Csizmar, Clifford M.

ABSTRACT The recently published BLAST clinical risk model for chronic myelomonocytic leukemia (CMML) and its molecular version (BLAST‐Mol) differentiate low, intermediate, and high‐risk groups. The objective of the current retrospective study was to examine the survival impact of allogeneic stem cell transplant (ASCT) in a consecutive series of Mayo Clinic patients ( n = 775; 68% males, median age 71 years) with CMML, overall and adjusted for baseline BLAST/BLAST‐Mol risk categories. At a median follow‐up of 77 months, 151 (20%) patients underwent ASCT. Overall survival (OS) was longer in the ASCT compared to the non‐ASCT cohorts (median 77 vs. 28 months; p < 0.01). The significant difference in OS between the ASCT and non‐ASCT cohorts was retained after adjusting for BLAST clinical risk groups: high‐risk (median 50 vs. 14 months; p < 0.01), intermediate‐risk (81 vs. 28 months; p < 0.01), and low‐risk (111 vs. 65 months; p = 0.04), respectively. The same was mostly the case when OS comparison was adjusted for BLAST‐Mol with respective p values of < 0.01, < 0.01, and 0.08, respectively. Time‐dependent cox models confirmed the favorable survival impact of ASCT in the BLAST/BLAST‐Mol high‐ and intermediate‐risk groups. The current study highlights the benefit of ASCT for BLAST/BLAST‐Mol high‐ and intermediate‐risk groups in CMML and its potential to overcome the adverse impact of some high risk cytogenetic or molecular abnormalities. These observations support the early use of ASCT in CMML, ideally before BT and regardless of genetic characteristics.

6588 Background: TET2 is the most frequently mutated gene in chronic myelomonocytic leukemia (CMML) and has been associated with favorable outcome, with recent studies suggesting a more pronounced prognostic impact in the presence of ≥2 TET2 mutations ( Csizmar et al. leukemia 2025;39:2030; Kynning et al. Br J Haematol. 2025 Nov 25. doi: 10.1111/bjh.70264 ). We sought to determine if this particular observation extended to patients with ≥3 TET2 mutations ( TET2 MUT ) and also held true in the context of recently reiterated genetic risk factors. Methods: The current study was conducted under institutional review board approved minimum risk protocols allowing retrospective patient data collection and analysis. Diagnostic criteria were according to the International Consensus Classification (Arber et al. Blood 2022. 140:1200). All statistical analyses were conducted using JMP 18 software. Results: 536 CMML patients who underwent CLIA-approved NGS testing were stratified according to the number of TET2 MUT : 221 (41%) wild-type, 148 (28%) with one, 153 (29%) 2, and 14 (2%) ≥3 TET2 MUT . Co-mutation frequencies with significant differences between one vs. two TET2 MUT included ASXL1 (46% vs. 29%; p<0.01), KRAS (7% vs. 14%; p=0.02) and SETBP1 (5% vs. 0.6%; p=0.02). Phenotypic comparisons revealed preponderance of prognostically favorable traits associated with wild-type TET2 but no significant differences between one vs. two TET2 MUT . In age-adjusted univariate analysis, transplant-censored overall survival (TCOS), calculated from the time of mutation detection, was superior in patients with one (p<0.01; HR 0.7) or 2 (p<0.01; HR 0.4) but not ≥3 (p=0.4) TET2 MUT , vs. those with wild-type TET2 . TCOS was also superior with 2 vs. 1 (p=0.02; HR 0.7) but not with ≥3 vs. 1 (p=0.7) TET2 MUT ; the results were similar when TCOS was calculated from the time of diagnosis. Multivariable analysis confirmed the survival advantage of exactly two TET2 MUT vs. wild-type TET2 (p<0.01; HR 0.5), one TET2 MUT (p<0.01; HR 0.6), or ≥3 TET2 MUT (p<0.01; HR 0.4), after adjusting for age, sex, anemia, circulating blasts ≥2%, and leukocyte count ≥13×10⁹/L. The favorable impact of exactly two TET2 MUT persisted in CMML-MD and CMML-MP and remained independent of TET2 VAF, mutation type, karyotype, and other prognostically relevant mutations. This association was sustained in the BLAST (p<0.01; HR 0.5) and CPSS-mol (p=0.02; HR 0.7) models, but not in BLAST-mol model (p=0.12). Conclusions: Mechanistic explanation for this novel observation includes the possibility that TET2 mutational categories based on mutation count reflect distinct biological states rather than having a simple linear effect. The persistence of this signal despite adjustment for established molecular and genetic risk factors supports its consideration in the development of future CMML prognostic models.

