Longitudinal tracking of drug induced transcriptomic reprogramming in triple negative breast cancers

Background Development of drug resistance is often dynamic and requires time series experiments to understand these processes. The most abundant source of information regarding such progressive activity is tracking single cell gene expression data. Our understanding of the imminent role of phenotype switching in modifying therapeutic response has considerably increased.The analysis of single cell transcriptomic data along with machine learning models offers a constructive tool to describe dynamic cellular processes to elucidate cell-to-cell variability within the population after drug exposure. Here we studied how genomic and non-genomic basis for transcriptome differences can influence the developing drug resistance and clonal fitness. Results: This study describes the scRNA-seq analysis of untreated, treated and drug holiday PDX transplants of TNBC exposed to platinum. This builds from the materials and findings of our paper by Salehi, Kabeer et al, Nature 2021 (PMID: 3416307), in which we established the measurement of fitness in this time series. In the new work, we have mapped single cell transcription in conjunction, allowing a dissection of copy number driven (in-cis) clonal transcription and non-clonal, ie non-genomic transcription. We developed an experimental and computational platform consisting of four major steps: (1) timeseries sampling of scRNA-seq 126,556 TNBC PDX cells >2.5 years; (2) clustering the tumor cells into groups reflecting their drug sensitivity, resistance and other characteristics; (3) performing trajectory inference to identify lineages of transitional processes along with drug and tumor progression; (4) estimating the longitudinal gene expression of each lineage; and identifying significant genes between-lineage differential expression modules. Interestingly, we identified a small group of genes (18.9%) that display a new cell state that is not towards the untreated (Un-Rx) nor towards treated Rx which are distant from untreated cell states. The pathway results are shared between two different quantitative measurements of clone-aware differential expression and pseudotime analysis.

• Type: Other
• Archiver: European Genome-Phenome Archive (EGA)