Integration Of Proteomic Data Research Articles

Reconstruction, simulation and analysis of enzyme-constrained metabolic models using GECKO Toolbox 3.0.

Genome-scale metabolic models (GEMs) are computational representations that enable mathematical exploration of metabolic behaviors within cellular and environmental constraints. Despite their wide usage in biotechnology, biomedicine and fundamental studies, there are many phenotypes that GEMs are unable to correctly predict. GECKO is a method to improve the predictive power of a GEM by incorporating enzymatic constraints using kinetic and omics data. GECKO has enabled reconstruction of enzyme-constrained metabolic models (ecModels) for diverse organisms, which show better predictive performance than conventional GEMs. In this protocol, we describe how to use the latest version GECKO 3.0; the procedure has five stages: (1) expansion from a starting metabolic model to an ecModel structure, (2) integration of enzyme turnover numbers into the ecModel structure, (3) model tuning, (4) integration of proteomics data into the ecModel and (5) simulation and analysis of ecModels. GECKO 3.0 incorporates deep learning-predicted enzyme kinetics, paving the way for improved metabolic models for virtually any organism and cell line in the absence of experimental data. The time of running the whole protocol is organism dependent, e.g., ~5 h for yeast.

Simultaneous application of enzyme and thermodynamic constraints to metabolic models using an updated Python implementation of GECKO.

The metabolism of biological cells is an intricate network of reactions that interconvert chemical compounds, gathering energy, and using that energy to grow. The static analysis of these metabolic networks can be turned into a computational model that can efficiently output the distribution of fluxes in the network. With the inclusion of enzymes in the network, we can also interpret the role and concentrations of the metabolic proteins. However, the models and the experimental data often clash, resulting in a network that cannot grow. Here, we tackle this situation with a suite of relaxation algorithms in a package called geckopy. Geckopy also integrates with other software to allow for adding thermodynamic and metabolomic constraints. In addition, to ensure that enzyme-constrained models follow the community standards, a format for the proteins is postulated. We hope that the package and algorithms presented here will be useful for the constraint-based modeling community.

Frontiers in mass spectrometry-based clinical proteomics for cancer diagnosis and treatment.

Numerous omics studies, primarily genomics analyses, have been conducted to fully understand the molecular biological characteristics of cancer. In recent years, the depth of proteomic analysis, which comprehensively analyzes proteins and molecules that function directly in vivo, has increased dramatically. Proteomics using mass spectrometry (MS) is a promising technology to directly examine proteoforms, including post-translational modifications and variants originating from genomic aberrations. Recent advances in MS-based proteomics have enabled direct, in depth, and quantitative analysis of the expression levels of various cancer-related proteins, as well as their cancer-specific proteoforms, and proteins that fluctuate with cancer initiation and progression in cell lines and tissue samples. Additionally, the integration of proteomic data with genomic, epigenomic, and transcriptomic data has formed the growing field of proteogenomics, which is already yielding new biological and diagnostic knowledge. Deep proteomic profiling provides clinically useful information in various aspects, including understanding the mechanisms of cancer development and progression and discovering targets for diagnosis and drug development. Furthermore, it is expected to make a significant contribution to the promotion of personalized medicine. In this review, recent advances and impacts in MS-based clinical proteomics are highlighted with a focus on oncology.

Integration of Proteomic Data Obtained from the Saliva of Children with Caries through Bioinformatic Analysis

