Dasatinib response in acute myeloid leukemia is correlated with FLT3/ITD, PTPN11 mutations and a unique gene expression signature

Decoding Acute Myeloid Leukemia: A Clinician's Guide to Functional Profiling

Acute myeloid leukemia (AML) is a complex clonal disorder characterized by clinical, genetic, metabolomic, and epigenetic heterogeneity resulting in the uncontrolled proliferation of aberrant blood-forming precursor cells. Despite advancements in the understanding of the genetic, metabolic, and epigenetic landscape of AML, it remains a significant therapeutic challenge. Functional profiling techniques, such as BH3 profiling (BP), gene expression profiling (GEP), proteomics, metabolomics, drug sensitivity/resistance testing (DSRT), CRISPR/Cas9, and RNAi screens offer valuable insights into the functional behavior of leukemia cells. BP evaluates the mitochondrial response to pro-apoptotic BH3 peptides, determining a cell's apoptotic threshold and its reliance on specific anti-apoptotic proteins. This knowledge can pinpoint vulnerabilities in the mitochondria-mediated apoptotic pathway in leukemia cells, potentially informing treatment strategies and predicting therapeutic responses. GEP, particularly RNA sequencing, evaluates the transcriptomic landscape and identifies gene expression alterations specific to AML subtypes. Proteomics and metabolomics, utilizing mass spectrometry and nuclear magnetic resonance (NMR), provide a detailed view of the active proteins and metabolic pathways in leukemia cells. DSRT involves exposing leukemia cells to a panel of chemotherapeutic and targeted agents to assess their sensitivity or resistance profiles and potentially guide personalized treatment strategies. CRISPR/Cas9 and RNAi screens enable systematic disruption of genes to ascertain their roles in leukemia cell survival and proliferation. These techniques facilitate precise disease subtyping, uncover novel biomarkers and therapeutic targets, and provide a deeper understanding of drug-resistance mechanisms. Recent studies utilizing functional profiling have identified specific mutations and gene signatures associated with aggressive AML subtypes, aberrant signaling pathways, and potential opportunities for drug repurposing. The integration of multi-omics approaches, advances in single-cell sequencing, and artificial intelligence is expected to refine the precision of functional profiling and ultimately improve patient outcomes in AML. This review highlights the diverse landscape of functional profiling methods and emphasizes their respective advantages and limitations. It highlights select successes in how these methods have further advanced our understanding of AML biology, identifies druggable targets that have improved outcomes, delineates challenges associated with these techniques, and provides a prospective view of the future where these techniques are likely to be increasingly incorporated into the routine care of patients with AML.

Understanding mechanisms of resistance to FLT3 inhibitors in adult FLT3-mutated acute myeloid leukemia to guide treatment strategy

The presence of FLT3 mutations, including the most common FLT3-ITD (internal tandem duplications) and FLT3-TKD (tyrosine kinase domain), is associated with an unfavorable prognosis in patients affected by acute myeloid leukemia (AML). In this setting, in recent years, new FLT3 inhibitors have demonstrated efficacy in improving survival and treatment response. Nevertheless, the development of primary and secondary mechanisms of resistance poses a significant obstacle to their efficacy. Understanding these mechanisms is crucial for developing novel therapeutic approaches to overcome resistance and improve the outcomes of patients. In this context, the use of novel FLT3 inhibitors and the combination of different targeted therapies have been studied. This review provides an update on the molecular alterations involved in the resistance to FLT3 inhibitors, and describes how the molecular monitoring may be used to guide treatment strategy in FLT3-mutated AML.

Clinical forecasting of acute myeloid leukemia using ex vivo drug-sensitivity profiling

Current treatment selection for acute myeloid leukemia (AML) patients depends on risk stratification based on cytogenetic and genomic markers. However, the forecasting accuracy of treatment response remains modest, with most patients receiving intensive chemotherapy. Recently, ex vivo drug screening has gained traction in personalized treatment selection and as a tool for mapping patient groups based on relevant cancer dependencies. Here, we systematically evaluated the use of drug sensitivity profiling for predicting patient survival and clinical response to chemotherapy in a cohort of AML patients. We compared computational methodologies for scoring drug efficacy and characterized tools to counter noise and batch-related confounders pervasive in high-throughput drug testing. We show that ex vivo drug sensitivity profiling is a robust and versatile approach to patient prognostics that comprehensively maps functional signatures of treatment response and disease progression. In conclusion, ex vivo drug profiling can assess risk for individual AML patients and may guide clinical decision-making.

