LSC17 Score Research Articles

Genetic deletion of JAM-C in preleukemic cells rewires leukemic stem cell gene expression program in AML

AbstractThe leukemic stem cell (LSC) score LSC-17 based on a stemness–related gene expression signature is an indicator of poor disease outcome in acute myeloid leukemia (AML). However, it is not known whether “niche anchoring” of LSC affects disease evolution. To address this issue, we conditionally inactivated the adhesion molecule JAM-C (Junctional Adhesion Molecule-C) expressed by hematopoietic stem cells (HSCs) and LSCs in an inducible mixed-lineage leukemia (iMLL)-AF9–driven AML mouse model. Deletion of Jam3 (encoding JAM-C) before induction of the leukemia–initiating iMLL-AF9 fusion resulted in a shift from long-term to short-term HSC expansion, without affecting disease initiation and progression. In vitro experiments showed that JAM-C controlled leukemic cell nesting irrespective of the bone marrow stromal cells used. RNA sequencing performed on leukemic HSCs isolated from diseased mice revealed that genes upregulated in Jam3-deficient animals belonged to activation protein-1 (AP-1) and tumor necrosis factor α (TNF-α)/NF-κB pathways. Human orthologs of dysregulated genes allowed to identify a score that was distinct from, and complementary to, the LSC-17 score. Substratification of patients with AML using LSC-17 and AP-1/TNF-α genes signature defined 4 groups with median survival ranging from <1 year to a median of “not reached” after 8 years. Finally, coculture experiments showed that AP-1 activation in leukemic cells was dependent on the nature of stromal cells. Altogether, our results identify the AP-1/TNF-α gene signature as a proxy of LSC anchoring in bone marrow niches, which improves the prognostic value of the LSC-17 score. This trial was registered at www.ClinicalTrials.gov as #NCT02320656.

Cellular abundance-based prognostic model associated with deregulated gene expression of leukemic stem cells in acute myeloid leukemia.

Background: Previous studies have reported that genes highly expressed in leukemic stem cells (LSC) may dictate the survival probability of patients and expression-based cellular deconvolution may be informative in forecasting prognosis. However, whether the prognosis of acute myeloid leukemia (AML) can be predicted using gene expression and deconvoluted cellular abundances is debatable. Methods: Nine different cell-type abundances of a training set composed of the AML samples of 422 patients, were used to build a model for predicting prognosis by least absolute shrinkage and selection operator Cox regression. This model was validated in two different validation sets, TCGA-LAML and Beat AML (n = 179 and 451, respectively). Results: We introduce a new prognosis predicting model for AML called the LSC activity (LSCA) score, which incorporates the abundance of 5cell types, granulocyte-monocyte progenitors, common myeloid progenitors, CD45RA + cells, megakaryocyte-erythrocyte progenitors, and multipotent progenitors. Overall survival probabilities between the high and low LSCA score groups were significantly different in TCGA-LAML and Beat AML cohorts (log-rank p-value = and , respectively). Also, multivariate Cox regression analysis on these two validation sets shows that LSCA score is independent prognostic factor when considering age, sex, and cytogenetic risk (hazard ratio, HR = 2.17; 95% CI 1.40-3.34; p < 0.001 and HR = 1.20; 95% CI 1.02-1.43; p < 0.03, respectively). The performance of the LSCA score was comparable to other prognostic models, LSC17, APS, and CTC scores, as indicated by the area under the curve. Gene set variation analysis with six LSC-related functional gene sets indicated that high and low LSCA scores are associated with upregulated and downregulated genes in LSCs. Conclusion: We have developed a new prognosis prediction scoring system for AML patients, the LSCA score, which uses deconvoluted cell-type abundance only.

Integration of Transcriptomic Features to Improve Prognosis Prediction of Pediatric Acute Myeloid Leukemia With KMT2A Rearrangement.

Lysine methyltransferase 2A-rearranged acute myeloid leukemia (KMT2A-r AML) is a special entity in the 2022 World Health Organization classification of myeloid neoplasms, characterized by high relapse rate and adverse outcomes. Current risk stratification was established on the treatment response and translocation partner of KMT2A. To study the transcriptomic feature and refine the current stratification of pediatric KMT2A-r AML, we analyzed clinical and RNA sequencing data of 351 patients. By implementing least absolute shrinkage and selection operator algorithm, we identified 7 genes (KIAA1522, SKAP2, EGFL7, GAB2, HEBP1, FAM174B, and STARD8) of which the expression levels were strongly associated with outcomes. We then developed a transcriptome-based score, dividing patients into 2 groups with distinct gene expression patterns and prognosis, which was further validated in an independent cohort and outperformed the LSC17 score. We also found cell cycle, oxidative phosphorylation, and metabolism pathways were upregulated in patients with inferior outcomes. By integrating clinical characteristics, we proposed a simple-to-use prognostic scoring system with excellent discriminability, which allowed us to distinguish allogeneic hematopoietic stem cell transplantation candidates more precisely. In conclusion, pediatric KMT2A-r AML is heterogenous on transcriptomic level and the newly proposed scoring system combining clinical characteristics and transcriptomic features can be instructive in clinical routines.

A Single Phenotypic Biomarker of Gemtuzumab Ozogamicin (GO) Response in Acute Myeloid Leukemia Using Ex-Vivo Testing

Background or Introduction: Gemtuzumab Ozogamicin (GO), a therapeutic antibody targeting CD33, gained FDA approval in 2017 for the treatment of patients with Acute Myeloid Leukemia (AML). Biomarkers to predict GO response were historically based on multiple indicators: CD33 expression levels, favorable cytogenetics and specific genetic alterations (NPM1, FLT3-internal tandem duplications, other mutations). Moreover, comprehensive analyses including stemness expression (LSC17 score) and molecularly derived scores such as the CD33_PGx6_Score (a composite score derived from six CD33 SNP of prognostic significance) have further refined the biomarkers to identify potential responders (Fenwarth et al, nt. J. Mol. Sci. 2020, 21, 5626). However, due to its toxicity profile and elevated cost, the identification of robust biomarkers of GO response in AML remains a challenge to optimize its therapeutic efficacy and worldwide clinical adoption. Methods: In this study, we utilized an innovative ex-vivo functional platform of Patient Micro Avatars (PMAs) developed at OncoPrecision (García et al, Blood 2022 140 (Supplement 1): 13027-13028) to assess the response to GO in a small cohort of AML patients. Patient-Derived Cells (PDCs) were obtained from five newly diagnosed AML patients and GO activity was assessed using high-throughput flow cytometry. The correlation between CD33 expression and the ex-vivo response to GO was also examined. Results: Our findings revealed a clear differential response to GO in AML patients, with contrasting outcomes observed within a dose-range that does not exceed its theoretical plasma concentration (Figure - Upper Panel). Interestingly, despite significant variations in CD33 expression levels among the evaluated patients, no obvious association was found between CD33 expression and the ex-vivo response to GO (Figure - Lower Panel). Conclusions: This study presents an innovative approach with the potential to predict GO responders versus non-responders in AML. A single phenotypic biomarker overcomes the need to determine CD33 expression levels in each patient or to assess the presence of specific polymorphic variants, as well as additional molecular biomarkers. Patient Micro Avatars present a compelling alternative to comprehensively identify patients who are most likely to benefit from GO treatment.