Background: Dental caries can affect the expression of salivary proteins. Proteomics allows us to analyze and identify many proteins in a single sample and experiment; bioinformatics is essential to analyze proteomic data. Objective: This research aims to identify and integrate the main differentially expressed proteins in the saliva of children with caries, infer their Gene Ontology and interactions, and identify regulatory factors. Materials and Methods: We extracted proteins from a bibliographic search in the Scopus and PubMed databases. We analyzed these proteins with the web application ShinyGO v0.76, ToppGene and NetworkAnalyst 3.0, and the Cytoscape platform. Results: In the literature search, we extracted 26 differentially expressed proteins. These proteins show enrichment in antioxidant activity, antimicrobial response, immune response, and vitamin and mineral metabolism. We found three transcription factors that regulate most of the genes of these proteins: TFDP1, SOX13, and BCL6. We also identified three microRNAs that highly restrict the expression of these proteins: hsa-mir-124-3p, hsa-mir-27a-3p, and hsa-mir-26b-5p. On the other hand, the main drugs associated with these proteins are potassium persulfate, aluminum, and cadmium. Conclusion: The differentially expressed proteins in the saliva of children with dental caries are involved in metabolic pathways related to folate, selenium, and vitamin B12 metabolism. In addition, some transcription factors (TFDP1, SOX13, and BCL6) miRNAs (hsa-mir-124-3p, hsa-mir-27a-3p, and hsa-mir-26b-5p) and chemical compounds (potassium persulfate, aluminum, and cadmium) can regulate the genes, mRNAs or proteins studied.

Gene Ontology (GO)-Driven Inference of Candidate Proteomic Markers Associated with Muscle Atrophy Conditions.

Skeletal muscle homeostasis is essential for the maintenance of a healthy and active lifestyle. Imbalance in muscle homeostasis has significant consequences such as atrophy, loss of muscle mass, and progressive loss of functions. Aging-related muscle wasting, sarcopenia, and atrophy as a consequence of disease, such as cachexia, reduce the quality of life, increase morbidity and result in an overall poor prognosis. Investigating the muscle proteome related to muscle atrophy diseases has a great potential for diagnostic medicine to identify (i) potential protein biomarkers, and (ii) biological processes and functions common or unique to muscle wasting, cachexia, sarcopenia, and aging alone. We conducted a meta-analysis using gene ontology (GO) analysis of 24 human proteomic studies using tissue samples (skeletal muscle and adipose biopsies) and/or biofluids (serum, plasma, urine). Whilst there were few similarities in protein directionality across studies, biological processes common to conditions were identified. Here we demonstrate that the GO analysis of published human proteomics data can identify processes not revealed by single studies. We recommend the integration of proteomics data from tissue samples and biofluids to yield a comprehensive overview of the human skeletal muscle proteome. This will facilitate the identification of biomarkers and potential pathways of muscle-wasting conditions for use in clinics.

Abstract 6348: A cloud-scalable software suite for large-cohort proteogenomics data analysis and visualization

Abstract Comprehensive assessment of the flow of genetic information through multi-omic data integration can reveal the molecular consequences of genetic variation underlying human disease. Next generation sequencing (NGS) is used to identify genetic variants and characterize gene function (e.g. transcriptome and epigenome), while mass spectrometry is used to assess the proteome through characterization of protein abundances, modifications, and interactions. A new plasma profiling platform, the Proteograph࣪ Product Suite, leverages multiple nanoparticles with distinct physiochemical properties to enable deep plasma proteome analyses at scale. Here, we present a cloud-based, data analysis software platform called Proteograph Analysis Suite (PAS) for proteogenomic data analyses through the integration of proteomics data derived from the Proteograph with genomic variant information derived from NGS experiments. PAS features include an experiment data management system, analysis protocols, an analysis setup wizard, and tools for reviewing and visualizing results. PAS can support both Data Independent Analysis (DIA) and Data Dependent Analysis (DDA) proteomics workflows and is compatible with variant call format (vcf) files from NGS workflows to enable personalized database searches. To assess quality of the resulting data PAS includes various metrics like peptide/protein group intensity, protein sequence coverage, relative protein abundance distribution, peptide and protein group counts. Visualizations including principal component analysis, hierarchical clustering, and heatmaps allow intuitive identification of experimental trends. To enable biological insights, differential expression analyses results are reported with interactive visualizations such as volcano plots, protein interaction maps, and protein-set enrichment. From data to insight, PAS provides an easy-to-use and efficient suite of functionality to enable proteogenomic data analysis. Integration of proteomics and genomics data require a variety of tools, many of which require command-line interfaces and operating system-specific requirements that can act as a barrier for researchers to adapt new data analysis tools. Here, we demonstrate the utility of PAS by analyzing samples from the Proteograph NSCLC plasma dataset1. PAS can analyze VCF files generated from NGS pipelines in combination with mass spec data to identify peptide variants using personalized libraries. Using the cloud-based architecture computational tasks are distributed for rapid analysis. The integrated proteogenomics viewers allow variant IDs to be interpreted in the context of genomic coordinates, protein sequence, functional domains and features. Together, these results show the utility of PAS for seamless and fast proteomic data analysis. Reference: 1 Blume, J. E. et al. Nature Communications, 2020 Citation Format: Aaron S. Gajadhar, Margaret K. Donovan, Harsharn Auluck, Yan Berk, Yuandan Lou, Theo Platt, Serafim Batzoglou. A cloud-scalable software suite for large-cohort proteogenomics data analysis and visualization [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2022; 2022 Apr 8-13. Philadelphia (PA): AACR; Cancer Res 2022;82(12_Suppl):Abstract nr 6348.

