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).

中文翻译：

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从大量肿瘤基因表达数据中同时枚举癌症和免疫细胞类型

浸润肿瘤的免疫细胞对肿瘤进展和治疗反应具有重要影响。我们提出了一种有效的算法，可以从大量肿瘤基因表达数据中同时估计癌症和免疫细胞类型的比例。我们的方法整合了肿瘤中发现的每种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 含量的再正常化，以及考虑未表征和可能高度可变的细胞类型的能力。通过对人类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。总而言之，我们的工作不仅提高了准确性，而且还扩大了肿瘤基因表达数据中绝对细胞分数预测的范围，