Early detection and prognosis prediction for colorectal cancer by circulating tumour DNA methylation haplotypes: A multicentre cohort study

Shaobo Mo, Weixing Dai, Hui Wang, Xiaoliang Lan, Chengcheng Ma, Zhixi Su, Wenqiang Xiang, Lingyu Han, Wenqin Luo, Long Zhang, Renjie Wang, Yaodong Zhang, Wenming Zhang, Lin Yang, Renquan Lu, Lin Guo, Ying Zheng, Mingzhu Huang, Ye Xu, Li Liang, Sanjun Cai, Guoxiang Cai, Shaobo Mo, Weixing Dai, Hui Wang, Xiaoliang Lan, Chengcheng Ma, Zhixi Su, Wenqiang Xiang, Lingyu Han, Wenqin Luo, Long Zhang, Renjie Wang, Yaodong Zhang, Wenming Zhang, Lin Yang, Renquan Lu, Lin Guo, Ying Zheng, Mingzhu Huang, Ye Xu, Li Liang, Sanjun Cai, Guoxiang Cai

Abstract

Background: Early detection and prognosis prediction of colorectal cancer (CRC) can significantly reduce CRC-related mortality. Recently, circulating tumour DNA (ctDNA) methylation has shown good application foreground in the early detection and prognosis prediction of multiple tumours.

Methods: This multicentre cohort study evaluated ctDNA methylation haplotype patterns based on archived plasma samples (collected between 2010 and 2018) from 1138 individuals at two medical centres: Fudan University Shanghai Cancer Center (Shanghai, China) and Southern Medical University Nanfang Hospital (Guangzhou, Guangdong, China), including 366 healthy individuals, 182 patients with advanced adenoma (AA), and 590 patients with CRC. Samples were processed using the ColonES assay, a targeted bisulfite sequencing method that detects ctDNA methylation haplotype patterns in 191 genomic regions. Among these 1138 samples, 748 were used to develop a classification model, and 390 served as a blinded cohort for independent validation. The study is registered at https://register.clinicaltrials.gov with the unique identifier NCT03737591.

Results: The model obtained from unblinded samples discriminated patients with CRC or AA from normal controls with high accuracy. In the blinded validation set, the ColonES assay achieved sensitivity values of 79.0% (95% confidence interval (CI), 66%-88%) in AA patients and 86.6% (95% CI, 81%-91%) in CRC patients with a specificity of 88.1% (95% CI, 81%-93%) in healthy individuals. The model area under the curve (AUC) for the blinded validation set was 0.903 for AA samples and 0.937 for CRC samples. Additionally, the prognosis of patients with high preoperative ctDNA methylation levels was worse than that of patients with low ctDNA methylation levels (p = 0.001 for relapse-free survival and p = 0.004 for overall survival).

Interpretation: We successfully developed and validated an accurate, noninvasive detection method based on ctDNA methylation haplotype patterns that may enable early detection and prognosis prediction for CRC.

Funding: The Grant of National Natural Science Foundation of China (No.81871958), National Natural Science Foundation of China (No. 82203215), Shanghai Science and Technology Committee (No. 19140902100), Scientific Research Fund of Fudan University (No.IDF159052), Shanghai Municipal Health Commission (SHWJRS 2021-99), and Shanghai Sailing Program (22YF1408800).

Keywords: Colorectal cancer; Early detection; Precancerous adenomas; Prognosis prediction; ctDNA methylation.

Conflict of interest statement

H.W., C.M. and Z.S. are employed by company Singlera Genomics (Shanghai). The other authors declare no competing interests.

© 2022 The Author(s).

