scholarly journals Genetic Characterization and Risk Stratification of Acute Myeloid Leukemia

2020 ◽  
Vol Volume 12 ◽  
pp. 2231-2253 ◽  
Author(s):  
Fatemeh Pourrajab ◽  
Mohamad Reza Zare-Khormizi ◽  
Azam Sadat Hashemi ◽  
Seyedhossein Hekmatimoghaddam
Keyword(s):  
Acute Myeloid Leukemia ◽  
Risk Stratification ◽  
Myeloid Leukemia ◽  
Genetic Characterization ◽  
Acute Myeloid

scholarly journals Clinical Impact of an Accurate Genetic Characterization of Older Acute Myeloid Leukemia Patients: A Report from the Northern Italy Leukemia Group (NILG) Randomized Trial 02/06

Blood ◽  
2018 ◽  
Vol 132 (Supplement 1) ◽  
pp. 434-434
Author(s):  
Chiara Caprioli ◽  
Tamara Intermesoli ◽  
Orietta Spinelli ◽  
Silvia Salmoiraghi ◽  
Pamela Zanghì ◽  
...  
Keyword(s):  
Acute Myeloid Leukemia ◽  
High Risk ◽  
Risk Stratification ◽  
Myeloid Leukemia ◽  
Genetic Characterization ◽  
Remission Induction ◽  
High Dose ◽  
Cell Transplant ◽  
High Dose Cytarabine ◽  
Acute Myeloid

Abstract Introduction In acute myeloid leukemia (AML) older age is independently associated with poor outcome, due to patient- and disease-related factors. Different genetic profiles characterize AML patients and their frequency varies according to age. Their identification can improve early risk stratification to select the most appropriate therapy, including alternative, not chemotherapy based, treatment modalities, such as hypomethylating and targeted agents (Döhner H et al., Blood 2017). We analyzed the clinical outcome of AML patients aged ≥60 years who were enrolled in the randomized multicentric trial NILG 02/06, and were deeply genetically characterized (Clinical Trials.gov Identifier: NCT00495287). Patients and Methods Five hundred seventy-four newly diagnosed AML patients were enrolled into the study and 168 were aged ≥60 years; all patients were randomized to receive conventional induction chemotherapy with idarubicin, cytarabine and etoposide (ICE) or sequential high-dose cytarabine and idarubicin (sHD), followed by consolidation courses with high dose cytarabine (Bassan R et al., annual congress EHA. Jun 9, 2016, abstr S485). Genetic characterization at diagnosis was obtained by conventional cytogenetics and RT-PCR for 145 and 168 patients, respectively, while Next Generation Sequencing was performed for 51 patients with normal karyotype. Patients were re-classified as per the 2017 European Leukemia Net (ELN) guidelines (Döhner H et al., Blood 2017). A myelodysplastic/myeloproliferative (MDS/MPN) related genetic signature was defined according to cytogenetic WHO criteria and/or molecular abnormalities known to be associated with MDS/MPN (Bullinger L et al., J Clin Oncol 2017) and used for outcome correlation. Results The characteristics of patients are summarized in Table 1. According to the ELN risk stratification, patients were classified as favorable, intermediate or adverse risk (23%, 38% and 39% of patients, respectively). A genetic MDS/MPN signature was demonstrated in 42% of patients (63/149), which was a higher proportion compared to that of patients with a clinical diagnosis of an antecedent MDS/MPN (19% of patients, 32/168). No significant difference was observed between the induction regimens regarding the achievement of complete remission (CR) (71% for sHD and 61% for ICE, P=0.23) and early death rate (12% and 10.6%, P=0.96). After achieving CR, a median of 2 consolidation courses was administered (range 1-5) within both treatment arms. A limited proportion of patients with high-risk genetic or clinical features (14%) had the opportunity to undergo an allogeneic hematopoietic stem cell transplant (alloHSCT), the majority of them (63%) receiving a reduced intensity conditioning. By intention to treat, 5-years overall survival (OS) and disease- free survival (DFS) on the whole study population were 29% and 32% respectively, without significant differences between the remission induction treatment (for sHD and ICE, OS: 29% and 28%, P=0.88; DFS: 34% and 29%, P=0.90). According to the ELN risk stratification, 5-years OS was 68%, 25% and 7% for favorable, intermediate and adverse groups (P<0.0001), while 3-years DFS was 73%, 28% and 13% (P<0.0001) (Figure 1A). According to the presence or absence of a MDS/MPN signature at diagnosis, 5-years OS was 11% vs 41% (P=0.0001) while 3-years DFS was 12% vs 49% (P<0.0001) (Figure 1B). AlloHSCT was associated with a significant benefit in terms of 5-years OS (57% vs 25%, P=0.0162) and DFS (53% vs 26%, P=0.0363) (Figure 1C). As expected, age had also an impact, with patients aged 60-64 years performing better than patients aged ≥65 years (5-years OS 38% vs 13%, P=0.003; 5-years DFS 43% vs 10%, P=0.002). Conclusions Older AML patients with favorable risk features according to ELN benefit from standard chemotherapy. The definition of an adverse genetic risk profile and particularly of a MDS/MPN signature is crucial to identify patients who have a very dismal outcome. These patients should be considered for alternative, innovative treatment options. In high-risk, ≥60 years old AML patients with a good performance status, alloHSCT significantly improves both OS and DFS and should always be considered as the most effective post consolidation treatment. Disclosures Cattaneo: GILEAD: Other: Advisory Board. Cortelezzi:janssen: Consultancy; novartis: Consultancy; abbvie: Consultancy; roche: Consultancy. Rambaldi:Italfarmaco: Consultancy; Omeros: Consultancy; Roche: Consultancy; Amgen Inc.: Consultancy; Novartis: Consultancy; Pfizer: Consultancy; Celgene: Consultancy.

