Hunyadi, Olivia

Abstract Myeloid neoplasia encompasses a heterogeneous and complex molecular landscape whereby the distinct combinatorial patterns associated with clinical phenotypes are difficult to delineate. Given that genetic heterogeneity can be a consequence of the topology of the fitness landscape (peaked/non-peaked=high vs low fitness), we investigated co-mutational patterns in TP53 context as an example of hallmark genomic heterogeneity. Indeed, TP53 gene is characterized by hypomorphic/loss-of-function lesions, truncating mutations (frameshifts, stop codons), missense mutations often having a dominant-negative effect and loss of heterozygosity by chromosome 17p deletion driving clonal expansion. Clinical phenotypes of TP53 mutants are also heterogeneous. Here, we stratify TP53 mutations based on our previous published data1 whereby patients with single-hit TP53 and variant allelic frequency (VAF) >50% were classified as obligatory biallelic, patients with VAF between 23-50% as probable biallelic and <23% as probable monoallelic. A threshold of VAF 23% was optimal to separate monoallelic vs biallelic and was based on all-cause mortality. A tree-based model (RandomForestSRC) was used to build a survival model with VAF as a single covariate and minimum node size set to 15 to optimize the cut-off. Using this cut-off, we delineate the epistatic landscape and potential interactions among different TP53 mutational spectra and its fitness. We curated a cohort of 800 patients with TP53 mutations from our institution. In total 36% of the cases carried single hits while 64% had double hits genomic configuration. About 70% of the mutations were missense and were detected at canonical sites (R175H, Y220C, M237I, R273H, R282W, R248Q). We took into consideration 35 features and clustered our cohort of TP53 mutants using factor analysis. When we stratified the cohort based on our predetermined VAF threshold, we found that majority of the variants with high VAF, to be deemed as obligatory biallelic, co-occurred with complex karyotype. These cases were very homogeneous hence had on average a low number of additional variants, likely due to TP53 clones having reduced fitness in the presence of additional mutations. Conversely, patients without complex karyotype were mostly assigned to the monoallelic group due to low to median VAFs. Consequently, these samples had on average a higher number of additional hits, hence a heterogeneous molecular landscape. To substantiate this finding, we noticed that among monoallelic patients two characteristic pairs were observed (TP53-TET2 and TP53-SF3B1) and showed different patterns according to karyotypic associations. Indeed, both pairs in normal karyotype (TP53-TET2, 65%; TP53-SF3B1, 23%) had the highest number of additional mutations on average compared to other monoallelic hits in complex karyotype suggesting that clones resulting by these pairs were advantageous and produced higher fitness driving heterogeneity when together with other mutations. As a preliminary analysis to characterize the fitness landscape of canonical missense TP53 mutations, we investigated whether distinct TP53 mutations conferred differential clonal fitness by implementing a previously published model of stochastic hematopoietic stem cell growth based on the distribution of allelic frequencies across different age groups. Given a fixed stem cell population pool and a constant mutation rate, the distribution of allelic frequencies in the population of corresponding mutations across age can inform the fitness/selection coefficient. The model predicts that high fit mutations will expand more in the population (high VAF) depending on the time the mutation is allowed to expand (age). Based on the literature, we set the stem cell pool to 100,000 and the mutation rate to 1.4E-8.2We limited our analysis to mutations found in at least 10 individual in our cohort and to single hits due to potential epistatic interactions across multiple mutations in the same gene. Given this criterion two mutations R273H and R248Q were analyzed. Interestingly, we observed a 15% increase in fitness gain for R273H compared to R248Q. In summary, mutational heterogeneity and clonal fitness might provide clues to the diversity of TP53 mutational configurations. Our model will be further optimized for the upcoming ASH meeting by incorporating temporal sampling and increasing sample size to define differences in fitness according to mutation site.

