MethCORR infers gene expression from DNA methylation and allows molecular analysis of ten common cancer types using fresh-frozen and formalin-fixed paraffin-embedded tumor samples.
MethCORR infers gene expression from DNA methylation and allows molecular analysis of ten common cancer types using fresh-frozen and formalin-fixed paraffin-embedded tumor samples.
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DOI：
10.1186/s13148-021-01000-0
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发表时间：
2021-01-28
影响因子：
5.7
通讯作者：
Bramsen JB
中科院分区：
文献类型：
--
作者：
Mattesen TB;Andersen CL;Bramsen JB
转录分析被广泛用于研究癌症的分子生物学，并具有用于临床患者分层的巨大生物标志物潜力。然而，准确的转录谱分析需要高质量的RNA，这通常不能从临床部门常规收集和存档的福尔马林固定、石蜡包埋（FFPE）肿瘤组织中检索到。为了克服这一临床测试的障碍，我们之前开发了MethCORR，这是一种从DNA甲基化数据推断基因表达的方法，该数据从FFPE组织中稳健地检索。MethCORR最初是为结直肠癌开发的，通过这项研究，我们的目标是：（1）将MethCORR方法扩展到10种其他癌症类型，（2）说明推断的基因表达是准确的和临床信息。使用来自癌症基因组图谱项目的匹配RNA测序和DNA甲基化谱（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。对于所有癌症类型，推断出稳健和准确的基因表达谱：平均而言，以良好的准确度对11，000个基因的表达进行建模，并且观察到推断和测量的基因表达之间的样本内相关性R2 = 0.90。对乳腺癌、前列腺癌和肺癌样品进行分子途径分析和转录亚型分型，以说明推断的基因表达谱的一般可用性：总体而言，当使用测量和推断的基因表达作为输入时，观察到途径富集评分的r = 0.96（Pearson）的高相关性和分子亚型调用的76%对应性。最后，与来自FFPE组织的RNA测序数据相比，来自FFPE组织的推断表达与来自匹配的新鲜冷冻组织的RNA测序数据的相关性更好（P <0.0001; Wilcoxon秩和检验）。在所有研究的癌症中，MethCORR能够进行基于DNA甲基化的转录分析，从而在高质量DNA而不是RNA可用的情况下，能够进行未来的癌症分析。在这里，我们提供了10种常见癌症类型的MethCORR建模的框架和资源，从而广泛扩展了档案FFPE材料的转录研究的可能性。
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 R2 = 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.
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实现癌症基因组数据的共同愿景。
DOI：
10.1056/nejmp1607591
发表时间：
2016-09-22
期刊：
The New England journal of medicine
影响因子：
--
作者：
Grossman RL;Heath AP;Ferretti V;Varmus HE;Lowy DR;Kibbe WA;Staudt LM
通讯作者：
Staudt LM
影响因子：
3.8
作者：
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通讯作者：
Lehmann BD
DOI：
10.1016/j.urolonc.2013.06.014
发表时间：
2014-04
期刊：
Urologic oncology
影响因子：
--
作者：
Sharron Lin X;Hu L;Sandy K;Correll M;Quackenbush J;Wu CL;Scott McDougal W
通讯作者：
Scott McDougal W
影响因子：
64.5
作者：
Liu J;Lichtenberg T;Hoadley KA;Poisson LM;Lazar AJ;Cherniack AD;Kovatich AJ;Benz CC;Levine DA;Lee AV;Omberg L;Wolf DM;Shriver CD;Thorsson V;Cancer Genome Atlas Research Network;Hu H
通讯作者：
Hu H
影响因子：
8.8
作者：
Bramsen, Jesper Bertram;Rasmussen, Mads Heilskov;Andersen, Claus Lindbjerg
通讯作者：
Andersen, Claus Lindbjerg