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Journal of Experimental & Clinical Cancer Research

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Open Access 01-12-2021 | Colorectal Cancer | Research

Immuno-genomic classification of colorectal cancer organoids reveals cancer cells with intrinsic immunogenic properties associated with patient survival

Authors: Eun Jeong Cho, Minsuh Kim, Daum Jo, Jihye Kim, Ji-Hye Oh, Hee Chul Chung, Sun-hye Lee, Deokhoon Kim, Sung-Min Chun, Jihun Kim, Hyeonjin Lee, Tae Won Kim, Chang Sik Yu, Chang Ohk Sung, Se Jin Jang

Published in: Journal of Experimental & Clinical Cancer Research | Issue 1/2021

Abstract

Background

The intrinsic immuno-ge7nomic characteristics of colorectal cancer cells that affect tumor biology and shape the tumor immune microenvironment (TIM) are unclear.

Methods

We developed a patient-derived colorectal cancer organoid (CCO) model and performed pairwise analysis of 87 CCOs and their matched primary tumors. The TIM type of the primary tumor was classified as immuno-active, immuno-exhausted, or immuno-desert.

Results

The gene expression profiles, signaling pathways, major oncogenic mutations, and histology of the CCOs recapitulated those of the primary tumors, but not the TIM of primary tumors. Two distinct intrinsic molecular subgroups of highly proliferative and mesenchymal phenotypes with clinical significance were identified in CCOs with various cancer signaling pathways. CCOs showed variable expression of cancer-specific immune-related genes such as those encoding HLA-I and HLA-II, and molecules involved in immune checkpoint activation/inhibition. Among these genes, the expression of HLA-II in CCOs was associated with favorable patient survival. K-means clustering analysis based on HLA-II expression in CCOs revealed a subgroup of patients, in whom cancer cells exhibited Intrinsically Immunogenic Properties (Ca-IIP), and were characterized by high expression of signatures associated with HLA-I, HLA-II, antigen presentation, and immune stimulation. Patients with the Ca-IIP phenotype had an excellent prognosis, irrespective of age, disease stage, intrinsic molecular type, or TIM status. Ca-IIP was negatively correlated with intrinsic E2F/MYC signaling. Analysis of the correlation between CCO immuno-genotype and TIM phenotype revealed that the TIM phenotype was associated with microsatellite instability, Wnt/β-catenin signaling, APC/KRAS mutations, and the unfolded protein response pathway linked to the FBXW7 mutation in cancer cells. However, Ca-IIP was not associated with the TIM phenotype.

Conclusions

We identified a Ca-IIP phenotype from a large set of CCOs. Our findings may provide an unprecedented opportunity to develop new strategies for optimal patient stratification in this era of immunotherapy.

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Sia D, Jiao Y, Martinez-Quetglas I, Kuchuk O, Villacorta-Martin C. Castro de Moura M, et al. identification of an immune-specific class of hepatocellular carcinoma, based on molecular features. Gastroenterology. 2017;153(3):812–26. https://doi.org/10.1053/j.gastro.2017.06.007.CrossRefPubMed

Moffitt RA, Marayati R, Flate EL, Volmar KE, Loeza SG, Hoadley KA, et al. Virtual microdissection identifies distinct tumor- and stroma-specific subtypes of pancreatic ductal adenocarcinoma. Nat Genet. 2015;47(10):1168–78. https://doi.org/10.1038/ng.3398.CrossRefPubMedPubMedCentral

Kang HJ, Oh JH, Chun SM, Kim D, Ryu YM, Hwang HS, et al. Immunogenomic landscape of hepatocellular carcinoma with immune cell stroma and EBV-positive tumor-infiltrating lymphocytes. J Hepatol. 2019;71(1):91–103. https://doi.org/10.1016/j.jhep.2019.03.018.CrossRefPubMed

Barkauskas CE, Chung MI, Fioret B, Gao X, Katsura H, Hogan BL. Lung organoids: current uses and future promise. Development. 2017;144(6):986–97. https://doi.org/10.1242/dev.140103.CrossRefPubMedPubMedCentral

Bartfeld S, Bayram T, van de Wetering M, Huch M, Begthel H, Kujala P, et al. In vitro expansion of human gastric epithelial stem cells and their responses to bacterial infection. Gastroenterol. 2015;148(1):126–36.e6.CrossRef

Broutier L, Andersson-Rolf A, Hindley CJ, Boj SF, Clevers H, Koo BK, et al. Culture and establishment of self-renewing human and mouse adult liver and pancreas 3D organoids and their genetic manipulation. Nat Protoc. 2016;11(9):1724–43. https://doi.org/10.1038/nprot.2016.097.CrossRefPubMed