Abstract Recent updates to monocyte count thresholds recognize oligomonocytic chronic myelomonocytic leukemia (OM-CMML) as an early form of CMML. However, the clinical validity of these changes remains uncertain without incorporating biological and genomic factors. In this study, we analyzed a cohort of 911 patients (249 with OM-CMML, 359 with overt CMML, and 303 with myelodysplastic syndromes) using unsupervised clustering to evaluate the role of genomic determinants in refining CMML diagnosis. Our findings show that CMML molecular signatures (biallelic TET2 mutations or SRSF2-TET2 comutations) are linked to a distinct transcriptome, monocytic bias, classical monocytosis, and a higher risk of progression to overt CMML in OM-CMML cases. We developed a weighted genomic model and diagnostic workflow showing that combining genomic signatures with bone marrow monocyte frequencies in OM-CMML more accurately predicts progression to overt CMML. These findings support integrating genomic determinants and our clinic-ready diagnostic workflow into the CMML diagnostic framework to improve accuracy. Significance: Through comprehensive clinical and genomic profiling of a large patient cohort, alongside immunophenotypic and transcriptional cellular analyses, this study provides evidence that incorporating genomic determinants into the diagnostic criteria for OM-CMML improves diagnostic accuracy and refines the identification of early-stage CMML, thereby preventing misclassification.

Abstract Introduction Management of acute leukemia (AL) during pregnancy remains challenging. In the first trimester, chemotherapy is toxic to the fetus, while delaying treatment may endanger maternal health, often prompting recommendations for pregnancy termination. In later trimesters, although the risk of congenital malformations decreases, complications such as preterm labor remain prevalent. Delivery prior to chemotherapy may be considered in advanced gestation. This study aimed to describe maternal and fetal outcomes of patients diagnosed with AL during pregnancy. Methods Following institutional review board approval, we retrospectively identified pregnant patients diagnosed with AL between 1995 and 2025 across Mayo Clinic sites (Minnesota, Arizona, Florida). We recorded leukemia subtype, treatment course, maternal and fetal outcomes. Results We identified 18 patients diagnosed with AL during pregnancy, including 11 with acute myeloid leukemia (AML) and 7 with acute lymphoblastic leukemia (ALL). Ten were diagnosed in the first trimester, 3 in the second, and 5 in the third. In the AML group (n=11; median age 30 years, range 18-42), one patient had acute promyelocytic leukemia (APL). ELN 2022 risk stratification was favorable in 5 (45%), intermediate in 4 (36%), and adverse in 2 (18%). In the ALL group (n=7; median age 23 years, range 19-34), 5 had B-ALL (one Philadelphia chromosome-positive) and 2 had T-ALL. All ALL cases were diagnosed in the first trimester. Among first-trimester AMLs, 2 of 3 had core-binding factor (CBF) AML. All eight patients diagnosed in the second and third trimesters had AML (1 APL, 2 CBF AML, 1 KMT2A-rearranged, 1 with DEK::NUP214 fusion, 1 complex karyotype including t(8;21), 2 with normal karyotype). Overall, 15 (83%) patients achieved complete remission (CR) or CR with incomplete count recovery (CRi) after initial induction. One patient had refractory disease and achieved partial remission after salvage, another had refractory disease and achieved CR after multiple lines of therapy, and a third achieved CR after FLAG-idarubicin and revumenib. Five patients received chemotherapy during pregnancy (3 in the first trimester, 1 in the second, 1 in the