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. 2024 Apr 9;8(7):1587-1599.
doi: 10.1182/bloodadvances.2023011425.

Biological signatures of the International Prognostic Index in diffuse large B-cell lymphoma

Yue Wang  1 , Qing Shi  1 , Zi-Yang Shi  1 , Shuang Tian  1 , Mu-Chen Zhang  1 , Rong Shen  1 , Di Fu  1 , Lei Dong  2 , Hong-Mei Yi  2 , Bin-Shen Ouyang  2 , Rong-Ji Mu  3 , Shu Cheng  1 , Li Wang  1   4 , Peng-Peng Xu  1 , Wei-Li Zhao  1   4
Affiliations

Biological signatures of the International Prognostic Index in diffuse large B-cell lymphoma

Yue Wang et al. Blood Adv. .

Abstract

Diffuse large B-cell lymphoma (DLBCL) is a highly aggressive subtype of lymphoma with clinical and biological heterogeneity. The International Prognostic Index (IPI) shows great prognostic capability in the era of rituximab, but the biological signatures of IPI remain to be discovered. In this study, we analyzed the clinical data in a large cohort of 2592 patients with newly diagnosed DLBCL. Among them, 1233 underwent DNA sequencing for oncogenic mutations, and 487 patients underwent RNA sequencing for lymphoma microenvironment (LME) alterations. Based on IPI scores, patients were categorized into 4 distinct groups, with 5-year overall survival of 41.6%, 55.3%, 71.7%, and 89.7%, respectively. MCD-like subtype was associated with age of >60 years, multiple extranodal involvement, elevated serum lactate dehydrogenase (LDH), and IPI scores ranging from 2 to 5, whereas ST2-like subtype showed an opposite trend. Patients with EZB-like MYC+ and TP53Mut subtypes exhibited poor clinical outcome independent of the IPI; integrating TP53Mut into IPI could better distinguish patients with dismal survival. The EZB-like MYC-, BN2-like, N1-like, and MCD-like subtypes had inferior prognosis in patients with IPI scores of ≥2, indicating necessity for enhanced treatment. Regarding LME categories, the germinal center-like LME was more prevalent in patients with normal LDH and IPI scores of 0 to 1. The mesenchymal LME served as an independent protective factor, whereas the germinal center-like, inflammatory, and depleted LME categories correlated with inferior prognosis for IPI scores of 2 to 5. In summary, our work explored the biological signatures of IPI, thus providing useful rationale for future optimization of the IPI-based treatment strategies with multi-omics information in DLBCL.

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Conflict of interest statement

Conflict-of-interest disclosure: The authors declare no competing financial interests.

Figures

None
Graphical abstract
Figure 1.
Figure 1.
Patient flow diagram.
Figure 2.
Figure 2.
Survival curves of patients treated with R-CHOP–based immunochemotherapy. (A) PFS for IPI risk groups. (B) OS for IPI risk groups.
Figure 3.
Figure 3.
Mutation profile of patients with DLBCL. (A) Relationship between oncogenic mutations and IPI risk groups. (B) Mantel-Haenszel χ2 of oncogenic mutations according to the IPI. (C) Relationship between oncogenic mutations and age. (D) Relationship between oncogenic mutations and Ann Arbor stage. (E) Relationship between oncogenic mutations and extranodal involvement. (F) Relationship between oncogenic mutations and LDH ratio. (G) Relationship between oncogenic mutations and ECOG performance status (PS); ∗P < .05, ∗∗P < .01, ∗∗∗P < .001, and ∗∗∗∗P < .0001.
Figure 3.
Figure 3.
Mutation profile of patients with DLBCL. (A) Relationship between oncogenic mutations and IPI risk groups. (B) Mantel-Haenszel χ2 of oncogenic mutations according to the IPI. (C) Relationship between oncogenic mutations and age. (D) Relationship between oncogenic mutations and Ann Arbor stage. (E) Relationship between oncogenic mutations and extranodal involvement. (F) Relationship between oncogenic mutations and LDH ratio. (G) Relationship between oncogenic mutations and ECOG performance status (PS); ∗P < .05, ∗∗P < .01, ∗∗∗P < .001, and ∗∗∗∗P < .0001.
Figure 4.
Figure 4.
Genetic subtypes of patients with DLBCL. (A) Mantel-Haenszel χ2 of genetic subtypes related to IPI risk group. (B-C) Forest plots visualize hazard ratios (HRs) and P values obtained from the multivariate analysis of genetic subtypes and IPI for PFS and OS. (D) PFS into 4 main risk groups stratified by molecularly-enhanced IPI scores in training cohort. (E) PFS for IPI risk groups in training cohort. (F) Distribution of IPI risk factors across different genetic subtypes (∗P < .05 and ∗∗P < .01).
Figure 4.
Figure 4.
Genetic subtypes of patients with DLBCL. (A) Mantel-Haenszel χ2 of genetic subtypes related to IPI risk group. (B-C) Forest plots visualize hazard ratios (HRs) and P values obtained from the multivariate analysis of genetic subtypes and IPI for PFS and OS. (D) PFS into 4 main risk groups stratified by molecularly-enhanced IPI scores in training cohort. (E) PFS for IPI risk groups in training cohort. (F) Distribution of IPI risk factors across different genetic subtypes (∗P < .05 and ∗∗P < .01).
Figure 5.
Figure 5.
LME categories of patients with DLBCL. (A) Kaplan-Meier models of PFS according to LME categories. (B) Kaplan-Meier models of OS according to LME categories. (C) Mantel-Haenszel χ2 of LME categories related to IPI risk group. (D-E) Forest plots visualize hazard ratios (HRs) and P values obtained from the multivariate analysis of LME categories and IPI for PFS and OS. (F) Distribution of IPI risk factors across different LME categories; ∗P < .05, ∗∗P < .01, and ∗∗∗P < .001.
Figure 5.
Figure 5.
LME categories of patients with DLBCL. (A) Kaplan-Meier models of PFS according to LME categories. (B) Kaplan-Meier models of OS according to LME categories. (C) Mantel-Haenszel χ2 of LME categories related to IPI risk group. (D-E) Forest plots visualize hazard ratios (HRs) and P values obtained from the multivariate analysis of LME categories and IPI for PFS and OS. (F) Distribution of IPI risk factors across different LME categories; ∗P < .05, ∗∗P < .01, and ∗∗∗P < .001.
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