Erlotinib is effective against FLT3-ITD mutant AML and helps to overcome intratumoral heterogeneity via targeting FLT3 and Lyn

Identification and validation of a novel prognostic model based on anoikis‑related genes in acute myeloid leukemia

Acute myeloid leukemia (AML) is a hematological cancer prevalent worldwide. Anoikis-related genes (ARGs) are crucial in the progression of cancer and metastasis of tumors. However, their role in AML needs to be clarified. In the present study, differential analysis was performed on data from The Cancer Genome Atlas database to identify differentially expressed ARGs (DE-ARGs). Subsequently, a prognostic model for patients with AML was constructed using univariate Cox, Least Absolute Shrinkage and Selection Operator and multivariate Cox regression analyses. This model was based on four key DE-ARGs [lectin galactoside-binding soluble 1 (LGALS1), integrin subunit α 4 (ITGA4), hepatocyte growth factor (HGF) and Ras homolog gene family member C (RHOC)]. Independent prognostic factors for AML included prior treatment, age, risk scores and diagnosis. A nomogram was constructed based on these factors to aid clinical decision-making. Furthermore, bone marrow samples were collected from individuals diagnosed with AML and healthy donors to validate the expression of the identified ARGs using reverse transcription-quantitative PCR. The mRNA levels of LGALS1 and RHOC were significantly higher, while those of ITGA4 and HGF were significantly lower in patients with AML than in healthy donors (all P<0.05). The results of the present study expands the understanding of the function of ARGs in AML, providing a new theoretical basis for the treatment of AML.

Establishment and validation of a gene mutation-based risk model for predicting prognosis and therapy response in acute myeloid leukemia

Background

Acute myeloid leukemia (AML) is a malignant clonal proliferative disease of hematopoietic system. Despite tremendous progress in uncovering the AML genome, only a small number of mutations have been incorporated into risk stratification and used as therapeutic targets. In this research, we performed to construct a predictive prognosis risk model for AML patients according to gene mutations.

Methods

Next-generation sequencing (NGS) technology was utilized to detect gene mutation from 118 patients. mRNA expression profiles and related clinical information were mined from TCGA and GEO databases. Consensus cluster analysis was applied to obtain molecular subtypes, and differences in clinicopathological features, prognosis, and immune microenvironment of different clusters were systematically compared. According to the differentially expressed genes (DEGs) between clusters, univariate and LASSO regression analysis were applied to identify gene signatures to build a prognostic risk model. Patients were classified into high-risk (HR) and low-risk (LR) groups according to the median risk score (RS). Differences in prognosis, immune profile, and therapeutic sensitivity between two groups were analyzed. The independent predictive value of RS was assessed and a nomogram was developed.

Results

NGS detected 24 mutated genes, with higher mutation frequencies in CBL (63 %) and SETBP1 (49 %). Two clusters exhibited different immune microenvironments and survival probability (p = 0.0056) were identified. A total of 444 DEGs were screened in two clusters, and a mutation-associated risk model was constructed, including MPO, HGF, SH2B3, SETBP1, HLA-DRB1, LGALS1, and KDM5B. Patients in LR had a superior survival time compared to HR. Predictive performance of this model was confirmed and the developed nomogram further improved the applicability of the risk model with the AUCs for predicting 1-, 3-, 5-year survival rate were 0.829, 0.81 and 0.811, respectively. HR cases were more sensitive to erlotinib, CI-1040, and AZD6244.

Conclusion

These findings supplemented the understanding of gene mutations in AML, and constructed models had good application prospect to provide effective information for predicting prognosis and treatment response of AML.

Identification of hub genes and potential molecular mechanisms related to drug sensitivity in acute myeloid leukemia based on machine learning

Background: Acute myeloid leukemia (AML) is the most common form of leukemia among adults and is characterized by uncontrolled proliferation and clonal expansion of hematopoietic cells. There has been a significant improvement in the treatment of younger patients, however, prognosis in the elderly AML patients remains poor. Methods: We used computational methods and machine learning (ML) techniques to identify and explore the differential high-risk genes (DHRGs) in AML. The DHRGs were explored through multiple in silico approaches including genomic and functional analysis, survival analysis, immune infiltration, miRNA co-expression and stemness features analyses to reveal their prognostic importance in AML. Furthermore, using different ML algorithms, prognostic models were constructed and validated using the DHRGs. At the end molecular docking studies were performed to identify potential drug candidates targeting the selected DHRGs. Results: We identified a total of 80 DHRGs by comparing the differentially expressed genes derived between AML patients and normal controls and high-risk AML genes identified by Cox regression. Genetic and epigenetic alteration analyses of the DHRGs revealed a significant association of their copy number variations and methylation status with overall survival (OS) of AML patients. Out of the 137 models constructed using different ML algorithms, the combination of Ridge and plsRcox maintained the highest mean C-index and was used to build the final model. When AML patients were classified into low- and high-risk groups based on DHRGs, the low-risk group had significantly longer OS in the AML training and validation cohorts. Furthermore, immune infiltration, miRNA coexpression, stemness feature and hallmark pathway analyses revealed significant differences in the prognosis of the low- and high-risk AML groups. Drug sensitivity and molecular docking studies revealed top 5 drugs, including carboplatin and austocystin-D that may significantly affect the DHRGs in AML. Conclusion: The findings from the current study identified a set of high-risk genes that may be used as prognostic and therapeutic markers for AML patients. In addition, significant use of the ML algorithms in constructing and validating the prognostic models in AML was demonstrated. Although our study used extensive bioinformatics and machine learning methods to identify the hub genes in AML, their experimental validations using knock-out/-in methods would strengthen our findings.

