SBIR Phase I: High multiplex visual mapping of heterogeneity in FFPE biospecimen

SBIR第一阶段项目将通过对活检的高级分析来解决癌症患者的耐药性问题。 不仅每个患者都是独特的，而且他们的肿瘤也含有高水平的遗传多样性。患者肿瘤内被忽视的多样性可能导致治疗不足，从而导致获得性耐药性，并且还显示出预测对某些药物的治疗反应。 所提出的技术能够提取肿瘤基因表达谱并将其与活检的空间信息和成像相关联，从而可以构建遗传多样性的物理图谱。 这种方法的最初应用将是免疫肿瘤学，其中对抗癌症的免疫细胞的空间位置已被证明可以预测对治疗的反应。下一步将是阐明标本中通常被传统诊断忽略的所有细胞类型，并使用这些信息来确定会影响该肿瘤中发现的所有类型恶性细胞的治疗组合。这直接解决了癌症登月计划对个性化药物联合治疗的关注。 该方法的成功商业化将导致药物生物标志物发现服务，患者的精确筛选，以及研究人员使用该工具研究各种疾病的生物学基础发现。 SBIR第一阶段项目的技术创新是能够将基因组分析与空间信息相关联，以创建整个标本的基因表达原位图谱。 成像和测序等大基因组数据是相互排斥的，将它们关联起来以可视化标本中的遗传多样性的能力将为筛选和研究提供信息。 该项目的目标是证明从存档的活检中创建基因表达的可视化热图的能力，并表明该表达图识别标准分析方法无法识别的遗传多样性。 对这种多样性的量化和肿瘤中偏差的位置将是未来药物反应性筛查以及对各种疾病的病因和治疗方法进行基础研究的基础。

This SBIR Phase I project will address drug resistance in cancer patients through the advanced analysis of biopsies. Not only is each patient unique but their tumor contains a high level of genetic diversity as well. Overlooked diversity within patient tumors can lead to inadequate treatment and consequently acquired drug resistance, and has also been shown to predict therapeutic response to certain drugs. The proposed technology is capable of extracting and correlating tumor gene expression profiles to the spatial information and imaging of a biopsy so that a physical map of genetic diversity can be constructed. The initial application of this method will be in immuno-oncology where the spatial locations of immune cells fighting cancer have been shown to predict response to treatment. The next step will be to elucidate all cell types in the specimen that are usually overlooked with conventional diagnostics and use this information for determining treatment combinations that would affect all types of malignant cells found in this tumor. This directly addresses the Cancer Moonshot Initiative's focus on combination treatments for personalized medicine. Successful commercialization of this method will result in drug biomarker discovery services, precision screening for patients, and fundamental discoveries in biology by researchers using this tool to investigate various diseases. The technical innovation in this SBIR Phase I Project is the ability to correlate genomic analysis with spatial information to create an in-situ map of gene expression across the specimen. Imaging and big genomic data such as sequencing have been mutually exclusive, and the ability to correlate them in order to visualize genetic diversity within a specimen would be informative for both screening and research. The goals of this project are to demonstrate the ability to create a visual heat map of gene expression from archived biopsies and to show that this expression map identifies genetic diversity that standard analysis methods do not. Quantification of this diversity and the locations of deviations in the tumor will be the basis of future screenings for drug responsiveness as well as basic research into the causes and treatments of various diseases.