Temporal single-cell tracing reveals clonal revival and expansion of precursor exhausted T cells during anti-PD-1 therapy in lung cancer

简介

肿瘤浸润T细胞既包含能够识别肿瘤抗原并杀伤癌细胞的肿瘤特异性T细胞，也包含专门识别非肿瘤抗原例如流感病毒的T细胞，而且在肿瘤中非肿瘤特异性T细胞占了很大一部分比例[1]。因此在分析过程中如何排除非肿瘤特异性T细胞的潜在影响，精准地研究肿瘤特异性T细胞的动态变化是一个挑战。之前的多个研究表明，由于肿瘤抗原的持续刺激，肿瘤中的肿瘤特异性CD8 T细胞克隆会同时高表达T细胞杀伤和“耗竭”相关基因，而非肿瘤特异性CD8 T细胞则不会表达“耗竭”相关基因[2-4]。因此在肿瘤中，耗竭CD8 T细胞可以当作肿瘤特异T细胞的一个替代[5]

研究人员开发了一套新的分析思路，首先通过聚类分析鉴定了耗竭CD8 T细胞类群，进而以耗竭CD8 T细胞克隆的TCR序列为基础，将所有的CD8 T细胞分为肿瘤特异性CD8 T细胞和非肿瘤特异性CD8 T细胞：根据上面提到的结论和假设，与耗竭CD8 T细胞有完全相同TCR序列的细胞为肿瘤特异性T细胞，剩下的为非肿瘤特异性T细胞。研究发现在有响应的肿瘤当中，治疗显著提高了耗竭信号低的肿瘤特异T细胞前体细胞的比例，表明了PD-1抗体可能阻断了肿瘤特异T细胞向耗竭状态的分化。相反，这一趋势在治疗前和治疗后无响应的肿瘤中并没有观察到。

有效治疗后肿瘤特异T细胞前体细胞的增加有三种可能：1. 耗竭T细胞的逆转；2. 肿瘤中之前存在的前体细胞的扩增；3. 来自肿瘤外例如外周血中的T细胞的补充。研究者通过相关分析排除了第一种可能，强调了后面两种模式的重要性。耗竭T细胞的逆转是领域内长期存在的一种假设，但是之前的小鼠研究表明耗竭T细胞的表观修饰和特征是稳定的，很难改变[6]。研究者对人类肿瘤的分析进一步支持了这一观点。

此外，之前斯坦福大学Howard Chang研究组提出了克隆替代（clonal replacement）的概念，认为治疗后肿瘤中的肿瘤特异T细胞的克隆型都是新出现的[7]。而该研究发现，在肺癌治疗的过程中，新的克隆和之前存在的克隆都会被招募到肿瘤中进而发挥功能。针对这一现象，研究人员提出了克隆复兴（clonal revival）的概念，拓展了clonal replacement的模式。