Figures

Fig. 1
Fig. 1
An outline of the study design.
Fig. 2
Fig. 2
Development of a Methylation Haplotype-based Classification Model. (A) The top 20 absolute value of coefficient methylation regions identified from tissue samples. (B) Heatmap of methylation regions in model building of training data set including control, advanced adenoma and CRC plasmas. (C) ROC curve of training data set with LR score of 191 methylation haplotype markers. The AUCs were 0.951 for AA samples and 0.963 for CRC samples. (D) Heatmap of methylation regions in model building of validation data set including control, advanced adenoma and CRC plasmas. (E) ROC curve of validation data set with LR score of 191 methylation haplotype markers. The AUCs were 0.902 for AA samples and 0.919 for CRC samples.
Fig. 3
Fig. 3
ColonES Detects Early-Stage Neoplasms with High Accuracy. (A) ROC curve of blind test data set. AUC for the blinded validation set was 0.903 for adenoma samples and 0.937 for CRC samples. (B) Logistic regression model score of control, advanced adenoma and CRC groups. The prediction table were also included. The sensitivity is 86.6% for CRC and 79.0% for AA with 88.1% specificity. (C) Bar plot with 95% confidence interval of the comparison between CEA and ColonES for different disease stages. (D) Bar plot with 95% confidence interval of the comparison between FIT and ColonES for different disease stages.
Fig. 4
Fig. 4
Influence of Covariates on ColonES Assay Performance. Covariate analysis of ColonES model for disease stages (A), tumor size of CRC patients (B), age of CRC patients (C), gender of CRC patients (D) and tumor location of CRC patients (E). The statical analysis of detection rate or sensitivity was done with z-test, Z-value was computed by the difference of two ratios and divided by the standard error of the overall ratio. The significant difference between any groups were labelled in figures. ∗p 

Fig. 5

Prognosis Prediction for Stage I–III…

Fig. 5

Prognosis Prediction for Stage I–III CRC Patients by ColonES Assay. (A) LR scores…

Fig. 5
Prognosis Prediction for Stage I–III CRC Patients by ColonES Assay. (A) LR scores between relapse (n = 72) and non-relapse (n = 356) groups. (B) LR scores between early relapse group (within 12 months, n = 39) and late relapse group (after 12 months, n = 33). (C) LR scores between alive (n = 366) and dead (n = 62) groups. (D) Kaplan–Meier curves of RFS between LR score-High and -Low groups (p = 0.001). (E) Comparison of relapse rate for 358 stage I–III CRC patients stratified by pre-LR score status on average. (F) Kaplan–Meier curves of OS between LR score-High and -Low groups (p = 0.004). (G) Comparison of overall survival rate for 358 stage I–III CRC patients stratified by pre-LR score status on average.
Fig. 5
Fig. 5
Prognosis Prediction for Stage I–III CRC Patients by ColonES Assay. (A) LR scores between relapse (n = 72) and non-relapse (n = 356) groups. (B) LR scores between early relapse group (within 12 months, n = 39) and late relapse group (after 12 months, n = 33). (C) LR scores between alive (n = 366) and dead (n = 62) groups. (D) Kaplan–Meier curves of RFS between LR score-High and -Low groups (p = 0.001). (E) Comparison of relapse rate for 358 stage I–III CRC patients stratified by pre-LR score status on average. (F) Kaplan–Meier curves of OS between LR score-High and -Low groups (p = 0.004). (G) Comparison of overall survival rate for 358 stage I–III CRC patients stratified by pre-LR score status on average.

