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Galectin-9 expression as a poor prognostic factor in patients with renal cell carcinoma

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Abstract

Recently, the effectiveness of anti-programmed death 1 (PD-1) antibody therapy in the treatment of renal cell carcinoma (RCC) has been established. Nevertheless, efficacy has been reported to be limited to only 10–30% of patients. To develop more effective immunotherapy for RCC, we analyzed the immunological characteristics in RCC tissues by immunohistochemistry (IHC). We prepared a tissue microarray that consisted of tumor tissue sections (1 mm in diameter) from 83 RCC patients in Kanagawa Cancer Center between 2006 and 2015. IHC analysis was performed with antibodies specific to immune-related (CD8 and Foxp3) and immune checkpoint (programmed death ligand 1 (PD-L1) and 2 (PD-L2), B7-H4 and galectin-9) molecules. The numbers and proportions of positively stained tumor cells or immune cells were determined in each section. From multivariate analysis of all 83 patients, higher galectin-9 expression was detected as a factor associated with worse overall survival (OS) (P = 0.029) and that higher stage and higher B7-H4 expression were associated with worse progression-free survival (PFS) (P < 0.001 and P = 0.021, respectively). Similarly, in multivariate analysis of 69 patients with clear cell RCC, though not statistically significant, there was a trend for association between higher galectin-9 expression and worse OS (P = 0.067), while higher stage was associated with worse PFS (P < 0.001). This study suggests that higher galectin-9 expression is an independent adverse prognostic factor of OS in RCC patients. Therefore, to develop more effective personalized immunotherapy to treat RCC, it may be important to target not only PD-1/PD-L1, but also other immune checkpoint molecules such as galectin-9.

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Abbreviations

ccRCC:

Clear cell renal cell carcinoma

CSS:

Cancer-specific survival

ICI:

Immune checkpoint inhibitor

IHC:

Immunohistochemistry

OS:

Overall survival

PD-1:

Programmed death 1

PD-L1:

Programmed death ligand 1

PD-L2:

Programmed death ligand 2

PFS:

Progression-free survival

RCC:

Renal cell carcinoma

RFS:

Recurrence-free survival

TIM-3:

T cell immunoglobulin and mucin domain-containing protein 3

TMA:

Tissue microarray

References

  1. Bray F, Ferlay J, Soerjomataram I, Siegel RL, Torre LA, Jemal A (2018) Global cancer statistics 2018: GLOBOCAN estimates of incidence and mortality worldwide for 36 cancers in 185 countries. CA Cancer J Clin 68:394–424. https://doi.org/10.3322/caac.21492

    Article  PubMed  Google Scholar 

  2. Topalian SL, Hodi FS, Brahmer JR et al (2012) Safety, activity, and immune correlates of anti-PD-1 antibody in cancer. N Engl J Med 366:2443–2454. https://doi.org/10.1056/NEJMoa1200690

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  3. Motzer RJ, Rini BI, McDermott DF et al (2015) Nivolumab for metastatic renal cell carcinoma: results of a randomized phase II trial. J Clin Oncol 33:1430–1437. https://doi.org/10.1200/JCO.2014.59.0703

    Article  CAS  PubMed  Google Scholar 

  4. Motzer RJ, Escudier B, McDermott DF et al (2015) Nivolumab versus everolimus in advanced renal-cell carcinoma. N Engl J Med 373:1803–1813. https://doi.org/10.1056/NEJMoa1510665

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  5. Weber JS, D'Angelo SP, Minor D et al (2015) Nivolumab versus chemotherapy in patients with advanced melanoma who progressed after anti-CTLA-4 treatment (CheckMate 037): a randomised, controlled, open-label, phase 3 trial. Lancet Oncol 16:375–384. https://doi.org/10.1016/S1470-2045(15)70076-8

    Article  CAS  PubMed  Google Scholar 

  6. Daud AI, Wolchok JD, Robert C et al (2016) Programmed death-ligand 1 expression and response to the anti-programmed death 1 antibody pembrolizumab in melanoma. J Clin Oncol 34:4102–4109. https://doi.org/10.1200/JCO.2016.67.2477

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  7. Gandhi L, Rodriguez-Abreu D, Gadgeel S et al (2018) Pembrolizumab plus chemotherapy in metastatic non-small-cell lung cancer. N Engl J Med 378:2078–2092. https://doi.org/10.1056/NEJMoa1801005

