The Novel Prognostic Index Model of Combining Circulating Tumor DNA and PINK-E Predicts the Clinical Outcomes for Newly Diagnosed Extranodal NK/T-cell Lymphoma

Copyright © 2022 the Author(s). Published by Wolters Kluwer Health, Inc. on behalf of the European Hematology Association..

Extranodal NK/T-cell lymphoma (ENKTL) is a highly aggressive and heterogeneous disease with poor clinical outcome. Our previous work had demonstrated that circulating tumor DNA (ctDNA) analyses were feasible in ENKTL, and dynamic tracing of ctDNA could be used to monitor the disease status. However, the prognostic value of ctDNA in ENKTL has not been fully investigated. Patients with newly diagnosed ENKTL from February 2017 to December 2021 (n = 70) were enrolled. The pretreatment ctDNA concentration (hGE/mL) was measured. The prognostic value of ctDNA, international prognostic index (IPI), Korean prognostic index (KPI), PINK-E, and the combination of PINK-E and ctDNA (PINK-EC) were investigated in our cohort. The IPI and PINK-E risk categories had a significant difference in progression-free survival (PFS) and overall survival (OS) between the low-risk and intermediate-risk groups. The KPI risk category had a difference in PFS and OS between the intermediate-risk and high-risk groups. Furthermore, integrating ctDNA into the PINK-E model could overcome the shortcomings of other prognostic models, which could significantly distinguish the different-risk groups. Overall, our results demonstrated that PINK-EC showed a superior prognostic prediction value and stability compared with IPI, KPI, and PINK-E. The integration of molecular features of the tumor into classic risk categories might better characterize a high-risk group where novel treatment approaches are most needed.

Medienart:

E-Artikel

Erscheinungsjahr:

2023

Erschienen:

2023

Enthalten in:

Zur Gesamtaufnahme - volume:7

Enthalten in:

HemaSphere - 7(2023), 1 vom: 01. Jan., Seite e822

Sprache:

Englisch

Beteiligte Personen:

Huang, Dezhi [VerfasserIn]
Li, Qiong [VerfasserIn]
Li, Xinlei [VerfasserIn]
Ma, Naya [VerfasserIn]
Duan, Yishuo [VerfasserIn]
Zhu, Lidan [VerfasserIn]
Li, Jiali [VerfasserIn]
Wen, Qin [VerfasserIn]
Gao, Lei [VerfasserIn]
Yang, Cheng [VerfasserIn]
Rao, Lingyi [VerfasserIn]
Gao, Li [VerfasserIn]
Zhang, Xi [VerfasserIn]
Rao, Jun [VerfasserIn]

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Date Revised 03.01.2023

published: Electronic-eCollection

Citation Status PubMed-not-MEDLINE

doi:

10.1097/HS9.0000000000000822

funding:

Förderinstitution / Projekttitel:

PPN (Katalog-ID):

NLM350821968