OTHR-42. Missing data tolerant integration of proteomic datasets enables the identification and characterization of brain cancer subtypes

Investigating the proteome can add a significant layer of information to manifold existing methylation, mutation, and transcriptome data on brain tumors as proteins represent the pharmacologically addressable phenotype of a disease. Small cohorts limit the usability and validity of statistical methods, and variable technical setups and high numbers of missing values make data integration from public sources challenging. Using a newly developed framework being able to reduce batch effects without the need for data reduction or missing value imputation, we show –based on in-house and publicly available datasets- successful integration of proteomic data across different tissue types, quantification platforms, and technical setups. Exemplarily, data of a Sonic hedgehog (Shh) medulloblastoma mouse model were analyzed, showing efficient data integration independent of tissue preservation strategy or batch. We further integrated batches of publicly available data of human brain tumors, confirming proposed proteomic cancer subtypes correlating with clinical features. We show that, missing value tolerant reduction of technical variances may be helpful to identify biomarkers, proteomic signatures, and altered pathways characteristic for molecular brain cancer subtypes.

Bioinformatic analysis of dysregulated proteins in prostate cancer patients reveals putative urinary biomarkers and key biological pathways.

Prostate cancer (PCa) is one of the most common cancer types among men. The quantification of prostate-specific antigen used for PCa detection has revealed limited applicability. Thus, it is crucial to identify new minimally invasive biomarkers for PCa. It is believed that the integration of proteomics data from different studies is vital for identifying new biomarkers for PCa, but studies carried out in this regard have few converging results. Using a different approach, this study aimed to unveil molecular features consistently dysregulated in PCa and potential urinary biomarkers for PCa. The novelty of this analysis relies on the comparison of urinary and tissue proteomes from PCa patients and consequent exclusion of kidney and bladder cancer interference. The conducted bioinformatic analysis revealed molecular processes dysregulated in urine from PCa patients that mirror the alterations in prostate tumor tissue. To identify putative urinary biomarkers, proteins previously detected in kidney and bladder tissues were eliminated from the final list of potential urinary biomarkers for PCa. After a detailed analysis, MSMB, KLK3, ITIH4, ITIH2, HPX, GP2, APOA2 and AZU1 proteins stood out as candidate urinary biomarkers for PCa.

Comprehending Meningioma Signaling Cascades Using Multipronged Proteomics Approaches & Targeted Validation of Potential Markers.