third); two of these resulted in live births. Among the 10 patients diagnosed in the first trimester, all underwent therapeutic or spontaneous abortion. Of the 3 second-trimester diagnoses, 2 underwent spontaneous or therapeutic abortion at 15 weeks. The third received 7+3 induction at 24 weeks for KMT2A-rearranged AML, followed by preterm delivery at 26 weeks and re-induction with FLAG-idarubicin plus revumenib, achieving CR. Among the 5 third trimester diagnoses, 4 delivered preterm infants (median gestational age 33 weeks) prior to chemotherapy. One patient received 7+3 at 29 weeks and delivered a viable preterm infant at 36 weeks. At a median follow-up of 6.5 years, 5 of 11 patients with AML (45%) and 3 of 7 patients with ALL (43%) died, mostly due to disease progression. Median overall survival was not reached in either group. Causes of death were as follows: 1 of AML, 1 of ALL, 3 of acute stem cell transplant (SCT) complications, 1 of chronic SCT complications, 1 of Hodgkin lymphoma, and 1 of pneumonia. Among patients diagnosed in the second and third trimesters (n=8, all with AML), 6 (75%) resulted in live births: 5 of 5 (100%) third-trimester diagnoses and 1 of 3 (33%) second-trimester diagnoses. All received intensive induction: 7+3 (n=5), 7+3 + ATRA (n=1), 7+3 + gilteritinib (n=1), and 7+3 + nilotinib (n=1). Of the second-trimester group, only one received induction prior to delivery; she later achieved CR with FLAG-ida and revumenib. Among the third-trimester group, one patient received chemotherapy prior to delivery and achieved CR followed by SCT. All but one of the 8 AML patients achieved CR after initial therapy. Conclusions Successful pregnancy outcomes were achieved in 75% of patients diagnosed with AML during the late second or third trimester. Notably, 5 (28%) of patients received chemotherapy during pregnancy. Larger, multi-institutional cohorts are warranted to further characterize the incidence and outcomes of AL in pregnancy.

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 Introduction Blast transformation (BT) in CMML occurs in 15-30% of patients (pts) with a median overall survival (OS) &lt;6 months (mo). While clonal evolution can be observed, biological mechanisms of BT remain unknown for many pts. Using paired samples from chronic phase (CP) and at BT, this study defines the cytogenetic and molecular landscape of BT-CMML. Methods BT-CMML pts from Mayo Clinic (n=153, 31%), MD Anderson (n=195, 40%), and 7 centers of the Groupe Francophone des Myélodysplasies (n=145, 29%) were included after IRB approval. ICC 2022 criteria defined CMML and AML. Pt data were collected at initial diagnosis or referral at CP and again at BT. Clonal dominance was inferred from a natural log-fold difference of mutation variant allele fractions (VAF). OS was censored at allogeneic stem cell transplant (alloSCT). Single-cell multiomic analyses used the Tapestri platform (Mission Bio). Statistics used R. Results Among 1,883 cases of CMML with a median follow up of 96 mo, 493 (26%) underwent BT. The median age at CP and BT was 69 and 71 years, respectively. BT-CMML pts were predominantly male (69%) and had proliferative CMML (53%) or CMML-1 (69%) in CP; 39% and 33% were considered high-risk by CPSS-Mol and BLAST-Mol criteria, respectively. The median time to BT was 16 mo (interquartile range 7-35 mo) and was not different in males vs females (p=0.60). Compared to their CP parameters (p&lt;0.001 each), pts at BT had lower median hemoglobin (10.8 vs 9.2 g/dL) and platelets (100 vs 44 x109/L) but higher leukocytes (13.9 vs 16.3 x109/L), peripheral blasts (0% vs 14%), and bone marrow blasts (6% vs 32%). Blasts exhibited mostly monocytic (57%) or