Molecular data representation based on gene embeddings for cancer drug response prediction

Cancer drug response prediction is a crucial task in precision medicine, but existing models have limitations in effectively representing molecular profiles of cancer cells. Specifically, when these models represent molecular omics data such as gene expression, they employ a one-hot encoding-based approach, where a fixed gene set is selected for all samples and omics data values are assigned to specific positions in a vector. However, this approach restricts the utilization of embedding-vector-based methods, such as attention-based models, and limits the flexibility of gene selection. To address these issues, our study proposes gene embedding-based fully connected neural networks (GEN) that utilizes gene embedding vectors as input data for cancer drug response prediction. The GEN allows for the use of embedding-vector-based architectures and different gene sets for each sample, providing enhanced flexibility. To validate the efficacy of GEN, we conducted experiments on three cancer drug response datasets. Our results demonstrate that GEN outperforms other recently developed methods in cancer drug prediction tasks and offers improved gene representation capabilities. All source codes are available at https://github.com/DMCB-GIST/GEN/ .

Analysis of the clinical characteristics and prognosis of adult de novo acute myeloid leukemia (none APL) with PTPN11 mutations

We discuss the clinical characteristics and prognostic significance of adult individuals with PTPN11 mutations who have developed acute myeloid leukemia (AML) (none acute promyelocytic leukemia). Next generation sequencing and Sanger sequencing were used to detect 51 gene mutations, and multiplex-PCR was used to detect 41 fusion genes from 232 de novo adult AML patients retrospectively. About 7.76% patients harbored PTPN11 mutations, 20 PTPN11 alterations were identified, all of which were missense mutations in the N-SH2 (n = 16) and PTP (n = 4) domains located in exon 3. Patients with PTPN11 mut had significantly higher platelet counts and hemoglobin levels (p < 0.001), which were mainly detected in M5 (n = 12, 66.67%, p < 0.001) subtype. Patients with MLL-AF6 positive showed a higher frequency of PTPN11 mut (p = 0.018) in the 118 AML cases. PTPN11 mut were accompanied by other mutations, which were NPM1 (44.44%), DNMT3A (38.89%), FLT3 (38.89%), and NRAS (17.2%). PTPN11 mut had a negative impact on the complete remission rate in M5 subtype patients (p < 0.001). However, no statistically significant effect on overall survival (OS) with PTPN11 mut patients in the whole cohort and age group (p > 0.05) was observed. Further analysis revealed no significant difference in OS among NPM1 mut/PTPN11 mut, NPM1 mut/PTPN11 wt, DNMT3A mut/PTPN11 mut, and DNMT3A mut/PTPN11 wt patients (p > 0.05). Multivariate analysis showed the proportion of bone marrow blasts ≥65.4% was a factor significantly affecting OS in PTPN11 mut patients (p = 0.043).

Isocitrate dehydrogenase 1 mutation drives leukemogenesis by PDGFRA activation due to insulator disruption in acute myeloid leukemia (AML)

Acute myeloid leukemia (AML) is characterized by complex molecular alterations and driver mutations. Elderly patients show increased frequencies of IDH mutations with high chemoresistance and relapse rates despite recent therapeutic advances. Besides being associated with global promoter hypermethylation, IDH1 mutation facilitated changes in 3D DNA-conformation by CTCF-anchor methylation and upregulated oncogene expression in glioma, correlating with poor prognosis. Here, we investigated the role of IDH1 p.R132H mutation in altering 3D DNA-architecture and subsequent oncogene activation in AML. Using public RNA-Seq data, we identified upregulation of tyrosine kinase PDGFRA in IDH1-mutant patients, correlating with poor prognosis. DNA methylation analysis identified CpG hypermethylation within a CTCF-anchor upstream of PDGFRA in IDH1-mutant patients. Increased PDGFRA expression, PDGFRA-CTCF methylation and decreased CTCF binding were confirmed in AML CRISPR cells with heterozygous IDH1 p.R132H mutation and upon exogenous 2-HG treatment. IDH1-mutant cells showed higher sensitivity to tyrosine kinase inhibitor dasatinib, which was supported by reduced blast count in a patient with refractory IDH1-mutant AML after dasatinib treatment. Our data illustrate that IDH1 p.R132H mutation leads to CTCF hypermethylation, disrupting DNA-looping and insulation of PDGFRA, resulting in PDGFRA upregulation in IDH1-mutant AML. Treatment with dasatinib may offer a novel treatment strategy for IDH1-mutant AML.

