Bahaj, Waled

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 Background: Higher-risk myelodysplastic syndromes/neoplasms (HR-MDS) and secondary acute myeloid leukemia (sAML) share overlapping molecular features but both remain clinically diverse with poor prognosis. Machine learning (ML)-based genomic clustering offers an opportunity to redefine disease subgroups beyond traditional clinical prognostic schemes or pathologic classifications. Prognostic scoring systems emphasize outcomes while pathologic classifications morphology resulting in inclusion of patients (pts) with different genetic background into same outcome or morphologic subgroup. We compared our ML-unsupervised clustering (UC) model named molecular nosology1 with the taxonomy model2 to evaluate differences in cluster architecture and clinical relevance in HR-MDS and sAML. Methods We analyzed HR-MDS (≥10% blasts) and sAML pts with available molecular data at diagnosis from a well-annotated cohort. Our molecular nosology model used UC via a binary latent-factor model to generate molecular clusters (MCs). The taxonomy model assigned pts to predefined genomic classes. We compared MC size, genomic enrichment, and overall survival (OS) across diagnostic categories. Concordance was assessed using Adjusted Rand Index (ARI) and Normalized Mutual Information (NMI). Purity and entropy were calculated to assess how concentrated (purity) or dispersed (entropy) each taxonomy group was across MCs. Results Among 898 pts, 367 (41%) had HR-MDS and 531 (59%) had sAML. Median age was 71 (IQR 65–77) years. Compared to sAML, HR-MDS pts had similar age (71 vs. 72, p=0.556) but higher hemoglobin (9.9 vs. 9.3, p<0.001) and platelet counts (91 vs. 50, p<0.001). Normal karyotype was more common in HR-MDS (55% vs. 42%, p<0.001). TET2 (24%), ASXL1 (24%), RUNX1 (23%), and SRSF2 (22%) were the most frequently mutated genes. Using our nosology model, we identified 14 MCs.1 The most frequent were MC2 (normal karyotype) at 22% and MC13 (TP53-complex) at 17%, followed by MC1 (+8/ASXL1/TET2/RUNX1, 12%), MC6 (SRSF2/RAS-pathway, 9%), and MC9 and MC14 (each 8%). Less common clusters included MC12 (5%) and MC3–MC5 (2–3%). For comparison, the taxonomy model included 18 genomic groups: TP53-complex (20%), mNOS (13%), bi-TET2, IDH-STAG2, and no event (each 10%), CCUS-like, SETBP1/-7, and AML-like (each 6%), SRSF2 (4%), del(5q), BCOR/L1, EZH2-ASXL1, and SF3B1 (each 3%), and five rare subgroups (DDX41, der(1;7), U2AF1157, U2AF134, ZRSR2; all <1%). Our MCs aligned closely with several taxonomy groups: 82% of TP53-complex cases were assigned to MC13, 99% of no event cases mapped to MC2, and 97% of del(5q) cases mapped to MC8. Bi-TET2 cases were distributed across MC6 and MC12 (both TET2-enriched); SETBP1/-7 to MC11; and EZH2-ASXL1 cases to MC1 and MC12. In contrast, mNOS, AML-like, and CCUS-like groups were genomically heterogeneous and spanned multiple MCs. ARI and NMI values were 0.391 and 0.458, indicating moderate concordance. Purity was highest in no event (0.989), del(5q) (0.967), and TP53-complex (0.823), and lowest in IDH-STAG2 (0.247), CCUS-like (0.241), and AML-like (0.347), all with high entropy (>2.5), reflecting genomic diversity not captured by taxonomy groups alone. Median OS for the entire cohort was 17 mo (95%CI: 14–20). HR-MDS pts had longer OS vs. sAML (26 vs. 12 mo, p<0.001). OS did not differ significantly between HR-MDS and sAML within most taxonomy groups or MCs, except for MC3 (NA vs. 11 mo, p=0.014), MC6 (111 vs. 15 mo, p<0.001), and MC9 (18 vs. 9 mo, p=0.027) for the nosology model and SF3B1 (129 vs. 23 mo, p=0.033), del(5q) (33 vs. 6 mo, p=0.022), bi-TET2 (25 vs. 8 mo, p=0.019), mNOS (34 vs. 9 mo, p<0.001) for taxonomy. Conclusions Some molecular taxonomy groups showed strong concordance with our nosology-defined clusters, which served as the gold standard for genomically distinct subsets (e.g., TP53-complex, del(5q), and no event), irrespective of diagnosis or blast percentage which may present the stage of the disease. In contrast, taxonomy-defined categories such as CCUS-like, AML-like, and mNOS were more genetically diverse and less aligned with specific clusters in the nosology model. Median OS within most clusters was not significantly influenced by blast count, underscoring the potential of molecular-driven clustering to reveal underlying disease biology not captured by morphology and to refine classification in MDS/AML and sAML with few exceptions (SF3B1 or biTET2). Ref 1.Kewan, Durmaz et al.Nat Commun 2023 2.Bernard, et al.Blood 2024