J Pathol Transl Med.  2020 Sep;54(5):351-360. 10.4132/jptm.2020.05.15.

Immune landscape and biomarkers for immuno-oncology in colorectal cancers

• Bae JM
• Yoo SY
• Kim JH
• Kang GH

• KMID: 2506268
• DOI: http://doi.org/10.4132/jptm.2020.05.15

Abstract

Recent advances in immuno-oncology have increased understanding of the tumor immune microenvironment (TIME), and clinical trials for immune checkpoint inhibitor treatment have shown remission and/or durable response in certain proportions of patients stratified by predictive biomarkers. The TIME in colorectal cancer (CRC) was initially evaluated several decades ago. The prognostic value of the immune response to tumors, including tumor-infiltrating lymphocytes, peritumoral lymphoid reaction, and Crohn’s-like lymphoid reaction, has been well demonstrated. In this review, we describe the chronology of TIME research and review the up-to-date high-dimensional TIME landscape of CRC. We also summarize the clinical relevance of several biomarkers associated with immunotherapy in CRC, such as microsatellite instability, tumor mutational burden, POLE/POLD mutation, consensus molecular subtype, and programmed death-ligand 1 expression.

Keyword

Colorectal neoplasms; Tumor immune microenvironment; Immunotherapy; Microsatellite instability

Figure

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  Fig. 1. Timeline with key milestones in immuno-oncology research and U.S. Food and Drug Administration (FDA)–approved anti-cancer therapy in colorectal cancers (CRCs). CTLA-4, cytotoxic T-lymphocyte antigen 4; PD-1, programmed death-1; PD-L1, programmed deathligand 1; TCGA, The Cancer Genome Atlas; mCRC, metastatic colorectal cancer; MSI, microsatellite instability; MMR-D, mismatch repair deficiency; MSI-H, MSI-high. Black diamond, milestone events in general immuno-oncology research; blue diamond, milestone events in immuno-oncology research in CRCs; black flags, cytotoxic chemotherapy; blue flags, targeted therapy; and red flags, immunotherapy.

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  Fig. 2. Calculation method of Immunoscore (adapted from Anitei et al. [17]). CT, center of tumor; IM, invasive margin; H, high; and L, low.

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  Fig. 3. Correlation of tumor mutational burden, microsatellite instability, OLE/POLD mutation, and programmed death-ligand 1 (PD-L1) expression in colorectal cancers. TMB-H, tumor mutational burden-high; MSI-H, microsatellite instability-high; ICI, immune checkpoint inhibitor.

Reference

References

1. Jung KW, Won YJ, Kong HJ, Lee ES. Cancer statistics in Korea: incidence, mortality, survival, and prevalence in 2016. Cancer Res Treat. 2019; 51:417–30.

2. Siegel RL, Miller KD, Fedewa SA, et al. Colorectal cancer statistics, 2017. CA Cancer J Clin. 2017; 67:177–93.

3. Bae JM, Kim JH, Kwak Y, et al. Distinct clinical outcomes of two CIMP-positive colorectal cancer subtypes based on a revised CIMP classification system. Br J Cancer. 2017; 116:1012–20.

4. Fridman WH, Zitvogel L, Sautès-Fridman C, Kroemer G. The immune contexture in cancer prognosis and treatment. Nat Rev Clin Oncol. 2017; 14:717–34.

5. Hodi FS, O'Day SJ, McDermott DF, et al. Improved survival with ipilimumab in patients with metastatic melanoma. N Engl J Med. 2010; 363:711–23.

6. Garon EB, Rizvi NA, Hui R, et al. Pembrolizumab for the treatment of non-small-cell lung cancer. N Engl J Med. 2015; 372:2018–28.

7. Le DT, Uram JN, Wang H, et al. PD-1 blockade in tumors with mismatch-repair deficiency. N Engl J Med. 2015; 372:2509–20.

8. Overman MJ, McDermott R, Leach JL, et al. Nivolumab in patients with metastatic DNA mismatch repair-deficient or microsatellite instability-high colorectal cancer (CheckMate 142): an open-label, multicentre, phase 2 study. Lancet Oncol. 2017; 18:1182–91.

9. Spratt JS Jr, Spjut HJ. Prevalence and prognosis of individual clinical and pathologic variables associated with colorectal carcinoma. Cancer. 1967; 20:1976–85.

