Simultaneous enumeration of cancer and immune cell types from bulk tumor gene expression data.
Simultaneous enumeration of cancer and immune cell types from bulk tumor gene expression data.
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DOI：
10.7554/elife.26476
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发表时间：
2017-11-13
期刊：
影响因子：
7.7
通讯作者：
Gfeller D
中科院分区：
文献类型：
--
作者：
Racle J;de Jonge K;Baumgaertner P;Speiser DE;Gfeller D
浸润肿瘤的免疫细胞可对肿瘤进展和对治疗的反应具有重要影响。我们提出了一种有效的算法，同时估计癌症和免疫细胞类型的分数从散装肿瘤基因表达数据。我们的方法整合了来自肿瘤中发现的每个主要非恶性细胞类型的新基因表达谱，基于细胞类型特异性mRNA含量的重正化，以及考虑未表征和可能高度可变的细胞类型的能力。通过对人黑色素瘤和结直肠肿瘤标本进行流式细胞术、免疫组织化学和单细胞RNA-Seq分析的验证证明了可行性。总之，我们的工作不仅提高了准确性，而且拓宽了从肿瘤基因表达数据预测绝对细胞分数的范围，并为癌症研究中的免疫基因组学分析提供了独特的新实验基准（http：epic.gfellerlab.org）。恶性肿瘤不仅含有癌细胞。来自身体的正常细胞也浸润肿瘤。这些通常包括各种免疫细胞，可以帮助检测和杀死癌细胞。许多证据表明，肿瘤中不同免疫细胞类型的比例可以影响肿瘤的生长以及哪些治疗是有效的。研究人员通常通过测量基因的表达来研究肿瘤，即，哪些基因在肿瘤中活跃然而，对于在基因表达水平上研究的肿瘤，通常不测量肿瘤中不同细胞类型的比例。Racle等人现在已经证明，一种新的基于计算机的工具可以直接从肿瘤中的基因表达中准确地检测出肿瘤中的所有主要细胞类型。该工具被称为“估计免疫细胞和癌细胞的比例”-或简称EPIC。它将肿瘤中基因的表达水平与可以在肿瘤中发现的特定细胞类型的基因表达谱库进行比较，并使用这些信息来预测每种类型细胞的数量。对几种人类肿瘤的实验测量证实了EPIC的预测是准确的。EPIC可在网上免费获得。由于许多患者的肿瘤中的活性基因已经与临床数据一起记录，研究人员可以使用EPIC来研究肿瘤中的细胞类型是否影响肿瘤的危害程度或特定治疗的效果。将来，这些信息可以帮助确定特定患者的最佳治疗方法，并可能揭示导致恶性肿瘤发展和生长的新基因。
Immune cells infiltrating tumors can have important impact on tumor progression and response to therapy. We present an efficient algorithm to simultaneously estimate the fraction of cancer and immune cell types from bulk tumor gene expression data. Our method integrates novel gene expression profiles from each major non-malignant cell type found in tumors, renormalization based on cell-type-specific mRNA content, and the ability to consider uncharacterized and possibly highly variable cell types. Feasibility is demonstrated by validation with flow cytometry, immunohistochemistry and single-cell RNA-Seq analyses of human melanoma and colorectal tumor specimens. Altogether, our work not only improves accuracy but also broadens the scope of absolute cell fraction predictions from tumor gene expression data, and provides a unique novel experimental benchmark for immunogenomics analyses in cancer research (http://epic.gfellerlab.org). Malignant tumors do not only contain cancer cells. Normal cells from the body also infiltrate tumors. These often include a variety of immune cells that can help detect and kill cancer cells. Many evidences suggest that the proportion of different immune cell types in a tumor can affect tumor growth and which treatments are effective. Researchers often study tumors by measuring the expression of genes, i.e., which genes are active in tumors. However, the proportion of different cell types in the tumor is often not measured for tumors studied at the gene expression level. Racle et al. have now demonstrated that a new computer-based tool can accurately detect all the main cell types in a tumor directly from the expression of genes in this tumor. The tool is called “Estimating the Proportion of Immune and Cancer cells” – or EPIC for short. It compares the level of expression of genes in a tumor with a library of the gene expression profiles from specific cell types that can be found in tumors and uses this information to predict how many of each type of cell are present. Experimental measurements of several human tumors confirmed that EPIC’s predictions are accurate. EPIC is freely available online. Since the active genes in tumors from many patients have already been documented together with clinical data, researchers could use EPIC to investigate whether the cell types in a tumor affect how harmful it is or how well a particular treatment works on it. In the future, this information could help to identify the best treatment for a particular patient and may reveal new genes that cause malignant tumors to develop and grow.