Dynamic Protein Networks Research Articles

The prognostic value of bioinformatics analysis of ECM receptor signaling pathways and LAMB1 identification as a promising prognostic biomarker of lung adenocarcinoma.

The extracellular matrix (ECM) is a complex and dynamic network of cross-linked proteins and a fundamental building block in multicellular organisms. Our study investigates the impact of genes related to the ECM receptor interaction pathway on immune-targeted therapy and lung adenocarcinoma (LUAD) prognosis. This study obtained LUAD chip data (GSE68465, GSE31210, and GSE116959) from NCBI GEO. Moreover, the gene data associated with the ECM receptor interaction pathway was downloaded from the Molecular Signature Database. Differentially expressed genes were identified using GEO2R, followed by analyzing their correlation with immune cell infiltration. Univariate Cox regression analysis screened out ECM-related genes significantly related to the survival prognosis of LUAD patients. Additionally, Lasso regression and multivariate Cox regression analysis helped construct a prognostic model. Patients were stratified by risk score and survival analyses. The prognostic models were evaluated using receiver operating characteristic curves, and risk scores and prognosis associations were analyzed using univariate and multivariate Cox regression analyses. A core gene was selected for gene set enrichment analysis and CIBERSORT analysis to determine its function and tumor-infiltrating immune cell proportion, respectively. The results revealed that the most abundant pathways among differentially expressed genes in LUAD primarily involved the cell cycle, ECM receptor interaction, protein digestion and absorption, p53 signaling pathway, complement and coagulation cascade, and tyrosine metabolism. Two ECM-associated subtypes were identified by consensus clustering. Besides, an ECM-related prognostic model was validated to predict LUAD survival, and it was associated with the tumor immune microenvironment. Additional cross-analysis screened laminin subunit beta 1 (LAMB1) for further research. The survival time of LUAD patients with elevated LAMB1 expression was longer than those with low LAMB1 expression. Gene set enrichment analysis and CIBERSORT analyses revealed that LAMB1 expression correlated with tumor immune microenvironment. In conclusion, a prognostic model of LUAD patients depending on the ECM receptor interaction pathway was constructed. Screening out LAMB1 can become a prognostic risk factor for LUAD patients or a potential target during LUAD treatment.

A Construction Method for a Dynamic Weighted Protein Network Using Multi-Level Embedding

The rapid development of high-throughput technology has generated a large amount of protein–protein interaction (PPI) data, which provide a large amount of data support for constructing dynamic protein–protein interaction networks (PPINs). Constructing dynamic PPINs and applying them to recognize protein complexes has become a hot research topic. Most existing methods for complex recognition cannot fully mine the information of PPINs. To address this problem, we propose a construction method of dynamic weighted protein network by multi-level embedding (DWPNMLE). It can reflect the protein network’s dynamics and the protein network’s higher-order proximity. Firstly, the protein active period is calculated to divide the protein subnetworks at different time points. Then, the connection probability is used for the proteins possessing the same time points to judge whether there is an interaction relationship between them. Then, the corresponding protein subnetworks (multiple adjacency matrices) are constructed. Secondly, the multiple feature matrices are constructed using one-hot coding with the gene ontology (GO) information. Next, the first embedding is performed using variational graph auto-encoders (VGAEs) to aggregate features efficiently, followed by the second embedding using deep attributed network embedding (DANE) to strengthen the node representations learned in the first embedding and to maintain the first-order and higher-order proximity of the original network; finally, we compute the cosine similarity to obtain the final dynamic weighted PPIN. To evaluate the effectiveness of DWPNMLE, we apply four classical protein-complex-recognition algorithms on the DWPNMLE and compare them with two other dynamic protein network construction methods. The experimental results demonstrate that DWPNMLE significantly enhances the accuracy of complex recognition with high robustness, and the algorithms’ efficiency is also within a reasonable range.

Exploring the properties and potential of the neural extracellular matrix for next-generation regenerative therapies.