Hyperleukocytosis Increases the Risk of Early Relapses Independently of Genetics in AML

Background. Higher white blood cell (WBC) count at diagnosis is associated with specific gene lesions and higher early death rates in AML patients (pts) treated intensively. Whether higher WBC count at diagnosis affects outcome beyond remission independently of genetics is unknown. Methods. 1,371 AML pts treated intensively in 3 ALFA trials (0702 18-60y, 0701 50-70y, 1200 60y+) with centralized genetics were studied. Genetic alterations found in &amp;gt;5% of pts (n=22) were analyzed. Hyperleukocytosis (HL) was defined as WBC &amp;gt; 50 x10 9/L. NPM1 MRD was stratified as published (Balsat, J Clin Oncol 2017). All prognostic analyses were stratified on trial. Time-dependent effects were introduced in multivariable Cox models when the proportional hazard (PH) assumption was violated. Results. The median WBC count at diagnosis was 7.1 x10 9/L (range 0.3-546.6), and 235 pts (17.1%) had HL. HL pts had poorer performance status (p&amp;lt;0.001), lower platelet counts (p=0.003), higher frequency of de novo AML (p=0.006) and more favorable genetic risk (ELN 2022 criteria, p&amp;lt;0.001). In a multivariable model, HL was independently associated with more frequent NPM1, FLT3-ITD and -TKD mutations, and less frequent STAG2 and -17/17p- alterations (all p&amp;lt;0.05, Figure 1A). Complete remission (CR [including CR with incomplete platelet recovery, CRp]) and early death (ED) rates were 77.4 vs 80.0% (p=0.38) and 8.9 vs 5.0% (p=0.03) in pts with and without HL, respectively (resp). In a multivariable model accounting for ELN22 risk and age, HL was independently associated with more frequent ED (OR =2.20, 95%CI 1.26-3.75, p=0.004), and a trend to more frequent primary induction failure (OR=1.40, 95%CI 0.95-2.05, p=0.08). In the 1,091 pts achieving CR/CRp (182 and 909 with and without HL resp), 1-y and 2-y cumulative incidence of relapse were 39.0% vs 26.1% and 44.0% vs 40.2% resp, suggestive of a time-dependent effect of HL on post-remission outcome ( Figure 1B). Considering relapse and death as competing events, a multivariable model revealed a significant impact of HL on relapse (subdistribution hazard ratio, sHR=1.59, p=0.0003), independent of ELN22 risk and age, but not on non-relapse mortality (sHR=0.69, p=0.23). HL violated the PH assumption in a univariable Cox model for DFS (p=0.0003). We thus performed multivariable Cox models for DFS separating early (&amp;lt; 1 year from CR) and late (≥1 year) effects for HL. In a multivariable time-dependent model, HL significantly impaired early (HR=1.89, p&amp;lt;10 -4), but not late (HR=0.79, p=0.29) DFS, independently of ELN risk and age. These results were confirmed considering WBC count as a (log-transformed) continuous variable, accounting for all differentially represented genetic lesions instead of ELN22 risk or censoring at allogeneic HCT. In a validation cohort of 1,089 pts &amp;lt;60y reaching CR after intensive chemotherapy in the BIG-1 trial (NCT02416388) where median WBC was 7.7 x10 9/L and 16.1% of pts were HL, the adverse risk of HL on CIR (sHR=1.39, p=0.007) but not NRM (sHR=1.02, p=0.95) and on early (HR=1.42, p=0.01), but not late (HR=1.24, p=0.23) DFS independent of ELN22 risk and age was validated. Finally, to explore the mechanism underlying the higher early relapse rate of HL pts, we analyzed NPM1 MRD (n=152) and LSC17 (n=504) data from the 0702 trial. A higher WBC count was predictive of suboptimal MRD (OR=1.97, p=0.05) independently of FLT3-ITD status. A higher WBC count was significantly correlated to lower LSC17 score (Spearman rho=0.23, p&amp;lt;10-5). Conclusion. A high WBC count at diagnosis predicts higher rates of early, but not late relapse, independent of genetic risk. Our results strengthen the hypothesis that distinct biological mechanisms underpin early vs late chemoresistance in AML.

The Ancestry-Related Landscape of Long Non-Coding RNAs (LncRNAs) in Acute Myeloid Leukemia (AML) and Its Impact on Patient (Pt) Survival

Background In addition to molecular genetic and cytogenetic findings, gene expression profiles provide independent prognostic information in AML. Aberrant expression of non-coding RNAs, including lncRNAs, further inform pt prognosis. However, non-coding expression profiles and their clinical relevance in AML almost exclusively stem from studies of pts of European ancestry (hereafter referred to as White). Given the inferior survival of African-American (hereafter referred to as Black) AML pts, we hypothesized that aberrant, possibly ancestry-associated lncRNA expression may contribute to survival disparities. Hence, we set out to (1) characterize the lncRNA landscape of African-American ancestry, (2) delineate associations of lncRNAs with clinical and molecular characteristics and (3) assess the prognostic significance of ancestry-associated lncRNAs in AML. Methods We used diagnostic samples from 681 adult AML pts (&amp;lt;60y) treated similarly on Alliance frontline protocols (White, n=616; Black, n=65) for RNAseq, mutational profiling and centrally reviewed karyotyping. A negative binomial model was fit to identify lncRNAs differentially expressed between Black and White pts. To determine prognostic relevance of identified lncRNAs, 10-fold cross-validation repeated 1000 times was performed on LASSO penalized Cox proportional hazards model to overall survival (OS). LncRNAs appearing in at least 900 of the 1000 models were retained and coefficient estimates were averaged then applied to form the “lncRNA score.” Ancestry-associated SNPs in regulatory regions of select lncRNAs were identified using the ENCODE registry for cCREs, SNP2TFBS and population allele frequency data from NCBI ALFA. Results Comparing Black and White AML pts, 135 lncRNAs were upregulated and 55 lncRNAs were downregulated. The differentially expressed lncRNAs were equally distributed throughout the genome and no lncRNA was ancestry-exclusive in its expression, indicating high conservation. lncRNAs with the highest differential expression include several lncRNAs previously implicated in cancer/leukemia, but also thus far unstudied lncRNAs. Using the 190 aberrantly expressed lncRNAs identified from comparison of Black and White pts, we derived a prognostic score (lncRNA score) consisting of 13 lncRNAs. LncRNA High strongly associated with inferior OS in all pts ( Fig. 1). In all pts combined, presence of lncRNA High positively associated with several adverse-risk features, and negatively associated with core-binding factor AML and CEBPA bZIP mutations. Notably, select genetic features differed in their lncRNA score associations based on ancestry, including a relative paucity of cytogenetically normal AML (29% vs 57%), NPM1 (13% vs 40%) and CEBPA bZIP (8% vs 17.5%) mutations in Black pts compared to White pts in the lncRNA Low group. lncRNA score was an independent prognosticator of worse OS in both Black and White pts that refined the prognostication of the European LeukemiaNet 2022 Favorable (P=.004) and Intermediate (P&amp;lt;.001) genetic risk groups. It also provided further prognostic information to the LSC17 score in both low (P&amp;lt;.001) and high (P&amp;lt;.001) LSC17 groups. Examination of the genomic context of regulatory regions within the 13 lncRNAs revealed multiple ancestry-associated SNPs that putatively altered transcription factor / chromatin regulator binding sites in cis-regulatory regions, including rs58515841 (MAF White vs Black, 24% vs 9%, CTCF binding site within ENSG00000284052) and rs1400262 (MAF, 62% vs 10%, REST binding site upstream of NCK1-DT). Conclusions We show an ancestry-related landscape of lncRNAs in AML, including an ancestry-associated expression profile of lncRNAs and evidence of ancestry-associated SNPs as expression modulators. A prognosis-associated lncRNA score based on differentially expressed lncRNAs in Black versus White AML pts carries prognostic significance irrespective of ancestry and refines both mutation/cytogenetically-based (ELN 2022) and gene-expression-based (LSC17) prognostic algorithms. Our findings highlight the importance of ancestry-diverse populations in the establishment of prognostic scoring systems to provide ancestry-connected survival prediction. Support: U10CA180821, U10CA180882, U24CA196171; https://acknowledgments.alliancefound.org. Clinicaltrials.gov Ids: NCT00048958, NCT00899223, NCT00900224

