scholarly journals Whole Genome Sequencing Provides Efficient and Comprehensive Genetic Risk Stratification in Acute Myeloid Leukemia and Myelodysplastic Syndrome

2021 ◽  
Vol 18 (4) ◽  
Author(s):  
Robert P. Hasserjian
Keyword(s):  
Acute Myeloid Leukemia ◽  
Risk Stratification ◽  
Myelodysplastic Syndrome ◽  
Whole Genome Sequencing ◽  
Genome Sequencing ◽  
Genetic Risk ◽  
Myeloid Leukemia ◽  
Whole Genome ◽  
Acute Myeloid

scholarly journals A Prospective Analysis of the Genomic Landscape of Patients with Acute Myeloid Leukemia and Its Impact on Clinical Outcomes - Data from a Tertiary Care Center in India

Blood ◽  
2019 ◽  
Vol 134 (Supplement_1) ◽  
pp. 5163-5163
Author(s):  
Hamza Yusuf Dalal ◽  
Sharat Damodar ◽  
Vidya Harini Veldore ◽  
Coral Miriam K ◽  
Shilpa Prabhu ◽  
...  
Keyword(s):  
Risk Stratification ◽  
Whole Genome Sequencing ◽  
Clinical Outcomes ◽  
Genome Sequencing ◽  
Myeloid Leukemia ◽  
Driver Mutations ◽  
Whole Genome ◽  
Intermediate Risk ◽  
Genomic Landscape ◽  
Acute Myeloid

BACKGROUND : Clinical Phenotype and outcomes of patients with Acute myeloid leukemia (AML) in the Indian subcontinent differs from published literature. A younger age at diagnosis and higher induction mortality complicate AML management in India(1). Metaphase Karyotyping represents the backbone of prognostication and risk stratification in AML. Optimal treatment strategies for the cohort of Cytogenetically normal AML are still under evaluation. Applications of Next generation Sequencing (NGS) techniques in AML have unravelled the genetic heterogeneity of this disease. Whole genome sequencing has identified many novel mutations leading to tremendous improvements in diagnosis and risk stratification. Development of therapies targeting these genetic alterations is enabling a gradual shift from non-specific approaches to personalised therapy tailored to an individual patient's genome. This will undoubtedly translate to better clinical outcomes for this disease, with otherwise poor prognosis. Whole genome sequencing is still in a nascent stage in Indian settings with no published literature on genomics in AML till date. We aimed to study the genomic landscape of AML in the Indian population and to co-relate this with clinical outcomes over the course of 1 year. METHODS: We recruited 34 newly diagnosed patients with AML who presented to our Centre (Mazumdar Shaw Medical Centre, Narayana Health City, Bangalore, India) between November 2017 and May 2018. Clinical and laboratory details of all patients were recorded. Bone marrow and paired peripheral blood samples were drawn before initiating therapy. Whole genome sequencing and Exome capture was done for each sample using Ilumina HiSeq platform. Patients were risk stratified as per ELN 2017 and treated as per NCCN guidelines. Patients were followed up prospectively for one year from initial diagnosis. Genetic results were stratified according to gene function and analysed with respect to predefined clinical outcomes (remission status post induction, relapse rates, progression free and overall survival). RESULTS: Amongst the 34 study participants, 5 patients failed QC during sequencing and were de-recruited. Hence 29 patients were available for final analysis. Median age of patients was 42 years with 13 patients (44.8%) less than 40 years of age.18 patients (60%) had normal cytogenetics at baseline.17 patients (58%) were classified as intermediate risk and 6 patients each as Standard and high risk, as per ELN 2017. 22 patients (79.3%) patients received standard Induction chemotherapy (3+7 regimen) while 6 patients received hypomethylating agents. Overall CR rate following induction at Day 28 was 50% and Induction mortality was 21.42%. 6 patients underwent an Allogenic Stem cell transplant. A total of 96 mutations (47 driver and 49 VUS mutations) in 123 genes were identified. The average number of Driver mutations was 1.48 per patient. IDH genes were the most frequently mutated Driver genes followed by FLT3 mutations. Frequency of NPM1 mutations was significantly low (17.25%). Highest frequency of VUS mutations was seen in the ETV6, ATM and CBLC genes. Highest frequency of somatic mutations were identified in the genes encoding for myeloid transcription factors and DNA methylation. Average driver mutations showed significant co-relation to Age (> 60 years) and high burden of Bone marrow blasts (>30%). An updated risk stratification incorporating mutation analysis findings resulted in re-stratification of 8 intermediate risk patients into high risk. 2 patients with detectable FLT3 ITD mutation by NGS were negative by PCR. Choice of consolidation therapy and Driver mutation status were found to show statistically significant association with both Event free survival and Overall survival at 1 year. Increased driver mutation burden was associated with increased refractoriness to chemotherapy and poor EFS and OS. Mutations in Tumour suppressor genes, were associated with suboptimal treatment outcomes and poor survival. CONCLUSIONS Genomic landscape of AML in Indian patients shows significant differences from published literature. This may hold clues to the differing biological characteristics of AML seen in this population. Genome based risk stratification and tailored therapy needs to be adapted into the management of AML. This data provides valuable insights into developing therapeutic strategies for Indian patients. Disclosures No relevant conflicts of interest to declare.

