Rokach, Lior

Abstract The comparative value of liposomal cytarabine/daunorubicin (CPX-351) versus venetoclax plus a hypomethylating agent (Ven-HMA) in the frontline treatment of older adults with primary ( de novo ) or secondary acute myeloid leukemia (AML) remains uncertain. In the current study, we retrospectively examined outcomes of 600 patients with newly diagnosed AML treated with CPX-351 (N = 112) or Ven-HMA (N = 488). AML subtypes included de novo (46%), post-myelodysplastic syndrome (MDS) (19%), post-myeloproliferative neoplasm (MPN) (12%), post-MDS/MPN (6%), therapy-related (17%) and AML with myelodysplasia-related genetic abnormalities (AML-MR; 46%). Patients receiving CPX-351 were younger (median 65 vs. 73 years; p < 0.01), predominantly female (50% vs. 38%; p = 0.02), more likely to have secondary AML (68% vs. 51%; p < 0.01), and less likely to harbor NPM1 MUT (5% vs. 12%; p = 0.02). Rates of complete response with or without count recovery (CR/CRi) were comparable between CPX-351 and Ven-HMA (55% vs. 60%; p = 0.30), including in AML-MR (60% vs. 63%; p = 0.70). Ven-HMA use was associated with fewer infectious complications (62% vs. 83%; p < 0.01) and yielded higher CR/CRi rates in males (60% vs. 45%; p = 0.04), de novo AML (68% vs. 50%; p = 0.03), and in the presence of STAG2 MUT (86% vs. 44%; p = 0.02), or CEBPA MUT (88% vs. 50%; p = 0.03). Overall survival censored for transplant, was similar (median 10 vs. 13 months; p = 0.90), with Ven-HMA being superior in post-MDS AML (median 12 vs. 7 months; p = 0.02) and CPX-351 in the presence of SF3B1 MUT (not reached vs. 14 months; p < 0.01). Our findings suggest that Ven-HMA is as effective and less toxic than CPX-351 in newly diagnosed AML, including AML-MR, despite selection of younger, fitter patients for CPX-351.

Abstract Venetoclax (Ven) in combination with hypomethylating agents (HMA) is widely used as frontline treatment in older or unfit patients with acute myeloid leukemia (AML). Liposomal cytarabine and daunorubicin (CPX-351) is FDA-approved for the treatment of secondary AML, including therapy-related (t-AML) and AML with myelodysplasia-related changes (AML-MR). The current study aimed to provide a comprehensive comparison between the two treatment strategies to better inform clinical decisions. Methods The current retrospective analysis was performed under an institutional review board approved minimal risk protocol at Mayo Clinic (MN, AZ, FL). AML-MR gene mutations or cytogenetic abnormalities were categorized according to the International Consensus Classification (Arber et al. Blood 2022;140:1200). Treatment response was assessed according to the 2022 European LeukemiaNet (ELN) criteria (Dohner et al. Blood 2022;140:1345). Overall survival (OS) analysis was censored for allogeneic stem cell transplant (ASCT). Statistical analyses were performed using JMP Pro (Version 18.0.0). Results A total of 600 patients with newly diagnosed AML (ND-AML) treated with CPX-351 (N-112, 19%), or Ven-HMA (N=488, 81%) were included. AML subtypes included de novo (N=277, 46%), post-myelodysplastic syndrome (post-MDS, N=114,19%), post-myeloproliferative neoplasm (post-MPN, N=70, 12%), post-MDS/MPN (N=36, 6%), t-AML (N=103, 17%) and AML-MR (N=274, 46%). Compared with those treated with Ven-HMA, patients receiving CPX-351 were younger (median age 65 vs 73 years; p&lt;0.01), more likely to be female (50% vs 38%; p=0.02), more frequently diagnosed withsecondaryAML (68% vs 51%; p&lt;0.01), and less likely to have NPM1MUT (5% vs 12%; p=0.02) whereas ELN 2022 cytogenetic risk distribution was similar between the two (adverse risk; 43% vs 40%; p=0.5). Comparison of treatment response and survival Complete response rates with (CR) or without (CRi) count recovery were similar between CPX-351 and Ven-HMA overall (55% vs 60%) as well as specifically in patients with AML-MR (60% vs 63%), adverse karyotype (44% vs 44%), NPM1MUT (100% vs 89%), TP53MUT(32% vs 45%), IDH1MUT (60% vs 76%), IDH2MUT (63% vs 77%), SF3B1MUT(71% vs 45%), and CBLMUT (33% vs 69%) [p&gt;0.05 in all instances]. Compared to those with CPX-351, CR/CRi