LSC17 score complements genetics and measurable residual disease in acute myeloid leukemia: an ALFA study

AbstractWhether the LSC17 gene expression can improve risk stratification in the context of next generation sequencing–based risk stratification and measurable residual disease (MRD) in patients with intensively treated AML has not been explored. We analyzed LSC17 in 504 adult patients prospectively treated in the ALFA-0702 trial. RUNX1 or TP53 mutations were associated with higher LSC1 scores while CEBPA and NPM1 mutations were associated with lower scores. Patients with high LSC17 scores had a lower rate of complete response (CR) in a multivariable analysis (odds ratio, 0.41; P = .0007), accounting for European LeukemiaNet 2022 (ELN22), age, and white blood cell count (WBC). LSC17-high status was associated with shorter overall survival (OS) (3-year OS: 70.0% vs 52.7% in patients with LSC17-low status; P < .0001). In a multivariable analysis considering ELN22, age, and WBC, patients with LSC17-high status had shorter disease-free survival (DFS) (hazard ratio [HR], 1.36; P = .048) than those with LSC17-low status. In 123 patients with NPM1-mutated AML in CR, LSC17-high status predicted poorer DFS (HR, 2.34; P = .01), independent of age, WBC, ELN22 risk, and NPM1-MRD. LSC-low status and negative NPM1-MRD identified a subset comprising 48% of patients with mutated NPM1 with a 3-year OS from CR of 93.1% compared with 60.7% in those with LSC17-high status and/or positive NPM1-MRD (P = .0001). Overall, LSC17 assessment refines genetic risk stratification in adult patients with AML treated intensively. Combined with MRD, LSC17 identifies a subset of patients with NPM1-mutated AML with excellent clinical outcome.

A three-gene leukaemic stem cell signature score is robustly prognostic in chronic myelomonocytic leukaemia.

Leukaemic stem cell (LSC) gene expression has recently been linked to prognosis in patients with acute myeloid leukaemia (17-gene LSC score, LSC-17) and myelodysplastic syndromes. Although chronic myelomonocytic leukaemia (CMML) is regarded as a stem cell disorder, the clinical and biological impact of LSCs on CMML patients remains elusive. Making use of multiple independent validation cohorts, we here describe a concise three-gene expression signature (LSC-3, derived from the LSC-17 score) as an independent and robust prognostic factor for leukaemia-free and overall survival in CMML. We propose that LSC-3 could be used to supplement existing risk stratification systems, to improve prognostic performance and guide management decisions.

The LSC17 Score Correlates with the ELN 2022 Classification of AML and Is an Independent Predictor of Detectable Measurable Residual Disease after Induction Chemotherapy

Introduction: An emerging trend for targeted treatment in AML has led to increased treatment options, however variable outcomes remain even with a targeted approach suggesting novel approaches are needed. To date, risk stratification of AML patients has relied on cytogenetic and genomic data as there has been no CLIA-certified biomarker to assess how leukemia stem cells (LSC) contribute to disease resistance at a clinical level. The LSC17 score predicts risk based on stemness features in AML patients at diagnosis and is strongly associated with survival after standard induction therapy (Ng, Nature 2016). A NanoString clinical assay for the LSC17 score has recently been validated in a CLIA-certified laboratory (Ng, Blood Advances 2022) and can provide a rapid result within 72 hours of diagnosis for incorporation into upfront treatment decisions. Recent updates in AML have led to an improved risk stratification at cytogenetic and genomic levels (ELN 2022). Here we carried out a new analysis to assess the value of the LSC17 score as an independent predictor of outcomes in AML patients in the context of the updated ELN 2022 classification. Methods: Data from recent validation and prospective feasibility studies were analyzed. Only patients that received induction chemotherapy were included in the analysis. Patients with a confirmed diagnosis of acute promyelocytic leukemia were excluded from analysis. In our studies the LSC17 score was measured in parallel with standard prognostic markers including cytogenetics, molecular studies and targeted sequencing using a standard myeloid panel. Patients’ ELN status was updated using the 2022 classification scheme and was correlated with treatment response and survival. Results: 190 patients were included in our analysis. LSC17 scores ranged from 0 to 1.25, and were classified as high or low according to the median score of 0.51 from our reference cohort (Ng, Blood Advances 2022). Of the 190 patients, 84 had a low LSC17 score and 106 had a high LSC17 score. Patients received induction chemotherapy with three different protocols: standard 3+7 (n = 141), Flag-IDA (n =40) and CPX-351 (n = 9). We first considered the impact of the ELN 2022 classification and how it altered prognostic categorization in our cohort. Using ELN 2017 criteria, 48 (27%), 51 (29%), and 77 (44%) of patients had favorable, intermediate and adverse risk disease, respectively. 14 patients were not classifiable as cytogenetic testing failed. Using the ELN 2022 hierarchical classification, patients were categorized into low (n=44), intermediate (n = 53) and high (n = 93) risk groups. Changes in prognostic classification were most evident in the intermediate risk category where 11 patients were upgraded to high-risk disease due to the presence of myelodysplasia-related disease mutations. When the LSC17 score was considered as a continuous variable, the median LSC17 score was statistically different between the three ELN 2022 risk groups (0.24 [range 0-0.8] vs 0.48 [range 0.04-1.13] vs 0.73 [range 0.14-1.25] for low, intermediate and high-risk, respectively; P<0.001, Figure 1). Measurable residual disease (RD) is associated with poor clinical outcomes. Prediction of RD can identify patients who may benefit from alternative treatment approaches. We therefore assessed the ability of the LSC17 score to predict RD positivity after induction chemo in the context of ELN 2022. In our cohort, 141 patients (74%) had RD testing performed after induction. In univariate analysis, RD positivity was associated with both ELN 2022 high-risk disease (P=0.001, OR 12.7) and a high LSC17 score (P<0.001, OR = 7.7). In multivariate analysis incorporating both variables, both ELN high risk (P=0.02, OR 5.56, 95% CI 1.35 -22.85) and high LSC17 score (P=0.007, OR 3.98, 95% CI 1.44 -11.01) retained independent prognostic value. Conclusion: The LSC17 score measured by the clinical NanoString-based assay correlates with the updated ELN 2022 risk classification and importantly is an independent predictor of RD positivity irrespective of mutational status. Measurement of the LSC17 score at diagnosis will enable identification of patients with high probability of treatment refractoriness. This study highlights the potential role of the LSC17 score in future risk-adapted clinical studies of novel induction candidates. Figure 1View largeDownload PPTFigure 1View largeDownload PPT Close modal

