancer Immunol Immunother 66:403–410Google Scholar
  29. 29.
    Tu YN, Tong WL, Fawcett TJ, Blanck G (2017) Lung tumor exome files with T-cell receptor recombinations: a mouse model of T-cell infiltrates reflecting mutation burdens. Lab Invest 97:1516–1520Google Scholar
  30. 30.
    In TSH, Trotman-Grant A, Fahl S, Chen ELY, Zarin P, Moore AJ, Wiest DL, Zuniga-Pflucker JC, Anderson MK (2017) HEB is required for the specification of fetal IL-17-producing gammadelta T cells. Nat Commun 8:2004Google Scholar
  31. 31.
    Arias-Pulido H, Cimino-Mathews A, Chaher N, Qualls C, Joste N, Colpaert C, Marotti JD, Foisey M, Prossnitz ER, Emens LA, Fiering S (2018) The combined presence of CD20 + B cells and PD-L1 + tumor-infiltrating lymphocytes in inflammatory breast cancer is prognostic of improved patient outcome, Breast Cancer Res Treat, 171(2):273–282Google Scholar
  32. 32.
    Lang Kuhs KA, Lin SW, Hua X, Schiffman M, Burk RD, Rodriguez AC, Herrero R, Abnet CC, Freedman ND, Pinto LA, Hamm D, Robins H, Hildesheim A, Shi J, Safaeian M (2018) T cell receptor repertoire among women who cleared and failed to clear cervical human papillomavirus infection: an exploratory proof-of-principle study. PLoS ONE 13:e0178167Google Scholar
  33. 33.
    Zeng G, Huang Y, Huang Y, Lyu Z, Lesniak D, Randhawa P (2016) Antigen-specificity of T cell infiltrates in biopsies with T cell-mediated rejection and BK polyomavirus viremia: analysis by next generation sequencing. Am J Transplant 16:3131–3138Google Scholar
  34. 34.
    Di Sante G, Tolusso B, Fedele AL, Gremese E, Alivernini S, Nicolo C, Ria F, Ferraccioli G (2015) Collagen specific T-cell repertoire and HLA-DR alleles: biomarkers of active refractory rheumatoid arthritis, EBioMedicine, 2:2037–2045Google Scholar
  35. 35.
    Klarenbeek PL, Doorenspleet ME, Esveldt RE, van Schaik BD, Lardy N, van Kampen AH, Tak PP, Plenge RM, Baas F, de Bakker PI, de Vries N (2015) Somatic variation of T-cell receptor genes strongly associate with HLA class restriction. PLoS ONE 10:e0140815Google Scholar
  36. 36.
    Loi S, Michiels S, Salgado R, Sirtaine N, Jose V, Fumagalli D, Kellokumpu-Lehtinen PL, Bono P, Kataja V, Desmedt C, Piccart MJ, Loibl S, Denkert C, Smyth MJ, Joensuu H, Sotiriou C (2014) 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 25:1544–1550Google Scholar
  37. 37.
    Xu T, He BS, Liu XX, Hu XX, Lin K, Pan YQ, Sun HL, Peng HX, Chen XX, Wang SK (2017) The predictive and prognostic role of stromal tumor-infiltrating lymphocytes in HER2−positive breast cancer with trastuzumab-based treatment: a meta-analysis and systematic review. J Cancer 8:3838–3848Google Scholar
  38. 38.
    Loi S (2013) Tumor-infiltrating lymphocytes, breast cancer subtypes and therapeutic efficacy, Oncoimmunology, 2:e24720Google Scholar
  39. 39.
    Loi S, Sirtaine N, Piette F, Salgado R, Viale G, Van Eenoo F, Rouas G, Francis P, Crown JP, Hitre E, de Azambuja E, Quinaux E, Di Leo A, Michiels S, Piccart MJ, Sotiriou C (2013) Prognostic and predictive value of tumor-infiltrating lymphocytes in a phase III randomized adjuvant breast cancer trial in node-positive breast cancer comparing the addition of docetaxel to doxorubicin with doxorubicin-based chemotherapy: BIG 02–98. J Clin Oncol 31:860–867Google Scholar

Copyright information

© Springer Science+Business Media, LLC, part of Springer Nature 2018

Authors and Affiliations

  1. 1.Department of Molecular MedicineMorsani College of Medicine, University of South FloridaTampaUSA
  2. 2.Department of ImmunologyH. Lee Moffitt Cancer Center and Research InstituteTampaUSA

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