Session: 637. Myelodysplastic Syndromes: Clinical and Epidemiological: Poster II
Hematology Disease Topics & Pathways:
Acute Myeloid Malignancies, AML, MDS, Clinical Practice (Health Services and Quality), Chronic Myeloid Malignancies, Diseases, Myeloid Malignancies
Methods: MN cases harboring TP53mut (variant allele frequency, VAF ≥2%) were classified based on bone marrow (BM) and peripheral blood (PB) blast % using the 4th revision of the WHO classification (WHO-4R). Biallelic loss was defined as (i) ≥2 mutations each with VAF ≥2%; (ii) single TP53mut VAF ≥2% plus 17p loss detected on karyotype, FISH, or SNP; or (iii) single TP53mut VAF ≥50%, whereas monoallelic loss was defined as single TP53mut 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%).
- 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 TP53mut with CK compared to monoallelic TP53mut without CK, who had the most favorable survival (12.7 vs. 13 vs. 34.8 months, P<0.0001).
- 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).
- 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.
- 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 5th 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 TP53mut 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 TP53mut. 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 TP53mut MN compared to the current models with a higher c- index.
Disclosures: 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: Morphosys: Other: Advisory Board; pfizer: Other: Advisory board; Takeda: Other: advisory board . Yeung: Pfizer: Honoraria; Novartis: Honoraria, Research Funding; Ascentage: Honoraria; Takeda: Honoraria; Amgen: Honoraria; BMS: Research Funding. Patnaik: Astra Zeneca: Membership on an entity's Board of Directors or advisory committees; Polaris: Research Funding; Solu therapeutics: Research Funding; StemLine: Research Funding; Kura Oncology: Research Funding; Epigenetix: 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.
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