Genomic Data Integration for Tailored Treatments

Proteogenomics refers to the integration of mass spectrometry-based proteomics with genomics, epigenomics, and transcriptomics next generation sequencing (NGS) data. This multiomic approach to translational research allows for novel insights into existing and future drug targets for the development of more effective and precise cancer treatments. The Clinical Proteomic Tumor Analysis Consortium (CPTAC) has performed proteogenomic characterization of over 1,000 treatment-naive primary tumors spanning 10 cancer types. A new study by Sarah Savage and larger team at the Baylor College of Medicine integrates the CPTAC dataset with other public datasets to provide insights into existing cancer drug targets while systematically identifying new candidate targets. Their team created a convenient web app to explore the dataset here: https://lnkd.in/efSKFxsj Pan-cancer proteogenomics expands the landscape of therapeutic targets. https://lnkd.in/e8w5Fcfx Methods overview: The authors analyzed harmonized CPTAC proteogenomics data from 1,043 tumor and 524 normal tissue samples across 10 cancer types. Drug target information was collated from DrugBank, Guide to Pharmacology, Drug Gene Interaction Database, and the in silico human surfaceome and classified into 5 tiers Integrative analysis of proteogenomics data and genetic screen data was used to identify synthetic lethal partners of genomically altered tumor suppressor genes. Neoantigen analysis was performed using the NeoFlow pipeline to identify mutation-derived neoantigens. A computational pipeline was developed to identify tumor-associated antigens as targets for immunotherapy Results overview: Proteomic analysis quantified 2,863 druggable proteins, which showed a wide range of abundance and heterogeneous mRNA-protein correlations across cancer types. Integration of proteomic and cell line data identified 51 pan-cancer targetable dependencies driven by protein overexpression and 31 driven by protein hyperactivation Evaluation showed the predicted targets were enriched for approved or investigational oncology drugs and could increase the success rate of identifying effective drugs by 2.6-fold. Integration of proteogenomics and cell line data revealed protein dependencies associated with loss of tumor suppressor genes like TP53, providing a strategy to target tumor suppressor loss Neoantigen analysis prioritized mutant KRAS peptides as promising public neoantigens. Computational prediction followed by experimental validation identified broadly applicable tumor-associated antigens as potential immunotherapy targets

Neo7’s Personalized Peptide Engineering: Integrating High-Definition OMICS, Proteomics, Molecular Modeling and Mapping Utilizing PBIMA-HI/AI PBIMA-HI/AI (Precision-Based ImmunoMolecular Augmentation IP) represents a cutting-edge approach in advancing precision and personalized peptides. This innovative technology integrates high-resolution molecular analysis with both human and artificial intelligence to engineer peptides that specifically target individual faulty molecular signal targets. By leveraging detailed molecular profiles and AI algorithms, This precision-based approach not only optimizes therapeutic efficacy but also minimizes adverse effects by precisely modulating molecular functions. At Neo7, we lead in personalized peptide engineering by integrating high-definition OMICS and proteomics, focusing on cancer treatment, disease management, and resilience building. This convergence transforms healthcare with tailored treatment strategies aligned to each patient’s unique molecular profile. Understanding High-Definition OMICS High-definition OMICS involves detailed analysis of genomics, transcriptomics, proteomics, and metabolomics at unprecedented resolution. It identifies critical molecular variations for disease mechanisms and treatment responses. The Role of Proteomics Proteomics, studying proteins at scale, provides insights into cellular functions and disease states. We employ advanced technologies like mass spectrometry and protein microarrays to map protein networks crucial for treatment efficacy. Personalized Peptide Engineering at Neo7 Using insights from high-definition proteomics, we design peptides tailored to individual profiles. Peptides are valued for their specificity and efficacy in therapies with minimal toxicity. Applications and Precision Customization 1. Cancer Treatment: Personalized peptides target unique tumor signal markers, enhancing efficacy and minimizing side effects. 2. Active Disease Management: Peptides modulate disease-specific signal proteins, customizing treatments for better outcomes. 3. Resilience Building: Peptides based on proteomic signatures boost immunity and overall health resilience. Future Directions Advancements in personalized peptide engineering promise refined therapies. Collaboration is key in translating scientific breakthroughs into clinical practice, supported by evolving regulatory frameworks. Conclusion Neo7 pioneers personalized peptide engineering with high-definition OMICS and proteomics, offering precise treatments tailored to individual molecular profiles. Join us in advancing personalized and precision biotechnology and personalized medicine. Connect on LinkedIn for insights into Neo7’s transformative healthcare innovations. www.neo7bioscience.com #PersonalizedMedicine #PrecisionMedicine #Biotechnology #HealthcareInnovation #Proteomics #OMICS #PeptideEngineering #CancerResearch #DiseaseManagement #HealthResilience

This newsletter explores the power of omics-guided microenvironment chips for functional target validation in disease research and drug development. By integrating multi-omics data such as genomics, transcriptomics, and proteomics into chip design, researchers can reconstruct complex and physiologically relevant disease microenvironments to reflect the molecular characteristics of individual patients or disease conditions. These chips co-culture key cell types (e.g., tumor cells, immune system cells, stromal cells) under dynamic microfluidic control and can monitor drug response, immune activation, and cell-cell interactions in real time. The platform enables researchers to validate therapeutic targets identified from omics analysis in a functional, human-relevant environment, thereby accelerating the discovery of effective personalized therapies. The platform bridges the gap between big data and translational biology, making precision medicine more predictive and actionable. #OmicsToChip #TargetValidation #OrganOnAChip #FunctionalBiology #PrecisionMedicine #TumorMicroenvironment #DrugDiscovery #MultiOmics #ImmunotherapyResearch #TranslationalResearch #CSTEAMBiotech

"Recent advancements in high-throughput technologies have ushered in the age of multi-omics [6], encompassing genomics [7], transcriptomics [8], proteomics [9], metabolomics [10], and epigenomics [11]. These technologies generate massive datasets that hold the key to understanding cancer at a molecular level, enabling researchers to identify biomarkers [12], elucidate disease mechanisms [13], and predict therapy responses [14]. Similarly, imaging modalities [15] have become indispensable tools in cancer diagnostics [16-18] and treatment planning [19, 20]. These modalities provide spatial and temporal information about tumor morphology and the surrounding microenvironment [21], supplementing the molecular insights derived from omics data [6-11]." "Clinically, these technological advancements are directly enhancing the translational pipeline, moving precision oncology from an aspirational goal to a clinical reality in a few years. The integrative methods reviewed here are yielding tangible improvements in early and non-invasive diagnostics, enabling more accurate prognostication, and personalizing therapeutic strategies by predicting patient response to specific treatments." "Despite this rapid progress, significant hurdles remain in the path to routine clinical deployment. The field must urgently address the need for standardized, multi-institutional validation protocols to ensure model robustness and generalizability, overcome challenges related to data harmonization, and enhance model interpretability to build clinical trust. Future efforts must be intensely focused on bridging the gap between computational innovation and real-world clinical utility. This will require fostering deep collaboration between data scientists and clinicians, promoting the development of accessible open-source tools, and establishing clear regulatory pathways to ensure that these transformative technologies can be safely and effectively integrated into patient care, ultimately realizing the promise of data-driven, personalized oncology." https://lnkd.in/efBQt9cJ