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Article Open Access

A cross‑platform transcriptomic risk score integrating LymphoMAP microenvironment archetypes and immune evasion‑associated programs in diffuse large B‑cell lymphoma

  • Authors:
    • Xiaochang Chen
    • Jin Zhang
    • Shenhe Jin
    • Luqiao Wang
    • Ye Zhang
  • View Affiliations / Copyright

    Affiliations: Department of Hematology, Sir Run Run Shaw Hospital, Zhejiang University School of Medicine, Hangzhou, Zhejiang 310016, P.R. China
    Copyright: © Chen et al. This is an open access article distributed under the terms of Creative Commons Attribution License.
  • Article Number: 326
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    Published online on: June 3, 2026
       https://doi.org/10.3892/ol.2026.15681
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Abstract

Diffuse large B‑cell lymphoma (DLBCL) is clinically heterogeneous and existing prognostic indices incompletely capture its biological diversity. The present study aimed to investigate how transcriptome‑inferred LymphoMAP archetypes (lymph node‑like, fibroblast‑macrophage‑rich and T cell‑exhausted) relate to genetic subtypes and immune evasion‑associated programs, and whether integrating these dimensions improves risk stratification beyond the International Prognostic Index (IPI). Using the DLBCL‑2018 cohort (n=562), LymphoMAP archetypes were inferred and immune programs were quantified by single‑sample gene set enrichment analysis, summarizing them into an immune evasion‑associated index. In the immunochemotherapy‑treated subset (n=234), overall survival was assessed and an elastic‑net Cox model was developed to derive a transcriptomic risk score (RScore‑Expr). The score was externally evaluated in four independent Gene Expression Omnibus cohorts (n=1,173) with a random‑effects meta‑analysis performed across studies. LymphoMAP archetypes were non‑randomly associated with genetic subtypes (χ²=20.85, P=0.0076) and exhibited distinct immune‑program patterns. While archetypes alone were not prognostic in the treated subset (log‑rank P=0.67), an integrated model identified high‑risk patients (log‑rank P=0.0026) with modest discrimination (C‑index: 0.624; time‑dependent area under the curve values: 0.662/0.628/0.642 at 12/36/60 months). When added to the IPI, the score improved corrected discrimination (0.639 to 0.687), improved model fit (likelihood‑ratio χ²=12.32, P=4.48x10‑4), and yielded higher net benefit on decision‑curve analysis at 24 and 60 months. In external cohorts, RScore‑Expr showed a consistent association with survival (pooled hazard ratio per 1 standard deviation, 1.13; 95% confidence interval 1.01‑1.26; P=0.033). These results support a cross‑platform framework linking microenvironment archetypes, immune evasion‑associated programs and tumor genetics. The novelty of the current study lies in integrating these features into an externally evaluated transcriptomic risk score and formally demonstrating added value beyond the IPI in DLBCL.
View Figures

Figure 1

Study design and analytical workflow.
(A) Discovery-cohort feature engineering. The full DLBCL-2018
cohort (n=562) was used for transcriptomic, genomic and
microenvironment association analyses, including LymphoMAP
inference, immune-program scoring and derivation of the IEAI. The
frontline immunochemotherapy-treated NCICCR subset (n=234) was used
as the discovery cohort for prognostic modeling. (B) Model
development, internal evaluation and external validation. An
elastic-net Cox model (α=0.5) with 10-fold cross-validation was
used to select the regularization parameter (λ) and derive the
transcriptomic risk score (RScore-Expr). Internal evaluation
included Harrell's C-index, time-dependent AUC, KM analysis,
multivariate Cox regression and calibration. External validation
was performed in four independent GEO cohorts using continuous HR
per 1 SD increase in RScore-Expr, random-effects meta-analysis and
PFS sensitivity analysis where available. AUC, area under the
curve; COO, cell-of-origin; DLBCL, diffuse large B-cell lymphoma;
FMAC, fibroblast-macrophage-rich; GEO, Gene Expression Omnibus; HR,
hazard ratio; IEAI, immune evasion-associated index; IPI,
International Prognostic Index; KM, Kaplan-Meier; LN, lymph
node-like; NCICCR, National Cancer Institute Center for Cancer
Research; PFS, progression-free survival; R-CHOP, rituximab plus
cyclophosphamide, doxorubicin, vincristine and prednisone; SD,
standard deviation; ssGSEA, single-sample gene set enrichment
analysis; TEX, T cell-exhausted; QC, quality control.

Figure 2

LymphoMAP archetypes are associated
with genetic subtypes and IEAI. (A) Heatmap showing the
distribution of genetic subtypes across LymphoMAP archetypes in the
diffuse large B-cell lymphoma-2018 cohort. The association was
significant as determined by χ2 test
(χ2=20.849; P=0.007559). (B) Distribution of the IEAI
across LymphoMAP archetypes. The y-axis shows IEAI (z), where z
denotes the standardized IEAI score (z-score). FMAC showed the
highest IEAI value, whereas TEX overlapped substantially with LN,
supporting partial but incomplete correspondence between
categorical archetype labels and the continuous immune-state score.
(C) Representative expression of antigen-processing genes in the
discovery cohort stratified by IEAI group. IEAI-high cases showed
lower expression of TAP1 and TAP2, supporting an immune
evasion-associated transcriptional phenotype centered on impaired
antigen-processing/presentation programs. FMAC,
fibroblast-macrophage-rich; IEAI, immune evasion-associated index;
LN, lymph node-like; TEX, T cell-exhausted.

