Campagna, Alessia

Myelodysplastic syndromes with isolated deletion of chromosome 5q [MDS-del(5q)] constitute a distinct biological entity traditionally associated with favorable outcomes, although up to one quarter of patients progress to acute myeloid leukemia (AML). Existing prognostic models, developed in heterogeneous MDS populations, may not adequately capture risk within this subgroup. We assembled an international cohort of 682 patients with MDS-del(5q) to evaluate the performance of the IPSS-R and IPSS-M, identify prognostic variables, and develop a disease-specific prognostic tool, the IPSS-del(5q). Most patients were classified as lower-risk by IPSS-R (94.4%) and IPSS-M (85.5%), yet both systems showed limited discriminatory ability (C-indices ≈0.5). Independent adverse prognostic factors included age ≥70 years, male sex, anemia (hemoglobin ≤10 g/dL), thrombocytopenia (platelets ≤100×10⁹/L), the presence of one additional chromosomal abnormality, ≥2 gene mutations, SF3B1 mutations, and high-risk TP53 status. Six variables were included in the IPSS-del(5q), stratifying patients into standard-risk (74.3%) and high-risk (25.7%) groups with significantly different LFS (69.2 vs. 32.0 months; p<0.01). Moreover, this model reclassified 19.1% of lower-risk IPSS-R and 14.6% of lower-risk IPSS-M patients into the high-risk IPSS-del(5q) group. However, its discriminative power remained modest, with a C-index of 0.60. Overall, this study provides the most comprehensive prognostic evaluation of MDS-del(5q) to date, demonstrates the limited discriminatory capacity of existing MDS scores in this entity, and underscores the need to develop refined disease-specific prognostic approaches for this MDS subtype.

Abstract Myelodysplastic syndromes (MDS) are clinically and biologically diverse disorders, emphasizing the need for personalized treatment approaches. The International Working Group for Prognostication of MDS (IWG_PM) recently introduced a molecular classification, referred to as the MDS taxonomy, that categorizes patients into 16 subgroups based on 21 gene mutations, 6 cytogenetic abnormalities, and loss of heterozygosity (LOH) at TP53 and TET2 loci. This study sought to validate and enhance the clinical relevance of the MDS taxonomy by analyzing a large retrospective cohort ( n = 5136) and transcriptomic data from a prospective cohort ( n = 477). The taxonomy successfully identified subgroups with distinct clinical characteristics and disease progression patterns. However, incorporating gene interactions from taxonomy subgroups did not improve the prognostic performance of the Molecular International Prognostic Scoring System (IPSS‐M). We further assessed whether the taxonomy could guide management in patients receiving disease‐modifying therapies. Except for the “ TP53 ‐complex” subgroup, taxonomy classifications were not predictive of hypomethylating agent response or transplant outcomes. Nonetheless, they correlated with overall survival, suggesting that while both IPSS‐M and the taxonomy capture disease biology, other non‐genetic factors may influence treatment response. RNA sequencing confirmed the biological distinctiveness of the taxonomy groups. Transcriptomic profiling of CD34+ bone marrow cells revealed unique, homogeneous gene expression patterns, particularly within the AML‐like, bi TET2 , SF3B1 , and TP53 ‐complex subgroups. Further integration of multi‐omics data may refine MDS classification, improving clinical decision‐making and guiding the development of targeted therapies.

PURPOSE Chronic myelomonocytic leukemia (CMML) is a rare myeloid neoplasm characterized by clinical heterogeneity and is associated with poor outcomes. To date, limited molecular information has been incorporated into disease classification and risk stratification. We aimed to integrate genomic features into the clinical decision-making process for CMML. PATIENTS AND METHODS We analyzed a retrospective cohort of 3013 patients with CMML (training set) and a prospective population of 516 patients (validation set). Using an innovative framework for multimodal data analysis, we developed molecular-based disease taxonomy and prognostication. RESULTS Unsupervised clustering identified nine entities with distinct genomic features and outcomes ( P < .001), including splicing machinery, transcription factors, signal transduction and tyrosine kinase pathways aberrations, and high-risk molecular signatures. Notably, 15% of patients showed molecular/clinical overlap with other myeloid neoplasms. We integrated molecular and clinical information to build the international CMML Prognostic Scoring System (iCPSS), incorporating mutations in nine genes together with hematologic parameters and cytogenetic abnormalities. The iCPSS identified five groups with distinct probability of overall and leukemia-free survival in both training and validation cohorts ( P < .001), outperforming existing prognostic models. Importantly, 55% of patients were reassigned to higher or lower risk groups by the iCPSS. Decision analysis demonstrated that iCPSS could refine the optimal timing of allogeneic transplantation at the individual level; compared with conventional prognostic tools, iCPSS-based decision modeling changed transplantation strategy in 31% of cases, resulting in a significant gain-in-life expectancy for eligible patient population ( P < .001). A federated learning platform was implemented to enable continuous, privacy-preserving model update across multiple centers. CONCLUSION Molecular information improves CMML classification and prognostication, supports more effective clinical decision making, and potentially refines the design of clinical trials.

