Simultaneous enumeration of cancer and immune cell types from tumor gene expression data

Cancers are composed of various cell types. We present an efficient algorithm to simultaneously Estimate the Proportion of Immune and Cancer cells (EPIC) 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. RNA-seq from lymph node bulk samples from 4 melanoma patients.

肿瘤由多种细胞类型构成。我们提出了一种可从实体瘤批量基因表达数据中同时估算免疫细胞与癌细胞占比的高效算法（EPIC，Estimate the Proportion of Immune and Cancer cells）。该方法整合了肿瘤中各主要非恶性细胞类型的新型基因表达谱、基于细胞类型特异性mRNA含量的重归一化策略，以及可纳入未表征且可能高度可变细胞类型的分析能力。本方法的可行性已通过对人类黑色素瘤与结直肠癌样本开展流式细胞术、免疫组化及单细胞RNA测序分析得到验证。综上，本研究不仅提升了基于肿瘤基因表达数据的绝对细胞占比预测精度，还拓展了其应用范围，同时为癌症研究中的免疫基因组学分析提供了全新的独特实验基准。本数据集包含4名黑色素瘤患者的淋巴结批量样本的RNA测序数据。