Meningiomas are one of the most prevalent primary brain tumors. Our study aims to obtain mechanistic insights of meningioma pathobiology using mass spectrometry-based label-free quantitative proteome analysis to identifying druggable targets and perturbed pathways for therapeutic intervention. Label-free based proteomics study was done from peptide samples of 21 patients and 8 non-tumor controls which were followed up with Phosphoproteomics to identify the kinases and phosphorylated components of the perturbed pathways. In silico approaches revealed perturbations in extracellular matrix remodeling and associated cascades. To assess the extent of influence of Integrin and PI3K-Akt pathways, we used an Integrin Linked Kinase inhibitor on patient-derived meningioma cell line and performed a transcriptomic analysis of the components. Furthermore, we designed a Targeted proteomics assay which to the best of our knowledge for very first-time enables identification of peptides from 54 meningioma patients via SRM assay to validate the key proteins emerging from our study. This resulted in the identification of peptides from CLIC1, ES8L2, and AHNK many of which are receptors and kinases and are difficult to be characterized using conventional approaches. Furthermore, we were also able to monitor transitions for proteins like NEK9 and CKAP4 which have been reported to be associated with meningioma pathobiology. We believe, this study can aid in designing peptide-based validation assays for meningioma patients as well as IHC studies for clinical applications.

Abstract 4385: Proteogenomics-driven synthetic lethality discovery to predict targetable protein dependencies induced by somatic deletions

Abstract Computational prediction of synthetic lethality has been shown as a promising approach to identify therapeutic vulnerabilities in many cancer types but has been limited to using genomics and transcriptomics data for discovery. Recent advancements on integrating mass spectrometry (MS)-based proteomics with genomics and transcriptomics in cancer research have produced proteogenomics datasets on large tumor cohorts, enabling the integration of proteomics data into synthetic lethality discovery. This is particularly attractive for tumors driven by loss-of-function alterations such as somatic deletions. Although deletions are challenging to target themselves, they may induce targetable protein dependencies, and this relationship can be identified through computational prediction of synthetic lethality. Here, we performed an unbiased discovery of such synthetic lethal interactions by identifying protein targets of drugs for which under-expression is observed significantly less than expected by chance with gene deletions using proteogenomics data from a breast cancer cohort with 105 tumors. We found many significant pairs uniquely driven by either protein under-expression (18,897 pairs) or mRNA under-expression (42,754 pairs) while only 5% (3,042 pairs) were supported by both protein and mRNA measurements. To systematically evaluate candidate synthetic lethal pairs, an AUROC analysis was performed using publicly available drug response data from breast cancer cells. Results showed synthetic lethal relationships supported by both protein and mRNA-based analyses were substantially more predictive of drug response (AUC = 81%) than protein alone (AUC = 65%) or mRNA alone (AUC = 61%). Despite having comparable AUCs, protein-based analysis showed much higher sensitivity than mRNA-based analysis when limiting false positive rate to less than 10%. An example of a synthetic lethal pair supported by both protein and mRNA-based analyses include tumors with low AURKA protein/mRNA and PARP2 deletions, which were significantly under-represented in the proteogenomics dataset (p = 0.003). Interestingly, these tumors were enriched for triple-negative breast tumors (p = 0.001), a type of breast cancer that lacks biomarkers to guide clinical treatment. This finding suggests AURKA as a putative dependency in triple-negative tumors with PARP2 deletion as a biomarker. Taken together, these results demonstrate that a multi-omics approach using proteomics to complement transcriptomics along with genomic changes may better predict therapeutic vulnerabilities in breast cancer with driver gene deletions than using proteomics or transcriptomics alone. As genomic testing has become more widely implemented as part of precision oncology programs, our approach to identifying protein dependencies induced by loss-of-function genomic alterations may provide new treatment opportunities for many patients with breast cancer or other tumor types. Citation Format: Jonathan T. Lei, Eric Jaehnig, Bing Zhang. Proteogenomics-driven synthetic lethality discovery to predict targetable protein dependencies induced by somatic deletions [abstract]. In: Proceedings of the Annual Meeting of the American Association for Cancer Research 2020; 2020 Apr 27-28 and Jun 22-24. Philadelphia (PA): AACR; Cancer Res 2020;80(16 Suppl):Abstract nr 4385.

