AI-driven discovery of blood xenobiotic biomarkers in neovascular age-related macular degeneration using iterative random forests.

PURPOSE: To investigate the xenobiotic profiles of patients with neovascular age-related macular degeneration (nAMD) undergoing anti-vascular endothelial growth factor (anti-VEGF) intravitreal therapy (IVT) to identify biomarkers indicative of clinical phenotypes through advanced AI methodologies. METHODS: In this cross-sectional observational study, we analyzed 156 peripheral blood xenobiotic features in a cohort of 46 nAMD patients stratified by choroidal neovascularization (CNV) control under anti-VEGF IVT. We employed Liquid Chromatography-Tandem Mass Spectrometry (LC-MS/MS) for measurement and leveraged an AI-driven iterative Random Forests (iRF) approach for robust pattern recognition and feature selection, aligning molecular profiles with clinical phenotypes. RESULTS: AI-augmented iRF models effectively refined the metabolite spectrum by discarding non-predictive elements. Perfluorooctanesulfonate (PFOS) and Ethyl ß-glucopyranoside were identified as significant biomarkers through this process, associated with various clinically relevant phenotypes. Unlike single metabolite classes, drug metabolites were distinctly correlated with subretinal fluid presence. CONCLUSIONS: This study underscores the enhanced capability of AI, particularly iRF, in dissecting complex metabolomic data to elucidate the xenobiotic landscape of nAMD and environmental impact on the disease. The preliminary biomarkers discovered offer promising directions for personalized treatment strategies, although further validation in broader cohorts is essential for clinical application.

Exploring the Impact of Saccharin on Neovascular Age-Related Macular Degeneration: A Comprehensive Study in Patients and Mice.

Purpose: We aimed to determine the impact of artificial sweeteners (AS), especially saccharin, on the progression and treatment efficacy of patients with neovascular age-related macular degeneration (nAMD) under anti-vascular endothelial growth factor (anti-VEGF-A) treatment. Methods: In a cross-sectional study involving 46 patients with nAMD undergoing intravitreal anti-VEGF therapy, 6 AS metabolites were detected in peripheral blood using liquid chromatography - tandem mass spectrometry (LC-MS/MS). Disease features were statistically tested against these metabolite levels. Additionally, a murine choroidal neovascularization (CNV) model, induced by laser, was used to evaluate the effects of orally administered saccharin, assessing both imaging outcomes and gene expression patterns. Polymerase chain reaction (PCR) methods were used to evaluate functional expression of sweet taste receptors in a retinal pigment epithelium (RPE) cell line. Results: Saccharin levels in blood were significantly higher in patients with well-controlled CNV activity (P = 0.004) and those without subretinal hyper-reflective material (P = 0.015). In the murine model, saccharin-treated mice exhibited fewer leaking laser scars, lesser occurrence of bleeding, smaller fibrotic areas (P < 0.05), and a 40% decrease in mononuclear phagocyte accumulation (P = 0.06). Gene analysis indicated downregulation of inflammatory and VEGFR-1 response genes in the treated animals. Human RPE cells expressed taste receptor type 1 member 3 (TAS1R3) mRNA and reacted to saccharin stimulation with changes in mRNA expression. Conclusions: Saccharin appears to play a protective role in patients with nAMD undergoing intravitreal anti-VEGF treatment, aiding in better pathological lesion control and scar reduction. The murine study supports this observation, proposing saccharin's potential in mitigating pathological VEGFR-1-induced immune responses potentially via the RPE sensing saccharin in the blood stream.

Systemic Blood Proteome Patterns Reflect Disease Phenotypes in Neovascular Age-Related Macular Degeneration.

There is early evidence of extraocular systemic signals effecting function and morphology in neovascular age-related macular degeneration (nAMD). The prospective, cross-sectional BIOMAC study is an explorative investigation of peripheral blood proteome profiles and matched clinical features to uncover systemic determinacy in nAMD under anti-vascular endothelial growth factor intravitreal therapy (anti-VEGF IVT). It includes 46 nAMD patients stratified by the level of disease control under ongoing anti-VEGF treatment. Proteomic profiles in peripheral blood samples of every patient were detected with LC-MS/MS mass spectrometry. The patients underwent extensive clinical examination with a focus on macular function and morphology. In silico analysis includes unbiased dimensionality reduction and clustering, a subsequent annotation of clinical features, and non-linear models for recognition of underlying patterns. The model assessment was performed using leave-one-out cross validation. The findings provide an exploratory demonstration of the link between systemic proteomic signals and macular disease pattern using and validating non-linear classification models. Three main results were obtained: (1) Proteome-based clustering identifies two distinct patient subclusters with the smaller one (n = 10) exhibiting a strong signature for oxidative stress response. Matching the relevant meta-features on the individual patient's level identifies pulmonary dysfunction as an underlying health condition in these patients. (2) We identify biomarkers for nAMD disease features with Aldolase C as a putative factor associated with superior disease control under ongoing anti-VEGF treatment. (3) Apart from this, isolated protein markers are only weakly correlated with nAMD disease expression. In contrast, applying a non-linear classification model identifies complex molecular patterns hidden in a high number of proteomic dimensions determining macular disease expression. In conclusion, so far unconsidered systemic signals in the peripheral blood proteome contribute to the clinically observed phenotype of nAMD, which should be examined in future translational research on AMD.