The extracellular matrix (ECM) is a dynamic and complex network of proteins and molecules that surrounds cells and tissues in the nervous system and orchestrates a myriad of biological functions. This review carefully examines the diverse interactions between cells and the ECM, as well as the transformative chemical and physical changes that the ECM undergoes during neural development, aging, and disease. These transformations play a pivotal role in shaping tissue morphogenesis and neural activity, thereby influencing the functionality of the central nervous system (CNS). In our comprehensive review, we describe the diverse behaviors of the CNS ECM in different physiological and pathological scenarios and explore the unique properties that make ECM-based strategies attractive for CNS repair and regeneration. Addressing the challenges of scalability, variability, and integration with host tissues, we review how advanced natural, synthetic, and combinatorial matrix approaches enhance biocompatibility, mechanical properties, and functional recovery. Overall, this review highlights the potential of decellularized ECM as a powerful tool for CNS modeling and regenerative purposes and sets the stage for future research in this exciting field. This article is categorized under: Implantable Materials and Surgical Technologies > Nanotechnology in Tissue Repair and Replacement Therapeutic Approaches and Drug Discovery > Nanomedicine for Neurological Disease Implantable Materials and Surgical Technologies > Nanomaterials and Implants.

Nε-Carboxymethyl-Lysine Modification of Extracellular Matrix Proteins Augments Fibroblast Activation.

The extracellular matrix (ECM) is a dynamic complex protein network that provides structural integrity and plays an active role in shaping fibroblast behavior both in health and disease. Despite its essential functions, the impact of age-associated post-translational modifications on ECM-driven fibroblast activities such as proliferation, survival, fibroblast-to-myofibroblast transformation (FMT), and extracellular matrix production remains largely unknown. Nε-carboxymethyl-lysine (CML) is one of the well-characterized advanced glycation end-products (AGEs) that can occur on lysine residues within ECM proteins through non-enzymatic glycation. In this study, we determined the accumulation and the effects of the CML-modified ECM (CML-ECM) on fibroblast activation. Immunostainings and immunoblot analysis demonstrated significant increases in CML-AGE content in idiopathic pulmonary fibrosis (IPF) compared to age-matched healthy lungs. Gene expression analysis and fibroblast activation assays collectively implicate the ECM as a negative regulator of fibroblast activation. Notably, the CML modification of the ECM resulted in a significant decrease in its anti-fibrotic effects including proliferation, FMT, apoptosis, and ECM production. Together, the results of this study revealed an unexplored pathological role played by the CML-ECM on fibroblast activation, which has wide implications in IPF and other fibrotic diseases.

Orphan quality control shapes network dynamics and gene expression

All eukaryotes require intricate protein networks to translate developmental signals into accurate cell fate decisions. Mutations that disturb interactions between network components often result in disease, but how the composition and dynamics of complex networks are established remains poorly understood. Here, we identify the E3 ligase UBR5 as a signaling hub that helps degrade unpaired subunits of multiple transcriptional regulators that act within a network centered on the c-Myc oncoprotein. Biochemical and structural analyses show that UBR5 binds motifs that only become available upon complex dissociation. By rapidly turning over unpaired transcription factor subunits, UBR5 establishes dynamic interactions between transcriptional regulators that allow cells to effectively execute gene expression while remaining receptive to environmental signals. We conclude that orphan quality control plays an essential role in establishing dynamic protein networks, which may explain the conserved need for protein degradation during transcription and offers opportunities to modulate gene expression in disease.

The unexpected versatility of ALP/Enigma family proteins.

One of the most intriguing features of multicellular animals is their ability to move. On a cellular level, this is accomplished by the rearrangement and reorganization of the cytoskeleton, a dynamic network of filamentous proteins which provides stability and structure in a stationary context, but also facilitates directed movement by contracting. The ALP/Enigma family proteins are a diverse group of docking proteins found in numerous cellular milieus and facilitate these processes among others. In vertebrates, they are characterized by having a PDZ domain in combination with one or three LIM domains. The family is comprised of CLP-36 (PDLIM1), Mystique (PDLIM2), ALP (PDLIM3), RIL (PDLIM4), ENH (PDLIM5), ZASP (PDLIM6), and Enigma (PDLIM7). In this review, we will outline the evolution and function of their protein domains which confers their versatility. Additionally, we highlight their role in different cellular environments, focusing specifically on recent advances in muscle research using Drosophila as a model organism. Finally, we show the relevance of this protein family to human myopathies and the development of muscle-related diseases.

