Biology subjects

Lae, M.

Publications and source records attributed to Lae, M..

3 recordsLinked to original sources

bioRxiv2023

Disequilibrium between BRCA1 and BRCA2 circular and messenger RNAs plays a role in breast cancer

AO_SCPLOWBSTRACTC_SCPLOWBreast cancer is a frequent disease for which the discovery of markers for early detection or prognostic assessment remains challenging. Circular RNAs (circRNAs) are single-stranded structures in closed loops, produced by backsplicing. CircRNA and messenger RNA (mRNA) are generated cotranscriptionally and backsplicing and linear splicing compete against each other. As mRNAs are key players in tumorigenesis, we hypothesize that a balance disruption between circRNAs and mRNAs could promote breast cancer. Hence, we developed an assay for a simultaneous study of circRNAs and mRNAs, called Splice and Expression Analyses by exon Ligation and High Throughput Sequencing (SEALigTHS). Following SEALigHTS validation for BRCA1 and BRCA2, our hypothesis was tested using an independent research set of 95 pairs of tumour and adjacent normal breast tissues. On this research set, ratios of BRCA1 and BRCA2 circRNAs/mRNAs were significantly lower in tumour breast tissue compared to normal tissue (p=1.6e-09 and p=4.4e-05 for BRCA1 and BRCA2, respectively). Overall, we developed an innovative method to study linear and backsplicing, described the repertoire of BRCA1 and BRCA2 circRNAs, including 10 novel ones, and showed for the first time that a disequilibrium between BRCA1 and BRCA2 circRNAs and mRNAs plays a role in breast cancer.

cancer biology↗

bioRxiv2022

Neural network for the prediction of treatment response in triple negative breast cancer

AO_SCPLOWBSTRACTC_SCPLOWThe automatic analysis of stained histological sections is becoming increasingly popular. Deep Learning is today the method of choice for the computational analysis of such data, and has shown spectacular results for large datasets for a large variety of cancer types and prediction tasks. On the other hand, many scientific questions relate to small, highly specific cohorts. Such cohorts pose serious challenges for Deep Learning, typically trained on large datasets. In this article, we propose a modification of the standard nested cross-validation procedure for hyper-parameter tuning and model selection, dedicated to the analysis of small cohorts. We also propose a new architecture for the particularly challenging question of treatment prediction, and apply this workflow to the prediction of response to neoadjuvant chemotherapy for Triple Negative Breast Cancer.

bioinformatics↗

bioRxiv2020

Establishment of multiple novel patient-derived models of desmoplastic small round cell tumor enabling functional characterization of ERBB pathway signaling and pre-clinical evaluation of a novel targeted therapy approach

Desmoplastic small round cell tumor (DSRCT) is characterized by the t(11;22)(p13;q12) chromosomal translocation, which fuses the transcriptional regulatory domain of EWSR1 with the zinc finger DNA-binding domain of WT1, resulting in the oncogenic transcription factor EWS-WT1. DSRCT primarily affects young males and has a 5-year overall survival of about 15%. Typical treatment approaches for patients with DSRCT involve a multi-modal combination of surgery, chemotherapy and radiation. The paucity of DSRCT disease models has hampered functional and pre-clinical therapeutic studies in this aggressive cancer. Here, we developed robust preclinical disease models and mined DSRCT expression profiling data to identify genetic vulnerabilities that could be leveraged for the identification of rational therapies. Specifically, we developed four new DSRCT cell lines and one patient-derived xenograft (PDX) model. Transcriptomic and proteomic profiling showed evidence of activation of the ERBB pathway. Ectopic expression of EWSR1-WT1 resulted in upregulation of ERRB family ligands and downstream signaling. Treatment of DSRCT cell lines with ERBB ligands resulted in activation of EGFR, ERBB2, ERK1/2 and AKT, and stimulation of cell growth. Conversely, targeting of EGFR using shRNA, small molecule inhibitors (afatinib, neratinib) or an anti-EGFR antibody (cetuximab) inhibited growth and induced apoptosis in DSRCT cells. Finally, treatment of mice bearing DSRCT xenografts with a combination of cetuximab and afatinib significantly reduced tumor growth. These data provide a rationale for the clinical evaluation of EGFR antagonists in patients with DSRCT.

cancer biology↗