10. Watt AG, House AK. Colonic carcinoma: a quantitative assessment of lymphocyte infiltration at the periphery of colonic tumors related to prognosis. Cancer. 1978; 41:279–82.

11. Pihl E, Malahy MA, Khankhanian N, Hersh EM, Mavligit GM. Immunomorphological features of prognostic significance in Dukes' Class B colorectal carcinoma. Cancer Res. 1977; 37:4145–9.

12. Maoz A, Dennis M, Greenson JK. The Crohn's-like lymphoid reaction to colorectal cancer-tertiary lymphoid structures with immunologic and potentially therapeutic relevance in colorectal cancer. Front Immunol. 2019; 10:1884.

13. Naito Y, Saito K, Shiiba K, et al. CD8⁺ T cells infiltrated within cancer cell nests as a prognostic factor in human colorectal cancer. Cancer Res. 1998; 58:3491–4.

14. Ogino S, Nosho K, Irahara N, et al. Lymphocytic reaction to colorectal cancer is associated with longer survival, independent of lymph node count, microsatellite instability, and CpG island methylator phenotype. Clin Cancer Res. 2009; 15:6412–20.

15. Nearchou IP, Lillard K, Gavriel CG, Ueno H, Harrison DJ, Caie PD. Automated analysis of lymphocytic infiltration, tumor budding, and their spatial relationship improves prognostic accuracy in colorectal cancer. Cancer Immunol Res. 2019; 7:609–20.

16. Galon J, Costes A, Sanchez-Cabo F, et al. Type, density, and location of immune cells within human colorectal tumors predict clinical outcome. Science. 2006; 313:1960–4.

17. Anitei MG, Zeitoun G, Mlecnik B, et al. Prognostic and predictive values of the immunoscore in patients with rectal cancer. Clin Cancer Res. 2014; 20:1891–9.

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

19. Mlecnik B, Van den Eynde M, Bindea G, et al. Comprehensive intrametastatic immune quantification and major impact of immunoscore on survival. J Natl Cancer Inst. 2018; 110:97–108.

20. Pages F, Mlecnik B, Marliot F, et al. International validation of the consensus immunoscore for the classification of colon cancer: a prognostic and accuracy study. Lancet. 2018; 391:2128–39.

21. Reichling C, Taieb J, Derangere V, et al. Artificial intelligence-guided tissue analysis combined with immune infiltrate assessment predicts stage III colon cancer outcomes in PETACC08 study. Gut. 2020; 69:681–90.

22. Yoo SY, Park HE, Kim JH, et al. Whole-slide image analysis reveals quantitative landscape of tumor-immune microenvironment in colorectal cancers. Clin Cancer Res. 2020; 26:870–81.

23. Guinney J, Dienstmann R, Wang X, et al. The consensus molecular subtypes of colorectal cancer. Nat Med. 2015; 21:1350–6.

24. Becht E, de Reynies A, Giraldo NA, 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.

25. Newman AM, Liu CL, Green MR, et al. Robust enumeration of cell subsets from tissue expression profiles. Nat Methods. 2015; 12:453–7.

26. Aran D, Hu Z, Butte AJ. xCell: digitally portraying the tissue cellular heterogeneity landscape. Genome Biol. 2017; 18:220.

27. Becht E, Giraldo NA, Lacroix L, et al. Estimating the population abundance of tissue-infiltrating immune and stromal cell populations using gene expression. Genome Biol. 2016; 17:218.

28. Jimenez-Sanchez A, Cast O, Miller ML. Comprehensive benchmarking and integration of tumor microenvironment cell estimation methods. Cancer Res. 2019; 79:6238–46.

29. Labib M, Kelley SO. Single-cell analysis targeting the proteome. Nat Rev Chem. 2020; 4:143–58.

30. Zhang L, Zhang Z. Recharacterizing tumor-infiltrating lymphocytes by single-cell RNA sequencing. Cancer Immunol Res. 2019; 7:1040–6.

31. Moncada R, Barkley D, Wagner F, et al. Integrating microarraybased spatial transcriptomics and single-cell RNA-seq reveals tissue architecture in pancreatic ductal adenocarcinomas. Nat Biotechnol. 2020; 38:333–42.

32. Jackson HW, Fischer JR, Zanotelli VR, et al. The single-cell pathology landscape of breast cancer. Nature. 2020; 578:615–20.

