Many cancers, including some types of breast cancer, are driven by alterations in the activity of cellular enzymes called kinases. Therapies that directly inhibit these cancer-promoting activities have proven to be effective for patients in which individual driving kinases can be diagnosed.

One major challenge to this therapeutic approach is to accurately quantify tumor kinases in human biopsy samples. Many kinases are not abundantly present and are therefore more difficult to measure accurately. Although currently there are methods to quantify small amounts of kinases, measuring multiple kinases concurrently is cumbersome and impractical in a clinical setting where rapid data return is critical.

It is crucial to develop methodologies to enrich kinases present in clinical samples, an important step toward effective personalized medicine. Now, researchers and the Baylor College of Medicine and collaborating institutions published a study “Kinase inhibitor pulldown (KiP) assay for clinical proteomics” in Clinical Proteomics about the development of a KiP that can optimally enrich and quantify the small amounts of kinases present in biopsy samples in combination with mass-spectrometry techniques.

The researchers established the coverage and quantitative fidelity of the assay for kinases in a single-shot approach, optimized a 100-kinase targeted panel and determined the effectiveness of KiP in subtyping breast cancer patient-derived animal models and two breast cancer patient sample cohorts.

“Optimized assays were initially evaluated in 16 patient derived xenograft models (PDX) where KiP identified multiple differentially expressed and biologically relevant kinases. From these analyses, an optimized single-shot parallel reaction monitoring (PRM) method was developed to improve quantitative fidelity. The PRM KiP approach was then reapplied to low quantities of proteins typical of yields from core needle biopsies of human cancers,” write the investigators.

Initial targeting

The initial prototype targeting 100 kinases recapitulated intrinsic subtyping of PDX models obtained from comprehensive proteomic and transcriptomic profiling. Luminal and HER2 enriched OCT-frozen patient biopsies subsequently analyzed through KiP-PRM also clustered by subtype. Finally, stable isotope labeled peptide standards were developed to define a prototype clinical method. Data are available via ProteomeXchange with identifiers PXD044655 and PXD046169.”

“Our study represents a convergence of advanced technologies, redefining basic medical research and paving the way for future clinical applications,” said first author Alexander Saltzman, PhD, senior bioinformatics analyst at the Mass Spectrometry Proteomics Core at Baylor.

“This paper emphasizes that new methods in protein mass spectrometry hold great promise for better definition of the individual druggable landscape present in each cancer and should be more widely used for research and, ultimately, clinical care,” added co-corresponding author Matthew Ellis, PhD, faculty at Baylor’s Lester and Sue Smith Breast Center.

“This methodology’s approach to identifying key kinases in cancer may even extend beyond these enzymes and into other low-abundance and biologically relevant targets,” said co-corresponding author Beom-Jun Kim, PhD, currently an associate director at AstraZeneca and an assistant professor at Baylor at the time of research.

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