Landscape Of Acute Myeloid Leukemia Research Articles

Combination therapy with novel agents for acute myeloid leukaemia: Insights into treatment of a heterogenous disease.

The treatment landscape of acute myeloid leukaemia (AML) is evolving rapidly. Venetoclax in combination with intensive chemotherapy or doublets or triplets with targeted or immune therapies is the focus of numerous ongoing trials. The development of mutation-targeted therapies has greatly enhanced the treatment armamentarium, with FLT3 inhibitors and isocitrate dehydrogenase inhibitors improving outcomes in frontline and relapsed/refractory (RR) AML, and menin inhibitors showing efficacy in RR NPM1mut and KMT2A-rearranged AML. With so many new drugs approved, the number of potential combinatorial approaches to leverage the maximal benefit of these agents has increased dramatically, while at the same time introducing clinical challenges, such as key preclinical and clinical data supporting the development of combinatorial therapy, how to optimally combine or sequence these novel agents, how to optimise dose and duration to maintain safety while enhancing efficacy, the optimal duration of therapy and the role of measurable residual disease in decision-making in both intensive and low-intensity therapy settings. In this review, we will outline the evidence leading to the approval of key agents in AML, their on-label current approvals and how they may be optimally combined in a safe and deliverable fashion to further improve outcomes in AML.

The ferroptosis landscape in acute myeloid leukemia.

Ferroptosis induction through the suppression of glutathione peroxidase 4 (GPX4) and apoptosis-inducing factor mitochondria-associated 2 (AIFM2) has proven to be an effective approach in eliminating chemotherapy-resistant cells of various types. However, a comprehensive understanding of the roles of GPX4 and AIFM2 in acute myeloid leukemia (AML) has not yet been achieved. Using cBioPortal, DepMap, GEPIA, Metascape, and ONCOMINE, we compared the transcriptional expression, survival data, gene mutation, methylation, and network analyses of GPX4- and AIFM2-associated signaling pathways in AML. The results revealed that high expression levels of GPX4 and AIFM2 are associated with an adverse prognosis for AML patients. Overexpression of AIFM2 correlated with elevated mutation frequencies in NPM1 and DNMT3A. GPX4 upregulation modulated the following pathways: GO:0045333, cellular respiration; R-HSA-5389840, mitochondrial translation elongation; GO:0009060, aerobic respiration; R-HSA-9609507, protein localization; and R-HSA-8953854, metabolism of RNA. On the other hand, the overexpression of AIFM2 influenced the following processes: GO:0048704, embryonic skeletal system morphogenesis; GO:0021546, rhombomere development; GO:0009954, proximal/distal pattern formation; and GO:0048732, gland development. This study identifies the high expression of GPX4 and AIFM2 as novel biomarkers predicting a poor prognosis for AML patients. Furthermore, ferroptosis induction may improve the stratified treatment of AML.

The Molecular Landscape of Acute Myeloid Leukemia with Trisomy 8: A Comprehensive Cancer Center Analysis of 627 Patients

1. Introduction Trisomy 8 is one of the most frequent cytogenetic abnormalities in acute myeloid leukemia (AML). Trisomy 8 frequently occurs with additional aberrant or complex karyotypes; it can also occur as the sole cytogenetic abnormality. While prior studies have shown that trisomy 8 cooperates with other somatic genetic mutations (e.g., RUNX1), there is persistent controversy about the prognostic impact of AML with trisomy 8. We analyzed the relationship between trisomy 8 and additional molecular lesions, the ELN 2022 revision, and the prognostic impact of trisomy 8 across modern treatment strategies. 2. Methods We analyzed 627 patients from the Project ERIS database with newly diagnosed AML treated from January 1, 2013 to April 18, 2023 at VCU Massey Comprehensive Cancer Center. We identified 86 patients with trisomy 8 independent of the presence of complex cytogenetics and a cohort of 541 historical controls without trisomy 8. We recorded baseline patient-related and disease characteristics, including age, ECOG, Charlson comorbidity index (CCI) scores, molecular profiling and ELN 2022 cytogenetic risk, dates of regimen initiation, and survival. We used the D'Agostino & Pearson method for normality testing and the t-test or Mann-Whitney (as applicable) tests for between-group comparisons. Categorical comparisons used Fischer's exact test. We applied the Bonferroni correction if multiple comparisons were made. The Wilson-Brown method was used for 95% confidence intervals. We analyzed survival by the Kaplan-Meier method with significance determined by the log-rank test. The event for calculating the overall survival (OS) was the date of death. Patients were otherwise censored at the date of last contact. 3. Results The most common cytogenetic abnormalities associated with trisomy 8 were the presence of a complex karyotype (45.2%, 95% CI: 35.0-55.9), loss of 5 (15.5%, 95% CI, 9.3-24.7%), loss of 7 (8.3%, 95% CI, 4.1-16.2), and -17p (13.1%, 95% CI, 7.5-21.9). ASXL1 mutations tended to occur significantly more frequently in trisomy 8 compared to those without (27.5% vs 13.0%, p = 0.029, Table A). Similarly, TP53 mutations occurred significantly more frequently in trisomy 8 than those without (27.5% vs 13.0%, p = 0.029). Conversely, DNMT3A mutations were less likely to be associated with trisomy 8, which approached statistical significance (10.0% vs 23.47%, p = 0.063). NPM1 mutations were significantly less likely to be associated with trisomy 8 compared to those without (7.5% vs 30.3%, p = 0.002). Consequently, trisomy 8 was significantly more likely to be associated with ELN 2022 adverse risk disease (odds ratio: 2.754, p < 0.0001). Next, we compared response and survival with intensive chemotherapy using conventional 7+3 in those with and without trisomy 8. In 51 patients with trisomy 8, the composite complete response rate (CRc; CR + CRi + CRh) was 34.1% (95% CI, 21.9-48.9), significantly worse compared to 330 patients without trisomy 8 (55.1%, 95% CI, 49.4-60.6, p = 0.010). The median overall survival was significantly worse in patients with trisomy 8 treated with 7+3 compared to those without trisomy 8 (7.1 m. vs 19.2 m., p = 0.009, Figure B). In 13 patients treated with venetoclax and a hypomethylating agent (HMA), the CRc was 25.0% (95% CI, 8.9-53.2), which was nonsignificantly worse compared with 87 patients without trisomy 8 that demonstrated a CRc of 48.4% (95% CI, 36.4-60.6, p = 0.206). The overall survival of patients treated with venetoclax + HMA nonsignificantly favored patients without trisomy 8 (8.2 m. vs 5.0 m., p = 0.268). 4. Discussion AML with trisomy 8 is significantly enriched in ASXL1 and TP53 mutations; conversely, trisomy 8 is significantly less likely to harbor NPM1 mutations. Patients with trisomy 8 are significantly more likely to have ELN 2022 adverse-risk disease. The CRc rate and median overall survival are significantly worse in patients with trisomy 8 treated with intensive chemotherapy. We observed no significant difference in response or survival in patients with or without trisomy 8 treated with venetoclax + HMA.

