Clinical and biological impact of plasma cytokine levels in newly diagnosed chronic lymphocytic leukemia

S Samir Mouhssine (32Division of Hematology, Department of Translational Medicine, University of Eastern Piedmont, Novara, Italy) C Chiara Cosentino (1Division of Hematology, Department of Translational Medicine, Università del Piemonte Orientale, Novara, Italy) M Mohammad Almasri (1Division of Hematology, Department of Translational Medicine, Università del Piemonte Orientale, Novara, Italy) N Nawar Maher (1Division of Hematology, Department of Translational Medicine, Università del Piemonte Orientale, Novara, Italy) J Jana Nabki (1Division of Hematology, Department of Translational Medicine, Università del Piemonte Orientale, Novara, Italy) L Luca Cividini (1Division of Hematology, Department of Translational Medicine, Università del Piemonte Orientale, Novara, Italy) B Bashar AL Deeban (1Division of Hematology, Department of Translational Medicine, Università del Piemonte Orientale, Novara, Italy) F Francesca Maiellaro (1Division of Hematology, Department of Translational Medicine, Università del Piemonte Orientale, Novara, Italy) R Riccardo Dondolin (1Division of Hematology, Department of Translational Medicine, Università del Piemonte Orientale, Novara, Italy) M Matteo Bellia (1Division of Hematology, Department of Translational Medicine, Università del Piemonte Orientale, Novara, Italy) E Eleonora Secomandi (1Division of Hematology, Department of Translational Medicine, Università del Piemonte Orientale, Novara, Italy) V Valeria Caneparo (2Center for Translational Research on Autoimmune & Allergic Diseases – CAAD, Università del Piemonte Orientale, Novara, Italy) A Abdurraouf Mahmoud (1Division of Hematology, Department of Translational Medicine, Università del Piemonte Orientale, Novara, Italy) C Clara Deambrogi (1Division of Hematology, Department of Translational Medicine, Università del Piemonte Orientale, Novara, Italy) S Sreekar Kogila (1Division of Hematology, Department of Translational Medicine, Università del Piemonte Orientale, Novara, Italy) J Joseph Ghanej (1Division of Hematology, Department of Translational Medicine, Università del Piemonte Orientale, Novara, Italy) S Silvia Rasi (1Division of Hematology, Department of Translational Medicine, Università del Piemonte Orientale, Novara, Italy) G Gianluca Gaidano (13Department of Translational Medicine, University of Eastern Piedmont, Novara, Italy) R Riccardo Moia (32Division of Hematology, Department of Translational Medicine, University of Eastern Piedmont, Novara, Italy)

Abstract

Abstract BACKGROUND: Concentrations of specific cytokines/chemokines are consistently associated with patient prognosis in different cancer types. In the context of chronic lymphocytic leukemia (CLL), the impact of baseline cytokine levels on biological features and clinical outcomes remains poorly understood. METHODS: Plasma from newly diagnosed CLL patients was extracted and then analyzed using a bead-based 27-plex enzymatic assay. Different optimal cut-offs for overall survival (OS), probability of Richter transformation (RT) and second malignancies risk analyses were defined using maximally selected rank statistics for every specific cytokine/chemokine. The machine-learning algorithms “self-organizing map” (SOM) and “K-means” were applied to divide patients into clusters based on cytokine/chemokine levels. RESULTS: A total of 244 patients with newly diagnosed CLL were included. Median age at diagnosis was 68.9 years; 73 patients (29.9%) had unmutated immunoglobulin heavy chain variable region (IGHV) genes, and 16 (6.6%) had TP53 disruption. After a median follow-up of 13.4 years, the median OS was 15.7 years. By correlating cytokines/chemokines with patient baseline clinical and molecular features, elevated MIP-1α levels were significantly associated with unmutated IGHV genes (p<0.0001), greater lymph node diameter (p<0.0001), and advanced Rai and Binet stages (both p<0.0001). Higher IP-10 levels significantly associated with advanced Rai (p=0.02) and Binet (p=0.01) stages. Elevated GM-CSF levels were associated with del(17p) (p=0.03). At diagnosis, multiple cytokines/chemokines with levels exceeding the optimal cut-offs for predicting OS, namely MIP-1α (p= 0.001), IP-10 (p= 0.003), G-CSF (p= 0.011), PDGF-BB (p= 0.023), IL-4 (p=0.023), GM-CSF (p=0.025), IL-5 (p=0.025), IL-6 (p=0.027), and IL-2 (p=0.027) were significantly associated with shorter survival. In multivariate analysis (adjusted for age, IGHV, and TP53 status), high levels of IL-4, using a cut-off of 5.78 pg/mL, independently associated with shorter OS (HR 2.2, p=0.008). Pathway gene enrichment analysis revealed that cytokines/chemokines predicting shorter OS are involved in key molecular pathways regulating cell proliferation, survival, and apoptosis. In particular, G-CSF, GM-CSF, IL-2, IL-4, IL-5, IL-6, and PDGF-BB clustered in the JAK-STAT signaling pathway (fold enrichment 109.9, p=2.23E-12), whereas G-CSF, IL-2, IL-4, IL-6, and PDGF-BB clustered in the PI3K-Akt pathway (fold enrichment 35.9, p=9.17E-07). Regarding RT, high levels of GM-CSF (p<0.0001), MIP1-α (p=0.034), IL-4 (p=0.034), and IL-10 (p=0.034) were significantly associated with increased risk of RT in univariate analysis. GM-CSF, with a cutoff of 0.86 pg/mL, independently predicted RT in multivariate analysis (HR 9.32, p=0.009). Moreover, among patients who developed a second malignancy after CLL diagnosis, high levels of MIP-1β (p=0.0001), RANTES (p=0.023), and MIP-1α (p=0.023) were linked to shorter time of cancer occurrence. Unsupervised clustering identified three patient groups, namely clusters 1, 2, and 3, with distinct cytokine/chemokine profiles. Cluster 1 is characterized by elevated levels of MIP-1α, MIP-1β, IL-6, TNF-α, IL-8, GCSF, IL-2, and VEGF. Cluster 2 is associated with generally low cytokine/chemokine expression, while cluster 3 shows higher concentrations of IL-10, IL-9, IL-7, IP-10, and RANTES. In the pathway gene enrichment analysis, cluster 1 displayed higher activation of inflammation-related pathways, such as the PI3K/Akt signaling pathway. Conversely, cluster 3 was characterized by the activation of autoimmunity and infection response pathways. Survival analysis demonstrated that patients in cluster 1 had significantly worse OS (p = 0.02), a finding confirmed in multivariate analysis (p = 0.02). Furthermore, Binet A and B patients in cluster 3 exhibited a significantly shorter time to first treatment (TTFT) compared to the other clusters (p = 0.02).CONCLUSIONS: Plasma levels of immunomodulatory cytokines/chemokines are associated with unfavorable clinical and biological features at the time of CLL diagnosis and might predict lower OS, higher risk of RT and second malignancies. Moreover, patients can be divided into groups based on their plasma cytokine/chemokine profile to better stratify OS and TTFT. If confirmed also in the context of patients treated with BTK inhibitors and BCL2 inhibitors, cytokine/chemokine levels might integrate current prognostic biomarkers in CLL.

