Intrinsic and evasive antiangiogenic drug (AAD) resistance is frequently developed in cancer patients, and molecular mechanisms underlying AAD resistance remain largely unknown. Here we describe AAD-triggered, lipid-dependent metabolic reprogramming as an alternative mechanism of AAD resistance. Unexpectedly, tumor angiogenesis in adipose and non-adipose environments is equally sensitive to AAD treatment. AAD-treated tumors in adipose environment show accelerated growth rates in the presence of a minimal number of microvessels. Mechanistically, AAD-induced tumor hypoxia initiates the fatty acid oxidation metabolic reprogramming and increases uptake of free fatty acid (FFA) that stimulates cancer cell proliferation. Inhibition of carnitine palmitoyl transferase 1A (CPT1) significantly compromises the FFA-induced cell proliferation. Genetic and pharmacological loss of CPT1 function sensitizes AAD therapeutic efficacy and enhances its anti-tumor effects. Together, we propose an effective cancer therapy concept by combining drugs that target angiogenesis and lipid metabolism.

Purpose: To investigate changes in subfoveal choroidal thickness (SFChT) during orthokeratology (Ortho-K) lens wear and after its cessation and the association of short-term change in SFChT with the long-term eye elongation in Ortho-K subjects. Design: A prospective clinical trial. Methods: Fifty myopic children aged between 9 and 14 years were enrolled. Twenty-nine subjects continuously wore Ortho-K lens for 12 months and discontinued for 1 month. Twenty-one subjects wearing single vision distance spectacles for 12 months were included as the control group. SFChT was assessed using optical coherence tomography. Ocular parameters, including axial length (AL), central corneal thickness (CCT), anterior chamber depth (ACD), lens thickness (LT) and apical corneal power (ACP), were also measured. Results: After 12 months of follow-up, AL elongation was larger and SFChT change was smaller in the control group compared to the Ortho-K group (both p < 0.001). In the Ortho-K group, SFChT increased by 16 lm from baseline at the 1-month visit (p < 0.001), and the magnitude of choroidal thickening remained unchanged at the 6-and 12-month visit (p = 0.289). One month after discontinuation of Ortho-K lens, SFChT and ocular parameters of the anterior segment, including ACP, CCT and ACD recovered to baseline level (All p > 0.05), and AL increased by 0.23 AE 0.18 mm compared to baseline (p = 0.018). SFChT change at 1-month was negatively associated with AL change at 13-month (standard b, À0.581, p = 0.001) after adjusting for other influencing factors, including baseline age and the ocular parameters. Conclusion: Subfoveal ChT (SFChT) significantly increased after short-term Ortho-K lens treatment and the increase maintained throughout the period of treatment. One month after Ortho-K lens cessation, SFChT, ACP, CCT and ACD returned to baseline. Short-term response in SFChT is associated with long-term change in AL in children undergoing Ortho-K lens and may be a predictor for the effectiveness of the treatment.

BackgroundElectronic medical records provide large-scale real-world clinical data for use in developing clinical decision systems. However, sophisticated methodology and analytical skills are required to handle the large-scale datasets necessary for the optimisation of prediction accuracy. Myopia is a common cause of vision loss. Current approaches to control myopia progression are effective but have significant side effects. Therefore, identifying those at greatest risk who should undergo targeted therapy is of great clinical importance. The objective of this study was to apply big data and machine learning technology to develop an algorithm that can predict the onset of high myopia, at specific future time points, among Chinese school-aged children.Methods and findingsReal-world clinical refraction data were derived from electronic medical record systems in 8 ophthalmic centres from January 1, 2005, to December 30, 2015. The variables of age, spherical equivalent (SE), and annual progression rate were used to develop an algorithm to predict SE and onset of high myopia (SE ≤ −6.0 dioptres) up to 10 years in the future. Random forest machine learning was used for algorithm training and validation. Electronic medical records from the Zhongshan Ophthalmic Centre (a major tertiary ophthalmic centre in China) were used as the training set. Ten-fold cross-validation and out-of-bag (OOB) methods were applied for internal validation. The remaining 7 independent datasets were used for external validation. Two population-based datasets, which had no participant overlap with the ophthalmic-centre-based datasets, were used for multi-resource validation testing. The main outcomes and measures were the area under the curve (AUC) values for predicting the onset of high myopia over 10 years and the presence of high myopia at 18 years of age. In total, 687,063 multiple visit records (≥3 records) of 129,242 individuals in the ophthalmic-centre-based electronic medical record databases and 17,113 follow-up records of 3,215 participants in population-based cohorts were included in the analysis. Our algorithm accurately predicted the presence of high myopia in internal validation (the AUC ranged from 0.903 to 0.986 for 3 years, 0.875 to 0.901 for 5 years, and 0.852 to 0.888 for 8 years), external validation (the AUC ranged from 0.874 to 0.976 for 3 years, 0.847 to 0.921 for 5 years, and 0.802 to 0.886 for 8 years), and multi-resource testing (the AUC ranged from 0.752 to 0.869 for 4 years). With respect to the prediction of high myopia development by 18 years of age, as a surrogate of high myopia in adulthood, the algorithm provided clinically acceptable accuracy over 3 years (the AUC ranged from 0.940 to 0.985), 5 years (the AUC ranged from 0.856 to 0.901), and even 8 years (the AUC ranged from 0.801 to 0.837). Meanwhile, our algorithm achieved clinically acceptable prediction of the actual refraction values at future time points, which is supported by the regressive performance and calibration curves. Although the algorithm achiev...

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