Biology subjects

Dar, A. C.

Publications and source records attributed to Dar, A. C..

4 recordsLinked to original sources

bioRxiv2025

Mechanisms of resistance to active state selective tri-complex RAS inhibitors

Tri-complex inhibitors (TCIs) act as molecular glues to recruit cyclophilin A (CYPA) to the active (GTP-bound or ON) conformation of RAS, which in turn prevents the activation of downstream effector proteins like RAF and PI3K. Emerging data demonstrate clinical activity, including tumor regressions, in patients with RAS driven cancers. Despite being promising therapeutic interventions, the mechanisms of resistance in patients treated with these inhibitors remain unknown. Here we studied matched baseline and post-progression specimens from patients treated with the RAS(ON) multi-selective inhibitor daraxonrasib (RMC-6236). Tissue or cell-free DNA specimens were collected from 40 patients with RAS-mutant non-small cell lung, colorectal, or other cancers. Eighteen patients (45%) were found to have acquired alterations in RAS signaling intermediates, including recurrent alterations in KRAS, BRAF, RAF1, MAP2K1/2 and PIK3CA. Preclinical resistance models mirrored the alterations observed in patients. We found that secondary KRAS Y64X mutations caused resistance by disrupting an important pi-pi interaction between KRAS and the indole ring of daraxonrasib, which lowers the affinity of the daraxonrasib:CYPA binary complex for active KRAS. We also identified kinase-dead and low-activity BRAF mutations in samples with acquired resistance. This is puzzling, because TCIs are expected to prevent the interaction between RAS and BRAF, which is needed for hypoactive mutants to dimerize and signal. We now show that RAF dimers are harder to displace from active RAS, as compared to their monomeric forms. Indeed, enhanced RAF dimerization attenuated the ability of TCIs to recruit CYPA to active RAS, resulting in diminished inhibition of oncogenic signaling and tumor growth. Thus, several clinical resistance alterations converge at attenuating the formation of the RAS:daraxonrasib:CYPA tri-complex, either by preventing daraxonrasib binding or by inducing RAF dimers.

cancer biology↗

bioRxiv2025

Paired primary-metastasis patient-derived organoids and mouse models identify phenotypic evolution and druggable dependencies of peritoneal metastasis from appendiceal cancer

Peritoneal carcinomatosis is a common yet deadly manifestation of gastrointestinal cancers, with few effective treatments. To identify targetable determinants of peritoneal metastasis, we focused on appendiceal adenocarcinoma (AC), a gastrointestinal cancer that metastasizes almost exclusively to the peritoneum. Current treatments are extrapolated from colorectal cancer (CRC), yet AC has distinct genomic alterations, mucinous morphology and peritoneum restricted metastatic pattern. Further, no stable preclinical models of AC exist, limiting drug discovery and representing an unmet clinical need. We establish a first-in-class stable biobank of 16 long-term cultured AC patient-derived organoids (PDOs), including 3 matched, simultaneously resected primary AC-peritoneal carcinomatosis (AC-PC) pairs. By enriching for cancer cells, AC PDOs enable accurate genomic characterization relative to paucicellular AC tissue. We establish an organoid orthotopic intraperitoneal xenograft model that recapitulates diffuse peritoneal carcinomatosis and show that PC-organoids retain increased metastatic capacity, decreased growth factor dependency and sensitivity to standard of care chemotherapy relative to matched primary AC organoids. Single cell profiling of AC-PC pairs reveals dedifferentiation from mucinous differentiated states in primary AC into intestinal stem cell and fetal progenitor states in AC-PC, with upregulation of oncogenic signaling pathways. Through hypothesis-driven drug testing, we identify KRASMULTI-ON inhibitor RMC-7977 and Wnt-targeting tyrosine kinase inhibitor WNTinib as novel, clinically actionable strategies to target AC-PC more effectively.

cancer biology↗

bioRxiv2019

PyVOL: a PyMOL plugin for visualization, comparison, and volume calculation of drug-binding sites

MotivationBinding pocket volumes are a simple yet important predictor of small molecule binding; however, generating visualizations of pocket topology and performing meaningful volume comparisons can be difficult with available tools. Current programs for accurate volume determination rely on extensive user input to define bulk solvent boundaries and to partition cavities into subpockets, increasing inter-user variability in measurements as well as time demands. ResultsWe developed PyVOL, a python package with a PyMOL interface and GUI, to visualize, to characterize, and to compare binding pockets. PyVOLs pocket identification algorithm is designed to maximize reproducibility through minimization of user-provided parameters, avoidance of grid-based methods, and automated subpocket identification. This approach permits efficient, scalable volume calculations. AvailabilityPyVOL is released under the MIT License. Source code and documentation are available through github (https://github.com/schlessingerlab/pyvol/) with distribution through PyPI (bio-pyvol). Contactavner.schlessinger@mssm.edu, arvin.dar@mssm.edu

pharmacology and toxicology↗

bioRxiv2018

Integrated in silico and Drosophila cancer model platform captures previously unappreciated chemicals perturbing a kinase network

Drosophila provides an inexpensive and quantitative platform for measuring whole animal drug response. A complementary approach is virtual screening, where chemical libraries can be efficiently screened against protein target(s). Here, we present a unique discovery platform integrating structure-based modeling with Drosophila biology and organic synthesis. We demonstrate this platform by developing chemicals targeting a Drosophila model of Medullary Thyroid Cancer (MTC) with disease-promoting kinase network activated by mutant dRetM955T. Structural models for kinases relevant to MTC were generated for virtually screening to identify initial hits that were dissimilar to known kinase inhibitors yet suppressed dRetM955T-induced oncogenicity. We then combined features from the hits and known inhibitors to create a hybrid molecule with improved dRetM955T phenotypic outcome. Our platform provides a framework to efficiently explore novel chemical spaces, develop compounds outside of the current inhibitor chemical space, and correct cancer-causing signaling networks to improve disease prognosis while minimizing whole body toxicity.\n\nAUTHOR SUMMARYEffective and safe treatment of multigenic diseases often involves drugs that modulate whole systems by interacting with multiple nodes in pathways and networks, i.e., polypharmacology. Polypharmacology is increasingly appreciated as a potential desirable property of kinase drugs; however, most known drugs that interact with multiple targets have been identified as such by chance, and most polypharmacological compounds are not chemically unique resembling to structures of known kinase inhibitors. The fruit fly Drosophila has been established as a robust screening platform that provides an inexpensive, rapid, and quantitative measure of whole animal drug response, complementing computational approaches. We present a chemical genetics approach that efficiently combines Drosophila with structural prediction and virtual screening, creating a unique discovery platform. We demonstrate the utility of our approach by developing useful small molecules targeting a kinase network in a Drosophila model of Medullary Thyroid Cancer (MTC) driven by the active mutant dRetM955T.

cancer biology↗