Kinase Inhibitor Combination Discovery
Make decisions to predict adaptive resistance to targeted inhibitor therapeutics in oncology1
Empower discovery of combination therapies and overcoming adaptive resistance in solid tumor targeted therapies.
How our Award-Winning IsoLight Single-Cell System is Making a Difference
Solution
IsoPlexis’ single-cell phosphoproteomic analytics of a human-derived in vivo GBM model of mTORki resistance demonstrated that drug resistance can proceed via non-genetic, adaptive mechanisms that are activated within days of drugging.
Finding
The measured adaptive response points to combination therapies tested in vivo were shown to halt tumor growth. This single-cell analytic approach provided clinically actionable insights into designing combination therapies against solid tumor.
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See the latest published data in Solid Tumor & Oncology
To evaluate the change in tumor heterogeneity across the three stages, we employed a functional heterogeneity index (FHI). The FHI reflects the dispersion of the functional protein levels across all single-cell assays at a specific condition. It is defined as the dissimilarity value in the agglomerative hierarchical clustering (AHC) of mean normalized single-cell data based upon Ward’s minimum variance method (Ward, 1963).
In the responsive state, there is a more than 4-fold drop in the FHI. The tumors were again probed at the resistant state (day 39 following the start of therapy). Resistance was also associated with a sharp increase in the FHI.
Solution
Detect Critical Differences
In vivo test results for the seven monotherapy or combination therapies based upon the predictions from the SCBC data analysis. Data are shown as mean ± SD; n=11 for vehicle, n=6 for C, n=4 for D, n=4 for U, n = 4 for each combinatorial treatment group.
All seven predictions proved correct. **p < 0.005 relative to samples after treatment stop versus responsive samples; ***p < 0.001 relative to responsive samples versus vehicle samples.1
[1] Reference: Wei et al, Cancer Cell 2016