Peripheral blood DNA methylation profiles predict future development of B-cell Non-Hodgkin Lymphoma

Abstract

Lack of accurate methods for early lymphoma detection limits the ability to cure patients. Since patients with Non-Hodgkin lymphomas (NHL) who present with advanced disease have worse outcomes, accurate and sensitive methods for early detection are needed to improve patient care. We developed a DNA methylation-based prediction tool for NHL, based on blood samples collected prospectively from 278 apparently healthy patients who were followed for up to 16 years to monitor for NHL development. A predictive score was developed using machine learning methods in a robust training/validation framework. Our predictive score incorporates CpG DNA methylation at 135 genomic positions, with higher scores predicting higher risk. It was 85% and 78% accurate for identifying patients at risk of developing future NHL, in patients with high or low epigenetic mitotic clock respectively, in a validation cohort. It was also sensitive at detecting active NHL (96.3% accuracy) and healthy status (95.6% accuracy) in additional independent cohorts. Scores optimized for specific NHL subtypes showed significant but lower accuracy for predicting other subtypes. Our score incorporates hyper-methylation of Polycomb and HOX genes, which have roles in NHL development, as well as PAX5 - a master transcriptional regulator of B-cell fate. Subjects with higher risk scores showed higher regulatory T-cells, memory B-cells, but lower naïve T helper lymphocytes fractions in the blood. Future prospective studies will be required to confirm the utility of our signature for managing patients who are at high risk for developing future NHL.

Fig. 1. Workflow of the study.

Blood samples from a large population of healthy volunteers were taken and frozen using liquid nitrogen (1). Throughout a 16 years follow-up, subjects that developed NHL were registered (2). At the end of the follow-up, a balanced cohort comprising individuals that developed NHL and a matching ratio of controls was identified (3) and omics analyses were conducted on the selected frozen blood samples (4). Data was preprocessed and analysed to develop a classifier for future NHL called Future LYmphoma Predictor or FLYP (5,6,7). Finally, the classifier was tested in a variety of independent NHL and healthy cohorts (8). BCLL = B-cell chronic lymphocytic leukaemia, MM = multiple myeloma.

Fig. 2. Top three CpGs corresponding to HOX and PAX genes in the prospective NHL and control groups.

List of CpGs corresponding to HOX and PAX genes significantly hyper-methylated in the group with high epiTOC estimated levels (cg00599770 P = 1.34 × 10⁻⁹, cg19542816 P = 4.10 × 10⁻⁸, cg22674699 P = 1.22 × 10⁻⁷, cg01783070 P = 2.43 × 10⁻⁸, cg11428724 P = 3.75 × 10⁻⁸, cg18988498 P = 3.32 × 10⁻⁸) identified using univariate regression analyses (N = 278). The box plot uses the median (horizontal line), the first and third quartiles (ends of box) and points more than 3/2 times the interquartile range (dots).

Fig. 3. Performance of the predictive models using the bulk DNA methylation.

Numbers are expressed in percentage and refer to the samples from the test set of the prospective cohort.

Fig. 4. Landscape of the future NHL signature.

a Number of CpGs from the signature from each chromatin state compared to the annotated genome. b Location of the CpGs from the signature compared to the annotated genome. c Number of hyper-/hypo-methylated CpGs from the signature from each chromatin state within the prospective cohort (reference: healthy controls from the prospective study). d Top 20 pathways from GSEA analysis (C2-curated gene sets).