QSEA-modelling of genome-wide DNA methylation from sequencing enrichment experiments

Genome-wide enrichment of methylated DNA followed by sequencing (MeDIP-seq) offers a reasonable compromise between experimental costs and genomic coverage. However, the computational analysis of these experiments is complex, and quantification of the enrichment signals in terms of absolute levels of methylation requires specific transformation. In this work, we present QSEA, Quantitative Sequence Enrichment Analysis, a comprehensive workflow for the modelling and subsequent quantification of MeDIP-seq data. As the central part of the workflow we have developed a Bayesian statistical model that transforms the enrichment read counts to absolute levels of methylation and, thus, enhances interpretability and facilitates comparison with other methylation assays. We suggest several calibration strategies for the critical parameters of the model, either using additional data or fairly general assumptions. By comparing the results with bisulfite sequencing (BS) validation data, we show the improvement of QSEA over existing methods. Additionally, we generated a clinically relevant benchmark data set consisting of methylation enrichment experiments (MeDIP-seq), BS-based validation experiments (Methyl-seq) aswell as gene expression experiments (RNA-seq) derived from non-small cell lung cancer patients, and show that the workflow retrieves well-known lung tumour methylation markers that are causative for gene expression changes, demonstrating the applicability of QSEA for clinical studies. QSEA is implemented in R and available from the Bioconductor repository 3.4 (www.bioconductor.org/packages/qsea)

Epigenomic profiling of non-small cell lung cancer xenografts uncover LRP12 DNA methylation as predictive biomarker for carboplatin resistance

Background: Non-small cell lung cancer (NSCLC) is the most common cause of cancer-related deaths worldwide and is primarily treated with radiation, surgery, and platinum-based drugs like cisplatin and carboplatin. The major challenge in the treatment of NSCLC patients is intrinsic or acquired resistance to chemotherapy. Molecular markers predicting the outcome of the patients are urgently needed. Methods: Here, we employed patient-derived xenografts (PDXs) to detect predictive methylation biomarkers for platin-based therapies. We used MeDIP-Seq to generate genome-wide DNA methylation profiles of 22 PDXs, their parental primary NSCLC, and their corresponding normal tissues and complemented the data with gene expression analyses of the same tissues. Candidate biomarkers were validated with quantitative methylation-specific PCRs (qMSP) in an independent cohort. Results: Comprehensive analyses revealed that differential methylation patterns are highly similar, enriched in PDXs and lung tumor-specific when comparing differences in methylation between PDXs versus primary NSCLC. We identified a set of 40 candidate regions with methylation correlated to carboplatin response and corresponding inverse gene expression pattern even before therapy. This analysis led to the identification of a promoter CpG island methylation of LDL receptor-related protein 12 (LRP12) associated with increased resistance to carboplatin. Validation in an independent patient cohort (n = 35) confirmed that LRP12 methylation status is predictive for therapeutic response of NSCLC patients to platin therapy with a sensitivity of 80% and a specificity of 84% (p < 0.01). Similarly, we find a shorter survival time for patients with LRP12 hypermethylation in the TCGA data set for NSCLC (lung adenocarcinoma). Conclusions: Using an epigenome-wide sequencing approach, we find differential methylation patterns from primary lung cancer and PDX-derived cancers to be very similar, albeit with a lower degree of differential methylation in primary tumors. We identify LRP12 DNA methylation as a powerful predictive marker for carboplatin resistance. These findings outline a platform for the identification of epigenetic therapy resistance biomarkers based on PDX NSCLC models

Additional file 2: of Epigenomic profiling of non-small cell lung cancer xenografts uncover LRP12 DNA methylation as predictive biomarker for carboplatin resistance

Additional Methods. Figure S1. Region used for the MSP of LRP12. Figure S2. MeDIP-Seq statistics of 54 samples including primary tumor tissues (P) and PDXs (X). Table S3. MeDIP-Sequencing statistics. Table S4 Methyl-Sequencing statistics. Figure S3. Correlation of methylation of overlapping DMRs in primary NSCLC and patient-derived xenografts. Table S5. Tumor content of primary tumors and genome-wide Spearman correlation of DMRs of primary tissue to the PDXs. Figure S4. Patientwise circos plots of overlapping DMRs of primary NSCLC and PDX. Figure S5. Comparison of methylation values of primary NSCLCs and PDXs. Figure S6. Methylation differences between non-responders and responders in large hypomethylated blocks (LHBs) on chromosomes 1, 2, and 4 as examples. Table S6. Histopathologic evaluation of primary tumor and PDX tumor. Figure S7. Ingenuity pathway and upstream regulator analyses of the 2380 genes differentially methylated. Figure S8. LRP12 knockdown induces carboplatin resistance. Table S9. Patient’s data and clinical characteristics of the validation cohort. Figure S9. LRP12 DNA hypermethylation as independent factor predictive for clinical outcome in NSCLC. Figure S10 LRP12 DNA hypermethylation as independent predictive factor for clinical outcome in 449 NSCLC patients from the TCGA data set. Additional references. (XLSX 141 kb

Additional file 1: of Epigenomic profiling of non-small cell lung cancer xenografts uncover LRP12 DNA methylation as predictive biomarker for carboplatin resistance

Table S1. Clinical characteristics of the study cohort. Table S2. Response rates of PDXs to carboplatin treatment. Table S7. Carboplatin therapy resistance-associated genes for which promoter DNA methylation is correlated to tumor volume. Table S8. Tumor suppressor genes with significant carboplatin rDMRs and differential expression. (DOC 4660 kb