Abstract Background: The recently published BLAST clinical and BLAST molecular (BLAST-Mol) risk models for chronic myelomonocytic leukemia (CMML) incorporate clinical and molecular variables, respectively, for prediction of overall survival (OS), while BLAST-BT (blast transformation) risk model was predictive of BT free survival (BTFS) [Blood 2025;doi:]. In the current study, the Mayo Clinic (USA) CMML cohort used in the aformentioned study was expanded from 457 to 600 patients and merged with an external cohort of 501 patients from the Humanitas Research Hospital (HRH; Italy) in order to validate the BLAST, BLAST-Mol and BLAST-BT risk models. Methods: CMML diagnosis and sub-categorization were according to the International Consensus Classification, which included CMML-1 (BM/PB blasts &lt;10%/&lt;5%) vs. CMML-2 (BM/PB blasts ≥10%/≥5%) and CMML-MP (leukocytes ≥13 x 10⁹/L) vs. CMML-MD (leukocytes &lt;13 x 10⁹/L) [Blood 2022;140:1200]. The BLAST score is based on clinical risk factors including PB blasts ≥2%, leukocytes ≥13 x 109/L, and severe or moderate anemia resulting in BLAST low, intermediate, and high risk groups. BLAST-Mol also accounts for mutations and karyotype and identifies PHF6 and TET2 as “favorable” and DNMT3A, U2AF1, BCOR, SETBP1, ASXL1, NRAS, PTPN11, RUNX1, TP53, as “unfavorable” mutations with adverse karyotype also considered unfavorable. BLAST-BT variables include absence of PHF6MUT, BM blasts ≥10%, presence of DNMT3AMUT, PB blasts ≥2%, leukocyte count ≥13 x 109 /L, and ASXL1MUT. OS analyses were censored for allogeneic stem cell transplant (ASCT). Conventional statistical methods were employed, using JMP Pro 18.0.0 software SAS Institute, Cary, NC, USA. Results: A total of 1,101 patients (median age 73 years; 67% males) with newly-diagnosed CMML were considered: Mayo cohort 600 and HRH cohort 501. Compared to the HRH cohort, the Mayo cohort was more likely to include CMML-MP (44% vs. 28%; p&lt;0.01), CMML-1 (87% vs. 81%; p&lt;0.01), ASXL1MUT (48% vs. 35%; p&lt;0.01), SRSF2MUT (43% vs. 33%; p&lt;0.01), NRASMUT (15% vs 9%; p&lt;0.01), PHF6MUT (7% vs. 4%; p&lt;0.01), and less likely to harbor TET2MUT (50% vs. 58%; p&lt;0.01), and SF3B1MUT (4% vs. 10%; p&lt;0.01) [Table 1]. At a median follow-up of 55 months (range 0-202), 686 (62%) deaths, 218 (20%) BTs, and 142 (13%) ASCTs were documented. CPSS-mol risk categories at diagnosis included low (N=190; median OS 67 months), intermediate-1(N=271; median 38 months), intermediate-2 (N=407; median 26 months), and high (N=231; median 16 months; p&lt;0.01) [AUC at 5 years 0.75]. BLAST clinical risk distribution was low (N=356; median OS 65 months), intermediate (N=393; median 30 months), and high (N= 352; median 16 months; p&lt;0.01) [Figure 1; AUC at 5 years 0.79]. The corresponding AUCs calculated for the Mayo and HRH cohorts, separately, were 0.81 and 0.76, respectively. BLAST-Mol risk assignments included low (N=357; median OS 66 months), intermediate (N=512; median 27 months) and high (N=232; median 15 months; p&lt;0.01) [Figure 2; AUC at 5 years 0.79]. The corresponding AUCs for the Mayo and HRH cohorts were 0.81 and 0.77, respectively. The 3-year cumulative incidence of BT (accounting for death from another cause) stratified by BLAST-BT risk model included 8% for very low risk (N=54), 11% for low risk (N=332), 20% for intermediate-risk (N=281), 27% for high risk (N=250), and 34% for very high risk (N=176) [p&lt;0.01; Figure 3]. Multivariable analysis of the combined Mayo-Humanitas cohort (N=1,101) identified age &gt;65 years (HR 1.7, 95% CI 1.3-2.1), CMML-2 subtype (HR 2.0; 95% CI 1.6-2.5), and a number of other mutations or co-mutations with BLAST-Mol-independent and CPSS-mol-independent prognostic contribution (to be elaborated further at the time of meeting presentation). Conclusions: The current study validates the predictive performance of the BLAST, BLAST-Mol and BLAST-BT risk models in a large two-center international cohort of patients with CMML. The possibility of further enhancement of the BLAST-Mol model is suggested by the demonstration of independent prognostic relevance from additional mutations and co-mutations.

