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　　腫瘤微環(huán)境是腫瘤局部浸潤的免疫細胞、間質(zhì)細胞及其分泌的活性介質(zhì)等與腫瘤細胞共同構成的局部內(nèi)環(huán)境。掌握腫瘤微環(huán)境的免疫細胞表型，對于明確癌癥進展機制和免疫治療效果必不可少。

　　2018年6月28日，全球自然科學三大旗艦期刊之一、愛思唯爾旗下《細胞》正刊在線發(fā)表紐約紀念醫(yī)院斯隆凱特林癌癥中心、紐約哥倫比亞大學、立陶宛維爾紐斯大學、美國勃林格殷格翰的研究報告，通過對乳腺腫瘤免疫微環(huán)境的單細胞分析和計算機分析，繪出乳腺癌T淋巴細胞活化和分化情況的免疫分布圖。

　　該研究通過單細胞核糖核酸（RNA）測序，分析了4.5萬個免疫細胞，這些細胞來自8例乳腺癌以及經(jīng)過匹配的正常乳腺組織、血液和淋巴結。該研究開發(fā)了一系列預處理方法、測序質(zhì)量控制（SEQC）、貝葉斯聚類和單細胞克隆歸一化法（BISCUIT），以解決單細胞數(shù)據(jù)固有的計算難題。雖然正常與腫瘤組織的定居免疫細胞非常相似，但是該研究發(fā)現(xiàn)腫瘤微環(huán)境相關連續(xù)表型擴增。通過對2.7萬個其他T細胞的配對單細胞RNA和T細胞受體（TCR）測序數(shù)據(jù)進行分析，表明TCR利用率對不同表型具有復合影響。

　　該研究結果表明T細胞連續(xù)活化分布模式并不符合癌癥巨噬細胞極化分布模式。該研究結果對于明確腫瘤浸潤免疫細胞特征具有重要意義。

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Cell. 2018 Jun 28. [Epub ahead of print]

Single-Cell Map of Diverse Immune Phenotypes in the Breast Tumor Microenvironment.

Elham Azizi, Ambrose J. Carr, George Plitas, Andrew E. Cornish, Catherine Konopacki, Sandhya Prabhakaran, Juozas Nainys, Kenmin Wu, Vaidotas Kiseliovas, Manu Setty, Kristy Choi, Rachel M. Fromme, Phuong Dao, Peter T. McKenney, Ruby C. Wasti, Krishna Kadaveru, Linas Mazutis, Alexander Y. Rudensky, Dana Pe'er.

Memorial Sloan Kettering Cancer Center, New York, NY, USA; Columbia University, New York, NY, USA; Vilnius University, Vilnius, Lithuania; Boehringer Ingelheim Pharmaceuticals, Inc., Ridgefield, CT, USA.

HIGHLIGHTS

• Single-cell RNA-seq reveals phenotypic expansion of intratumoral immune cells

• Biscuit identifies cell populations that differ in co-expression patterns

• T cells reside on continuous activation and differentiation trajectories

• Combinatorial environmental inputs and TCR usage shape T cell phenotypes

Single-cell analysis of the breast tumor immune microenvironment, coupled with computational analysis, yields an immune map of breast cancer that points to continuous T cell activation and differentiation states.

Knowledge of immune cell phenotypes in the tumor microenvironment is essential for understanding mechanisms of cancer progression and immunotherapy response. We profiled 45,000 immune cells from eight breast carcinomas, as well as matched normal breast tissue, blood, and lymph nodes, using single-cell RNA-seq. We developed a preprocessing pipeline, SEQC, and a Bayesian clustering and normalization method, Biscuit, to address computational challenges inherent to single-cell data. Despite significant similarity between normal and tumor tissue-resident immune cells, we observed continuous phenotypic expansions specific to the tumor microenvironment. Analysis of paired single-cell RNA and T cell receptor (TCR) sequencing data from 27,000 additional T cells revealed the combinatorial impact of TCR utilization on phenotypic diversity. Our results support a model of continuous activation in T cells and do not comport with the macrophage polarization model in cancer. Our results have important implications for characterizing tumor-infiltrating immune cells.

KEYWORDS: single-cell RNA-seq, tumor microenvironment, tumor-infiltrating immune cells, breast cancer, T cell activation, TCR utilization, Bayesian modeling

DOI: 10.1016/j.cell.2018.05.060