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Lymphocyte-specific kinase expression is a prognostic indicator in ovarian cancer and correlates with a prominent B cell transcriptional signature

  • Emily HinchcliffEmail author
  • Cherie Paquette
  • Jason Roszik
  • Sarah Kelting
  • Mark H. Stoler
  • Samuel C. Mok
  • Tsz-Lun Yeung
  • Qian Zhang
  • Melinda Yates
  • Weiyi Peng
  • Patrick Hwu
  • Amir Jazaeri
Original Article

Abstract

Objective

To investigate the prognostic and biologic significance of immune-related gene expression in high grade serous ovarian cancer (HGSOC).

Methods

Gene expression dependent survival analyses for a panel of immune related genes were evaluated in HGSOC utilizing The Cancer Genome Atlas (TCGA). Prognostic value of LCK was validated using IHC in an independent set of 72 HGSOC. Prognostic performance of LCK was compared to cytolytic score (CYT) using RNAseq across multiple tumor types. Differentially expressed genes in LCK high samples and gene ontology enrichment were analyzed.

Results

High pre-treatment LCK mRNA expression was found to be a strong predictor of survival in a set of 535 ovarian cancers. Patients with high LCK mRNA expression had a longer median progression free survival (PFS) of 29.4 months compared to 16.9 months in those without LCK high expression (p = 0.003), and longer median overall survival (OS) of 95.1 months versus 44.5 months (p = 0.001), which was confirmed in an independent cohort by IHC (p = 0.04). LCK expression was compared to CYT across tumor types available in the TCGA and was a significant predictor of prognosis in HGSOC where CYT was not predictive. Unexpectedly, LCK high samples also were enriched in numerous immunoglobulin-related and other B cell transcripts.

Conclusions

LCK is a better prognostic factor than CYT in ovarian cancer. In HGSOC, LCK high samples were characterized by higher expression of immunoglobulin and B-cell related genes suggesting that a cooperative interaction between tumor infiltrating T and B cells may correlate with better survival in this disease.

Keywords

Ovarian cancer Lymphocyte specific kinase Biomarker Cytolytic activity score B lymphocyte 

Abbreviations

BCR

B cell receptor

CYT

Cytolytic Activity Score

GZMA

Granzyme A

HGSOC

High grade serous ovarian cancer

LCK

Lymphocyte specific tyrosine kinase

MHC

Major histocompatibility complex

PRF1

Perforin

RPPA

Reverse phase protein array

TCGA

The Cancer Genome Atlas

TCR

T cell receptor

TLS

Tertiary lymphoid structures

TMA

Tissue microarray

TPM

Transcripts per million

CYT

Cytolytic Activity Score

HGSOC

High grade serous ovarian cancer

LCK

Lymphocyte specific tyrosine kinase

TCGA

The Cancer Genome Atlas

TLS

Tertiary lymphoid structures

TMA

Tissue microarray

Notes

Author contributions

EH and AJ were the principle investigators. CP, SK and MHS performed immunohistochemistry and analysis. JR helped in TCGA analysis including comparison to CYT and related statistical analyses, while WP and PH aided in research question formulation and study design. SCM, TLY, QZ, MY contributed samples and support for analysis of independent cohort. EH wrote the manuscript, on which all co-authors commented.

Funding

This research was supported in part by the MD Anderson Cancer Center Support Grant (P30 CA016672), a T32 training grant for gynecologic oncology (CA101642; to K.H. Lu), and the Ovarian Cancer Research Program grants, Department of Defense (W81XWH-17-1-0126 and W81XWH-16-1-0038; to S.C. Mok).

Compliance with ethical standards:

Conflict of interest

The authors declare no potential conflicts of interest.

Ethical approval and ethical standards

Independent validation cohorts were enrolled on tissue and clinical data collection protocol approved by MD Anderson Cancer Center institutional review board (IRB, protocol #: LAB06-0412). All tissue included in the tissue microarray was obtained under an IRB approved protocol at the University of Virginia (protocol #:14461).

Informed consent

Because all information from the Cancer Genome Atlas is de-identified and publically available, informed consent by the study participants and approval of an ethics committee were unnecessary to perform this portion of the analyses in this study. All patients contributing tissue were enrolled under translational protocols as listed above and consent was obtained for the use of their specimens and data for research and for publication.

