Abstract 286: Transcriptomic features predicting drug sensitivity and resistance in acute myeloid leukemia
Authors: Ashwini Kumar, Disha Malani, Bhagwan Yadav, Mika Kontro, Matti Kankainen, Swapnil Potdar, Simon Anders, Kimmo Porkka, Olli Kallioniemi Kallioniemi, Caroline Heckman
Journal: Cancer Research (2018)

Abstract Acute myeloid leukemia (AML) is an aggressive, heterogeneous disease with poor survival after disease recurrence. Although new targeted therapies have recently been approved for specific AML subtypes, the majority is treated with conventional cytotoxic therapy with variable outcome. To identify novel therapies, we performed comprehensive ex vivo drug sensitivity testing with 515 drugs and RNA sequencing on 127 AML patient samples, allowing us to identify associations between transcriptomic profiles and drug responses. Bone marrow or peripheral blood mononuclear cells (MNCs) were collected from diagnostic (n=66), relapsed (n=39) and refractory (n=22) AML patients. RNA was prepared, sequenced and analyzed as described previously (PMID:28818039). Drug sensitivity and resistance testing was performed on the MNCs with 515 approved and investigational oncology chemical compounds (PMID:24056683). To identify expression profiles associated with drug response, generalized linear regression and elastic net regression models were applied. In the analysis, confounding factors including the patient's gender, RNA extraction and library preparation methods were taken into account. Elastic net regression analysis resulted in significant (FDR&amp;lt;0.1) positive or negative associations between 1110 genes and 105 drugs. Clustering of the genes depicted 4 major hubs where drugs with the same mode of action grouped together, e.g. chemotherapeutics, BCL-2, and FLT3 inhibitors. Functional enrichment analysis of each hub using DAVID tool revealed genes involved in regulation of cell proliferation (43 genes, FDR=9.19E-18), which can be explained by the cytotoxic chemotherapy drugs. Genes involved in cell death (68 genes, FDR=1.67E-5) were associated with BCL-2 inhibitor response, while genes involved in cell surface receptor linked signal transduction (51 genes, FDR=2.33E-11) were associated with FLT3 inhibitors. The fourth hub was enriched with cell adhesion (45 genes, FDR=1.11E-21) and focal adhesion (50 genes, FDR=5.11E-28), specifically integrin family (29 genes, FDR=3.17E-18) genes, that play an important role in drug resistance and were negatively correlated with cytotoxic drugs. Finally, the linear regression analysis revealed significant positive correlation between tyrosine kinase inhibitors (sorafeninb, sunitinib, tivozanib) and FLT3LG and KITLG and negative correlation with BEX2 and BEX5 genes. Regression analysis for MEK inhibitors resulted in expected positive correlation with RRAS and JAK2 gene expression. In conclusion, identifying associations between transcriptomic profiles and drug responses may reveal clinically actionable drugs for AML patients characterized by specific molecular features. Our results indicate potential gene expression biomarkers for key targeted drugs, which can be used to identify AML patients likely to benefit from these therapies. Citation Format: Ashwini Kumar, Disha Malani, Bhagwan Yadav, Mika Kontro, Matti Kankainen, Swapnil Potdar, Simon Anders, Kimmo Porkka, Olli Kallioniemi Kallioniemi, Caroline Heckman. Transcriptomic features predicting drug sensitivity and resistance in acute myeloid leukemia [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2018; 2018 Apr 14-18; Chicago, IL. Philadelphia (PA): AACR; Cancer Res 2018;78(13 Suppl):Abstract nr 286.