IPDS: A semantic mediator‐based system using Spark for the integration of heterogeneous proteomics data sources

SummaryWith the constant rise of data volumes in many disciplines, various new Big data management systems have emerged to provide scalable tools for efficient data integration, processing, and analysis. In this article, we provide an overview of biomedical data integration systems focusing on ontology‐based semantic systems and Big data technologies based systems such as Apache Spark. We also propose a new semantic data integration system, called Integrated Proteomics Data System (IPDS), which uses a mediator approach. IPDS provides users a unified interface for query processing and data exploration. This system takes advantage of the Apache Spark framework to perform the query transformation and execution needed to question the integrated data sources. We develop a domain ontology that allows the user to formulate its queries in terms defined in the ontology. IPDS is a case study of semantic proteomics data integration linking four data sources UniProt (protein annotation), String (protein‐protein interaction), PDB (protein structure), and Pubmed (biomedical citation).

Protein sorting into protein bodies during barley endosperm development is putatively regulated by cytoskeleton members, MVBs and the HvSNF7s

Cereal endosperm is a short-lived tissue adapted for nutrient storage, containing specialized organelles, such as protein bodies (PBs) and protein storage vacuoles (PSVs), for the accumulation of storage proteins. During development, protein trafficking and storage require an extensive reorganization of the endomembrane system. Consequently, endomembrane-modifying proteins will influence the final grain quality and yield. However, little is known about the molecular mechanism underlying endomembrane system remodeling during barley grain development. By using label-free quantitative proteomics profiling, we quantified 1,822 proteins across developing barley grains. Based on proteome annotation and a homology search, 94 proteins associated with the endomembrane system were identified that exhibited significant changes in abundance during grain development. Clustering analysis allowed characterization of three different development phases; notably, integration of proteomics data with in situ subcellular microscopic analyses showed a high abundance of cytoskeleton proteins associated with acidified PBs at the early development stages. Moreover, endosomal sorting complex required for transport (ESCRT)-related proteins and their transcripts are most abundant at early and mid-development. Specifically, multivesicular bodies (MVBs), and the ESCRT-III HvSNF7 proteins are associated with PBs during barley endosperm development. Together our data identified promising targets to be genetically engineered to modulate seed storage protein accumulation that have a growing role in health and nutritional issues.

Viewing Cortical Collecting Duct Function Through Phenotype-guided Single-Tubule Proteomics.

The revolution of the omics technologies has enabled profiling of the molecules of any sample. However, the heterogeneity of the kidney with highly specialized nephron segments like the cortical collecting duct (CCD) poses a challenge regarding integration of omics data and functional analysis. We examined function and proteome from the same single CCDs of C57Bl6 mice by investigating them in a double-barreled perfusion system before targeted mass spectrometry. Transepithelial voltage (Vte), transepithelial resistance, as well as amiloride-sensitive voltage (ΔVteamil) were recorded. CCDs were of 400–600 µm of length, showed lumen negative Vte between −8.5 and −32.5 mV and an equivalent short circuit current I’sc between 54 and 192 µA/cm2. On a single-tubule proteome level, intercalated cell (IC) markers strongly correlated with other intercalated cell markers and negatively with principal cell markers. Integration of proteome data with phenotype data revealed that tubular length correlated with actin and Na+-K+-ATPase expression. ΔVte(amil) reflected the expression level of the β-subunit of the epithelial sodium channel. Intriguingly, ΔVte(amil) correlated inversely with the water channel AQP2 and the negative regulator protein NEDD4L (NEDD4-2). In pendrin knockout (KO) mice, the CCD proteome was accompanied by strong downregulation of other IC markers like CLCNKB, BSND (Barttin), and VAA (vH+-ATPase), a configuration that may contribute to the salt-losing phenotype of Pendred syndrome. Proteins normally coexpressed with pendrin were decreased in pendrin KO CCDs. In conclusion, we show that functional proteomics on a single nephron segment scale allows function–proteome correlations, and may potentially help predicting function from omics data.