Supplementary material

262_2019_2385_MOESM1_ESM.pdf (7 mb)
Supplementary material 1 (PDF 7205 kb)

References

  1. 1.
    Hinchcliff EM, Paquette C, Roszik J, Kelting S, Stoler MH, Mok SC, Yeung T, Zhang Q, Yates M, Peng W (2019) Lymphocyte-specific protein tyrosine kinase expression predicts survival in ovarian high-grade serous carcinoma. In: Society for Gynecologic Oncology, Annual Meeting, Hawaii, March 2019Google Scholar
  2. 2.
    Torre LA, Trabert B, DeSantis CE, Miller KD, Samimi G, Runowicz CD, Gaudet MM, Jemal A, Siegel RL (2018) Ovarian cancer statistics, 2018. CA Cancer J Clin 68(4):284–296CrossRefGoogle Scholar
  3. 3.
    Hayashi K et al (1999) Clonal expansion of T cells that are specific for autologous ovarian tumor among tumor-infiltrating T cells in humans1. Gynecol Oncol 74(1):86–92CrossRefGoogle Scholar
  4. 4.
    Ioannides CG, Freedman RS, Platsoucas CD, Rashed S, Kim YP (1991) Cytotoxic T cell clones isolated from ovarian tumor-infiltrating lymphocytes recognize multiple antigenic epitopes on autologous tumor cells. J Immunol 146(5):1700–1707Google Scholar
  5. 5.
    Peoples GE, Schoof DD, Andrews JV, Goedegebuure PS, Eberlein TJ (1993) T-cell recognition of ovarian cancer. Surgery 114(2):227–234Google Scholar
  6. 6.
    Preston CC et al (2013) The ratios of CD8+ T cells to CD4+ CD25+ FOXP3+ and FOXP3- T cells correlate with poor clinical outcome in human serous ovarian cancer. PLoS One 8(11):1–10CrossRefGoogle Scholar
  7. 7.
    Sato E et al (2005) Intraepithelial CD8+ tumor-infiltrating lymphocytes and a high CD8+/regulatory T cell ratio are associated with favorable prognosis in ovarian cancer. Proc Natl Acad Sci 102(51):18538–18543CrossRefGoogle Scholar
  8. 8.
    Webb JR, Milne K, Watson P, DeLeeuw RJ, Nelson BH (2014) Tumor-infiltrating lymphocytes expressing the tissue resident memory marker cd103 are associated with increased survival in high-grade serous ovarian cancer. Clin Cancer Res 20(2):434–444CrossRefGoogle Scholar
  9. 9.
    Zhang L et al (2003) Intratumoral T cells, recurrence, and survival in epithelial ovarian cancer. N Engl J Med 348(3):203–213CrossRefGoogle Scholar
  10. 10.
    Curiel TJ et al (2004) Specific recruitment of regulatory T cells in ovarian carcinoma fosters immune privilege and predicts reduced survival. Nat Med 10(9):942–949CrossRefGoogle Scholar
  11. 11.
    Hwang WT et al (2012) Prognostic significance of tumor-infiltrating T-cells in ovarian cancer: a meta-analysis. Gynecol Oncol 124(2):192–198CrossRefGoogle Scholar
  12. 12.
    Yildirim N et al (2017) Do tumor-infiltrating lymphocytes really indicate favorable prognosis in epithelial ovarian cancer? Eur J Obstet Gynecol Reprod Biol 215:55–61CrossRefGoogle Scholar
  13. 13.
    Milne K et al (2009) Systematic analysis of immune infiltrates in high-grade serous ovarian cancer reveals CD20, FoxP3 and TIA-1 as positive prognostic factors. PLoS One 4(7):e6412CrossRefGoogle Scholar
  14. 14.
    Bindea G et al (2013) Spatiotemporal dynamics of intratumoral immune cells reveal the immune landscape in human cancer. Immunity 39(4):782–795CrossRefGoogle Scholar
  15. 15.
    Rooney MS, Shukla SA, Wu CJ, Getz G, Hacohen N (2015) Molecular and genetic properties of tumors associated with local immune cytolytic activity. Cell 160(1–2):48–61CrossRefGoogle Scholar
  16. 16.
    Narayanan S, Kawaguchi T, Yan L, Peng X, Qi Q, Takabe K (2018) Cytolytic activity score to assess anticancer immunity in colorectal cancer. Ann Surg Oncol 25(8):2323–2331CrossRefGoogle Scholar
  17. 17.
    Balli D, Rech AJ, Stanger BZ, Vonderheide RH (2017) Immune cytolytic activity stratifies molecular subsets of human pancreatic cancer. Clin Cancer Res 23(12):3129–3138CrossRefGoogle Scholar
  18. 18.
    Roufas C et al. (2018) The expression and prognostic impact of immune cytolytic activity-related markers in human malignancies: a comprehensive meta-analysis. Front Oncol 8:27CrossRefGoogle Scholar
  19. 19.
    Cancer Genome Atlas Research Network (2011) Integrated genomic analyses of ovarian carcinoma. Nature 474(7353):609–615CrossRefGoogle Scholar
  20. 20.
    Cerami E et al (2012) The cBio cancer genomics portal: an open platform for exploring multidimensional cancer genomics data. Cancer Discov 2(5):401–404CrossRefGoogle Scholar
  21. 21.
    Gao J et al (2013) Integrative analysis of complex cancer genomics and clinical profiles using the cBioPortal. Sci Signal 6(269):11Google Scholar
  22. 22.
    Salgado R et al (2015) The evaluation of tumor-infiltrating lymphocytes (TILS) in breast cancer: recommendations by an International TILS Working Group 2014. Ann Oncol 26(2):259–271CrossRefGoogle Scholar
  23. 23.
    Diedenhofen B, Musch J (2015) Cocor: a comprehensive solution for the statistical comparison of correlations. PLoS One 10(3):e0121945CrossRefGoogle Scholar
  24. 24.
    Goode EL et al (2017) Dose-response association of CD8+ tumor-infiltrating lymphocytes and survival time in high-grade serous ovarian cancer. JAMA Oncol 3(12):e173290CrossRefGoogle Scholar
  25. 25.
    James FR et al (2017) Association between tumour infiltrating lymphocytes, histotype and clinical outcome in epithelial ovarian cancer. BMC Cancer 17(1):657CrossRefGoogle Scholar
  26. 26.
    Sharma P, Allison JP (2015) The future of immune checkpoint therapy. Science (80-) 348(6230):56–61CrossRefGoogle Scholar
  27. 27.
    Pakish JB, Jazaeri AA (2017) Immunotherapy in gynecologic cancers: are we there yet? Curr Treat Options Oncol 18(10):59CrossRefGoogle Scholar
  28. 28.
    Varga A et al (2015) Antitumor activity and safety of pembrolizumab in patients (pts) with PD-L1 positive advanced ovarian cancer: Interim results from a phase Ib study. J Clin Oncol 33(15_suppl):5510Google Scholar
  29. 29.
    Brahmer JR et al. (2012) Safety and activity of anti–PD-L1 antibody in patients with advanced cancer. N Engl J Med 366(26):2455–2465CrossRefGoogle Scholar
  30. 30.
    Brownlie RJ, Zamoyska R (2013) T cell receptor signalling networks: branched, diversified and bounded. Nat Rev Immunol 13(4):257–269CrossRefGoogle Scholar
  31. 31.
    Molina TJ et al (1992) Profound block in thymocyte development in mice lacking p56lck. Nature 357(6374):161–164CrossRefGoogle Scholar
  32. 32.
    Nielsen JS et al (2012) CD20+ tumor-infiltrating lymphocytes have an atypical CD27—memory phenotype and together with CD8+ T cells promote favorable prognosis in ovarian cancer. Clin Cancer Res 18(12):3281–3292CrossRefGoogle Scholar
  33. 33.
    Iglesia MD et al (2014) Prognostic B-cell signatures using mRNA-seq in patients with subtype-specific breast and ovarian cancer. Clin Cancer Res 20(14):3818–3829CrossRefGoogle Scholar
  34. 34.
    Maddur MS et al (2014) Human B cells induce dendritic cell maturation and favour Th2 polarization by inducing OX-40 ligand. Nat Commun 5:4092CrossRefGoogle Scholar
  35. 35.
    DiLillo DJ, Yanaba K, Tedder TF (2010) B cells are required for optimal CD4+ and CD8+ T cell tumor immunity: therapeutic B cell depletion enhances B16 melanoma growth in mice. J Immunol 184(7):4006–4016CrossRefGoogle Scholar