33. Xiong Y, Wang K, Zhou H, Peng L, You W, Fu Z. Profiles of immune infiltration in colorectal cancer and their clinical significant: a gene expression-based study. Cancer Med. 2018; 7:4496–508.

34. Marisa L, Svrcek M, Collura A, et al. The balance between cytotoxic T-cell lymphocytes and immune checkpoint expression in the prognosis of colon tumors. J Natl Cancer Inst. 2018; 110:68–77.

35. Zhang L, Yu X, Zheng L, et al. Lineage tracking reveals dynamic relationships of T cells in colorectal cancer. Nature. 2018; 564:268–72.

36. Miyazaki K, Miyazaki M, Guo Y, et al. The role of the basic helixloop-helix transcription factor Dec1 in the regulatory T cells. J Immunol. 2010; 185:7330–9.

37. Lin CC, Bradstreet TR, Schwarzkopf EA, et al. Bhlhe40 controls cytokine production by T cells and is essential for pathogenicity in autoimmune neuroinflammation. Nat Commun. 2014; 5:3551.

38. Zhang L, Li Z, Skrzypczynska KM, et al. Single-cell analyses inform mechanisms of myeloid-targeted therapies in colon cancer. Cell. 2020; 181:442–59.

39. de Vries NL, van Unen V, Ijsselsteijn ME, et al. High-dimensional cytometric analysis of colorectal cancer reveals novel mediators of antitumour immunity. Gut. 2020; 69:691–703.

40. Norton SE, Ward-Hartstonge KA, McCall JL, et al. High-dimensional mass cytometric analysis reveals an increase in effector regulatory T cells as a distinguishing feature of colorectal tumors. J Immunol. 2019; 202:1871–84.

41. Di J, Liu M, Fan Y, et al. Phenotype molding of T cells in colorectal cancer by single-cell analysis. Int J Cancer. 2020; 146:2281–95.

42. Boland CR, Goel A. Microsatellite instability in colorectal cancer. Gastroenterology. 2010; 138:2073–87.

43. Dudley JC, Lin MT, Le DT, Eshleman JR. Microsatellite instability as a biomarker for PD-1 blockade. Clin Cancer Res. 2016; 22:813–20.

44. Llosa NJ, Cruise M, Tam A, et al. The vigorous immune microenvironment of microsatellite instable colon cancer is balanced by multiple counter-inhibitory checkpoints. Cancer Discov. 2015; 5:43–51.

45. Le DT, Durham JN, Smith KN, et al. Mismatch repair deficiency predicts response of solid tumors to PD-1 blockade. Science. 2017; 357:409–13.

46. Schrock AB, Ouyang C, Sandhu J, et al. Tumor mutational burden is predictive of response to immune checkpoint inhibitors in MSIhigh metastatic colorectal cancer. Ann Oncol. 2019; 30:1096–103.

47. Klempner SJ, Fabrizio D, Bane S, et al. Tumor mutational burden as a predictive biomarker for response to immune checkpoint inhibitors: a review of current evidence. Oncologist. 2020; 25:e147–59.

48. Chalmers ZR, Connelly CF, Fabrizio D, et al. Analysis of 100,000 human cancer genomes reveals the landscape of tumor mutational burden. Genome Med. 2017; 9:34.

49. Merino DM, McShane LM, Fabrizio D, et al. Establishing guidelines to harmonize tumor mutational burden (TMB): in silico assessment of variation in TMB quantification across diagnostic platforms: phase I of the Friends of Cancer Research TMB Harmonization Project. J Immunother Cancer. 2020; 8:e000147.

50. Cancer Genome Atlas Network. Comprehensive molecular characterization of human colon and rectal cancer. Nature. 2012; 487:330–7.

51. Parikh AR, He Y, Hong TS, et al. Analysis of DNA damage response gene alterations and tumor mutational burden across 17,486 tubular gastrointestinal carcinomas: implications for therapy. Oncologist. 2019; 24:1340–7.

52. Lee DW, Han SW, Bae JM, et al. Tumor mutation burden and prognosis in patients with colorectal cancer treated with adjuvant fluoropyrimidine and oxaliplatin. Clin Cancer Res. 2019; 25:6141–7.

53. Vanderwalde A, Spetzler D, Xiao N, Gatalica Z, Marshall J. Microsatellite instability status determined by next-generation sequencing and compared with PD-L1 and tumor mutational burden in 11,348 patients. Cancer Med. 2018; 7:746–56.