Genomics of Acute Myeloid Leukemia Diagnosis and Pathways

2017 ◽  
Vol 35 (9) ◽  
pp. 934-946 ◽  
Author(s):  
Lars Bullinger ◽  
Konstanze Döhner ◽  
Hartmut Döhner
Keyword(s):  
Acute Myeloid Leukemia ◽  
Risk Stratification ◽  
Myeloid Leukemia ◽  
Clinical Care ◽  
Clonal Expansion ◽  
Disease Classification ◽  
Disease Evolution ◽  
Myeloid Neoplasms ◽  
Increased Risk ◽  
Acute Myeloid

In recent years, our understanding of the molecular pathogenesis of myeloid neoplasms, including acute myeloid leukemia (AML), has been greatly advanced by genomics discovery studies that use novel high-throughput sequencing techniques. AML, similar to most other cancers, is characterized by multiple somatically acquired mutations that affect genes of different functional categories, a complex clonal architecture, and disease evolution over time. Patterns of mutations seem to follow specific and temporally ordered trajectories. Mutations in genes encoding epigenetic modifiers, such as DNMT3A, ASXL1, TET2, IDH1, and IDH2, are commonly acquired early and are present in the founding clone. The same genes are frequently found to be mutated in elderly individuals along with clonal expansion of hematopoiesis that confers an increased risk for the development of hematologic cancers. Furthermore, such mutations may persist after therapy, lead to clonal expansion during hematologic remission, and eventually lead to relapsed disease. In contrast, mutations involving NPM1 or signaling molecules (eg, FLT3, RAS) typically are secondary events that occur later during leukemogenesis. Genetic data are now being used to inform disease classification, risk stratification, and clinical care of patients. Two new provisional entities, AML with mutated RUNX1 and AML with BCR- ABL1, have been included in the current update of the WHO classification of myeloid neoplasms and AML, and mutations in three genes— RUNX1, ASXL1, and TP53—have been added in the risk stratification of the 2017 European LeukemiaNet recommendations for AML. Integrated evaluation of baseline genetics and assessment of minimal residual disease are expected to further improve risk stratification and selection of postremission therapy. Finally, the identification of disease alleles will guide the development and use of novel molecularly targeted therapies.

Analysis of Efficacy and Prognostic Factors of CLAG Treatment in Chinese Patients with Refractory or Relapsed Acute Myeloid Leukemia

2018 ◽  
Vol 141 (1) ◽  
pp. 43-53 ◽  
Author(s):  
Li Wang ◽  
Jun Xu ◽  
Xiaolong Tian ◽  
Tingting Lv ◽  
Guolin Yuan
Keyword(s):  
Acute Myeloid Leukemia ◽  
High Risk ◽  
Risk Stratification ◽  
Salvage Therapy ◽  
Myeloid Leukemia ◽  
Median Overall Survival ◽  
Remission Rate ◽  
Chinese Patients ◽  
Acute Myeloid

Background/Aims: The aim of this work was to investigate the efficacy and predictive factors of CLAG treatment in refractory or relapsed (R/R) acute myeloid leukemia (AML) patients. Methods: Sixty-seven R/R AML patients were enrolled in this prospective cohort study and treated by a CLAG regimen: 5 mg/m2/day cladribine (days 1–5), 2 g/m2/day cytarabine (days 1–5), and 300 μg/day filgrastim (days 0–5). The median follow-up duration was 10 months. Results: A total of 57 out of 67 patients were evaluable for remission after CLAG therapy, of whom 57.9% achieved a complete remission (CR) and the overall remission rate was 77.2%. The median overall survival (OS) was 10.0 months, with a 1-year OS of 40.3 ± 6.0% and 3-year OS of 16.7 ± 5.7%. CR at first induction after the initial diagnosis was associated with a favorable CR. Age above 60 years, high risk stratification, second or higher salvage therapy, and bone marrow (BM) blasts ≥42.1% were correlated with an unfavorable CR. Secondary disease, age ≥60 years, high risk stratification, and second or higher salvage therapy were associated with worse OS. Patients developed thrombocytopenia (41, 61%), febrile neutropenia (37, 55%), leukopenia (33, 49%), neutropenia (18, 27%), and anemia (9, 13%). Conclusion: CLAG was effective and well tolerated for R/R AML. BM blasts ≥42.1%, age ≥60 years, high risk stratification, and second or higher salvage therapy were independent factors for a poor prognosis.