Abstract Outgrowth of a founder mutant clone is an initial event in leukemogenesis. The corresponding somatic hit likely conveys a fitness advantage over a polyclonal background. Several factors may modulate the competitiveness of hematopoietic stem or progenitor cells, including aging, genotoxic stress, repeated cycles of stress hematopoiesis, and inherited genetic fitness deficiency. The nature of these conditions provides a particularly fertile environment in which only certain mutant clones thrive. For instance, chemotherapy facilitates expansion of PPMD1MT or TP53MT clones, aging-related loss of fitness predisposes to outgrowth of TET2MT clones, while many inherited bone marrow failure (BMF) syndromes are associated with specific clonal acquisitions due to somatic gene rescue. Identification of factors enabling the expansion of clones with specific mutations may help to develop targeted preventive, screening, or therapeutic strategies. Recently, high levels of erythropoietin (EPO) induced by frequent blood donation were shown to favor the emergence of DNMT3AR882 clones due to their increased EPO sensitivity1. We theorized that this may be a more general phenomenon operating in other diseases characterized by high EPO levels. Thus, we selected 644 patients in whom endogenous serum EPO levels had been assessed and who underwent mutational NGS analysis. Given that this cohort consisted of patients with hematologic neoplasms, EPO levels were broad with the majority falling outside the normal clinical reference range. For this study we used EPO levels ≥100mU/mL as the threshold to define high and low EPO groups. This threshold laid above the 75th percentile for our cohort (M=31.1; IQR=109.7) to capture outliers more likely to reflect biologically significant erythropoietic stress. DNMT3AMT were present in 14% of high EPO and in 12% of low EPO groups (p=0.5). However, in the high EPO group, R882 constituted 32% of DNMT3AMT while in the low EPO group R882 was found in only 11% of DNMT3AMT cases (p=.02). In a reverse fashion, the mean level of EPO in DNMT3AR882 was 441.7mU/mL while it was 215.3mU/mL in other DNMT3AMT carriers (p=0.03) and 186.2mU/mL in DNMT3AWT cases (p<0.001). To assess whether the effect on DNMT3A was dependent on EPO levels rather than anemia, we determined mean hemoglobin levels in R882 (µ=10.2g/dL), other DNMT3AMT (µ=10.4g/dL), and wild type cases (µ=10.9g/dL) and found no significant differences (p=0.33). To assess whether R882-EPO sensitivity was related to EPO-R expression levels, we compared EPO-R expression in R882 (µ=2.7), non-R882 (µ=2.6) mutations, and WT cases (µ=2.8) and found no significant difference. To assess whether the impact of high EPO states was specific to DNMT3AMT we next analyzed the distribution of other mutations. We observed that frameshift mutations in BCOR (p = 0.013) and BCORL1 (p=0.036) were significantly more frequent in high EPO patients. BCORMT was also overall more prevalent in the high EPO group (p=.001), while BCORL1 frequency was similar across EPO strata (p=0.77) showing a similar trend to DNMT3AMT. Beyond these genes, other patterns emerged, particularly within CH-associated genes. Individuals with U2AF1MT had elevated EPO (p=.0087), with variant-specific enrichment: p.S34F was more common in high EPO (p=.038) whereas p.Q157P predominated in low EPO patients (p=0.038). No significant EPO associations were observed for other CH-related genes such as ASXL1, CBL, and TET2. The limitations of this study notwithstanding, these findings support a biologically meaningful relationship between elevated EPO levels and the acquisition or enrichment of specific CH-associated mutations in patients with hematologic malignancies. The association of high EPO with DNMT3AR882, frameshift mutations in BCOR/L1, and variant-specific enrichment in U2AF1 suggests that EPO-driven stress may contribute to clonal selection or expansion. Our study of high EPO levels and DNMT3AMT analysis illustrates that external conditions may influence selection pressure for specific mutations. However, selection pressure may be a complex combination of factors. Non-malignant conditions with high EPO, such as hypoxia or intrinsic BMF, may also create conditions facilitating the acquisition of specific somatic hits. Recognition of evolutionary pressures and their associations with specific mutations may improve our understanding of CH pathogenesis, and conceptualization of therapeutic or preventative measures.