The Composition of Acute Myeloid Leukemia Cell Differentiation States Predicts Response to Immune Checkpoint Blockade

Background: Given the activity of allogeneic T cells in acute myeloid leukemia (AML) patients, immunotherapies stimulating antitumor T cell responses have been explored as a promising therapeutic option. However, these therapies have been associated with modest clinical activity, and further research is needed to identify response and resistance mechanisms. A spectrum of primitive and differentiated cells comprise AML in each individual patient, and this cell composition has been previously shown to predict outcomes. In particular, high proportions of AML stem and progenitor cells have been associated with worse clinical outcomes and lack of response to induction chemotherapy (Ng et al Nature 2016; van Galen et al Cell 2019; Zeng et al Nat Med 2022). The composition of AML cell differentiation states in relation to immunotherapy response or resistance has not been elucidated; herein, we explored whether this composition may be associated with response in AML patients treated with immune checkpoint blockade. Methods: Pre- and posttreatment whole bone marrow (BM) samples were collected from 31 newly diagnosed and relapsed/refractory AML patients treated with azacitidine and pembrolizumab (NCT02845297) and 18 relapsed/refractory AML patients treated with high-dose cytarabine (HiDAC) and pembrolizumab (NCT02768792; Zeidner et al Blood Cancer Discov 2022) on two separate phase II trials. Primary patient samples and associated clinical data were obtained via informed consent in accordance with the Declaration of Helsinki as per research protocols approved by the Institutional Review Boards of the participating institutions. Bulk RNA sequencing was performed on all BM samples. Differential gene expression analysis was performed using the DESeq2 package, and gene set enrichment analysis (GSEA) was performed using the clusterProfiler package. Proportions of AML cell populations present in BM samples were deconvoluted using CIBERSORTx as per Zeng et al Nat Med 2022. The LSC17 score and LinClass7 score were calculated as per Ng et al Nature 2016 and Zeng et al Nat Med 2022, respectively. All analyses and graphs were generated using R (v4.2.1). Results: GSEA using gene sets from a range of primitive and differentiated AML and normal cell types as per van Galen et al Cell 2019 was performed on pre- and posttreatment samples. Patients achieving a complete response (CR) to either immunotherapy regimen were enriched at baseline for the following differentiated AML gene sets: pro monocyte (Promono)-like AML; monocyte-like AML; conventional dendritic cell (cDC)-like AML; and myeloid-like AML (Figure 1A). Patients not responding (NR) to azacitidine/pembrolizumab were enriched for the following stem and progenitor AML gene sets: hematopoietic stem cell (HSC)-like AML; progenitor (Prog)-like AML; and HSC progenitor (HSCProg)-like AML (Figure 1A). To validate these findings, BM samples were deconvoluted into inferred fractions of AML differentiation states using CIBERSORTx and single-cell RNA sequencing expression data from Zeng et al Nat Med 2022. Patients not responding to azacitidine/pembrolizumab had a higher proportion of leukemia stem and progenitor quiescent cells (LSPC Quiescent) at baseline, while patients achieving CR to either regimen had higher baseline proportions of pro monocyte-like AML cells (Figure 1B). Additionally, patients not responding to azacitidine/pembrolizumab had higher baseline LSC17 and LinClass7 scores, two gene expression scores that are reflective of leukemia stemness (Figure 1B). In the posttreatment setting for both regimens, HSC-like, Prog-like, and HSCProg-like gene sets were associated with NR; monocyte-like and myeloid-like gene sets were associated with CR (Figure 1A). Discussion: Patients achieving CR to azacitidine/pembrolizumab and HiDAC/pembrolizumab had higher proportions of differentiated AML cells, while NR patients had a higher proportion of AML stem and progenitor cells. We hypothesize that antitumor T cells may preferentially target differentiated AML cells over AML stem and progenitor cells. Further research is needed to validate these findings in a prospective and mechanistic manner and in other immunotherapy regimens. Analyses of the proportions of AML differentiation states in patients relapsing after allogeneic hematopoietic cell transplantation are ongoing. Figure 1View largeDownload PPTFigure 1View largeDownload PPT Close modal

LSC17 Score Is Complementary with Minimal Residual Disease to Stratify NPM1-Mutated Acute Myeloid Leukemia: An ALFA Study

