Single-molecule epiallelic profiling of DNA derived from routinely collected Pap specimens for noninvasive detection of ovarian cancer.
DNA methylation
Pap smear
digital melt
microfluidics
ovarian cancer
Journal
Clinical and translational medicine
ISSN: 2001-1326
Titre abrégé: Clin Transl Med
Pays: United States
ID NLM: 101597971
Informations de publication
Date de publication:
Aug 2024
Aug 2024
Historique:
revised:
22
05
2024
received:
17
02
2024
accepted:
09
07
2024
medline:
31
7
2024
pubmed:
31
7
2024
entrez:
31
7
2024
Statut:
ppublish
Résumé
Recent advances in molecular analyses of ovarian cancer have revealed a wealth of promising tumour-specific biomarkers, including protein, DNA mutations and methylation; however, reliably detecting such alterations at satisfactorily high sensitivity and specificity through low-cost methods remains challenging, especially in early-stage diseases. Here we present PapDREAM, a new approach that enables detection of rare, ovarian-cancer-specific aberrations of DNA methylation from routinely-collected cervical Pap specimens. The PapDREAM approach employs a microfluidic platform that performs highly parallelized digital high-resolution melt to analyze locus-specific DNA methylation patterns on a molecule-by-molecule basis at or near single CpG-site resolution at a fraction (< 1/10th) of the cost of next-generation sequencing techniques. We demonstrate the feasibility of the platform by assessing intermolecular heterogeneity of DNA methylation in a panel of methylation biomarker loci using DNA derived from Pap specimens obtained from a cohort of 43 women, including 18 cases with ovarian cancer and 25 cancer-free controls. PapDREAM leverages systematic multidimensional bioinformatic analyses of locus-specific methylation heterogeneity to improve upon Pap-specimen-based detection of ovarian cancer, demonstrating a clinical sensitivity of 50% at 99% specificity in detecting ovarian cancer cases with an area under the receiver operator curve of 0.90. We then establish a logistic regression model that could be used to identify high-risk patients for subsequent clinical follow-up and monitoring. The results of this study support the utility of PapDREAM as a simple, low-cost screening method with the potential to integrate with existing clinical workflows for early detection of ovarian cancer. KEY POINTS: We present a microfluidic platform for detection and analysis of rare, heterogeneously methylated DNA within Pap specimens towards detection of ovarian cancer. The platform achieves high sensitivity (fractions <0.00005%) at a suitably low cost (∼$25) for routine screening applications. Furthermore, it provides molecule-by-molecule quantitative analysis to facilitate further study on the effect of heterogeneous methylation on cancer development.
Substances chimiques
Biomarkers, Tumor
0
DNA
9007-49-2
Types de publication
Journal Article
Langues
eng
Sous-ensembles de citation
IM
Pagination
e1778Subventions
Organisme : NIH HHS
ID : 1R01CA260628
Pays : United States
Organisme : NIH HHS
ID : P50CA22899
Pays : United States
Organisme : NIH HHS
ID : R33CA272321-01
Pays : United States
Organisme : Honorable Tina Brozman Foundation
Informations de copyright
© 2024 The Author(s). Clinical and Translational Medicine published by John Wiley & Sons Australia, Ltd on behalf of Shanghai Institute of Clinical Bioinformatics.
Références
Torre LA, Trabert B, DeSantis CE, et al. Ovarian cancer statistics, 2018. CA Cancer J Clin. 2018;68(4):284‐296. doi:10.3322/caac.21456
Cho KR, Shih IM. Ovarian cancer. Annu Rev Pathol Mech Dis. 2009;4:287‐313. doi:10.1146/annurev.pathol.4.110807.092246
National Cancer Institute. SEER*Explorer Application. Accessed May 2, 2023. Available from: https://seer.cancer.gov/statistics‐network/explorer/
Matteson KA, Members S, Gunderson C, Richardson DL. The role of the obstetrician‐gynecologist in the early detection of epithelial ovarian cancer in women at Average Risk Committee on Gynecologic Practice Society of Gynecologic Oncology Recommendations and Conclusions. Replace Comm Opin Number. 2017:716.
