PAIRWISE: Deep Learning-based Prediction of Effective Personalized Drug Combinations in Cancer.

Combination therapies offer promise for improving cancer treatment efficacy and preventing recurrence. However, identifying optimal drug combinations tailored to specific cancer subtypes and individual patients is extremely challenging due to the vast number of possible combinations and tumor heterogeneity. To address this gap, we take a machine learning approach combining deep learning with transfer learning to incorporate prior scientific knowledge and predict drug synergy based on tumor-specific transcriptome profiles.

Understanding tumour growth variability in breast cancer xenograft models identifies PARP inhibition resistance biomarkers.

Understanding the mechanisms of resistance to PARP inhibitors (PARPi) is a clinical priority, especially in breast cancer. We developed a novel mathematical framework accounting for intrinsic resistance to olaparib, identified by fitting the model to tumour growth metrics from breast cancer patient-derived xenograft (PDX) data. Pre-treatment transcriptomic profiles were used with the calculated resistance to identify baseline biomarkers of resistance, including potential combination targets.

Clinical comparison of the TruVision Omni and four progressive addition lenses.

The TruVision Omni is a progressive addition lens (PAL) using a bipolar design and the Dirichlet principle to minimize optical aberrations. The design was evaluated by having each patient elect to keep one lens type after masked paired comparison of the TruVision Omni with one of four other PAL types: VIP, Varilux, New Super NoLine, and Gradal HS. The Omni was chosen most frequently in each case: 16 to 8, 17 to 6, 18 to 6, and 15 to 3, respectively.