Systematic evaluation of the prognostic impact and intratumour heterogeneity of clear cell renal cell carcinoma biomarkers.
Systematic evaluation of the prognostic impact and intratumour heterogeneity of clear cell renal cell carcinoma biomarkers.
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DOI：
10.1016/j.eururo.2014.06.053
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发表时间：
2014-11
期刊：
影响因子：
23.4
通讯作者：
Gerlinger M
中科院分区：
文献类型：
--
作者：
Gulati S;Martinez P;Joshi T;Birkbak NJ;Santos CR;Rowan AJ;Pickering L;Gore M;Larkin J;Szallasi Z;Bates PA;Swanton C;Gerlinger M
已经确定了透明细胞肾细胞癌（ccRCC）患者的候选生物标志物，但大多数尚未得到验证。在独立患者队列中验证已发表的ccRCC预后生物标志物，并评估最有希望的标志物的瘤内异质性（ITH），以指导生物标志物优化。在350例ccRCC患者中评估了28种已鉴定的遗传或转录组学生物标志物的癌症特异性生存期（CSS）。在10个ccRCC的多区域活检数据集中询问ITH。通过单变量和多变量分析来分析与CSS相关的生物标志物。在单变量分析中，28种生物标志物中的总共17种（TP 53突变;染色体8 q、12、20q11.21q13.32和20的扩增以及4p、9 p、9p21.3p24.1和22 q的缺失; EDNRB和TSPAN 7低表达和6种基因表达特征）被验证为CSS差的预测因子。在多变量分析中，肿瘤分期和ccB表达特征是唯一的独立预测因子。在10个肿瘤中的8个中鉴定出ccB签名的ITH。在单变量分析中显著的几个遗传改变被富集，并且在表达ccB签名的样品中染色体不稳定性指数增加。这项研究可能不足以验证低患病率的生物标志物。ccB特征是唯一的独立预后生物标志物。在ccB样本中富集多个预后不良的遗传改变表明，可能需要几个事件来建立这种侵袭性表型，在某些肿瘤中由染色体不稳定性催化。多区域评估可以提高该生物标志物的精确度。我们在一个独立的患者队列中评估了已发表的生物标志物预测透明细胞肾癌患者生存的能力。只有一种分子检测可以为常规临床评估提供预后信息。该标志物在大多数癌症个体中显示出良好和不良的预后结果。未来的生物标志物需要考虑肿瘤内的变异，以提高准确性。在28种已发表的肾透明细胞癌生物标志物中，共有17种已被验证为生存预测因子。ccA/ccB特征优于所有其他特征，并增加了预后信息。这种生物标志物存在肿瘤内异质性，肿瘤的多区域评估可能会进一步提高其准确性。
Candidate biomarkers have been identified for clear cell renal cell carcinoma (ccRCC) patients, but most have not been validated. To validate published ccRCC prognostic biomarkers in an independent patient cohort and to assess intratumour heterogeneity (ITH) of the most promising markers to guide biomarker optimisation. Cancer-specific survival (CSS) for each of 28 identified genetic or transcriptomic biomarkers was assessed in 350 ccRCC patients. ITH was interrogated in a multiregion biopsy data set of 10 ccRCCs. Biomarker association with CSS was analysed by univariate and multivariate analyses. A total of 17 of 28 biomarkers (TP53 mutations; amplifications of chromosomes 8q, 12, 20q11.21q13.32, and 20 and deletions of 4p, 9p, 9p21.3p24.1, and 22q; low EDNRB and TSPAN7 expression and six gene expression signatures) were validated as predictors of poor CSS in univariate analysis. Tumour stage and the ccB expression signature were the only independent predictors in multivariate analysis. ITH of the ccB signature was identified in 8 of 10 tumours. Several genetic alterations that were significant in univariate analysis were enriched, and chromosomal instability indices were increased in samples expressing the ccB signature. The study may be underpowered to validate low-prevalence biomarkers. The ccB signature was the only independent prognostic biomarker. Enrichment of multiple poor prognosis genetic alterations in ccB samples indicated that several events may be required to establish this aggressive phenotype, catalysed in some tumours by chromosomal instability. Multiregion assessment may improve the precision of this biomarker. We evaluated the ability of published biomarkers to predict the survival of patients with clear cell kidney cancer in an independent patient cohort. Only one molecular test adds prognostic information to routine clinical assessments. This marker showed good and poor prognosis results within most individual cancers. Future biomarkers need to consider variation within tumours to improve accuracy. A total of 17 of 28 published biomarkers for clear cell renal cell carcinoma have been validated as predictors of survival. The ccA/ccB signature outperforms all others and adds prognostic information. Intratumour heterogeneity was seen for this biomarker, and multiregion assessment of tumours may further improve its accuracy.
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DOI：
10.1158/1078-0432.ccr-12-3886
发表时间：
2013-06-15
期刊：
Clinical cancer research : an official journal of the American Association for Cancer Research
影响因子：
--
作者：
Hakimi AA;Ostrovnaya I;Reva B;Schultz N;Chen YB;Gonen M;Liu H;Takeda S;Voss MH;Tickoo SK;Reuter VE;Russo P;Cheng EH;Sander C;Motzer RJ;Hsieh JJ;ccRCC Cancer Genome Atlas (KIRC TCGA) Research Network investigators
通讯作者：
ccRCC Cancer Genome Atlas (KIRC TCGA) Research Network investigators
影响因子：
45.3
作者：
Motzer, RJ;Mazumdar, M;Ferrara, J
通讯作者：
Ferrara, J
影响因子：
7.3
作者：
Martinez, Pierre;Birkbak, Nicolai Juul;Swanton, Charles
通讯作者：
Swanton, Charles
影响因子：
7.5
作者：
Brunelli, Matteo;Eccher, Albino;Martignoni, Guido
通讯作者：
Martignoni, Guido
影响因子：
11.5
作者：
Kosari, F;Parker, AS;Vasmatzis, G
通讯作者：
Vasmatzis, G