Interplay between local protein interactions and water bridging of a proton antenna carboxylate cluster

Proteins that bind protons at cell membrane interfaces often expose to the bulk clusters of carboxylate and histidine sidechains that capture protons transiently and, in proton transporters, deliver protons to an internal site. The protonation-coupled dynamics of bulk-exposed carboxylate clusters, also known as proton antennas, is poorly described. An essential open question is how water-mediated bridges between sidechains of the cluster respond to protonation change and facilitate transient proton storage. To address this question, here I studied the protonation-coupled dynamics at the proton-binding antenna of PsbO, a small extrinsinc subunit of the photosystem II complex, with atomistic molecular dynamics simulations and systematic graph-based analyses of dynamic protein and protein-water hydrogen-bond networks. The protonation of specific carboxylate groups is found to impact the dynamics of their local protein-water hydrogen-bond clusters. Regardless of the protonation state considered for PsbO, carboxylate pairs that can sample direct hydrogen bonding, or bridge via short hydrogen-bonded water chains, anchor to nearby basic or polar protein sidechains. As a result, carboxylic sidechains of the hypothesized antenna cluster are part of dynamic hydrogen bond networks that may rearrange rapidly when the protonation changes.

A protein complex recognition method based on spatial-temporal graph convolution neural network

To propose a new method for mining complexes in dynamic protein network using spatiotemporal convolution neural network. The edge strength, node strength and edge existence probability are defined for modeling of the dynamic protein network. Based on the time series information and structure information on the graph, two convolution operators were designed using Hilbert-Huang transform, attention mechanism and residual connection technology to represent and learn the characteristics of the proteins in the network, and the dynamic protein network characteristic map was constructed. Finally, spectral clustering was used to identify the protein complexes. The simulation results on several public biological datasets showed that the F value of the proposed algorithm exceeded 90% on DIP dataset and MIPS dataset. Compared with 4 other recognition algorithms (DPCMNE, GE-CFI, VGAE and NOCD), the proposed algorithm improved the recognition efficiency by 34.5%, 28.7%, 25.4% and 17.6%, respectively. The application of deep learning technology can improve the efficiency in analysis of dynamic protein networks.

A Key Protein Recognition Algorithm Based on High-order Complex Protein Network

Although the existing protein recognition methods have improved the recognition accuracy of key proteins to a certain extent, they have ignored the biological features of the proteins. In view of this shortcoming, this paper constructed a high-order dynamic complex protein network for key protein recognition. At first, this paper presented a method for feature selection and candidate set evaluation of complex protein network; a weighted network was constructed based on the obtained topological features of the complex protein network and the semantic similarity of protein gene ontology annotations. Then, this paper proposed an algorithm for recognizing key proteins in high-order dynamic protein network based on a Fruit fly optimization algorithm. At last, the effectiveness of the proposed model was verified by experimental results.

An in vitro model of fibrosis using crosslinked native extracellular matrix-derived hydrogels to modulate biomechanics without changing composition

