Discover how advanced proteomic profiling is bridging the gap between preclinical research and clinical reality. While genomic data has long been the standard for characterizing tumor models, it often fails to accurately predict actual protein levels due to complex post-translational regulations.
This research introduces a high-throughput proteomic platform that directly quantifies thousands of proteins across hundreds of Patient-Derived Xenograft (PDX) models, providing a more precise map of the "druggable" landscape.
By analyzing over 500 PDX models across various cancer types—including lung, colorectal, and breast cancers—this study demonstrates that direct protein measurement is essential for identifying the most effective therapeutic targets. The findings reveal that for many critical biomarkers, mRNA levels do not strongly correlate with protein expression, making this proteomic data a vital asset for selecting the right models for drug testing. This comprehensive dataset serves as a powerful resource for refining target discovery and improving the translational accuracy of oncology research.
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Overcome Genomic Limitations: Learn why mRNA data alone can be misleading and how direct proteomic quantification provides a more accurate assessment of therapeutic targets.
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Access a Massive Proteomic Dataset: Gain insights from a study covering over 500 PDX models, identifying more than 10,000 unique proteins to help you find the perfect model for your research.
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Improve Translational Success: Understand the methodology used to correlate preclinical protein expression with clinical outcomes, reducing the risk of failure when moving into human trials.
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Identify Novel Biomarkers: Discover how high-depth proteomics can uncover low-abundance proteins and signaling pathways that are often missed by standard analytical methods.