References

    1. Siegel R.L., Miller K.D., Fuchs H.E., Jemal A. Cancer statistics, 2021. CA Cancer J Clin. 2021;71(1):7–33.
    1. Siegel R.L., Miller K.D., Goding Sauer A., et al. Colorectal cancer statistics, 2020. CA Cancer J Clin. 2020;70(3):145–164.
    1. Bray F., Ferlay J., Soerjomataram I., Siegel R.L., Torre L.A., 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(6):394–424.
    1. Chen W., Zheng R., Baade P.D., et al. Cancer statistics in China, 2015. CA Cancer J Clin. 2016;66(2):115–132.
    1. Nguyen L.H., Goel A., Chung D.C. Pathways of colorectal carcinogenesis. Gastroenterology. 2020;158(2):291–302.
    1. Simon K. Colorectal cancer development and advances in screening. Clin Interv Aging. 2016;11:967–976.
    1. Provenzale D., Ness R.M., Llor X., et al. NCCN guidelines insights: colorectal cancer screening, version 2.2020. J Natl Compr Canc Netw. 2020;18(10):1312–1320.
    1. Dekker E., Tanis P.J., Vleugels J.L.A., Kasi P.M., Wallace M.B. Colorectal cancer. Lancet. 2019;394(10207):1467–1480.
    1. Curry S.J., Krist A.H., Owens D.K., et al. Screening for cervical cancer: US Preventive Services Task Force recommendation statement. JAMA. 2018;320(7):674–686.
    1. Siu A.L. Screening for breast cancer: U.S. Preventive Services Task Force recommendation statement. Ann Intern Med. 2016;164(4):279–296.
    1. Moyer V.A. Screening for lung cancer: U.S. Preventive Services Task Force recommendation statement. Ann Intern Med. 2014;160(5):330–338.
    1. O'Connell J.B., Maggard M.A., Ko C.Y. Colon cancer survival rates with the new American Joint Committee on Cancer sixth edition staging. J Natl Cancer Inst. 2004;96(19):1420–1425.
    1. Joranger P., Nesbakken A., Sorbye H., Hoff G., Oshaug A., Aas E. Survival and costs of colorectal cancer treatment and effects of changing treatment strategies: a model approach. Eur J Health Econ. 2020;21(3):321–334.
    1. Rex D.K., Boland C.R., Dominitz J.A., et al. Colorectal cancer screening: recommendations for physicians and patients from the U.S. Multi-Society Task Force on colorectal cancer. Gastroenterology. 2017;153(1):307–323.
    1. Regula J., Rupinski M., Kraszewska E., et al. Colonoscopy in colorectal-cancer screening for detection of advanced neoplasia. N Engl J Med. 2006;355(18):1863–1872.
    1. Levin B., Lieberman D.A., McFarland B., et al. Screening and surveillance for the early detection of colorectal cancer and adenomatous polyps, 2008: a joint guideline from the American Cancer Society, the US Multi-Society Task Force on Colorectal Cancer, and the American College of Radiology. CA Cancer J Clin. 2008;58(3):130–160.
    1. Gimeno García A.Z. Factors influencing colorectal cancer screening participation. Gastroenterol Res Pract. 2012;2012
    1. Inadomi J.M., Vijan S., Janz N.K., et al. Adherence to colorectal cancer screening: a randomized clinical trial of competing strategies. Arch Intern Med. 2012;172(7):575–582.
    1. Helsingen L.M., Vandvik P.O., Jodal H.C., et al. Colorectal cancer screening with faecal immunochemical testing, sigmoidoscopy or colonoscopy: a clinical practice guideline. BMJ. 2019;367:l5515.
    1. Quintero E., Castells A., Bujanda L., et al. Colonoscopy versus fecal immunochemical testing in colorectal-cancer screening. N Engl J Med. 2012;366(8):697–706.
    1. Buskermolen M., Cenin D.R., Helsingen L.M., et al. Colorectal cancer screening with faecal immunochemical testing, sigmoidoscopy or colonoscopy: a microsimulation modelling study. BMJ. 2019;367:l5383.
    1. Imperiale T.F., Gruber R.N., Stump T.E., Emmett T.W., Monahan P.O. Performance characteristics of fecal immunochemical tests for colorectal cancer and advanced adenomatous polyps: a systematic review and meta-analysis. Ann Intern Med. 2019;170(5):319–329.