Abstract Background & aim: Myelodysplastic syndromes with isolated deletion of chromosome 5q (MDS-del(5q)) are associated with low-risk features and favorable prognosis. In contrast, TP53 multihit alterations, typically reflecting biallelic inactivation, are associated with high-risk features and adverse outcomes across MDS subtypes. Although current classification systems exclude MDS-del(5q) cases with TP53 multihit from low-risk categories, the prognostic significance of these alterations in this specific context remains unclear. Methods: We conducted an international, multicenter, retrospective study comparing MDS-del(5q) per 2017 WHO criteria with TP53 multihit alterations (MDS-del(5q) TP53 multihit) from 21 international institutions, to MDS with low blast counts with TP53 multihit alterations without isolated del(5q) (MDS-LB TP53 multihit) from the public data from the study of Bernard et al., Nat Med 2020. Clinical and biological data were collected at diagnosis. Genetic profiling included G-banding and NGS of myeloid genes. TP53 multihit was defined as &gt;1 TP53 mutation or a single mutation with 17p deletion, copy-neutral loss of heterozygosity (LOH), or variant allele frequency (VAF) ≥50%. Patients were stratified as lower- or higher-risk based on IPSS-R and IPSS-M scores. Lower-risk included those with IPSS-R ≤3.5 points or very-low, low, or moderate-low IPSS-M, while higher-risk included those with IPSS-R &gt;3.5 points or moderate-high, high, or very-high IPSS-M. Clinical, molecular features, overall survival (OS), and AML progression were compared between MDS-del(5q) TP53 multihit and MDS-LB TP53 multihit groups. Finally, outcomes between patients with likely biallelic TP53 multihit status, defined as harboring a single TP53 mutation accompanied by a co-occurring deletion/LOH of 17p or a TP53 VAF ≥50%, versus those considered less likely to be biallelic, thus still classified as multihit, defined as having &gt;1 TP53 mutation but a combined VAF &lt;50%, were compared in both MDS-del(5q) and MDS-LB groups. Statistical analysis was performed using R (version 4.2.2). Results: We included 113 patients with a median follow-up of 81.1 months (95% CI 74.9–NR). A total of 45 (39.8%) were MDS-del(5q) TP53 multihit, and 68 (60.2%) MDS-LB TP53 multihit. Compared to MDS-LB TP53 multihit, patients with MDS-del(5q) TP53 multihit had significantly higher platelet counts (209 ×10⁹/L vs. 77 ×10⁹/L) and were more frequently classified as lower-risk by both IPSS-R (75.4% vs. 9.8%) and IPSS-M (90.2% vs. 24.6%). The median number of mutations per patient, excluding TP53, was 1 in both groups, and only SF3B1 mutations were significantly more frequent in the MDS-del(5q) TP53 multihit group compared to MDS-LB TP53 multihit (20.5% vs. 2.9%; p&lt;0.01). Regarding survival, MDS-del(5q) TP53 multihit patients showed an improved OS compared to MDS-LB TP53 multihit (median OS of 57.0 months (95%CI, 43.0–112.0) vs 14.0 months (95%CI, 8.6–19.0); p&lt;0.01). Moreover, while the risk of progression to AML at 48 months were comparable between groups (34.9% for MDS-del(5q) TP53 multihit and 33.0% for MDS-LB TP53 multihit; p=0.8), a significant difference was observed in the timing of AML progression: median time to AML evolution of 31.7 months (IQR 23.4–55.3) in the MDS-del(5q) TP53 multihit group and 7.2 months (IQR 4.1–12.9) in the MDS-LB TP53 multihit group (p&lt;0.01). Notably, no significant differences in outcomes were found between TP53 multihit and most likely biallelic cases in both MDS-del(5q) and MDS-LB cohorts. The impact of complex karyotype (CK) in MDS-LB TP53 multihit was also explored. MDS-LB TP53 multihitwith CK showed a significantly worse OS compared to MDS-LB TP53 multihitwithout CK (median OS of 10.0 months (95% CI 7.5–17.0) vs 40.0 months (95% CI 14.0–NA); respectively; p&lt;0.001. However, the incidence of AML and median time to AML evolution were comparable between both groups. Finally, OS between MDS-LB TP53 multihit patients without CK and the MDS-del(5q) TP53 multihit cohort was compared. Median OS was significantly shorter in the former group (median of 40 months) compared to the latter (median of 61 months) (p &lt;0.01) Conclusions: These findings underscore that, in contrast to other TP53-multihit MDS subtypes, MDS-del(5q) with TP53 multihit alterations constitute a clinically distinct entity with better outcomes, thereby warranting reconsideration of its place in current classification systems.