Context. A 17-gene Leukemic Stem Cell gene expression signature (LSC17) has been shown to be prognostic in AML (Ng et al., 2016), independently of genetic risk stratification (Bill et al., 2020; Duployez et al., 2019). Whether assessment of LSC17 adds prognostic value on top of measurable residual disease (MRD) has not been explored. Objective. We analyzed the prognostic value of LSC17 in adult AML patients prospectively treated in the intensive chemotherapy ALFA-0702 trial (NCT00932412) in the context of ELN 2017 risk stratification (ELN17) and NPM1 MRD. Methods. The LSC17 score was determined by Nanostring technology (Duployez et al., 2019). NPM1 MRD negativity was defined as a 4-log reduction in peripheral blood NPM1c expression (Balsat et al., 2017; Lesieur et al., 2021). Multiple testing was adjusted for using the Benjamini-hochberg procedure. Results. The study population included 504 AML patients (237 female, 267 male) with a median age of 48 years (range 18-60). One hundred eighty-seven (37%) patients had an NPM1 mutation. ELN17 risk was favorable in 176 (35%), intermediate in 151 (30%) and adverse in 170 (34%) patients. A higher LSC17 score was positively associated with RUNX1 (median difference [md]=0.13, corrected p-value [q]=0.0005) and negatively correlated with NPM1 mutation (md=-0.14, q<0.0001) and double-mutated CEBPA (md=-0.30, q<0.0001). Regarding recurrent cytogenetic lesions, presence of monosomy 7 or del7q (md=0.29, q<0.0001), monosomy 5 or del5q (md=0.25, q=0.0004), monosomy 17, del17p or abnormal 17p (md=0.24, q=0.01), monosomal karyotype (md=0.17, q=0.02) and complex karyotype (md=0.13, q=0.03) were each associated with higher LSC17, whereas normal karyotype was associated with a lower LSC17 score (md=-0.11, q<0.0001) (Figure 1A). In the subgroup of NPM1-mutated AML (n=187), a higher LSC17 score was only correlated with FLT3-ITD (md=0.13, q=0.0002) but not DNMT3A (md=-0.0007, q=1) mutations. Patients were dichotomized into LSC17-low (n=252) and LSC17-high (n=252) groups using the median LSC17 score as cutoff. Overall, 400 (79%) patients achieved complete response (CR) or CR with incomplete platelet recovery (CRp) after one cycle of induction and an additional 31 (6%) after a second course. LSC17-high patients had a lower rate of CR/CRp after one course of induction (69% vs 89% in LSC17-low patients) in univariate (p<0.0001) or multivariate analysis (OR=0.41, p=0.001) considering ELN17 risk, age and white blood cell (WBC) count. LSC17-high patients had lower 3-year overall survival (OS) from CR/CRp (60.6%, 95% CI, 54.2-67.8%) than LSC17-low patients (75.4%, 95% CI, 70.0-81.2%, p=0.0003). In multivariate analysis considering ELN17, age and WBC, LSC17-high population had shorter DFS (HR=1.39, p=0.038) and a trend for shorter OS from CR/CRp (HR=1.40, p=0.069). In univariate analyses, LSC17-high status was associated with a higher cumulative incidence of relapse (p=0.002) without impact on non-relapse mortality (p=0.27). Considering hematopoietic cell transplantation (allo-HCT) as a time-dependent covariate, there was no interaction on OS from CR/CRp between allo-HCT in first CR and LSC17-high status (p=0.84). In NPM1-mutated patients (n=187), the rate of CR/CRp after one induction course was 90% in LSC17-high patients versus 96% in LSC17-low patients (p=0.20). In 123 NPM1-mutated patients in CR after the first induction course and with available MRD, LSC17-high status (n=42) predicted a poorer DFS (HR:2.47, p=0.005) in multivariate analysis independently of age (p=0.55), higher WBC (p=0.84), intermediate ELN17 risk (p=0.97) and MRD positivity (p=0.0002). LSC17-high status also independently predicted poorer OS from CR/CRp (HR:2.73, p=0.007) in a similar multivariate model. Three-year OS from CR was 93.1% (95% CI, 86.8-99.9%) in the 59 (48%) NPM1-mutated LSC17-low patients achieving negative MRD compared to 60.7% (95% CI, 49.8-74.0%) in those (n=64, 52%) with LSC17-high status and/or positive MRD (p=0.0001) (Figure 1B). Conclusion. The LSC17 score complements genetic risk stratification in AML patients treated intensively. Combining LSC17 and MRD assessment identifies a subset of NPM1-mutated AML with excellent long-term prognosis. Figure 1View largeDownload PPTFigure 1View largeDownload PPT Close modal

A Novel Role for PLK1 in Leukemia Stem Cell Function in Acute Myeloid Leukemia

Acute myeloid leukemia (AML) is an aggressive hematologic malignancy with poor prognosis. While most patients achieve initial remission following standard chemotherapy, the majority will relapse and ultimately die. Poor outcomes in AML are tied to the properties of leukemia stem cells (LSC), which drive therapy resistance and relapse. For successful therapeutic targeting, it is critical to understand the pathways that operate in LSC function and survival and how they are impacted by drug activity. We carried out an innovative stemness-focused high-throughput drug screen employing a hierarchical AML cell model and a Nanostring-based LSC signature assay to probe for genes and pathways that impact LSC function. All 4 small molecule inhibitors of Polo-like Kinase 1 (PLK1) included in the compound library were among the top hits. PLK1 inhibitor treatment selectively targeted the CD34⁺CD38⁻ fraction of OCI-AML-8227 cells, which is enriched for quiescent LSCs. PLK1 is an important cell cycle regulator; although there are reports for cell cycle-independent roles, they have not been systematically characterized. Importantly, the role of PLK1 has not been previously studied in the context of cancer stem cells. To investigate the role of PLK1 in LSCs, we performed single-cell (sc) multi-omics incorporating both scRNAseq and scATACseq on the CD34⁺CD38⁻ compartment of OCI-AML-8227 cells. Enrichment analysis of previously-identified chromatin accessibility signatures of primitive long-term hematopoietic stem cells (LT-HSC) and progenitors was used to identify cell clusters with distinct stemness and differentiation properties. Interestingly, PLK1 mRNA expression in OCI-AML-8227 was restricted to cells enriched for the LT-HSC epigenetic signature (Fig 1A). In healthy human bone marrow, PLK1 expression was restricted to committed progenitors and not detected in HSCs (Fig 1B). These data strongly suggest a previously unexplored role of PLK1 in regulating stemness that may be specific LSCs and not normal HSCs. To determine the effects of PLK1 against LSCs in vivo, we carried out PLK1 inhibitor treatment using the specific PLK1 inhibitor volasertib in a cohort of 9 primary AML samples in PDX models. Volasertib treatment significantly reduced leukemic burden in 7 of 9 samples, all of which had a high LSC17 score (associated with poor prognosis and standard chemotherapy response). To quantify effects on LSCs, we serially transplanted 2 samples at limiting doses into untreated secondary recipients and demonstrated a 5- to 25-fold reduction in LSC frequency following volasertib treatment, indicating that functional LSCs were targeted by PLK1 inhibition. Together, these data strongly suggest that PLK1 impacts LSC function and stemness, and that PLK1 inhibitors are acting through an uncharacterized cell-cycle independent mechanism to target quiescent LSCs. To discover novel PLK1 signaling pathways, we carried out proximity-dependent biotinylation (PDB) to characterize the PLK1 interactome in serum-starved cells. Preliminary analysis has revealed a number of previously uncharacterized PLK1 interactors, including components of autophagy and apoptosis machinery, that are currently being validated in the hematopoietic cell context. Our study highlights PLK1 as a specific vulnerability of LSCs, and furthers the understanding of malignant PLK1 signaling in non-cell cycle related processes. Further insights into the underlying mechanisms will enable the development of effective therapies to target these pathways and improve patient outcomes in AML. Figure 1View largeDownload PPTFigure 1View largeDownload PPT Close modal

Gene Expression Analysis Reveals a Subset of TP53mutant-like AML with Wild Type TP53 and Poor Prognosis