Article Details

Journal Blood
Volume / Issue Vol. 146, Issue Supplement 1
Published November 03, 2025
Pages 7404-7404
ISSN 0006-4971
Publisher Elsevier BV

Journal Info

Blood

Elsevier BV

ISSN: 0006-4971 Health Sciences

Authors (19)

S

Samir Mouhssine

32Division of Hematology, Department of Translational Medicine, University of Eastern Piedmont, Novara, Italy

C

Chiara Cosentino

1Division of Hematology, Department of Translational Medicine, Università del Piemonte Orientale, Novara, Italy

M

Mohammad Almasri

1Division of Hematology, Department of Translational Medicine, Università del Piemonte Orientale, Novara, Italy

N

Nawar Maher

1Division of Hematology, Department of Translational Medicine, Università del Piemonte Orientale, Novara, Italy

J

Jana Nabki

1Division of Hematology, Department of Translational Medicine, Università del Piemonte Orientale, Novara, Italy

L

Luca Cividini

1Division of Hematology, Department of Translational Medicine, Università del Piemonte Orientale, Novara, Italy

B

Bashar AL Deeban

1Division of Hematology, Department of Translational Medicine, Università del Piemonte Orientale, Novara, Italy

F

Francesca Maiellaro

1Division of Hematology, Department of Translational Medicine, Università del Piemonte Orientale, Novara, Italy

R

Riccardo Dondolin

1Division of Hematology, Department of Translational Medicine, Università del Piemonte Orientale, Novara, Italy

M

Matteo Bellia

1Division of Hematology, Department of Translational Medicine, Università del Piemonte Orientale, Novara, Italy

E

Eleonora Secomandi

1Division of Hematology, Department of Translational Medicine, Università del Piemonte Orientale, Novara, Italy

V

Valeria Caneparo

2Center for Translational Research on Autoimmune & Allergic Diseases – CAAD, Università del Piemonte Orientale, Novara, Italy

A

Abdurraouf Mahmoud

1Division of Hematology, Department of Translational Medicine, Università del Piemonte Orientale, Novara, Italy

C

Clara Deambrogi

1Division of Hematology, Department of Translational Medicine, Università del Piemonte Orientale, Novara, Italy

S

Sreekar Kogila

1Division of Hematology, Department of Translational Medicine, Università del Piemonte Orientale, Novara, Italy

J

Joseph Ghanej

1Division of Hematology, Department of Translational Medicine, Università del Piemonte Orientale, Novara, Italy

S

Silvia Rasi

1Division of Hematology, Department of Translational Medicine, Università del Piemonte Orientale, Novara, Italy

G

Gianluca Gaidano

13Department of Translational Medicine, University of Eastern Piedmont, Novara, Italy

R

Riccardo Moia

32Division of Hematology, Department of Translational Medicine, University of Eastern Piedmont, Novara, Italy