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Identification of an immune overdrive high-risk subpopulation with aberrant expression of FOXP3 and CTLA4 in colorectal cancer

Abstract

Colorectal cancer (CRC) is characterized by a heterogeneous tumor microenvironment (TME) that regulates cancer progression and therapeutic response. Overexpression of FOXP3 and CTLA4 is associated with immunosuppressive TME and poor prognosis in many cancer types. However, opposite results were reported in CRC. Thus, we performed comprehensive analyses to evaluate the exact prognostic value of FOXP3 and CTLA4 in CRC. Here, the expression levels of FOXP3 and CTLA4 were used to construct a subtyping system based on >1200 CRC patients from multiple independent public datasets. We revealed that, in CRC patients with relatively high expression of FOXP3, there exist two different subpopulations with opposite survival patterns according to CLTA4 expression. We further established a method for evaluating all cohorts and identified a novel FOXP3HighCTLA4High* CRC risk subpopulation that accounts for 5–10% of CRC patients. Moreover, different methods of functional enrichment and immune evaluation were used to analyze the TME characteristics of different FOXP3/CTLA4 subtypes. The FOXP3HighCTLA4High* CRC risk subpopulation was characterized by an immune overdrive TME phenotype, including high immune cell infiltration, low tumor purity, high immune checkpoint levels, and TGF-β activation. Finally, the constructed FOXP3/CTLA4 subtyping system was further validated by quantitative RT-PCR, immunochemistry staining, and multicolor immunofluorescence in an independent CRC cohort we collected. This high-risk subpopulation was also observed in kidney cancers and low-grade glioma patients by a Pan-cancer analysis. Together, our study revealed that the established FOXP3/CTLA4 molecular subtyping system could be used to select treatment and management strategies for CRC and other cancers.

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Fig. 1: Two subpopulations with high CTLA4 expression show opposite 5-year OS in FOXP3-overexpressing CRC patients.
Fig. 2: The prognostic value of CTLA4 is dependent on FOXP3 expression in CRC.
Fig. 3: FOXP3HighCTLA4High* patients represent a novel high-risk subpopulation in CRC.
Fig. 4: Immune characteristics of the FOXP3HighCTLA4High* risk subpopulation.
Fig. 5: Molecular subtypes characteristics of the FOXP3HighCTLA4High* risk subpopulation.
Fig. 6: Independent validation of the FOXP3/CTLA4 subtyping system in Asian validation cohorts.
Fig. 7: Pan-cancer prognostic significance of FOXP3HighCTLA4High* subpopulation.
Fig. 8

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References

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

    Article  PubMed  Google Scholar 

  2. Punt CJ, Koopman M, Vermeulen L. From tumour heterogeneity to advances in precision treatment of colorectal cancer. Nat Rev Clin Oncol. 2017;14:235–46.

    Article  CAS  PubMed  Google Scholar 

  3. Giraldo NA, Sanchez-Salas R, Peske JD, Vano Y, Becht E, Petitprez F, et al. The clinical role of the TME in solid cancer. Br J Cancer. 2019;120:45–53.

    Article  PubMed  Google Scholar 

  4. Yin Y, Yao S, Hu Y, Feng Y, Li M, Bian Z, et al. The immune-microenvironment confers chemoresistance of colorectal cancer through macrophage-derived IL6. Clin Cancer Res. 2017;23:7375–87.

    Article  CAS  PubMed  Google Scholar 

  5. Riera-Domingo C, Audigé A, Granja S, Cheng W-C, Ho P-C, Baltazar F, et al. Immunity, hypoxia, and metabolism-the ménage à trois of cancer: implications for immunotherapy. Physiol Rev. 2020;100:1–102.

    Article  CAS  PubMed  Google Scholar 

  6. Becht E, de Reynies A, Giraldo NA, Pilati C, Buttard B, Lacroix L, et al. Immune and stromal classification of colorectal cancer is associated with molecular subtypes and relevant for precision immunotherapy. Clin Cancer Res. 2016;22:4057–66.

    Article  CAS  PubMed  Google Scholar 

  7. Iglesia MD, Parker JS, Hoadley KA, Serody JS, Perou CM, Vincent BG. Genomic analysis of immune cell infiltrates across 11 tumor types. J Natl Cancer Inst. 2016;108:djw144.

    Article  PubMed Central  CAS  Google Scholar 

  8. Charoentong P, Finotello F, Angelova M, Mayer C, Efremova M, Rieder D, et al. Pan-cancer immunogenomic analyses reveal genotype-immunophenotype relationships and predictors of response to checkpoint blockade. Cell Rep. 2017;18:248–62.