scholarly journals Prospective Identification of Acute Myeloid Leukemia Patients Who Benefit from Gene-Expression Based Risk Stratification

Blood ◽  
2019 ◽  
Vol 134 (Supplement_1) ◽  
pp. 1397-1397
Author(s):  
Diego Chacon ◽  
Ali Braytee ◽  
Yizhou Huang ◽  
Julie Thoms ◽  
Shruthi Subramanian ◽  
...  
Keyword(s):  
Gene Expression ◽  
Acute Myeloid Leukemia ◽  
Risk Stratification ◽  
Genetic Risk ◽  
Myeloid Leukemia ◽  
Research Funding ◽  
Risk Groups ◽  
High Fidelity ◽  
Improve Outcome ◽  
Acute Myeloid

Background: Acute myeloid leukemia (AML) is a highly heterogeneous malignancy and risk stratification based on genetic and clinical variables is standard practice. However, current models incorporating these factors accurately predict clinical outcomes for only 64-80% of patients and fail to provide clear treatment guidelines for patients with intermediate genetic risk. A plethora of prognostic gene expression signatures (PGES) have been proposed to improve outcome predictions but none of these have entered routine clinical practice and their role remains uncertain. Methods: To clarify clinical utility, we performed a systematic evaluation of eight highly-cited PGES i.e. Marcucci-7, Ng-17, Li-24, Herold-29, Eppert-LSCR-48, Metzeler-86, Eppert-HSCR-105, and Bullinger-133. We investigated their constituent genes, methodological frameworks and prognostic performance in four cohorts of non-FAB M3 AML patients (n= 1175). All patients received intensive anthracycline and cytarabine based chemotherapy and were part of studies conducted in the United States of America (TCGA), the Netherlands (HOVON) and Germany (AMLCG). Results: There was a minimal overlap of individual genes and component pathways between different PGES and their performance was inconsistent when applied across different patient cohorts. Concerningly, different PGES often assigned the same patient into opposing adverse- or favorable- risk groups (Figure 1A: Rand index analysis; RI=1 if all patients were assigned to equal risk groups and RI =0 if all patients were assigned to different risk groups). Differences in the underlying methodological framework of different PGES and the molecular heterogeneity between AMLs contributed to these low-fidelity risk assignments. However, all PGES consistently assigned a significant subset of patients into the same adverse- or favorable-risk groups (40%-70%; Figure 1B: Principal component analysis of the gene components from the eight tested PGES). These patients shared intrinsic and measurable transcriptome characteristics (Figure 1C: Hierarchical cluster analysis of the differentially expressed genes) and could be prospectively identified using a high-fidelity prediction algorithm (FPA). In the training set (i.e. from the HOVON), the FPA achieved an accuracy of ~80% (10-fold cross-validation) and an AUC of 0.79 (receiver-operating characteristics). High-fidelity patients were dichotomized into adverse- or favorable- risk groups with significant differences in overall survival (OS) by all eight PGES (Figure 1D) and low-fidelity patients by two of the eight PGES (Figure 1E). In the three independent test sets (i.e. form the TCGA and AMLCG), patients with predicted high-fidelity were consistently dichotomized into the same adverse- or favorable- risk groups with significant differences in OS by all eight PGES. However, in-line with our previous analysis, patients with predicted low-fidelity were dichotomized into opposing adverse- or favorable- risk groups by the eight tested PGES. Conclusion: With appropriate patient selection, existing PGES improve outcome predictions and could guide treatment recommendations for patients without accurate genetic risk predictions (~18-25%) and for those with intermediate genetic risk (~32-35%). Figure 1 Disclosures Hiddemann: Celgene: Consultancy, Honoraria; Roche: Consultancy, Honoraria, Research Funding; Bayer: Research Funding; Vector Therapeutics: Consultancy, Honoraria; Gilead: Consultancy, Honoraria; Janssen: Consultancy, Honoraria, Research Funding. Metzeler:Celgene: Honoraria, Research Funding; Otsuka: Honoraria; Daiichi Sankyo: Honoraria. Pimanda:Celgene: Honoraria, Membership on an entity's Board of Directors or advisory committees, Research Funding. Beck:Gilead: Research Funding.

