Biology subjects

Bahreini, A.

Publications and source records attributed to Bahreini, A..

4 recordsLinked to original sources

bioRxiv2021

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↗

bioRxiv2020

ESR1 mutant breast cancers show elevated basal cytokeratins and immune activation

Estrogen receptor alpha (ER/ESR1) is mutated in 30-40% of endocrine resistant ER-positive (ER+) breast cancer. ESR1 mutations cause ligand-independent growth and increased metastasis in vivo and in vitro. Despite the distinct clinical features and changes in therapeutic response associated with ESR1 mutations, there are no data about their potential role in intrinsic subtype switching. Applying four luminal and basal gene set pairs, ESR1 mutant cell models and clinical samples showed a significant enrichment of basal subtype markers. Among them, the six basal cytokeratins (BCKs) were the most enriched genes. Induction of BCKs was independent of ER binding and instead associated with chromatin reprogramming centered around a progesterone receptor-orchestrated topological associated domain at the KRT14/16/17 genomic region. Unexpectedly, high BCK expression in ER+ primary breast cancer is associated with good prognosis, and these tumors show enriched activation of a number of immune pathways, a distinctive feature shared with ESR1 mutant tumors. S100A8 and S100A9 were among the most highly induced immune mediators shared between high-BCKs ER+ and ESR1 mutant tumors, and single-cell RNA-seq analysis inferred their involvement in paracrine crosstalk between epithelial and stromal cells. Collectively, these observations demonstrate that ESR1 mutant tumors gain basal features with induction of basal cytokeratins via epigenetic mechanisms in rare subpopulation of cells. This is associated with increased immune activation, encouraging additional studies of immune therapeutic vulnerabilities in ESR1 mutant tumors.

cancer biology↗

bioRxiv2018

AUTOMAGIC: Standardized Preprocessing of Big EEG Data

Electroencephalography (EEG) recordings have been rarely included in large-scale studies. This is arguably not due to a lack of information that lies in EEG recordings but mainly on account of methodological issues. In many cases, particularly in clinical, pediatric and aging populations, the EEG has a high degree of artifact contamination and the quality of EEG recordings often substantially differs between subjects. Although there exists a variety of standardized preprocessing methods to clean EEG from artifacts, currently there is no method to objectively quantify the quality of preprocessed EEG. This makes the commonly accepted procedure of excluding subjects from analyses due to exceeding contamination of artifacts highly subjective. As a consequence, P-hacking is fostered, the replicability of results is decreased, and it is difficult to pool data from different study sites. In addition, in large-scale studies, data are collected over years or even decades, requiring software that controls and manages the preprocessing of ongoing and dynamically growing studies. To address these challenges, we developed Automagic, an open-source MATLAB toolbox that acts as a wrapper to run currently available preprocessing methods and offers objective standardized quality assessment for growing studies. The software is compatible with the Brain Imaging Data Structure (BIDS) standard and hence facilitates data sharing. In the present paper we outline the functionality of Automagic and examine the effect of applying combinations of methods on a sample of resting EEG data. This examination suggests that applying a pipeline of algorithms to detect artifactual channels in combination with Multiple Artifact Rejection Algorithm (MARA), an independent component analysis (ICA)-based artifact correction method, is sufficient to reduce a large extent of artifacts.

neuroscience↗

bioRxiv2018

Targeted mutation detection in breast cancer using MammaSeqTM

BackgroundBreast cancer is the most common invasive cancer among women worldwide. Next-generation sequencing (NGS) has revolutionized the study of cancer across research labs around the globe, however genomic testing in clinical settings remain limited. Advances in sequencing reliability, pipeline analysis, accumulation of relevant data, and the reduction of costs are rapidly increasing the feasibility of NGS-based clinical decision making.\n\nMethodsWe report the development of MammaSeq, a breast cancer specific NGS panel, targeting 79 genes and 1369 mutations, optimized for use in primary and metastatic breast cancer. To validate the panel, 46 solid tumor and 14 plasma circulating-free cfDNA samples were sequenced to a mean depth of 2311X and 1820 X respectively. Variants were called using Ion Torrent Suite 4.0 and annotated with cravat CHASM. CNVKit was used to call copy number variants in the solid tumor cohort. The oncoKB Precision Oncology Database was used to identify clinically actionable variants. ddPCR was used to validate select cfDNA mutations.\n\nResultsIn cohorts of 46 solid tumors and 14 cfDNA samples from patients with advanced breast cancer we identified 592 and 43 protein coding mutations. Mutations per sample in the solid tumor cohort ranged from 1 to 128 (median 3) and the cfDNA cohort ranged from 0 to 26 (median 2.5). Copy number analysis in the solid tumor cohort identified 46 amplifications and 35 deletions. We identified 26 clinically actionable variants (levels 1-3) annotated by OncoKB, distributed across 20 out of 46 cases (40%), in the solid tumor cohort. Allele frequencies of ESR1 and FOXA1 mutations correlated with CA.27.29 levels in patient matched blood draws.\n\nConclusionsIn solid tumors biopsies and cfDNA, MammaSeq detects clinicaly actionable mutations (oncoKB levels 1-3) in 22/46 (48%) solid tumors and in 4/14 (29%) of cfDNA samples. MammaSeq is a targeted panel suitable for clinically actionable mutation detection in breast cancer.

cancer biology↗