Photolithographic Tumor DNA Isolation

光刻肿瘤 DNA 分离

• 批准号: 10670402
• 负责人: Darryl K Shibata
• 金额: $ 18.14万
• 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/10670402
• 关键词: 3D Print; Algorithmic Analysis; Algorithms; Bioinformatics; Biological Markers; Cell Count; Cell Extracts; Cell Nucleus; Clinical; Complex; Complex Mixtures; DNA; DNA Methylation; DNA sequencing; Diagnosis; Diploidy; Documentation; Engineering; Ensure; Exposure to; Feedback; Gene Frequency; Genome; Gland; Goals; Heterozygote; 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; Printing; Reproducibility; Research; Resolution; Robotics; Sampling; Scanning; Short Waves; Slide; Specimen; Spottings; Stains; Standardization; System; Technology; Testing; The Cancer Genome Atlas; Three-Dimensional Image; Tissues; Translations; Tumor Tissue; Ultraviolet Rays; United States National Institutes of Health; anticancer research; 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 分离 个性化肿瘤学基于癌症突变决定最佳治疗的理念。 通过更新的下一代测序仪和生物信息学，突变检测变得越来越可能，但是 目前DNA分离的第一步是临时的、手动的和非标准化的。人类肿瘤很复杂 正常细胞和肿瘤细胞的混合物，如果可以的话，突变检测将变得更加可靠和可重复 提取出接近纯的肿瘤DNA。在这里，我们建议开发一个自动化系统，可以提取 通过将光刻技术与传统 H&E 染色显微镜载玻片相结合，获得 >90% 纯肿瘤 DNA 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 算法可以区分肿瘤和正常细胞，这些信息将被传输到3D打印机， 它将不透明材料直接放置在载玻片上的肿瘤核上。然后将载玻片暴露于短波 紫外线会破坏未受保护的正常细胞中的 DNA，而肿瘤 DNA 则受到选择性保护 光刻掩模。可以从整个载玻片中提取DNA，并且仅提取受保护的肿瘤细胞中的DNA 可以进行测序（全外显子组或靶向测序）或测量 CpG 甲基化。现场 3D打印机的分辨率约为40微米，因此形貌非常不规则复杂且体积小 像单个肿瘤腺体这样的特征可以通过光刻来保护。整个系统（扫描、分析、 打印、照射、提取）可以在大约 24 小时内从 H&E 载玻片中产生 >90% 纯的肿瘤 DNA。 变革的潜力在于，该系统将当前临时的、高度劳动密集型的 自动化、有据可查且可重复的提取的技术步骤，可以添加信息和 学习是因为准确提取的细胞数量、它们的表型和空间位置是已知的。因为 它使用图像算法来选择肿瘤细胞，任何人都可以进行“相同”的DNA分离 世界任何地方。此外，图像算法可以根据反馈“学习”更好地提取肿瘤DNA 来自DNA测序。将该系统集成到测序流程中将改善 通过提取近乎纯的 (>90%) 肿瘤 DNA，实现突变调用的可靠性、记录和再现性， 这将推动可重复的癌症研究和精准肿瘤学的临床转化。