Predictive biomarker for surgical outcome in patients with advanced primary high-grade serous ovarian cancer. Are we there yet? An analysis of the prospective biobank for ovarian cancer.

BACKGROUND: High-grade serous ovarian cancer (HGSOC) is the most common subtype of ovarian cancer and is associated with high mortality rates. Surgical outcome is one of the most important prognostic factors. There are no valid biomarkers to identify which patients may benefit from a primary debulking approach. OBJECTIVE: Our study aimed to discover and validate a predictive panel for surgical outcome of residual tumor mass after first-line debulking surgery. STUDY DESIGN: Firstly, "In silico" analysis of publicly available datasets identified 200 genes as predictors for surgical outcome. The top selected genes were then validated using the novel Nanostring method, which was applied for the first time for this particular research objective. 225 primary ovarian cancer patients with well annotated clinical data and a complete debulking rate of 60% were compiled for a clinical cohort. The 14 best rated genes were then validated through the cohort, using immunohistochemistry testing. Lastly, we used our biomarker expression data to predict the presence of miliary carcinomatosis patterns. RESULTS: The Nanostring analysis identified 37 genes differentially expressed between optimal and suboptimal debulked patients (p < 0.05). The immunohistochemistry validated the top 14 genes, reaching an AUC Ø0.650. The analysis for the prediction of miliary carcinomatosis patterns reached an AUC of Ø0.797. CONCLUSION: The tissue-based biomarkers in our analysis could not reliably predict post-operative residual tumor. Patient and non-patient-associated co-factors, surgical skills, and center experience remain the main determining factors when considering the surgical outcome at primary debulking in high-grade serous ovarian cancer patients.

A framework for validating AI in precision medicine: considerations from the European ITFoC consortium.

BACKGROUND: Artificial intelligence (AI) has the potential to transform our healthcare systems significantly. New AI technologies based on machine learning approaches should play a key role in clinical decision-making in the future. However, their implementation in health care settings remains limited, mostly due to a lack of robust validation procedures. There is a need to develop reliable assessment frameworks for the clinical validation of AI. We present here an approach for assessing AI for predicting treatment response in triple-negative breast cancer (TNBC), using real-world data and molecular -omics data from clinical data warehouses and biobanks. METHODS: The European "ITFoC (Information Technology for the Future Of Cancer)" consortium designed a framework for the clinical validation of AI technologies for predicting treatment response in oncology. RESULTS: This framework is based on seven key steps specifying: (1) the intended use of AI, (2) the target population, (3) the timing of AI evaluation, (4) the datasets used for evaluation, (5) the procedures used for ensuring data safety (including data quality, privacy and security), (6) the metrics used for measuring performance, and (7) the procedures used to ensure that the AI is explainable. This framework forms the basis of a validation platform that we are building for the "ITFoC Challenge". This community-wide competition will make it possible to assess and compare AI algorithms for predicting the response to TNBC treatments with external real-world datasets. CONCLUSIONS: The predictive performance and safety of AI technologies must be assessed in a robust, unbiased and transparent manner before their implementation in healthcare settings. We believe that the consideration of the ITFoC consortium will contribute to the safe transfer and implementation of AI in clinical settings, in the context of precision oncology and personalized care.

Classification of Molecular Subtypes of High-Grade Serous Ovarian Cancer by MALDI-Imaging.

Despite the correlation of clinical outcome and molecular subtypes of high-grade serous ovarian cancer (HGSOC), contemporary gene expression signatures have not been implemented in clinical practice to stratify patients for targeted therapy. Hence, we aimed to examine the potential of unsupervised matrix-assisted laser desorption/ionization imaging mass spectrometry (MALDI-IMS) to stratify patients who might benefit from targeted therapeutic strategies. Molecular subtyping of paraffin-embedded tissue samples from 279 HGSOC patients was performed by NanoString analysis (ground truth labeling). Next, we applied MALDI-IMS paired with machine-learning algorithms to identify distinct mass profiles on the same paraffin-embedded tissue sections and distinguish HGSOC subtypes by proteomic signature. Finally, we devised a novel approach to annotate spectra of stromal origin. We elucidated a MALDI-derived proteomic signature (135 peptides) able to classify HGSOC subtypes. Random forest classifiers achieved an area under the curve (AUC) of 0.983. Furthermore, we demonstrated that the exclusion of stroma-associated spectra provides tangible improvements to classification quality (AUC = 0.988). Moreover, novel MALDI-based stroma annotation achieved near-perfect classifications (AUC = 0.999). Here, we present a concept integrating MALDI-IMS with machine-learning algorithms to classify patients according to distinct molecular subtypes of HGSOC. This has great potential to assign patients for personalized treatment.

