Biology subjects

Kugener, G.

Publications and source records attributed to Kugener, G..

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↗

bioRxiv2018

Inhibition of mitochondrial ferredoxin 1 (FDX1) prevents adaptation to proteotoxic stress

The mechanisms used by cancer cells to resist the severe disruption in protein homeostasis caused by proteasome inhibitors remain obscure. Here, we show this resistance correlates with a metabolic shift from glycolysis to oxidative phosphorylation (OXPHOS). Employing small molecule screens, we identified a striking overlap between compounds that preferentially impede the growth of proteasome inhibitor-resistant cancer cells and those that block the growth of high OXPHOS cells. Elesclomol potently exhibits both characteristics. Using genome-wide CRISPR/Cas9-based screening, in vitro validation and NMR spectroscopy we identify mitochondrial protein ferredoxin 1 (FDX1), a critical component of mitochondrial iron-sulfur (Fe-S) cluster biosynthesis, as the primary target of elesclomol. In a mouse model of multiple myeloma, inhibition of FDX1 with elesclomol significantly attenuated the emergence of proteasome inhibitor-resistance and markedly prolonged survival. Our work reveals that the mitochondrial Fe-S cluster pathway is a targetable vulnerability in cancers that are resistant to increased proteotoxic burden.

cancer biology↗