British Journal of CancerCancer BiologyPeer-reviewed journal article
Integrative proteo-genomic profiling uncovers key biomarkers of lapatinib resistance in HER2-positive breast cancer
J Steggall, Vinothini RajeeveiD, Najah M. AlsubaieiD, Atif NaeemiD, Aamer IkramiD, Ateequllah HayatiD
Abstract
Drug resistance is a major obstacle to the long-term effectiveness of cancer therapies. Approximately 70% of breast cancer patients relapse after 5 years of treatment, and the lack of biomarkers associated with drug resistance translates to poor prognosis in clinic. Previous research has utilised omics approaches to uncover biomarkers driving drug resistance, with a strong emphasis on genetic mutations. Here, we identified a nine-marker signature associated with resistance to lapatinib in a HER2-positive breast cancer model using a target discovery approach by employing an integrative multi-omics strategy, combining ATAC-seq, RNA-seq, and proteomics. We found that 7 markers in the drug resistance-signature had not been previously found to be implicated in HER2 positive breast cancer. We counterintuitively found that drug resistant cells have restrictive chromatin accessibility with reduced gene expression associated with limited total proteome changes. However, upon closer look, we identified that the drug resistance-signature had increased chromatin accessibility closer to the transcriptional start sites of those genes and are highly differentially expressed across the three datasets. Our data show that despite the overall transcriptional and proteomic landscape showing limited changes, there are several markers that are highly expressed, which correlate with increased anchorage-independent and invasive phenotype in vitro in lapatinib resistant cells compared to cancer cells. Our results demonstrate that disease aggressiveness can be related to reduced chromatin and gene expression dynamics. We anticipate that the resistant signature identified here using integrative target discovery approach can be applied to complex, representative models and validated before they can be targeted by suitable therapeutic agents.
- organism: human
- assay: ATAC-seq
- assay: proteomics
- assay: bulk RNA-seq
- research focus: differential expression
- research focus: cell type annotation
- approach: deep learning
- approach: statistical model
- Citations
- 6
- FWCI
- 2.28
- Field percentile
- top 10%
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