Metabolomic Prediction of Human Prostate Cancer Aggressiveness: Magnetic Resonance Spectroscopy of Histologically Benign Tissue.
Metabolomic Prediction of Human Prostate Cancer Aggressiveness: Magnetic Resonance Spectroscopy of Histologically Benign Tissue.
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DOI：
10.1038/s41598-018-23177-w
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发表时间：
2018-03-26
影响因子：
4.6
通讯作者：
Cheng LL
中科院分区：
文献类型：
--
作者：
Vandergrift LA;Decelle EA;Kurth J;Wu S;Fuss TL;DeFeo EM;Halpern EF;Taupitz M;McDougal WS;Olumi AF;Wu CL;Cheng LL
前列腺癌通过可能发生在癌症形态形成之前的事件改变细胞代谢。基于磁共振波谱（MRS）的代谢组学可以预测癌变前列腺良性组织的侵袭性，提供临床可翻译的预后信息。本回顾性研究纳入185例患者（2002-2009），包括来自前列腺切除术（365例）、良性前列腺增生（BPH）（15例）和来自癌阴性患者的活检（14例）的前列腺组织。采用高分辨率魔角旋转（HRMAS） MRS测量组织，随后采用预后分级组（PGG）系统进行定量组织学检查。仅从365个癌性前列腺的组织学良性组织中的338个组织中测量代谢谱，并将其分为训练测试队列，可以确定肿瘤的分级和分期，并预测复发。具体而言，代谢谱：(1)在高侵袭性癌症患者中显示肌醇升高，肌醇是一种内源性肿瘤抑制因子和潜在的机械治疗靶点；(2)如果在活检中分析，确定一个侵袭性较低的前列腺癌患者亚组，以避免过度治疗；(3)将临床病理上不可分割的PGG2组细分为两个不同的Kaplan-Meier复发组，从而识别出复发风险更高的患者。这些发现可以通过活检或前列腺切除术组织测量来实现，可以为治疗策略提供信息。代谢组学信息可以通过调用癌症生物学来改善对癌症环境中组织学良性组织的评估，从而帮助改变基于形态学的诊断系统。
Prostate cancer alters cellular metabolism through events potentially preceding cancer morphological formation. Magnetic resonance spectroscopy (MRS)-based metabolomics of histologically-benign tissues from cancerous prostates can predict disease aggressiveness, offering clinically-translatable prognostic information. This retrospective study of 185 patients (2002–2009) included prostate tissues from prostatectomies (n = 365), benign prostatic hyperplasia (BPH) (n = 15), and biopsy cores from cancer-negative patients (n = 14). Tissues were measured with high resolution magic angle spinning (HRMAS) MRS, followed by quantitative histology using the Prognostic Grade Group (PGG) system. Metabolic profiles, measured solely from 338 of 365 histologically-benign tissues from cancerous prostates and divided into training-testing cohorts, could identify tumor grade and stage, and predict recurrence. Specifically, metabolic profiles: (1) show elevated myo-inositol, an endogenous tumor suppressor and potential mechanistic therapy target, in patients with highly-aggressive cancer, (2) identify a patient sub-group with less aggressive prostate cancer to avoid overtreatment if analysed at biopsy; and (3) subdivide the clinicopathologically indivisible PGG2 group into two distinct Kaplan-Meier recurrence groups, thereby identifying patients more at-risk for recurrence. Such findings, achievable by biopsy or prostatectomy tissue measurement, could inform treatment strategies. Metabolomics information can help transform a morphology-based diagnostic system by invoking cancer biology to improve evaluation of histologically-benign tissues in cancer environments.
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影响因子：
64.5
作者：
Boelens MC;Wu TJ;Nabet BY;Xu B;Qiu Y;Yoon T;Azzam DJ;Twyman-Saint Victor C;Wiemann BZ;Ishwaran H;Ter Brugge PJ;Jonkers J;Slingerland J;Minn AJ
通讯作者：
Minn AJ
影响因子：
16.6
作者：
Avgustinova A;Iravani M;Robertson D;Fearns A;Gao Q;Klingbeil P;Hanby AM;Speirs V;Sahai E;Calvo F;Isacke CM
通讯作者：
Isacke CM
影响因子：
10.3
作者：
Hong, JongWook;Tobin, Nicholas P.;Genove, Guillem
通讯作者：
Genove, Guillem
DOI：
10.1016/j.neo.2016.11.003
发表时间：
2017-03
期刊：
Neoplasia (New York, N.Y.)
影响因子：
--
作者：
Reed MAC;Singhal R;Ludwig C;Carrigan JB;Ward DG;Taniere P;Alderson D;Günther UL
通讯作者：
Günther UL
影响因子：
2.9
作者：
Kaebisch E;Fuss TL;Vandergrift LA;Toews K;Habbel P;Cheng LL
通讯作者：
Cheng LL