Epigenomic profiling of non-small cell lung cancer xenografts uncover LRP12 DNA methylation as predictive biomarker for carboplatin resistance.

BACKGROUND: Non-small cell lung cancer (NSCLC) is the most common cause of cancer-related deaths worldwide and is primarily treated with radiation, surgery, and platinum-based drugs like cisplatin and carboplatin. The major challenge in the treatment of NSCLC patients is intrinsic or acquired resistance to chemotherapy. Molecular markers predicting the outcome of the patients are urgently needed. METHODS: Here, we employed patient-derived xenografts (PDXs) to detect predictive methylation biomarkers for platin-based therapies. We used MeDIP-Seq to generate genome-wide DNA methylation profiles of 22 PDXs, their parental primary NSCLC, and their corresponding normal tissues and complemented the data with gene expression analyses of the same tissues. Candidate biomarkers were validated with quantitative methylation-specific PCRs (qMSP) in an independent cohort. RESULTS: Comprehensive analyses revealed that differential methylation patterns are highly similar, enriched in PDXs and lung tumor-specific when comparing differences in methylation between PDXs versus primary NSCLC. We identified a set of 40 candidate regions with methylation correlated to carboplatin response and corresponding inverse gene expression pattern even before therapy. This analysis led to the identification of a promoter CpG island methylation of LDL receptor-related protein 12 (LRP12) associated with increased resistance to carboplatin. Validation in an independent patient cohort (n = 35) confirmed that LRP12 methylation status is predictive for therapeutic response of NSCLC patients to platin therapy with a sensitivity of 80% and a specificity of 84% (p < 0.01). Similarly, we find a shorter survival time for patients with LRP12 hypermethylation in the TCGA data set for NSCLC (lung adenocarcinoma). CONCLUSIONS: Using an epigenome-wide sequencing approach, we find differential methylation patterns from primary lung cancer and PDX-derived cancers to be very similar, albeit with a lower degree of differential methylation in primary tumors. We identify LRP12 DNA methylation as a powerful predictive marker for carboplatin resistance. These findings outline a platform for the identification of epigenetic therapy resistance biomarkers based on PDX NSCLC models.

Pathway deregulation and expression QTLs in response to Actinobacillus pleuropneumoniae infection in swine.

Actinobacillus (A.) pleuropneumoniae is among the most important pathogens in pig. The agent causes severe economic losses due to decreased performance, the occurrence of acute or chronic pleuropneumonia, and an increase in death incidence. Since therapeutics cannot be used in a sustainable manner, and vaccination is not always available, new prophylactic measures are urgently needed. Recent research has provided evidence for a genetic predisposition in susceptibility to A. pleuropneumoniae in a Hampshire × German Landrace F2 family with 170 animals. The aim of the present study is to characterize the expression response in this family in order to unravel resistance and susceptibility mechanisms and to prioritize candidate genes for future fine mapping approaches. F2 pigs differed distinctly in clinical, pathological, and microbiological parameters after challenge with A. pleuropneumoniae. We monitored genome-wide gene expression from the 50 most and 50 least susceptible F2 pigs and identified 171 genes differentially expressed between these extreme phenotypes. We combined expression QTL analyses with network analyses and functional characterization using gene set enrichment analysis and identified a functional hotspot on SSC13, including 55 eQTL. The integration of the different results provides a resource for candidate prioritization for fine mapping strategies, such as TF, TFRC, RUNX1, TCN1, HP, CD14, among others.

LGALS3BP regulates centriole biogenesis and centrosome hypertrophy in cancer cells.

Centrosome morphology and number are frequently deregulated in cancer cells. Here, to identify factors that are functionally relevant for centrosome abnormalities in cancer cells, we established a protein-interaction network around 23 centrosomal and cell-cycle regulatory proteins, selecting the interacting proteins that are deregulated in cancer for further studies. One of these components, LGALS3BP, is a centriole- and basal body-associated protein with a dual role, triggering centrosome hypertrophy when overexpressed and causing accumulation of centriolar substructures when downregulated. The cancer cell line SK-BR-3 that overexpresses LGALS3BP exhibits hypertrophic centrosomes, whereas in seminoma tissues with low expression of LGALS3BP, supernumerary centriole-like structures are present. Centrosome hypertrophy is reversed by depleting LGALS3BP in cells endogenously overexpressing this protein, supporting a direct role in centrosome aberration. We propose that LGALS3BP suppresses assembly of centriolar substructures, and when depleted, causes accumulation of centriolar complexes comprising CPAP, acetylated tubulin and centrin.