Immunologic Targets in AML.

In this issue of Blood Cancer Discovery, Nelde and colleagues used a sensitive mass spectrometry-based immunopeptidomics approach to characterize the antigenic landscape of acute myeloid leukemia (AML) and were able to identify immunogenic peptides presented by both leukemia stem cells (LSC) and bulk primary AML blasts. These immunogenic peptides elicit primarily CD4 T-cell responses and the diversity of the HLA class II immunopeptidome and presence of CD4 memory T-cell responses were both associated with improved clinical outcome. See related article by Nelde et al., p. 468 (1) .

AML-395 Genomic Landscape of Acute Myeloid Leukemia With t(8;21) and inv(16)

AML-395 Genomic Landscape of Acute Myeloid Leukemia With t(8;21) and inv(16)

Construction and validation of an NAD + metabolism-related lncRNA signature for predicting the prognosis and immune landscape of acute myeloid leukemia

BackgroundThis study aimed to investigate the potential of a NAD+ metabolism-related lncRNA signature as a reliable prognostic biomarker for acute myeloid leukemia (AML).MethodsTranscriptome profiles and clinical data of AML patients were obtained from The Cancer Genome Atlas (TCGA) database. NAD+ metabolism-related genes (NMRGs) were identified from the KEGG and Reactome databases. Coexpression analysis was used to screen NAD+ metabolism-related lncRNAs. The NAD+ metabolismrelated lncRNA signature was constructed using univariate analysis, LASSO regression, and multivariate analysis. High- and low-risk groups were compared for survival, tumor mutation burden, immune cell infiltration, and response to immunotherapy. Enrichment analysis explored the biological functions.ResultsLINC01679, AC079922.2, TRAF3IP2-AS1, and LINC02465 were identified to construct the risk model. The model exhibited good predictive power and outperformed age and gender as an independent prognostic marker. High-risk patients showed poorer survival, distinct TP53 mutations, and altered immune cell infiltration compared to low-risk patients. Additionally, low-risk patients exhibited greater sensitivity to immunotherapy. Enriched biological functions included leukocyte migration and positive regulation of cytokine production.ConclusionsThe NAD+ metabolism-related lncRNA signature shows promise in predicting clinical outcomes for AML patients.

Charting the Differentiation Landscape of Acute Myeloid Leukemia

Charting the Differentiation Landscape of Acute Myeloid Leukemia

Bioinformatics Analysis Identifies EPAS1 as a Novel Prognostic Marker Correlated with Immune Infiltration in Acute Myeloid Leukemia

EPAS1 plays an important role in the development and progression of multiple tumor types by interacting with a series of other molecules. However, the prognostic and diagnostic values of EPAS1 in acute myeloid leukemia (AML) remain unknown. Here, we systematically explored and clarified the potential functions of EPAS1 in AML using data from Xena Browser and TCGA database. The expression of EPAS1 was significantly lower in AML patients than that in healthy people. The GO, KEGG, GSEA, and GSVA were performed to explore the potential functions and signaling pathways. The survival analysis was conducted using Cox regression analysis and the Kaplan-Meier method. Immune cell infiltration was evaluated via single-sample GSEA (ssGSEA). The results of enrichment analyses suggested that low-EPAS1 expression was related to the initiation, development, and prognosis of AML. The immune microenvironment landscape in AML was described by ssGSEA. ROC analysis of EPAS1 showed high discrimination ability between AML patients and healthy people. Kaplan-Meier method indicated that low-EPAS1 expression correlated significantly with a poor overall survival. Multivariate Cox regression analysis revealed that both age and EPAS1 expression were independent prognostic factors in AML patients. Furthermore, the nomogram based on these two variables performed well in discrimination and calibration. In summary, our study may provide new insights into the molecular mechanisms underlying AML and demonstrate the diagnostic and prognostic value of EPAS1 in AML for the first time.

