Biology subjects

Giner, G.

Publications and source records attributed to Giner, G..

4 recordsLinked to original sources

bioRxiv2025

Thioredoxin reductase 3 inhibition sensitizes triple-negative breast cancer cells to EGFR inhibitors

Triple-negative breast cancer (TNBC) is an aggressive subtype with poor prognosis and limited treatments. Although 40-70% of TNBC cases overexpress EGFR, anti-EGFR therapies show minimal clinical benefit. This may result from inherent resistance, inactive EGFR, or its absence on the plasma membrane. Here, we used genome wide CRISPR knock out library screening to identify factors that mediate resistance to EGFR inhibitor. We discovered that depletion of a redox protein, thioredoxin reductase 3 (TXNRD3), in MDA-MB-231 cells reduces cell survival after Erlotinib treatment suggesting that loss of TXNRD3 may sensitize TNBC cells to EGFR inhibitors. siRNA-induced knockdown or pharmacological inhibition of TXNRD3 using an FDA-approved drug Auranofin significantly sensitized EGFR-high TNBC cells to EGFR inhibitors. Mechanistically, TXNRD3 knockdown or inhibition using Auranofin increased oxidative stress-mediated accumulation of phosphorylated EGFR at Y1068 and increased surface accumulation. Interestingly, combination of Auranofin with EGFR inhibition markedly induced antibody-dependent cell mediated cytotoxicity (ADCC) and exerted a significant anti-cancer activity in vivo model. Overall, our findings indicate that targeting TXNRD3 with Auranofin can activate EGFR and enhance its surface localization, thereby sensitizing TNBC cells to anti-EGFR therapies. This approach offers a promising strategy for treating TNBC patients who are resistant to current EGFR-targeted treatments.

cancer biology↗

bioRxiv2022

Genome-wide in vivo CRISPR screens identify GATOR1 as a TP53 induced tumour suppressor

Identifying tumor suppressor genes is predicted to inform on the development of novel strategies for cancer therapy. To identify new lymphoma driving processes that cooperate with oncogenic MYC (which is abnormally highly expressed in [~]70% of human cancers) we have used a genome-wide CRISPR knockout screen in E{micro}-Myc;Cas9 transgenic hematopoietic stem and progenitor cells in vivo. We discovered that loss of any of the GATOR1 complex components - NPRL3, DEPDC5, NPRL2 - significantly accelerated c-MYC-driven lymphoma development in mice. Low expression of the GATOR1 complex genes correlated with poor survival outcomes for human patients with high MYC- expressing cancers. Murine lymphomas lacking GATOR1 were highly sensitive to mTOR inhibitors as a single agent therapy, both in vitro and in vivo. These findings identify inhibition of mTORC1 as a potent tumor suppressive mechanism in c-MYC-driven lymphomagenesis and suggest a new avenue for therapeutic intervention in GATOR1-deficient lymphomas through mTOR inhibition. Key PointsO_LIIn vivo CRISPR/Cas9 whole genome knockout screens identified the GATOR1 complex as a potent suppressor of c-MYC-driven lymphomagenesis. C_LIO_LIGATOR1 deficiency in MYC-driven lymphomas confers sensitivity to mTOR inhibition, suggesting a novel therapeutic approach. C_LI

cancer biology↗

bioRxiv2019

A new local covariance matrix estimation for the classification of gene expression profiles in RNA-Seq data

Background and ObjectiveRecent developments in the next-generation sequencing (NGS) based on RNA-sequencing (RNA-Seq) allow researchers to measure the expression levels of thousands of genes for multiple samples simultaneously. In order to analyze these kind of data sets, many classification models have been proposed in the literature. Most of the existing classifiers assume that genes are independent; however, this is not a realistic approach for real RNA-Seq classification problems. For this reason, some other classification methods, which incorporates the dependence structure between genes into a model, are proposed. qtQDA proposed by Kochan et al. [1] is one of those classifiers, which estimates covariance matrix by Maximum Likelihood Estimator.\n\nMethodsIn this study, we use a another approach based on local dependence function to estimate the covariance matrix to be used in the qtQDA classification model. We investigate the impact of different covariance estimates on RNA-Seq data classification.\n\nResultsThe performances of qtQDA classifier based on two different covariance matrix estimates are compared over two real RNA-Seq data sets, in terms of classification error rates. The results show that using local dependence function approach yields a better estimate of covariance matrix and increases the performance of qtQDA classifier.\n\nConclusionIncorporating the true/accurate covariance matrix into the classification model is an important and crucial step particularly for cancer prediction. The local covariance matrix estimate allows researchers to classify cancer patients based on gene expression profiles more accurately. R code for local dependence function is available at https://github.com/Necla/LocalDependence.

bioinformatics↗

bioRxiv2019

qtQDA: quantile transformed quadratic discriminant analysis for high-dimensional RNA-seq data

Classification on the basis of gene expression data derived from RNA-seq promises to become an important part of modern medicine. We propose a new classification method based on a model where the data is marginally negative binomial but dependent, thereby incorporating the dependence known to be present between measurements from different genes. The method, called qtQDA, works by first performing a quantile transformation (qt) then applying Gaussian Quadratic Discriminant Analysis (QDA) using regularized covariance matrix estimates. We show that qtQDA has excellent performance when applied to real data sets and has advantages over some existing approaches. An R package implementing the method is also available.

bioinformatics↗