scholarly journals A Predictor Combining Clinical and Genetic Factors for AML1-ETO Leukemia Patients

2022 ◽  
Vol 11 ◽  
Author(s):  
Min Yang ◽  
Bide Zhao ◽  
Jinghan Wang ◽  
Yi Zhang ◽  
Chao Hu ◽  
...  

Core Binding Factor (CBF)-AML is one of the most common somatic mutations in acute myeloid leukemia (AML). t(8;21)/AML1-ETO-positive acute myeloid leukemia accounts for 5-10% of all AMLs. In this study, we consecutively included 254 AML1-ETO patients diagnosed and treated at our institute from December 2009 to March 2020, and evaluated molecular mutations by 185-gene NGS platform to explore genetic co-occurrences with clinical outcomes. Our results showed that high KIT VAF(≥15%) correlated with shortened overall survival compared to other cases with no KIT mutation (3-year OS rate 26.6% vs 59.0% vs 69.6%, HR 1.50, 95%CI 0.78-2.89, P=0.0005). However, no difference was found in patients’ OS whether they have KIT mutation in two or three sites. Additionally, we constructed a risk model by combining clinical and molecular factors; this model was validated in other independent cohorts. In summary, our study showed that c-kit other than any other mutations would influence the OS in AML1-ETO patients. A proposed predictor combining both clinical and genetic factors is applicable to prognostic prediction in AML1-ETO patients.

Blood ◽  
2006 ◽  
Vol 108 (11) ◽  
pp. 2307-2307
Author(s):  
Der-Cherng Liang ◽  
Lee-Yung Shih ◽  
Chein-Fuang Huang ◽  
Ya-Tzu Chang ◽  
Huei-Ying Li ◽  
...  

Abstract C-KIT is a member of the type III receptor tyrosine kinase family and plays a crucial role in normal hematopoiesis and acute myeloid leukemia (AML). C-KIT mutations have been described in core-binding factor (CBF) AML at initial diagnosis. The role of C-KIT mutations in the relapse of CBF AML is not clear. In the present study, we analyzed C-KIT mutations on paired diagnosis and relapse samples in CBF AML. Among 1014 adults and 162 children with AML, CBF AML was detected in 11.4% of adults and 25.3% of children. Mutational analysis of C-KIT was performed by direct sequencing for all cDNA PCR products amplified with 5 overlapping primer pairs, which cover the whole coding sequences of C-KIT gene from exon 1 through exon 21. In AML with t(8;21)/AML1-ETO, 33.0 % (29/88) of adults and 44.4 % (12/27) of children had C-KIT mutations. In AML with inv(16)/CBFβ-MYH11, 22.2 % (6/27) of adults and 38.5 %(5/13) of children had C-KIT mutations. Taken together, C-KIT mutations were present in 30.4 % (35/115) of adults and 42.5 % (17/40) of children with CBF AML. Forty-two patients with CBF AML relapsed. Twenty-two(18 adults and 4 children) of the 23 patients with CBF AML and C-KIT(+) at diagnosis had relapse samples available for comparative analysis. All the 22 patients relapsed with C-KIT mutations, 21 of them showed the identical C-KIT mutation patterns as those at diagnosis. Of the 20 relapsed patients with t(8;21)/AML1-ETO and C-KIT(+), 3 had mutations in exon 8: T417_D419delinsY, Y418_D419delinsA, and [Y418N;Y418_D419insFF], respectively; one had mutation in exon 9: I478V; another one had mutation in exon 11: [D572_P573insL; E561_D572dup]; 14 had mutations in exon 17: 5 D816V, 3 N822K, 3 D816Y, and one each with D816H, D820G, and D820Y; the remaining one patient relapsed twice, the patterns of C-KIT mutations changed but remained in exon 17: D816A at diagnosis, D816V at the first relapse, and N822K at the second relapse. Genotyping analysis with 15 loci of short tandem repeats at 13 different chromosomes showed identity for the diagnosis and the two relapse samples. Of the 2 adults with inv(16)/CBFβ-MYH11 and C-KIT(+) who relapsed, both had mutations in exon 17: N822K and D816Y, respectively. C-KIT mutations were absent in all of the 35 complete remission samples examined. In those with CBF AML and C-KIT(−) at diagnosis, 19 patients including 16 adults and 3 children relapsed; C-KIT mutations were not present in all the relapse samples except one who acquired D816H mutation. The present study showed that all patients with de novo CBF AML harboring C-KIT mutations at diagnosis retained the mutations at relapse, indicating that C-KIT mutations play a crucial role in the leukemogenesis in a substantial proportion of patients with CBF AML.


Blood ◽  
2019 ◽  
Vol 134 (Supplement_1) ◽  
pp. 2710-2710
Author(s):  
Nikhil Patkar ◽  
Chinmayee Kakirde ◽  
Anam Fatima Shaikh ◽  
Shrinidhi Nathany ◽  
Gaurav Chatterjee ◽  
...  

