Integrated time course omics analysis distinguishes immediate therapeutic response from acquired resistance

Abstract

BACKGROUND: Targeted therapies specifically act by blocking the activity of proteins that are encoded by genes critical for tumorigenesis. However, most cancers acquire resistance and long-term disease remission is rarely observed. Understanding the time course of molecular changes responsible for the development of acquired resistance could enable optimization of patients' treatment options. Clinically, acquired therapeutic resistance can only be studied at a single time point in resistant tumors. To determine the dynamics of these molecular changes, we obtained high throughput omics data weekly during the development of cetuximab resistance in a head and neck cancer in vitro model. RESULTS:An unsupervised algorithm, CoGAPS, was used to quantify the evolving transcriptional and epigenetic changes. Applying a PatternMarker statistic to the results from CoGAPS enabled novel heatmapbased visualization of the dynamics in these time course omics data. We demonstrate that transcriptional changes result from immediate therapeutic response or resistance, whereas epigenetic alterations only occur with resistance. Integrated analysis demonstrates delayed onset of changes in DNA methylation relative to transcription, suggesting that resistance is stabilized epigenetically. CONCLUSIONS:Genes with epigenetic alterations associated with resistance that have concordant expression changes are hypothesized to stabilize resistance. These genes include FGFR1, which was associated with EGFR inhibitor resistance previously. Thus, integrated omics analysis distinguishes the timing of molecular drivers of resistance. Our findings provide a relevant towards better understanding of the time course progression of changes resulting in acquired resistance to targeted therapies. This is an important contribution to the development of alternative treatment strategies that would introduce new drugs before the resistant phenotype develops.

A Mechanism of Resistance to Antibody-Targeted Immune Attack

“…Analysis of ADCCS1 and ADCCR1 cells from challenges 30 to 35 was performed with the CoGAPS algorithm (9), using the timecourse analysis pipeline from Stein-O'Brien and colleagues (18). The PatternMarker statistic for CoGAPS (10) identified 300 genes with consistent upregulation and 450 genes with consistent downregulation in ADCCR1 cells compared with ADCCS1 cells across all challenges.…”

Integrated single-cell and bulk gene expression and ATAC-seq reveals heterogeneity and early changes in pathways associated with resistance to cetuximab in HNSCC-sensitive cell lines

“…In another study, we have shown that while SCC25 acquires cetuximab resistance due to chronic exposure, 44 the transcriptional changes occur a few weeks prior to the promoter hyper-or hypomethylation, with the latter being detected when cells are already resistant. Here, we investigated the hypothesis that some of the genes involved in resistance are controlled by chromatin remodelling that occurs prior to methylation, while the cells are still sensitive to the therapy, and drive a proportion of the expression changes.…”

Extracellular Vesicles Released by Tumor Endothelial Cells Spread Immunosuppressive and Transforming Signals Through Various Recipient Cells

“…This algorithm was applied to the gene expression time-course data of ASC stimulated with TEC-EV. CoGAPS was previously used to successfully delineate the time-course data of therapeutic response in breast and HNSCC (Hill et al, 2016;Stein-O'Brien et al, 2018). In our case, it detected the critical role of inflammatory response gene set to respond to TEC-EV stimuli within 24 h (Figure 4).…”