Photolithographic Tumor DNA Isolation

光刻肿瘤 DNA 分离

• 批准号: 10495070
• 负责人: Darryl K Shibata
• 金额: $ 22.73万
• 依托单位: UNIVERSITY OF SOUTHERN CALIFORNIA
• 依托单位国家: 美国
• 财政年份: 2022
• 资助国家: 美国
• 起止时间: 2022-07-22 至 2025-05-31
• 项目状态: 未结题
• 来源: https://reporter.nih.gov/project-details/10495070
• 关键词: 3D Print; Algorithmic Analysis; Algorithms; Bioinformatics; Biological Markers; Cell Count; Cell Extracts; Cell Nucleus; Clinical; Clinical Research; Complex; Complex Mixtures; DNA; DNA Methylation; DNA sequencing; Diagnosis; Diploidy; Documentation; Engineering; Ensure; Exposure to; Feedback; Gene Frequency; Genome; Gland; Goals; Hour; Human; Image; Image Analysis; Individual; Intervention; Laboratories; Learning; Location; Machine Learning; Malignant Neoplasms; Malignant neoplasm of lung; Manuals; Masks; Measurement; Measures; Medical; Methods; Methylation; Microdissection; Microscope; Microscopic; Modernization; Mutation; Mutation Detection; Normal Cell; Outcome; Phenotype; Population; Reproducibility; Resolution; Robotics; Sampling; Scanning; Short Waves; Slide; Specimen; Spottings; Stains; Standardization; System; Systems Integration; Testing; The Cancer Genome Atlas; Three-Dimensional Image; Tissues; Translations; Tumor Tissue; Ultraviolet Rays; United States National Institutes of Health; anticancer research; base; cancer cell; cancer diagnosis; cancer genome; cancer therapy; clinical translation; cost; design; exome sequencing; genetic variant; human DNA; improved; laser capture microdissection; machine learning algorithm; mortality; mutant; neoplastic cell; next generation; next generation sequencing; optimal treatments; personalized medicine; precision oncology; programs; skills; success; targeted sequencing; targeted treatment; tumor; tumor DNA

Abstract: Photolithographic Tumor DNA Isolation Personalized oncology is based on the idea that the mutations in a cancer determine optimal therapy. Mutation detection is increasingly possible with newer next generation sequencers and bioinformatics, but currently the first step of DNA isolation is ad hoc, manual, and non-standardized. Human tumors are complex mixtures of normal and tumor cells, and mutation detection would become more reliable and reproducible if nearly pure tumor DNA was extracted. Here we propose to develop an automated system that can extract >90% pure tumor DNA from conventional H&E stained microscope slides by integrating photolithography with high resolution slide scanners, image analysis algorithms, and modern 3D printers. Machine learning image algorithms can distinguish tumor from normal cells, and this information will be transferred to the 3D printer, which places opaque material directly over tumor nuclei on the slide. The slide is then exposed to short wave UV light to destroy the DNA in unprotected normal cells whereas tumor DNA is selectively protected by the photolithographic mask. DNA can be extracted from the entire slide, and only DNA in the protected tumor cells can be sequenced (whole exome or targeted sequencing) or measured for CpG methylation. The spot resolution of the 3D printer is about 40 microns, and therefore very irregular complex topography and small features like a single tumor gland can be protected by photolithography. The entire system (scan, analyze, print, irradiate, extract) could yield >90% pure tumor DNA from an H&E slide in about 24 hours. The transformational potential is that the system converts a currently ad hoc, highly labor-intensive technical step into an automated, well-documented and reproducible extraction that can add information and learning because the exact extracted cell numbers, their phenotypes and spatial locations are known. Because it uses an image algorithm to select the tumor cells, the “same” DNA isolation can be performed by anyone anywhere in the world. Moreover, image algorithms can “learn” to better extract tumor DNA based on feedback from the DNA sequencing. The integration of this system into a sequencing pipeline would improve the reliability, documentation, and reproducibility of mutation calling by extracting nearly pure (>90%) tumor DNA, which will advance both reproducible cancer research and the clinical translation of precision oncology.

摘要：光刻肿瘤DNA隔离 个性化的肿瘤学是基于癌症中的突变决定最佳治疗的想法。 对于新的下一代测序师和生物信息学，越来越可能的突变检测是可能的，但是 目前，DNA隔离的第一步是临时，手动和非标准化。人肿瘤很复杂 正常和肿瘤细胞的混合物以及突变检测将变得更可靠和可再现的话 提取几乎纯肿瘤DNA。在这里，我们建议开发一个可以提取的自动化系统 传统H＆E染色显微镜幻灯片的> 90％的纯肿瘤DNA通过将光刻造影与 高分辨率幻灯片扫描仪，图像分析算法和现代3D打印机。机器学习图像 算法可以将肿瘤与正常细胞区分开，该信息将转移到3D打印机， 哪个将不透明的材料直接放在幻灯片上的肿瘤核上。然后将幻灯片暴露于短波 紫外线在未受保护的正常细胞中破坏DNA，而肿瘤DNA则被选择性保护 光刻面具。 DNA可以从整个载玻片中提取，仅在受保护的肿瘤细胞中提取DNA 可以测序（整个外显子组或靶向测序）或用于CpG甲基化的测量。这个地方 3D打印机的分辨率约为40微米，因此非常不规则的复杂地形和小 诸如单个肿瘤腺之类的特征可以受到光刻的保护。整个系统（扫描，分析， 打印，辐照，提取物）在大约24小时内从H＆E载体中产生> 90％的纯肿瘤DNA。 转变潜力是该系统转换了当前临时，高度劳动力密集型 技术步骤迈入自动化，有据可查且可再现的提取，可以添加信息和 学习，因为精确提取的细胞数量，它们的表型和空间位置是已知的。因为 它使用图像算法选择肿瘤细胞，任何人都可以执行“相同”的DNA隔离 世界上任何地方。此外，图像算法可以“学习”以根据反馈更好地提取肿瘤DNA 来自DNA测序。该系统集成到测序管道中将改善 通过提取几乎纯净（> 90％）肿瘤DNA的可靠性，文档和可重复性， 这将提高可复制的癌症研究和精度肿瘤学的临床翻译。