Introduction Acute myeloid leukemia (AML) is a fatal blood cancer with a 2-year survival rate of less than 50%. AML with TP53 mutations (TP53mut) has a particularly poor prognosis with a 1-year survival rate of less than 10%. Gene expression profiling (GEP) has been used to identify poor prognostic features in AML and other cancers. In acute lymphoblastic leukemia (ALL), GEP has been instrumental in defining new molecular subgroups with poor prognostic features such as Ph-like ALL. Because TP53mut AML is uniquely treatment refractory, we hypothesize that this AML subtype harbors a distinct GEP and TP53 wild type (TP53wt) AMLs that express features of the TP53mut GEP would have similarly poor clinical outcomes. Methods and results: We analyzed RNA sequencing data of AML samples that have associated mutational profiling in the BEAT-AML (n=413) and TCGA (n=178) datasets. We compared the GEP of TP53mut and TP53 wild type (TP53wt) samples. Using unsupervised hierarchical clustering methods, we detected no significant clustering according to TP53 status. Therefore, we applied a machine-learning approach to detect whether a unique TP53mut GEP could be detected. Using the BEAT-AML dataset, we randomly divided the samples in half to generate training and testing datasets. The TCGA dataset was reserved as a validation dataset. We trained a Ridge regression model on the training dataset using 10-fold cross validation to define a model that could be used to classify TP53mut and TP53wt cases. We evaluated our model on the test dataset and found that this model was highly accurate in distinguishing TP53mut versus TP53wt cases (93% sensitivity and 97% specificity). When we applied this model to the validation dataset, we found a similar performance (93% sensitivity and 99% specificity) demonstrating that this model robustly defines a TP53mut GEP that generalizes across datasets. Although our machine learning model exhibited high accuracy in distinguishing TP53mut samples from TP53wt samples, we noted there was a cohort of TP53wt samples that were consistently scored highly by our classifier, suggesting they had features that made them TP53 mutant-like (TP53mut-like). Interestingly, we found that the TP53wt cases with the highest scores (representing highest similarity to the TP53mut profile) shared several clinical features with TP53mut samples. Most notably, the survival rate of TP53mut-like cases (TP53wt cases with high scores) was significantly lower than TP53wt cases with low scores. In the BEAT-AML dataset, we observed that the 42 TP53wt samples with the highest score had worse overall survival (OS) rates among the TP53wt cases (median survival: 347 days versus 568 days, p=0.014). We reasoned that these TP53mut-like may represent a previously unappreciated subtype, and thus, we specifically trained a Ridge regression model to distinguish these TP53mut-like cases from TP53wt in the BEAT-AML dataset. This model was also highly accurate when tested on the held-out BEAT-AML samples (87.50% sensitivity and 95.52% specificity). When applying the resulting TP53mut-like model to the TCGA GEP data, we found that the 23 TP53wt patients with the highest scores had the worse OS rate among the TP53wt cases (median survival 335 days versus 800 days, p=0.031). Furthermore, we find that the TP53mut-like cases displayed other similar features to TP53mut cases. For example, TP53mut and TP53mut-like samples have higher LSC17 scores (Ng et al. Nature 2016) relative to TP53wt cases (p<0.0001). Clinically, we find that TP53mut cases have lower bone marrow blast percentage and white blood cell count (WBC) relative to TP53wt cases in both datasets (consistent with prior reports [Tashakori et al. Blood 2022]). Accordingly, TP53mut-like cases also have lower bone marrow blast percentage (35 versus 63%, p<0.0001) and WBC (22 versus 42%, p<0.0001) relative to TP53wt cases. The cytogenetic profiles of TP53mut-like cases resembled those of TP53mut cases, but these cytogenetic profiles were insufficient identify TP53mut-like cases and were detected in some TP53wt cases. Finally, we identified a panel of 15 genes whose expression identifies TP53mut-like cases. Conclusion: We use a statistical regression model to define the GEP of TP53mut AML and define a new subset of TP53mut-like AML that lacks TP53 mutations but shares many of the same clinical and laboratory features of TP53mut AML, including poor survival. Figure 1View largeDownload PPTFigure 1View largeDownload PPT Close modal

The 17-gene stemness score associates with relapse risk and long-term outcomes following allogeneic haematopoietic cell transplantation in acute myeloid leukaemia.

A 17‐gene stemness (LSC17) score determines risk in acute myeloid leukaemia patients treated with standard chemotherapy regimens. The present study further analysed the impact of the LSC17 score at diagnosis on outcomes following allogeneic haematopoietic cell transplantation (HCT). Out of 452 patients with available LSC17 score, 123 patients received allogeneic HCT. Transplant outcomes, including overall (OS), leukaemia‐free survival (LFS), relapse incidence (RI) and non‐relapse mortality (NRM), were compared according to the LSC17 scored group. The patients with a low LSC17 score had higher OS (56.2%) and LFS (54.4%) at 2 years compared to patients with high LSC17 score (47.2%, p = 0.0237 for OS and 46.0%, p = 0.0181 for LFS). The low LSC17 score group also had a lower relapse rate at 2 years (12.7%) compared to 25.3% in the high LSC17 score group (p = 0.017), but no difference in NRM (p = 0.674). Worse outcomes in the high LSC17 score group for OS, LFS and relapse were consistently observed across all stratified sub‐groups. The use of more intensive conditioning did not improve outcomes for either group. In contrast, chronic graft‐versus‐host‐disease was associated with more favourable outcomes in both groups. The 17‐gene stemness score is highly prognostic for survival and relapse risk following allogeneic HCT.

A clinical laboratory–developed LSC17 stemness score assay for rapid risk assessment of patients with acute myeloid leukemia

AbstractLeukemia stem cells (LSCs) are linked to relapse in acute myeloid leukemia (AML). The LSC17 gene expression score robustly captures LSC stemness properties in AML and can be used to predict survival outcomes and response to therapy, enabling risk-adapted, upfront treatment approaches. The LSC17 score was developed and validated in a research setting. To enable widespread use of the LSC17 score in clinical decision making, we established a laboratory-developed test (LDT) for the LSC17 score that can be deployed broadly in clinical molecular diagnostic laboratories. We extensively validated the LSC17 LDT in a College of American Pathologists/Clinical Laboratory Improvements Act (CAP/CLIA)-certified laboratory, determining specimen requirements, a synthetic control, and performance parameters for the assay. Importantly, we correlated values from the LSC17 LDT to clinical outcome in a reference cohort of patients with AML, establishing a median assay value that can be used for clinical risk stratification of individual patients with newly diagnosed AML. The assay was established in a second independent CAP/CLIA-certified laboratory, and its technical performance was validated using an independent cohort of patient samples, demonstrating that the LSC17 LDT can be readily implemented in other settings. This study enables the clinical use of the LSC17 score for upfront risk-adapted management of patients with AML.

Deconvolution of Bulk Gene Expression Profiles with Single-Cell Transcriptomics to Develop a Cell Type Composition-Based Prognostic Model for Acute Myeloid Leukemia.

Acute myeloid leukemia (AML) is one of the malignant hematologic cancers with rapid progress and poor prognosis. Most AML prognostic stratifications focused on genetic abnormalities. However, none of them was established based on the cell type compositions (CTCs) of peripheral blood or bone marrow aspirates from patients at diagnosis. Here we sought to develop a novel prognostic model for AML in adults based on the CTCs. First, we applied the CIBERSORT algorithm to estimate the CTCs for patients from two public datasets (GSE6891 and TCGA-LAML) using a custom gene expression signature reference constructed by an AML single-cell RNA sequencing dataset (GSE116256). Then, a CTC-based prognostic model was established using least absolute shrinkage and selection operator Cox regression, termed CTC score. The constructed prognostic model CTC score comprised 3 cell types, GMP-like, HSC-like, and T. Compared with the low-CTC-score group, the high-CTC-score group showed a 1.57-fold [95% confidence interval (CI), 1.23 to 2.00; p = 0.0002] and a 2.32-fold (95% CI, 1.53 to 3.51; p < 0.0001) higher overall mortality risk in the training set (GSE6891) and validation set (TCGA-LAML), respectively. When adjusting for age at diagnosis, cytogenetic risk, and karyotype, the CTC score remained statistically significant in both the training set [hazard ratio (HR) = 2.25; 95% CI, 1.20 to 4.24; p = 0.0119] and the validation set (HR = 7.97; 95% CI, 2.95 to 21.56; p < 0.0001]. We further compared the performance of the CTC score with two gene expression-based prognostic scores: the 17-gene leukemic stem cell score (LSC17 score) and the AML prognostic score (APS). It turned out that the CTC score achieved comparable performance at 1-, 2-, 3-, and 5-years timepoints and provided independent and additional prognostic information different from the LSC17 score and APS. In conclusion, the CTC score could serve as a powerful prognostic marker for AML and has great potential to assist clinicians to formulate individualized treatment plans.