Figure 3

Discovery-cohort prognostic analyses.
(A) Kaplan-Meier OS curves for the National Cancer Institute Center
for Cancer Research -treated discovery cohort stratified by
LymphoMAP archetype, showing no significant separation (log-rank
P=0.67). (B) Multivariate Cox regression including LymphoMAP
archetype, genetic subtype, IEAI, COO, age and IPI group. MCD and
N1 genetic subtypes, ABC COO and increasing age were associated
with inferior OS, whereas neither FMAC archetype, TEX archetype or
IEAI were independently associated with OS. (C) Kaplan-Meier OS
curves stratified by high-RScore-Expr versus low-RScore-Expr using
the prespecified discovery cutpoint, showing a significant
difference (log-rank P=0.0026). (D) Time-dependent AUC values of
RScore-Expr at 12, 36 and 60 months. ABC, activated B-cell-like;
AUC, area under the curve; COO, cell-of-origin; FMAC,
fibroblast-macrophage-rich; GCB, germinal center B-cell-like; IEAI,
immune evasion-associated index; IPI, International Prognostic
Index; LN, lymph node-like; OS, overall survival; TEX, T
cell-exhausted.

Figure 4

Added value of RScore-Expr beyond IPI
and clinical utility in the discovery cohort. (A) Comparison of
IPI-only, RScore-Expr-only and IPI + RScore-Expr models. Adding
RScore-Expr to IPI improved corrected discrimination (C-index 0.639
to 0.687), reduced AIC and improved model fit (likelihood-ratio
P=4.5×10−4). Decision-curve analyses for (B) 24-month
and (C) 60-month overall survival. Across multiple clinically
relevant threshold ranges, the combined IPI + RScore-Expr model
yielded higher net benefit than either model alone. (D) Calibration
plot comparing mean predicted and observed KM 60-month survival
probabilities in the discovery cohort. AIC, Akaike information
criterion; IPI, International Prognostic Index; KM,
Kaplan-Meier.

Figure 5

External validation of RScore-Expr
across independent Gene Expression Omnibus cohorts. (A) Forest plot
summarizing cohort-specific and pooled hazard ratios for overall
survival per 1 SD increase in RScore-Expr across four external
cohorts. The pooled random-effects estimate was 1.13 (95%
confidence interval 1.01–1.26; P=0.0328), with I2=18.7%.
(B) Cohort-specific C-index values for overall survival in the
external cohorts, showing modest but directionally consistent
discrimination. SD, standard deviation.
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Spandidos Publications style
Chen X, Zhang J, Jin S, Wang L and Zhang Y: A cross‑platform transcriptomic risk score integrating LymphoMAP microenvironment archetypes and immune evasion‑associated programs in diffuse large B‑cell lymphoma. Oncol Lett 32: 326, 2026.
APA
Chen, X., Zhang, J., Jin, S., Wang, L., & Zhang, Y. (2026). A cross‑platform transcriptomic risk score integrating LymphoMAP microenvironment archetypes and immune evasion‑associated programs in diffuse large B‑cell lymphoma. Oncology Letters, 32, 326. https://doi.org/10.3892/ol.2026.15681
MLA
Chen, X., Zhang, J., Jin, S., Wang, L., Zhang, Y."A cross‑platform transcriptomic risk score integrating LymphoMAP microenvironment archetypes and immune evasion‑associated programs in diffuse large B‑cell lymphoma". Oncology Letters 32.2 (2026): 326.
Chicago
Chen, X., Zhang, J., Jin, S., Wang, L., Zhang, Y."A cross‑platform transcriptomic risk score integrating LymphoMAP microenvironment archetypes and immune evasion‑associated programs in diffuse large B‑cell lymphoma". Oncology Letters 32, no. 2 (2026): 326. https://doi.org/10.3892/ol.2026.15681
Copy and paste a formatted citation
x
Spandidos Publications style
Chen X, Zhang J, Jin S, Wang L and Zhang Y: A cross‑platform transcriptomic risk score integrating LymphoMAP microenvironment archetypes and immune evasion‑associated programs in diffuse large B‑cell lymphoma. Oncol Lett 32: 326, 2026.
APA
Chen, X., Zhang, J., Jin, S., Wang, L., & Zhang, Y. (2026). A cross‑platform transcriptomic risk score integrating LymphoMAP microenvironment archetypes and immune evasion‑associated programs in diffuse large B‑cell lymphoma. Oncology Letters, 32, 326. https://doi.org/10.3892/ol.2026.15681
MLA
Chen, X., Zhang, J., Jin, S., Wang, L., Zhang, Y."A cross‑platform transcriptomic risk score integrating LymphoMAP microenvironment archetypes and immune evasion‑associated programs in diffuse large B‑cell lymphoma". Oncology Letters 32.2 (2026): 326.
Chicago
Chen, X., Zhang, J., Jin, S., Wang, L., Zhang, Y."A cross‑platform transcriptomic risk score integrating LymphoMAP microenvironment archetypes and immune evasion‑associated programs in diffuse large B‑cell lymphoma". Oncology Letters 32, no. 2 (2026): 326. https://doi.org/10.3892/ol.2026.15681
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