Sato T, Stange DE, Ferrante M, Vries RG, Van Es JH, Van den Brink S, et al. Long-term expansion of epithelial organoids from human colon, adenoma, adenocarcinoma, and Barrett's epithelium. Gastroenterology. 2011;141(5):1762–72. https://doi.org/10.1053/j.gastro.2011.07.050.CrossRefPubMed

Kim M, Mun H, Sung CO, Cho EJ, Jeon HJ, Chun SM, et al. Patient-derived lung cancer organoids as in vitro cancer models for therapeutic screening. Nat Commun. 2019;10(1):3991. https://doi.org/10.1038/s41467-019-11867-6.CrossRefPubMedPubMedCentral

Broutier L, Mastrogiovanni G, Verstegen MM, Francies HE, Gavarro LM, Bradshaw CR, et al. Human primary liver cancer-derived organoid cultures for disease modeling and drug screening. Nat Med. 2017;23(12):1424–35. https://doi.org/10.1038/nm.4438.CrossRefPubMedPubMedCentral

10.

Sachs N, de Ligt J, Kopper O, Gogola E, Bounova G, Weeber F, et al. A Living Biobank of Breast Cancer Organoids Captures Disease Heterogeneity. Cell. 2018;172(1–2):373–86.e10.CrossRefPubMed

11.

van de Wetering M, Francies HE, Francis JM, Bounova G, Iorio F, Pronk A, et al. Prospective derivation of a living organoid biobank of colorectal cancer patients. Cell. 2015;161(4):933–45. https://doi.org/10.1016/j.cell.2015.03.053.CrossRefPubMedPubMedCentral

12.

Ooft SN, Weeber F, Dijkstra KK, McLean CM, Kaing S, van Werkhoven E, et al. Patient-derived organoids can predict response to chemotherapy in metastatic colorectal cancer patients. Sci Transl Med. 2019;11(513):eaay2574.CrossRefPubMed

13.

Vlachogiannis G, Hedayat S, Vatsiou A, Jamin Y, Fernández-Mateos J, Khan K, et al. Patient-derived organoids model treatment response of metastatic gastrointestinal cancers. Science. 2018;359(6378):920–6. https://doi.org/10.1126/science.aao2774.CrossRefPubMedPubMedCentral

14.

Weeber F, van de Wetering M, Hoogstraat M, Dijkstra KK, Krijgsman O, Kuilman T, et al. Preserved genetic diversity in organoids cultured from biopsies of human colorectal cancer metastases. Proc Natl Acad Sci U S A. 2015;112(43):13308–11. https://doi.org/10.1073/pnas.1516689112.CrossRefPubMedPubMedCentral

15.

Yan HHN, Siu HC, Ho SL, Yue SSK, Gao Y, Tsui WY, et al. Organoid cultures of early-onset colorectal cancers reveal distinct and rare genetic profiles. Gut. 2020;69(12):2165–79. https://doi.org/10.1136/gutjnl-2019-320019.CrossRefPubMed

16.

Oh J-H, Jang SJ, Kim J, Sohn I, Lee J-Y, Cho EJ, et al. Spontaneous mutations in the single TTN gene represent high tumor mutation burden 2020;5(1):1–11.

17.

Chun SM, Sung CO, Jeon H, Kim TI, Lee JY, Park H, et al. Next-generation sequencing using S1 nuclease for poor-quality formalin-fixed, Paraffin-Embedded Tumor Specimens. J Mol Diagn. 2018;20(6):802–11. https://doi.org/10.1016/j.jmoldx.2018.06.002.CrossRefPubMed

18.

Li H, Durbin R. Fast and accurate long-read alignment with burrows–wheeler transform. Bioinformatics. 2010;26(5):589–95. https://doi.org/10.1093/bioinformatics/btp698.CrossRefPubMedPubMedCentral

19.

McKenna A, Hanna M, Banks E, Sivachenko A, Cibulskis K, Kernytsky A, et al. The genome analysis toolkit: a MapReduce framework for analyzing next-generation DNA sequencing data. Genome Res. 2010;20(9):1297–303. https://doi.org/10.1101/gr.107524.110.CrossRefPubMedPubMedCentral

20.

Cibulskis K, Lawrence MS, Carter SL, Sivachenko A, Jaffe D, Sougnez C, et al. Sensitive detection of somatic point mutations in impure and heterogeneous cancer samples. Nat Biotechnol. 2013;31(3):213–9. https://doi.org/10.1038/nbt.2514.CrossRefPubMedPubMedCentral

21.

