Purpose:

The detection of ctDNA, which allows noninvasive tumor molecular profiling and disease follow-up, promises optimal and individualized management of patients with cancer. However, detecting small fractions of tumor DNA released when the tumor burden is reduced remains a challenge.

Experimental Design:

We implemented a new, highly sensitive strategy to detect bp resolution methylation patterns from plasma DNA and assessed the potential of hypomethylation of long interspersed nuclear element-1 retrotransposons as a noninvasive multicancer detection biomarker. The Detection of Long Interspersed Nuclear Element Altered Methylation ON plasma DNA method targets 30 to 40,000 young long interspersed nuclear element-1 retrotransposons scattered throughout the genome, covering about 100,000 CpG sites and is based on a reference-free analysis pipeline.

Results:

Resulting machine learning–based classifiers showed powerful correct classification rates discriminating healthy and tumor plasmas from six types of cancers (colorectal, breast, lung, ovarian, and gastric cancers and uveal melanoma, including localized stages) in two independent cohorts (AUC = 88%–100%, N = 747). The Detection of Long Interspersed Nuclear Element Altered Methylation ON plasma DNA method can also be used to perform copy number alteration analysis that improves cancer detection.

Conclusions:

This should lead to the development of more efficient noninvasive diagnostic tests adapted to all patients with cancer, based on the universality of these factors.

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©2024 The Authors; Published by the American Association for Cancer Research
2024
American Association for Cancer Research
This open access article is distributed under the Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International (CC BY-NC-ND 4.0) license.
This work is licensed under a Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International License .
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Supplementary data

Supplementary Tables S1-S22

Table S1. Targeted bisulfite sequencing primers Table S2. Target copies and CpG sites relative to the number of sequencing reads Table S3. Statistical results of global methylation differences between healthy samples and cancer subgroups using Mann–Whitney U test Table S4. List of plasma samples. Sample IDs were not known to anyone outside the research group Table S5. Statistical results of proportion differences between healthy samples and cancer subgroups for the 372 haplotypes using Mann–Whitney U test Table S6. List of paired plasma and tissues samples and their spearman correlation for various features Table S7. Breast cancer subtype composition of M0 and M+ subgroups in C1 and C2 Table S8. Mutation screening of ovarian cancer samples from the discovery cohort Table S9. Statistical results of proportion differences between healthy samples from C1 and C2 for 14 haplotypes selected using Mann–Whitney U test Table S10. Statistical results of global methylation differences between healthy samples and cancer subgroups from C1 and C2 using Mann–Whitney U test Table S11. Statistical results of proportion differences between healthy samples and cancer subgroups from C1 and C2 for 14 haplotypes selected using Mann–Whitney U test Table S12. Statistical results of global methylation differences between original data and age-adjusted data using Mann–Whitney U test Table S13. Statistical results of global methylation differences between cancer stages using Mann–Whitney U test Table S14. Statistical results of global methylation differences between primary and metastatic tissues using Mann–Whitney U test Table S15. Statistical results of Genome-wide z-score differences between healthy samples and cancer subgroups using Mann–Whitney U test Tables S16-S22. Cancer prediction and sample labelling with the 2-step classification integrating CNA analysis - healthy vs each type of model

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Supplementary Figure S1

Fig S1. cfDNA extraction methods did not impact the L1PA methylation patterns

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Supplementary Figure S2

Fig S2. Methylation profiles obtained with bisulfite or enzymatic conversion are similar

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Supplementary Figure S3

Fig S3. Preparation of L1PA targeted bisulfite sequencing libraries and analysis workflow

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Supplementary Figure S4

Fig S4. DIAMOND features: CpG calling and contribution of CG positions or haplotypes

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Supplementary Figure S5

Fig S5. Comparison of tumor and plasma paired samples

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Supplementary Figure S6

Fig S6. Classifier performances: feature types, calculation parameters, cancer subtypes and stages

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Supplementary Figure S7

Fig S7. Extraction of L1PA DNA hypomethylation signal from whole genome data

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Supplementary Figure S8

Fig S8. DIAMOND profiles and performances in the validation versus discovery cohorts

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Supplementary Figure S9

Fig S9. Age has a minor effect on L1PA DNA methylation patterns and is not a confounding factor in this study

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Supplementary Figure S10

Fig S10. Comparison of multiple classifiers (expert, all, stack and blind models) and prognostic value of L1PA hypomethylation

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Supplementary Figure S11

Fig S11. 2 step-models integrating CNA signal extracted from DIAMOND data

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