# Single cell modeling of cancer mutations > **NIH NIH R33** · STANFORD UNIVERSITY · 2023 · $375,297 ## Abstract ABSTRACT Hundreds of thousands of mutations have been identified in cancer. However, the vast majority of cancer mutations lack functional biological characterization. Therefore, very little is known about their impact on gene function beyond in silico predictions. Developing experimental models to study the biological consequences of these mutations is a daunting challenge. We developed a technology called transcript-informed single cell CRISPR sequencing (TISCC-seq) that provides modeling of cancer mutations at single cell resolution. CRISPR engineering introduces cancer gene mutations into cells. Single-cell RNA sequencing (scRNA-seq) is a power tool for evaluating the genomic features of cancer. By combining these two approaches TISCC-seq has the potential for dramatic increases in parallelization and scalability of experimental cancer models. We use DNA base editors to introduce specific cancer mutations into target genes among individual cells. Single molecule nanopore sequencing of the cDNA target directly identifies the mutation in each cell. By integrating single cell long and short read sequence data, each cell’s newly introduced mutation is matched to the same cell’s gene expression data. We will develop TISCC-seq as a new single cell genomic platform for engineering cancer mutations into cell lines and primary tissue cultures. Single base mutations are the most commonly reported type of cancer genetic alteration. For Aim 1, we will develop TISCC-seq for highly multiplexed functional screening of substitution mutations at single cell resolution while matching the mutation genotype to the same single cell’s transcriptome. We will identify reported cancer mutations with known biological effects and others which are not characterized identified in colorectal or gastric cancer. Next, we will determine which of these mutations can be engineered using base editing methods. Then, we will deliver base editors and guide RNAs to engineer up to 500 substitution mutations across different cell lines and organoids. Post-editing, the cells will undergo scRNA-seq with both short and long-read platform. These data sets will be integrated to provide a single readout where the single cell mutation is matched to the corresponding cell’s gene expression. Alternative splicing is increasingly recognized as an important feature of cancer. Some cis-based cancer mutations occur in exon-intron junctions that lead to alternative splicing of mRNAs. For Aim 2, we will develop TISCC-seq as a method to evaluate this category of mutations. First, we will identify a set of 100 cancer genes that have cis-based mutations at exon-intron junctions as reported in colorectal or gastric cancer. We will increase the scalability of this process such that at least 500 of this class of mutations can be studied in parallel using an integrated long and short read sequencing. These mutations will be introduced across different cell lines and organoids. Overall, we will develop a ... ## Key facts - **NIH application ID:** 10612689 - **Project number:** 1R33CA278469-01 - **Recipient organization:** STANFORD UNIVERSITY - **Principal Investigator:** Hanlee P Ji - **Activity code:** R33 (R01, R21, SBIR, etc.) - **Funding institute:** NIH - **Fiscal year:** 2023 - **Award amount:** $375,297 - **Award type:** 1 - **Project period:** 2023-06-01 → 2026-05-31 ## Primary source NIH RePORTER: https://reporter.nih.gov/project-details/10612689 ## Citation > US National Institutes of Health, RePORTER application 10612689, Single cell modeling of cancer mutations (1R33CA278469-01). Retrieved via AI Analytics 2026-05-24 from https://api.ai-analytics.org/grant/nih/10612689. Licensed CC0. --- *[NIH grants dataset](/datasets/nih-grants) · CC0 1.0*