Transcriptomic Features of Immune Exhaustion and Senescence Predict Outcomes and Define Checkpoint Blockade-Unresponsive Microenvironments in Acute Myeloid Leukemia

Acute myeloid leukemia (AML) is a molecularly and clinically heterogeneous disease. Reinstating immunological control of AML is highly desirable to eradicate chemotherapy-resistant clones and provide long-term disease control. We recently identified bone marrow (BM) microenvironmental transcriptomic profiles that stratify patients with newly diagnosed AML into an immune-infiltrated and an immune-depleted subtype and that refine the accuracy of survival prediction beyond that afforded by current prognosticators (Vadakekolathu J et al., 2020). We have also shown that CD8 + T cells from patients with AML exhibit features of immune exhaustion and senescence (IES), including heightened expression of killer cell lectin-like receptor subfamily G member 1 (KLRG1) and B3GAT1 (encoding CD57) (Knaus H et al., 2018). Whether deranged T-cell functions affect the likelihood of responding to antitumor therapy, including immune checkpoint blockade (ICB), is an outstanding question in AML.In the current study, we analyzed 183 BM samples collected longitudinally at time of AML onset, response assessment and disease relapse from multiple cohorts of patients with AML treated with standard-of-care induction chemotherapy, and from 33 elderly AML patients with newly diagnosed or chemotherapy-refractory/relapsed AML treated with azacitidine, and the PD-1 checkpoint inhibitor pembrolizumab (NCT02845297). Primary patient specimens and associated clinical data were obtained via informed consent in accordance with the Declaration of Helsinki on research protocols approved by the Institutional Review Boards of the participating Institutions. RNA (150-200 ng) was extracted from BM aspirates and was processed on the nCounter FLEX analysis system (NanoString Technologies, Seattle, WA) using the PanCancer Immune profiling panel, as previously published (Vadakekolathu J et al., 2020). The correlation between transcriptomic features of IES, clinical characteristics, therapeutic response and patient outcome was validated using publicly available RNA-sequencing and NanoString data from 1,698 patients with AML, including samples from the TCGA-AML (n=147 cases), Beat-AML Master Trial (n=264 cases, of which 240 with survival data and 195 with chemotherapy response data) and Children's Oncology Group (COG)-TARGET AML series (n=145 cases).We initially showed that, compared with their non-senescent CD8 +CD57 -KLRG1 - counterpart, senescent CD8 +CD57 +KLRG1 + T cells are functionally impaired in terms of their ability to effect AML-blast killing mediated by an anti-CD33/CD3 bi-specific T-cell engager antibody construct (kindly provided by Amgen, USA; effector/target [E/T] ratio = 1:5). We then used gene set enrichment analysis (GSEA) to derive a transcriptomic signature of IES encompassing natural killer (NK)-cell and stem-like CD8 + T-cell markers, and showed that IES states correlate with lymphoid infiltration, adverse-risk molecular lesions (TP53 and RUNX1 mutations), experimental gene signatures of leukemia stemness (LSC17 score; Ng et al., 2016) and poor outcome in response to standard induction chemotherapy (Fig. 1A). In independent validation cohorts of children and adults with AML, the IES score was higher at baseline in patients with primary induction failure (following a standard 2 cycles of chemotherapy) compared with complete remission, increased in post-chemotherapy BM specimens, and predicted survival with greater accuracy than the ELN cytogenetic risk classifier (Fig. 1B). In the immunotherapy setting, high IES scores at baseline defined a checkpoint blockade-unresponsive AML tumor microenvironment and correlated with significantly shorter overall survival (9.1 versus 15.56 months in patients with high and low IES scores, respectively; HR = 3.32 (95% CI = 1.19-9.25); log-rank P = 0.021; Fig. 1C). Finally, the IES-related gene set also predicted for long-term outcomes and objective responses, based on RECIST criteria, to single-agent nivolumab or pembrolizumab, or combination anti-PD-1 + anti-CTLA-4, in 106 patients with melanoma (PRJEB23709 and GSE93157 series), a tumor type known to derive durable clinical benefit from ICB (Fig. 1D).Our findings encourage the pursuit of immune senescence reversal as a strategy to functionally reinvigorate T cells and could inform the delivery of ICB and other T cell-targeting immunotherapies to patients who are likely to benefit. [Display omitted] DisclosuresRadojcic: Syndax Pharmaceuticals: Research Funding; Regeneron Pharmaceuticals: Membership on an entity's Board of Directors or advisory committees; Allakos: Membership on an entity's Board of Directors or advisory committees. Minden: Astellas: Consultancy. Tasian: Aleta Biotherapeutics: Consultancy; Gilead Sciences: Research Funding; Kura Oncology: Consultancy; Incyte Corporation: Research Funding.

The LSC17 Leukemic Stem Cell Signature Predicts Outcome in Pediatric Acute Myeloid Leukemia

