Peripheral blood immune cell dynamics reflect antitumor immune responses and predict clinical response to immunotherapy.
Peripheral blood immune cell dynamics reflect antitumor immune responses and predict clinical response to immunotherapy.
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DOI：
10.1136/jitc-2022-004688
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发表时间：
2022-06
影响因子：
10.9
通讯作者：
中科院分区：
文献类型：
--
作者：
尽管免疫治疗取得了进展，但我们对反应的理解依赖于基于组织的静态肿瘤特征，如肿瘤突变负荷（TMB）和程序性死亡配体1（PD-L1）表达。这些方法在捕获免疫检查点阻断的选择性压力下肿瘤-免疫系统相互作用的可塑性以及预测治疗反应和长期结果方面受到限制。在这里，我们研究了在免疫检查点阻断期间不断发展的肿瘤生态系统的背景下，外周血细胞计数的系列评估与肿瘤负荷动态之间的关系。利用机器学习，我们整合了239例转移性非小细胞肺癌（NSCLC）患者外周血免疫细胞亚群的动态，包括嗜中性粒细胞-淋巴细胞比率（NLR），并预测了免疫检查点阻断的临床结果。然后，我们试图解释26例接受新辅助免疫检查点阻断的早期NSCLC患者的转录组学和T细胞库轨迹背景下的NLR动态。我们进一步确定了NLR动力学、病理反应和循环肿瘤DNA（ctDNA）清除率之间的关系。外周血细胞计数的综合动力学，主要是NLR动力学和嗜酸性粒细胞水平的变化，预测临床结果，优于TMB和PD-L1表达。由于NLR的早期变化是反应的关键预测因子，因此我们将NLR动力学与连续RNA测序去卷积和T细胞受体测序相关联，以研究外周NLR减少患者治疗期间的差异肿瘤微环境重塑。NLR的减少与干扰素-γ应答的诱导相关，所述干扰素-γ应答驱动抗原呈递和促炎基因集的表达，以及肿瘤内T细胞库的重塑。此外，NLR动力学反映了通过病理学反应评估的肿瘤消退，并补充了预测长期结局的ctDNA动力学。在转移性和早期队列中，免疫检查点阻断期间外周嗜酸性粒细胞水平升高与治疗应答相关。我们的研究结果表明，外周血免疫细胞亚群的早期动态反映了肿瘤微环境的变化，并捕获了抗肿瘤免疫应答，最终反映了免疫检查点阻断的临床结果。
Despite treatment advancements with immunotherapy, our understanding of response relies on tissue-based, static tumor features such as tumor mutation burden (TMB) and programmed death-ligand 1 (PD-L1) expression. These approaches are limited in capturing the plasticity of tumor–immune system interactions under selective pressure of immune checkpoint blockade and predicting therapeutic response and long-term outcomes. Here, we investigate the relationship between serial assessment of peripheral blood cell counts and tumor burden dynamics in the context of an evolving tumor ecosystem during immune checkpoint blockade. Using machine learning, we integrated dynamics in peripheral blood immune cell subsets, including neutrophil-lymphocyte ratio (NLR), from 239 patients with metastatic non-small cell lung cancer (NSCLC) and predicted clinical outcome with immune checkpoint blockade. We then sought to interpret NLR dynamics in the context of transcriptomic and T cell repertoire trajectories for 26 patients with early stage NSCLC who received neoadjuvant immune checkpoint blockade. We further determined the relationship between NLR dynamics, pathologic response and circulating tumor DNA (ctDNA) clearance. Integrated dynamics of peripheral blood cell counts, predominantly NLR dynamics and changes in eosinophil levels, predicted clinical outcome, outperforming both TMB and PD-L1 expression. As early changes in NLR were a key predictor of response, we linked NLR dynamics with serial RNA sequencing deconvolution and T cell receptor sequencing to investigate differential tumor microenvironment reshaping during therapy for patients with reduction in peripheral NLR. Reductions in NLR were associated with induction of interferon-γ responses driving the expression of antigen presentation and proinflammatory gene sets coupled with reshaping of the intratumoral T cell repertoire. In addition, NLR dynamics reflected tumor regression assessed by pathological responses and complemented ctDNA kinetics in predicting long-term outcome. Elevated peripheral eosinophil levels during immune checkpoint blockade were correlated with therapeutic response in both metastatic and early stage cohorts. Our findings suggest that early dynamics in peripheral blood immune cell subsets reflect changes in the tumor microenvironment and capture antitumor immune responses, ultimately reflecting clinical outcomes with immune checkpoint blockade.
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影响因子：
64.5
作者：
Litchfield K;Reading JL;Puttick C;Thakkar K;Abbosh C;Bentham R;Watkins TBK;Rosenthal R;Biswas D;Rowan A;Lim E;Al Bakir M;Turati V;Guerra-Assunção JA;Conde L;Furness AJS;Saini SK;Hadrup SR;Herrero J;Lee SH;Van Loo P;Enver T;Larkin J;Hellmann MD;Turajlic S;Quezada SA;McGranahan N;Swanton C
通讯作者：
Swanton C
影响因子：
64.8
作者：
Helmink BA;Reddy SM;Gao J;Zhang S;Basar R;Thakur R;Yizhak K;Sade-Feldman M;Blando J;Han G;Gopalakrishnan V;Xi Y;Zhao H;Amaria RN;Tawbi HA;Cogdill AP;Liu W;LeBleu VS;Kugeratski FG;Patel S;Davies MA;Hwu P;Lee JE;Gershenwald JE;Lucci A;Arora R;Woodman S;Keung EZ;Gaudreau PO;Reuben A;Spencer CN;Burton EM;Haydu LE;Lazar AJ;Zapassodi R;Hudgens CW;Ledesma DA;Ong S;Bailey M;Warren S;Rao D;Krijgsman O;Rozeman EA;Peeper D;Blank CU;Schumacher TN;Butterfield LH;Zelazowska MA;McBride KM;Kalluri R;Allison J;Petitprez F;Fridman WH;Sautès-Fridman C;Hacohen N;Rezvani K;Sharma P;Tetzlaff MT;Wang L;Wargo JA
通讯作者：
Wargo JA
影响因子：
28.2
作者：
Chen PL;Roh W;Reuben A;Cooper ZA;Spencer CN;Prieto PA;Miller JP;Bassett RL;Gopalakrishnan V;Wani K;De Macedo MP;Austin-Breneman JL;Jiang H;Chang Q;Reddy SM;Chen WS;Tetzlaff MT;Broaddus RJ;Davies MA;Gershenwald JE;Haydu L;Lazar AJ;Patel SP;Hwu P;Hwu WJ;Diab A;Glitza IC;Woodman SE;Vence LM;Wistuba II;Amaria RN;Kwong LN;Prieto V;Davis RE;Ma W;Overwijk WW;Sharpe AH;Hu J;Futreal PA;Blando J;Sharma P;Allison JP;Chin L;Wargo JA
通讯作者：
Wargo JA
影响因子：
50.3
作者：
Li H;Xiao Y;Li Q;Yao J;Yuan X;Zhang Y;Yin X;Saito Y;Fan H;Li P;Kuo WL;Halpin A;Gibbons DL;Yagita H;Zhao Z;Pang D;Ren G;Yee C;Lee JJ;Yu D
通讯作者：
Yu D
影响因子：
8.4
作者：
Ameratunga, Malaka;Chenard-Poirier, Maxime;Lopez, Juanita
通讯作者：
Lopez, Juanita