Guarnera, Luca

ABSTRACT Myelodysplastic syndrome (MDS) and chronic myelomonocytic leukemia (CMML) are clonal myeloid neoplasms shaped by genetic lesions and immune dysregulation, both of which contribute to disease progression and poor outcomes. Existing prognostic systems, such as IPSS‐R and IPSS‐M, do not incorporate immune alterations. We assessed the biological and clinical relevance of an immune dysfunction signature (IDS) across multi‐center MDS and CMML cohorts. IDS scores, derived from bulk transcriptomic data, were significantly associated with inferior leukemia‐free and overall survival. In multivariable analyses, IDS retained independent prognostic value alongside IPSS‐R, IPSS‐M, and bi‐allelic TP53 inactivation. Incorporation of IDS into existing models improved prognostic discrimination and time‐dependent predictive accuracy. Longitudinal analyses revealed that rising IDS scores paralleled disease progression and acute transformation, whereas declining scores were observed in remission. Biologically, IDS‐high cases demonstrated reduced cytotoxic T‐cell activity, expansion of regulatory T cells, enrichment of primitive progenitor signals, and increased expression of checkpoint pathway genes. These findings were validated across multiple independent MDS cohorts and consistently reproduced in CMML, where IDS also stratified risk and tracked disease evolution. Finally, integration with drug response signatures from the BeatAMLv2 cohort suggested potential therapeutic vulnerability of IDS‐high cases to multikinase and NF‐κB pathway inhibitors. These results establish IDS as a robust and dynamic biomarker in MDS and CMML, with applications in refined risk stratification, longitudinal disease monitoring, and guiding personalized therapeutic strategies targeting immune dysfunction. Trial Registration: #201709072RINC, #202109078RINB, #202207050RINB

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.