Pharmacogenomic Profiling of Pediatric Acute Myeloid Leukemia to Identify Therapeutic Vulnerabilities and Inform Functional Precision Medicine

Despite the expanding portfolio of targeted therapies for adults with acute myeloid leukemia (AML), direct implementation in children is challenging due to inherent differences in underlying genetics. Here we established the pharmacologic profile of pediatric AML by screening myeloblast sensitivity to approved and investigational agents, revealing candidates of immediate clinical relevance. Drug responses ex vivo correlated with patient characteristics, exhibited age-specific alterations, and concorded with activities in xenograft models. Integration with genomic data uncovered new gene-drug associations, suggesting actionable therapeutic vulnerabilities. Transcriptome profiling further identified gene-expression signatures associated with on- and off-target drug responses. We also demonstrated the feasibility of drug screening-guided treatment for children with high-risk AML, with two evaluable cases achieving remission. Collectively, this study offers a high-dimensional gene-drug clinical data set that could be leveraged to research the unique biology of pediatric AML and sets the stage for realizing functional precision medicine for the clinical management of the disease.

SIGNIFICANCE

We conducted integrated drug and genomic profiling of patient biopsies to build the functional genomic landscape of pediatric AML. Age-specific differences in drug response and new gene-drug interactions were identified. The feasibility of functional precision medicine-guided management of children with high-risk AML was successfully demonstrated in two evaluable clinical cases. This article is highlighted in the In This Issue feature, p. 476.

Transcriptional signatures of the BCL2 family for individualized acute myeloid leukaemia treatment

BACKGROUND

Although anti-apoptotic proteins of the B-cell lymphoma-2 (BCL2) family have been utilized as therapeutic targets in acute myeloid leukaemia (AML), their complicated regulatory networks make individualized therapy difficult. This study aimed to discover the transcriptional signatures of BCL2 family genes that reflect regulatory dynamics, which can guide individualized therapeutic strategies.

METHODS

From three AML RNA-seq cohorts (BeatAML, LeuceGene, and TCGA; n = 451, 437, and 179, respectively), we constructed the BCL2 family signatures (BFSigs) by applying an innovative gene-set selection method reflecting biological knowledge followed by non-negative matrix factorization (NMF). To demonstrate the significance of the BFSigs, we conducted modelling to predict response to BCL2 family inhibitors, clustering, and functional enrichment analysis. Cross-platform validity of BFSigs was also confirmed using NanoString technology in a separate cohort of 47 patients.

RESULTS

We established BFSigs labeled as the BCL2, MCL1/BCL2, and BFL1/MCL1 signatures that identify key anti-apoptotic proteins. Unsupervised clustering based on BFSig information consistently classified AML patients into three robust subtypes across different AML cohorts, implying the existence of biological entities revealed by the BFSig approach. Interestingly, each subtype has distinct enrichment patterns of major cancer pathways, including MAPK and mTORC1, which propose subtype-specific combination treatment with apoptosis modulating drugs. The BFSig-based classifier also predicted response to venetoclax with remarkable performance (area under the ROC curve, AUROC = 0.874), which was well-validated in an independent cohort (AUROC = 0.950). Lastly, we successfully confirmed the validity of BFSigs using NanoString technology.

CONCLUSIONS

This study proposes BFSigs as a biomarker for the effective selection of apoptosis targeting treatments and cancer pathways to co-target in AML.

Overcoming Resistance: FLT3 Inhibitors Past, Present, Future and the Challenge of Cure

FLT3 ITD and TKD mutations occur in 20% and 10% of Acute Myeloid Leukemia (AML), respectively, and they represent the target of the first approved anti-leukemic therapies in the 2000s. Type I and type II FLT3 inhibitors (FLT3i) are active against FLT3 TKD/ITD and FLT3 ITD mutations alone respectively, but they still fail remissions in 30-40% of patients due to primary and secondary mechanisms of resistance, with variable relapse rate of 30-50%, influenced by NPM status and FLT3 allelic ratio. Mechanisms of resistance to FLT3i have recently been analyzed through NGS and single cell assays that have identified and elucidated the polyclonal nature of relapse in clinical and preclinical studies, summarized here. Knowledge of tumor escape pathways has helped in the identification of new targeted drugs to overcome resistance. Immunotherapy and combination or sequential use of BCL2 inhibitors and experimental drugs including aurora kinases, menin and JAK2 inhibitors will be the goal of present and future clinical trials, especially in patients with FLT3-mutated (FLT3mut) AML who are not eligible for allogeneic transplantation.

