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Denkert, C.

Publications and source records attributed to Denkert, C..

3 recordsLinked to original sources

Hotspot ESR1 mutations are multimodal and contextual drivers of breast cancer metastasis

Constitutively active estrogen receptor- (ER/ESR1) mutations have been identified in approximately one third of ER+ metastatic breast cancer. Although these mutations are known mediators of endocrine resistance, their potential role in promoting metastatic disease has not yet been mechanistically addressed. In this study, we show the presence of ESR1 mutations exclusively in distant, but not local recurrences. In concordance with transcriptomic profiling of ESR1 mutant tumors, genome-edited Y537S and D538G cell models have a reprogrammed cell adhesive gene network via alterations in desmosome/gap junction genes and the TIMP3/MMP axis, which functionally confers enhanced cell-cell contacts while decreased cell-ECM adhesion. Context-dependent migratory phenotypes revealed co-targeting of Wnt and ER as vulnerability. Mutant ESR1 exhibits non-canonical regulation of several metastatic pathways including secondary transactivation and de novo FOXA1-driven chromatin remodeling. Collectively, our data supports evidence for ESR1 mutation-driven metastases and provides insight for future preclinical therapeutic strategies. SignificanceContext and allele-dependent transcriptome and cistrome reprogramming in genome-edited ESR1 mutation cell models elicit diverse metastatic phenotypes, including but not limited to alterations in cell adhesion and migration. The gain-of-function mutations can be pharmacologically targeted, and thus may be key components of novel therapeutic treatment strategies for ER-mutant metastatic breast cancer.

cancer biology↗

Acquired mutations and transcriptional remodeling in long-term estrogen-deprived locoregional breast cancer recurrences

Endocrine therapy resistance is a hallmark of advanced estrogen receptor (ER) positive breast cancer. In this study, we performed DNA/RNA hybrid-capture sequencing on 12 locoregional recurrences after long-term estrogen-deprivation along with each tumors matched primary. Despite being up to 7 years removed from the primary lesion, most recurrences harbored similar intrinsic transcriptional and copy number profiles. Only two genes, AKAP9 and KMT2C, were found to have single nucleotide variant (SNV) enrichments in more than one recurrence. Enriched mutations in single cases included SNVs within transcriptional regulators such as ARID1A, TP53, FOXO1, BRD1, NCOA1 and NCOR2 with one local recurrence gaining three PIK3CA mutations. In contrast to DNA-level changes, we discovered recurrent outlier mRNA expression alterations were common--including outlier gains in TP63 (n=5 cases [42%]), NTRK3 [n=5 [42%]), NTRK2 (n=4 [33%]), PAX3 (n=4 [33%]), FGFR4 (n=3 [25%]) and TERT (n=3 [25%]). Recurrent losses involved ESR1 (n=5 [42%]), RELN (n=5 [42%]), SFRP4 (n=4 [33%]) and FOSB (n=4 [33%]). ESR1-depleted recurrences harbored shared transcriptional remodeling events including upregulation of PROM1 and other basal cancer markers. Taken together, this study defines acquired genomic changes in long-term, estrogen-deprived disease, highlights longitudinal RNA-profiling and identifies a common endocrine-resistant breast cancer subtype with basal-like transcriptional reprogramming.

cancer biology↗

Prioritization of metabolic genes as novel therapeutic targets in estrogen-receptor negative breast tumors using multi-omics data and text mining

Estrogen-receptor negative (ERneg) breast cancer is an aggressive breast cancer subtype in the need for new therapeutic options. We have analyzed metabolomics, proteomics and transcriptomics data for a cohort of 276 breast tumors (MetaCancer study) and nine public transcriptomics datasets using univariate statistics, meta-analysis, Reactome pathway analysis, biochemical network mapping and text mining of metabolic genes. In the MetaCancer cohort, a total of 29% metabolites, 21% proteins and 33% transcripts were significantly different (raw p < 0.05) between ERneg and ERpos breast tumors. In the nine public transcriptomics datasets, on average 23% of all genes were significantly different (raw p < 0.05). Specifically, up to 60% of the metabolic genes were significantly different (meta-analysis raw p < 0.05) across the transcriptomics datasets. Reactome pathway analysis of all omics showed that energy metabolism, and biosynthesis of nucleotides, amino acids, and lipids were associated with ERneg status. Text mining revealed that several significant metabolic genes and enzymes have been rarely reported to date, including PFKP, GART, PLOD1, ASS1, NUDT12, FAR1, PDE7A, FAHD1, ITPK1, SORD, HACD3, CDS2 and PDSS1. Metabolic processes associated with ERneg tumors were identified by multi-omics integration analysis of metabolomics, proteomics and transcriptomics data. Overall results suggested that TCA anaplerosis, proline biosynthesis, synthesis of complex lipids and mechanisms for recycling substrates were activated in ERneg tumors. Under-reported genes were revealed by text mining which may serve as novel candidates for drug targets in cancer therapies. The workflow presented here can also be used for other tumor types.

bioinformatics↗