Introduction: Core binding factor acute myeloid leukemia (CBF-AML) is one of the commonest subtypes of AML characterized presence of t(8;21)(q22;q22) or inv(16)(p13q22)/t(16;16)(p13;q22). It is characterised by a high frequency of somatic mutations especially in RAS and tyrosine kinase signalling pathways. Here we investigated the feasibility of improving risk prediction of CBF-AML using machine learning algorithms. Methods: We developed a next generation sequencing panel that targeted 50 genes implicated in the pathogenesis of myeloid malignancies using single molecule molecular inversion probes. This panel was used to sequence 106 patients of CBF-AML accrued over a six year period (March 2012 - December 2018) treated with conventional "3 + 7" chemotherapy. Post data analysis, we devised a supervised machine learning (ML) approach for identification of mutations most likely to predict for favorable outcome in CBF-AML. We included somatic mutations in genes occurring in CBF-AML at a frequency of >5%. A total of 11 variables were included for feature selection to predict for favorable outcome (including mutations in ASXL2, CSF3R,FLT3, KIT, NF1, NRAS, RAD21, TET2 and WT1 genes as well as mutation burden). Approaches for supervised ML were naïve bayes, generalized linear model, logistic regression, deep learning and random forest methods. Based on the ML results top 6 selected variables were allotted an individual score. A final score for that case was devised as a sum total of the individual scores. These sum were used to generate a genetic risk for a patient. Overall survival (OS) was calculated from date of diagnosis to time of last follow up or death. Relapse free survival (RFS) was calculated from date of CR till time to relapse or death or last follow up if in CR. Results of the genetic risk were analyzed for their impact on OS and RFS using log rank test. Multivariate analysis was performed using cox proportional hazards regression model. Results: The median follow up of the cohort was 27.6 months. A total of 181 somatic mutations were identified in this subset of AML with 86.7% harbouring at least one somatic mutation (median = 2). Based on ML data, a genetic score was formulated that incorporated mutations in RAD21, FLT3, KIT D816, ASXL2, NRAS genes as well as high mutation burden (≥2) into two genetic risk classes (favorable risk and poor ML derived genetic genetic risk). Patients classified as poor genetic risk had a significantly lower OS [median OS: 34.8 months; 95% confidence interval (CI) (14.2-34.8); p=0.0086] and RFS [median RFS: 17.9 months; 95%CI (12.7-33.6); p=0.0043] as compared to patients with favorable genetic risk (median OS and RFS not reached). These results can be seen in Figure 1. On multivariate analysis poor genetic risk was the most important independent risk factor that predicted for inferior OS [hazard ratio(HR), 2.7; 95% CI 1.3 to 5.7] and RFS (HR, 2.6; 95% CI:1.3 to 5.1). Conclusions In a proof of concept, we describe a novel ML derived genomics scoring model that provides a mechanism to risk stratify CBF-AML, a seemingly homogeneous disease entity. This study, to the best of our knowledge represents a novel application of ML to CBF mutated AML. Our data indicates that this scoring system will be useful in identifying CBF mutated AML patients who are at higher risk of relapse and distinguishes them from patients who are truly good risk. Figure 1 Disclosures No relevant conflicts of interest to declare.


2018 ◽  
Vol 97 (6) ◽  
pp. 955-965 ◽  
Author(s):  
Silvia Park ◽  
Hangseok Choi ◽  
Hee Je Kim ◽  
Jae-Sook Ahn ◽  
Hyeoung-Joon Kim ◽  
...  

2016 ◽  
Vol 35 (4) ◽  
pp. 810-813 ◽  
Author(s):  
Uday Deotare ◽  
Marwan Shaheen ◽  
Joseph M. Brandwein ◽  
Bethany Pitcher ◽  
Suzanne Kamel-Reid ◽  
...  

2017 ◽  
Vol 92 (9) ◽  
pp. 845-850 ◽  
Author(s):  
Brittany Knick Ragon ◽  
Naval Daver ◽  
Guillermo Garcia-Manero ◽  
Farhad Ravandi ◽  
Jorge Cortes ◽  
...  

2014 ◽  
Vol 38 (7) ◽  
pp. 773-780 ◽  
Author(s):  
Andrew M. Brunner ◽  
Traci M. Blonquist ◽  
Hossein Sadrzadeh ◽  
Ashley M. Perry ◽  
Eyal C. Attar ◽  
...  

Leukemia ◽  
2018 ◽  
Vol 32 (7) ◽  
pp. 1621-1630 ◽  
Author(s):  
Peter Paschka ◽  
Richard F Schlenk ◽  
Daniela Weber ◽  
Axel Benner ◽  
Lars Bullinger ◽  
...  

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