Translatome proteomics identifies autophagy as a resistance mechanism to on-target FLT3 inhibitors in acute myeloid leukemia

Abstract

Internal tandem duplications (ITD) in the receptor tyrosine kinase FLT3 occur in 25 % of acute myeloid leukemia (AML) patients, drive leukemia progression and confer a poor prognosis. Primary resistance to FLT3 kinase inhibitors (FLT3i) quizartinib, crenolanib and gilteritinib is a frequent clinical challenge and occurs in the absence of identifiable genetic causes. This suggests that adaptive cellular mechanisms mediate primary resistance to on-target FLT3i therapy. Here, we systematically investigated acute cellular responses to on-target therapy with multiple FLT3i in FLT3-ITD + AML using recently developed functional translatome proteomics (measuring changes in the nascent proteome) with phosphoproteomics. This pinpointed AKT-mTORC1-ULK1-dependent autophagy as a dominant resistance mechanism to on-target FLT3i therapy. FLT3i induced autophagy in a concentration- and time-dependent manner specifically in FLT3-ITD + cells in vitro and in primary human AML cells ex vivo. Pharmacological or genetic inhibition of autophagy increased the sensitivity to FLT3-targeted therapy in cell lines, patient-derived xenografts and primary AML cells ex vivo. In mice xenografted with FLT3-ITD + AML cells, co-treatment with oral FLT3 and autophagy inhibitors synergistically impaired leukemia progression and extended overall survival. Our findings identify a molecular mechanism responsible for primary FLT3i treatment resistance and demonstrate the pre-clinical efficacy of a rational combination treatment strategy targeting both FLT3 and autophagy induction.

Mapping the proteogenomic landscape enables prediction of drug response in acute myeloid leukemia

“…Machine learning offers an effective tool for synthesizing and analyzing the amassing omics data to extract prominent features that can be used to develop mechanistic models for hypothesis generation. 49 , 64 As a first step, we look forward to the harmonization of our dataset with other recently published proteomic studies in AML 18 , 23 , 36 , 37 , 39 , 40 to improve prognostication, and better understand the biology of drug response in AML patients.…”

“… 24 , 25 , 26 , 27 This motivated the National Cancer Institute (NCI) to create the Clinical Proteomic Tumor Analysis Consortium (CPTAC), in which patient-derived samples were assayed using state-of-the-art mass spectrometry (MS) pipelines to produce proteomic and phosphoproteomic measurements of hundreds of tumors in breast, ovary, kidney, head and neck, endometrium, brain, and other tissues. 28 , 29 , 30 , 31 , 32 , 33 , 34 , 35 Efforts to study the impact of the proteomic signatures on drug response have been previously evaluated in AML cell lines 24 , 36 , 37 and in primary AML patient samples. 18 , 36 , 38 , 39 , 40 In each study, these proteomic measurements demonstrated patterns that were not evident at the genomic or transcriptomic level.…”

“… 28 , 29 , 30 , 31 , 32 , 33 , 34 , 35 Efforts to study the impact of the proteomic signatures on drug response have been previously evaluated in AML cell lines 24 , 36 , 37 and in primary AML patient samples. 18 , 36 , 38 , 39 , 40 In each study, these proteomic measurements demonstrated patterns that were not evident at the genomic or transcriptomic level. 18 , 41 Recent studies have shown that the use of proteomic signatures can also improve modeling of ex vivo drug response, 38 , 42 providing impetus for the proteomic characterization of a larger cohort to further map the molecular diversity of AML in response to drug treatment.…”

Mass Spectrometry–Based Proteogenomics: New Therapeutic Opportunities for Precision Medicine

“…MS-based proteomic approaches mapped the effects of targeted agents on AML cell lines and primary samples and identified key pathways affected by drug treatment ( 33 – 35 ) and mutational status ( 36 ) or targetable pathways associated with drug resistance ( 37 , 38 ). We previously used computational approaches to integrate proteomic profiling with genomics, transcriptomics, and small-molecule inhibitor sensitivity data sets to create models that recapitulate patient biology, which can be leveraged to prioritize treatment strategies ( 37 , 39 ).…”

Proteomic Characterization of Acute Myeloid Leukemia for Precision Medicine

“…Koschade et al. ( 112 ) used quantification of translation changes by proteomics to reveal global attenuation of translation, but an increase in the transcription and synthesis of proteins involved in autophagy, upon FLT3 inhibition. Phosphoproteomics analysis showed an increase in the phosphorylation of proteins related to autophagy and MTOR signaling after pharmacological inhibition of FLT3.…”