Machine learning models predict the primary sites of head and neck squamous cell carcinoma metastases based on DNA methylation.
DNA methylation
cancer of unknown primary
head and neck squamous cell carcinoma
machine learning
Journal
The Journal of pathology
ISSN: 1096-9896
Titre abrégé: J Pathol
Pays: England
ID NLM: 0204634
Informations de publication
Date de publication:
04 2022
04 2022
Historique:
revised:
24
10
2021
received:
26
07
2021
accepted:
06
12
2021
pubmed:
9
12
2021
medline:
28
4
2022
entrez:
8
12
2021
Statut:
ppublish
Résumé
In head and neck squamous cell cancers (HNSCs) that present as metastases with an unknown primary (HNSC-CUPs), the identification of a primary tumor improves therapy options and increases patient survival. However, the currently available diagnostic methods are laborious and do not offer a sufficient detection rate. Predictive machine learning models based on DNA methylation profiles have recently emerged as a promising technique for tumor classification. We applied this technique to HNSC to develop a tool that can improve the diagnostic work-up for HNSC-CUPs. On a reference cohort of 405 primary HNSC samples, we developed four classifiers based on different machine learning models [random forest (RF), neural network (NN), elastic net penalized logistic regression (LOGREG), and support vector machine (SVM)] that predict the primary site of HNSC tumors from their DNA methylation profile. The classifiers achieved high classification accuracies (RF = 83%, NN = 88%, LOGREG = SVM = 89%) on an independent cohort of 64 HNSC metastases. Further, the NN, LOGREG, and SVM models significantly outperformed p16 status as a marker for an origin in the oropharynx. In conclusion, the DNA methylation profiles of HNSC metastases are characteristic for their primary sites, and the classifiers developed in this study, which are made available to the scientific community, can provide valuable information to guide the diagnostic work-up of HNSC-CUP. © 2021 The Authors. The Journal of Pathology published by John Wiley & Sons Ltd on behalf of The Pathological Society of Great Britain and Ireland.
Types de publication
Journal Article
Research Support, Non-U.S. Gov't
Langues
eng
Sous-ensembles de citation
IM
Pagination
378-387Informations de copyright
© 2021 The Authors. The Journal of Pathology published by John Wiley & Sons Ltd on behalf of The Pathological Society of Great Britain and Ireland.
Références
Sung H, Ferlay J, Siegel RL, et al. Global cancer statistics 2020: GLOBOCAN estimates of incidence and mortality worldwide for 36 cancers in 185 countries. CA Cancer J Clin 2021; 71: 209-249.
Müller von der Grün J, Tahtali A, Ghanaati S, et al. Diagnostic and treatment modalities for patients with cervical lymph node metastases of unknown primary site - current status and challenges. Radiat Oncol 2017; 12: 82.
Pavlidis N, Pentheroudakis G, Plataniotis G. Cervical lymph node metastases of squamous cell carcinoma from an unknown primary site: a favourable prognosis subset of patients with CUP. Clin Transl Oncol 2009; 11: 340-348.
Davis KS, Byrd JK, Mehta V, et al. Occult primary head and neck squamous cell carcinoma: utility of discovering primary lesions. Otolaryngol Head Neck Surg 2014; 151: 272-278.
Haas I, Hoffmann TK, Engers R, et al. Diagnostic strategies in cervical carcinoma of an unknown primary (CUP). Eur Arch Otorhinolaryngol 2002; 259: 325-333.
Maghami E, Ismaila N, Alvarez A, et al. Diagnosis and management of squamous cell carcinoma of unknown primary in the head and neck: ASCO guideline. J Clin Oncol 2020; 38: 2570-2596.
Geltzeiler M, Doerfler S, Turner M, et al. Transoral robotic surgery for management of cervical unknown primary squamous cell carcinoma: updates on efficacy, surgical technique and margin status. Oral Oncol 2017; 66: 9-13.