Oh J-H, Sung CO. Comprehensive characteristics of somatic mutations in the normal tissues of patients with cancer and existence of somatic mutant clones linked to cancer development; 2020. https://doi.org/10.1136/jmedgenet-2020-106905.CrossRef

22.

McLaren W, Pritchard B, Rios D, Chen Y, Flicek P, Cunningham F. Deriving the consequences of genomic variants with the Ensembl API and SNP effect predictor. Bioinformatics. 2010;26(16):2069–70. https://doi.org/10.1093/bioinformatics/btq330.CrossRefPubMedPubMedCentral

23.

Robinson JT, Thorvaldsdottir H, Winckler W, Guttman M, Lander ES, Getz G, et al. Integrative genomics viewer. Nat Biotechnol. 2011;29(1):24–6. https://doi.org/10.1038/nbt.1754.CrossRefPubMedPubMedCentral

24.

Talevich E, Shain AH, Botton T, Bastian BC. CNVkit: genome-wide copy number detection and visualization from targeted DNA sequencing. PLoS Comput Biol. 2016;12(4):e1004873. https://doi.org/10.1371/journal.pcbi.1004873.CrossRefPubMedPubMedCentral

25.

Nilsen G, Liestol K, Van Loo P, Moen Vollan HK, Eide MB, Rueda OM, et al. Copynumber: efficient algorithms for single- and multi-track copy number segmentation. BMC Genomics. 2012;13(1):591. https://doi.org/10.1186/1471-2164-13-591.CrossRefPubMedPubMedCentral

26.

Wang K, Singh D, Zeng Z, Coleman SJ, Huang Y, Savich GL, et al. MapSplice: accurate mapping of RNA-seq reads for splice junction discovery. Nucleic Acids Res. 2010;38(18):e178. https://doi.org/10.1093/nar/gkq622.CrossRefPubMedPubMedCentral

27.

Li B, Dewey CN. RSEM: accurate transcript quantification from RNA-Seq data with or without a reference genome. BMC Bioinformatics. 2011;12(1):323. https://doi.org/10.1186/1471-2105-12-323.CrossRefPubMedPubMedCentral

28.

Hoshida Y. Nearest template prediction: a single-sample-based flexible class prediction with confidence assessment. PLoS One. 2010;5(11):e15543. https://doi.org/10.1371/journal.pone.0015543.CrossRefPubMedPubMedCentral

29.

Newman AM, Liu CL, Green MR, Gentles AJ, Feng W, Xu Y, et al. Robust enumeration of cell subsets from tissue expression profiles. Nat Methods. 2015;12(5):453–7. https://doi.org/10.1038/nmeth.3337.CrossRefPubMedPubMedCentral

30.

Becht E, Giraldo NA, Lacroix L, Buttard B, Elarouci N, Petitprez F, et al. Estimating the population abundance of tissue-infiltrating immune and stromal cell populations using gene expression. Genome Biol. 2016;17(1):218. https://doi.org/10.1186/s13059-016-1070-5.CrossRefPubMedPubMedCentral

31.

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 U S A. 2005;102(43):15545–50. https://doi.org/10.1073/pnas.0506580102.CrossRefPubMedPubMedCentral

32.

Hanzelmann S, Castelo R, Guinney J. GSVA: gene set variation analysis for microarray and RNA-seq data. BMC Bioinformatics. 2013;14(1):7. https://doi.org/10.1186/1471-2105-14-7.CrossRefPubMedPubMedCentral

33.

Wherry EJ, Ha SJ, Kaech SM, Haining WN, Sarkar S, Kalia V, et al. Molecular signature of CD8+ T cell exhaustion during chronic viral infection. Immunity. 2007;27(4):670–84. https://doi.org/10.1016/j.immuni.2007.09.006.CrossRefPubMed

34.

Haas BJ, Dobin A, Li B, Stransky N, Pochet N, Regev A. Accuracy assessment of fusion transcript detection via read-mapping and de novo fusion transcript assembly-based methods. Genome Biol. 2019;20(1):213. https://doi.org/10.1186/s13059-019-1842-9.CrossRefPubMedPubMedCentral

35.

Lagstad S, Zhao S, Hoff AM, Johannessen B, Lingjaerde OC, Skotheim RI. Chimeraviz: a tool for visualizing chimeric RNA. Bioinformatics. 2017;33(18):2954–6. https://doi.org/10.1093/bioinformatics/btx329.CrossRefPubMedPubMedCentral

36.

