Clinical Forecasting using Ex Vivo Drug Sensitivity Profiling of Acute Myeloid Leukemia

Abstract

Acute Myeloid Leukemia (AML) is a heterogeneous malignancy involving the clonal expansion of myeloid stem and progenitor cells in the bone marrow and peripheral blood. Most AML patients eligible for potentially curative treatment receive intensive chemotherapy. Risk stratification is used to optimize treatment intensity and transplant strategy, and is mainly based on cytogenetic screening for structural chromosomal alterations and targeted sequencing of a selection of common mutations. However, the forecasting accuracy of treatment response remains modest. Recently, ex vivo drug screening has gained traction for its potential in personalized treatment selection, as well as a tool for identifying and mapping patient groups based on relevant cancer dependencies. We systematically evaluated the use of drug sensitivity profiling for predicting patient survival and clinical response to chemotherapy in a cohort of AML patients. We compared computational methodologies for scoring drug efficacy and characterized tools to counter noise and batch-related confounders pervasive in high-throughput drug testing. We show that ex vivo drug sensitivity profiling is a robust and versatile approach to patient prognostics that comprehensively maps functional signatures of treatment response and disease progression. In conclusion, ex vivo drug profiling can accurately assess risk of individual AML patients and may guide clinical decision-making.