Abstract
Background: Background: Acute myeloid leukemia (AML) and myelodysplastic syndromes (MDS) exhibit clinical heterogeneity, and genomic profiles play a crucial role in disease characteristics and survival outcomes. Elli Papaemmanuil et al. classified AML into 11 genomic subgroups (NEJM, 2016), where TP53 mutations and aneuploidy predicted dismal survival. Cases with TP53 mutations are often accompanied by complex chromosomal abnormalities (CAs), but the prognostic impact and target genes of CAs have not been fully studied. Methods: To clarify this issue, specimens of AML and MDS with excess blasts (MDS-EB) were collected from 1,001 patients enrolled at collaborating institutes of Kyoto Hematology Study Group between July 2017 and January 2023. Targeted capture sequencing with 1,216 single nucleotide polymorphism sites was used to analyze primary samples, allowing assessment of copy number alteration (CNA) profiles. The target gene panel included common mutations in myeloid neoplasms. Mutation calling was performed using the established pipeline Genomon 2. Prognostic impact of CNA, single nucleotide variants, and insertion/deletion with 10% or more frequency was evaluated, and the frequencies of CNA were calculated for the short and long arms of chromosomes 1-22 and the X chromosome. Univariate and multivariate analyses were conducted using statistical significance criteria of p < 0.05. Results: The average sequencing depth was 548. Among 1,001 patients, 224 (22.4%) harbored TP53 mutations. TP53-mutated cases had a median age of 70 (24-95) years old at diagnosis, with 117 (52.2%) being AML and 107 (47.8%) MDS-EB. TP53 biallelic mutation, defined by 2 or more mutations in TP53 or loss of heterogeneity of TP53 locus, was present in 200 (89.3%) of cases, more commonly associated with 3 or more CAs or CNA (complex karyotype (CK)-like) than monoallelic mutation (99% vs. 42%, p = 1.9x10 -13). The most frequent affected lesions were del(5q), del(7q), del(7p), del(12p), del(18q), gain(11q), and gain(21q) with frequencies of 76%, 59%, 34%, 33%, 28%, 27%, and 26%, respectively. In survival analysis, gain(21q), gain(19p), del(16q), del(3q), del(19p), del(13q), and del(4q), along with diagnosis of AML ( p = 2.0x10 -2), TP53 biallelic mutation ( p = 6.8x10 -3), and CK-like ( p = 3.4x10 -3), were associated with poor outcomes in univariate analysis. Multivariate analysis revealed gain(19p), del(3q), del(13q), del(4q), diagnosis of AML, and CK-like as independent poor prognostic factors. Here we have defined gain(19p), del(3q), del(13q), and del(4q) as “Risk CNA”. A significant difference in outcomes was observed between patients with and without Risk CNA (n = 110 and n = 114, respectively, Hazard ratio = 2.6, p = 6.0x10 -8): The 6-month survival rate was 70 (95% Confidence interval [60-79]) % for patients without Risk CNA and 42 [31-52] % for those with Risk CNA. Patients with two or more Risk CNAs had worse prognosis than those with one ( p = 2.7x10 -2), indicating independent or additive contributions to outcomes. Similar trends were observed when limiting the analysis to TP53-biallelic cases. Further investigation of Risk CNA target genes was performed by identifying commonly amplified/deleted lesions. Target genes of gain(19p) included DNM2, CARM1, SMARCA4, LDLR, and EPOR, while RB1 and TET2 were potential candidates of del(13q) and del(4q), respectively. Identification of candidate genes for del(3q) was challenging due to broad common deleted lesions. Conclusion: Chromosomal abnormalities stratify the prognosis of AML and MDS-EB with TP53 mutation. The number of Risk CNA also impacts survival. Investigation of target genes of Risk CNA suggests potential therapeutic targets for TP53-mutated myeloid neoplasms, warranting further study for treatment optimization.
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