Recurrent chromosomal alterations in epithelial stem cells link chronic inflammation to colorectal cancer

Abstract

Chronic inflammation in inflammatory bowel disease (IBD) is a recognized risk factor for colorectal cancer (CRC), but the early cellular and molecular events that drive this transition remain poorly understood. To address this, we analyzed over 600,000 single-cell transcriptomes from healthy colon, IBD lesions, and CRC tumors. Focusing on epithelial stem cells (ESCs), we identified a distinct IBD-associated subset that harbors copy number variations (CNVs) resembling those in mismatch repair-deficient (MMRd) tumors, including amplifications of chromosomes 4 and 6 and deletion of chromosome 16. Pseudotime and diffusion map analyses revealed that a fraction of IBD-derived ESCs acquire transcriptional features characteristic of cancer stem cells and follow a trajectory toward cancer-like states. Chromosome 6 amplification was associated with upregulation of MHC class II genes in both IBD and MMRd tumors; however, increased CD8⁺ T-cell infiltration was observed only in MMRd tumors. Comparative pathway analysis of cells with chromosome 4 or 6 amplification showed activation of interferon-response programs, including IL-27 signaling and CXCL10/CXCL11 expression. DNA-level validation in independent cohorts confirmed the presence of these amplifications in both CRC and IBD patients. In ESC-enriched colorectal tumors, a gene signature derived from these amplifications correlated with improved prognosis. Collectively, these findings define a pre-malignant ESC state in IBD that shares genomic and immunological features with MMRd CRC and suggest that inflammation-driven CNVs in ESCs may serve as early biomarkers for CRC risk stratification.

Keywords

Copy number variation / Inflammatory bowel disease / Colorectal cancer / Stem cell / Genomic instability / Mismatch repair deficiency / Single-cell transcriptomics

References

[1] Balkwill F, Mantovani A. Inflammation and cancer: back to Virchow? Lancet. 2001; 357(9255): 539-545. https://doi.org/10.1016/S0140—6736(00)04046—0

[2] Zhao H, Wu L, Yan G, et al. Inflammation and tumor progression: signaling pathways and targeted intervention. Sig Transduct Target Ther. 2021; 6(1): 263. https://doi.org/10.1038/s41392—021—00658—5

[3] Eaden JA, Abrams KR, Mayberry JF. The risk of colorectal cancer in ulcerative colitis: a meta—analysis. Gut. Apr. 2001; 48(4): 526-535. https://doi.org/10.1136/gut.48.4.526

[4] Sato Y, Tsujinaka S, Miura T, Kitamura Y, Suzuki H, Shibata C. Inflammatory bowel disease and colorectal cancer: epidemiology, etiology, surveillance, and management. Cancers. 2023; 15(16): 4154. https://doi.org/10.3390/cancers15164154

[5] Afify SM, Hassan G, Seno A, Seno M. Cancer—inducing niche: the force of chronic inflammation. Br J Cancer. 2022; 127(2): 193-201. https://doi.org/10.1038/s41416—022—01775—w

[6] Le DT, Durham JN, Smith KN, et al. Mismatch repair deficiency predicts response of solid tumors to PD—1 blockade. Science. 2017; 357(6349): 409-413. https://doi.org/10.1126/science.aan6733

[7] Ganesh K, Stadler ZK, Cercek A, et al. Immunotherapy in colorectal cancer: rationale, challenges and potential. Nat Rev Gastroenterol Hepatol. 2019; 16(6): 361-375. https://doi.org/10.1038/s41575—019—0126—x

[8] Boland CR, Goel A. Microsatellite instability in colorectal cancer. Gastroenterology. 2010; 138(6): 2073-2087.e3. https://doi.org/10.1053/j.gastro.2009.12.064

[9] Sinicrope FA, Foster NR, Thibodeau SN, et al. DNA Mismatch Repair Status and Colon Cancer Recurrence and Survival in Clinical Trials of 5—Fluorouracil—Based Adjuvant Therapy. JNCI J Natl Cancer Inst. 2011; 103(11): 863-875. https://doi.org/10.1093/jnci/djr153

[10] Australian Pancreatic Cancer Genome Initiative, ICGC Breast Cancer Consortium, ICGC MMML—Seq Consortium, et al. Signatures of mutational processes in human cancer. Nature. 2013; 500(7463): 415-421. doi: 10.1038/nature12477.

