Machine Learning Identifies Stemness Features Associated with Oncogenic Dedifferentiation.
Machine Learning Identifies Stemness Features Associated with Oncogenic Dedifferentiation.
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DOI：
10.1016/j.cell.2018.03.034
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发表时间：
2018-04-05
期刊：
影响因子：
64.5
通讯作者：
Wiznerowicz M
中科院分区：
文献类型：
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作者：
Malta TM;Sokolov A;Gentles AJ;Burzykowski T;Poisson L;Weinstein JN;Kamińska B;Huelsken J;Omberg L;Gevaert O;Colaprico A;Czerwińska P;Mazurek S;Mishra L;Heyn H;Krasnitz A;Godwin AK;Lazar AJ;Cancer Genome Atlas Research Network;Stuart JM;Hoadley KA;Laird PW;Noushmehr H;Wiznerowicz M
癌症进展涉及分化表型的逐渐丧失以及祖细胞和干细胞样特征的获得。在这里，我们提供了新的干性指标来评估致癌去分化的程度。我们使用了一种创新的一类逻辑回归机器学习算法（OSTO）来提取来自非转化多能干细胞及其分化后代的转录组和表观遗传特征集。使用Oscillation，我们能够确定以前未发现的与去分化致癌状态相关的生物学机制。对肿瘤微环境的分析揭示了癌症干性与免疫检查点表达和浸润免疫系统细胞的意外相关性。我们发现，去分化的致癌表型通常在转移性肿瘤中最为突出。将我们的干性指数应用于单细胞数据揭示了肿瘤内分子异质性的模式。最后，这些指数允许识别新的靶点和可能的靶向治疗，旨在肿瘤分化。从TCGA肿瘤的转录组学和表观遗传学数据中提取的干性特征揭示了抗癌疗法的新药物靶点
Cancer progression involves the gradual loss of a differentiated phenotype and acquisition of progenitor and stem cell-like features. Here, we provide novel stemness indices for assessing the degree of oncogenic dedifferentiation. We used an innovative one-class logistic regression machine learning algorithm (OCLR) to extract transcriptomic and epigenetic feature sets derived from non-transformed pluripotent stem cells and their differentiated progeny. Using OCLR, we were able to identify previously undiscovered biological mechanisms associated with the dedifferentiated oncogenic state. Analyses of the tumor microenvironment revealed unanticipated correlation of cancer stemness with immune checkpoint expression and infiltrating immune system cells. We found that the dedifferentiated oncogenic phenotype was generally most prominent in metastatic tumors. Application of our stemness indices to single cell data revealed patterns of intra-tumor molecular heterogeneity. Finally, the indices allowed for the identification of novel targets and possible targeted therapies aimed at tumor differentiation. Stemness features extracted from transcriptomic and epigenetic data from TCGA tumors reveals new drug targets for anti-cancer therapies
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影响因子：
14.9
作者：
Colaprico A;Silva TC;Olsen C;Garofano L;Cava C;Garolini D;Sabedot TS;Malta TM;Pagnotta SM;Castiglioni I;Ceccarelli M;Bontempi G;Noushmehr H
通讯作者：
Noushmehr H
影响因子：
120.7
作者：
Gentles, Andrew J.;Plevritis, Sylvia K.;Majeti, Ravindra;Alizadeh, Ash A.
通讯作者：
Alizadeh, Ash A.
影响因子：
50.3
作者：
Dolma S;Selvadurai HJ;Lan X;Lee L;Kushida M;Voisin V;Whetstone H;So M;Aviv T;Park N;Zhu X;Xu C;Head R;Rowland KJ;Bernstein M;Clarke ID;Bader G;Harrington L;Brumell JH;Tyers M;Dirks PB
通讯作者：
Dirks PB
影响因子：
82.9
作者：
Eppert, Kolja;Takenaka, Katsuto;Dick, John E.
通讯作者：
Dick, John E.
头颈鳞状细胞癌的免疫治疗。
影响因子：
--
作者：
Fuereder T
通讯作者：
Fuereder T