Changing the strategic paradigm for the treatment of adult acute myeloid leukemia

Background: Acute myeloid leukemia (AML) is a representative blood cancer, accounting for most adult leukemia cases in Korea. Until recently, intensive chemotherapy and hematopoietic stem cell transplantation were the only curative treatment options for AML. However, the recent introduction of new drugs is bringing about changes in the strategic paradigm for the treatment of AML.Current Concepts: Along with the clinical eligibility for receiving intensive treatment and hematopoietic stem cell transplantation, the most critical determinants in treating AML are precise classification and risk stratification based on cytogenetic and molecular information. The recently revised World Health Organization classification, newly proposed International Consensus Classification, and the latest version of the European LeukemiaNet risk stratification reflect the importance of cytogenetic and molecular information. Although there have been no significant changes for a long time in the landscape of AML, especially in the field of treatment, the treatment paradigm has started to evolve with the introduction of new drugs. This evolution is led by FLT3 inhibitors, Bcl-2 inhibitors, isocitrate dehydrogenase inhibitors, target agents against CD33 antigens, and liposomal formulations of chemotherapeutics.Discussion and Conclusion: Successful initial treatment to induce complete remission followed by post-remission treatment to remove residual disease can lead to the achievement of long-term survival and cure goals in AML. We hope that new drugs will markedly improve the treatment outcomes for patients with AML.

Abstract 3172: Mapping the molecular landscape of acute myeloid leukemia enables prediction of drug response from proteogenomic data

Abstract Acute myeloid leukemia (AML) is a deadly blood cancer that remains largely classified by genetic aberrations, which inform therapy stratification. However, therapeutic response cannot be predicted or explained by genetic abnormalities alone. The integration of multiple omics, consisting of genomic, transcriptomic, proteomic, and phosphoproteomic measurements, offers a holistic view to resolve the underlying pathophysiology of AML that influences response to therapy. In this work, we pair multi-omic characterization together with ex vivo drug sensitivity assays accrued with 145 small molecule inhibitors for 210 AML patient samples (Bottomly et al., Cancer Cell 2022). We showcase how the integration of these data can guide drug sensitivity exploration and prediction. We first expanded the dataset by generating matching comprehensive proteomics and phosphoproteomics data for the Beat AML samples and integrated these data using non-negative matrix factorization. This analysis identified four distinct proteogenomic subtypes of AML, each representing distinct clinical and biological features including differences in survival and biological pathway activation. We then sought distinct patterns of drug sensitivity across the subtypes of the patient cohort and found one pair of drugs, venetoclax and panobinostat, to be sensitive in complementary sets of patients, suggesting that they could be more effective in combination. Lastly, we further enhanced the proteogenomic subtypes by a building machine learning based model of distinct drug response that we then evaluated in vitro. Our results show that the four proteogenomic subtypes are independent yet complementary to existing mutational profiles, and can be used to improve drug treatment stratification. We tested the combination of panobinostat and venetoclax in patient samples and show that they are more effective in combination than as single agents. We then tested drug-specific machine learning models to predict drug response on AML cell lines that were in varying stages of resistance to the FLT3 inhibitor quizartinib. The models predicted a change across the proteogenomic landscape as quizartinib resistance evolves, resulting in a shift in drug sensitivities that we experimentally validated. This work represents a seminal effort in the integration of proteogenomic and ex vivo drug sensitivity datasets. In summary, we show how multi-omic characterization of AML maps a proteogenomic landscape that enables improved exploration of patient drug response and ultimately patient treatment. Citation Format: James C. Pino, Camilo Posso, Sunil K. Joshi, Michael Nestor, Jamie Moon, Joshua R. Hansen, Marina A. Gritsenko, Chelsea Hutchinson-Bunch, Karl K. Weitz, Kevin Watanabe-Smith, Jason E. McDermott, Brian J. Druker, Tao Liu, Jeffrey W. Tyner, Anupriya Agarwal, Elie Traer, Paul D. Piehowski, Cristina E. Tognon, Karin D. Rodland, Sara J. Gosline. Mapping the molecular landscape of acute myeloid leukemia enables prediction of drug response from proteogenomic data [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2023; Part 1 (Regular and Invited Abstracts); 2023 Apr 14-19; Orlando, FL. Philadelphia (PA): AACR; Cancer Res 2023;83(7_Suppl):Abstract nr 3172.

The clinical impact of the molecular landscape of acute myeloid leukemia

Research into the underlying pathogenic mechanisms of acute myeloid leukemia (AML) has led to remarkable advances in our understanding of the disease. Mutations now allow us to explore the enormous diversity among cytogenetically defined subsets of AML, particularly the large subset of cytogenetically normal AML. Despite the progress in unraveling the tumor genome, only a small number of recurrent mutations have been incorporated into risk-stratification schemes and have been proven to be clinically relevant, targetable lesions. The current World Health Organization Classification of myeloid neoplasms and leukemia includes eight AML categories defined by recurrent genetic abnormalities as well as three categories defined by gene mutations. We here discuss the utility of molecular markers in AML in prognostication and treatment decision-making. New therapies based on targetable markers include IDH inhibitors (ivosidenib, enasidenib), venetoclax-based therapy, FLT3 inhibitors (midostaurin, gilteritinib, and quizartinib), gemtuzumab ozogamicin, magrolimab and menin inhibitors.

Biological therapy in elderly patients with acute myeloid leukemia

ABSTRACT Introduction The introduction of target molecules and immunological therapies is changing the treatment landscape of acute myeloid leukemia (AML). Areas covered We recapitulate the biological therapies that can be employed in the treatment of elderly patients with AML. Alongside small molecules inhibitors that target specific gene mutations, antibodies, tumor microenvironment modulators, and cellular therapies are being developed for the cure of the disease. Here, we report the biological activities, the efficacy and toxicities of humanized antibodies and antibody-drug conjugates that targets surface antigens as CD33 (gemtuzumab ozogamicine) or CD123 (pivekimab sunirine). We further explore mechanisms and effectiveness of medications that modify the microenvironment, such as glasdegib, or that harness the immune system against leukemia, such as CD47 antibody magrolimab, PD1/PDL1 inhibitors pembrolizumab and nivolumab, TIM3 inhibitor sabatolimab, T-cell and NK-cell engagers. Cellular therapies are considered, even if a large trial is still pending for the feasibility of the approach. In this scenario, a brief overview of the mechanism of action of target agents is provided, particularly with respect to their biological mechanisms. Expert opinion Overall, this therapeutic armamentarium will constitute the basis for multimodal and personalized combinations that, in the idea of precision medicine, will enormously benefit elderly AML patients.