Circ_0035381 Regulates Acute Myeloid Leukemia Development by Modulating YWHAZ Expression via Adsorbing miR-582-3p

Acute myeloid leukemia (AML) is a common hematopoietic disorder. Many circular RNAs (circRNAs) are abnormally expressed in AML, including hsa_circ_0035381 (circ_0035381). Nevertheless, the function and mechanism of circ_0035381 in AML remain mostly unclear. Expression of circ_0035381 was determined by qRT-PCR. The impacts of circ_0035381 on AML cell proliferation, apoptosis, and mitochondrial damage were validated via performing loss-of-function experiments. Targeting relationship was predicted by bioinformatics analysis and verified via dual-luciferase reporter and RNA immunoprecipitation assays. Circ_0035381 was upregulated in AML bone marrow samples and cells. Circ_0035381 downregulation decreased AML cell growth in nude mice and restrained AML cell proliferation and contributed to AML apoptosis and mitochondrial damage in vitro. Circ_0035381 acted as a miR-582-3p sponge, and miR-582-3p downregulation mitigated the impacts of circ_0035381 interference on AML cell proliferation, apoptosis, and mitochondrial damage. MiR-582-3p targeted Tyrosine3-monooxygenase/tryptophan5-monooxygenase activation protein zeta (YWHAZ), and it restrained AML cell proliferation and facilitated AML cell apoptosis and mitochondrial damage by decreasing YWHAZ expression. Notably, circ_0035381 regulated YWHAZ expression via miR-582-3p. Circ_0035381 knockdown repressed cell proliferation and promoted cell apoptosis and mitochondrial damage via regulating the miR-582-3p/YWHAZ axis in AML.

The Combination of Gefitinib With ATRA and ATO Induces Myeloid Differentiation in Acute Promyelocytic Leukemia Resistant Cells

In approximately 15% of patients with acute myeloid leukemia (AML), total and phosphorylated EGFR proteins have been reported to be increased compared to healthy CD34⁺ samples. However, it is unclear if this subset of patients would benefit from EGFR signaling pharmacological inhibition. Pre-clinical studies on AML cells provided evidence on the pro-differentiation benefits of EGFR inhibitors when combined with ATRA or ATO in vitro. Despite the success of ATRA and ATO in the treatment of patients with acute promyelocytic leukemia (APL), therapy-associated resistance is observed in 5-10% of the cases, pointing to a clear need for new therapeutic strategies for those patients. In this context, the functional role of EGFR tyrosine-kinase inhibitors has never been evaluated in APL. Here, we investigated the EGFR pathway in primary samples along with functional in vitro and in vivo studies using several APL models. We observed that total and phosphorylated EGFR (Tyr992) was expressed in 28% and 19% of blast cells from APL patients, respectively, but not in healthy CD34⁺ samples. Interestingly, the expression of the EGF was lower in APL plasma samples than in healthy controls. The EGFR ligand AREG was detected in 29% of APL patients at diagnosis, but not in control samples. In vitro, treatment with the EGFR inhibitor gefitinib (ZD1839) reduced cell proliferation and survival of NB4 (ATRA-sensitive) and NB4-R2 (ATRA-resistant) cells. Moreover, the combination of gefitinib with ATRA and ATO promoted myeloid cell differentiation in ATRA- and ATO-resistant APL cells. In vivo, the combination of gefitinib and ATRA prolonged survival compared to gefitinib- or vehicle-treated leukemic mice in a syngeneic transplantation model, while the gain in survival did not reach statistical difference compared to treatment with ATRA alone. Our results suggest that gefitinib is a potential adjuvant agent that can mitigate ATRA and ATO resistance in APL cells. Therefore, our data indicate that repurposing FDA-approved tyrosine-kinase inhibitors could provide new perspectives into combination therapy to overcome drug resistance in APL patients.