    1. Niedermaier T., Weigl K., Hoffmeister M., Brenner H. Diagnostic performance of flexible sigmoidoscopy combined with fecal immunochemical test in colorectal cancer screening: meta-analysis and modeling. Eur J Epidemiol. 2017;32(6):481–493.
    1. Niedermaier T., Tikk K., Gies A., Bieck S., Brenner H. Sensitivity of fecal immunochemical test for colorectal cancer detection differs according to stage and location. Clin Gastroenterol Hepatol. 2020;18(13):2920–2928.e6.
    1. Shaukat A., Levin T.R. Current and future colorectal cancer screening strategies. Nat Rev Gastroenterol Hepatol. 2022;19(8):521–531.
    1. Adler A., Geiger S., Keil A., et al. Improving compliance to colorectal cancer screening using blood and stool based tests in patients refusing screening colonoscopy in Germany. BMC Gastroenterol. 2014;14:183.
    1. Palmqvist R., Engarås B., Lindmark G., et al. Prediagnostic levels of carcinoembryonic antigen and CA 242 in colorectal cancer: a matched case-control study. Dis Colon Rectum. 2003;46(11):1538–1544.
    1. Macdonald J.S. Carcinoembryonic antigen screening: pros and cons. Semin Oncol. 1999;26(5):556–560.
    1. Schwarzenbach H., Hoon D.S., Pantel K. Cell-free nucleic acids as biomarkers in cancer patients. Nat Rev Cancer. 2011;11(6):426–437.
    1. Alix-Panabières C., Pantel K. Clinical applications of circulating tumor cells and circulating tumor DNA as liquid biopsy. Cancer Discov. 2016;6(5):479–491.
    1. Merker J.D., Oxnard G.R., Compton C., et al. Circulating tumor DNA analysis in patients with cancer: American Society of Clinical Oncology and College of American Pathologists joint review. J Clin Oncol. 2018;36(16):1631–1641.
    1. Ye Q., Ling S., Zheng S., Xu X. Liquid biopsy in hepatocellular carcinoma: circulating tumor cells and circulating tumor DNA. Mol Cancer. 2019;18(1):114.
    1. Hai L., Li L., Liu Z., Tong Z., Sun Y. Whole-genome circulating tumor DNA methylation landscape reveals sensitive biomarkers of breast cancer. MedComm (2020) 2022;3(3):e134.
    1. Cheng M.L., Pectasides E., Hanna G.J., Parsons H.A., Choudhury A.D., Oxnard G.R. Circulating tumor DNA in advanced solid tumors: clinical relevance and future directions. CA Cancer J Clin. 2021;71(2):176–190.
    1. Xie H., Kim R.D. The application of circulating tumor DNA in the screening, surveillance, and treatment monitoring of colorectal cancer. Ann Surg Oncol. 2021;28(3):1845–1858.
    1. Kulis M., Esteller M. DNA methylation and cancer. Adv Genet. 2010;70:27–56.
    1. Klutstein M., Nejman D., Greenfield R., Cedar H. DNA methylation in cancer and aging. Cancer Res. 2016;76(12):3446–3450.
    1. Lam K., Pan K., Linnekamp J.F., Medema J.P., Kandimalla R. DNA methylation based biomarkers in colorectal cancer: a systematic review. Biochim Biophys Acta. 2016;1866(1):106–120.
    1. deVos T., Tetzner R., Model F., et al. Circulating methylated SEPT9 DNA in plasma is a biomarker for colorectal cancer. Clin Chem. 2009;55(7):1337–1346.
    1. Church T.R., Wandell M., Lofton-Day C., et al. Prospective evaluation of methylated SEPT9 in plasma for detection of asymptomatic colorectal cancer. Gut. 2014;63(2):317–325.
    1. Bibbins-Domingo K., Grossman D.C., Curry S.J., et al. Screening for colorectal cancer: US Preventive Services Task Force recommendation statement. JAMA. 2016;315(23):2564–2575.
    1. Wang J., Chang S., Li G., Sun Y. Application of liquid biopsy in precision medicine: opportunities and challenges. Front Med. 2017;11(4):522–527.
    1. Redshaw N., Huggett J.F., Taylor M.S., Foy C.A., Devonshire A.S. Quantification of epigenetic biomarkers: an evaluation of established and emerging methods for DNA methylation analysis. BMC Genomics. 2014;15(1):1174.