    Article  CAS  PubMed  Google Scholar 

  8. Fridman WH, Zitvogel L, Sautes-Fridman C, Kroemer G (2017) The immune contexture in cancer prognosis and treatment. Nat Rev Clin Oncol 14:717–734. https://doi.org/10.1038/nrclinonc.2017.101

    Article  CAS  PubMed  Google Scholar 

  9. Giraldo NA, Becht E, Vano Y et al (2017) Tumor-infiltrating and peripheral blood T-cell immunophenotypes predict early relapse in localized clear cell renal cell carcinoma. Clin Cancer Res 23:4416–4428. https://doi.org/10.1158/1078-0432.CCR-16-2848

    Article  CAS  PubMed  Google Scholar 

  10. Tanaka A, Sakaguchi S (2017) Regulatory T cells in cancer immunotherapy. Cell Res 27:109–118. https://doi.org/10.1038/cr.2016.151

    Article  CAS  PubMed  Google Scholar 

  11. Polimeno M, Napolitano M, Costantini S et al (2013) Regulatory T cells, interleukin (IL)-6, IL-8, vascular endothelial growth factor (VEGF), CXCL10, CXCL11, epidermal growth factor (EGF) and hepatocyte growth factor (HGF) as surrogate markers of host immunity in patients with renal cell carcinoma. BJU Int 112:686–696. https://doi.org/10.1111/bju.12068

    Article  CAS  PubMed  Google Scholar 

  12. Leite KR, Reis ST, Junior JP, Zerati M, Gomes Dde O, Camara-Lopes LH, Srougi M (2015) PD-L1 expression in renal cell carcinoma clear cell type is related to unfavorable prognosis. Diagn Pathol 10:189. https://doi.org/10.1186/s13000-015-0414-x

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  13. Shin SJ, Jeon YK, Kim PJ, Cho YM, Koh J, Chung DH, Go H (2016) Clinicopathologic analysis of PD-L1 and PD-L2 expression in renal cell carcinoma: association with oncogenic proteins status. Ann Surg Oncol 23:694–702. https://doi.org/10.1245/s10434-015-4903-7

    Article  PubMed  Google Scholar 

  14. Iacovelli R, Nole F, Verri E et al (2016) Prognostic role of PD-L1 expression in renal cell carcinoma. a systematic review and meta-analysis. Target Oncol 11:143–148. https://doi.org/10.1007/s11523-015-0392-7

    Article  PubMed  Google Scholar 

  15. Kim SH, Park WS, Park EY, Park B, Joo J, Joung JY, Seo HK, Lee KH, Chung J (2017) The prognostic value of BAP1, PBRM1, pS6, PTEN, TGase2, PD-L1, CA9, PSMA, and Ki-67 tissue markers in localized renal cell carcinoma: a retrospective study of tissue microarrays using immunohistochemistry. PLoS ONE 12:e0179610. https://doi.org/10.1371/journal.pone.0179610

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  16. Simon I, Zhuo S, Corral L, Diamandis EP, Sarno MJ, Wolfert RL, Kim NW (2006) B7-H4 is a novel membrane-bound protein and a candidate serum and tissue biomarker for ovarian cancer. Cancer Res 66:1570–1575. https://doi.org/10.1158/0008-5472.CAN-04-3550

    Article  CAS  PubMed  Google Scholar 

  17. Krambeck AE, Thompson RH, Dong H et al (2006) B7–H4 expression in renal cell carcinoma and tumor vasculature: associations with cancer progression and survival. Proc Natl Acad Sci USA 103:10391–10396. https://doi.org/10.1073/pnas.0600937103

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  18. Shen L, Qian Y, Wu W et al (2017) B7-H4 is a prognostic biomarker for poor survival in patients with pancreatic cancer. Hum Pathol 66:79–85. https://doi.org/10.1016/j.humpath.2017.05.023

    Article  CAS  PubMed  Google Scholar 

  19. Thijssen VL, Heusschen R, Caers J, Griffioen AW (2015) Galectin expression in cancer diagnosis and prognosis: a systematic review. Biochim Biophys Acta 1855:235–247. https://doi.org/10.1016/j.bbcan.2015.03.003