rates with Ven-HMA were superior in males (60% vs. 45%; p=0.04) and in patients with de novo AML (68% vs. 50%; p=0.03), STAG2MUT (86% vs. 44%; p=0.02), or CEBPAMUT (88% vs. 50%; p=0.03); infectious complications were significantly more frequent with CPX-351 (83% vs 62%, p&lt;0.01). At a median follow-up of 12 months (range; 1-88), 416 (69%) deaths, 179 relapses (44% in CPX-351 and 52% in Ven-HMA responders) and 149 ASCTs (53% in CPX-351 and 18% in Ven-HMA) were documented. OS rates were similar between CPX-351 and Ven-HMA, in general (median 10 vs 13 months), as well as in patients with adverse karyotype, TP53MUT, IDH1MUT, and IDH2MUT (p&gt;0.05 in all instances). By contrast, OS was superior in Ven-HMA-treated patients with post-MDS AML (median 12 vs. 7 months; age-adjusted p=0.02) and in CPX-351-treated patients harboring SF3B1MUT(median not reached vs 14 months; age-adjusted p&lt;0.01). In addition, a borderline-significant OS advantage for CPX-351 was apparent in patients with PTPN11MUT (13.5 vs 8 months; p=0.08). Comparison in ICC defined AML-myelodysplasia-related CR/CRi and OS rates were comparable between patients treated with CPX-351 (N=57) or Ven-HMA (N=217), with response rates of 60% vs 63% (p=0.68) and median OS of 10 vs 14 months (p=0.61), respectively. However, OS was superior with Ven-HMA in patients with post-MDS AML (15 vs 8 months; age-adjusted p=0.02) and with CPX-351 in those with SF3B1MUT (NR vs 14 months; age-adjusted p=0.02). Conclusions: The current study suggests that Ven-HMA was as effective and possibly less toxic than CPX-351 for the treatment of ND-AML, including AML-MR, despite the bias for younger and fitter patients treated with the latter regimen. Observed differences involving patients with SF3B1MUT or post-MDS AML require validation in prospective studies and provide parameters for developing treatment-specific predictive models that will be presented at the meeting.

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 Background Red blood cell distribution width (RDW) is an easily accessible diagnostic test that is underutilized as a prognostic marker, despite substantial supporting evidence in cancer, cardiovascular diseases, and helathy individuals (Pilling et al. PLoS One 2028;13:e0203504). The association between increased RDW and incident acute myeloid leukemia (AML) has long been suggested in healthy volunteers (Abelson et al. Nature 2019;559:400). We looked into a similar phenomenon in patients with polycythemia vera (PV) or essential thrombocythemia (ET) in large discovery and validation cohorts. Methods A machine learning (ML) approach was used to identify baseline clinical and laboratory parameters that were predictive of leukemic transformation (LT) and develop a predictive model in a large discovery cohort of PV and ET, obtained from electronic medical records of the Maccabi Healthcare Services (MHS; Tel-Aviv, Israel). Receiver operating characteristic (ROC) curve analysis was utilized to determine the optimal cutoff points for RDW and other continuous variables. Mayo Clinic databases (Rochester, MN, USA) for patients with PV and ET were utilized as validation cohorts. Identification of study patients in the latter was according to the International Consensus Classification (Arber et al. Blood 2022; 140:1200) while in the discovery cohort was based on presence of ICD-9 codes associated with documentation of disease-specific driver mutations or treatment records that are consistent. Statistical analyses were performed using the R statistical software 4.4.2 (November, 2024) (Foundation for Statistical Computing, Vienna, Austria) or JMP Pro 18.0.0 software (SAS Institute, Cary, NC, USA). Results The discovery cohorts from Israel included 4,592 patients with PV (median age 63 yesrs; 53% males) and 5,968 with ET (median age 61 yesrs; males 40%). The validation cohorts from the Mayo Clinic included 633 PV (median 65 years; 51% males) and 634 ET (median 60 years; females 64%) patients that were informative for RDW. In the discovery cohorts, median follow-up (incident AML) was 6.1 years (N=186; 4.1%) in PV and 6.1 