A multiparametric niche-like drug screening platform in acute myeloid leukemia

Functional precision medicine in AML often relies on short-term in vitro drug sensitivity screening (DSS) of primary patient cells in standard culture conditions. We designed a niche-like DSS assay combining physiologic hypoxia (O₂ 3%) and mesenchymal stromal cell (MSC) co-culture with multiparameter flow cytometry to enumerate lymphocytes and differentiating (CD11/CD14/CD15+) or leukemic stem cell (LSC)-enriched (GPR56+) cells within the leukemic bulk. After functional validation of GPR56 expression as a surrogate for LSC enrichment, the assay identified three patterns of response, including cytotoxicity on blasts sparing LSCs, induction of differentiation, and selective impairment of LSCs. We refined our niche-like culture by including plasma-like amino-acid and cytokine concentrations identified by targeted metabolomics and proteomics of primary AML bone marrow plasma samples. Systematic interrogation revealed distinct contributions of each niche-like component to leukemic outgrowth and drug response. Short-term niche-like culture preserved clonal architecture and transcriptional states of primary leukemic cells. In a cohort of 45 AML samples enriched for NPM1c AML, the niche-like multiparametric assay could predict morphologically (p = 0.02) and molecular (NPM1c MRD, p = 0.04) response to anthracycline-cytarabine induction chemotherapy. In this cohort, a 23-drug screen nominated ruxolitinib as a sensitizer to anthracycline-cytarabine. This finding was validated in an NPM1c PDX model.

Current status of drug repositioning in hematology

INTRODUCTION

Drug repositioning (DR) is defined as determining new therapeutic applications for existing drugs. This approach is advantageous over de novo drug discovery in accelerating clinical development, in terms of lower costs, a shortened development period, a well-known action mechanism, a feasible dosage, and an acceptable safety profile.

AREAS COVERED

This work was aimed at reviewing agents with successful DR in hematology.

EXPERT OPINION

Thalidomide and plerixafor have been successfully repositioned for treating multiple myeloma and harvesting peripheral blood stem cells, respectively. The former was originally developed as a sedative and the latter as an anti-HIV drug. Currently, the feasibility of repositioning various agents is being explored (e.g. an anti-influenza virus drug oseltamivir for primary immune thrombocytopenia, an anti-HIV drug abacavir for adult T-cell leukemia, and a macrolide antibiotic clarithromycin for multiple myeloma). Furthermore, bosutinib for chronic myeloid leukemia or the antiplatelet drug cilostazol have been suggested to have clinical benefits for the management of amyotrophic lateral sclerosis and ischemic stroke, respectively. To promote DR, effective application of artificial intelligence or stem cell models, comprehensive database construction shared between academia and pharmaceutical companies, suitable handling of drug patents, and wide cooperation in the area of specialty are warranted.

Bayesian multi-source regression and monocyte-associated gene expression predict BCL-2 inhibitor resistance in acute myeloid leukemia

The FDA recently approved eight targeted therapies for acute myeloid leukemia (AML), including the BCL-2 inhibitor venetoclax. Maximizing efficacy of these treatments requires refining patient selection. To this end, we analyzed two recent AML studies profiling the gene expression and ex vivo drug response of primary patient samples. We find that ex vivo samples often exhibit a general sensitivity to (any) drug exposure, independent of drug target. We observe that this "general response across drugs" (GRD) is associated with FLT3-ITD mutations, clinical response to standard induction chemotherapy, and overall survival. Further, incorporating GRD into expression-based regression models trained on one of the studies improved their performance in predicting ex vivo response in the second study, thus signifying its relevance to precision oncology efforts. We find that venetoclax response is independent of GRD but instead show that it is linked to expression of monocyte-associated genes by developing and applying a multi-source Bayesian regression approach. The method shares information across studies to robustly identify biomarkers of drug response and is broadly applicable in integrative analyses.