Researchers at St. Jude have developed a transcriptomics-based (RNA-seq) predictive biomarker to predict sensitivity of patients with T-ALL to dasatinib and ponatinib using a pre-defined data-driven T-ALL interactome and the NetBID algorithm to computationally transform the RNA-Seq profile, and then use the activity scores of 14 genes and a random forest–based model to predict the sensitivity to dasatinib and ponatinib. The 14-gene predictive model is being refined by including new data recently generated.
This invention can be used to identify and select patients with T-ALL that will respond to dasatinib and ponatinib. Both dasatinib and ponatinib are FDA-approved agents for CML and Ph+ ALL, and researchers discovered that dasatinib and ponatinib have potential anti-leukemic effects in ~40% of children with T-ALL by inhibiting the TCR signaling pathway (e.g., LCK phosphorylation). This biomarker may be extended to other T-lineage hematological malignancies.
Existing biomarkers for dasatinib and ponatinib are based on ABL-translocation or fusion rearrangements, however, these genomic alternations are rare (<5%) in T-ALL. We discovered that ~40% of patients with T-ALL are sensitive to dasatinib and ponatinib. This transcriptomics-based biomarker is the first to predict patient response to these two tyrosine kinase inhibitors and select beneficial patients most of whom don’t have ABL-rearrangements, predicting ~43% of patients with T-ALL in the TARGET cohort (N=261) are sensitive to dasatinib and ponatinib, consistent with the response rate in the index cohort
The technology offers a more robust predictive biomarker by increasing the signal to noise ratio compared to single-gene expression–based prediction. There is also artificial intelligence to increase the accuracy of the prediction and identify a host of other items.
Diagnostic, transcriptomics-based (RNA-seq) predictive biomarker, T-ALL, dasatinib, ponatinib, hematological malignancies.
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