Evidence-Based Risk Stratification of TP53-Mutated Myeloid Neoplasms

Background:TP53-mutated (TP53^mut) myeloid neoplasms (MN) have particularly poor survival. Discrepancies in defining TP53^mut MN have impact on diagnostic work up, therapeutic decisions, and clinical trial enrolment.

Methods: MN cases harboring TP53^mut (variant allele frequency, VAF ≥2%) were classified based on bone marrow (BM) and peripheral blood (PB) blast % using the 4^th revision of the WHO classification (WHO-4R). Biallelic loss was defined as (i) ≥2 mutations each with VAF ≥2%; (ii) single TP53^mut VAF ≥2% plus 17p loss detected on karyotype, FISH, or SNP; or (iii) single TP53^mut VAF ≥50%, whereas monoallelic loss was defined as single TP53^mut VAF <50% without 17p loss. Primary endpoint was overall survival (OS) from diagnosis. Conditional Inference Trees (CIT) analysis was used to determine the true effect of a predictor.

Results: We identified 580 cases including WHO-4R defined acute myeloid leukemia (AML, BM/PB blasts ≥20%, n=219, 37.8%), myelodysplastic syndrome (MDS) with excess blasts-2 (EB-2, BM 10-19% or PB 5%-19%, n=92, 15.9%), MDS with excess blasts-1 (EB-1, BM 5-9% or PB 2-4%, n=75, 12.9%), and the remaining were MDS with low blasts (-LB, BM <5% and PB ≤1%, n=194, 33.4%).

Of the 578 cases evaluable for allelic status, 437 (75.6%) and 141 (24.4%) had biallelic and monoallelic TP53 loss, respectively. Higher frequency of biallelic loss was observed with increasing blasts. 63.2%, 73.3%, 78% and 86.3% of cases with MDS-LB, MDS-EB1, MDS-EB2, and AML had bi-allelic TP53 loss (P<0.0001).

In a multivariable analysis, WHO-4R categories, VAF ≥10%, and biallelic loss were independent predictors of poor survival. CIT analysis further stratified the cohort into 4 groups with distinct survival: (1) MDS-LB (n=194, 33.4%); (2) MDS-EB1/EB2 and AML with VAF <10% (n=38, 6.6%); (3) MDS-EB1/EB2 with VAF ≥10% (n=149, 25.7%); and (4) AML with VAF ≥10% (n=199, 34.3%).

1. MDS-LB cases could be further stratified based on allelic status and complex karyotype (CK): OS was poor for cases with biallelic TP53 loss and monoallelic TP53^mut with CK compared to monoallelic TP53^mut without CK, who had the most favorable survival (12.7 vs. 13 vs. 34.8 months, P<0.0001).

2. MDS-EB1/EB2/AML with VAF<10% could also be stratified by CK: MDS-EB1/EB2/AML with VAF<10% cases without CK had the favorable survival compared to their counterparts with CK and MDS-EB1/EB2 with VAF ≥10% cases (26.2 vs. 5.6 vs. 6.3 months, P=0.003).

3. MDS-EB1/EB2 with VAF ≥10% was a homogenous subgroup: Biological characteristics including the proportion of cases with biallelic TP53 loss, CK (P>0.9), monosomal karyotype (MK, P>0.9), del 5q (P=0.6), del 7q (P=0.4), and del 17p (P=0.2) were comparable between MDS-EB1 and -EB2 with VAF ≥10%. Importantly, median OS of MDS-EB1 and EB2 with VAF ≥10% was comparable (9.6 vs. 7.2 months, P=0.12). OS in this group was comparable regardless of the blast %, allelic status, or presence of CK.

4. AML with VAF ≥10% had extremely poor survival: Median OS was 3.9 months. Survival of AML was equally poor irrespective of the mono- or biallelic (3.6 vs. 3.9, P=0.89) and non-CK vs. CK (1.8 vs. 3.9 months, P=0.09).

Combined, our model would acknowledge poor survival of 514 (89.1%) cases. In contrast, retrospective application of the 5^th edition of the WHO classification (WHO-5) and International Consensus Classification (ICC) would classify only 211 (36.4%) and 468 (80.7%) cases as biallelic/multi-hit TP53^mut MN, respectively. Finally, the Harrell's c-index of the proposed model, WHO-5, and ICC was 0.66, 0.571, and 0.617, respectively.

Conclusion: We provide evidence for subsequent refinements in classifying MN harboring TP53^mut. Our salient findings include: (i) acknowledgement of CK as ‘biallelic equivalent’ in MDS-LB; as well as EB1/EB2/AML with VAF 2 to <10% cases; and (ii) demonstration of poor survival of MDS-EB1 and -EB2 with VAF ≥10% regardless of the allelic status. In conclusion, our proposed model acknowledged poor survival of a higher proportion of cases as TP53^mut MN compared to the current models with a higher c- index.

Begna:Novartis: Membership on an entity's Board of Directors or advisory committees. Litzow:Abbvie: Research Funding; Amgen: Research Funding, Speakers Bureau; Actinium: Research Funding; Astellas: Research Funding; Pluristem: Research Funding; Sanofi: Research Funding; Beigene: Speakers Bureau; Biosight: Other: Data Safety Monitoring Committee. Badar:Takeda: Other: advisory board ; pfizer: Other: Advisory board; Morphosys: Other: Advisory Board. Yeung:Pfizer: Honoraria; Novartis: Honoraria, Research Funding; Ascentage: Honoraria; Takeda: Honoraria; Amgen: Honoraria; BMS: Research Funding. Patnaik:Solu therapeutics: Research Funding; Kura Oncology: Research Funding; Astra Zeneca: Membership on an entity's Board of Directors or advisory committees; Epigenetix: Research Funding; Polaris: Research Funding; StemLine: Research Funding. Gangat:Agios: Other: Advisory Board; DISC Medicine: Consultancy, Other: Advisory Board . Mangaonkar:BMS: Research Funding; Incyte: Research Funding; Novartis: Research Funding. Hiwase:Abbvie: Honoraria; Astella Pharma: Honoraria; Otsuka: Honoraria.