    Article  CAS  PubMed  Google Scholar 

  20. Mok TSK, Wu YL, Kudaba I et al (2019) Pembrolizumab versus chemotherapy for previously untreated, PD-L1-expressing, locally advanced or metastatic non-small-cell lung cancer (KEYNOTE-042): a randomised, open-label, controlled, phase 3 trial. Lancet 393:1819–1830. https://doi.org/10.1016/S0140-6736(18)32409-7

    Article  CAS  PubMed  Google Scholar 

  21. Yeong J, Zhao Z, Lim JCT et al (2020) PD-L1 expression is an unfavourable prognostic indicator in Asian renal cell carcinomas. J Clin Pathol. https://doi.org/10.1136/jclinpath-2019-206092

    Article  PubMed  Google Scholar 

  22. Kanda Y (2013) Investigation of the freely available easy-to-use software 'EZR' for medical statistics. Bone Marrow Transpl 48:452–458. https://doi.org/10.1038/bmt.2012.244

    Article  CAS  Google Scholar 

  23. Kageshita T, Kashio Y, Yamauchi A et al (2002) Possible role of galectin-9 in cell aggregation and apoptosis of human melanoma cell lines and its clinical significance. Int J Cancer 99:809–816. https://doi.org/10.1002/ijc.10436

    Article  CAS  PubMed  Google Scholar 

  24. Irie A, Yamauchi A, Kontani K et al (2005) Galectin-9 as a prognostic factor with antimetastatic potential in breast cancer. Clin Cancer Res 11:2962–2968. https://doi.org/10.1158/1078-0432.CCR-04-0861

    Article  CAS  PubMed  Google Scholar 

  25. Liang M, Ueno M, Oomizu S, Arikawa T, Shinonaga R, Zhang S, Yamauchi A, Hirashima M (2008) Galectin-9 expression links to malignant potential of cervical squamous cell carcinoma. J Cancer Res Clin Oncol 134:899–907. https://doi.org/10.1007/s00432-008-0352-z

    Article  CAS  PubMed  Google Scholar 

  26. Zhang ZY, Dong JH, Chen YW, Wang XQ, Li CH, Wang J, Wang GQ, Li HL, Wang XD (2012) Galectin-9 acts as a prognostic factor with antimetastatic potential in hepatocellular carcinoma. Asian Pac J Cancer Prev 13:2503–2509. https://doi.org/10.7314/apjcp.2012.13.6.2503

    Article  PubMed  Google Scholar 

  27. Fu H, Liu Y, Xu L, Liu W, Fu Q, Liu H, Zhang W, Xu J (2015) Galectin-9 predicts postoperative recurrence and survival of patients with clear-cell renal cell carcinoma. Tumour Biol 36:5791–5799. https://doi.org/10.1007/s13277-015-3248-y

    Article  CAS  PubMed  Google Scholar 

  28. Qi Y, Chang Y, Wang Z et al (2019) Tumor-associated macrophages expressing galectin-9 identify immunoevasive subtype muscle-invasive bladder cancer with poor prognosis but favorable adjuvant chemotherapeutic response. Cancer Immunol Immunother 68:2067–2080. https://doi.org/10.1007/s00262-019-02429-2

    Article  CAS  PubMed  Google Scholar 

  29. Li H, Wu K, Tao K et al (2012) Tim-3/galectin-9 signaling pathway mediates T-cell dysfunction and predicts poor prognosis in patients with hepatitis B virus-associated hepatocellular carcinoma. Hepatology 56:1342–1351. https://doi.org/10.1002/hep.25777

    Article  CAS  PubMed  Google Scholar 

  30. Zhu C, Anderson AC, Schubart A, Xiong H, Imitola J, Khoury SJ, Zheng XX, Strom TB, Kuchroo VK (2005) The Tim-3 ligand galectin-9 negatively regulates T helper type 1 immunity. Nat Immunol 6:1245–1252. https://doi.org/10.1038/ni1271

    Article  CAS  PubMed  Google Scholar 

  31. Granier C, Dariane C, Combe P et al (2017) Tim-3 expression on tumor-infiltrating PD-1(+)CD8(+) T cells correlates with poor clinical outcome in renal cell carcinoma. Cancer Res 77:1075–1082. https://doi.org/10.1158/0008-5472.CAN-16-0274

