Dataset Information

Molecular Responses of Anti-VEGF Therapy in Neovascular Age-Related Macular Degeneration: Integrative Insights From Multi-Omics and Clinical Imaging.

ABSTRACT:

Purpose

The purpose of this study was to investigate the molecular mechanisms underlying anti-vascular endothelial growth factor (anti-VEGF) efficacy and response variability in neovascular age-related macular degeneration (nAMD) using longitudinal proteomic and metabolomic analysis alongside three-dimensional lesion measurements.

Methods

In this prospective study, 54 treatment-naive patients with nAMD underwent "3+ pro re nata" (3+PRN) anti-VEGF regimens followed for at least 12 weeks. Aqueous humors were collected pre- and post-treatment for proteomic and metabolomic analysis. Three-dimensional optical coherence tomography (OCT) and OCT angiography assessed different types of nAMD lesion volumes and areas.

Results

There were 1350 proteins and 1268 metabolites that were identified in aqueous humors, with 301 proteins and 353 metabolites significantly altered during anti-VEGF treatment, enriched in pathways of angiogenesis, energy metabolism, signal transduction, and neurofunctional regulation. Sixty-seven changes of (Δ) molecules significantly correlated with at least one type of ΔnAMD lesion. Notably, proteins FGA, TALDO1, and ASPH significantly decreased during treatment, with their reductions correlating with greater lesion regression in at least two lesion types. Conversely, despite that YIPF3 also showed significant downregulation, its decrease was associated with poorer regression in total nAMD lesion and subretinal hyper-reflective material.

Conclusions

This study identifies FGA, TALDO1, and ASPH as potential key molecules in the efficacy of anti-VEGF therapy, whereas YIPF3 may be a key factor in poor response. The integration of longitudinal three-dimensional lesion analysis with multi-omics provides valuable insights into the mechanisms and response variability of anti-VEGF treatment in nAMD.

SUBMITTER: Zhuang X

PROVIDER: S-EPMC11328887 | biostudies-literature | 2024 Aug

REPOSITORIES: biostudies-literature

ACCESS DATA

Publications

Molecular Responses of Anti-VEGF Therapy in Neovascular Age-Related Macular Degeneration: Integrative Insights From Multi-Omics and Clinical Imaging.

Zhuang Xuenan X Li Miaoling M Mi Lan L Zhang Xiongze X Pu Jiaxin J He Guiqin G Zhang Liang L Yu Honghua H Yao Liwei L Chen Hui H Ji Yuying Y Zuo Chengguo C Su Yongyue Y Gan Yuhong Y Hao Xinlei X Zhang Yining Y Chen Xuelin X Wen Feng F

Investigative ophthalmology & visual science 20240801 10

<h4>Purpose</h4>The purpose of this study was to investigate the molecular mechanisms underlying anti-vascular endothelial growth factor (anti-VEGF) efficacy and response variability in neovascular age-related macular degeneration (nAMD) using longitudinal proteomic and metabolomic analysis alongside three-dimensional lesion measurements.<h4>Methods</h4>In this prospective study, 54 treatment-naive patients with nAMD underwent "3+ pro re nata" (3+PRN) anti-VEGF regimens followed for at least 12 ...[more]