Henderson JT, Webber EM, Sawaya GF. Screening for ovarian cancer updated evidence report and systematic review for the US Preventive Services Task Force. JAMA. 2018;319(6):595‐606. doi:10.1001/jama.2017.21421
Kinde I, Bettegowda C, Wang Y, et al. Evaluation of DNA from the Papanicolaou test to detect ovarian and endometrial cancers. Sci Transl Med. 2013;5(167):167ra4. doi:10.1126/scitranslmed.3004952
Wang Y, Li L, Douville C, et al. Evaluation of liquid from the Papanicolaou test and other liquid biopsies for the detection of endometrial and ovarian cancers. Sci Transl Med. 2018;10(433):eaap8793. doi:10.1126/scitranslmed.aap8793
Zhang L, Hu C, Huang Z, Li Z, Zhang Q, He Y. In silico screening of circulating tumor DNA, circulating microRNAs, and long non‐coding RNAs as diagnostic molecular biomarkers in ovarian cancer: a comprehensive meta‐analysis. PLoS One. 2021;16(4):e0250717. doi:10.1371/journal.pone.0250717
Giannopoulou L, Chebouti I, Pavlakis K, et al. RASSF1A promoter methylation in high‐grade serous ovarian cancer: a direct comparison study in primary tumors, adjacent morphologically tumor cell‐free tissues and paired circulating tumor DNA. Oncotarget. 2017;8(13):21429‐21443. doi:10.18632/ONCOTARGET.15249
Swisher EM, Wollan M, Mahtani SM, et al. Tumor‐specific p53 sequences in blood and peritoneal fluid of women with epithelial ovarian cancer. Am J Obstet Gynecol. 2005;193(3):662‐667. doi:10.1016/J.AJOG.2005.01.054
Lee Y, Miron A, Drapkin R, et al. A candidate precursor to serous carcinoma that originates in the distal fallopian tube. J Pathol. 2007;211(1):26‐35. doi:10.1002/PATH.2091
Eckert MA, Pan S, Hernandez KM, et al. Genomics of ovarian cancer progression reveals diverse metastatic trajectories including intraepithelial metastasis to the fallopian tube. Cancer Discov. 2016;6(12):1342. doi:10.1158/2159‐8290.CD‐16‐0607
Labidi‐Galy SI, Papp E, Hallberg D, et al. High grade serous ovarian carcinomas originate in the fallopian tube. Nat Commun. 2017;8:1093. doi:10.1038/s41467‐017‐00962‐1
Shih IM, Wang Y, Wang TL. The origin of ovarian cancer species and precancerous landscape. Am J Pathol. 2021;191(1):26‐39. doi:10.1016/J.AJPATH.2020.09.006
Wu RC, Wang P, Lin SF, et al. Genomic landscape and evolutionary trajectories of ovarian cancer precursor lesions. J Pathol. 2019;248(1):41‐50. doi:10.1002/path.5219
Schnatz PF, Guile M, O'Sullivan DM, Sorosky JI. Clinical significance of atypical glandular cells on cervical cytology. Obstet Gynecol. 2006;107(3):701‐708. doi:10.1097/01.AOG.0000202401.29145.68
Chang CC, Wang HC, Liao YP, et al. The feasibility of detecting endometrial and ovarian cancer using DNA methylation biomarkers in cervical scrapings. J Gynecol Oncol. 2018;29(1):17. doi:10.3802/jgo.2018.29.e17
Earp MA, Cunningham JM. DNA methylation changes in epithelial ovarian cancer histotypes. Genomics. 2015;106(6):311‐321. doi:10.1016/j.ygeno.2015.09.001
Sánchez‐Vega F, Gotea V, Petrykowska HM, et al. Recurrent patterns of DNA methylation in the ZNF154, CASP8, and VHL promoters across a wide spectrum of human solid epithelial tumors and cancer cell lines. Epigenetics. 2013;8(12):1355‐1372. doi:10.4161/epi.26701
Singh A, Gupta S, Badarukhiya JA, Sachan M. Detection of aberrant methylation of HOXA9 and HIC1 through multiplex MethyLight assay in serum DNA for the early detection of epithelial ovarian cancer. Int J Cancer. 2020;147(6):1740‐1752. doi:10.1002/ijc.32984
Pisanic TR, Cope LM, Lin SFSF, et al. Methylomic analysis of ovarian cancers identifies tumor‐specific alterations readily detectable in early precursor lesions. Clin Cancer Res. 2018;24(24):6536‐6547. doi:10.1158/1078‐0432.CCR‐18‐1199
Belinsky SA, Nikula KJ, Palmisano WA, et al. Aberrant methylation of p16(INK4a) is an early event in lung cancer and a potential biomarker for early diagnosis. Proc Natl Acad Sci U S A. 1998;95(20):11891‐11896. doi:10.1073/pnas.95.20.11891
Herman JG, Baylin SB. Gene silencing in cancer in association with promoter hypermethylation. N Engl J Med. 2003;34921349:2042‐2054. Accessed June 15, 2017. http://www.nejm.org/doi/pdf/10.1056/NEJMra023075
Pisanic TR, Athamanolap P, Poh W, et al. DREAMing: a simple and ultrasensitive method for assessing intratumor epigenetic heterogeneity directly from liquid biopsies. Nucleic Acids Res. 2015;43(22):e154. doi:10.1093/nar/gkv795
O'Keefe CM, Pisanic TR, Zec H, Overman MJ, Herman JG, Wang TH. Facile profiling of molecular heterogeneity by microfluidic digital melt. Sci Adv. 2018;4(9):6459‐6485. doi:10.1126/sciadv.aat6459
