DNA single nucleotide variants are a major cause of drug resistance in cancer, but for most variants their effects on drug response are yet unknown. While new SNVs are discovered at an increasing rate, the interpretation of their impacts presents a major bottleneck in clinical use.
To address this bottleneck, we developed a suite of statistical analysis tools that allowed the creation of a prospective map of mutational impact from new experimental techniques that combine gene editing data with RNA and DNA sequencing readout at the single-cell level. Our tools shed light on the degree of malignancy of individual mutations, on changes in gene regulation resulting from mutations, and on potential drug targets, and also include methods to model the specific noise structure of single-cell data for the gene editing context.
First, we studied IFNγ response across different mutations to the JAK1 gene in colon cancer cells[1], and demonstrated the accuracy of our computational tools by linking genotype with transcriptional phenotype in 9,908 cells for scDNA-seq and 18,978 cells for scRNA-seq, encompassing 97 unique genotypes with low error-rates for known genotype-phenotype relationships.
In a second application[2], we studied the transcriptional profiles of drug-resistant colon cancer cells at scale, following exposure to the drugs dabrafenib and cetuximab. Our approach shed light on transcriptional differences between different types of drug resistance, including drug addiction.
References:
1. Cooper*, Coelho*, Strauss*, et al. Genome Biol 25, 20 (2024).
2. Coelho, Strauss, Watterson, et al. Nat. Genet. (2024).