Spatial Organization and Molecular Correlation of Tumor-Infiltrating Lymphocytes Using Deep Learning on Pathology Images.
Spatial Organization and Molecular Correlation of Tumor-Infiltrating Lymphocytes Using Deep Learning on Pathology Images.
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利用深度学习对病理图像进行肿瘤浸润淋巴细胞的空间组织和分子相关性研究
DOI：
10.1016/j.celrep.2018.03.086
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发表时间：
2018-04-03
期刊：
影响因子：
8.8
通讯作者：
Thorsson V
中科院分区：
文献类型：
--
作者：
Saltz J;Gupta R;Hou L;Kurc T;Singh P;Nguyen V;Samaras D;Shroyer KR;Zhao T;Batiste R;Van Arnam J;Cancer Genome Atlas Research Network;Shmulevich I;Rao AUK;Lazar AJ;Sharma A;Thorsson V
除了样本管理和基本病理学表征，TCGA样本的数字化H& E染色图像仍然未得到充分利用。为了突出这一资源，我们提出了基于13种TCGA肿瘤类型的H&E图像的肿瘤浸润淋巴细胞（TIL）的映射。这些TIL图是使用经过训练以对图像块进行分类的卷积神经网络通过计算染色得出的。亲和传播揭示了TIL模式的局部空间结构和与总体生存的相关性。使用标准组织病理学参数对TIL图谱结构模式进行分组。这些模式在源自分子测量的特定T细胞亚群中富集。TIL密度和空间结构在肿瘤类型、免疫亚型和肿瘤分子亚型之间差异富集，这意味着空间浸润状态可以反映特定的肿瘤细胞畸变状态。获得与TCGA样品的丰富基因组表征相关的空间淋巴细胞模式证明了TCGA图像档案的一种用途，其具有对肿瘤免疫微环境的洞察。通过Saltz等人开发的深度学习衍生的“计算染色”，从标准病理学癌症图像中识别出肿瘤浸润淋巴细胞（TIL）。他们处理了来自13种癌症类型的5，202张数字图像。所得到的TIL图谱与TCGA分子数据相关，将TIL含量与存活率、肿瘤亚型和免疫谱相关。
Beyond sample curation and basic pathologic characterization, the digitized H&E-stained images of TCGA samples remain underutilized. To highlight this resource, we present mappings of tumor-infiltrating lymphocytes (TILs) based on H&E images from 13 TCGA tumor types. These TIL maps are derived through computational staining using a convolutional neural network trained to classify patches of images. Affinity propagation revealed local spatial structure in TIL patterns and correlation with overall survival. TIL map structural patterns were grouped using standard histopathological parameters. These patterns are enriched in particular T cell subpopulations derived from molecular measures. TIL densities and spatial structure were differentially enriched among tumor types, immune subtypes, and tumor molecular subtypes, implying that spatial infiltrate state could reflect particular tumor cell aberration states. Obtaining spatial lymphocytic patterns linked to the rich genomic characterization of TCGA samples demonstrates one use for the TCGA image archives with insights into the tumor-immune microenvironment. Tumor-infiltrating lymphocytes (TILs) were identified from standard pathology cancer images by a deep-learning-derived “computational stain” developed by Saltz et al. They processed 5,202 digital images from 13 cancer types. Resulting TIL maps were correlated with TCGA molecular data, relating TIL content to survival, tumor subtypes, and immune profiles.
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影响因子：
6.7
作者：
Hendry S;Salgado R;Gevaert T;Russell PA;John T;Thapa B;Christie M;van de Vijver K;Estrada MV;Gonzalez-Ericsson PI;Sanders M;Solomon B;Solinas C;Van den Eynden GGGM;Allory Y;Preusser M;Hainfellner J;Pruneri G;Vingiani A;Demaria S;Symmans F;Nuciforo P;Comerma L;Thompson EA;Lakhani S;Kim SR;Schnitt S;Colpaert C;Sotiriou C;Scherer SJ;Ignatiadis M;Badve S;Pierce RH;Viale G;Sirtaine N;Penault-Llorca F;Sugie T;Fineberg S;Paik S;Srinivasan A;Richardson A;Wang Y;Chmielik E;Brock J;Johnson DB;Balko J;Wienert S;Bossuyt V;Michiels S;Ternes N;Burchardi N;Luen SJ;Savas P;Klauschen F;Watson PH;Nelson BH;Criscitiello C;O'Toole S;Larsimont D;de Wind R;Curigliano G;André F;Lacroix-Triki M;van de Vijver M;Rojo F;Floris G;Bedri S;Sparano J;Rimm D;Nielsen T;Kos Z;Hewitt S;Singh B;Farshid G;Loibl S;Allison KH;Tung N;Adams S;Willard-Gallo K;Horlings HM;Gandhi L;Moreira A;Hirsch F;Dieci MV;Urbanowicz M;Brcic I;Korski K;Gaire F;Koeppen H;Lo A;Giltnane J;Rebelatto MC;Steele KE;Zha J;Emancipator K;Juco JW;Denkert C;Reis-Filho J;Loi S;Fox SB
通讯作者：
Fox SB
影响因子：
5.8
作者：
Bodenhofer, Ulrich;Kothmeier, Andreas;Hochreiter, Sepp
通讯作者：
Hochreiter, Sepp
影响因子：
64.8
作者：
Bailey, Peter;Chang, David K.;Grimmond, Sean M.
通讯作者：
Grimmond, Sean M.
影响因子：
1.9
作者：
BANFIELD, JD;RAFTERY, AE
通讯作者：
RAFTERY, AE
影响因子：
64.5
作者：
Cancer Genome Atlas Network
通讯作者：
Cancer Genome Atlas Network