Spatial subsetting enables integrative modeling of oral squamous cell carcinoma multiplex imaging data.
Spatial subsetting enables integrative modeling of oral squamous cell carcinoma multiplex imaging data.
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空间子集划分使口腔鳞状细胞癌多重复合成像数据的综合建模成为可能。
DOI：
10.1016/j.isci.2023.108486
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发表时间：
2023-12-15
期刊：
影响因子：
5.8
通讯作者：
Han, Xiaoyuan
中科院分区：
文献类型：
--
作者：
Einhaus, Jakob;Gaudilliere, Dyani K.;Hedou, Julien;Feyaerts, Dorien;Ozawa, Michael G.;Sato, Masaki;Ganio, Edward A.;Tsai, Amy S.;Stelzer, Ina A.;Bruckman, Karl C.;Amar, Jonas N.;Sabayev, Maximilian;Bonham, Thomas A.;Gillard, Joshua;Diop, Maigane;Cambriel, Amelie;Mihalic, Zala N.;Valdez, Tulio;Liu, Stanley Y.;Feirrera, Leticia;Lam, David K.;Sunwoo, John B.;Schuerch, Christian M.;Gaudilliere, Brice;Han, Xiaoyuan
口腔鳞状细胞癌（OSCC）是一种常见且侵袭性强的肿瘤，由于预后不良以及预后生物标志物有限，带来了重大挑战。利用高度多重成像质谱流式细胞技术，我们研究了口腔鳞状细胞癌活检组织中的肿瘤免疫微环境（TIME），对肿瘤浸润前沿的免疫细胞分布和信号活动进行了表征。我们的空间细分方法根据组织区域对细胞群进行标准化，提高了特征的可重复性，并揭示了伴随分化缺失的肿瘤免疫微环境模式。采用将可靠的特征选择与多变量建模相结合的机器学习流程，我们实现了准确的组织学分级分类（曲线下面积 = 0.88）。三个模型特征与一个独立队列的临床结果相关：肿瘤前沿的粒细胞MAPKAPK2信号、基质CD4 + 记忆T细胞大小以及成纤维细胞与肿瘤边界的距离。这项研究建立了一个强大的建模框架，用于提取复杂的成像数据，揭示口腔鳞状细胞癌肿瘤免疫微环境的关键特征，以促进用于复发风险分层和免疫调节治疗开发的预后生物标志物的发现。
我们开发了一个用于对成像质谱流式细胞技术（IMC）数据进行综合多变量分析的强大框架
将成像质谱流式细胞技术图像细分为空间组织区域可提高特征的可重复性
口腔鳞状细胞癌不同组织区域之间的细胞类型特异性免疫激活存在差异
口腔鳞状细胞癌组织区域的免疫特征与肿瘤分级和结果相关
免疫学；细胞生物学；癌症；机器学习
Oral squamous cell carcinoma (OSCC), a prevalent and aggressive neoplasm, poses a significant challenge due to poor prognosis and limited prognostic biomarkers. Leveraging highly multiplexed imaging mass cytometry, we investigated the tumor immune microenvironment (TIME) in OSCC biopsies, characterizing immune cell distribution and signaling activity at the tumor-invasive front. Our spatial subsetting approach standardized cellular populations by tissue zone, improving feature reproducibility and revealing TIME patterns accompanying loss-of-differentiation. Employing a machine-learning pipeline combining reliable feature selection with multivariable modeling, we achieved accurate histological grade classification (AUC = 0.88). Three model features correlated with clinical outcomes in an independent cohort: granulocyte MAPKAPK2 signaling at the tumor front, stromal CD4+ memory T cell size, and the distance of fibroblasts from the tumor border. This study establishes a robust modeling framework for distilling complex imaging data, uncovering sentinel characteristics of the OSCC TIME to facilitate prognostic biomarkers discovery for recurrence risk stratification and immunomodulatory therapy development. We developed a robust framework for integrative, multivariable analysis of IMC data Subsetting of IMC images into spatial tissue zones improves feature reproducibility Cell type-specific immune activation differs between tissue zones in OSCC Immune features of OSCC tissue zones are associated with tumor grade and outcomes Immunology; Cell biology; Cancer; Machine learning
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影响因子：
46.9
作者：
Greenwald, Noah F.;Miller, Geneva;Moen, Erick;Kong, Alex;Kagel, Adam;Dougherty, Thomas;Fullaway, Christine Camacho;McIntosh, Brianna J.;Leow, Ke Xuan;Schwartz, Morgan Sarah;Pavelchek, Cole;Cui, Sunny;Camplisson, Isabella;Bar-Tal, Omer;Singh, Jaiveer;Fong, Mara;Chaudhry, Gautam;Abraham, Zion;Moseley, Jackson;Warshawsky, Shiri;Soon, Erin;Greenbaum, Shirley;Risom, Tyler;Hollmann, Travis;Bendall, Sean C.;Keren, Leeat;Graf, William;Angelo, Michael;Van Valen, David
通讯作者：
Van Valen, David
DOI：
10.1126/science.1245075
发表时间：
2015-05-15
期刊：
Science (New York, N.Y.)
影响因子：
--
作者：
Ginzberg MB;Kafri R;Kirschner M
通讯作者：
Kirschner M
影响因子：
48
作者：
Giesen, Charlotte;Wang, Hao A. O.;Bodenmiller, Bernd
通讯作者：
Bodenmiller, Bernd
影响因子：
82.9
作者：
Binnewies M;Roberts EW;Kersten K;Chan V;Fearon DF;Merad M;Coussens LM;Gabrilovich DI;Ostrand-Rosenberg S;Hedrick CC;Vonderheide RH;Pittet MJ;Jain RK;Zou W;Howcroft TK;Woodhouse EC;Weinberg RA;Krummel MF
通讯作者：
Krummel MF
DOI：
10.4103/0973-029x.84485
发表时间：
2011-05
期刊：
Journal of oral and maxillofacial pathology : JOMFP
影响因子：
--
作者：
Akhter M;Hossain S;Rahman QB;Molla MR
通讯作者：
Molla MR