Binder A, Bockmayr M, Hägele M, et al. Morphological and molecular breast cancer profiling through explainable machine learning. Nat Mach Intell 2021; 3: 355-366.
Campanella G, Hanna MG, Geneslaw L, et al. Clinical-grade computational pathology using weakly supervised deep learning on whole slide images. Nat Med 2019; 25: 1301-1309.
Klauschen F, Müller KR, Binder A, et al. Scoring of tumor-infiltrating lymphocytes: from visual estimation to machine learning. Semin Cancer Biol 2018; 52: 151-157.
Seegerer P, Binder A, Saitenmacher R, et al. Interpretable deep neural network to predict estrogen receptor status from haematoxylin-eosin images. In Artificial Intelligence and Machine Learning for Digital Pathology: State-of-the-Art and Future Challenges. Lecture Notes in Computer Science, Holzinger A, Goebel R, Mengel M, et al. (eds). Springer International Publishing: Cham, 2020; 16-37.
Stenzinger A, Alber M, Allgäuer M, et al. Artificial intelligence and pathology: from principles to practice and future applications in histomorphology and molecular profiling. Semin Cancer Biol 2021. https://doi.org/10.1016/j.semcancer.2021.02.011.
Pfeifer B, Saranti A, Holzinger A. Network module detection from multi-modal node features with a greedy decision forest for actionable explainable AI. arXiv.org 2021; 2108.11674 [Not peer reviewed].
Capper D, Jones DTW, Sill M, et al. DNA methylation-based classification of central nervous system tumours. Nature 2018; 555: 469-474.
Hackeng WM, Dreijerink KMA, de Leng WWJ, et al. Genome methylation accurately predicts neuroendocrine tumor origin: an online tool. Clin Cancer Res 2021; 27: 1341-1350.
Moran S, Martínez-Cardús A, Sayols S, et al. Epigenetic profiling to classify cancer of unknown primary: a multicentre, retrospective analysis. Lancet Oncol 2016; 17: 1386-1395.
Jurmeister P, Bockmayr M, Seegerer P, et al. Machine learning analysis of DNA methylation profiles distinguishes primary lung squamous cell carcinomas from head and neck metastases. Sci Transl Med 2019; 11: eaaw8513.
Jurmeister P, Schöler A, Arnold A, et al. DNA methylation profiling reliably distinguishes pulmonary enteric adenocarcinoma from metastatic colorectal cancer. Mod Pathol 2019; 32: 855-865.
Maros ME, Capper D, Jones DTW, et al. Machine learning workflows to estimate class probabilities for precision cancer diagnostics on DNA methylation microarray data. Nat Protoc 2020; 15: 479-512.
Basu B, Chakraborty J, Chandra A, et al. Genome-wide DNA methylation profile identified a unique set of differentially methylated immune genes in oral squamous cell carcinoma patients in India. Clin Epigenetics 2017; 9: 13.
Degli Esposti D, Sklias A, Lima SC, et al. Unique DNA methylation signature in HPV-positive head and neck squamous cell carcinomas. Genome Med 2017; 9: 33.
Edgar R, Domrachev M, Lash AE. Gene Expression Omnibus: NCBI gene expression and hybridization array data repository. Nucleic Acids Res 2002; 30: 207-210.
Clark K, Vendt B, Smith K, et al. The Cancer Imaging Archive (TCIA): maintaining and operating a public information repository. J Digit Imaging 2013; 26: 1045-1057.
Zuley ML, Jarosz R, Kirk S, et al. Radiology Data from The Cancer Genome Atlas Head-Neck Squamous Cell Carcinoma [TCGA-HNSC] collection. The Cancer Imaging Archive. 2016. http://doi.org/10.7937/K9/TCIA.2016.LXKQ47MS.
World Health Organization. In International Classification of Diseases for Oncology (ICD-O) (3rd edn, 1st Revision edn), Fritz A, Percy C, Jack A, et al. (eds). World Health Organization, 2013. https://apps.who.int/iris/handle/10665/96612.