immature myeloid (40%) differentiation. Abnormal karyotypes (AK) were more prevalent at BT (25% vs 62%, p&lt;0.001). Among 318 pts with paired karyotype data, 221 (69%) had a normal karyotype (NK) in CP, of which 123 (56%) remained normal at BT and 98 (44%) acquired new abnormalities. Conversely, 97 (31%) pts had an AK in CP, of which 67 (69%) acquired abnormalities or increased clonal metaphases (6%), 23 (24%) remained stable, and 7 (7%) reverted to a NK at BT. Overall, 46% acquired and 8% lost a clonal cytogenetic abnormality, while 46% remained unchanged. Numeric (37%) and structural (30%) changes were most common. Complex (12% vs 3%) and monosomal (5% vs 2%) karyotypes were more frequent at BT than in CP. Only 6 (2%) pts acquired an AML-defining rearrangement at BT. Mutational data were available for 248 paired cases. The number of somatic mutations was higher at BT (median 3 vs 4, p&lt;0.001). Mutations in RUNX1 (27% vs 39%), NRAS (23% vs 33%), KRAS (11% vs 17%), FLT3 (6% vs 15%), NPM1 (0% vs 11%), IDH2 (5% vs 11%), and CEBPA (3% vs 10%) were more frequent at BT (all p&lt;0.05). At BT, the average VAF of the dominant clone increased in 32%, decreased in 35%, and remained unchanged (delta ≤5%) in 33%. The most frequent dominant clones both in CP and at BT had mutations in TET2 (8% vs 4%) or co-dominant mutations involving SRSF2-TET2 (7% vs 3%). Mutational clonal evolution was observed in 72%, with mutations gained in 45% and lost from the dominant clone in 19%. The most gained mutations at BT were in NRAS (15%) and RUNX1 (13%). Single cell proteogenomic analyses in 4 selected CP and BT pts shed light on the impact of NRAS and RUNX1 mutations on clonal hierarchies and differentiation. Among 196 fully evaluable cases, a new cytogenetic and/or molecular aberration was uniquely gained in 106 (54%) cases and lost in 9 (5%), with 40 (20%) both gaining and losing a genetic alteration. Forty-one (21%) patients had no identifiable genetic change between CP and BT, suggesting driver alterations outside of that detected by routine analyses. Median OS was 31 mo from CMML diagnosis and 5.6 mo from BT. Compared to supportive care, treatment in CP did not prolong median time to BT (14 vs 17 mo, p=0.91) or OS (35 vs 29 mo, p=0.05), regardless of intensity. After BT, median OS was 1.2 mo with supportive care (n=83), 8.7 mo with a hypomethylating agent (HMA, n=39), 10 mo with HMA plus venetoclax (n=37), 7.8 mo with chemotherapy (n=91), and 31 mo in pts who underwent alloSCT (n=57). Conclusions In this large BT-CMML cohort, cytogenetic (46% gained, 8% lost) or mutational (45% gained, 19% lost) driver events were identified in 79% of pts, with 21% having no identifiable changes by conventional testing. Whole genome/transcriptome analyses are underway in this subgroup to identify novel drivers of BT.

Abstract Background: While mutations in the fms-like tyrosine kinase 3 (FLT3) gene are well-established adverse drivers in acute myeloid leukemia (AML), their role in myelodysplastic syndromes (MDS) and chronic myelomonocytic leukemia (CMML) remains insufficiently defined. Methods: We conducted a retrospective multicenter study of 115 patients with FLT3-mutated (FLT3mut) MDS (n=63) or CMML (n=52), comparing their clinical, genomic, and therapeutic characteristics at diagnosis or first referral to 677 FLT3 wild-type (FLT3wt) controls (MDS n=388; CMML n=289) using multivariate analyses and propensity score matching (PSM). Results: FLT3mut MDS and CMML exhibited a proliferative phenotype with higher leukocyte and neutrophil counts and lower platelet levels (p&lt;0.02); in MDS, median white blood cell counts remained below 13,000/µL. In MDS, FLT3mut cases were enriched