Phosphotyrosine-based Phosphoproteomics for Target Identification and Drug Response Prediction in AML Cell Lines

Abstract

Acute myeloid leukemia (AML) is a clonal disorder arising from hematopoietic myeloid progenitors. Aberrantly activated tyrosine kinases (TK) are involved in leukemogenesis and are associated with poor treatment outcome. Kinase inhibitor (KI) treatment has shown promise in improving patient outcome in AML. However, inhibitor selection for patients is suboptimal.In a preclinical effort to address KI selection, we analyzed a panel of 16 AML cell lines using phosphotyrosine (pY) enrichment-based, label-free phosphoproteomics. The Integrative Inferred Kinase Activity (INKA) algorithm was used to identify hyperphosphorylated, active kinases as candidates for KI treatment, and efficacy of selected KIs was tested.Heterogeneous signaling was observed with between 241 and 2764 phosphopeptides detected per cell line. Of 4853 identified phosphopeptides with 4229 phosphosites, 4459 phosphopeptides (4430 pY) were linked to 3605 class I sites (3525 pY). INKA analysis in single cell lines successfully pinpointed driver kinases (PDGFRA, JAK2, KIT and FLT3) corresponding with activating mutations present in these cell lines. Furthermore, potential receptor tyrosine kinase (RTK) drivers, undetected by standard molecular analyses, were identified in four cell lines (FGFR1 in KG-1 and KG-1a, PDGFRA in Kasumi-3, and FLT3 in MM6). These cell lines proved highly sensitive to specific KIs. Six AML cell lines without a clear RTK driver showed evidence of MAPK1/3 activation, indicative of the presence of activating upstream RAS mutations. Importantly, FLT3 phosphorylation was demonstrated in two clinical AML samples with a FLT3 internal tandem duplication (ITD) mutation.Our data show the potential of pY-phosphoproteomics and INKA analysis to provide insight in AML TK signaling and identify hyperactive kinases as potential targets for treatment in AML cell lines. These results warrant future investigation of clinical samples to further our understanding of TK phosphorylation in relation to clinical response in the individual patient.

Focal adhesion kinase inhibition synergizes with nab-paclitaxel to target pancreatic ductal adenocarcinoma

“…These results might be the result of the high penetrance of the driving KRAS mutation in these tumors [39] and lack of other activating mutations of oncogenes. Previous studies of our lab in different cancer types showed more heterogeneity in kinase ranking, affiliated by driver mutations [14,40,41]. Previously, Kim et al [42] evaluated three primary cell lines from different metastatic origins of one patient and found clonal heterogeneity in PDAC, with a distinct (phospho)proteomic profiles of each metastatic site.…”

Drug ranking using machine learning systematically predicts the efficacy of anti-cancer drugs

“…Projects such as the Cancer Target Discovery and Development and Genomics of Drug Sensitivity in Cancer have evaluated ML as a means to predict drug responses by associating genomic features, gene expression patterns and copy number alterations to drug sensitivity [17][18][19][20][21] . However, this approach has not been systematically applied using large scale proteomics and phosphoproteomics data, even though anecdotal evidence suggests that proteomic-derived features may be able to predict drug responses more accurately that genomic alternatives [22][23][24][25][26] . A limitation has been the low sample throughput of proteomics and phosphoproteomics by liquid chromatography coupled to tandem mass spectrometry (LC-MS/ MS) compared to other omics techniques.…”

FLT3-ITD transduces autonomous growth signals during its biosynthetic trafficking in acute myelogenous leukemia cells

“…The anti-FLT3 cytoplasmic domain antibody also stained the perinuclear region in these ITD-positive cells (Supplementary Figure S1), supporting the results of FLT3-ITD mislocalization. Since these three cell lines have different ITD sequences (Quentmeier et al, 2003;Furukawa et al, 2007;van Alphen et al, 2020), the accumulation of FLT3 in the perinuclear region was independent of inserted amino acid sequences but dependent on an ITD insertion. These results suggest that ITD causes FLT3 retention in the perinuclear compartment in AML cells.…”

“…To examine the localization of endogenous FLT3, we performed confocal immunofluorescence microscopic analyses on human leukemia cell lines with an anti-FLT3 luminal-faced N-terminal region antibody. For immunostaining, we chemically fixed and permeabilized THP-1 (acute monocytic leukemia, FLT3 WT/WT ), RS4-11 (AML, FLT3 WT/WT ), MV4-11 (AML, FLT3 ITD/ITD ), MOLM-14 (AML, FLT3 WT/ITD ), and Kasumi-6 (AML, FLT3 ITD/ITD ) (Quentmeier et al, 2003;Furukawa et al, 2007;Wang et al, 2018;van Alphen et al, 2020; Figure 1A). In FLT3-wt leukemia cell lines (THP-1 and RS4-11), the wild-type receptor was mainly found at the PM ( Figure 1B, upper panels).…”