[MET/ERK and MET/JNK Pathway Activation Is Involved in BCR-ABL Inhibitor-resistance in Chronic Myeloid Leukemia]

Expression of genes potentially involved in loss of response in patients with chronic myeloid leukemia

Chronic Myeloid Leukemia (CML) is a hematological malignancy characterized by the presence of the BCR::ABL1 fusion gene, which leads to uncontrolled cell growth and survival. Tyrosine kinase inhibitors (TKIs) have revolutionized the treatment of CML, but a significant proportion of patients develop resistance or lose response to these drugs. Understanding the molecular mechanisms underlying treatment response and resistance is crucial for improving patient outcomes. This study aimed to analyze the expression patterns of genes involved in treatment response and resistance in CML patients receiving TKI therapy. The expression levels of MET, FOXO3, p15, p16, HCK, and FYN genes were examined in CML patients and compared to healthy donors. Gene expression levels were compared between optimal responders (OR) and resistant patients (R) vs. healthy donors (HD). The MET and FOXO3 OR group showed significant differences compared with the HD, (p < 0.0001) and (p = 0.0003), respectively. p15 expression showed significant differences between OR and HD groups (p = 0.0078), while no significant differences were found in p16 expression between the HD groups. FYN showed a statistically significant difference between R vs. HD (p = 0.0157). The results of HCK expression analysis revealed significant differences between OR and HD (p = 0.0041) and between R and HD (p = 0.0026). When we analyzed OR patients with undetectable BCR::ABL1 transcripts, a greater expression of HCK was observed in the R group. These findings suggest that monitoring the expression levels of MET and FOXO3 genes could be valuable in predicting treatment response and relapse in CML patients. Our study provides important insights into the potential use of gene expression analysis as a tool for predicting treatment response and guiding treatment decisions in CML patients. This knowledge may ultimately contribute to the development of personalized treatment strategies to improve patient outcomes in CML.

Discovering the mechanism of action of drugs with a sparse explainable network

BACKGROUND

Although Deep Neural Networks (DDNs) have been successful in predicting the efficacy of cancer drugs, the lack of explainability in their decision-making process is a significant challenge. Previous research proposed mimicking the Gene Ontology structure to allow for interpretation of each neuron in the network. However, these previous approaches require huge amount of GPU resources and hinder its extension to genome-wide models.

METHODS

We developed SparseGO, a sparse and interpretable neural network, for predicting drug response in cancer cell lines and their Mechanism of Action (MoA). To ensure model generalization, we trained it on multiple datasets and evaluated its performance using three cross-validation schemes. Its efficiency allows it to be used with gene expression. In addition, SparseGO integrates an eXplainable Artificial Intelligence (XAI) technique, DeepLIFT, with Support Vector Machines to computationally discover the MoA of drugs.

FINDINGS

SparseGO's sparse implementation significantly reduced GPU memory usage and training speed compared to other methods, allowing it to process gene expression instead of mutations as input data. SparseGO using expression improved the accuracy and enabled its use on drug repositioning. Furthermore, gene expression allows the prediction of MoA using 265 drugs to train it. It was validated on understudied drugs such as parbendazole and PD153035.

INTERPRETATION

SparseGO is an effective XAI method for predicting, but more importantly, understanding drug response.

FUNDING

The Accelerator Award Programme funded by Cancer Research UK [C355/A26819], Fundación Científica de la AECC and Fondazione AIRC, Project PIBA_2020_1_0055 funded by the Basque Government and the Synlethal Project (RETOS Investigacion, Spanish Government).

Myricetin is effective and selective in inhibiting imatinib-resistant chronic myeloid leukemia stem and differentiated cells through targeting eIF4E

Although imatinib has revolutionized the treatment of chronic myeloid leukemia (CML), s develop resistance to imatinib when progress to blast phase and relapse. Myricetin, a flavonoid compound found in natural plants, has multiple biological functions. In this study, we show that myricetin demonstrated potent efficacy in imatinib-resistant CML CD34 + stem/progenitor cells with less toxicity in normal bone marrow. Myricetin is also active against imatinib-resistant CML bulk cells. The in vitro observations on the therapeutic effects of myricetin were translatable to in vivo imatinib-resistant CML xenograft mouse models. Mechanism studies showed that myricetin decreased the phosphorylation of eIF4E and Ak strain transforming, and the protein level of c-Myc and Cyclin D1. Rescue studies using eIF4E (S209D) and (S209A) confirmed that eIF4E phosphorylation inhibition was the mechanism of myricetin's action in CML. Our results suggest that myricetin may be a potential lead for drug development to overcome imatinib resistance in CML.

Inference of kinase-signaling networks in human myeloid cell line models by Phosphoproteomics using kinase activity enrichment analysis (KAEA)

BACKGROUND

Despite the introduction of targeted therapies, most patients with myeloid malignancies will not be cured and progress. Genomics is useful to elucidate the mutational landscape but remains limited in the prediction of therapeutic outcome and identification of targets for resistance. Dysregulation of phosphorylation-based signaling pathways is a hallmark of cancer, and therefore, kinase-inhibitors are playing an increasingly important role as targeted treatments. Untargeted phosphoproteomics analysis pipelines have been published but show limitations in inferring kinase-activities and identifying potential biomarkers of response and resistance.

METHODS

We developed a phosphoproteomics workflow based on titanium dioxide phosphopeptide enrichment with subsequent analysis by liquid chromatography tandem mass spectrometry (LC-MS). We applied a novel Kinase-Activity Enrichment Analysis (KAEA) pipeline on differential phosphoproteomics profiles, which is based on the recently published SetRank enrichment algorithm  with reduced false positive rates. Kinase activities were inferred by this algorithm using an extensive reference database comprising five experimentally validated kinase-substrate meta-databases complemented with the NetworKIN in-silico prediction tool. For the proof of concept, we used human myeloid cell lines (K562, NB4, THP1, OCI-AML3, MOLM13 and MV4-11) with known oncogenic drivers and exposed them to clinically established kinase-inhibitors.

RESULTS

Biologically meaningful over- and under-active kinases were identified by KAEA in the unperturbed human myeloid cell lines (K562, NB4, THP1, OCI-AML3 and MOLM13). To increase the inhibition signal of the driving oncogenic kinases, we exposed the K562 (BCR-ABL1) and MOLM13/MV4-11 (FLT3-ITD) cell lines to either Nilotinib or Midostaurin kinase inhibitors, respectively. We observed correct detection of expected direct (ABL, KIT, SRC) and indirect (MAPK) targets of Nilotinib in K562 as well as indirect (PRKC, MAPK, AKT, RPS6K) targets of Midostaurin in MOLM13/MV4-11, respectively. Moreover, our pipeline was able to characterize unexplored kinase-activities within the corresponding signaling networks.

CONCLUSIONS

We developed and validated a novel KAEA pipeline for the analysis of differential phosphoproteomics MS profiling data. We provide translational researchers with an improved instrument to characterize the biological behavior of kinases in response or resistance to targeted treatment. Further investigations are warranted to determine the utility of KAEA to characterize mechanisms of disease progression and treatment failure using primary patient samples.