Tumors reside within a complex multicellular ecosystem comprised of malignant and non- malignant cells, where interacting cells and molecules are organized in space and time. The diversity of these cells and their interactions affect cancer progression and drug response and resistance, and present opportunities for more precise diagnostics and therapeutics. In this proposal, we will develop Slide-seq, a novel spatial transcriptomic method, into a high- resolution spatial genomics platform for cancer precision medicine through a set of robust protocols, pipelines and computational algorithms. Our tools will allow pathologists to apply Slide-seq on a broad range of tumor specimens in the clinic with standard equipment and minimal training. Our novel computational pipelines will allow the seamless integration of molecular, cellular and histological understanding in tumors: they will enable the spatial localization of cell types within complex tumor environments, the identification of spatially varying gene expression patterns driven by pathology, as well as the organization of cellular niches. Applying these approaches will revolutionize our ability to discover changes in tumor spatial and molecular organization during disease progression and treatment, provide new biomarkers for diagnostics and prognostics, and highlight new therapeutic avenues.
Cells are the unit building blocks of tissues and organs, thus, to understand how tumors progress and develop in tissues and organs, it is important to understand where different types of healthy and tumor cells reside in the tissue. Here, we are developing new technologies that allows clinical scientists to simultaneously look at who cells are, via the genes they express, and where they reside across many different tumor types.
