Follow the Data: Make decisions to predict adaptive resistance to targeted inhibitor therapeutics in oncology1

Research Area: Solid tumor & oncology

Development Stage: Optimization

Goal: Uncovering the ability to predict adaptive resistance to glioblastoma and other solid tumors to targeted inhibitor therapeutics.

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How our single-cell systems are making a difference

Solution: IsoPlexis’ single-cell phosphoproteomic analytics of a human-derived in vivo GBM model of mTORki resistance demonstrated show that drug resistance can proceed via a 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.

Follow the data

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 (left). 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.1

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IsoPlexis finds 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