Abstract Background Blast transformation (BT) occurs in approximately 15-30% of patients with chronic myelomonocytic leukemia (CMML) and remains a leading cause of death. Allogeneic stem cell transplantation (ASCT) is currently the only treatment modality with the potential to cure the disease or prolong survival. Optimal timing of ASCT is critical for maximizing benefit while minimizing risks. To that end, contemporary risk models have focused on the prognostic relevance of individual, as opposed to concurrent mutations. In the current study, we looked into the possibility of prognostic prominence from concurrent mutations in predicting BT in CMML. Methods CMML diagnostic criteria were according to the International Consensus Classification (Arber et al. Blood 2022; 140:1200). A machine-learning hierarchical clustering algorithm was developed and tailored for patient stratification using survival outcomes and co-expression of genomic alterations. To reduce complexity and improve interpretability, we generalized each cluster using existing rule induction algorithms, including the in Trees framework and JRip. The final output was a set of mutation-based cluster definitions, each representing a distinct patient subgroup with similar survival trajectories. Competing risk analysis and cumulative incidence functions were used in downstream evaluation to validate the clinical distinctiveness of the clusters. For all survival analysis, patients were censored at the time of ASCT. Time-to-BT was calculated from the date of diagnosis to the date of BT or ASCT or last contact. BT-free-survival was calculated from the date of diagnosis to the date of BT, ASCT, death, or last contact. Results The core study cohort included 605 patients from the Mayo Clinic, USA, and the external validation cohort 501 patients from Humanitas Cancer Center, Milan, Italy. Using the patient cohort from the Mayo Clinic, machine-learning algorithms identified five molecular clusters with 3-year blast transformation (BT) rates ranging from 0% to 100% (AUC at 3 years 0.78): the order of molecular signature assignment (probability of BT/death from another cause) was i) PHF6MUT/ASXL1WT (0%/17% at 3 years; N=32), ii) NPM1MUT OR BCORMUT/ASXL1MUT OR SETBP1MUT/NRASMUT (48%/30% at 1 year; N=24), iii) RUNX1MUT/ASXL1MUT OR SRSF2MUT/NRASMUT OR EZH2MUT/ASXL1MUT OR SETBP1MUT OR BCORMUT (31%/55% at 3 years; N=132), iv) ASXL1MUT/TET2MUT OR DNMT3AMUT OR JAK2MUT (24%/28% at 3 years; N=123), and v) all other permutations (10%/40% at 3 years; N=294) [Figure 1]. Additional analysis confirmed significant differences in survival between RUNX1MUT/ASXL1MUT vs. RUNX1MUT/ASXL1WT (p&lt;0.01) OR RUNX1WT/ASXL1WT (p&lt;0.01) OR RUNX1WT/ASXL1MUT (p=0.038) [Figure 2]. Similar patterns of differences in survival were also documented for NRAS/SETBP1, ASXL1/EZH2, and NRAS/SRSF2 mutation combinations (Figure 2). A subsequent Cox regression analysis confirmed independent prognostic contributions from “PHF6MUT/ASXL1WT” (HR 5.43e-10; p&lt;0.01), NPM1MUT (HR 26.7; p&lt;0.01), “SETBP1MUT/NRASMUT” (HR 12.7; p&lt;0.01), BCORMUT(HR 5.8; p&lt;0.01), “RUNX1MUT/ASXL1MUT” (HR 2.3, p&lt;0.01), JAK2MUT (HR 2.1; p&lt;0.01), and “ASXL1MUT/TET2MUT” (HR 1.7; p=0.02). The prognostic relevance of “SETBP1MUT/NRASMUT”, “RUNX1MUT/ASXL1MUT”, NPM1MUT, and BCORMUT was validated in the external cohort from Italy (N=501). In the Mayo Clinic cohort, presence of any of the latter mutations was associated with 1-, 3-, and 5-year BT (death from other cause) rates of 27% (21%), 44% (49%), and 44% (51%), respectively (Figure 3). The corresponding values in the absence of high risk mutations were 7% (15%), 15% (37%), and 18% (52%) [Gray's p value &lt;0.01 for both BT and death from another cause; Figure 3]. Similarly, the 1-, 3-, and 5-year BT (death from another cause) rates in the Italian cohort were 21% (19%), 37% (47%), and 42% (54%) in the presence and 8% (14%), 20% (35%), and 24% (45%) in the absence of high-risk mutations (Gray's p value &lt;0.01 for BT and 0.18 for death from another cause; Figure 4). Conclusions In the current study, machine-learning algorithms enabled the discovery of concurrent mutations in CMML that were shown to be prognostically more significant than their individual constituents. Such prognostic interaction might have contributed to some of the discrepancies noted in current literature regarding the prognostic relevance of certain mutations in CMML and should be accounted for in the development of future risk models.