Novel investigational approaches for high-risk genetic subsets of AML: TP53, KMT2A, FLT3.

The treatment landscape in acute myeloid leukemia (AML) is rapidly evolving, with multiple new therapies approved in recent years. However, the prognosis for patients with high-risk genetic subsets of AML remains poor, and the development of more effective treatment options for these patients is ongoing. Three of these high-risk AML patient subsets include TP53-mutated AML, FLT3-internal tandem duplication (ITD)-mutated AML, and AML harboring rearrangements affecting the KMT2A locus (KMT2A-r AML). The prognosis for TP53-mutated AML remains poor with both intensive and targeted regimens, including those incorporating the BCL-2 inhibitor, venetoclax. Allogeneic hematopoietic stem cell transplantation is the only potentially curative therapy for these patients, but posttransplant relapse rates remain high. Patients with FLT3-ITD-mutated AML continue to have suboptimal outcomes with standard therapies and experience high rates of relapse following transplant. KMT2A-r AML is also associated with poor outcomes with current treatment approaches, and effective standards of care are lacking for patients with relapsed/refractory disease. This article discusses current treatment approaches, along with the investigational agents being explored for the treatment of these 3 AML subsets, focusing primarily on agents that are further along in development.

The RNA editing landscape in acute myeloid leukemia reveals associations with disease mutations and clinical outcome.

Several studies have documented aberrant RNA editing patterns across multiple tumors across large patient cohorts from The Cancer Genome Atlas (TCGA). However,studies on understanding the role of RNA editing in acute myeloid leukemia(AML) have been limited to smaller sample sizes. Using high throughput transcriptomic data from the TCGA, we demonstrated higher levels of editing as a predictor of poor outcome within the AML patient samples. Moreover, differential editing patterns were observed across individual AML genotypes. We also could demonstrate a negative association between the degree of editing and mRNA abundance for some transcripts, identifying the potential regulatory potentialof RNA-editing in altering gene expression in AML. Further edQTL analysis suggests potential cis-regulatory mechanisms in RNA editing variation. Our work suggests a functional and regulatory role of RNA editing in the pathogenesis of AML and we extended our analysis to gain insight into the factors influencingaltered levels of editing.

Comparison of Risk Stratification Tools (ELN-2022, ELN-2017, Lindsley et al., Papaemmanuil et al. and ALFA classifier) in Predicting Outcomes of Patients with Acute Myeloid Leukemia

Introduction: Characterization of molecular landscape in acute myeloid leukemia (AML) leads to update of known prognostic classifier or to create new ones. We analyzed the prognostic impact of ELN 2017 (Döhner et al., Blood 2017), ELN 2022 (Döhner et al., Blood 2022), ALFA classifier (Itzykson et al. Blood 2021), Lindsley classifier (Lindsley et al. Blood 2015) and Papaemmanuil classifier (aml-risk-model.com/calculator) in a large cohort of AML adult patients. Methods: In this monocentric study (Nice University Hospital), the prognostic scores were calculated using all five risk stratification models (ELN 2017, ELN 2022, ALFA classifier, Lindsley classifier and Papaemmanuil classifier) and risk categories were determined for each adult patient treated for an AML between January 2015 and January 2022. Overall survival (OS) was calculated from the time of diagnosis of AML to the time of death or last known follow-up. Median OS was estimated using the Kaplan−Meier method, and differences between subgroups were assessed using the log-rank test. Cox proportional hazards models were fit to assess association of prognostic systems with OS. Corrected Akaike Information Criteria (AICc) were used to compare these fitted models. Results: Two hundred ninety consecutive AML patients treated in our institution were included from 2015 to 2022. Median age was 68 years old (18-93, range). Sex ratio M/F was 160/125. Performans status was ≤ 2 in 93% of patients. AML were de novo in 74%, therapy related in 5%, secondary in 21%. Intensive chemotherapy, non-intensive chemotherapy and best supportive care were used in 53%, 40% and 6% of patients, respectively. ELN 2017, ELN 2022, ALFA classifier, Lindsley classifier and Papaemmanuil classifier were calculated in 96%, 99%, 87%, 88% and 93% of patients, respectively. With a median follow up of 7.9 months (0-78, range), median OS in the entire cohort was 40.2 months (95% CI, 14.6-65.9). We observed significant differences using ELN 2017 (Favorable : 83.3 months / Intermediate : 40.2 months / Adverse : 12.4 months, p<0.0001) ; ELN 2022 (Favorable : 83.3 months / Intermediate : 43.2 months / Adverse : 9.2 months, p<0.0001) ; ALFA classifier (Go go : 83.3 months / Slow go : 34.7 months / No go : 9.5 months, p<0.0001) ; Lindsley classifier (De novo/pan AML : 83.3 months / Secondary : 13.3 months / TP53 mutated : 10.1 months, p<0.0001) and Papaemmanuil classifier (Favorable : 83.3 months / Intermediate : 40.2 months / Adverse : 10.7 months, p<0.0001). The AICcs were used to compare the univariable Cox models containing prognostic scoring systems as covariates (lower is associated to a better prediction). The AICcs for the OS models were 2510 (ELN 2017), 2621 (ELN 2022), 2979 (Lindsley classifier), 3441 (Papaemmanuil classifier) and 5843 (ALFA classifier) (Table). ELN 2017, ELN 2022 and Lindsley classifier were significantly better in the entire cohort of treated AML patients. To assess if the relative prognostic discrimination for the five prognostic tools was influenced by age or type of treatment, we calculated the AICc scores for the five prognostic tools in each of these subgroups (Table). We observed a trend for a better stratification of patients with ELN 2022 in age < or ≥ 60 years old and in patients treated with intensive and non-intensive chemotherapy. In our cohort, Papaemmanuil and ALFA classifier were significantly less suitable for stratification in younger patients (< 60 years old) (p=0.026) and in patients treated intensively (p<0.0001), respectively. ALFA classifier was significantly less suitable for stratification in patients treated by intensive chemotherapy (p<0.0001). Lindsley classifier was significantly less suitable for stratification in patients treated by non-intensive chemotherapy (p<0.0001). Conclusions: Among all risk stratification tools available in AML, ELN 2022 seems to be the best prognostic score in younger and older patients independently of the received treatment. These observations need to be validated in a larger cohort. Figure 1View largeDownload PPTFigure 1View largeDownload PPT Close modal

