Multiomics Approaches in Precision Medicine

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

"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

the NIH recently announced a $50M program for funding multi-omic research. the goal is to support the aggregation and analysis of multi-modal biological data, such as genomics, epigenomics, transcriptomics, and proteomics. while this will be an impactful initiative, there remains an under-investigation more broadly of theoretical frameworks for multi-omic biology. specifically, how do different modalities of biological data interrelate? how do interactions across omic levels relate to function and disease? and, most critically, what is the optimal level of analysis for a given biological question? computational neuroscientist david marr famously posited that information processing systems, like the brain, ought to be analyzed at 3 distinct levels: the computational level (defining the problem the system addresses), the algorithmic level (outlining high-level processes to solve that problem), and the implementation level (the physical mechanisms, such as neural circuits, that carry out these processes). marr's framework is a highly effective approach for understanding complex systems, and analogous theories should be adapted more generally across biology. disease results from dysfunction across multiple levels of biology, from genetic sequences to gene expression to cell, tissue, and organ morphology. by incorporating the overall functional goals of the system and characterizing how changes at each of these levels propagate across the system, we will a develop a more comprehensive understanding of biological function. this will result in a more structured approach to multi-omic research. it will help scientists determine the appropriate scale and modality of data to focus on for a particular biological question, and will maximize the potential of the field.