Landan G, Cohen NM, Mukamel Z, et al. Epigenetic polymorphism and the stochastic formation of differentially methylated regions in normal and cancerous tissues. Nat Genet. 2012;44(11):1207‐1214. doi:10.1038/ng.2442
Timp W, Feinberg AP. Cancer as a dysregulated epigenome allowing cellular growth advantage at the expense of the host. Nat Rev Cancer. 2013;13(7):497‐510. doi:10.1038/nrc3486
Teschendorff AE, Jones A, Fiegl H, et al. Epigenetic variability in cells of normal cytology is associated with the risk of future morphological transformation. Genome Med. 2012;4(3). doi:10.1186/gm323
Sheffield NC, Pierron G, Klughammer J, et al. DNA methylation heterogeneity defines a disease spectrum in Ewing sarcoma. Nat Med. 2017;23(3):386‐395. doi:10.1038/nm.4273
Klughammer J, Kiesel B, Roetzer T, et al. The DNA methylation landscape of glioblastoma disease progression shows extensive heterogeneity in time and space. Nat Med 2018 2410. 2018;24(10):1611‐1624. doi:10.1038/s41591‐018‐0156‐x
Hu X, Estecio MR, Chen R, et al. Evolution of DNA methylome from precancerous lesions to invasive lung adenocarcinomas. Nat Commun. 2021;12(1):687. doi:10.1038/S41467‐021‐20907‐Z
Batra RN, Lifshitz A, Vidakovic AT, et al. DNA methylation landscapes of 1538 breast cancers reveal a replication‐linked clock, epigenomic instability and cis‐regulation. Nat Commun. 2021;12(1). doi:10.1038/S41467‐021‐25661‐W
Yakovchuk P, Protozanova E, Frank‐Kamenetskii MD. Base‐stacking and base‐pairing contributions into thermal stability of the DNA double helix. Nucleic Acids Res. 2006;34(2):564‐574. doi:10.1093/nar/gkj454
Pisanic TR, Wang Y, Sun H, et al. Methylomic landscapes of ovarian cancer precursor lesions. Clin Cancer Res. 2020;26(23):6310. doi:10.1158/1078‐0432.CCR‐20‐0270
Miller BF, Pisanic TR, Margolin G, et al. Leveraging locus‐specific epigenetic heterogeneity to improve the performance of blood‐based DNA methylation biomarkers. Clin Epigenetics. 2020;12(1). doi:10.1186/S13148‐020‐00939‐W
Teschendorff AE, Widschwendter M. Differential variability improves the identification of cancer risk markers in DNA methylation studies profiling precursor cancer lesions. Bioinformatics. 2012;28(11):1487‐1494. doi:10.1093/bioinformatics/bts170
Kitchener HC, Gittins M, Desai M. In: Walley T, ed. A Study of Cellular Counting to Determine Minimum Thresholds for Adequacy for Liquid‐Based Cervical Cytology Using a Survey and Counting Protocol. NIH Journals Library; 2015. doi:10.3310/hta19220
Krimmel‐Morrison JD, Ghezelayagh TS, Lian S, et al. Characterization of TP53 mutations in Pap test DNA of women with and without serous ovarian carcinoma. Gynecol Oncol. 2020;156(2):407. doi:10.1016/J.YGYNO.2019.11.124
Torre LA, Trabert B, DeSantis CE, et al. Ovarian cancer statistics, 2018. CA Cancer J Clin. 2018;68(4):284‐296. doi:10.3322/caac.21456
Salk JJ, Loubet‐Senear K, Maritschnegg E, et al. Ultra‐sensitive TP53 sequencing for cancer detection reveals progressive clonal selection in normal tissue over a century of human lifespan. Cell Rep. 2019;28(1):132‐144. doi:10.1016/J.CELREP.2019.05.109 e3
Maritschnegg E, Wang Y, Pecha N, et al. Lavage of the uterine cavity for molecular detection of Müllerian duct carcinomas: a proof‐of‐concept study. J Clin Oncol. 2015;33(36):4293. doi:10.1200/JCO.2015.61.3083
Locke WJ, Guanzon D, Ma C, et al. DNA methylation cancer biomarkers: translation to the clinic. Front Genet. 2019;10:1150. doi:10.3389/fgene.2019.01150
Harrell FE, Lee KL, Mark DB. Multivariable prognostic models: issues in developing models, evaluating assumptions and adequacy, and measuring and reducing errors. Stat Med. 1996;15(4):361‐387. doi:10.1002/(sici)1097‐0258(19960229)15:4<361::aid‐sim168>3.0.co;2‐4
Zhao Y, O'Keefe CM, Hsieh K, et al. Multiplex digital methylation‐specific PCR for noninvasive screening of lung cancer. Adv Sci. 2023;10(16):2206518. doi:10.1002/ADVS.202206518
Lu J, Johnston A, Berichon P, Ru KL, Korbie D, Trau M. PrimerSuite: a high‐throughput web‐based primer design program for multiplex bisulfite PCR. Sci Rep. 2017;7(January):1‐12. doi:10.1038/srep41328
Woods RP, Grafton ST, Watson JDG, Sicotte NLMJ. Automated image registration: II. Intersubject validation of linear and nonlinear models. J Comput Assist Tomogr. 1998;2:153‐165.
Dwight Z, Palais R, Wittwer CT. uMELT: prediction of high‐resolution melting curves and dynamic melting profiles of PCR products in a rich web application. Bioinformatics. 2011;27(7):1019‐1020. doi:10.1093/bioinformatics/btr065