Biology subjects

Vazquez, F.

Publications and source records attributed to Vazquez, F..

3 recordsLinked to original sources

bioRxiv2026

Synergistic targeting of EP300/CBP and EYA co-activators collapses the rhabdomyosarcoma core regulatory circuit

Rhabdomyosarcoma (RMS) is a multi-subtype, high-risk pediatric sarcoma with a low mutational burden. The mutations found in RMS often alter genes involved in transcriptional control. Approaches to target dysregulated RMS transcription have remained elusive. Here, we develop a novel approach to target RMS transcription comprising simultaneous targeting of two distinctly acting transcriptional co-activators. We discover a common identity-controlling pan-RMS core regulatory circuit (CRC) composed of oncogenic and lineage-specific myogenic master transcription factors (mTFs). Using a super-enhancer-based reporter screen, we identify the EP300/CBP inhibitor A485 as a potent inhibitor of the pan-RMS CRC, though with efficacy-limiting toxicities. To enhance efficacy, we identify the mTF-binding co-activator EYA2 as a co-factor of this pan-RMS CRC and exploit a new second-generation EYA1/2 inhibitor, LG1-34, to disrupt its function. Combined co-activator inhibition inactivates the CRC and synergistically reduces RMS growth. This strategy dually targets CRC-associated co-activators to cooperatively suppress the RMS transcriptome and enforce cell death.

cancer biology↗

bioRxiv2018

Improved estimation of cancer dependencies from large-scale RNAi screens using model-based normalization and data integration

The availability of multiple datasets together comprising hundreds of genome-scale RNAi viability screens across a diverse range of cancer cell lines presents new opportunities for understanding cancer vulnerabilities. Integrated analyses of these data to assess differential dependency across genes and cell lines are challenging due to confounding factors such as batch effects and variable screen quality, as well as difficulty assessing gene dependency on an absolute scale. To address these issues, we incorporated estimation of cell line screen quality parameters and hierarchical Bayesian inference into an analytical framework for analyzing RNAi screens (DEMETER2; https://depmap.org/R2-D2). We applied this model to individual large-scale datasets and show that it substantially improves estimates of gene dependency across a range of performance measures, including identification of gold-standard essential genes as well as agreement with CRISPR-Cas9-based viability screens. This model also allows us to effectively integrate information across three large RNAi screening datasets, providing a unified resource representing the most extensive compilation of cancer cell line genetic dependencies to date.

genomics↗

bioRxiv2017

Computational correction of copy-number effect improves specificity of CRISPR-Cas9 essentiality screens in cancer cells

The CRISPR-Cas9 system has revolutionized gene editing both on single genes and in multiplexed loss-of-function screens, enabling precise genome-scale identification of genes essential to proliferation and survival of cancer cells. However, previous studies reported that an anti-proliferative effect of Cas9-mediated DNA cleavage confounds such measurement of genetic dependency, particularly in the setting of copy number gain1-4. We performed genome-scale CRISPR-Cas9 essentiality screens on 342 cancer cell lines and found that this effect is common to all lines, leading to false positive results when targeting genes in copy number amplified regions. We developed CERES, a computational method to estimate gene dependency levels from CRISPR-Cas9 essentiality screens while accounting for the copy-number-specific effect, as well as variable sgRNA activity. We applied CERES to sets of screens performed with different sgRNA libraries and found that it reduces false positive results and provides meaningful estimates of sgRNA activity. As a result, the application of CERES improves confidence in the interpretation of genetic dependency data from CRISPR-Cas9 essentiality screens of cancer cell lines.

genomics↗