PROJECT SUMMARY Ongoing cancer genomic studies have documented numerous mutations across all tumor types. Most cancer mutations have not been functionally characterized, as conducting experiments with individual cell line models for each mutation is neither scalable nor practical. Moreover, primary tumors and cancer cell lines exhibit extensive genetic and transcriptional heterogeneity, with multiple subclones coexisting within the same cancer population. Each subclone possesses different biological properties. Therefore, it is crucial to closely monitor this heterogeneity to achieve clear and reproducible results. There is a lack of integrated experimental systems and computational approaches capable of analyzing the genomic complexity of subclonal populations. To address this need, we will integrate two distinct approaches— one genomic and one bioinformatic—to engineer, track, and study the impact of various mutations on the subclonal properties of cancer, including growth. Transcript-informed single-cell CRISPR sequencing (TISCC- seq), developed by the Ji group at Stanford, can experimentally model cancer mutations of interest at single-cell resolution by leveraging CRISPR engineering and single-cell RNA sequencing (scRNA-seq). CLONEID, developed by the Andor group at Moffitt Cancer Center, is a computational tool for monitoring heterogeneous cell populations by taking daily microscopic cell images and incorporating various assays, including single-cell sequencing. Using this integrated system, we will characterize the functional consequences of mutations in clinically relevant genes (TP53, PTEN, and MET) in gastric cancers by monitoring and modeling subclonal dynamics over time. The objective of this project is to develop an integrated system combining TISCC-seq for single-cell mutation analysis with the CLONEID framework to evaluate the functional consequences of introduced mutations by monitoring and modeling subclonal dynamics. We have two specific aims: 1) adapting the CLONEID framework for TISCC-seq single cell clonal tracking and readouts and 2) Single cell longitudinal monitoring of TISCC-seq engineered clonal growth phenotypes. This project aims to demonstrate how an integrated system can provide accurate and reproducible insights into the functional consequences of known cancer mutations.