Whole Genome Sequencing of Therapy-Related Acute Myeloid Leukemia

Blood ◽  
2012 ◽  
Vol 120 (21) ◽  
pp. 784-784
Author(s):  
Giridharan Ramsingh ◽  
Dong Shen ◽  
Tamara Lamprecht ◽  
Sharon Heath ◽  
Robert S. Fulton ◽  
...  
Keyword(s):  
Acute Myeloid Leukemia ◽  
Whole Genome Sequencing ◽  
Genome Sequencing ◽  
Myeloid Leukemia ◽  
Somatic Mutations ◽  
De Novo ◽  
Whole Genome ◽  
Hematopoietic Stem ◽  
De Novo Aml ◽  
Acute Myeloid

Abstract Abstract 784 Whole Genome Sequencing of Therapy-Related Acute Myeloid Leukemia Giridharan Ramsingh, Dong Shen, Tamara L. Lamprecht, Sharon E. Heath, Robert S. Fulton, Elaine Mardis, Li Ding, Peter Westervelt, John Welch, Matthew J. Walter, Timothy A. Graubert, John F. DiPersio, Timothy J. Ley, Richard K. Wilson, and Daniel C. Link. Therapy related therapy-related acute myeloid leukemia (t-AML) accounts for 10–20% of all new cases of AML, and its incidence is rising. A fundamental difference in the pathogenesis of de novo AML and t-AML is prior treatment with chemotherapy and/or radiotherapy. The exposure of hematopoietic stem/progenitors cells (HSPCs) to this genotoxic stress is hypothesized to alter the number and spectrum of mutations that arise in t-AML. Moreover, the genotoxic stress may exert selective pressure to expand those HSPC clones that are inherently resistant to chemotherapy, a common feature in t-AML. To test these hypotheses, we sequenced the genomes of 23 cases of t-AML and compared them to the genomes of 24 cases of de novo AML, which we recently reported (Welch et al., Cell, July 2012). We choose to focus our initial studies on the subset of t-AML with normal cytogenetics or simple balanced translocations. Specifically, MLL gene rearrangements were observed in 22% of cases, other balanced translocations in 22%, trisomy 8 in 22%, normal karyotype in 31%, and a complex karyotype in a single case. All patients had received prior alkylator chemotherapy (62%), topoisomerase inhibitor chemotherapy (65%), or radiotherapy (77%). To identify somatic mutations, whole genome sequencing was performed on leukemic bone marrow (average 65% blasts) and skin (normal) DNA. Average haploid coverage was 37.5X and 34.7X for the leukemia and skin genomes, respectively. All somatic mutations were verified using patient-specific custom NimbleGen capture arrays, followed by Illumina sequencing. Although the total number of somatic single nucleotide variants in older patients (>50 years) with t-AML was similar to that observed in de novo AML (484 ± 68 vs. 506 ± 45, respectively), significantly more mutations were present in younger (≤ 50 years) patients with t-AML (743 ± 228) compared with de novo AML (336 ± 179, P=0.04). Exposure to chemotherapy is associated with an increased rate of transversions in relapsed AML (Ding et al., Nature 2012). However, the percentage of somatic mutations that were transversions in t-AML (35.8 ± 1.91%) was similar to that seen in de novo AML (33.5 ± 0.93%), regardless of age. In the 23 t-AML genomes, we identified recurring mutations (present in at least 2 cases) in 20 genes. Many of these mutations were also observed in de novo AML genomes (Figure 1). The most commonly mutated gene in t-AML was TET2, which was mutated in 35% of cases. Of interest, missense mutations of the ABC transporter gene ABCG2 were significantly enriched in t-AML (2/23, 8.7%) compared with de novo AML (0 in 200 cases, P=0.01). ABCG2 (also known as breast cancer resistance protein, BCRP) has been implicated in chemotherapy resistance. ABCG2 is expressed at high levels in hematopoietic stem cells, where it is known to function as a key drug transporter. Studies are underway to define the frequency of ABCG2 mutations (and other ABC transporter genes) in a larger cohort of t-AML, including cases with alterations in chromosome 5 or 7 or with complex cytogenetic abnormalities. In summary, in younger patients with t-AML, the mutational burden is higher than that of de novo AML patients, possibly reflecting prior exposure to chemoradiotherapy, though no increase in transversions was observed. Mutations of ABCG2 may contribute to chemotherapy resistance in a subset of t-AML. Figure 1. Recurring mutations in t-AML (n = 23) compared with de novo AML (n = 24). Figure 1. Recurring mutations in t-AML (n = 23) compared with de novo AML (n = 24). Disclosures: Ley: Washington University: Patents & Royalties.