    1. Provenzale D., Gupta S., Ahnen D.J., et al. NCCN guidelines insights: colorectal cancer screening, version 1.2018. J Natl Compr Canc Netw. 2018;16(8):939–949.
    1. Weiser M.R. AJCC 8th edition: colorectal cancer. Ann Surg Oncol. 2018;25(6):1454–1455.
    1. Edge S.B., Compton C.C. The American Joint Committee on Cancer: the 7th edition of the AJCC cancer staging manual and the future of TNM. Ann Surg Oncol. 2010;17(6):1471–1474.
    1. Comprehensive molecular characterization of human colon and rectal cancer. Nature. 2012;487(7407):330–337.
    1. Cai G., Cai M., Feng Z., et al. A multilocus blood-based assay targeting circulating tumor DNA methylation enables early detection and early relapse prediction of colorectal cancer. Gastroenterology. 2021;161(6):2053–2056.e2.
    1. von Elm E., Altman D.G., Egger M., Pocock S.J., Gøtzsche P.C., Vandenbroucke J.P. The Strengthening the Reporting of Observational Studies in Epidemiology (STROBE) statement: guidelines for reporting observational studies. Int J Surg. 2014;12(12):1495–1499.
    1. Chen X., Gole J., Gore A., et al. Non-invasive early detection of cancer four years before conventional diagnosis using a blood test. Nat Commun. 2020;11(1):3475.
    1. Chen C.C., Yang S.H., Lin J.K., et al. Is it reasonable to add preoperative serum level of CEA and CA19-9 to staging for colorectal cancer? J Surg Res. 2005;124(2):169–174.
    1. Corley D.A., Jensen C.D., Marks A.R., et al. Adenoma detection rate and risk of colorectal cancer and death. N Engl J Med. 2014;370(14):1298–1306.
    1. Imperiale T.F., Ransohoff D.F., Itzkowitz S.H., et al. Multitarget stool DNA testing for colorectal-cancer screening. N Engl J Med. 2014;370(14):1287–1297.
    1. Ahlquist D.A., Zou H., Domanico M., et al. Next-generation stool DNA test accurately detects colorectal cancer and large adenomas. Gastroenterology. 2012;142(2):248–256. quiz e25-6.
    1. Xu R.H., Wei W., Krawczyk M., et al. Circulating tumour DNA methylation markers for diagnosis and prognosis of hepatocellular carcinoma. Nat Mater. 2017;16(11):1155–1161.
    1. Guo S., Diep D., Plongthongkum N., Fung H.L., Zhang K., Zhang K. Identification of methylation haplotype blocks aids in deconvolution of heterogeneous tissue samples and tumor tissue-of-origin mapping from plasma DNA. Nat Genet. 2017;49(4):635–642.
    1. Warren J.D., Xiong W., Bunker A.M., et al. Septin 9 methylated DNA is a sensitive and specific blood test for colorectal cancer. BMC Med. 2011;9:133.
    1. Ma Z.Y., Law W.L., Ng E.K.O., et al. Methylated septin 9 and carcinoembryonic antigen for serological diagnosis and monitoring of patients with colorectal cancer after surgery. Sci Rep. 2019;9(1)
    1. Sun J., Fei F., Zhang M., et al. The role of (m)SEPT9 in screening, diagnosis, and recurrence monitoring of colorectal cancer. BMC Cancer. 2019;19(1):450.
    1. Nassar F.J., Msheik Z.S., Nasr R.R., Temraz S.N. Methylated circulating tumor DNA as a biomarker for colorectal cancer diagnosis, prognosis, and prediction. Clin Epigenetics. 2021;13(1):111.
    1. Xu J.M., Liu X.J., Ge F.J., et al. KRAS mutations in tumor tissue and plasma by different assays predict survival of patients with metastatic colorectal cancer. J Exp Clin Cancer Res. 2014;33(1):104.
    1. Wang J.Y., Hsieh J.S., Chang M.Y., et al. Molecular detection of APC, K- ras, and p53 mutations in the serum of colorectal cancer patients as circulating biomarkers. World J Surg. 2004;28(7):721–726.
    1. Luo H., Zhao Q., Wei W., et al. Circulating tumor DNA methylation profiles enable early diagnosis, prognosis prediction, and screening for colorectal cancer. Sci Transl Med. 2020;12(524)
    1. Hauptman N., Glavač D. Colorectal cancer blood-based biomarkers. Gastroenterol Res Pract. 2017;2017:2195361.

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