years (N=85; 1.4%) in ET. The corresponding values for the validation cohorts were 6.6 years (N=24; 3.8%) in PV and 7.0 years (N=22; 3.5%) in ET. Discovery cohort analyses Covariate-adjusted Cox regression analysis was utilized to estimate associations between LT and several clinical variables followed by machine learning pipeline to predict LT in a given timeframe. Conversion from continuous to binary variables was accompliched by ROC analysis. Shapley Additive exPlanations (SHAP) application enabled selection of top-ranked features that included RDW &gt;15.5 (HR 3.2, 1.1-9.5; p=0.03), age &gt;60 years (HR 2.9, 1.4-5.9; p&lt;0.01), and LDH &gt;1.5 x UNL (HR 4.0 (1.6-10.0; p&lt;0.01). A similar analysis in ET also identified RDW &gt;15.5 (p&lt;0.01), age &gt;60 years (p=0.01) as well as male sex (p=0.03) as independent risk factors for LT. Subseqent risk models based on these risk factors were externally validated and enabled robust risk stratification with 10-20 year AUCs rangening from 0.75 to 0.81. Validation cohorts analyses The indpendent predictive value of increased RDW observed in the discovery cohort was confirmed in the validation cohorts for both PV and ET and further gender-specified: female/male RDW ≥16.4/15.8 in PV (HR 7.9, 1.9-33.8) and ≥16.1/14.4 in ET (HR 5.1, 2.2-11.9). Also confirmed were the additional prognostic contributions of age ≥60 years (HR 3.8, 1.3-10.9) and LDH &gt;1.5 x UNL (HR 3.9, 1.6-9.4), in PV, while no other clinical risk factor showed RDW-independent prognostic relevance in ET. The prognostic impact of RDW in ET was most apparent in females (p&lt;0.01), as opposed to males (p=0.32) while the effect was equally significant in PV. In multivariable analysis that included previously recognized high risk mutations in PV (SRSF2) and ET (SF3B1), both RDW and mutations sustained significance (p&lt;0.05). Conclusion The current study identifies higher RDW as a key risk factor for LT in both PV and ET, independent of other clinical or genetic risk factors. Possible underlying mechanisms include increased RDW as a marker of long-term pre-leukemic clones with sub-clinical ineffective erythropoiesis or an associated pro-leukemic inflammatory state.

ABSTRACT Survival prediction models in essential thrombocythemia (ET) include the International Prognostic Scoring System (IPSET) and the more recently introduced triple‐A (AAA) prognostic score. The latter enlists age and absolute neutrophil (ANC) and lymphocyte (ALC) counts as risk variables. In the current study, a Mayo Clinic discovery cohort of 658 patients with ET was used to identify AAA‐independent risk variables. Accordingly, multivariable analysis‐derived HRs (95% CI) were 15.7 (8.4–29.5) for age > 70 years (8 points); 4.2 (2.3–7.5) for age 50 to 70 years (2 points); 1.8 (1.2–2.5) for ANC ≥ 8 × 10 9 /L (1 point); 1.4 (1.03–1.9) for ALC < 1.7 × 10 9 /L (1 point); 1.8 (1.2–2.6) for absolute monocyte count (AMC) ≥ 0.5 × 10 9 /L (1 point); 1.8 (1.2–2.3) for male sex (1 point); 1.8 (1.3–2.4) for arterial hypertension (1 point); and 1.6 (1.2–2.3) for arterial thrombosis (1 point). HR‐weighted scoring enabled a 4‐tiered risk classification: ultra‐low (0–1 points; N = 94; median survival 42.7 years), low (2–4 points; N = 297; 23 years), intermediate (5 points; N = 66; 17.3 years), and high (6–14 points; N = 201; 10.8 years). Time‐dependent predictive performance at 20/25 years favored AAA + (AUC 0.92/0.91) vs. AAA (0.86/0.86) vs. IPSET (0.81/0.84). The AAA + risk model was subsequently validated by two external cohorts from Israel ( N = 5968) and Italy ( N = 682). In the cohort from Israel, disease‐specific mortality was assessed by comparing observed survival to an age‐ and sex‐matched reference population, which suggested near‐normal life expectancy in ultra‐low risk patients. The current study highlights host‐related factors as the primary determinants of longevity in ET and provides a composite risk score (AAA + ) that is based on complete blood count‐derived parameters and host‐related factors. Predictive performance of the new model was shown to be superior to that of IPSET and AAA.