Validation of blast and blast-mol risk models in chronic myelomonocytic leukemia: Mayo-humanitas collaborative project involving 1,101 patients

S Saubia Fathima (1Mayo Clinic, Hematology, Rochester, United States) M Muhammad Yousuf P Priyansh Faldu (1Division of Hematology, Department of Medicine, Mayo Clinic, Rochester, Minnesota, USA, Rochester, United States) A Ali Alsugair (1Mayo Clinic, Hematology, Rochester, United States) C Clifford Csizmar (1Mayo Clinic, Hematology, Rochester, United States) M Merry Nakhleh (1Mayo Clinic, Hematology, Rochester, United States) A Abhishek Mangaonkar (1Mayo Clinic, Rochester, United States) A Animesh Pardanani (4Mayo Clinic, Scottsdale, United States) L Luca Lanino (3Yale University, New Haven, United States) A Alessia Campagna (2Humanitas Research Hospital, Istituto di Ricovero e Cura a Carattere Scientifico, Milan, Italy) G Giulia Maggioni (2Humanitas Research Hospital, Istituto di Ricovero e Cura a Carattere Scientifico, Milan, Italy) K Kaaren Reichard (4Mayo Clinic, Department of Laboratory Medicine and Pathology, Rochester, United States) R Rong He N Naseema Gangat (4Mayo Clinic, Scottsdale, United States) M Mrinal Patnaik (5Mayo Clinic, Rochester, United States) M Matteo Della Porta (1IRCCS Humanitas Research Hospital, AI Center, Rozzano, Italy) A Ayalew Tefferi (4Mayo Clinic, Scottsdale, United States)

Abstract

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 <10%/<5%) vs. CMML-2 (BM/PB blasts ≥10%/≥5%) and CMML-MP (leukocytes ≥13 x 10⁹/L) vs. CMML-MD (leukocytes <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<0.01), CMML-1 (87% vs. 81%; p<0.01), ASXL1MUT (48% vs. 35%; p<0.01), SRSF2MUT (43% vs. 33%; p<0.01), NRASMUT (15% vs 9%; p<0.01), PHF6MUT (7% vs. 4%; p<0.01), and less likely to harbor TET2MUT (50% vs. 58%; p<0.01), and SF3B1MUT (4% vs. 10%; p<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<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<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<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<0.01; Figure 3]. Multivariable analysis of the combined Mayo-Humanitas cohort (N=1,101) identified age >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.

Article Details

Journal Blood
Volume / Issue Vol. 146, Issue Supplement 1
Published November 03, 2025
Pages 2077-2077
ISSN 0006-4971
Publisher Elsevier BV

Journal Info

Blood

Elsevier BV

ISSN: 0006-4971 Health Sciences

Authors (17)

S

Saubia Fathima

1Mayo Clinic, Hematology, Rochester, United States

M

Muhammad Yousuf

P

Priyansh Faldu

1Division of Hematology, Department of Medicine, Mayo Clinic, Rochester, Minnesota, USA, Rochester, United States

A

Ali Alsugair

1Mayo Clinic, Hematology, Rochester, United States

C

Clifford Csizmar

1Mayo Clinic, Hematology, Rochester, United States

M

Merry Nakhleh

1Mayo Clinic, Hematology, Rochester, United States

A

Abhishek Mangaonkar

1Mayo Clinic, Rochester, United States

A

Animesh Pardanani

4Mayo Clinic, Scottsdale, United States

L

Luca Lanino

3Yale University, New Haven, United States

A

Alessia Campagna

2Humanitas Research Hospital, Istituto di Ricovero e Cura a Carattere Scientifico, Milan, Italy

G

Giulia Maggioni

2Humanitas Research Hospital, Istituto di Ricovero e Cura a Carattere Scientifico, Milan, Italy

K

Kaaren Reichard

4Mayo Clinic, Department of Laboratory Medicine and Pathology, Rochester, United States

R

Rong He

N

Naseema Gangat

4Mayo Clinic, Scottsdale, United States

M

Mrinal Patnaik

5Mayo Clinic, Rochester, United States

M

Matteo Della Porta

1IRCCS Humanitas Research Hospital, AI Center, Rozzano, Italy

A

Ayalew Tefferi

4Mayo Clinic, Scottsdale, United States