Molecular Landscape in Acute Myeloid Leukemia (AML) Patients (pts) from Mexico As an Initial Study to Identify Healthcare Disparities in Hispanic Populations: Alliance for Clinical Trials in Oncology [Alliance

Health care disparities are associated with social, economic and environmental disadvantages and may adversely affect minority groups. "Hispanic or Latino” refers to a person of Cuban, Mexican, Puerto Rican, South or Central American, or other Spanish origin or culture regardless of race. To begin to understand disparities in Hispanic pts with AML, we thought to characterize and compare clinical features, mutationallandscape and disease outcomes of AML pts from Mexico and Hispanic (Hisp) AML pts living in the US and compare them with those of non-Hispanic White (NHW) pts. Methods: We assessed molecular features and outcomes of 48 adult AML pts diagnosed and treated with anthracycline and cytarabine combination (90%) at the Hospital General De Mexico (HGM, a urban public tertiary hospital and a referral institution for AML pts in Mexico City) (cohort 1: Mex), as well as 48 adult AML pts who self-identified as Hispanic and 1496 non-Hispanic White pts similarly treated on frontline Alliance protocols (cohort 2: A-Hisp, A-NHW). No pt received an allogeneic transplant in 1st complete remission. Mutational status of 55 protein-coding genes in addition to the main fusion gene rearrangements at time of diagnosis were determined by both study groups centrally using targeted NGS platforms. Last, we used 2 additional reported Hispanic cohorts in the US and large international cohorts for validation of the identified molecular and clinical features (cohort 3 [] and 4). Results: Median age at diagnosis for cohorts 1 and 2 was younger than reported for AML, 38yo (15-86) for cohort 1 vs 45yo (17-83) for cohort 2. Somatic mutations in the cohort 1 were detected in 96% of pts, similar to the A-Hisp subset of cohort 2 (94%). The most prevalent mutations in the cohorts were FLT3 (Mex 29%, A-Hisp 33%, A-NHW 42%, p= 0.654), followed by CEBPA (Mex 21%, A-Hisp 15%, A-NHW 13%, p=0.214), TET2(Mex 19%, A-Hisp 8%, A-NHW 16%, p=0.322), DNMT3A (Mex 19%, A-Hisp 18%, A-NHW 24%, p=0.5498), RUNX1 (Mex 15%, A-Hisp 0%, A-NHW 12%, p<0.001) and NPM1 (Mex 12%, A-Hisp 29%, A-NHW 35%, p=0.002). Fusion genes were detected in 21% of pts, with t(8;21)(RUNX1-RUNX1T1) being the most commonly found (Mex 13%, A-Hisp 6%, A-NHW 3%, p=0.005), followed by MLL rearrangements (Mex 4%, A-Hisp 16%, A-NHW 5%, p=0.015) and inv(16)(CBFB-MYH11) (Mex 2%, A-Hisp 10%, A-NHW 2%, p=0.003). We found some commonalities in the genetic alterations between these two Hispanic cohorts (i.e., CEBPA, FLT3, DNMT3A), however median overall survival (OS) for the Mexican cohort was 9mo, worse than OS for cohort 2 (A-Hisp 15mo, A-NHW 14mo). Those molecular differences were validated in 2 additional international cohorts (TCGA and Pappaemmanuil et al., NEJM, 2016). Notably, there was an overrepresentation of CEBPA mutated AML in both cohort 1 and A-Hisp (cohort 2). The most notable difference between the two Hispanic cohorts (Mex vs A-Hisp) was the prevalence of mutated NPM1, which was higher in the US A-Hisp dataset, similar to what is described in other international cohorts for de novo AML. Conclusion: Mexican AML pts have a distinct genetic makeup and overall poorer outcomes compared to other international cohorts. These differences in genetic alterations point to differences in host genetic susceptibility and environmental factors. Because the term Hispanic reflects a culture rather than a genetically defined ethnicity we will perform ancestry-specific analysis to better characterize the genetic differences and determine who is represented within this heterogeneous population, particularly in the self-reported population living in the US. Some of the detected variants are targetable mutations that could influence disease management decisions. It is important to use ancestry-specific analysis, as compared with self-reported race and ethnicity, to better understand the impact on disparities and highlighting the importance of increasing enrollment of Hispanic pts in clinical trials. Knowing who is accessing healthcare in the US as self-reported Hispanic will help understand whether the differences in outcome are related to healthcare access or there is a genetic component that could explain some of the differences in age of onset and molecular findings observed in cohorts of similar ethnic background.