DIPSBC--data integration platform for systems biology collaborations.

BACKGROUND: Modern biomedical research is often organized in collaborations involving labs worldwide. In particular in systems biology, complex molecular systems are analyzed that require the generation and interpretation of heterogeneous data for their explanation, for example ranging from gene expression studies and mass spectrometry measurements to experimental techniques for detecting molecular interactions and functional assays. XML has become the most prominent format for representing and exchanging these data. However, besides the development of standards there is still a fundamental lack of data integration systems that are able to utilize these exchange formats, organize the data in an integrative way and link it with applications for data interpretation and analysis. RESULTS: We have developed DIPSBC, an interactive data integration platform supporting collaborative research projects, based on Foswiki, Solr/Lucene, and specific helper applications. We describe the main features of the implementation and highlight the performance of the system with several use cases. All components of the system are platform independent and open-source developments and thus can be easily adopted by researchers. An exemplary installation of the platform which also provides several helper applications and detailed instructions for system usage and setup is available at http://dipsbc.molgen.mpg.de. CONCLUSIONS: DIPSBC is a data integration platform for medium-scale collaboration projects that has been tested already within several research collaborations. Because of its modular design and the incorporation of XML data formats it is highly flexible and easy to use.

Construction of a pig physical interactome using sequence homology and a comprehensive reference human interactome.

The analysis of interaction networks is crucial for understanding molecular function and has an essential impact for genomewide studies. However, the interactomes of most species are largely incomplete and computational strategies that take into account sequence homology can help compensating for this lack of information using cross-species analysis. In this work we report the construction of a porcine interactome resource. We applied sequence homology matching and carried out bi-directional BLASTp searches for the currently available protein sequence collections of human and pig. Using this homology we were able to recover, on average, 71% of the proteins annotated for human pathways for the pig. Porcine protein-protein interactions were deduced from homologous proteins with known interactions in human. The result of this work is a resource comprising 204,699 predicted porcine interactions that can be used in genome analyses in order to enhance functional interpretation of data. The data can be visualized and downloaded from http://cpdb.molgen.mpg.de/pig.

Gene ontology analysis of the centrosome proteomes of Drosophila and human.

The centrosome is a complex cell organelle in higher eukaryotic cells that functions in microtubule organization and is integrated into major cellular signaling pathways.1-3 For example, a tight link exists between cell cycle regulation and centrosome duplication, as centrosome numbers must be precisely controlled to ensure high fidelity of chromosome segregation.4 The analysis of the centrosome's protein composition provides the opportunity for a better understanding of centrosome function and to identify possible links to cellular signaling pathways.5,6 Our proteomics study of the Drosophila centrosome recently identified 251 centrosome candidate proteins that we subsequently characterized by RNAi in Drosophila SL2 cells and classified according to their function in centrosome duplication/segregation, structure maintenance and cell cycle regulation.7 Interestingly, functional characterization of their human orthologous proteins revealed the highest functional conservation in the process of centrosome duplication and separation. To analyze functional and biochemical interdependencies further, we carried out an analysis of the gene ontology (GO) annotation of the identified Drosophila centrosome proteins, as well as of the human centrosome proteome.5 The GO analysis of the group of proteins that did not show a centrosome, chromosome segregation or cell cycle related phenotype in our RNAi assays suggests that these molecules may constitute linker proteins to other cellular signaling pathways. Furthermore, the results of our GO analysis of components of the human and of the Drosophila centrosome reflect the somatic and embryonic origin, respectively, of the isolated centrosomes, implicating the Drosophila centrosome proteins in developmental signaling and cell differentiation.

Proteomic and functional analysis of the mitotic Drosophila centrosome.

Regulation of centrosome structure, duplication and segregation is integrated into cellular pathways that control cell cycle progression and growth. As part of these pathways, numerous proteins with well-established non-centrosomal localization and function associate with the centrosome to fulfill regulatory functions. In turn, classical centrosomal components take up functional and structural roles as part of other cellular organelles and compartments. Thus, although a comprehensive inventory of centrosome components is missing, emerging evidence indicates that its molecular composition reflects the complexity of its functions. We analysed the Drosophila embryonic centrosomal proteome using immunoisolation in combination with mass spectrometry. The 251 identified components were functionally characterized by RNA interference. Among those, a core group of 11 proteins was critical for centrosome structure maintenance. Depletion of any of these proteins in Drosophila SL2 cells resulted in centrosome disintegration, revealing a molecular dependency of centrosome structure on components of the protein translation machinery, actin- and RNA-binding proteins. In total, we assigned novel centrosome-related functions to 24 proteins and confirmed 13 of these in human cells.