方法要点

1. To remove batch effects between patients we performed the BBKNN pipeline[8] to obtain the batch-corrected space, and further used the Leiden clustering approach implemented in scanpy to identify each cell cluster.
2. Clusters of T and natural killer (NK) cells were identified by unsupervised clustering following the strategy described above, characterized by high expression of CD3D, CD3E and NKG7. A second round of unsupervised clustering was further performed to identify NK, CD4+ and CD8+ T cells based on the expression of signature genes including CD8A, CD8B, CD4, IL7R and NKG7。（细胞注释不是一步完成，而是several rounds)
3. scTCR-seq data processing:The TCR sequence data from 10X Genomics were processed using Cell Ranger software (v.3.1) with the manufacturer-supplied human VDJ reference genome. For each sample, the output file filtered_contig_annotations.csv, containing TCR α- and β-chain CDR3 nucleotide sequences, was used for downstream analysis. Only those assembled chains that were productive, highly confident, full length, with a valid cell barcode and an unambiguous chain type (for example, alpha) assignment were retained. If a cell had two or more qualified chains of the same type, only that chain with the highest UMI count was qualified and retained. For each patient, cells with an identical α/β-chain pair were considered as having originated from the same clonotype and were therefore identified as clonal cells.
4. Bulk TCR-seq data processing and analysis:To identify the expansion patterns of Tex clonotypes in peripheral blood, we performed bulk TCR-seq using pre- and post-treatment blood samples from P010, P013, P019 and P030.Sequences were processed and analyzed using the MiXCR method. Bulk TCR-seq clonotypes were linked to intratumoral Tex clonotypes by matching the TCR β-chain to any TCR β-chain from a clonotype in the scTCR-seq data.
5. 他们做proportion analysis的时候也不是仅仅只用了t-test，而是包括the Dirichlet-multinomial regression model, Fisher’s exact test and t-test.
6. RNA velocity: scVelo, dynamical model
7. 对人类样本耗竭T细胞的认定：CXCL13 has been shown to be exclusively expressed by terminal Tex cells in treatment-naïve tumors[9-11] and its expression has now also been observed in post-treatment Texp but not in Tex-irrelevant cells。We included only high-confidence Tex clones in subsequent analysis, with two additional filters: (1) clone size at least five cells and (2) cells from a certain clone expressing CXCL13—that is, normalized average CXCL13 expression of a certain clone >0.1. The identities of Tex cells from the remaining samples were determined by supervised cell-type classification using SciBet[12]
8. Metacell analysis[13]
9. Because our calculation of exhaustion score was based on metacells, which addresses the potential bias introduced by dropout events, we used only four well-defined exhaustion markers (HAVCR2, ENTPD1, LAYN and LAG3) to improve the accuracy of terminal Tex cell identification. Exhaustion score was defined as the sum of the expression of these four genes. In addition, proliferation score was defined as the sum of the expression of STMN1 and TUBB (top two highly expressed genes in proliferative cells).
   10.An important question here is whether the new Tex clonotypes detected by scTCR-seq were really absent before treatment or were missed in the first biopsy. Here we considered bulk TCR-seq data(PB) as ground truth.We observed that the false-positive rate of new Tex clonotypes could be reduced to 17% by filtering out those clonotypes detected in pretreatment blood。如果要看T细胞亚克隆是否真的是新增的，很重要的一点就是和外周血bulk TCR-seq 的结果进行比较。

文章知识要点：

1. 4-1BB (TNFRSF9), a known Treg activation marker；with immunosuppressive functions (IL1R2, REL and LAYN)
2. 对CD4+ T和CD8+ T分开分析
3. CD8+ T-cell infiltration into tumors could predict survival [14,15] and response to immunotherapy[16]
4. Emerging evidence demonstrates that terminal Tex cells in tumors are specifically derived from tumor-specific Tcells, whereas Tcells responsible for acute infections do not give rise to Tex cells. Thus, a terminal Tex subset could be used as a proxy for a tumor-reactive T-cell compartment. To further support this notion, we analyzed previously reported signature genes of tumor antigen-specific T cells—ENTPD1 and ITGAE—and found that they were specifically expressed in the terminal Tex subset

5. Terminal Tex cells were characterized by high expression of exhaustion signatures, including PDCD1, CTLA4 and HAVCR2

   
   

6. 如何找到Tex precursor?常规方法：细胞聚类后找与终末耗竭T有最多相同TCR序列的细胞亚群。问题：such clusters may contain TCRs independently connected with terminal Tex and blood effector cells, implying that non-exhausted tumor-reactive T cells and potential bystander cells may fall into the same cluster due to similar transcriptional phenotypes.所以直接基于TCR寻找Texp更为可靠

   
   
7. Co-expression of CD39 and CD103 identifies tumor-reactive CD8 T cells in human solid tumors[17]

8. 发现本文章中RNA velocity的图并不是放的整合的图，而是放的一个人的一部分细胞。应该提取目的细胞，在diffusion map上面embed velocity stream。这真是一种很好的方法！

   
   RNA velocities visualized on diffusion map of cells from the largest Tex clone from pre- and post-treatment P019 tumor biopsies, color coded by cluster.
9. Previous mouse studies have defined detailed developmental frameworks for Tex cells[18-20]。 Stem, transitory and terminal Tex cells（human):
   
10. Together, using a TCR-based selection approach, we showed that GZMK+NR4A2–, GZMK+NR4A2+, terminal and proliferative Tex cells are four transcriptionally distinct Tex populations present in human lung tumors. For clarity, GZMK+NR4A2–/+ subsets are referred to here as Texp1 (GZMK+NR4A2–) and Texp2 (GZMK+NR4A2+) cells.（辅助：SciBet（好吧，就是他们自己团队开发的自动注释包）,MetaCell---Metacell-based in silico FACS
    
11. CXCL13 is upregulated only when cells are in the tumor microenvironment
12. STARTRAC,之后应该会看到这篇文献

本文章用到的方法：

1. scRNA-seq
2. scTCR-seq
3. bulk TCR-seq
4. IHC--验证

参考文献：
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