Discovery of methylated circulating DNA biomarkers for comprehensive non-invasive monitoring of treatment response in metastatic colorectal cancer

Abstract

Objective: Mutations in cell-free circulating DNA (cfDNA) have been studied for tracking disease relapse in colorectal cancer (CRC). This approach requires personalised assay design due to the lack of universally mutated genes. In contrast, early methylation alterations are restricted to defined genomic loci allowing comprehensive assay design for population studies. Our objective was to identify cancer-specific methylated biomarkers which could be measured longitudinally in cfDNA (liquid biopsy) to monitor therapeutic outcome in patients with metastatic CRC (mCRC).

Design: Genome-wide methylation microarrays of CRC cell lines (n=149) identified five cancer-specific methylated loci (EYA4, GRIA4, ITGA4, MAP3K14-AS1, MSC). Digital PCR assays were employed to measure methylation of these genes in tumour tissue DNA (n=82) and cfDNA from patients with mCRC (n=182). Plasma longitudinal assessment was performed in a patient subset treated with chemotherapy or targeted therapy.

Results: Methylation in at least one marker was detected in all tumour tissue samples and in 156 mCRC patient cfDNA samples (85.7%). Plasma marker prevalence was 71.4% for EYA4, 68.5% for GRIA4, 69.7% for ITGA4, 69.1% for MAP3K14-AS1% and 65.1% for MSC. Dynamics of methylation markers was not affected by treatment type and correlated with objective tumour response and progression-free survival.

Conclusion: This five-gene methylation panel can be used to circumvent the absence of patient-specific mutations for monitoring tumour burden dynamics in liquid biopsy under different therapeutic regimens. This method might be proposed for assessing pharmacodynamics in clinical trials or when conventional imaging has limitations.

Keywords: chemotherapy; colorectal cancer; methylation; screening; tumour markers.

Conflict of interest statement

Competing interests: AB reports personal fees (scientific advisory board member) from Horizon Discovery, personal fees (scientific advisory board member) from Biocartis, personal fees (Consultant) from Novartis, personal fees (Consultant) from Roche, personal fees (Consultant) from Illumina. AB and FDN reports grants from Trovagene, outside the submitted work. In addition, FDN and PZ have a patent 102017000072650 pending. All the other authors have nothing to disclose.

© Article author(s) (or their employer(s) unless otherwise stated in the text of the article) 2018. All rights reserved. No commercial use is permitted unless otherwise expressly granted.

Figures

Figure 1. Workflow of the study

A multistep marker discovery analysis was first performed to identify highly prevalent cancer specific markers. After design, assay probes were further validated in-silico. Assays were optimized to achieve linear quantification over a wide methylation range (0.09% to 100%). Marker prevalence was first evaluated in a total of 83 individuals with mCRC. Then marker prevalence was evaluated in cfDNA in a total of 232 donors enrolled in the study among which 50 were de-identified healthy self-declared donors, and 182 mCRC patients. Among mCRC cases, 47 were followed longitudinally and treated either with conventional chemotherapy, targeted therapy regimen, or with temozolomide (TMZ) as part of a clinical trial. Methylation was analyzed longitudinally for cases with positivity in at least one marker at baseline sample. Methylation dynamics was then compared to additional available clinical or molecular features. In green: Unpublished data; in blue: bench experiments; in orange: bioinformatics or statistical analyses with clinical correlates; in grey: sample exclusion. *GSE32146 was used after removal of ulcerative colitis cases. **in-silico validation was performed again restricting the analysis to the probes included in the assay amplicon. *** Only normal healthy and prei-tumoral tissues were used from GSE48684.

Figure 2

Prevalence of methylated markers in tissue DNA from mCRC patients. A total of 82 cases were analyzed. A first cohort was composed of 32 cases from which tumor and peri-tumoral tissue DNA were available. A second cohort of independent tumor tissue was assembled from remaining DNA extracted during the process of two clinical trials.

Figure 3. Prevalence of methylated markers in cfDNA and total amount of cfDNA

Fifty self-declared healthy donors (blue) and 182 mCRC patients (red) were analyzed for the six selected markers. Group mean is represented by a horizontal bar. Mann-Whitney U test was performed to compare distribution in healthy and cancer patients which were all significantly different (with p-valueA: EYA4,B: GRIA4,C: ITGA4, D: MAP3K14-AS1E: MSC,F:Genome equivalent/ml (GE/ml). G: Representation of average methylation signal. Two healthy donors presented an average methylation value above positivity threshold (purple, orange and green arrow), which was due to high positivity in GRIA4 and/or EYA4. For each marker, the dashed-line correspond to the threshold established by ROC analyses available in supplementary data XX. H: Heatmap of methylation values in mCRC cases sorted by average methylation.

Figure 3. Average of selected markers (ASM) in cfDNA dynamics in eight mCRC cases treated with conventional chemotherapy regimens

ASM is plotted in blue, while KRAS mutations are plotted in pink and BRAF in red. Methylation and genetic mutations evolve in parallel demonstrating the possibility to use methylation instead of genetic alterations for tracking response. Response status is indicated with arrows and the following abbreviations: PR: Partial Response, SD: Stable Disease, PD: Progressive Disease. Treatment periods are indicated as horizontal black arrows with corresponding chemotherapy regimens.

Figure 4

Average of selected markers (ASM) in cfDNA dynamics in five mCRC cases treated with panitumumab for whom resistance causative mutations were discovered at progression and retrospectively assessed longitudinally. A–C: Resistance was acquired through the emergence of a KRAS alteration, while D: Resistance was acquired through the emergence of amplification of MET. In each case, increase in ASM follows the emergence of resistance alterations. E: A case in which resistance mechanism remained unidentified but for which ASM could detect relapse.

Figure 5. Average of selected markers (ASM) incfDNA dynamics assessment in mCRC patients treated with temozolomide within a clinical trial

A: Comparison of ASM changes to response status and RECIST. The best ASM changes were plotted as a waterfall plot. Response status of patients evaluated by RECIST is plotted as a heatmap. B: Progression free survival according to best ASM change. Negative ASM change shows a trend for improved PFS.