    Article  CAS  PubMed  Google Scholar 

  32. Podojil JR, Miller SD (2017) Potential targeting of B7-H4 for the treatment of cancer. Immunol Rev 276:40–51. https://doi.org/10.1111/imr.12530

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  33. Ishida Y, Agata Y, Shibahara K, Honjo T (1992) Induced expression of PD-1, a novel member of the immunoglobulin gene superfamily, upon programmed cell death. EMBO J 11:3887–3895

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  34. Barber DL, Wherry EJ, Masopust D, Zhu B, Allison JP, Sharpe AH, Freeman GJ, Ahmed R (2006) Restoring function in exhausted CD8 T cells during chronic viral infection. Nature 439:682–687. https://doi.org/10.1038/nature04444

    Article  CAS  PubMed  Google Scholar 

  35. Zou W, Chen L (2008) Inhibitory B7-family molecules in the tumour microenvironment. Nat Rev Immunol 8:467–477. https://doi.org/10.1038/nri2326

    Article  CAS  PubMed  Google Scholar 

  36. Ohigashi Y, Sho M, Yamada Y et al (2005) Clinical significance of programmed death-1 ligand-1 and programmed death-1 ligand-2 expression in human esophageal cancer. Clin Cancer Res 11:2947–2953. https://doi.org/10.1158/1078-0432.CCR-04-1469

    Article  CAS  PubMed  Google Scholar 

  37. Wu C, Zhu Y, Jiang J, Zhao J, Zhang XG, Xu N (2006) Immunohistochemical localization of programmed death-1 ligand-1 (PD-L1) in gastric carcinoma and its clinical significance. Acta Histochem 108:19–24. https://doi.org/10.1016/j.acthis.2006.01.003

    Article  PubMed  Google Scholar 

  38. Hamanishi J, Mandai M, Iwasaki M et al (2007) Programmed cell death 1 ligand 1 and tumor-infiltrating CD8+ T lymphocytes are prognostic factors of human ovarian cancer. Proc Natl Acad Sci USA 104:3360–3365. https://doi.org/10.1073/pnas.0611533104

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  39. Droeser RA, Hirt C, Viehl CT et al (2013) Clinical impact of programmed cell death ligand 1 expression in colorectal cancer. Eur J Cancer 49:2233–2242. https://doi.org/10.1016/j.ejca.2013.02.015

    Article  CAS  PubMed  Google Scholar 

  40. Schalper KA, Velcheti V, Carvajal D, Wimberly H, Brown J, Pusztai L, Rimm DL (2014) In situ tumor PD-L1 mRNA expression is associated with increased TILs and better outcome in breast carcinomas. Clin Cancer Res 20:2773–2782. https://doi.org/10.1158/1078-0432.CCR-13-2702

    Article  CAS  PubMed  Google Scholar 

  41. Lipson EJ, Vincent JG, Loyo M et al (2013) PD-L1 expression in the Merkel cell carcinoma microenvironment: association with inflammation, Merkel cell polyomavirus and overall survival. Cancer Immunol Res 1:54–63. https://doi.org/10.1158/2326-6066.CIR-13-0034

    Article  CAS  PubMed  Google Scholar 

  42. Topalian SL, Taube JM, Anders RA, Pardoll DM (2016) Mechanism-driven biomarkers to guide immune checkpoint blockade in cancer therapy. Nat Rev Cancer 16:275–287. https://doi.org/10.1038/nrc.2016.36

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  43. Bu M, Shen Y, Seeger WL, An S, Qi R, Sanderson JA, Cai Y (2016) Ovarian carcinoma-infiltrating regulatory T cells were more potent suppressors of CD8(+) T cell inflammation than their peripheral counterparts, a function dependent on TIM3 expression. Tumour Biol 37:3949–3956. https://doi.org/10.1007/s13277-015-4237-x

    Article  CAS  PubMed  Google Scholar 

  44. Liu Z, McMichael EL, Shayan G, Li J, Chen K, Srivastava R, Kane LP, Lu B, Ferris RL (2018) Novel effector phenotype of Tim-3(+) regulatory T cells leads to enhanced suppressive function in head and neck cancer patients. Clin Cancer Res 24:4529–4538. https://doi.org/10.1158/1078-0432.CCR-17-1350

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  45. Liu JF, Wu L, Yang LL, Deng WW, Mao L, Wu H, Zhang WF, Sun ZJ (2018) Blockade of TIM3 relieves immunosuppression through reducing regulatory T cells in head and neck cancer. J Exp Clin Cancer Res 37:44. https://doi.org/10.1186/s13046-018-0713-7

    Article  CAS  PubMed  PubMed Central  Google Scholar 

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Acknowledgements

This study was supported by Kanagawa Cancer Center Hospital-Research Institute Joint Study.

