Department of Molecular Medicine, Aarhus University Hospital, Palle Juul-Jensens Boulevard 99, 8200, Aarhus N, Denmark.
Clin Epigenetics. 2021 Jan 28;13(1):20. doi: 10.1186/s13148-021-01000-0.
Transcriptional analysis is widely used to study the molecular biology of cancer and hold great biomarker potential for clinical patient stratification. Yet, accurate transcriptional profiling requires RNA of a high quality, which often cannot be retrieved from formalin-fixed, paraffin-embedded (FFPE) tumor tissue that is routinely collected and archived in clinical departments. To overcome this roadblock to clinical testing, we previously developed MethCORR, a method that infers gene expression from DNA methylation data, which is robustly retrieved from FFPE tissue. MethCORR was originally developed for colorectal cancer and with this study, we aim to: (1) extend the MethCORR method to 10 additional cancer types and (2) to illustrate that the inferred gene expression is accurate and clinically informative.
Regression models to infer gene expression information from DNA methylation were developed for ten common cancer types using matched RNA sequencing and DNA methylation profiles (HumanMethylation450 BeadChip) from The Cancer Genome Atlas Project. Robust and accurate gene expression profiles were inferred for all cancer types: on average, the expression of 11,000 genes was modeled with good accuracy and an intra-sample correlation of R = 0.90 between inferred and measured gene expression was observed. Molecular pathway analysis and transcriptional subtyping were performed for breast, prostate, and lung cancer samples to illustrate the general usability of the inferred gene expression profiles: overall, a high correlation of r = 0.96 (Pearson) in pathway enrichment scores and a 76% correspondence in molecular subtype calls were observed when using measured and inferred gene expression as input. Finally, inferred expression from FFPE tissue correlated better with RNA sequencing data from matched fresh-frozen tissue than did RNA sequencing data from FFPE tissue (P < 0.0001; Wilcoxon rank-sum test).
In all cancers investigated, MethCORR enabled DNA methylation-based transcriptional analysis, thus enabling future analysis of cancer in situations where high-quality DNA, but not RNA, is available. Here, we provide the framework and resources for MethCORR modeling of ten common cancer types, thereby widely expanding the possibilities for transcriptional studies of archival FFPE material.
转录分析被广泛用于研究癌症的分子生物学，并且在临床患者分层方面具有很大的生物标志物潜力。然而，准确的转录谱分析需要高质量的 RNA，而从临床科室常规收集和存档的福尔马林固定、石蜡包埋（FFPE）组织中往往无法获得这种 RNA。为了克服这一临床检测障碍，我们之前开发了 MethCORR，这是一种从 FFPE 组织中可靠获取的 DNA 甲基化数据推断基因表达的方法。MethCORR 最初是为结直肠癌开发的，在这项研究中，我们旨在：（1）将 MethCORR 方法扩展到另外 10 种常见癌症类型；（2）说明推断的基因表达是准确且具有临床意义的。
使用来自癌症基因组图谱计划的匹配 RNA 测序和 DNA 甲基化图谱（HumanMethylation450 BeadChip），为十种常见癌症类型开发了从 DNA 甲基化推断基因表达信息的回归模型。所有癌症类型都推断出了稳健且准确的基因表达谱：平均而言，有 11000 个基因的表达以良好的准确性进行建模，并且观察到推断的基因表达与测量的基因表达之间的样本内相关性 R=0.90。对乳腺癌、前列腺癌和肺癌样本进行了分子途径分析和转录亚型分析，以说明推断的基因表达谱的一般可用性：总体而言，使用测量和推断的基因表达作为输入时，途径富集评分的相关性 r=0.96（皮尔逊）和分子亚型调用的一致性为 76%。最后，FFPE 组织中的推断表达与匹配的新鲜冷冻组织中的 RNA 测序数据的相关性优于 FFPE 组织中的 RNA 测序数据（P<0.0001；Wilcoxon 秩和检验）。
在所研究的所有癌症中，MethCORR 实现了基于 DNA 甲基化的转录分析，从而能够在只有高质量 DNA 而不是 RNA 的情况下对癌症进行未来分析。在这里，我们提供了十种常见癌症类型的 MethCORR 建模框架和资源，从而广泛扩展了对存档 FFPE 材料进行转录研究的可能性。
