Bomane, A. | bioRxiv Science

bioRxiv2026

Reverse molecular pharmacology identifies the non-canonical axis of IRAK as a chemoresistance factor in neuroblastoma

Owing to chemoresistance, the prognosis of relapsed neuroblastoma is dismal with less than 10% of patients surviving after 5 years. We developed a reverse molecular pharmacology approach that is based on high-throughput drug screening coupled with chemo-informatic and transcriptomic analyses. This led to the identification of IRAK1 as a key chemoresistance factor in neuroblastoma. By performing functional and pharmacological drug combination screens targeting IRAK1, we revealed a synergy between IRAK1 inhibition/silencing and BET, EGFR and mTOR inhibitors as well as microtubule-targeting agents. The synergistic combination of microtubule-targeting agent, vincristine and IRAK inhibitors was then confirmed in tumor spheroids, patient-derived tumoroids and a syngeneic orthotopic mouse model. Mechanistically, IRAK inhibition potentiated the pro-apoptotic and cell cycle arrest properties of vincristine via a pathway involving the PIDDosome complex rather than its canonical MyDDosome axis. Altogether, this study represents a proof-of-concept of our reverse molecular pharmacology approach to quickly develop biology-guided drug combinations, that could be applied to any other human diseases.

cancer biology↗

bioRxiv2018

Machine learning models to predict in vivo drug response via optimal dimensionality reduction of tumour molecular profiles

Inter-tumour heterogeneity is one of cancers most fundamental features. Patient stratification based on drug response prediction is hence needed for effective anti-cancer therapy. However, lessons from the past indicate that single-gene markers of response are rare and/or often fail to achieve a significant impact in clinic. In this context, Machine Learning (ML) is emerging as a particularly promising complementary approach to precision oncology. Here we leverage comprehensive Patient-Derived Xenograft (PDX) pharmacogenomic data sets with dimensionality-reducing ML algorithms with this purpose. Results show that combining multiple gene alterations via ML leads to better discrimination between sensitive and resistant PDXs in 19 of the 26 analysed cases. Highly predictive ML models employing concise gene lists were found for three cases: Paclitaxel (breast cancer), Binimetinib (breast cancer) and Cetuximab (colorectal cancer). Interestingly, each of these ML models identify some responsive PDXs not harbouring the best actionable mutation for that case (such PDXs were missed by those single-gene markers). Moreover, ML multi-gene predictors generally retrieve a much higher proportion of treatment-sensitive PDXs than the corresponding single-gene marker. As PDXs often recapitulate clinical outcomes, these results suggest that many more patients could benefit from precision oncology if multiple ML algorithms were applied to existing clinical pharmacogenomics data, especially those algorithms generating classifiers combining data-selected gene alterations.

bioinformatics↗