Detection and verification of 2.3 million cancer mutations in NCI60 cancer cell lines with a cloud search engine

Today we have unprecedented access to human genomic and proteomic data that appear to be rapidly approaching our current understanding of comprehensive coverage. Combining genomic information with shotgun proteomics remains challenging due to the large increase in proteomics search space. However, making this connection between genomic and proteomic information is critical for cancer studies to vaccine development. Furthermore, as we progress towards personalized medicine, it will be essential for proteomics analysis to identify individual mutations and variants in order to fully understand protein networks and to develop personalized therapies. While these advantages are well-established, only a few studies have demonstrated the successful integration of proteomic data with large genomic input. We present and examine the abilities of Bolt, a new cloud-based proteomics search engine to search for the presence of over 2.3 million known cancer mutations in a matter of minutes while still performing a standard proteomics search that includes 31 post translational modifications. We use previously published proteomics data sets and identify mutations that are verified using genomic studies as well as previous proteomics efforts. Our results also emphasize the need to search for mutations in a comprehensive manner while still searching for both common and rare PTMs. SignificanceWe present and examine the abilities of Bolt, a new cloud-based proteomics search engine to search for the presence of over 2.3 million known cancer mutations in a matter of minutes while still performing a standard proteomics search that includes 31 post translational modifications. No other proteomics search software can do so.

Plant-Microbe Symbiosis: What Has Proteomics Taught Us?

Beneficial microbes have a positive impact on the productivity and fitness of the host plant. A better understanding of the biological impacts and underlying mechanisms by which the host derives these benefits will help to address concerns around global food production and security. The recent development of omics-based technologies has broadened our understanding of the molecular aspects of beneficial plant-microbe symbiosis. Specifically, proteomics has led to the identification and characterization of several novel symbiosis-specific and symbiosis-related proteins and post-translational modifications that play a critical role in mediating symbiotic plant-microbe interactions and have helped assess the underlying molecular aspects of the symbiotic relationship. Integration of proteomic data with other "omics" data can provide valuable information to assess hypotheses regarding the underlying mechanism of symbiosis and help define the factors affecting the outcome of symbiosis. Herein, an update is provided on the current and potential applications of symbiosis-based "omic" approaches to dissect different aspects of symbiotic plant interactions. The application of proteomics, metaproteomics, and secretomics as enabling approaches for the functional analysis of plant-associated microbial communities is also discussed.

Identification and validation of common molecular targets of hydroxytyrosol

Hydroxytyrosol (HT) is involved in healthful activities and is beneficial to lipid metabolism. Many investigations focused on finding tissue-specific targets of HT through the use of different omics approaches such as transcriptomics and proteomics. However, it is not clear which (if any) of the potential molecular targets of HT reported in different studies are concurrently affected in various tissues. Following the bioinformatic analyses of publicly available data from a selection of in vivo studies involving HT-supplementation, we selected differentially expressed lipid metabolism-related genes and proteins common to more than one study, for validation in rodent liver samples from the entire selection. Four miRNAs (miR-802-5p, miR-423-3p, miR-30a-5p, and miR-146b-5p) responded to HT supplementation. Of note, miR-802-5p was commonly regulated in the liver and intestine. Our premise was that, in an organ crucial for lipid metabolism such as the liver, consistent modulation should be found for a specific target of HT even if different doses and duration of HT supplementation were used in vivo. Even though our results show inconsistency regarding differentially expressed lipid metabolism-related genes and proteins across studies, we found Fgf21 and Rora as potential novel targets of HT. Omics approaches should be fine-tuned to better exploit the available databases.

Polyol Pathway Shed Light on Non-Obstructive Azoospermia Testicular Sperm Extraction (TESE) Negative ROS Imbalance

In our recent study regarding non-obstructive azoospermia by metabolomics technology, we clearly demonstrated that testicular sperm extraction negative patients are in extreme oxidative imbalance. With a reanalysis of our data and the integration of proteomics data, we found the Polyol pathway is up-regulated in non-obstructive azoospermia testicular sperm extraction negative patients. In this regard, we suggest the contribution of this biochemical pathway in the extreme oxidative imbalance of non-obstructive azoospermia testicular sperm extraction negative patients.

