Integrative Tumor and Immune Cell Multi-omic Analyses Predict Response to Immune Checkpoint Blockade in Melanoma.
Integrative Tumor and Immune Cell Multi-omic Analyses Predict Response to Immune Checkpoint Blockade in Melanoma.
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DOI：
10.1016/j.xcrm.2020.100139
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发表时间：
2020-11-17
期刊：
影响因子：
--
通讯作者：
Velculescu VE
中科院分区：
文献类型：
--
作者：
Anagnostou V;Bruhm DC;Niknafs N;White JR;Shao XM;Sidhom JW;Stein J;Tsai HL;Wang H;Belcaid Z;Murray J;Balan A;Ferreira L;Ross-Macdonald P;Wind-Rotolo M;Baras AS;Taube J;Karchin R;Scharpf RB;Grasso C;Ribas A;Pardoll DM;Topalian SL;Velculescu VE
在这项研究中，我们将全基因组序列和结构变化的分析与免疫治疗前和治疗中的转录和T细胞谱系特征结合在一起，这些患者接受了免疫检查点阻断治疗。虽然肿瘤突变负担与改善的治疗反应有关，但表达基因的突变频率在预测结果方面更具优势。基线肿瘤中T细胞密度的增加以及治疗期间T细胞谱系退缩或扩大的动态变化将有效与无效区分开来。转录组分析显示，来自应答者的肿瘤中B细胞亚群增加，以及与表达的突变消除或保留相关的分子反应模式，反映了临床结果。高维基因组、转录和免疫谱系数据被整合到一个多模式反应预测因子中。这些发现确定了预测免疫检查点阻断反应的肿瘤和免疫细胞的基因组和转录学特征，并强调了先前存在的T和B细胞免疫在治疗结果中的重要性。对解释免疫治疗反应的集成分子模型的需求尚未得到满足通过机器学习来整合基因组、转录和T和B细胞序列数据T细胞动力学是对免疫检查点阻断的反应的标志B、T和肿瘤细胞特征的联合作用预测临床结果Anagnostou等人。整合基因组、表达和免疫细胞谱系分析，以深入了解黑色素瘤免疫治疗期间癌症和免疫细胞之间的相互作用。这些发现表明，肿瘤免疫浸润物的复杂表型与肿瘤细胞的基因组特征相结合对决定临床结果是相关的。
In this study, we incorporate analyses of genome-wide sequence and structural alterations with pre- and on-therapy transcriptomic and T cell repertoire features in immunotherapy-naive melanoma patients treated with immune checkpoint blockade. Although tumor mutation burden is associated with improved treatment response, the mutation frequency in expressed genes is superior in predicting outcome. Increased T cell density in baseline tumors and dynamic changes in regression or expansion of the T cell repertoire during therapy distinguish responders from non-responders. Transcriptome analyses reveal an increased abundance of B cell subsets in tumors from responders and patterns of molecular response related to expressed mutation elimination or retention that reflect clinical outcome. High-dimensional genomic, transcriptomic, and immune repertoire data were integrated into a multi-modal predictor of response. These findings identify genomic and transcriptomic characteristics of tumors and immune cells that predict response to immune checkpoint blockade and highlight the importance of pre-existing T and B cell immunity in therapeutic outcomes. Unmet need for integrated molecular models that interpret immunotherapy response Genomic, transcriptomic, and T and B cell sequence data integration by machine learning T cell dynamism is a hallmark of response to immune checkpoint blockade The combined contributions of B, T, and tumor cell features predict clinical outcome Anagnostou et al. integrate genomic, expression, and immune cell repertoire analyses to gain insights into the crosstalk between cancer and immune cells during immunotherapy for melanoma. These findings suggest that the complex phenotype of tumor immune infiltrates combined with genomic features of tumor cells are relevant for determining clinical outcomes.
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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：
28.2
作者：
Grasso CS;Giannakis M;Wells DK;Hamada T;Mu XJ;Quist M;Nowak JA;Nishihara R;Qian ZR;Inamura K;Morikawa T;Nosho K;Abril-Rodriguez G;Connolly C;Escuin-Ordinas H;Geybels MS;Grady WM;Hsu L;Hu-Lieskovan S;Huyghe JR;Kim YJ;Krystofinski P;Leiserson MDM;Montoya DJ;Nadel BB;Pellegrini M;Pritchard CC;Puig-Saus C;Quist EH;Raphael BJ;Salipante SJ;Shin DS;Shinbrot E;Shirts B;Shukla S;Stanford JL;Sun W;Tsoi J;Upfill-Brown A;Wheeler DA;Wu CJ;Yu M;Zaidi SH;Zaretsky JM;Gabriel SB;Lander ES;Garraway LA;Hudson TJ;Fuchs CS;Ribas A;Ogino S;Peters U
通讯作者：
Peters U
影响因子：
64.8
作者：
Cabrita, Rita;Lauss, Martin;Jonsson, Goran
通讯作者：
Jonsson, Goran
影响因子：
82.9
作者：
Auslander N;Zhang G;Lee JS;Frederick DT;Miao B;Moll T;Tian T;Wei Z;Madan S;Sullivan RJ;Boland G;Flaherty K;Herlyn M;Ruppin E
通讯作者：
Ruppin E