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Authors and Affiliations

  1. Department of Urology, Kanagawa Cancer Center, 2-3-2 Nakao, Asahi-ku, Yokohama, Kanagawa, 241-8515, Japan

    Ryosuke Jikuya, Takeshi Kishida, Masato Yasui, Akihito Hashizume, Tomoyuki Tatenuma, Nobuhiko Mizuno, Kentaro Muraoka, Susumu Umemoto & Masaki Kawai

  2. Research Institute, Kanagawa Cancer Center, 2-3-2 Nakao, Asahi-ku, Yokohama, Kanagawa, 241-8515, Japan

    Masahiko Sakaguchi, Mitsuyo Yoshihara, Yoshiyasu Nakamura, Yohei Miyagi & Tetsuro Sasada

  3. Department of Pathology, Kanagawa Cancer Center, 2-3-2 Nakao, Asahi-ku, Yokohama, Kanagawa, 241-8515, Japan

    Tomoyuki Yokose

  4. Cancer Vaccine and Immunotherapy Center, Kanagawa Cancer Center, 2-3-2 Nakao, Asahi-ku, Yokohama, Kanagawa, 241-8515, Japan

    Tetsuro Sasada

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  1. Ryosuke Jikuya
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  2. Takeshi Kishida
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  3. Masahiko Sakaguchi
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Contributions

RJ, TK, and TS were involved in conceptualization; RJ, TK, TY, MY, AH, TT, NM, KM, SU, MK, MY, YN, YM, and TS contributed to data acquisition and methodology; RJ, TK, MS, TY, and TS contributed to formal analysis and investigation; RJ contributed to writing—original draft preparation; TK, MS, and TS contributed to writing—review and editing; RJ and TS contributed to funding acquisition; and all authors read and approved the final manuscript.

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Correspondence to Takeshi Kishida or Tetsuro Sasada.

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262_2020_2608_MOESM1_ESM.pdf

Figure 1. Kaplan–Meier analysis of OS in the subgroups of RCC patients separated by stage or grade. Kaplan–Meier analysis of OS was performed according to galectin-9 expression in the subgroups of RCC patients (n=83) separated by stage or grade. The patients were separated by the stage to (a) lower stage subgroup consisting of stage I and II and (b) higher stage subgroup consisting of stage III and IV. They were also separated by the grade to (c) lower grade subgroup consisting of grade 1 and 2 and (d) higher grade subgroup consisting of grade 3. The patients were divided into “high” and “low” groups in each subgroup by setting the median value of galectin-9 expression as the cutoff. Log-rank p value was shown. (PDF 239 kb)

262_2020_2608_MOESM2_ESM.pdf

Figure 2. Kaplan–Meier analysis of OS in the subgroups of clear cell RCC patients separated by stage or grade. Kaplan–Meier analysis of OS was performed according to galectin-9 expression in the subgroups of clear cell RCC patients (n=69) separated by stage or grade. The patients were separated by the stage to (a) lower stage subgroup consisting of stage I and II and (b) higher stage subgroup consisting of stage III and IV. They were also separated by the grade to (c) lower grade subgroup consisting of grade 1 and 2 and (d) higher grade subgroup consisting of grade 3. The patients were divided into “high” and “low” groups in each subgroup by setting the median value of galectin-9 expression as the cutoff. Log-rank p value was shown. (PDF 237 kb)

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Jikuya, R., Kishida, T., Sakaguchi, M. et al. Galectin-9 expression as a poor prognostic factor in patients with renal cell carcinoma. Cancer Immunol Immunother 69, 2041–2051 (2020). https://doi.org/10.1007/s00262-020-02608-6

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