VANTED: A Tool for Integrative Visualization and Analysis of -Omics Data.

The investigation of biological systems from different perspectives leads, due to novel -omics technologies, to large-scale, heterogeneous, and complex datasets. To elucidate molecular programs that control biological systems growth and development the integration and analysis of these -omics data remains challenging. Network-integrated visualizations based on graphical standards support intuitive exploration and interpretation of -omics data within the functional context. This integrated vision of the biological system to be studied tries to extract all hidden information for deepening our understanding and reveals new biological insights.The method described here gives detailed instructions on the generation of such an integrative visualization of -omics data in the context of networks presented in the Systems Biology Graphical Notation (SBGN) using VANTED; a software tool for systems biology applications. An example illustrates the application of the method for metabolomics and proteomics data integration and analysis using a primary metabolic pathway, for the model crop potato.

Systems biology integration of proteomic data in rodent models of depression reveals involvement of the immune response and glutamatergic signaling.

The pathophysiological basis of major depression is incompletely understood. Recently, numerous proteomic studies have been performed in rodent models of depression to investigate the molecular underpinnings of depressive-like behaviours with an unbiased approach. The objective of the study is to integrate the results of these proteomic studies in depression models to shed light on the most relevant molecular pathways involved in the disease. Network analysis is performed integrating preexisting proteomic data from rodent models of depression. The IntAct mouse and the HRPD are used as reference protein-protein interaction databases. The functionality analyses of the networks are then performed by testing overrepresented GO biological process terms and pathways. Functional enrichment analyses of the networks revealed an association with molecular processes related to depression in humans, such as those involved in the immune response. Pathways impacted by clinically effective antidepressants are modulated, including glutamatergic signaling and neurotrophic responses. Moreover, dysregulations of proteins regulating energy metabolism and circadian rhythms are implicated. The comparison with protein pathways modulated in depressive patients revealed significant overlapping. This systems biology study supports the notion that animal models can contribute to the research into the biology and therapeutics of depression.

Personalized Preclinical Trials in BRAF Inhibitor-Resistant Patient-Derived Xenograft Models Identify Second-Line Combination Therapies.

To test second-line personalized medicine combination therapies, based on genomic and proteomic data, in patient-derived xenograft (PDX) models. We established 12 PDXs from BRAF inhibitor-progressed melanoma patients. Following expansion, PDXs were analyzed using targeted sequencing and reverse-phase protein arrays. By using multi-arm preclinical trial designs, we identified efficacious precision medicine approaches. We identified alterations previously described as drivers of resistance: NRAS mutations in 3 PDXs, MAP2K1 (MEK1) mutations in 2, BRAF amplification in 4, and aberrant PTEN in 7. At the protein level, re-activation of phospho-MAPK predominated, with parallel activation of PI3K in a subset. Second-line efficacy of the pan-PI3K inhibitor BKM120 with either BRAF (encorafenib)/MEK (binimetinib) inhibitor combination or the ERK inhibitor VX-11e was confirmed in vivo Amplification of MET was observed in 3 PDX models, a higher frequency than expected and a possible novel mechanism of resistance. Importantly, MET amplification alone did not predict sensitivity to the MET inhibitor capmatinib. In contrast, capmatinib as single agent resulted in significant but transient tumor regression in a PDX with resistance to BRAF/MEK combination therapy and high pMET. The triple combination capmatinib/encorafenib/binimetinib resulted in complete and sustained tumor regression in all animals. Genomic and proteomic data integration identifies dual-core pathway inhibition as well as MET as combinatorial targets. These studies provide evidence for biomarker development to appropriately select personalized therapies of patients and avoid treatment failures. See related commentary by Hartsough and Aplin, p. 1550.