    Article  CAS  PubMed  Google Scholar 

  9. Mlecnik B, Bindea G, Angell HK, Maby P, Angelova M, Tougeron D, et al. Integrative analyses of colorectal cancer show immunoscore is a stronger predictor of patient survival than microsatellite instability. Immunity. 2016;44:698–711.

    Article  CAS  PubMed  Google Scholar 

  10. Pages F, Mlecnik B, Marliot F, Bindea G, Ou FS, Bifulco C, et al. International validation of the consensus Immunoscore for the classification of colon cancer: a prognostic and accuracy study. Lancet. 2018;391:2128–39.

    Article  PubMed  Google Scholar 

  11. Toor SM, Sasidharan Nair V, Decock J, Elkord E. Immune checkpoints in the tumor microenvironment. Semin Cancer Biol. 2020;65:1–12.

    Article  CAS  PubMed  Google Scholar 

  12. Deng L, Gyorffy B, Na F, Chen B, Lan J, Xue J, et al. Association of PDCD1 and CTLA-4 Gene expression with clinicopathological factors and survival in non¨CSmall-Cell lung cancer: results from a large and pooled microarray database. J Thorac Oncol. 2015;10:1020–6.

    Article  CAS  PubMed  Google Scholar 

  13. Heinzerling L, Ott PA, Hodi FS, Husain AN, Tajmir-Riahi A, Tawbi H, et al. Cardiotoxicity associated with CTLA4 and PD1 blocking immunotherapy. J Immunother Cancer. 2016;4:50.

    Article  PubMed  PubMed Central  Google Scholar 

  14. Herrmann A, Lahtz C, Nagao T, Song JY, Chan WC, Lee H, et al. CTLA4 promotes Tyk2-STAT3¨C dependent B-cell oncogenicity. Cancer Res. 2017;77:5118–28.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  15. Yu H, Yang J, Jiao S, Li Y, Zhang W, Wang J. Cytotoxic T lymphocyte antigen 4 expression in human breast cancer: implications for prognosis. Cancer Immunol Immunother. 2015;64:853–60.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  16. Lu L, Bai Y, Wang Z. Elevated T cell activation score is associated with improved survival of breast cancer. Breast Cancer Res Treat. 2017;164:689–96.

    Article  CAS  PubMed  Google Scholar 

  17. Xiang Z, Chen W, Zhang J, Song S, Xia G-K, Huang X-Y, et al. Identification of discrepancy between CTLA4 expression and CTLA4 activation in gastric cancer. Immunopharmacol Immunotoxicol. 2018;41:386–93.

    Article  PubMed  CAS  Google Scholar 

  18. Zaravinos A, Roufas C, Nagara M, de Lucas Moreno B, Oblovatskaya M, Efstathiades C, et al. Cytolytic activity correlates with the mutational burden and deregulated expression of immune checkpoints in colorectal cancer. J Exp Clin Cancer Res. 2019;38:364.

    Article  PubMed  PubMed Central  CAS  Google Scholar 

  19. Ciardiello D, Vitiello PP, Cardone C, Martini G, Troiani T, Martinelli E, et al. Immunotherapy of colorectal cancer: challenges for therapeutic efficacy. Cancer Treat Rev. 2019;76:22–32.

    Article  CAS  PubMed  Google Scholar 

  20. Raffin C, Vo LT, Bluestone JA. Treg cell-based therapies: challenges and perspectives. Nat Rev Immunol. 2020;20:158–72.

    Article  CAS  PubMed  Google Scholar 

  21. Punt S, Houwing-Duistermaat JJ, Schulkens IA, Thijssen VL, Osse EM, de Kroon CD, et al. Correlations between immune response and vascularization qRT-PCR gene expression clusters in squamous cervical cancer. Mol Cancer. 2015;14:71.

    Article  PubMed  PubMed Central  CAS  Google Scholar 

  22. Wei YF, Chu CY, Chang CC, Lin SH, Su WC, Tseng YL, et al. Different pattern of PD-L1, IDO, and FOXP3 Tregs expression with survival in thymoma and thymic carcinoma. Lung Cancer. 2018;125:35–42.

    Article  PubMed  Google Scholar 

  23. Wolf D. The expression of the regulatory T cell-specific forkhead box transcription factor Foxp3 is associated with poor prognosis in ovarian cancer. Clin Cancer Res. 2005;11:8326–31.

    Article  CAS  PubMed  Google Scholar 

  24. Shi J-Y, Ma L-J, Zhang J-W, Duan M, Ding Z-B, Yang L-X, et al. FOXP3 is a HCC suppressor gene and acts through regulating the TGF-β/Smad2/3 signaling pathway. BMC Cancer. 2017;17:648.

    Article  PubMed  PubMed Central  CAS  Google Scholar 

  25. Yang S, Liu Y, Li M-Y, Ng CSH, Yang S-l, Wang S, et al. FOXP3 promotes tumor growth and metastasis by activating Wnt/β-catenin signaling pathway and EMT in non-small cell lung cancer. Mol Cancer. 2017;16:124.

