Data Processing, Analysis and Modeling Unit

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PROJECT SUMMARY (Data Analysis Unit) Metastatic tumors are the leading cause of cancer deaths and are difficult to treat. The biology underlying cell state plasticity and the distinct molecular programs that govern adaptation to foreign microenvironments will require a much deeper understanding of the complex environment of tumor growth. We aim to address this significant knowledge gap by building a spatial-temporal atlas of the metastatic transition of three exceptionally lethal sets of malignancies – lung cancer, pancreatic cancer, and CNS metastases- by combining single-cell genomics with multi- dimensional spatial mapping in deeply annotated patient-derived primary and metastatic clinical samples. Towards this goal, our first aim will be to develop experimental design methods to select the patients, samples, and experimental parameters for creation of these three atlases. This aim is based on the rationale that atlas construction poses novel statistical challenges in experimental design that must be developed to maximally utilize resources towards atlas construction. In our second aim we will construct and implement the infrastructure of the data analysis unit at scale. The rationale for this aim is that quality control, and scalable quantification and annotation of the data at large scale will guide the use of the atlas for searching, comparing and interpreting samples. Finally, we will develop novel computational methods for data integration and interpretation towards a spatial-temporal atlas. In this instance we posit that data from different technologies and platforms often include redundant, biologically informative information that can be extracted for producing an annotated atlas. We will illustrate the impact of our atlas in the use case of predicting those early stage lung adenocarcinomas that are likely to metastasize to the brain. Ultimately the constructed atlases will provide insight on convergent events and bottlenecks in the metastatic transition, suggesting potential therapeutic targets and opportunities for intervention.