scholarly journals BCOR Mutations in Acute Myeloid Leukemia: Clonal Evolution and Prognosis

Blood ◽  
2020 ◽  
Vol 136 (Supplement 1) ◽  
pp. 4-4
Author(s):  
Ashley Zhang ◽  
Yuntao Liu ◽  
Shuning Wei ◽  
Benfa Gong ◽  
Chunlin Zhou ◽  
...  
Keyword(s):  
Bone Marrow ◽  
Acute Myeloid Leukemia ◽  
Risk Stratification ◽  
Myeloid Leukemia ◽  
Statistical Difference ◽  
Tp53 Mutation ◽  
Clonal Evolution ◽  
Normal Karyotype ◽  
Abnormal Karyotype ◽  
Acute Myeloid

Background BCOR gene is a transcription repressor that may influence normal hematopoiesis and is associated with poor prognosis in acute myeloid leukemia (AML) with normal karyotype. However, due to the rare mutation frequency in AML (3.8%-5%), clinical characteristics and prognosis of AML patients with BCOR mutation including abnormal karyotype are still unknown. In addition, the clonal evolution of AML patients with BCOR mutation has not been fully investigated. Methods By means of next generation of sequencing, we performed sequencing of 114 genes related to hematological diseases including BCOR on 509 newly diagnosed AML patients (except for acute promyelocytic leukemia) from March 2017 to April 2019. The 2017 European Leukemia Net (ELN) genetic risk stratification was used to evaluate prognosis. Overall survival (OS) was defined as the time from diagnosis to death or last follow-up. Relapse-free survival (RFS) was measured from remission to relapse or death. Clonal evolution was investigated through analyzing bone marrow samples at diagnosis, complete remission (CR) and relapse from the same patient. Result Among 509 AML patients, we found BCOR mutations in 23 patients (4.5%). BCOR mutations were enriched in patients with mutations of RUNX1 (p = 0.008) and BCORL1 (p = 0.0003). Patients with BCOR mutation were more at adverse ELN risk category compared to patients without BCOR mutation (p = 0.007). Besides, there was a larger proportion of patients with normal karyotype in BCOR mutation group but it had not reached statistical difference (62.5% vs 45.5%, p = 0.064). The abnormal karyotype in patients with BCOR mutations included trisomy 8, t(9;11), inv(3), -7 and complex karyotype.There were no significant differences in age, sex, white blood cell count, hemoglobin or platelet count between the two groups. More patients died during induction (13.0% vs 3.5%, p = 0.56) and fewer patients achieved CR after 2 cycles of chemotherapy when patients had BCOR mutations (69.6% vs 82.5%, p = 0.115) but the difference had not reached statistical difference . Patients with BCOR mutations had inferior 2-year OS (52.1% vs 70.7%, p = 0.0094) and 2-year RFS (29.8% vs 61.1%, p = 0.0090). After adjustment for ELN risk stratification, BCOR mutation was still remain a poor prognostic factor. However, the adverse prognostic impact of BCOR mutation is overcome by hematopoietic stem cell transplantation (HSCT), in which there was no difference between BCOR mutation group and wild type group (p = 0.474) (Figure 1). Through analysis of paired bone marrow sample at diagnosis, remission and relapse, we revealed the clonal evolution that BCOR mutation was only detected at diagnosis sample as a subclone and diminished at CR and relapse while TP53 mutation was only detected at relapse with a variant allele frequency (VAF) of 25.5%. We also found BCOR mutation at another patient's diagnosis and relapse sample while TP53 mutation was detected at relapse with VAF of 11.8%. Conclusion BCOR is associated with RUNX1 mutation and higher ELN risk. AML patients with BCOR mutation including normal and abnormal karyotype conferred a worse impact on OS that can be overcome by HSCT. BCOR mutation is a subclone at diagnosis or relapse in some patients, in which TP53 mutation clone occurred at relapse. Disclosures No relevant conflicts of interest to declare.

The stem cell-associated gene expression signature allows risk stratification in pediatric acute myeloid leukemia

Leukemia ◽  
2018 ◽  
Vol 33 (2) ◽  
pp. 348-357 ◽  
Author(s):  
Nicolas Duployez ◽  
Alice Marceau-Renaut ◽  
Céline Villenet ◽  
Arnaud Petit ◽  
Alexandra Rousseau ◽  
...  
Keyword(s):  
Gene Expression ◽  
Acute Myeloid Leukemia ◽  
Stem Cell ◽  
Risk Stratification ◽  
Myeloid Leukemia ◽  
Gene Expression Signature ◽  
Pediatric Acute Myeloid Leukemia ◽  
Expression Signature ◽  
Acute Myeloid
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