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Stem cell origin of death-from-cancer phenotypes of human prostate and breast cancers.

机译:人类前列腺癌和乳腺癌的癌症死亡表型的干细胞起源。

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In clinical terms, all human cancers diagnosed in individuals can be divided in two major categories: malignant tumors that will be cured with the existing cancer therapies and tumors that have therapy-resistant phenotypes and will return after initial treatment as incurable metastatic disease. These tumors manifesting clinically lethal death-from-cancer phenotypes represent the most formidable challenge of experimental, translational, and clinical cancer research. Clinical genomics data demonstrate that gene expression signatures associated with the "stemness" state of a cell are informative as molecular predictors of cancer therapy outcome and can help to identify cancer patients with therapy-resistant tumors. Here, we present experimental and clinical evidence in support of the BMI1 pathway rule indicating a genetic link between the stemness state and therapy-resistant death-from-cancer phenotypes. Our analysis demonstrates that therapy-resistant and therapy-responsive cancer phenotypes manifest distinct patterns of association with stemness/differentiation pathways, suggesting that therapy-resistant and therapy-responsive tumors develop within genetically distinct stemness/differentiation programs. These differences can be exploited for development of prognostic and therapy selection genetic tests utilizing a microarray-based cancer therapy outcome predictor algorithm. One of the major regulatory pathways manifesting distinct patterns of association with therapy-resistant and therapy-responsive cancer phenotypes is the Polycomb group proteins chromatin silencing pathway.
机译:从临床上讲,所有在个体中诊断出的人类癌症可以分为两大类:可以用现有的癌症疗法治愈的恶性肿瘤和具有治疗耐药性表型的恶性肿瘤,并且在初始治疗后会作为不可治愈的转移性疾病复发。这些表现出临床上致命的死于癌症的表型的肿瘤代表了实验,转化和临床癌症研究的最艰巨挑战。临床基因组学数据表明,与细胞“干性”状态相关的基因表达特征可作为癌症治疗结果的分子预测因子,可提供有用信息,并有助于鉴定具有治疗耐药性肿瘤的癌症患者。在这里,我们目前的实验和临床证据支持BMI1通路规则,表明干性状态与治疗耐药的癌症死亡表型之间存在遗传联系。我们的分析表明,对治疗有抗性和对治疗有反应的癌症表型表现出与干性/分化途径相关的不同模式,这表明在具有遗传学意义的干性/分化程序中发展了对治疗有抗性和治疗反应性的肿瘤。利用基于微阵列的癌症治疗结果预测因子算法,可以利用这些差异来开发预后和治疗选择基因测试。表现出与治疗抗性和治疗响应性癌症表型相关的独特模式的主要调控途径之一是Polycomb组蛋白染色质沉默途径。

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