Dobin A, Davis CA, Schlesinger F, Drenkow J, Zaleski C, Jha S, et al. STAR: ultrafast universal RNA-seq aligner. Bioinformatics. 2013;29(1):15–21. https://doi.org/10.1093/bioinformatics/bts635.CrossRefPubMed

37.

Cho EJ, Chun SM, Park H, Sung CO, Kim KR. Whole transcriptome analysis of gestational trophoblastic neoplasms reveals altered PI3K signaling pathway in epithelioid trophoblastic tumor. Gynecol Oncol. 2020;157(1):151–60. https://doi.org/10.1016/j.ygyno.2019.09.022.CrossRefPubMed

38.

Boegel S, Lower M, Schafer M, Bukur T, de Graaf J, Boisguerin V, et al. HLA typing from RNA-Seq sequence reads. Genome Med. 2012;4(12):102. https://doi.org/10.1186/gm403.CrossRefPubMedPubMedCentral

39.

Sidney J, Peters B, Frahm N, Brander C, Sette A. HLA class I supertypes: a revised and updated classification. BMC Immunol. 2008;9(1):1. https://doi.org/10.1186/1471-2172-9-1.CrossRefPubMedPubMedCentral

40.

Kang HJ, Chun SM, Kim KR, Sohn I, Sung CO. Clinical relevance of gain-of-function mutations of p53 in high-grade serous ovarian carcinoma. PLoS One. 2013;8(8):e72609. https://doi.org/10.1371/journal.pone.0072609.CrossRefPubMedPubMedCentral

41.

Petitjean A, Mathe E, Kato S, Ishioka C, Tavtigian SV, Hainaut P, et al. Impact of mutant p53 functional properties on TP53 mutation patterns and tumor phenotype: lessons from recent developments in the IARC TP53 database. Hum Mutat. 2007;28(6):622–9. https://doi.org/10.1002/humu.20495.CrossRefPubMed

42.

Gu Z, Eils R, Schlesner M. Complex heatmaps reveal patterns and correlations in multidimensional genomic data. Bioinformatics. 2016;32(18):2847–9. https://doi.org/10.1093/bioinformatics/btw313.CrossRefPubMed

43.

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(1):16618. https://doi.org/10.1038/s41598-017-16747-x.CrossRefPubMedPubMedCentral

44.

Kim JE, Chun SM, Hong YS, Kim KP, Kim SY, Kim J, et al. Mutation burden and I index for detection of microsatellite instability in colorectal Cancer by targeted next-generation sequencing. J Mol Diagn. 2019;21(2):241–50. https://doi.org/10.1016/j.jmoldx.2018.09.005.CrossRefPubMed

45.

Oh JH, Jang SJ, Kim J, Sohn I, Lee JY, Cho EJ, et al. Spontaneous mutations in the single TTN gene represent high tumor mutation burden. NPJ Genom Med. 2020;5:33.CrossRefPubMedPubMedCentral

46.

Mootha VK, Lindgren CM, Eriksson KF, Subramanian A, Sihag S, Lehar J, et al. PGC-1alpha-responsive genes involved in oxidative phosphorylation are coordinately downregulated in human diabetes. Nat Genet. 2003;34(3):267–73. https://doi.org/10.1038/ng1180.CrossRefPubMed

47.

Yang D, Khan S, Sun Y, Hess K, Shmulevich I, Sood AK, et al. Association of BRCA1 and BRCA2 mutations with survival, chemotherapy sensitivity, and gene mutator phenotype in patients with ovarian cancer. Jama. 2011;306(14):1557–65. https://doi.org/10.1001/jama.2011.1456.CrossRefPubMedPubMedCentral

48.

Barretina J, Caponigro G, Stransky N, Venkatesan K, Margolin AA, Kim S, et al. The Cancer cell line encyclopedia enables predictive modelling of anticancer drug sensitivity. Nature. 2012;483(7391):603–7. https://doi.org/10.1038/nature11003.CrossRefPubMedPubMedCentral

49.

Iorio F, Knijnenburg TA, Vis DJ, Bignell GR, Menden MP, Schubert M, et al. A landscape of Pharmacogenomic interactions in Cancer. Cell. 2016;166(3):740–54. https://doi.org/10.1016/j.cell.2016.06.017.CrossRefPubMedPubMedCentral

50.

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(6):594–603. https://doi.org/10.1038/s41588-020-0636-z.CrossRefPubMed

51.

