Biology subjects

Madhukar, N.

Publications and source records attributed to Madhukar, N..

3 recordsLinked to original sources

bioRxiv2019

Imatinib overrides taxane resistance by selective inhibition of novel CLIP1 variant obstructing the microtubule pore

Despite its widespread use, the majority of patients with gastric cancer (GC) will not respond to taxane chemotherapy due to resistance mechanisms. Here, we report the discovery of a novel truncated variant of the microtubule plus-end binding protein (+TIP) CLIP-170, hereafter CLIP-170S, whose expression is enriched in taxane resistant cell lines and patients with GC. To establish causation, we knocked-down (KD) CLIP-170S which completely reversed taxane resistance. Mass-spec proteomics and 5-RACE further showed that CLIP-170S lacked the first 150 amino acids, including the Cap-Gly motif required for microtubule (MT) plus-end localization. Mechanistically, we show that CLIP-170S was mislocalized from the MT plus-end to the MT lattice obstructing the MT pore surface site required for taxane entry into the MT lumen. Computational analysis of RNA-seq data from taxane-sensitive and resistant GC cell lines, predicted imatinib as the top candidate drug to overcome drug resistance. Imatinib treatment completely reversed taxane resistance, as predicted, and did so unexpectedly by selective depletion of CLIP-170S. Importantly, CLIP170S was found to be highly prevalent in tumor biopsies from patients with GC. Taken together, these data identify CLIP-170S as a novel, clinically prevalent +TIP variant that obstructs the MT pore and confers taxane resistance. The discovery of this previously unrecognized variant together with the computational discovery of Imatinib as a selective CLIP-170S inhibitor, implicate the MT pore in clinical taxane resistance and provide new therapeutic opportunities for treatment of GC and beyond.

cancer biology↗

bioRxiv2019

A machine learning and network framework to discover new indications for small molecules

Drug repurposing, identifying novel indications for drugs, bypasses common drug development pitfalls to ultimately deliver therapies to patients faster. However, most repurposing discoveries have been led by anecdotal observations (e.g. Viagra) or experimental-based repurposing screens, which are costly, time-consuming, and imprecise. Recently, more systematic computational approaches have been proposed, however these rely on utilizing the information from the diseases a drug is already approved to treat. This inherently limits the algorithms, making them unusable for investigational molecules. Here, we present a computational approach to drug repurposing, CATNIP, that requires only biological and chemical information of a molecule. CATNIP is trained with 2,576 diverse small molecules and uses 16 different drug similarity features, such as structural, target, or pathway based similarity. This model obtains significant predictive power (AUC = 0.841). Using our model, we created a repurposing network to identify broad scale repurposing opportunities between drug types. By exploiting this network, we identified literature-supported repurposing candidates, such as the use of systemic hormonal preparations for the treatment of respiratory illnesses. Furthermore, we demonstrated that we can use our approach to identify novel uses for defined drug classes. We found that adrenergic uptake inhibitors, specifically amitriptyline and trimipramine, could be potential therapies for Parkinsons disease. Additionally, using CATNIP, we predicted the kinase inhibitor, vandetanib, as a possible treatment for Type 2 Diabetes. Overall, this systematic approach to drug repurposing lays the groundwork to streamline future drug development efforts.

pharmacology and toxicology↗

bioRxiv2019

A machine learning approach predicts essential genes and pharmacological targets in cancer

Loss-of-function (LoF) screenings have the potential to reveal novel cancer-specific vulnerabilities, prioritize drug treatments, and inform precision medicine therapeutics. These screenings were traditionally done using shRNAs, but with the recent emergence of CRISPR technology there has been a shift in methodology. However, recent analyses have found large inconsistencies between CRISPR and shRNA essentiality results. Here, we examined the DepMap project, the largest cancer LoF effort undertaken to date, and find a lack of correlation between CRISPR and shRNA LoF results; we further characterized differences between genes found to be essential by either platform. We then introduce ECLIPSE, a machine learning approach, which combines genomic, cell line, and experimental design features to predict essential genes and platform specific essential genes in specific cancer cell lines. We applied ECLIPSE to known drug targets and found that our approach strongly differentiated drugs approved for cancer versus those that have not, and can thus be leveraged to identify potential cancer repurposing opportunities. Overall, ECLIPSE allows for a more comprehensive analysis of gene essentiality and drug development; which neither platform can achieve alone.

cancer biology↗