Extracellular matrix (ECM) is a dynamic network of proteins, proteoglycans and glycosaminoglycans, providing structure to the tissue and biochemical and biomechanical instructions to the resident cells. In fibrosis, the composition and the organization of the ECM are altered, and these changes influence cellular behaviour. Biochemical (i. e. protein composition) and biomechanical changes in ECM take place simultaneously in vivo. Investigating these changes individually in vitro to examine their (patho)physiological effects has been difficult. In this study, we generated an in vitro model to reflect the altered mechanics of a fibrotic microenvironment through applying fibre crosslinking via ruthenium/sodium persulfate crosslinking on native lung ECM-derived hydrogels. Crosslinking of the hydrogels without changing the biochemical composition of the ECM resulted in increased stiffness and decreased viscoelastic stress relaxation. The altered stress relaxation behaviour was explained using a generalized Maxwell model. Fibre analysis of the hydrogels showed that crosslinked hydrogels had a higher percentage of matrix with a high density and a shorter average fibre length. Fibroblasts seeded on ruthenium-crosslinked lung ECM-derived hydrogels showed myofibroblastic differentiation with a loss of spindle-like morphology together with greater α-smooth muscle actin (α-SMA) expression, increased nuclear area and circularity without any decrease in the viability, compared with the fibroblasts seeded on the native lung-derived ECM hydrogels. In summary, ruthenium crosslinking of native ECM-derived hydrogels provides an exciting opportunity to alter the biomechanical properties of the ECM-derived hydrogels while maintaining the protein composition of the ECM to study the influence of mechanics during fibrotic lung diseases. Statement of significanceFibrotic lung disease is characterized by changes in composition and excessive deposition of extracellular matrix (ECM). ECM fibre structure also changes due to crosslinking, which results in mechanical changes. Separating the changes in composition and mechanical properties has been difficult to date. In this study, we developed an in vitro model that allows alteration of the mechanical changes alone by applying fibre crosslinking in native lung ECM-derived hydrogels. Characterisations of the crosslinked hydrogels indicated the model mimicked mechanical properties of fibrotic lung tissue and reflected altered fibre organisation. This ECM-based fibrosis model provides a method to preserve the native protein composition while altering the mechanical properties providing an important tool, not only for lung but also other organ fibrosis.

Mechanisms and Minimization of False Discovery of Metabolic Bioorthogonal Noncanonical Amino Acid Proteomics.

Metabolic proteomics has been widely used to characterize dynamic protein networks in many areas of biomedicine, including in the arena of tissue aging and rejuvenation. Bioorthogonal noncanonical amino acid tagging (BONCAT) is based on mutant methionine-tRNA synthases (MetRS) that incorporates metabolic tags, for example, azidonorleucine [ANL], into newly synthesized proteins. BONCAT revolutionizes metabolic proteomics, because mutant MetRS transgene allows one to identify cell type-specific proteomes in mixed biological environments. This is not possible with other methods, such as stable isotope labeling with amino acids in cell culture, isobaric tags for relative and absolute quantitation and tandem mass tags. At the same time, an inherent weakness of BONCAT is that after click chemistry-based enrichment, all identified proteins are assumed to have been metabolically tagged, but there is no confirmation in mass spectrometry data that only tagged proteins are detected. As we show here, such assumption is incorrect and accurate negative controls uncover a surprisingly high degree of false positives in BONCAT proteomics. We show not only how to reveal the false discovery and thus improve the accuracy of the analyses and conclusions but also approaches for avoiding it through minimizing nonspecific detection of biotin, biotin-independent direct detection of metabolic tags, and improvement of signal to noise ratio through machine learning algorithms.

A Dynamic Network of Proteins Facilitate Cell Envelope Biogenesis in Gram-Negative Bacteria.

Bacteria must maintain the ability to modify and repair the peptidoglycan layer without jeopardising its essential functions in cell shape, cellular integrity and intermolecular interactions. A range of new experimental techniques is bringing an advanced understanding of how bacteria regulate and achieve peptidoglycan synthesis, particularly in respect of the central role played by complexes of Sporulation, Elongation or Division (SEDs) and class B penicillin-binding proteins required for cell division, growth and shape. In this review we highlight relationships implicated by a bioinformatic approach between the outer membrane, cytoskeletal components, periplasmic control proteins, and cell elongation/division proteins to provide further perspective on the interactions of these cell division, growth and shape complexes. We detail the network of protein interactions that assist in the formation of peptidoglycan and highlight the increasingly dynamic and connected set of protein machinery and macrostructures that assist in creating the cell envelope layers in Gram-negative bacteria.

