Methods for predicting relative benefit of therapy options and devices thereof

Abstract

Methods, non-transitory computer-readable media, and devices are disclosed that identify and validate a signature for predicting relative benefit of two therapies for frontline and therapy sequencing for patients. Described and illustrated by way of the example herein are machine learning approaches for gaining data-driven insights from the mutational landscape in metastatic pancreatic adenocarcinoma (mPDAC) and validating the signature in predicting relative benefit from FOLFIRINOX (FFX) and Gemcitabine/nab-Paclitaxel (GA) therapies. This technology inputs a patient's genomics findings and clinical data and generates predictions of relative effectiveness for the two distinct FFA and GA chemotherapy options. The predictions for an individual patient provide personalized guidance on treatment sequencing to improve patient health outcomes.

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