Aryee MJ, Jaffe AE, Corrada-Bravo H, et al. Minfi: a flexible and comprehensive bioconductor package for the analysis of Infinium DNA methylation microarrays. Bioinformatics 2014; 30: 1363-1369.
Triche TJ Jr, Weisenberger DJ, Van Den Berg D, et al. Low-level processing of Illumina Infinium DNA Methylation BeadArrays. Nucleic Acids Res 2013; 41: e90.
Zhou W, Laird PW, Shen H. Comprehensive characterization, annotation and innovative use of Infinium DNA methylation BeadChip probes. Nucleic Acids Res 2017; 45: e22.
Kuhn M. Building predictive models in R using the caret package. J Stat Softw 2008; 28: 1-26.
van der Maaten L, Hinton G. Visualizing data using t-SNE. J Mach Learn Res 2008; 9: 2579-2605.
Johann PD, Jäger N, Pfister SM, et al. RF_Purify: a novel tool for comprehensive analysis of tumor-purity in methylation array data based on random forest regression. BMC Bioinformatics 2019; 20: 428.
Qin Y, Feng H, Chen M, et al. InfiniumPurify: an R package for estimating and accounting for tumor purity in cancer methylation research. Genes Dis 2018; 5: 43-45.
Begum S, Gillison ML, Ansari-Lari MA, et al. Detection of human papillomavirus in cervical lymph nodes: a highly effective strategy for localizing site of tumor origin. Clin Cancer Res 2003; 9: 6469-6475.
El-Mofty SK, Zhang MQ, Davila RM. Histologic identification of human papillomavirus (HPV)-related squamous cell carcinoma in cervical lymph nodes: a reliable predictor of the site of an occult head and neck primary carcinoma. Head Neck Pathol 2008; 2: 163-168.
Gillison ML. Human papillomavirus-associated head and neck cancer is a distinct epidemiologic, clinical, and molecular entity. Semin Oncol 2004; 31: 744-754.
Pillai R, Deeter R, Rigl CT, et al. Validation and reproducibility of a microarray-based gene expression test for tumor identification in formalin-fixed, paraffin-embedded specimens. J Mol Diagn 2011; 13: 48-56.
Erlander MG, Ma XJ, Kesty NC, et al. Performance and clinical evaluation of the 92-gene real-time PCR assay for tumor classification. J Mol Diagn 2011; 13: 493-503.
Tothill RW, Kowalczyk A, Rischin D, et al. An expression-based site of origin diagnostic method designed for clinical application to cancer of unknown origin. Cancer Res 2005; 65: 4031-4040.
Bockmayr T, Erdmann G, Treue D, et al. Multiclass cancer classification in fresh frozen and formalin-fixed paraffin-embedded tissue by DigiWest multiplex protein analysis. Lab Invest 2020; 100: 1288-1299.
Zhang PW, Chen L, Huang T, et al. Classifying ten types of major cancers based on reverse phase protein array profiles. PLoS One 2015; 10: e0123147.
Bloom GC, Eschrich S, Zhou JX, et al. Elucidation of a protein signature discriminating six common types of adenocarcinoma. Int J Cancer 2007; 120: 769-775.
Rassy E, Assi T, Pavlidis N. Exploring the biological hallmarks of cancer of unknown primary: where do we stand today? Br J Cancer 2020; 122: 1124-1132.
Pezzuto F, Buonaguro L, Caponigro F, et al. Update on head and neck cancer: current knowledge on epidemiology, risk factors, molecular features and novel therapies. Oncology 2015; 89: 125-136.
van Kempen PM, Noorlag R, Braunius WW, et al. Differences in methylation profiles between HPV-positive and HPV-negative oropharynx squamous cell carcinoma. Epigenetics 2014; 9: 194-203.
Ritchie ME, Phipson B, Wu D, et al. limma powers differential expression analyses for RNA-sequencing and microarray studies. Nucleic Acids Res 2015; 43: e47.