Probabilistic Critical Controllability Analysis of Protein Interaction Networks Integrating Normal Brain Ageing Gene Expression Profiles.

Recently, network controllability studies have proposed several frameworks for the control of large complex biological networks using a small number of life molecules. However, age-related changes in the brain have not been investigated from a controllability perspective. In this study, we compiled the gene expression profiles of four normal brain regions from individuals aged 20–99 years and generated dynamic probabilistic protein networks across their lifespan. We developed a new algorithm that efficiently identified critical proteins in probabilistic complex networks, in the context of a minimum dominating set controllability model. The results showed that the identified critical proteins were significantly enriched with well-known ageing genes collected from the GenAge database. In particular, the enrichment observed in replicative and premature senescence biological processes with critical proteins for male samples in the hippocampal region led to the identification of possible new ageing gene candidates.

Characterization of Dynamic Regulatory Gene and Protein Networks in Wheat Roots Upon Perceiving Water Deficit Through Comparative Transcriptomics Survey.

A well-developed root system benefits host plants by optimizing water absorption and nutrient uptake and thereby increases plant productivity. In this study we have characterized the root transcriptome using RNA-seq and subsequential functional analysis in a set of drought tolerant and susceptible genotypes. The goal of the study was to elucidate and characterize water deficit-responsive genes in wheat landraces that had been through long-term field and biochemical screening for drought tolerance. The results confirm genotype differences in water-deficit tolerance in line with earlier results from field trials. The transcriptomics survey highlighted a total of 14,187 differentially expressed genes (DEGs) that responded to water deficit. The characterization of these genes shows that all chromosomes contribute to water-deficit tolerance, but to different degrees, and the B genome showed higher involvement than the A and D genomes. The DEGs were mainly mapped to flavonoid, phenylpropanoid, and diterpenoid biosynthesis pathways, as well as glutathione metabolism and hormone signaling. Furthermore, extracellular region, apoplast, cell periphery, and external encapsulating structure were the main water deficit-responsive cellular components in roots. A total of 1,377 DEGs were also predicted to function as transcription factors (TFs) from different families regulating downstream cascades. TFs from the AP2/ERF-ERF, MYB-related, B3, WRKY, Tify, and NAC families were the main genotype-specific regulatory factors. To further characterize the dynamic biosynthetic pathways, protein-protein interaction (PPI) networks were constructed using significant KEGG proteins and putative TFs. In PPIs, enzymes from the CYP450, TaABA8OH2, PAL, and GST families play important roles in water-deficit tolerance in connection with MYB13-1, MADS-box, and NAC transcription factors.

Research Progress on PATJ and Underlying Mechanisms Associated with Functional Outcomes After Stroke.

Cell polarity is an intrinsic property of epithelial cells regulated by scaffold proteins. The CRB (crumbs) complex is known to play a predominant role in the dynamic cooperative network of polarity scaffold proteins. PATJ (PALS1-associated tight junction) is the core component in the CRB complex and has been highly conserved throughout evolution. PATJ is crucial to several important events in organisms’ survival, including embryonic development, cell polarity, and barrier establishment. A recent study shows that PATJ plays an important role in functional outcomes of stroke. In this article, we elaborate on the biological structure and physiological functions of PATJ and explore the underlying mechanisms of PATJ genetic polymorphism that are associated with poor functional outcomes in ischemic stroke.

Megakaryocyte Cytoskeletal Proteins in Platelet Biogenesis and Diseases.

Thrombopoiesis governs the formation of blood platelets in bone marrow by converting megakaryocytes into long, branched proplatelets on which individual platelets are assembled. The megakaryocyte cytoskeleton responds to multiple microenvironmental cues, including chemical and mechanical stimuli, sustaining the platelet shedding. During the megakaryocyte's life cycle, cytoskeletal networks organize cell shape and content, connect them physically and biochemically to the bone marrow vascular niche, and enable the release of platelets into the bloodstream. While the basic building blocks of the cytoskeleton have been studied extensively, new sets of cytoskeleton regulators have emerged as critical components of the dynamic protein network that supports platelet production. Understanding how the interaction of individual molecules of the cytoskeleton governs megakaryocyte behavior is essential to improve knowledge of platelet biogenesis and develop new therapeutic strategies for inherited thrombocytopenias caused by alterations in the cytoskeletal genes.