PMID: 39140961

Similar Datasets

Project description:PurposeTo probabilistically forecast needed anti-vascular endothelial growth factor (anti-VEGF) treatment frequency using volumetric spectral domain-optical coherence tomography (SD-OCT) biomarkers in neovascular age-related macular degeneration from real-world settings.MethodsSD-OCT volume scans were segmented with a custom deep-learning-based analysis pipeline. Retinal thickness and reflectivity values were extracted for the central and the four inner Early Treatment Diabetic Retinopathy Study (ETDRS) subfields for six retinal layers (inner retina, outer nuclear layer, inner segments [IS], outer segments [OS], retinal pigment epithelium-drusen complex [RPEDC] and the choroid). Machine-learning models were probed to predict the anti-VEGF treatment frequency within the next 12 months. Probabilistic forecasting was performed using natural gradient boosting (NGBoost), which outputs a full probability distribution. The mean absolute error (MAE) between the predicted versus actual anti-VEGF treatment frequency was the primary outcome measure.ResultsIn a total of 138 visits of 99 eyes with neovascular AMD (96 patients) from two clinical centers, the prediction of future anti-VEGF treatment frequency was observed with an accuracy (MAE [95% confidence interval]) of 2.60 injections/year [2.25-2.96] (R2 = 0.390) using random forest regression and 2.66 injections/year [2.31-3.01] (R2 = 0.094) using NGBoost, respectively. Prediction intervals were well calibrated and reflected the true uncertainty of NGBoost-based predictions. Standard deviation of RPEDC-thickness in the central ETDRS-subfield constituted an important predictor across models.ConclusionsThe proposed, fully automated pipeline enables probabilistic forecasting of future anti-VEGF treatment frequency in real-world settings.Translational relevancePrediction of a probability distribution allows the physician to inspect the underlying uncertainty. Predictive uncertainty estimates are essential to highlight cases where human-inspection and/or reversion to a fallback alternative is warranted.

Project description:PurposeNo established biomarkers currently exist for therapeutic efficacy and durability of anti-VEGF therapy in neovascular age-related macular degeneration (nAMD). This study evaluated radiomic-based quantitative OCT biomarkers that may be predictive of anti-VEGF treatment response and durability.DesignAssessment of baseline biomarkers using machine learning (ML) classifiers to predict tolerance to anti-VEGF therapy.ParticipantsEighty-one participants with treatment-naïve nAMD from the OSPREY study, including 15 super responders (patients who achieved and maintained retinal fluid resolution) and 66 non-super responders (patients who did not achieve or maintain retinal fluid resolution).MethodsA total of 962 texture-based radiomic features were extracted from fluid, subretinal hyperreflective material (SHRM), and different retinal tissue compartments of OCT scans. The top 8 features, chosen by the minimum redundancy maximum relevance feature selection method, were evaluated using 4 ML classifiers in a cross-validated approach to distinguish between the 2 patient groups. Longitudinal assessment of changes in different texture-based radiomic descriptors (delta-texture features) between baseline and month 3 also was performed to evaluate their association with treatment response. Additionally, 8 baseline clinical parameters and a combination of baseline OCT, delta-texture features, and the clinical parameters were evaluated in a cross-validated approach in terms of association with therapeutic response.Main outcome measuresThe cross-validated area under the receiver operating characteristic curve (AUC), accuracy, sensitivity, and specificity were calculated to validate the classifier performance.ResultsThe cross-validated AUC by the quadratic discriminant analysis classifier was 0.75 ± 0.09 using texture-based baseline OCT features. The delta-texture features within different OCT compartments between baseline and month 3 yielded an AUC of 0.78 ± 0.08. The baseline clinical parameters sub-retinal pigment epithelium volume and intraretinal fluid volume yielded an AUC of 0.62 ± 0.07. When all the baseline, delta, and clinical features were combined, a statistically significant improvement in the classifier performance (AUC, 0.81 ± 0.07) was obtained.ConclusionsRadiomic-based quantitative assessment of OCT images was shown to distinguish between super responders and non-super responders to anti-VEGF therapy in nAMD. The baseline fluid and SHRM delta-texture features were found to be most discriminating across groups.

Dataset Information

Molecular Responses of Anti-VEGF Therapy in Neovascular Age-Related Macular Degeneration: Integrative Insights From Multi-Omics and Clinical Imaging.

Purpose

Methods

Results

Conclusions

Publications

Molecular Responses of Anti-VEGF Therapy in Neovascular Age-Related Macular Degeneration: Integrative Insights From Multi-Omics and Clinical Imaging.

Similar Datasets

OmicsDI is part of the ELIXIR infrastructure

Tweets