Background. Study of myeloid neoplasms (MN) has been rapidly transformed by genome characterization. Gene mutations have been reported to influence disease phenotype and risk of disease progression, and the evaluation of mutational status can provide valuable insights to improve the decision-making process. In this international study, we evaluated the clinical impact of mutational screening in patients with CMML, which is characterized by significant clinical and genomic heterogeneity, including a substantial proportion of patients with poor outcomes and unmet needs. Currently, only limited molecular information has been incorporated into CMML classification and prognostication. Methods. We retrospectively collected 3182 CMML patients. We used MOSAIC, an AI-based framework for multimodal analysis in rare cancers (PMID: 38875514) to develop innovative, molecular-based tools for classification and prognostication. An independent validation of the results on 516 prospectively collected patients was performed. Results. Bayesian Networs and Hierarchical Dirichlet Processes were used to identify genomic associations and define a CMML molecular classification. We identified 11 clusters with distinct clinical and genomic features, including splicing machinery, signal transduction and tyrosine kinase pathways aberrations, and high-risk molecular signatures (i.e., AML-like and TP53 mutations). Up to 15% of patients showed clear genomic overlap with other MN. Importantly, extensive multi-color flow cytometry on T lymphocytes, NK and myeloid cells (BD FACSymphony A5 Cell Analyzer) revealed specific immunologic and inflammation signatures associated with molecular subgroups. We then integrated molecular and clinical information to build an international CMML Prognostic Scoring System (iCPSS). We assessed the performance of different statistical and AI-based methods; L1-penalized Cox Model resulted as the best performing method. Selected features included hematological parameters (WBC, Hb, PLT and marrow blasts), cytogenetic abnormalities, and mutations in 10 genes (ASXL1, DNMT3A, EZH2, NRAS, RUNX1, SETBP1, STAG2, TET2, TP53, U2AF1). By Bayesian thresholds optimization, we identified 5 risk classes (i.e., very-low, low, intermediate, high and very-high) with median overall survival (OS) ranging from 99 to 9 months (P&lt;.001), and median leukemia-free survival (LFS) ranging from 210 to 18 months (P&lt;.001). iCPSS provided better patient discrimination across all clinical endpoints compared to currently available prognostic tools, with a Concordance Index (CI) for OS of 0.75 versus 0.62-0.64, respectively. Notably, compared to available scoring systems, up to 40% of patients were reassigned to higher or lower risk classes by the iCPSS. External validation on an independent prospective cohort confirmed that the performance of iCPSS is superior to existing prognostic models (CI for OS 0.71 vs 0.54-0.61, respectively) To demonstrate the clinical utility of iCPSS, we focused on 753 patients who underwent allogeneic stem cell transplantation (HSCT). In multivariable analysis, iCPSS stratified the probability of OS post-HSCT (P&lt;.001) and identified groups of patients with different probabilities of disease relapse, ranging from 9% to 62% (P&lt;.001). We employed a clinical- and genomic-based decision support system (PMID: 38723212) to determine the optimal timing of HSCT. Preliminary analyses indicated that higher-risk patients according to iCPSS (i.e., intermediate, high, and very high) benefit from an immediate HSCT strategy. Lower-risk patients (i.e., very low and low) had life expectancy maximized with delayed HSCT. Modeling decision analysis using iCPSS compared to conventional scores, resulted in a change in transplantation policy for a significant proportion of patients (up to 25%). Conclusion. Molecular information significantly improves the classification of CMML patients, providing a basis for refining diagnostic boundaries with other MN and for a more rational inclusion of patients in clinical trials. The iCPSS demonstrated superior performance over currently available scoring systems and improved HSCT decision making process.