Network CGA. Comprehensive molecular characterization of human colon and rectal cancer. Nature. 2012;487(7407):330–7. https://doi.org/10.1038/nature11252.CrossRef

52.

Donehower LA, Soussi T, Korkut A, Liu Y, Schultz A, Cardenas M, et al. Integrated Analysis of TP53 Gene and Pathway Alterations in The Cancer Genome Atlas. Cell Rep. 2019;28(5):1370–84.e5.CrossRefPubMedPubMedCentral

53.

Lindeboom RG, Supek F, Lehner B. The rules and impact of nonsense-mediated mRNA decay in human cancers. Nat Genet. 2016;48(10):1112–8. https://doi.org/10.1038/ng.3664.CrossRefPubMedPubMedCentral

54.

Park HJ, Kim YJ, Kim DH, Kim J, Park KH, Park JW, et al. HLA allele frequencies in 5802 Koreans: varied allele types associated with SJS/TEN according to culprit drugs. Yonsei Med J. 2016;57(1):118–26. https://doi.org/10.3349/ymj.2016.57.1.118.CrossRefPubMed

55.

Kim HS, Lee C, Kim WH, Maeng YH, Jang BG. Expression profile of intestinal stem cell markers in colitis-associated carcinogenesis. Sci Rep. 2017;7(1):6533. https://doi.org/10.1038/s41598-017-06900-x.CrossRefPubMedPubMedCentral

56.

Michels BE, Mosa MH, Grebbin BM, Yepes D, Darvishi T, Hausmann J, et al. Human colon organoids reveal distinct physiologic and oncogenic Wnt responses. J Exp Med. 2019;216(3):704–20. https://doi.org/10.1084/jem.20180823.CrossRefPubMedPubMedCentral

57.

Thorsson V, Gibbs DL, Brown SD, Wolf D, Bortone DS, Ou Yang TH, et al. The Immune Landscape of Cancer. Immunity. 2018;48(4):812–30.e14.CrossRefPubMedPubMedCentral

58.

Hanggi K, Ruffell B. Oncogenic KRAS drives immune suppression in colorectal Cancer. Cancer Cell. 2019;35(4):535–7. https://doi.org/10.1016/j.ccell.2019.03.008.CrossRefPubMed

59.

Schadt L, Sparano C, Schweiger NA, Silina K, Cecconi V, Lucchiari G, et al. Cancer-Cell-Intrinsic cGAS Expression Mediates Tumor Immunogenicity. Cell Rep. 2019;29(5):1236–48.e7.CrossRefPubMed

60.

Van Allen EM, Miao D, Schilling B, Shukla SA, Blank C, Zimmer L, et al. Genomic correlates of response to CTLA-4 blockade in metastatic melanoma. Science. 2015;350(6257):207–11. https://doi.org/10.1126/science.aad0095.CrossRefPubMedPubMedCentral

61.

Sconocchia G, Eppenberger-Castori S, Zlobec I, Karamitopoulou E, Arriga R, Coppola A, et al. HLA class II antigen expression in colorectal carcinoma tumors as a favorable prognostic marker. Neoplasia. 2014;16(1):31–42. https://doi.org/10.1593/neo.131568.CrossRefPubMedPubMedCentral

62.

Spranger S, Bao R, Gajewski TF. Melanoma-intrinsic beta-catenin signalling prevents anti-tumour immunity. Nature. 2015;523(7559):231–5. https://doi.org/10.1038/nature14404.CrossRefPubMed

63.

Grasso CS, Giannakis M, Wells DK, Hamada T, Mu XJ, Quist M, et al. Genetic mechanisms of immune evasion in colorectal Cancer. Cancer Discov. 2018;8(6):730–49. https://doi.org/10.1158/2159-8290.CD-17-1327.CrossRefPubMedPubMedCentral

Title: Immuno-genomic classification of colorectal cancer organoids reveals cancer cells with intrinsic immunogenic properties associated with patient survival
Authors: Eun Jeong Cho
Minsuh Kim
Daum Jo
Jihye Kim
Ji-Hye Oh
Hee Chul Chung
Sun-hye Lee
Deokhoon Kim
Sung-Min Chun
Jihun Kim
Hyeonjin Lee
Tae Won Kim
Chang Sik Yu
Chang Ohk Sung
Se Jin Jang
Publication date: 01-12-2021
Publisher: BioMed Central
Keywords: Colorectal Cancer
Colorectal Cancer
Published in: Journal of Experimental & Clinical Cancer Research / Issue 1/2021
Electronic ISSN: 1756-9966
DOI: https://doi.org/10.1186/s13046-021-02034-1

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