Usher Syndrome: Genetics of a Human Ciliopathy.

Usher syndrome (USH) is an autosomal recessive syndromic ciliopathy characterized by sensorineural hearing loss, retinitis pigmentosa and, sometimes, vestibular dysfunction. There are three clinical types depending on the severity and age of onset of the symptoms; in addition, ten genes are reported to be causative of USH, and six more related to the disease. These genes encode proteins of a diverse nature, which interact and form a dynamic protein network called the “Usher interactome”. In the organ of Corti, the USH proteins are essential for the correct development and maintenance of the structure and cohesion of the stereocilia. In the retina, the USH protein network is principally located in the periciliary region of the photoreceptors, and plays an important role in the maintenance of the periciliary structure and the trafficking of molecules between the inner and the outer segments of photoreceptors. Even though some genes are clearly involved in the syndrome, others are controversial. Moreover, expression of some USH genes has been detected in other tissues, which could explain their involvement in additional mild comorbidities. In this paper, we review the genetics of Usher syndrome and the spectrum of mutations in USH genes. The aim is to identify possible mutation associations with the disease and provide an updated genotype–phenotype correlation.

Integrative Proteomic Analysis of Multiple Posttranslational Modifications in Inflammatory Response

Posttranslational modifications (PTMs) of proteins, particularly acetylation, phosphorylation, and ubiquitination, play critical roles in the host innate immune response. PTMs’ dynamic changes and the crosstalk among them are complicated. To build a comprehensive dynamic network of inflammation-related proteins, we integrated data from the whole-cell proteome (WCP), acetylome, phosphoproteome, and ubiquitinome of human and mouse macrophages. Our datasets of acetylation, phosphorylation, and ubiquitination sites helped identify PTM crosstalk within and across proteins involved in the inflammatory response. Stimulation of macrophages by lipopolysaccharide (LPS) resulted in both degradative and non-degradative ubiquitination. Moreover, this study contributes to the interpretation of the roles of known inflammatory molecules and the discovery of novel inflammatory proteins.

Novel Regulators of the IGF System in Cancer.

The insulin-like growth factor (IGF) system is a dynamic network of proteins, which includes cognate ligands, membrane receptors, ligand binding proteins and functional downstream effectors. It plays a critical role in regulating several important physiological processes including cell growth, metabolism and differentiation. Importantly, alterations in expression levels or activation of components of the IGF network are implicated in many pathological conditions including diabetes, obesity and cancer initiation and progression. In this review we will initially cover some general aspects of IGF action and regulation in cancer and then focus in particular on the role of transcriptional regulators and novel interacting proteins, which functionally contribute in fine tuning IGF1R signaling in several cancer models. A deeper understanding of the biological relevance of this network of IGF1R modulators might provide novel therapeutic opportunities to block this system in neoplasia.

Comparative Analysis for Differential Drug Response between Early T-Cell Precursor Acute Lymphoblastic Leukemia (ETP-ALL) and T-Cell Acute Lymphoblastic Leukemia (T-ALL) Patients Using the Cellworks Omics Biology Model (CBM): Mycare-021-03

