Abstract
Transcriptional characterization and classification has potential to resolve the inter-tumor heterogeneity of colorectal cancer and improve patient management. Yet, robust transcriptional profiling is difficult using formalin-fixed, paraffin-embedded (FFPE) samples, which complicates testing in clinical and archival material. We present MethCORR, an approach that allows uniform molecular characterization and classification of fresh-frozen and FFPE samples. MethCORR identifies genome-wide correlations between RNA expression and DNA methylation in fresh-frozen samples. This information is used to infer gene expression information in FFPE samples from their methylation profiles. MethCORR is here applied to methylation profiles from 877 fresh-frozen/FFPE samples and comparative analysis identifies the same two subtypes in four independent cohorts. Furthermore, subtype-specific prognostic biomarkers that better predicts relapse-free survival (HR = 2.66, 95%CI [1.67–4.22], P value < 0.001 (log-rank test)) than UICC tumor, node, metastasis (TNM) staging and microsatellite instability status are identified and validated using DNA methylation-specific PCR. The MethCORR approach is general, and may be similarly successful for other cancer types.
Highlights
Transcriptional characterization and classification has potential to resolve the inter-tumor heterogeneity of colorectal cancer and improve patient management
The 11,222 genes were denoted MethCORR genes and the expressioncorrelated CpGs of these define the COREAD MethCORR matrix (≤200 CpGs × 11,222 genes; Supplementary Data 3) that was used for calculation of MethCORR score (MCS) from DNA methylation profiles of all samples analyzed in this study (Fig. 1c)
We investigated if RNA expression was better modeled using the ≤200 expressionassociated CpGs for each gene directly, instead of using MCSs, but found no improvement in overall performance (R2 and root mean square error (RMSE); Supplementary Fig. 1f)
Summary
Transcriptional characterization and classification has potential to resolve the inter-tumor heterogeneity of colorectal cancer and improve patient management. High-quality RNA is often not recovered from the formalin-fixed, paraffin-embedded (FFPE) tissue that is routinely archived in the clinic This can preclude confident transcriptional profiling and hereby complicate clinical testing of molecular classification and exploratory analysis in well-annotated, archival FFPE material[5,6,7,8,9]. Many biological traits, such as RNA expression and cell-type identity, are associated with specific and robust DNA methylation patterns in the genome[18,19] This suggests that both gene expression and celltype information may be extracted from DNA methylation profiles of FFPE samples and used for molecular classification and prognostication, as an alternative to RNA profiling. Hereby MethCORR provides a path for improved, subtype-specific prognostication of CRC using clinical FFPE samples
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