Choudhry, N. | Search bioRxiv

bioRxiv2023

MYC amplifies mitotic perturbations elicited by LXY18 to enable synthetic lethality

The MYC oncoprotein represents an intriguing target for cancer treatment, but its therapeutic potential has been hindered by the absence of specific pharmacological inhibitors. In this study, we demonstrate that the phenoxy quinoline compound LXY18 selectively targets and eliminates cells overexpressing MYC, leaving non-transformed cells unharmed. This synthetic lethality arises from an acute induction of multipolarity, resulting in a persistent arrest in early mitosis followed by cell death in mitosis or after mitotic slippage. Distinctively, LXY18s action contrasts with other antimitotic compounds, as they either fail to induce mitotic arrest or elicit mitotic arrest irrespective of MYC abundance. Furthermore, the MYC abundance in a panel of 98 tumor cell lines correlates with their sensitivity to LXY18. Collectively, our findings uncover LXY18 as an MYC- enabled mitotic blocker and open a new avenue to selectively target MYC-overexpressing tumor cells without affecting normal cells.

cancer biology↗

bioRxiv2020

Automated Deep Learning-based Multi-class Fluid Segmentation in Swept-Source Optical Coherence Tomography Images

PurposeTo evaluate the performance of a deep learning-based, fully automated, multi-class, macular fluid segmentation algorithm relative to expert annotations in a heterogeneous population of confirmed wet age-related macular degeneration (wAMD) subjects. MethodsTwenty-two swept-source optical coherence tomography (SS-OCT) volumes of the macula from 22 from different individuals with wAMD were manually annotated by two expert graders. These results were compared using cross-validation (CV) to automated segmentations using a deep learning-based algorithm encoding spatial information about retinal tissue as an additional input to the network. The algorithm detects and delineates fluid regions in the OCT data, differentiating between intra- and sub-retinal fluid (IRF, SRF), as well as fluid resulting from in serous pigment epithelial detachments (PED). Standard metrics for fluid detection and quantification were used to evaluate performance. ResultsThe per slice receiver operating characteristic (ROC) area under the curves (AUCs) for each of these fluid types were 0.90, 0.94 and 0.94 for IRF, SRF and PED, respectively. Per volume results were 0.94 and 0.88 for IRF and PED (SRF being present in all cases). The correlation of fluid volume between the expert graders and the algorithm were 0.99 for IRF, 0.99 for SRF and 0.82 for PED. ConclusionsAutomated, deep learning-based segmentation is able to accurately detect and quantify different macular fluid types in SS-OCT data on par with expert graders.

bioinformatics↗