Real-World Outcomes of IDH Mutant AML Patients Treated with or without IDH Inhibitors

Background: The molecular landscape of acute myeloid Leukemia (AML) is heterogeneous, yet only a few mutations are targetable. Mutations in the isocitrate dehydrogenase genes (IDH1, IDH2) are heterozygous gain of function point mutations with an approximate frequency of 20%. FDA approved IDH2 inhibitor enasidenib in 2017 and IDH1 inhibitor ivosidenib in 2018 for adults with relapsed/refractory (R/R) IDH2 and IDH1MT AML. In May 2022, FDA approved ivosidenib combined with azacitidine for adults aged 75 years or older with newly diagnosed AML who cannot undergo intensive induction chemotherapy. Since IDH inhibitors (IDHi) have been in clinical use within and outside clinical trials for the last few years, we sought to retrospectively analyze our experience and clinical outcomes of patients with IDH mutations who were treated with/without IDHi at our center, especially there is some recent evidence of better response to venetoclax based therapy for patients with IDH mutations. Methods: We included all patients diagnosed with a myeloid neoplasm with IDH1 or IDH2 or both mutations as identified by targeted Next Generation Sequencing using the commercially available Illumina TruSight Myeloid Panel and were treated between October 2014 and September 2021. Baseline demographic and clinical characteristics were noted. The Chi-square test was used to study various parameters as described in the results, and Kaplan-Meier curves were used for survival analyses. Results: Among 65 patients with myeloid neoplasms, 53 (82%) had AML, 8 (12%) had Myelodysplastic syndrome, 2 (3%) had Blastic Plasmacytoid Dendritic Cell Neoplasm (BPDCN), 1 (1.5%) had essential thrombocythemia, and 1 (1.5%) had myelofibrosis secondary to polycythemia vera. Of the 65, 54% (35/65) were male, and the median age at disease diagnosis was 65 years [range, 35-97]. 42% (27/65) in the cohort had IDH1 mutation, 55% (36/65) had IDH2 mutation, and 3% (2/65) had both. Only 29% (19/65) of patients in the cohort received IDHi therapy, and all had AML. 12 (63%) were female. 15 (79%) patients received IDHi therapy in the R/R setting and 4 (21%) in the first line. The median age of patients receiving IDHi was 66 years [range, 44-80]. Of the 19 patients who received IDHi, 6 (32%) achieved complete remission (CR), 2 (10%) achieved complete remission with incomplete count recovery (CRi), 1 (5%) had a partial response (PR), and 10 (53%) had refractory disease. The median time to best response was 3.5 months [range, 0.3-11.25]. IDHi-induced differentiation syndrome (DS) was reported in 6 patients (32%); 4 (21%) who developed DS received Olutasidenib, and 2 (11%) received Enasidenib. Three patients (50%) developed severe DS, two of which died from it. One had moderate DS, and 2 had indeterminate DS. All patients with DS were treated with medication discontinuation and intravenous dexamethasone. Median overall survival for the IDHi group was 28.6 months (95% CI 16.0-41.3) and 30.4 months (95% CI 5.9-54.9) for the non-IDHi group (p = 0.363). Conclusion: Only a minority of patients with IDHMT AML had received IDHi-based therapy, which is attributed to patients diagnosed and treated before the FDA approval. Surprisingly, the median survival for patients who didn't receive IDHi therapy seemed not different, underscoring the other therapies' effects, including the influence of the venetoclax-based regimen. More extensive randomized studies are needed to compare the efficacy of IDHi to other therapies in treating IDHMT AML. Figure 1View largeDownload PPTFigure 1View largeDownload PPT Close modal

Single-Cell Profiling of CD8 Landscape in Treatment Naïve and Relapsed/Refractory AML Patients Reveals Distinct Effector Cellular States Predictive of Outcomes

Background: The success of allogeneic stem cell transplantation mediated by the graft versus leukemia effect of engrafted T-cells strongly supports the premise to investigate T cell biology in acute myeloid leukemia (AML). Of particular interest are CD8 cells, which are key players in eliciting anti-cancer cytotoxic activities but are understudied in AML. In this study, we conducted single cell RNA profiling in bone marrows from healthy donors, treatment naïve (RxNaive) and relapsed/refractory (R/R) AML patients to investigate the CD8 landscape in AML. Methods: We conducted paired single cell gene expression (scRNA) and T-cell repertoire (scTCR) profiling of bone marrow mononuclear cells from 3 healthy donors and 23 RxNaive adult AML patients (median age 71.81 years; range 51-86 years) and leveraged our previously published data of 8 relapsed/refractory AML patients (median age 73 years; range 64-88 years). After doublet removal and quality assessment, a total of 51,219 T cells passed the filtering criteria, of which 23,320 (45.5%) were CD8 cells. A total of 17,782/23,320 (74.1%) CD8 cells had paired scTCR. We applied principal component analysis to identify sources of variability across CD8 cells, then used Harmony-based linear correction integrated with K-nearest neighbors for cluster identification. We then applied non-linear dimensionality reduction (UMAP) to project cells into 2 dimensions. Results: In total, 6 CD8 T cell subtypes were defined including naïve, memory, effector memory, cytotoxic and exhausted T cell subtypes. Panel 1 shows the UMAP projection of CD8 cells overlaid with neighborhoods demonstrating differential abundance of cells by patient type (healthy, RxNaive or R/R). Naïve, memory, MAIT and exhausted clusters were shared across healthy, RxNaive and R/R patients (Panel 1, left). Remarkably, the effector clusters (effector memory and cytotoxic) demonstrated distinct patterns of disease states. Specifically, the Top effector clusters (Panel 1, top right) were largely contributed by R/R AML patients. However, the Bottom effector clusters (Panel 1, bottom right) were enriched by cells from healthy and RxNaive. Only 2.5% of R/R effector cells were part of the bottom effector clusters. This indicated that naïve and memory cells of RxNaive and R/R AML patients are biologically similar, however, effector cells took diverging paths in RxNaive versus R/R AML patients. We next explored the biological programs that govern the Top clusters and thus associate with R/R state as compared to the Bottom clusters, associated with RxNaive AML patients. We conducted consensus non-negative matrix factorization coupled with gene expression profiling and pathway analysis. This revealed positive enrichment for IFNa and IFNg signaling and significant negative enrichment for TNFa and TGFb signaling in the Top clusters, suggesting a cytokine dependent remodeling of CD8 effector function in R/R cells. We next explored whether the top 25 differential genes (herein referred to as CD8 Dysfunctional Signature) in the Top clusters (enriched for R/R CD8 cells) can predict outcomes in two cohorts of newly diagnosed AML patients (Abbas et al 2021 and TCGA AML cohorts). Interestingly, the CD8 Dysfunctional Signature was indeed predictive of outcomes, with patients harboring higher CD8 Dysfunctional Signature scores having worse outcomes (p=0.0054, Panel 2). We next explored whether the TCR repertoires are also different in AML of RxNaive and R/R patients. Indeed, TCR profiling revealed lower clonotype diversity and significant clonotype expansion in R/R patients, with 34% of R/R cells with TCR data exhibiting hyperexpansion (clone size >100). However, these hyperexpanded clonotypes were largely derived from dysfunctional CD8 cells. Conclusion: We characterized the CD8 landscape in RxNaive and R/R AML patients and defined distinct states of effector CD8 cells predictive of outcomes. CD8+ T-cells in R/R patients are markedly different from healthy/RxNaive patients, and characterized by a dysfunctional state, skewed distribution, lower clonal diversity, and significant clonal hyperexpansion. This dysfunctional state may be due to the AML microenvironment actively shaping the T-cell repertoires or the chemotherapy received by the patient, which may narrow the repertoire by eliminating active T-cells. Figure 1View largeDownload PPTFigure 1View largeDownload PPT Close modal

