Evolutionary Action Score of Missense TP53 Mutations Can Predict Outcome in Patients with Myelodysplastic Syndrome and Acute Myeloid Leukemia

Abstract Introduction Gene mutations carry a significant prognostic value and influence the management of MDS and AML. However, until now, data on the impact of the type and functional effects of mutations on outcome, especially in genes lacking a hotspot such as TP53, are limited. TP53 mutation (TP53mut) is an independent predictor of poor outcome in MDS/AML. Majority of TP53mut are missense, few are nonsense, frameshift and splice-site. These occur across the entire coding region, and result in either loss-of-function, gain-of-function (GOF) or exert dominant negative effect on wild-type allele. Until now, there is limited data on the impact of the different types of TP53mut on the outcome in MDS/AML. There are several quantitative predictors to assess the molecular effect of TP53mut. Evolutional Action (EA) scoring system quantifies the deleterious effect of missense TP53mut based on the location of AA residue involved by the mutation and the effect of the AA change. Within head and neck cancer, this scoring system has identified a subset of mutations that have an adverse outcome. In this study, we assessed the impact of different parameters of TP53mut and the utility of EA score in well-characterized MDS and AML cohorts. Methods A total of 593 MDS, 139 CMML and 808 AML patients were selected for analysis. Coding region of TP53 was assessed by NGS for mutations by targeted amplicon-based NGS panel. Gain-of-function (GOF) mutations were defined as R172H, R175H, R270H, G245S, R248W, G248Q or R273H. Overall survival (OS) was calculated from the time of diagnosis to death or last follow-up. Event-free survival (EFS) was calculated from the time from therapy to progression/death. Various parameters of TP53mut, including the number, VAF, location, functional effect were correlated to outcome. In patients with missense TP53mut, EA score was correlated to outcome. Results Among a total of 732 MDS/CMML patients, 208 TP53 mutations were identified in 159 (21.7%) patients [99 men; 60 women; median age of 68 years (range, 18-98)]. The median VAF of TP53 mutations was 36.7% (range, 1.0-93.6). These included 164 missense, 21 frameshift, 17 nonsense and 6 splice-site mutations. Majority (n=149, 94%) involved DNA-binding domain (DBD). Twenty-four (15%) patients had a GOF type mutation. The median EA score was 76.7 (range, 16.6-97.7). The median OS was 33 months (12 months in TP53mut, 112 months in TP5wt; p 87.4 could predict a significantly worse EFS in MDS/CMML (p=0.01). Among 808 AML patients, 197 TP53 mutations were identified in 161 (~20%) patients [90 men, 71 women; median age of 70 years (range, 20-90)]. The median VAF of TP53 mutations was 42.7% (range, 1.2-94.4). These included 171 missense, 13 frameshift, 6 nonsense and 7 splice-site mutations. Majority (n=151, 77%) involved DBD. Twenty-six (16%) were of GOF type. The median EA score was 78.5 (range, 4.2-97.6). 139 TP53 mutated patients had a complex karyotype. The median OS was 16 months (6 month, TP53mut; 20 months, TP53wt; p 1 TP53mut showed worse OS than patients with one mutation (7 months, single; 7 months, multiple; p=0.006). The presence of CK with TP53mut showed worse OS compared to TP53mut alone (median 5 months vs. 11 months; p=0.002). The presence of deletion 17p did not affect OS in TP53mut patients (del17p + TP53mut, 5 months versus TP53mut alone 7 months; p=0.136). By univariate analysis, the presence, number (2 vs. 1) and VAF of TP53 mutation(s) were prognostic factors for OS, EFS and LFS. GOF type mutation was prognostic for EFS but showed a trend for OS and LFS. EA score and site of mutation did not influence the outcome in AML. In AML, EA score of >57.7 could predict a significantly worse OS (EA score 57.7, 9 months; p=0.02). Conclusion The presence, number and VAF of TP53 mutation(s) are associated with worse outcome in AML and MDS. EA score can be helpful to further stratify MDS and AML patients with missense mutations. Disclosures Sasaki: Otsuka Pharmaceutical: Honoraria. Kadia: Abbvie: Consultancy; Jazz: Consultancy, Research Funding; Takeda: Consultancy; Takeda: Consultancy; Celgene: Research Funding; Abbvie: Consultancy; Novartis: Consultancy; Celgene: Research Funding; Amgen: Consultancy, Research Funding; Jazz: Consultancy, Research Funding; Pfizer: Consultancy, Research Funding; Novartis: Consultancy; Amgen: Consultancy, Research Funding; BMS: Research Funding; Pfizer: Consultancy, Research Funding; BMS: Research Funding. Ravandi: Abbvie: Research Funding; Bristol-Myers Squibb: Research Funding; Astellas Pharmaceuticals: Consultancy, Honoraria; Macrogenix: Honoraria, Research Funding; Seattle Genetics: Research Funding; Bristol-Myers Squibb: Research Funding; Sunesis: Honoraria; Astellas Pharmaceuticals: Consultancy, Honoraria; Seattle Genetics: Research Funding; Sunesis: Honoraria; Amgen: Honoraria, Research Funding, Speakers Bureau; Macrogenix: Honoraria, Research Funding; Orsenix: Honoraria; Orsenix: Honoraria; Abbvie: Research Funding; Amgen: Honoraria, Research Funding, Speakers Bureau; Xencor: Research Funding; Jazz: Honoraria; Jazz: Honoraria; Xencor: Research Funding.