[11] Turajlic S, Swanton C. Metastasis as an evolutionary process. Science. 2016; 352(6282): 169-175. https://doi.org/10.1126/science.aaf2784

[12] Zack TI, Schumacher SE, Carter SL, et al. Pan—cancer patterns of somatic copy number alteration. Nat Genet. 2013; 45(10): 1134-1140. https://doi.org/10.1038/ng.2760

[13] Guan Y, Liu L, Jia Q, et al. The role of cell growth—related gene copy number variation in autoimmune thyroid disease. Biol Trace Elem Res. 2020; 195(2): 409-416. https://doi.org/10.1007/s12011—019—01880—7

[14] Beroukhim R, Mermel CH, Porter D, et al. The landscape of somatic copy—number alteration across human cancers. Nature. 2010; 463(7283): 899-905. https://doi.org/10.1038/nature08822

[15] Brønstad I, Wolff AS, Løvås K, Knappskog PM, Husebye ES. Genome—wide copy number variation (CNV) in patients with autoimmune Addison’s disease. BMC Med Genet. 2011; 12(1): 111. https://doi.org/10.1186/1471—2350—12—111

[16] Maley CC, Galipeau PC, Finley JC, et al. Genetic clonal diversity predicts progression to esophageal adenocarcinoma. Nat Genet. 2006; 38(4): 468-473. https://doi.org/10.1038/ng1768

[17] Hanahan D, Weinberg RA. Hallmarks of cancer: the next generation. Cell. 2011; 144(5): 646-674. https://doi.org/10.1016/j.cell.2011.02.013

[18] Yizhak K, Aguet F, Kim J, et al. RNA sequence analysis reveals macroscopic somatic clonal expansion across normal tissues. Science. 2019; 364(6444): eaaw0726. https://doi.org/10.1126/science.aaw0726

[19] Li S, Lu C, Zhang Y, et al. Genetic association and single—cell transcriptome analyses reveal distinct features connecting autoimmunity with cancers. iScience. 2022; 25(7): 104631. https://doi.org/10.1016/j.isci.2022.104631

[20] Kong L, Pokatayev V, Lefkovith A, et al. The landscape of immune dysregulation in Crohn’s disease revealed through single—cell transcriptomic profiling in the ileum and colon. Immunity. 2023; 56(2): 444-458.e5. https://doi.org/10.1016/j.immuni.2023.01.002

[21] Smillie CS, Biton M, Ordovas—Montanes J, et al. Intra— and inter—cellular rewiring of the human colon during ulcerative colitis. Cell. 2019; 178(3): 714-730.e22. https://doi.org/10.1016/j.cell.2019.06.029

[22] Pelka K, Hofree M, Chen JH, et al. Spatially organized multicellular immune hubs in human colorectal cancer. Cell. 2021; 184(18): 4734-4752.e20. https://doi.org/10.1016/j.cell.2021.08.003

[23] The Cancer Genome Atlas Research Network. Weinstein JN, Collisson EA, et al. The cancer genome atlas pan—cancer analysis project. Nat Genet. 2013; 45(10): 1113-1120. https://doi.org/10.1038/ng.2764

[24] Wolf FA, Angerer P, Theis FJ. SCANPY: large—scale single—cell gene expression data analysis. Genome Biol. 2018; 19(1): 15. https://doi.org/10.1186/s13059—017—1382—0

[25] Heumos L, Schaar AC, Lance C, et al. Best practices for single—cell analysis across modalities. Nat Rev Genet. 2023; 24(8): 550-572. https://doi.org/10.1038/s41576—023—00586—w

[26] Wolock SL, Lopez R, Klein AM. Scrublet: computational identification of cell doublets in single—cell transcriptomic data. Cell Syst. 2019; 8(4): 281-291.e9. https://doi.org/10.1016/j.cels.2018.11.005

[27] Lopez R, Regier J, Cole MB, Jordan MI, Yosef N. Deep generative modeling for single—cell transcriptomics. Nat Methods. 2018; 15(12): 1053-1058. https://doi.org/10.1038/s41592—018—0229—2

[28] Reed AD, Pensa S, Steif A, et al. A single—cell atlas enables mapping of homeostatic cellular shifts in the adult human breast. Nat Genet. 2024; 56(4): 652-662. https://doi.org/10.1038/s41588—024—01688—9

[29] Traag VA, Waltman L, Van Eck NJ. From Louvain to Leiden: guaranteeing well—connected communities. Sci Rep. 2019; 9(1): 5233. https://doi.org/10.1038/s41598—019—41695—z

[30] Schubert M, Klinger B, Klünemann M, et al. Perturbation—response genes reveal signaling footprints in cancer gene expression. Nat Commun. 2018; 9(1): 20. https://doi.org/10.1038/s41467—017—02391—6

[31] Colaprico A, Silva TC, Olsen C, et al. TCGAbiolinks: an R/Bioconductor package for integrative analysis of TCGA data. e71 Nucleic Acids Res. 2016; 44(8): e71. https://doi.org/10.1093/nar/gkv1507