in the MDS with increased blasts subtype (57% vs 39%, p=0.01) and favorable-risk cytogenetics (64% vs 41%, p&lt;0.01). Genomically, FLT3mut MDS showed higher frequencies of NRAS, KRAS, PTPN11, RUNX1 and CBL co-mutations (p&lt;0.05), and a markedly lower prevalence of TP53 mutations (2% vs 36%, p&lt;0.001). In CMML, FLT3mut cases were more often transfusion-dependent (50% vs 23%, p&lt;0.01), classified as CMML-2 (39% vs 25%, p=0.04), and enriched in ASXL1 (68% vs 47%; p&lt;0.01) and SETBP1 (24% vs 8%; p&lt;0.01) mutations. Notably, they exhibited multi-hit TET2 mutations less frequently than FLT3wt CMML (27% vs 53%, p=0.01). After adjusting for age, sex, and allogeneic stem cell transplantation, FLT3 mutations were independently associated with worse outcomes. To further assess the prognostic impact of FLT3 mutations, we conducted PSM analyses using a 1:1 ratio of FLT3mut to FLT3wt patients, adjusting for key clinical and biological confounders. In MDS, matching was based on age, sex, bone marrow blast percentage, and cytogenetic risk category; in CMML, variables included age, CMML-2 subtype, peripheral blood counts, normal karyotype, and ASXL1 mutation status. After matching, FLT3mut MDS cases showed significantly shorter overall survival (OS; 19.9 vs 44.2 months, p=0.046) and leukemia-free survival (LFS; 13.8 vs 37.1 months, p=0.023) compared to FLT3wt patients. Similarly, in CMML, FLT3mut patients experienced inferior OS (30.4 vs 67.2 months, p=0.017) and LFS (17.2 vs 65.6 months, p=0.014). Competing risk models confirmed a markedly increased cumulative incidence of AML transformation in both FLT3mut MDS and CMML (p&lt;0.001), without significant differences in the risk of non-leukemic death. Among FLT3mut cases, tyrosine kinase domain mutations (FLT3-TKD) were associated with superior survival compared to internal tandem duplications (FLT3-ITD; MDS hazard ratio [HR]: 0.28, p=0.003; CMML HR: 0.34, p=0.012). FLT3 allelic ratio (AR), assessed by conventional PCR in 63 patients (MDS n=34, CMML n=29), provided additional prognostic value. An AR ≥0.21 identified a subgroup with significantly shorter OS (MDS: 24.4 vs 38.8; CMML: 6.7 vs 30.4 months) and LFS (MDS: 9.0 vs 18.5 months; CMML: 5.8 vs 30.4 months; all p&lt;0.05), supporting the utility of quantitative FLT3 assessment in clinical risk stratification. FLT3 inhibitors (FLT3i) were administered in combination with hypomethylating agents (HMA) to 16 (27%) FLT3mut MDS and 15 (33%) CMML patients. Compared to HMA, FLT3i treatment was associated with higher overall response rates (MDS: 69% vs 22%, p&lt;0.01; CMML: 73% vs 35%, p=0.06). Molecular clearance, defined as post-treatment reduction in AR to ≤0.01, was observed more frequently with FLT3i than with HMA (MDS: 56% vs 10%, p=0.03; CMML: 60% vs 20%, p=0.09). FLT3 clearance was associated with prolonged survival. In MDS, median OS and LFS were 66.0 vs 14.5 (p=0.12) and 29.0 vs 9.9 months (p=0.056), respectively; in CMML, it was 37.6 vs 14.6 months for both OS (p=0.043) and LFS (p=0.032). Conclusions: This study expands the current understanding of FLT3mut MDS and CMML by characterizing their clinical, molecular, and therapeutic features in the largest multicenter cohort reported to date. These cases exhibit a proliferative phenotype, frequent co-mutations in RAS pathway genes, and an elevated risk of leukemic transformation. Quantitative assessment of FLT3 allelic burden refines prognostication beyond mutation status alone. The observed associations between FLT3 inhibitor use, molecular clearance, and improved outcomes support further evaluation of targeted strategies in selected patients.

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.