    Article  PubMed  PubMed Central  CAS  Google Scholar 

  26. Lal A, Chan L, Devries S, Chin K, Scott GK, Benz CC, et al. FOXP3-positive regulatory T lymphocytes and epithelial FOXP3 expression in synchronous normal, ductal carcinoma in situ, and invasive cancer of the breast. Breast Cancer Res Treat. 2013;139:381–90.

    Article  CAS  PubMed  Google Scholar 

  27. Weller P, Bankfalvi A, Gu X, Dominas N, Lehnerdt GTF, Zeidler R, et al. The role of tumour FoxP3 as prognostic marker in different subtypes of head and neck cancer. Eur J Cancer. 2014;50:1291–300.

    Article  CAS  PubMed  Google Scholar 

  28. Ganapathi SK, Beggs AD, Hodgson SV, Kumar D. Expression and DNA methylation of TNF, IFNG and FOXP3 in colorectal cancer and their prognostic significance. Br J Cancer. 2014;111:1581–9.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  29. Sun X, Feng Z, Wang Y, Qu Y, Gai Y. Expression of Foxp3 and its prognostic significance in colorectal cancer. Int J Immunopathol Pharm. 2017;30:201–6.

    Article  CAS  Google Scholar 

  30. Chew A, Salama P, Robbshaw A, Klopcic B, Zeps N, Platell C, et al. SPARC, FOXP3, CD8 and CD45 correlation with disease recurrence and long-term disease-free survival in colorectal cancer. PLoS ONE. 2011;6:e22047.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  31. Newman AM, Steen CB, Liu CL, Gentles AJ, Chaudhuri AA, Scherer F, et al. Determining cell type abundance and expression from bulk tissues with digital cytometry. Nat Biotechnol. 2019;37:773–82.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  32. Yoshihara K, Shahmoradgoli M, Martinez E, Vegesna R, Kim H, Torres-Garcia W, et al. Inferring tumour purity and stromal and immune cell admixture from expression data. Nat Commun. 2013;4:2612.

    Article  PubMed  CAS  Google Scholar 

  33. Li T, Fan J, Wang B, Traugh N, Chen Q, Liu JS, et al. TIMER: a web server for comprehensive analysis of tumor-infiltrating immune cells. Cancer Res. 2017;77:e108–10.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  34. Petitprez F, Levy S, Sun CM, Meylan M, Linhard C, Becht E, et al. The murine microenvironment cell population counter method to estimate abundance of tissue-infiltrating immune and stromal cell populations in murine samples using gene expression. Genome Med. 2020;12:86.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  35. Guinney J, Dienstmann R, Wang X, de Reynies A, Schlicker A, Soneson C, et al. The consensus molecular subtypes of colorectal cancer. Nat Med. 2015;21:1350–6.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  36. Thorsson V, Gibbs DL, Brown SD, Wolf D, Bortone DS, Ou YTH, et al. The immune landscape of cancer. Immunity .2018;48:812–30.e814.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  37. Eide PW, Bruun J, Lothe RA, Sveen A. CMScaller: an R package for consensus molecular subtyping of colorectal cancer pre-clinical models. Sci Rep. 2017;7:16618.

    Article  PubMed  PubMed Central  CAS  Google Scholar 

  38. Batlle E, Massague J. Transforming growth factor-beta signaling in immunity and cancer. Immunity. 2019;50:924–40.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  39. Lee HO, Hong Y, Etlioglu HE, Cho YB, Pomella V, Van den Bosch B, et al. Lineage-dependent gene expression programs influence the immune landscape of colorectal cancer. Nat Genet. 2020;52:594–603.

    Article  CAS  PubMed  Google Scholar 

  40. Wu C, Li M, Meng H, Liu Y, Niu W, Zhou Y, et al. Analysis of status and countermeasures of cancer incidence and mortality in China. Sci China Life Sci. 2019;62:640–7.

    Article  PubMed  Google Scholar 

  41. Freeman ZT, Nirschl TR, Hovelson DH, Johnston RJ, Engelhardt JJ, Selby MJ, et al. A conserved intratumoral regulatory T cell signature identifies 4-1BB as a pan-cancer target. J Clin Invest. 2020;130:1405–16.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  42. Ramos-Casals M, Brahmer JR, Callahan MK, Flores-Chavez A, Keegan N, Khamashta MA, et al. Immune-related adverse events of checkpoint inhibitors. Nat Rev Dis Prim. 2020;6:38.

    Article  PubMed  Google Scholar 

  43. Andrews LP, Yano H, Vignali DAA. Inhibitory receptors and ligands beyond PD-1, PD-L1 and CTLA-4: breakthroughs or backups. Nat Immunol. 2019;20:1425–34.

