Early Prediction of Cancer Progression by Depth-Resolved Nanoscale Mapping of Nuclear Architecture from Unstained Tissue Specimens.
Early Prediction of Cancer Progression by Depth-Resolved Nanoscale Mapping of Nuclear Architecture from Unstained Tissue Specimens.
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DOI：
10.1158/0008-5472.can-15-1274
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发表时间：
2015-11-15
期刊：
影响因子：
11.2
通讯作者：
Liu Y
中科院分区：
文献类型：
--
作者：
Uttam S;Pham HV;LaFace J;Leibowitz B;Yu J;Brand RE;Hartman DJ;Liu Y
早期癌症检测目前依赖于筛查整个高危人群，如结肠镜检查和乳房X光检查。因此，对有患癌症风险的患者进行频繁的侵入性监测会带来经济、身体和情感上的负担，因为临床医生缺乏准确预测哪些患者实际上会发展为恶性肿瘤的工具。在这里，我们提出了一种新的方法来预测癌症进展的风险，通过纳米级核结构映射（nanoNAM）的未染色的组织切片的基础上的内在密度改变的核结构，而不是染色摄取量。我们证明，nanoNAM在结肠癌发生的动物模型和溃疡性结肠炎患者的恶性转化过程中检测到核结构密度变化的逐渐增加，即使在病理学家认为组织学正常的组织中也是如此。我们评估了nanoNAM预测溃疡性结肠炎患者“未来”癌症进展的能力，这些患者在首次结肠镜检查后长达13年没有发生结肠癌。NanoNAM对最初的活检结果进行了正确分类，最终患结肠癌的15名患者中有12名，未患结肠癌的18名患者中有15名，总体准确率为85%。总的来说，我们的研究结果表明nanoNAM在预测癌症进展风险方面具有巨大的潜力，并表明在具有较大队列的多中心研究中的进一步验证可能最终推动这种方法成为常规临床测试。
Early cancer detection currently relies on screening the entire at-risk population, as with colonoscopy and mammography. Therefore, frequent, invasive surveillance of patients at risk for developing cancer carries financial, physical, and emotional burdens because clinicians lack tools to accurately predict which patients will actually progress into malignancy. Here we present a new method to predict cancer progression risk via nanoscale nuclear architecture mapping (nanoNAM) of unstained tissue sections based on the intrinsic density alteration of nuclear structure rather than the amount of stain uptake. We demonstrate that nanoNAM detects a gradual increase in the density alteration of nuclear architecture during malignant transformation in animal models of colon carcinogenesis and in human patients with ulcerative colitis, even in tissue that appears histologically normal according to pathologists. We evaluated the ability of nanoNAM to predict “future” cancer progression in patients with ulcerative colitis who did and did not develop colon cancer up to 13 years after their initial colonoscopy. NanoNAM of the initial biopsies correctly classified 12 out of 15 patients who eventually developed colon cancer and 15 out of 18 who did not, with an overall accuracy of 85%. Taken together, our findings demonstrate great potential for nanoNAM in predicting cancer progression risk, and suggest that further validation in a multi-center study with larger cohorts may eventually advance this method to become a routine clinical test.