Copyright information

© Springer-Verlag GmbH Germany, part of Springer Nature 2019

Authors and Affiliations

  • Emily Hinchcliff
    • 1
    Email author
  • Cherie Paquette
    • 2
  • Jason Roszik
    • 5
  • Sarah Kelting
    • 3
  • Mark H. Stoler
    • 4
  • Samuel C. Mok
    • 1
    • 6
  • Tsz-Lun Yeung
    • 1
  • Qian Zhang
    • 1
  • Melinda Yates
    • 1
    • 6
  • Weiyi Peng
    • 5
  • Patrick Hwu
    • 5
    • 6
  • Amir Jazaeri
    • 1
    • 6
  1. 1.Department of Gynecologic Oncology and Reproductive MedicineThe University of Texas MD Anderson Cancer CenterHoustonUSA
  2. 2.Department of Pathology and Laboratory MedicineWomen and Infants Hospital of Rhode Island and The Warren Alpert Medical School of Brown UniversityProvidenceUSA
  3. 3.Department of PathologyUniversity of New MexicoAlbuquerqueUSA
  4. 4.Department of PathologyUniversity of Virginia Health SystemCharlottesvilleUSA
  5. 5.Department of Melanoma Medical OncologyThe University of Texas MD Anderson Cancer CenterHoustonUSA
  6. 6.The University of Texas Graduate School of Biomedical Sciences at HoustonHoustonUSA

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