    Article  CAS  PubMed  Google Scholar 

  44. Dienstmann R, Vermeulen L, Guinney J, Kopetz S, Tejpar S, Tabernero J. Consensus molecular subtypes and the evolution of precision medicine in colorectal cancer. Nat Rev Cancer. 2017;17:79–92.

    Article  CAS  PubMed  Google Scholar 

  45. Fakih M, Ouyang C, Wang C, Tu TY, Gozo MC, Cho M, et al. Immune overdrive signature in colorectal tumor subset predicts poor clinical outcome. J Clin Invest. 2019;129:4464–76.

    Article  PubMed  PubMed Central  Google Scholar 

  46. Melaiu O, Lucarini V, Giovannoni R, Fruci D, Gemignani F. News on immune checkpoint inhibitors as immunotherapy strategies in adult and pediatric solid tumors. Semin Cancer Biol. 2020.

  47. Bai X, Yi M, Jiao Y, Chu Q, Wu K. Blocking TGF-beta signaling to enhance the efficacy of immune checkpoint inhibitor. Onco Targets Ther. 2019;12:9527–38.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  48. Fiegle E, Doleschel D, Koletnik S, Rix A, Weiskirchen R, Borkham-Kamphorst E, et al. Dual CTLA-4 and PD-L1 blockade inhibits tumor growth and liver metastasis in a highly aggressive orthotopic mouse model of colon cancer. Neoplasia .2019;21:932–44.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  49. Rotte A. Combination of CTLA-4 and PD-1 blockers for treatment of cancer. J Exp Clin Cancer Res. 2019;38:255.

    Article  PubMed  PubMed Central  Google Scholar 

  50. Xie YH, Chen YX, Fang JY. Comprehensive review of targeted therapy for colorectal cancer. Signal Transduct Target Ther. 2020;5:22.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  51. Wu F, Wang ZL, Wang KY, Li GZ, Chai RC, Liu YQ, et al. Classification of diffuse lower-grade glioma based on immunological profiling. Mol Oncol. 2020;14:2081–95.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  52. Liu J, Lichtenberg T, Hoadley KA, Poisson LM, Lazar AJ, Cherniack AD, et al. An integrated TCGA pan-cancer clinical data resource to drive high-quality survival outcome analytics. Cell .2018;173:400–16. e411.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  53. Subramanian A, Tamayo P, Mootha VK, Mukherjee S, Ebert BL, Gillette MA, et al. Gene set enrichment analysis: a knowledge-based approach for interpreting genome-wide expression profiles. Proc Natl Acad Sci USA. 2005;102:15545–50.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  54. Yu G, Wang L-G, Han Y, He Q-Y. clusterProfiler: an R package for comparing biological themes among gene clusters. OMICS. Biol. 2012;16:284–7.

    CAS  Google Scholar 

  55. Hänzelmann S, Castelo R, Guinney J. GSVA: gene set variation analysis for microarray and RNA-Seq data. BMC Bioinforma. 2013;14:7.

    Article  Google Scholar 

  56. Yin Y, Zhang B, Wang W, Fei B, Quan C, Zhang J, et al. miR-204-5p inhibits proliferation and invasion and enhances chemotherapeutic sensitivity of colorectal cancer cells by downregulating RAB22A. Clin Cancer Res. 2014;20:6187–99.

    Article  CAS  PubMed  Google Scholar 

  57. Stack EC, Wang C, Roman KA, Hoyt CC. Multiplexed immunohistochemistry, imaging, and quantitation: a review, with an assessment of Tyramide signal amplification, multispectral imaging and multiplex analysis. Methods. 2014;70:46–58.

    Article  CAS  PubMed  Google Scholar 

  58. Cui K, Liu C, Li X, Zhang Q, Li Y. Comprehensive characterization of the rRNA metabolism-related genes in human cancer. Oncogene. 2020;39:786–800.

    Article  CAS  PubMed  Google Scholar 

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Acknowledgements

We thank the TCGA, GEO, and CPTAC project. We thank the Affiliated Hospital of Jiangnan University for providing the CRC samples. This work was supported by grants from the National Natural Science Foundation of China (82002550, 81672328, and 81972220), Jiangsu Key Research and Development Plan (BE2019632), Medical Key Professionals Program of Jiangsu Province (AF052141), National First-class Discipline Program of Food Science and Technology (JUFSTR20180101), and Wuxi Medical Innovation Team (CXTP003).

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Cui, K., Yao, S., Zhang, H. et al. Identification of an immune overdrive high-risk subpopulation with aberrant expression of FOXP3 and CTLA4 in colorectal cancer. Oncogene 40, 2130–2145 (2021). https://doi.org/10.1038/s41388-021-01677-w

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