Molecular and Clinical PHF6 Mutant Myeloid Neoplasia Provides Clues As to Their Pathogenesis and Therapeutic Targeting

Congenital PHF6 mutations (PHF6MT) cause the X-linked neurodevelopmental disorder, Börjeson-Forssman-Lehmann syndrome (BFLS). In contrast, acquired PHF6MT are frequently detected in hematologic neoplasms including acute myeloid leukemia (AML), myelodysplastic syndrome (MDS), and T-cell acute lymphoblastic leukemia (T-ALL). Indeed, PHF6 is highly expressed in brain/developing CNS as well as in hematopoietic subpopulations suggesting its role in neurogenesis and hematopoiesis consistent with the neurologic BFSL presentation of the germline and leukemias with somatic mutations of this gene. This study focuses on involvement of PHF6 lesions in AML. Here, we hypothesize that there are functional links between lymphoid and myeloid neoplasia (MN) with PHF6-deficiency that can be instructive in terms of the impact of this gene as a tumor suppressor gene (TSG). In total, we analyzed sequencing results of 6886 AML cases, including 616 cases from open sources such as The Beat AML Master dataset (Tyner JW , Tognon CE, Bottomly D, et al. Functional genomic landscape of acute myeloid leukaemia. Nature. 2018;562(7728):526-531). We first investigated the clinical phenotype associated with PHF6MT. We detected a total of 111 (1.6% of cases) PHF6MT. While frameshifts were found across all the coding regions, missense and stop-gains were concentrated in the two PHD-type zinc finger domains and were overlapping between other MN and the previously reported T-ALL cases. PHF6MT were significantly more common in male cases (2.3 vs. 0.8%; p<.001) similar to other X-genes such as UBA1, ZRSR2, and STAG2 but different from ATRX, PIGA, and KDM6A (UTX). When comparing primary AML (pAML) with secondary AML (sAML), sAML had more PHF6MT than pAML (2.9 vs. 1.4%; p<.001). While PHF6MT were not associated with chromosomal abnormalities in male cases, female cases with X chromosome deletion (delX) showed higher frequency of PHF6MT compared to females with diploid X chromosome (6.5 vs. 0.8%; p=.0019). Consequently, when delX were included, a total of 2.3% of female patients had PHF6 hits with 10% of them in a homo/hemizygous configuration. While RUNX1, U2AF1, and ASXL1 mutations coincided with PHF6 hits in males, in female cases, delX and ASXL1 mutations were the most common associated defects (Fig.1). Interestingly, RUNX1 mutations (RUNX1MT) in females were enriched in cases with delX. As a result, PHF6MT were enriched into two distinct subclasses: while PHF6MT were enriched in male cases with sAML and RUNX1MT (8.8%), all female cases with sAML, RUNX1MT, and delX also acquired PHF6MT (Fig.2). A higher frequency of PHF6MT in advanced MN than early diseases (e.g., low-risk MDS), suggestive of a secondary rather than a founder role of PHF6MT prompted us to study the clonal architecture of PHF6MT cases using variant allelic frequencies. RUNX1MT were dominant/co-dominant to PHF6 hits in the clonal evolution of AML in contrast to T-ALL, in which PHF6MT appears to be an earlier event. Similar relationship was found with regard to PHF6 hits and other myeloid gene mutations (e.g., ASXL1, DNMT3A, or SRSF2) suggesting a lineage-restricted TSG function of PHF6 in AML following myeloid commitment (consistent with the physiologic expression of PHF6 in pre-B, pre-T, and erythroid progenitors during hematopoietic ontogeny). PHF6 mutations also affected AML patients’ outcome. Overall, PHF6MT showed worse outcome in male cases, but not in female cases. However, if PHF6MT cases also had RUNX1 mutations, the outcome was extremely low in both sexes. Unsupervised RNAseq analysis of PHF6 mutational status vs. expression pattern revealed that PHF6MT cases showed higher mRNA levels of several lymphoid genes (GCSAM, LY9, or BLNK). This finding was also confirmed by RNAseq of in-house PHF6 knock-out K562 and U-937 clones. Immunohistochemistry unveiled that LY9 was positive in illustrative PHF6MT cases and flow cytometry revealed the presence of lymphoid markers in 2/3 PHF6MT cases. Our study confirms that PHF6MT are more common in male AML. Also we demonstrate the intricate relationships between PHF6MT and RUNX1MT in male sAML patients which extend also to female cases with delX. In AML, PHF6MT are a late event compared to T-ALL, in which they occur early in leukemogenesis. This is also in line with higher frequency of PHF6MT in sAML. Some PHF6MT cases have higher expression of lymphoid genes, which may be candidate for molecular targeted therapy. Figure 1View largeDownload PPTFigure 1View largeDownload PPT Close modal

