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.
In a study published in , researchers at Baylor College of Medicine and collaborating institutions report the development of a kinase inhibitor pulldown assay (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.
“Our study represents a convergence of advanced technologies, redefining basic medical research and paving the way for future clinical applications,” said first author , senior bioinformatics analyst at the 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,” said co-corresponding author , faculty at Baylor’s .
“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 Dr. Beom-Jun Kim, currently an associate director at AstraZeneca and an assistant professor at Baylor at the time of research.
Doug W. Chan, Matthew V. Holt, Junkai Wang, Eric J. Jaehnig, Meenakshi Anurag, Purba Singh and Anna Malovannaya also contributed to this work. The authors are affiliated with Baylor College of Medicine, the Lester and Sue Smith Breast Center and/or the Dan L Duncan Comprehensive Cancer Center.
This work was supported by CPTAC PTRC grant ³Ô¹ÏÍøÕ¾ Cancer Institute’s Specialized Programs of Research Excellence (SPORE) (U01 CA214125) and a CPTAC PGDAC Award (U24 CA210954). Ellis received support from a CPRIT Established Investigator Award (RR140033) and from Ralph and Lisa Eads. Ellis also is a McNair Medical Institute Scholar. The BCM Mass Spectrometry Proteomics Core is supported in part by a Dan L Duncan Comprehensive Cancer Center Award (P30 CA125123), CPRIT Core Facility Awards (RP170005 and RP210227) and an NIH High-End Instrumentation Award (S10 OD026804).