[32] Somineni HK, Nagpal S, Venkateswaran S, et al. Whole—genome sequencing of African Americans implicates differential genetic architecture in inflammatory bowel disease. Am J Human Genet. 2021; 108(3): 431-445. https://doi.org/10.1016/j.ajhg.2021.02.001

[33] Pedersen BS, Quinlan AR. Mosdepth: quick coverage calculation for genomes and exomes. In: Hancock J, ed. 2018; 2018: 867-868. https://doi.org/10.1093/bioinformatics/btx699. Bioinformatics; 34

[34] Liberzon A, Birger C, Thorvaldsdóttir H, Ghandi M, Mesirov JP, Tamayo P. The molecular signatures database hallmark gene set collection. Cell Syst. 2015; 1(6): 417-425. https://doi.org/10.1016/j.cels.2015.12.004

[35] Barretina J, Caponigro G, Stransky N, et al. The cancer cell line encyclopedia enables predictive modelling of anticancer drug sensitivity. Nature. 2012; 483(7391): 603-607. https://doi.org/10.1038/nature11003

[36] Seabold S, Perktold J. Statsmodels Econom Stat Model Python. 2010: 92-96. https://doi.org/10.25080/Majora—92bf1922—011

[37] Büttner M, Ostner J, Müller CL, Theis FJ, Schubert B. scCODA is a Bayesian model for compositional single—cell data analysis. Nat Commun. 2021; 12(1): 6876. https://doi.org/10.1038/s41467—021—27150—6

[38] Robinson MD, McCarthy DJ, Smyth GK. edgeR: a Bioconductor package for differential expression analysis of digital gene expression data. Bioinformatics. 2010; 26(1): 139-140. https://doi.org/10.1093/bioinformatics/btp616

[39] Muñoz, Stange DE J, Schepers AG, et al. The Lgr5 intestinal stem cell signature: robust expression of proposed quiescent ‘+4′ cell markers. EMBO J. 2012; 31(14): 3079-3091. https://doi.org/10.1038/emboj.2012.166

[40] Munro MJ, Wickremesekera SK, Peng L, Tan ST, Itinteang T. Cancer stem cells in colorectal cancer: a review. J Clin Pathol. 2018; 71(2): 110-116. https://doi.org/10.1136/jclinpath—2017—204739

[41] Hamilton JA. GM—CSF in inflammation and autoimmunity. Trends Immunol. 2002; 23(8): 403-408. https://doi.org/10.1016/S1471—4906(02)02260—3

[42] Baniyash M, Sade—Feldman M, Kanterman J. Chronic inflammation and cancer: suppressing the suppressors. Cancer Immunol Immunother. 2014; 63(1): 11-20. https://doi.org/10.1007/s00262—013—1468—9

[43] Veglia F, Perego M, Gabrilovich D. Myeloid—derived suppressor cells coming of age. Nat Immunol. 2018; 19(2): 108-119. https://doi.org/10.1038/s41590—017—0022—x

[44] Jasso GJ, Jaiswal A, Varma M, et al. Colon stroma mediates an inflammation—driven fibroblastic response controlling matrix remodeling and healing. In: Eaves CJ, ed. 2022; 2022e3001532. https://doi.org/10.1371/journal.pbio.3001532. PLoS Biol; 20

[45] Zang R, Xu C, Fan Z, et al. The role of fibroblasts in chronic inflammatory and proliferative skin diseases. Exp Dermatol. 2025; 34(2): e70066. https://doi.org/10.1111/exd.70066

[46] Yuan S, Hu Z, Xu Z, Qian J, Wang X. Identification of shared diagnostic biomarkers LCN2 and DUOX2 for inflammatory bowel disease and cholangiocarcinoma through integrative bioinformatics and machine learning. JIR. 2025; 18: 15939-15958. https://doi.org/10.2147/JIR.S558859

[47] Mercado—Pimentel ME, Onyeagucha BC, Li Q, Pimentel AC, Jandova J, Nelson MA. The S100P/RAGE signaling pathway regulates expression of microRNA—21 in colon cancer cells. FEBS Lett. 2015; 589(18): 2388-2393. https://doi.org/10.1016/j.febslet.2015.07.010

[48] Eberhardt W, Haeussler K, Nasrullah U, Pfeilschifter J. Multifaceted roles of TRIM proteins in colorectal carcinoma. IJMS. 2020; 21(20): 7532. https://doi.org/10.3390/ijms21207532

[49] Zhang JJ, Vinkemeier U, Gu W, et al. Two contact regions between Stat1 and CBP/p300 in interferon gamma signaling. Proc Natl Acad Sci U S A. 1996; 93(26): 15092-15096. https://doi.org/10.1073/pnas.93.26.15092