Abstract Recent updates to the monocyte count thresholds for diagnosing chronic myelomonocytic leukemia (CMML) have recognized oligomonocytic CMML (OM-CMML) as an early stage within the CMML spectrum. However, the clinical validity of these revised criteria remains uncertain without considering biological factors and might misclassify myelodysplastic syndromes (MDS) with mild monocytosis as CMML. To characterize the genomic landscape of OM-CMML, we evaluated OM-CMML (n=249), MDS (n=304) and myelodysplastic CMML (MD-CMML, n=358) patients (pts) treated at three academic institutions. Targeted next-generation sequencing was performed on bone marrow (BM) at initial diagnosis or first referral. OM-CMML was characterized by lower frequencies of biallelic TET2 (biTET2) and RAS pathway (RASp) gene mutations (BRAF, CBL, KRAS, NF1, NRAS and PTPN11) compared to MD-CMML (p&lt;0.001) but higher frequencies than MDS (p&lt;0.001). BM monocyte frequencies, assessed morphologically, were higher in OM-CMML than in MDS (p&lt;0.001) but lower than in MD-CMML (p&lt;0.001), indicating that OM-CMML is a heterogenous group including true CMML and distinct MDS subsets. Unsupervised hierarchical clustering based on genomic alterations across all cases (n=911) identified three distinct clusters: 1) TP53 alterations (predominantly multihit TP53) and complex karyotype (TP53-complex); 2) biTET2 or TET2-SRSF2 co-mutation (biTET2/SRSF2); 3) a heterogenous group lacking these features (not otherwise specifiable [NOS]). Among OM-CMML pts, 11%, 34% and 55% belonged to these clusters, respectively. Model-based unsupervised clustering further identified 9 unique clusters that could be consolidated into 6 groups: 1) TP53-complex; 2) biTET2/SRSF2; 3) SF3B1 (without biTET2 or SRSF2); 4) SRSF2 without TET2 and frequent IDH2 mutations (SRSF2-IDH2); 5) ASXL1 and U2AF1 mutations and copy number alterations (CNA-ASXL1/U2AF1); 6) no somatic mutations (No mutation). biTET2/SRSF2 group exhibited monocytic bias with higher AMC, PB and BM monocyte % even among OM-CMML cases. This bias was not attributed to higher RAS pathway mutation frequencies, as biTET2/SRSF2 cases had similar RASp mutation frequencies but lower median variant allele frequencies for RASp mutations compared to NOS cases (9% vs. 21%, p=0.029). PB monocyte partitioning using flow cytometry (FC) in a subset of OM-CMML pts (n=50) demonstrated higher frequency of classical monocytosis (&gt;94% CD14+/CD16- monocytes) in biTET2/SRSF2 vs other OM-CMML. To further determine if biTET2/SRSF2 genotype defines a distinct biological subgroup, we performed RNA-sequencing of BM CD34+ cells from healthy donors (n=15) and pts with OM-CMML (n=12), MD-CMML (n=14) and MDS (n=55). Unsupervised clustering based on gene expression profiles revealed that biTET2/SRSF2 clustered irrespective of their diagnostic group with greater dispersion being observed based on diagnosis. MDS and CMML with biTET2/SRSF2 had less differentially expressed genes compared to MDS and CMML with biTET2/SRSF2 relative to other MDS and CMML genotypes. Among 138 OM-CMML pts in whom progression to overt CMML was evaluable, 37 (27%) progressed to overt CMML of which 26 (70%) belonged to the biTET2/SRSF2 group. Competing risk analysis confirmed higher cumulative incidence and shorter time to CMML progression among biTET2/SRSF2 OM-CMML. Using ElasticNet regression we identified BM monocytosis and 14 genomic variables (including biTET2 and TET2-SRSF2) as predictors of overt CMML progression among OM-CMML. We developed a score-based model able to predict progression with high reliability (AUC 0.805, sensitivity 0.70, specificity 0.76). Model accuracy was confirmed by optimism-corrected bootstrap internal validation (AUC 0.668). Monocytic bias, BM classical monocytosis (&gt;94% CD16- monocytes) and higher overt CMML progression in biTET2/SRSF2 was confirmed in a validation cohort (28, 66 and 22 pts with OM-CMML, MDS and MD-CMML, respectively). BM monocytosis of biTET2/SRSF2 was confirmed in this cohort with morphologic and FC monocyte frequency quantification having high concordance (r=0.737, p&lt;0.001). Genomic subgroup analysis discriminated survival outcomes more reliably than diagnostic categories alone. Our findings suggest that the presence of biTET2 or TET2-SRSF2 co-mutations, combined with BM monocytosis, can more reliably identify true CMML cases within the OM-CMML spectrum. Integrating genomic determinants into the CMML diagnostic framework enhances diagnostic accuracy.