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.

Comprehensive Genetic Diagnostics of Acute Myeloid Leukemia By Next Generation Sequencing

Blood ◽  
2016 ◽  
Vol 128 (22) ◽  
pp. 1665-1665
Author(s):  
Elisabeth Mack ◽  
Danny Langer ◽  
André Marquardt ◽  
Alfred Ultsch ◽  
Michael G Kiehl ◽  
...  
Keyword(s):  
Acute Myeloid Leukemia ◽  
Next Generation Sequencing ◽  
Genome Sequencing ◽  
Myeloid Leukemia ◽  
Genetic Diagnostics ◽  
Whole Genome ◽  
Genome Library ◽  
Low Coverage ◽  
Generation Sequencing ◽  
Acute Myeloid

Abstract Background Acute Myeloid Leukemia (AML) is the most common acute leukemia in adults with a poor overall prognosis. Although the disease has been extensively characterized on the molecular level, this knowledge is translating only slowly into the clinic, particularly with regard to novel therapeutic concepts. Presumably, this striking imbalance substantially is due to the long time required to complete genetic analyses so that results are not available when treatment has to be initiated. Specifically, cytogenetic examinations to determine the karyotype of the malignant blasts, which has been the most important parameter for risk stratification for more than thirty years, take up to two weeks. Next generation sequencing (NGS) technology essentially catalyzed efforts to dissect the genomic landscape of AML, leading to the identification of a large variety of AML driver genes and distinct molecular risk groups. However, these emerging molecular classes of AML do not cover all patients, implying that karyotyping is not dispensable for AML diagnostics at this point. Here we present an integrated approach to AML diagnostics that incorporates these complementary genetic examinations - focused mutational screening of AML-related genes and karyotyping - in one NGS assay. Methods We combined targeted resequencing of DNA and RNA using commercially available panels (TruSigth Myeloid, Illumina and FusionPlex Heme, ArcherDx) to detect AML-associated short sequence variants and gene fusions with low coverage whole genome sequencing for copy number variation analysis. Sequencing was performed on an Illumina MiSeq instrument with a read length of 2x150 bp and a coverage of 3.75 M reads for the TruSight Myeloid panel, 2.25 M reads for the FusionPlex panel and 1.5 M reads for the whole genome library. Variants and fusions were called using the manufacturers' analysis software and a previously published algorithm to identify ITDs (ITD-seek, Au et al., 2016). CNV analysis was performed by comparing read distribution in an AML whole genome library to in silico randomly sampled reads from the reference genome using an in house-developed algorithm. Results Initial testing of our approach on leukemia cell lines and peripheral blood leukocytes from healthy donors revealed sensitivities of 2% and 1-25% for the detection of DNA variants and fusions, respectively. Applying stringent filter criteria, we recovered 75% of verified COSMIC variants and 100% of known fusions in undiluted AML samples without false positives. Chromosomal gains and losses were detected with high confidence with a sensitivity of 10%. We were able to reliably distinguish between normal and complex karyotypes, although NGS-karyotyping based on known fusions and CNV-analysis missed some details of highly aberrant karyotypes such as derivative chromosomes and chromosomal translocations that did not involve genes included in the FusionPlex panel. Our preliminary experience on our method in a diagnostic setting confirms high correlation with reference laboratory results and no relevant differences with regard to treatment decisions. Moreover, we find that NGS considerably accelerates genetic diagnostics of AML as the entire workflow from sample to report including three parallel library preparations, sequencing and data analysis can be completed within 5 days. Operational costs amount approximately 1,700 USD (1,500 EUR) per sample with the low throughput equipment used in this work, which is in the range of expenses for currently established AML diagnostics. Conclusions NGS allows for comprehensive translocation and mutation screening, however, some technical and bioinformatics optimization is required to achieve consistently high sensitivity and specificity for all target genes. CNV analysis of low coverage whole genome sequencing data adds valuable information on numerical chromosomal aberrations, thus allowing construction of a virtual karyotype to substitute for difficult and time-consuming cytogenetics. In summary, we present a reliable, fast and cost-effective strategy to combine molecular and cytogenetics for AML diagnostics in a single NGS run in order to pave the way for a more differentiated clinical management of AML patients in the near future. Disclosures Kiehl: Roche: Consultancy, Other: Travel grants, Speakers Bureau.

Sign in / Sign up

Export Citation Format

Share Document