Price, Zoe

Abstract This retrospective analysis aimed to provide evidence-based risk stratification of TP53-mutated (TP53mut) myeloid neoplasms (MNs). Of 580 MNs harboring TP53mut with variant allele frequency (VAF) ≥2%, 219 (37.8%), 194 (33.4%), 92 (15.9%), and 75 (12.9%) were classified as acute myeloid leukemia (AML), myelodysplastic syndrome (MDS) with low blasts (MDS-LB), MDS with excess blasts (MDS-EB)-2, and -EB1 according to the revised fourth edition of the World Health Organization (WHO) classification, respectively. Hierarchical analysis identified the following 4 risk groups with distinct survival: (1) MDS-LB, (2) MDS-EB1/EB2/AML VAF <10%, (3) MDS-EB1/EB2 VAF ≥10%, and (4) AML VAF ≥10%. We next evaluated the impact of allelic status, VAF, and complex karyotype (CK). In our cohort, the significance of biallelic status was limited to MDS with <5% blasts and not for blasts 5% to 9%, as proposed by the International Consensus Classification (ICC), or 5% to 19%, as proposed by the fifth edition of the WHO (WHO-5). MDS-EB1 and -EB2 with VAF ≥10% had comparable survival (9.6 vs 7.2 months; P = .12), regardless of allelic status. Contrary to the ICC proposal, MDS-EB1/EB2 with VAF <10% and CK had poor survival compared with those without CK, comparable to MDS-EB1/EB2 with VAF ≥10% (5.6 vs 26.2 vs 6.3 months; P = .003). Survival of TP53mut AML was poor (median 3.9 months) regardless of allelic/CK status. Thus, using ICC or WHO-5 may underestimate prognosis of MDS with blasts 5% to 19% and 5% to 9%, respectively. Collectively, the hierarchical model acknowledges poor survival of 91.9% TP53mut MDS and AML compared with 36.5% and 80.7% by WHO-5 and ICC, respectively.

Background: TP53-mutated (TP53mut) myeloid neoplasms (MN) have particularly poor survival. Discrepancies in defining TP53mut MN have impact on diagnostic work up, therapeutic decisions, and clinical trial enrolment. 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.

Background: TP53 mutations (TP53mut) are highly prevalent in therapy-related myeloid neoplasms (t-MN) compared to de novo MN (35-40% vs 7-13%) and are associated with a poor outcome. Understanding the risk factors involved in the pathogenesis of TP53mut t-MN is critical to prevent its development. Methods: We characterized t-MN cases by examining primary cancer type vs autoimmune disease diagnosis vs solid organ transplantation, treatment modality, and latency (interval between primary cancer/disease and t-MN diagnosis) to identify patient and disease-related factors associated with TP53mut t-MN. Allelic loss of TP53mut MN was defined according to International Consensus Classification (ICC) criteria (Arber et al., Blood 2022). Results: Of 587 t-MN patients, 33% (n=194) had acute myeloid leukemia (t-AML) and 67% (n=393) had myelodysplastic syndrome (t-MDS, blasts<20%). Primary diagnosis comprised of 243 (41.6%) solid cancers and 300 (51.4%) hematological malignancies, while 7% (n=41) of t-MN cases had autoimmune disease or underwent solid organ transplant. Overall, 94 (16.1%) patients received prior radiotherapy (RT) alone and 75.6% (n=444) received chemotherapy (CT) with/without RT. 109 patients (18.7%) underwent autologous stem cell transplant (ASCT). The median latency from primary diagnosis to t-MN was 83.2 months (IQR 41.3, 153.2), and there was a trend towards shorter latency in t-AML compared to t-MDS (70.5 vs 85.8 months; P=0.07). Of the t-MN patients, 280 (47.7%) harbored TP53mut, which was associated with significantly poorer survival compared to wild-type (TP53wt) t-MN (9.7 vs 21.9 months; P <0.001). In contrast to TP53wt (59.0% vs 41.0%), the majority of TP53mut t-MN presented with t-MDS vs t-AML (75.7% vs 24.3%; P <0.001). According to ICC criteria, multi-hit MDS (n=131; 47.8%) was the most common entity, followed by AML (n=68; 24.8%), MDS/AML (n=34; 12.4%), and single-hit MDS (n=8, 2.9%). Poor prognostic karyotype changes e.g., del 5q, del 7q, complex karyotype and monosomal karyotype, were more prevalent in TP53mut compared to TP53wt t-MN. Co-mutations in other genes were enriched in TP53wt t-MN. As TP53mut t-MN is associated with a dismal outcome, we assessed the factors associated with its development. There were no significant differences in median age at primary cancer/disease diagnosis (59.0 vs 59.9 years; P=0.4) or gender (females 43.3% vs 39.3%; P=0.3) between TP53mut and TP53wt t-MN. However, TP53mut patients were older at t-MN diagnosis (69 vs 67.4 years; P=0.05). Correlations between gene mutations and prior malignancies revealed a significant association between TP53mut and hematological primary cancers (odds ratio [OR] 1.6; P=0.006), especially multiple myeloma (MM, OR 4.9; P<0.001) and lymphoproliferative disease (LPD, OR 2.0; P=0.013). Among solid cancers, head and neck malignancies were significantly associated with increased risk of developing TP53mut t-MN (OR 7.7; P=0.01). Compared to RT alone, ASCT (OR 2.2; P=0.004) and CT (OR 2.0; P=0.008) were associated with an increased risk of TP53mut t-MN. Recent studies indicate that CT/RT confer a selective advantage to preexisting TP53mut hematopoietic stem and progenitor cells, allowing clonal expansion/evolution and progression to t-MN. We hypothesized that TP53mut t-MN with high-risk disease features is a function of TP53mut clonal expansion/evolution rate and would predict latency between primary cancer and t-MN. Indeed, the latency between primary cancer and t-MN diagnosis was longer in TP53mut (87.8 months, IQR 45.5, 154.1) compared to TP53wt (74.7 months, IQR 37.5, 146.9 months) t-MN (P=0.05). Interestingly, 54% and 29% of TP53mut t-MN had a latency of ≥5 and ≥10 years, respectively. There were no correlations between latency and TP53 variant allele frequency (VAF) (r=0.38 and P=0.7) or between TP53mut t-MDS and t-AML (85.2 vs 109.3 months; P=0.9). Together, latency between primary cancer and t-MN was not associated with TP53mut t-MN phenotype, VAF or other poor-risk disease features. Conclusion: Hematological malignancies, especially MM and LPD treated with high-dose CT, were strongly associated with TP53mut t-MN development, highlighting the importance of careful and ongoing screening in this population. Also, the long latency of TP53mut t-MN provides an opportunity to devise effective surveillance strategies and preventative interventions long before TP53mut t-MN development.