Single Cell Analysis Identifies Immune Marker IL4I1 As Critical for Leukemogenesis and Immunity

The treatment landscape of acute myeloid leukemia (AML) is evolving with new targets entering clinical translation. However, patients’ responses are still heterogeneous. Bone marrow (BM) is considered an immune privilege organ, but recent evidence suggests that inflammatory insults can increase cell cycling in hematopoietic stem cells (HSCs) gradually accumulating DNA damage. Thus, discovering immune targets on leukemia cells is an urgent unmet need. Here, we used scRNA seq to compare BM aspirates from 12 complete response (CR) and 10 refractory patients. We found 24 cell clusters expressing established markers of hematopoietic population. Differentially expressed genes between CR and refractory patients were determined using EdgeR and illustrated by volcano plots (Log2FC>|0.58|, x-axis, and Log10Adjusted P, y-axis) followed by enrichment for immune markers. Interleukin 4-induced gene 1 (IL4I1), in cluster 5 (promonocyte-like AML) was the most significant immune marker overexpressed in refractory patients (Fig. 1A). IL4I1 encodes a secreted oxidase protein (Molinier-Frenkel et al. FEBS 2017) that induces cancer immune escape by limiting proliferation of cytotoxic T cells (Lasoudris et al. Eur J Immunol. 2011). IL4I1 also activates the aryl hydrocarbon receptor and correlates with lower survival in glioma patients (Sadik et al. Cell. 2020). Its role in AML is unknown. To determine its clinical relevance in AML, we generated Kaplan-Meier curves using TCGA (n=132) and TARGET AML (n=187) databases and showed that patients with high IL4I1 had decreased survival (Fig. 1B&C). IL4I1 expression is increased in AML patients’ samples as compared to normal blood samples (Fig. 1D) and found in patients with mixed lineage leukemia (MLL) or complex cytogenetics (Fig. 1E). We next interrogated IL4I1 expression on leukemia stem cells (LSCs) defined as CD34+CD38- in the Princess Margaret Leukemia database (n=75) and noted IL4I1 expression was higher on LSCs vs normal HSCs (Fig. 1F). We then knocked out the IL4I1 gene in MOLM14 leukemic cells using CRISPR/Cas9 technology. IL4I1 silencing inhibited leukemia proliferation measured by Ki67 (Fig. 1G) and increased leukemia apoptosis measured by Annexin-V (Fig. 1H) compared to wild type (WT) leukemic cells. IL4I1 deficiency in leukemic cells arrested G2/S/M cell cycle progression which accelerated cell differentiation as compared to WT leukemic cells (Fig. 1I). We also performed Pearson correlations of IL4I1 with leukemic oncogenic drivers such as homeobox A cluster (HOXA) genes which strongly associate with the MLL translocation (Ernst et al. Curr. Biol. 2004) in TCGA- and TARGET-AML databases. Radar plots showed that IL4I1 was significantly correlated with these HOXA family genes (Fig. 1J, TCGA shown). We next interrogated immune regulatory functions of IL4I1 in AML patients and showed that enrichment of IL4I1 high genes vs low were in 4 signaling pathways: (i) cytokines/inflammation, (ii) innate immunity, (iii) antigen presentation and (iv) adaptive immunity (Fig. 1K). Representative genes signatures in these 4 pathways are summarized in Fig. 1L. We further tested whether IL4I1 expressing leukemia cells (WT) suppress T cells expansion in vitro. We used CFSE dilution to measure CD8+ T cells proliferation. WT MOLM14egfp cells inhibited CD8+ T cell proliferation while IL4I1 blockade in MOLM14egfp cells restored their proliferation (Fig. 1M). To test the role of IL4I1 in AML progression in vivo, we injected NOD.Cg-Prkdcscid Il2rgtm1Wjl/SzJ (NSG) mice with IL4I1-CRISPR/Cas9 KO or WT MOLM14egfp cells (Fig. 2A). Mice injected with WT MOLM14egfp cells had higher leukemia burden (Fig. 2B), higher Ki67 proliferation rate (Fig. 2C), and lower apoptosis rate (Fig. 2D) than IL4I1-KO MOLM14egfp recipients at Day 14 post-challenge. Self-renewal ability of leukemia cells is correlated to reactive oxygen species (ROS) (Testa U. et al. Exp Hematol. 2016). We found that ROS levels in IL4I1 KO leukemic cells are diminished compared to WT leukemic cells (Fig. 2E). Finally, we tested IL4I1 expression in response to Fedratinib, a JAK2 inhibitor, and showed that IL4I1 expression was decreased in responders (R) vs nonresponders (NR) (Fig. 2F). We conclude that IL4I1 is a rational immune therapeutic target for AML and testing of IL4I1 Inhibitor CB-668 through a collaboration with Calithera is underway. Figure 1View largeDownload PPTFigure 1View largeDownload PPT Close modal