Background:Early T-cell Precursor Acute Lymphoblastic Leukemia (ETP-ALL), an orphan disease, is a sub-type of T-Cell Acute Lymphoblastic Leukemia (T-ALL) with very poor prognosis and limited therapy options. ETP-ALL is a heterogeneous disease with many distinct genomic profiles, often with more myeloid than lymphoid characteristics. However, standard of care (SOC) drugs for acute myeloid leukemia (AML) have shown limited efficacy for ETP-ALL (PMID: 32733662, 25435716). The genomic profiles of ETP-ALL patients have more complex cytogenetics and larger numbers of genomic aberrations when compared to non-ETP-ALL (T-ALL) profiles (PMID: 22237106, 30641417). We present an alternative multi-gene analysis approach using the Cellworks Omics Biology Model (CBM) workflow to identify unique, intersecting protein pathways in patient-specific disease profiles. The CBM predictive workflow was used to design novel personalized therapy options for an ETP-ALL representative PEER human lymphoid cell line in comparison to a T-ALL JURKAT cell line. The predicted combination therapies were then validated in a lab model. Methods:A PEER cell line was selected to represent ETP-ALL and a JURKAT cell line was selected as a representative for non-ETP T-ALL. Next Generation Sequencing (NGS) was performed for the PEER cell line. For the JURKAT cell line, publicly available NGS whole exome sequencing from cBioPortal and Sanger, along with array CGH from Agilent, were used. The genomic data for the PEER and JURKAT cell lines were used as inputs to the CBM to generate dynamic patient-specific disease protein network maps. Biomarkers and pathway characteristics unique to the PEER and JURKAT cell lines were identified. A digital drug library of targeted FDA-approved agents was simulated on the disease models using both single drug agents and drug combinations at varying doses. The treatment impact was assessed by quantitatively measuring drug effect on a cell growth score, which is a composite of the quantified values of cell proliferation, survival and apoptosis along with impact on the patient-specific disease biomarker score. Comparative dose response studies were run to assess IC50 differences for both cell lines. Cellworks VenturaTM predicted novel therapy combinations for the ETP-ALL representative PEER cell line, which were then prospectively validated by in vitro experiments. The same therapy options were predicted to be less effective in the T-ALL representative JURKAT cell line, which was also confirmed by in vitro studies. Results:The CBM predicted three novel combination therapies for the ETP-ALL representative PEER cell line: nilotinib + cytarabine, bortezomib + cytarabine and bortezomib + idarubicin. All three therapies were predicted to be less effective in JURKAT cells. In vitro, PEER cells were sensitive to all 3 combinations, as predicted by the CBM; whereas, JURKAT cell lines were not sensitive to the first 2 combinations (as predicted), but were sensitive to bortezomib + idarubicin. The CBM analysis is supported by scientific rationales for these combinations based on the genomics-driven disease characteristics of the cell-line. The reasons for drug sensitivity and resistance were determined. These combinations were then prospectively validated in vitro on both cell lines and the experimental responses matched the predicted outcomes. Conclusion:The Cellworks Omics Biology Model integrates the multiple genomic abnormalities in a patient to identify disease network characteristics unlike other NGS analytic tools that attempt to interpret the impact of each genomic alteration in isolation. CBM identified 3 novel therapy options for ETP-ALL that were validated in vitro, similar to anecdotal experience in vivo. This predictive technology can improve clinical decision-making and identify novel treatment options. Disclosures Howard: Cellworks:Consultancy;Servier:Consultancy, Other: Speaker;EUSA Pharma:Consultancy;Sanofi:Consultancy, Other: Speaker;Boston Scientific:Consultancy.Kumar:Cellworks Research India Private Limited:Current Employment.Pampana:Cellworks Research India Private Limited:Current Employment.Ullal:Cellworks Research India Private Limited:Current Employment.Tyagi:Cellworks Research India Private Limited:Current Employment.Lala:Cellworks Research India Private Limited:Current Employment.Kumari:Cellworks Research India Private Limited:Current Employment.Joseph:Cellworks Research India Private Limited:Current Employment.Raju:Cellworks Research India Private Limited:Current Employment.Balakrishnan:Cellworks Research India Private Limited:Current Employment.Mundkur:Cellworks Group Inc.:Current Employment.Macpherson:Cellworks Group Inc.:Current Employment.Nair:Cellworks Research India Private Limited:Current Employment.Kapoor:Cellworks Research India Private Limited:Current Employment.