[50] Wang X, Bondar VV, Davis OB, et al. Rab12 is a regulator of LRRK2 and its activation by damaged lysosomes. eLife. 2023; 12: e87255. https://doi.org/10.7554/eLife.87255

[51] Runkle KB, Kharbanda A, Stypulkowski E, et al. Inhibition of DHHC20—mediated EGFR palmitoylation creates a dependence on EGFR signaling. Mol Cell. 2016; 62(3): 385-396. https://doi.org/10.1016/j.molcel.2016.04.003

[52] Patel AP, Tirosh I, Trombetta JJ, et al. Single—cell RNA—seq highlights intratumoral heterogeneity in primary glioblastoma. Science. 2014; 344(6190): 1396-1401. https://doi.org/10.1126/science.1254257

[53] 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(2): 79-92. https://doi.org/10.1038/nrc.2016.126

[54] Sade—Feldman M, Yizhak K, Bjorgaard SL, et al. Defining T cell states associated with response to checkpoint immunotherapy in melanoma. Cell. 2018; 175(4): 998-1013.e20. https://doi.org/10.1016/j.cell.2018.10.038

[55] Burov A, Grigorieva E, Lebedev T, et al. Multikinase inhibitors modulate non—constitutive proteasome expression in colorectal cancer cells. Front Mol Biosci. 2024; 11: 1351641. https://doi.org/10.3389/fmolb.2024.1351641

[56] Tokunaga R, Zhang W, Naseem M, et al. CXCL9, CXCL10, CXCL11/CXCR3 axis for immune activation — A target for novel cancer therapy. Cancer Treat Rev. 2018; 63: 40-47. https://doi.org/10.1016/j.ctrv.2017.11.007

[57] Yu Z, Hong W, Green A. A new era of convergence: molecular and digital medicine in the age of AI. Mol Digit Med. 2026; 1(1): 100003. https://doi.org/10.1016/j.mdmed.2026.100003

[58] Robles AI, Traverso G, Zhang M, et al. Whole—exome sequencing analyses of inflammatory bowel disease—associated colorectal cancers. Gastroenterology. 2016; 150(4): 931-943. https://doi.org/10.1053/j.gastro.2015.12.036

[59] Yang Y, Yang J, Liang Y, Yang Q. Breast cancer organoids: a precision platform for modeling heterogeneity and advancing personalized therapy. Mol Digit Med. 2026; 1(1): 100004. https://doi.org/10.1016/j.mdmed.2026.100004

[60] Hause RJ, Pritchard CC, Shendure J, Salipante SJ. Classification and characterization of microsatellite instability across 18 cancer types. Nat Med. 2016; 22(11): 1342-1350. https://doi.org/10.1038/nm.4191

[61] Vilar E, Gruber SB. Microsatellite instability in colorectal cancer—the stable evidence. Nat Rev Clin Oncol. 2010; 7(3): 153-162. https://doi.org/10.1038/nrclinonc.2009.237

[62] Burrell RA, McGranahan N, Bartek J, Swanton C. The causes and consequences of genetic heterogeneity in cancer evolution. Nature. 2013; 501(7467): 338-345. https://doi.org/10.1038/nature12625

[63] Huang L, Zhang J, Wei B, et al. Small—molecule MHC—II inducers promote immune detection and anti—cancer immunity via editing cancer metabolism. Cell Chem Biol. 2023; 30(9): 1076-1089.e11. https://doi.org/10.1016/j.chembiol.2023.05.003

[64] Yang Z, Li F, Huang Y, et al. Dynamic tumor—specific MHC—II immuno—PET predicts checkpoint inhibitor immunotherapy efficacy in melanoma. Published online February 24, J Nucl Med. 2022:jnumed.121.263151. https://doi.org/10.2967/jnumed.121.263151 Published online February 24.

[65] Agrawal Y, Hong V, Boopathy W. GTK. P2Y2 signaling in cancer: mechanisms, biomarkers, and therapeutic horizons. Mol Digit Med. 2026; 1(1): 100001. https://doi.org/10.1016/j.mdmed.2026.100001

[66] Zhu W, Mou Y, Yu L, et al. Cell—free DNA analysis for cancer management: technologies, features, and clinical translation. Mol Digit Med. 2026; 1(1): 100005. https://doi.org/10.1016/j.mdmed.2026.100005

[67] Qiao J, Zhou Z, Zhang Z, Yang H, Zhang W, Sun J. Integrating multimodal neuroimaging genetics and deep learning to predict Alzheimer’s disease: a systematic review. Mol Digit Med. 2026; 1(1): 100006. https://doi.org/10.1016/j.mdmed.2026.100006