54. Liu Y, Sethi NS, Hinoue T, et al. Comparative molecular analysis of gastrointestinal adenocarcinomas. Cancer Cell. 2018; 33:721–35.

55. Hino H, Shiomi A, Kusuhara M, et al. Clinicopathological and mutational analyses of colorectal cancer with mutations in the POLE gene. Cancer Med. 2019; 8:4587–97.

56. Domingo E, Freeman-Mills L, Rayner E, et al. Somatic POLE proofreading domain mutation, immune response, and prognosis in colorectal cancer: a retrospective, pooled biomarker study. Lancet Gastroenterol Hepatol. 2016; 1:207–16.

57. Wang C, Gong J, Tu TY, Lee PP, Fakih M. Immune profiling of microsatellite instability-high and polymerase epsilon (POLE)-mutated metastatic colorectal tumors identifies predictors of response to anti-PD-1 therapy. J Gastrointest Oncol. 2018; 9:404–15.

58. Silberman R, Steiner DF, et al. Complete and prolonged response to immune checkpoint blockade in POLE-mutated colorectal cancer. JCO Precis Oncol. 2019 Jun 21 [Epub]. https://doi.org/10.1200/PO.18.00214.

59. De Sousa EM, Wang X, Jansen M, et al. Poor-prognosis colon cancer is defined by a molecularly distinct subtype and develops from serrated precursor lesions. Nat Med. 2013; 19:614–8.

60. Sadanandam A, Lyssiotis CA, Homicsko K, et al. A colorectal cancer classification system that associates cellular phenotype and responses to therapy. Nat Med. 2013; 19:619–25.

61. Marisa L, de Reynies A, Duval A, et al. Gene expression classification of colon cancer into molecular subtypes: characterization, validation, and prognostic value. PLoS Med. 2013; 10:e1001453.

62. Karpinski P, Rossowska J, Sasiadek MM. Immunological landscape of consensus clusters in colorectal cancer. Oncotarget. 2017; 8:105299–311.

63. Soldevilla B, Carretero-Puche C, Gomez-Lopez G, et al. The correlation between immune subtypes and consensus molecular subtypes in colorectal cancer identifies novel tumour microenvironment profiles, with prognostic and therapeutic implications. Eur J Cancer. 2019; 123:118–29.

64. Thorsson V, Gibbs DL, Brown SD, et al. The immune landscape of cancer. Immunity. 2018; 48:812–30.

65. Rosenbaum MW, Bledsoe JR, Morales-Oyarvide V, Huynh TG, Mino-Kenudson M. PD-L1 expression in colorectal cancer is associated with microsatellite instability, BRAF mutation, medullary morphology and cytotoxic tumor-infiltrating lymphocytes. Mod Pathol. 2016; 29:1104–12.

66. Lee LH, Cavalcanti MS, Segal NH, et al. Patterns and prognostic relevance of PD-1 and PD-L1 expression in colorectal carcinoma. Mod Pathol. 2016; 29:1433–42.

67. Kim JH, Park HE, Cho NY, Lee HS, Kang GH. Characterisation of PD-L1-positive subsets of microsatellite-unstable colorectal cancers. Br J Cancer. 2016; 115:490–6.

68. Inaguma S, Lasota J, Wang Z, Felisiak-Golabek A, Ikeda H, Miettinen M. Clinicopathologic profile, immunophenotype, and genotype of CD274 (PD-L1)-positive colorectal carcinomas. Mod Pathol. 2017; 30:278–85.

69. Eriksen AC, Sorensen FB, Lindebjerg J, et al. Programmed death ligand-1 expression in stage II colon cancer - experiences from a nationwide populationbased cohort. BMC Cancer. 2019; 19:142.

70. Li Y, He M, Zhou Y, et al. The prognostic and clinicopathological roles of PD-L1 expression in colorectal cancer: a systematic review and meta-analysis. Front Pharmacol. 2019; 10:139.

71. Yamada R, Yamaguchi T, Iijima T, et al. Differences in histological features and PD-L1 expression between sporadic microsatellite instability and Lynch-syndrome-associated disease in Japanese patients with colorectal cancer. Int J Clin Oncol. 2018; 23:504–13.

72. Ganesh K, Stadler ZK, Cercek A, et al. Immunotherapy in colorectal cancer: rationale, challenges and potential. Nat Rev Gastroenterol Hepatol. 2019; 16:361–75.