{'fraction_non_alphanumeric': 0.0349566055930569, 'fraction_numerical': 0.017357762777242044, 'mean_word_length': 5.101470588235294, 'pattern_counts': {'":': 0, '<': 1, '<?xml version=': 0, '>': 0, 'https://': 0, 'lorem ipsum': 0, 'www.': 0, 'xml': 0}, 'pii_count': 0, 'substrings_counts': 0, 'word_list_counts': {'cursed_substrings.json': 0, 'profanity_word_list.json': 0, 'sexual_word_list.json': 0, 'zh_pornsignals.json': 0}}
Whole Genome Sequencing As an Unbiased and Simplified Approach to Molecular Karyotyping in AML/MDS


 
 Introduction
 Copy number aberrations (CNAs) constitute the backbone of the diagnostic classification and risk stratification in patients with acute myeloid leukemia (AML) or myelodysplastic syndromes (MDS). Although conventional cytogenetic approaches has been implemented in clinical routine to detect recurrent genomic abnormalities in AML/MDS, it presents a few obvious shortcomings, such as time-consuming in vitro cell-culturing procedure, low resolution (no smaller than 10 Mbp), and subjective bias introduced from the visual interpretation by cytogeneticists. With the technological advances of next generation sequencing (NGS), the longstanding conventional cytogenetic test is likely to be challenged by whole-genome sequencing (WGS), which is a more unbiased method to detect all types of genomic aberrations not only those well-characterized clinically actionable mutations but also cryptic alternations yet to be defined and annotated.
 Methods
 In this study a shallow WGS (sWGS) was utilized to depict genomic profiles of CNAs for 113 patients with AML/MDS, 103 of whom had undergone cytogenetic test. Bone marrow was collected and mononuclear cells were separated to harvest genomic DNA which was sequenced without capture at approximately 1x coverage depth. Genome-wide CNAs greater than 5 Mbp was analyzed for each sample. European Leukemia Net (ELN)-defining CNAs were selected and used to assign patients to a genetic risk group through the same classification systems that are used for cytogenetic test.
 
 Results
 sWGS identified 156 DNA segments with CNAs in 68 (60.2%) of all 113 patients. At chromosome arm level, most recurrent events were gains of chr 8 (observed in 9 AML and 6 MDS patients), losses of chr Y (observed in 4 AML and 1 MDS patients) which are consistent with previously reported cytogenetic events in AML/MDS. With 103 patients whose cytogenetic test results were available, 79 CNAs were described by cytogenetic test. Compared between cytogenetic test and sWGS, a total of 52 CNAs (65.8%) were unambiguously matched. 42 and 32 CNAs were identified by sWGS only and cytogenetic test only respectively. Of the 42 aberrations detected only by sWGS, 19 (45.2%) were shorter than normally detected by cytogenetic test (<10 Mbp). Next, the concordance between sWGS and cytogenetic test for ELN-defining CNAs [del(5q), del(17p), and monosomy 7] was specifically evaluated. A total of 22 ELN-defining CNAs events were detected, 12 (54.5%) of which were concordantly identified by both sWGS and cytogenetic test whereas the other 10 (45.5%) were solely reported by sWGS but not detected by cytogenetic test. In regard to risk-group assignments based on ELN sWGS and cytogenetic test results, sWGS provided new genetic information in 16 (15.5%) patients and six (5.8%) patients were reassigned to a different risk category due to new adverse-risk findings that were identified by sWGS. Noteworthily, concordant CNA profiles were observed between the genomic DNA of bone marrow cells and the cell-free DNA (cfDNA) of plasmas from the same patients, which indicates peripheral blood can be a less-invasive alternative to characterize CNAs by sWGS analysis so that bone marrow aspiration can be spared.
 Conclusions
 In this study, a streamlined sWGS provided an accurate, convenient and cost-effective approach to describe genomic profiles of CNAs in patients with AML/MDS. It also brought greater diagnostic yield and risk stratification information than cytogenetic test based on the standard ELN categories.
 
 
 Zhu: Clinical Laboratories, Shenyou Bio: Current Employment. Zhang: Clinical Laboratories, Shenyou Bio: Current Employment. Chen: SeekIn Inc: Current Employment, Current holder of individual stocks in a privately-held company. Li: SeekIn Inc.: Current Employment, Current holder of individual stocks in a privately-held company. Chang: Clinical Laboratories, Shenyou Bio: Current Employment. Mao: SeekIn Inc: Current Employment, Current holder of individual stocks in a privately-held company.