IntroductionThe model of the leukemic stem cell was proposed nearly 60 years ago as a mechanism of leukemic pathogenesis. Recently, a 17-gene leukemic stem cell (LSC) prognostic signature, termed the LSC17, was developed (Ng et al., 2016). The LSC17 score was shown to reliably differentiate adult acute myeloid leukemia (AML) patients into high and low risk groups. However, its applicability to pediatric AML remains unknown.Prognostic indicators are sensitive to differences in age. For example, NPM1 mutations, which are highly prognostic of favorable outcome, have no significance in patients older than 65 years (Ostronoff et al., 2015). Similarly, core-binding factor AML, also associated with favorable outcomes, loses prognostic value in older patients (Prébet et al., 2009).Given that age remained a significant prognostic factor in multivariate analyses with the LSC17, we evaluated the impact of the signature in older versus younger adults in the TCGA AML dataset and further, applied it to transcriptome sequence data from pediatric AML to assess the prognostic power in younger patients.Patients and MethodsPediatric AML biological samples were collected from patients enrolled in the Children's Oncology Group protocol AAML0531 and RNA sequencing was performed by the British Columbia Genome Sciences Center (Vancouver, BC). The Cancer Genome Atlas AML RNA sequencing data was downloaded from the Broad Institute Firehose (TCGA Network et al., 2013; Broad Institute TCGA Genome Data Analysis Center, 2016). LSC17 scores were calculated from log2 RPKM normalized gene expression as described by Ng et al .ResultsWe initially verified that the LSC17 score is effective for risk stratification in adult AML by applying the method to the TCGA AML cohort, excluding PML-RARA, BCR-ABL1 fusions, and those who received no treatment (n = 155, range: 18 - 88 yrs). Patients with low LSC17 scores had significantly better overall survival (OS) compared to the high scoring group after 5 years (low-LSC17 OS: 38.0%, high-LSC17 OS: 15.2%, hazard ratio (HR) = 2.08, p < 0.001). To investigate age effects on LSC17, we divided TCGA into two groups based on the median age of 58 yrs. The low (n = 81) and high age groups (n = 78) were reanalyzed. Younger patients with low LSC17 scores fared much better (low-LSC17 OS at 5yrs: 57.9%, high-LSC17 OS at 5yrs: 16.0% p < 0.001, Fig. 1A). Strikingly, for TCGA patients above 58 years, the LSC17 signature showed no prognostic significance (p = 0.714, Fig. 1B).We then evaluated the LSC17 signature to identify risk groups using RNA sequencing from our pediatric AML cohort (n = 446, range: 0.02 - 28 yrs). Univariate survival analysis revealed strong prognostic value for both overall survival (low-LSC17 OS at 5yrs: 72.8%, high-LSC17 OS at 5yrs: 51.4%, HR = 2.06, p < 0.001, Fig. 1C) and event-free survival (low-LSC17 EFS at 5yrs: 60.3%, high-LSC17 EFS at 5yrs: 36.4%, HR = 2.04, p < 0.001). Although dividing the cohort by LSC17 scores above and below the median (as proposed by Ng. et al.) demonstrated prognostic value in pediatric AML, the choice of median as a threshold may not be ideal. Dividing pediatric AML patients into four quartiles, we found no statistical significance between the survival curves for the middle two groups. In contrast, the top and bottom quartiles show very strong trends (LSC17-q1: 77.0% at 5 years, LCS17-q4: 41.1% OS at 5yrs, HR = 3.34, p < 0.001, Fig.1D). The high-LSC17 cohort carried a higher incidence of adverse cytogenetics and recurrent mutations, such as FLT3-ITD, and conversely, the low-scoring group was found to have a higher percentage of favorable cytogenetics (Fig.1E). To determine if the prognostic significance would remain, we performed a multivariate analysis; though WBC, age, and NMP1 mutations continued to maintain prognostic significance, we found the LSC17 score to be a strong independent prognostic indicator (HR = 2.031, p < 0.001, Fig. 1F).ConclusionIn this study, we validated that the LSC17 score can be used as an effective risk stratification tool in children and young adults. The LSC17 remains a strong independent prognostic factor after controlling for age, WBC, adverse FLT3-ITD mutations and favorable NPM1 mutations. While the LSC17 can be used as an informative indicator of long term survival, and event-free survival, we showed that this signature loses prognostic value in older AML patients. [Display omitted] DisclosuresNo relevant conflicts of interest to declare.

Simultaneous Identification and Stratification of Low Molecular Risk AML Patients Using a Single LSC17-Based Nanostring Assay at Diagnosis

Refractoriness to induction chemotherapy and relapse after achievement of remission are the main barriers to cure in acute myeloid leukemia (AML). Currently, most AML patients receive standard induction chemotherapy because risk stratification depends on cytogenetic and molecular tests that typically take weeks to return results. We recently reported a rapid 17-gene leukemia stem cell score (LSC17, Ng et al., Nature 2016) that can accurately determine risk within 24 to 48 hours of diagnosis. Patients with high LSC17 scores have poor outcomes with current treatments and could be enrolled in clinical trials evaluating novel upfront treatment strategies.A small subset of AML patients with normal cytogenetics, NPM1 mutation and no FLT3-ITD are classified as low molecular risk (LMR) and are considered to have better outcomes, but ~35% relapse within 2 years following conventional therapy. In our prior study, we derived an optimized, re-weighted sub-score for LMR patients in which only 3 of the 17 genes contribute to the calculated score (LSC3). A high LSC3 score identifies LMR patients who have worse outcomes following standard induction therapy, and who might benefit from novel frontline therapy. Currently however, the LSC3 score can only be employed for risk-adapted therapy decisions in the post-remission setting, as LMR patients are only identified following cytogenetic and molecular testing. Earlier identification of high-risk LMR patients will facilitate clinical trials of novel frontline therapy in this patient subset.To determine whether a subset of the LSC17 signature genes could be used to identify LMR cases at diagnosis, we applied sparse binomial regression to relate the expression of the 17 signature genes to LMR vs. non-LMR cases in a large training cohort comprising AML patients of all subtypes (GSE6891, n=495 patients including 48 [9%] LMR cases). The resulting 13-gene sub-score (LMR13) can accurately identify LMR patients in 6 large independent validation cohorts spanning n=1559 cytogenetically and molecularly diverse AML patients using gene expression data from several different platforms (Figure 1; microarray datasets: GSE37642, n=542, area under the receiver operating characteristic curve (AUROC)=0.88; GSE15434, n=251, AUROC=0.92; TCGA AML, n=183, AUROC=0.92; ALFA-0701, n=192, AUROC=0.89; RNA-Seq dataset: TCGA AML, n=169, AUROC=0.92; NanoString datasets: GSE76004, n=237, AUROC=0.96; PM AML, n=154, AUROC=0.86). Importantly, the LMR13 score for each patient can be calculated from the same NanoString assay used to determine the patient's LSC17 and LSC3 risk scores. The ability to identify LMR cases rapidly at diagnosis enhances the clinical utility of the LSC17 assay, as it will allow clinicians to use the most appropriate risk score (LSC3 vs LSC17) for all newly-diagnosed AML patients, and enable evaluation of novel upfront treatment strategies for higher-risk LMR cases. Furthermore, integration of our functionally defined stem cell gene list with patient survival data using statistical learning approaches provides a basis for ongoing characterization of the molecular pathways associated with clinical outcomes, irrespective of mutational profile. [Display omitted] DisclosuresNo relevant conflicts of interest to declare.

A KDM4A-PAF1-mediated epigenomic network is essential for acute myeloid leukemia cell self-renewal and survival

Epigenomic dysregulation is a common pathological feature in human hematological malignancies. H3K9me3 emerges as an important epigenomic marker in acute myeloid leukemia (AML). Its associated methyltransferases, such as SETDB1, suppress AML leukemogenesis, whilst H3K9me3 demethylases KDM4C is required for mixed-lineage leukemia rearranged AML. However, the specific role and molecular mechanism of action of another member of the KDM4 family, KDM4A has not previously been clearly defined. In this study, we delineated and functionally validated the epigenomic network regulated by KDM4A. We show that selective loss of KDM4A is sufficient to induce apoptosis in a broad spectrum of human AML cells. This detrimental phenotype results from a global accumulation of H3K9me3 and H3K27me3 at KDM4A targeted genomic loci thereby causing downregulation of a KDM4A-PAF1 controlled transcriptional program essential for leukemogenesis, distinct from that of KDM4C. From this regulatory network, we further extracted a KDM4A-9 gene signature enriched with leukemia stem cell activity; the KDM4A-9 score alone or in combination with the known LSC17 score, effectively stratifies high-risk AML patients. Together, these results establish the essential and unique role of KDM4A for AML self-renewal and survival, supporting further investigation of KDM4A and its targets as a potential therapeutic vulnerability in AML.