← Search

Excel · study · 2026

Dissecting diffuse large B cell lymphoma heterogeneity: new insights from a state-specific molecular characterization

Listed in NCBI GEO

Diffuse large B cell lymphoma (DLBCL) is a biologically and clinically heterogeneous disease.

Description

In this study, we analyzed a real-world cohort of 178 newly diagnosed DLBCL patients homogeneously treated with R-CHOP–like regimens, integrating RNA sequencing, targeted mutational profiling, and digital deconvolution using the EcoTyper algorithm. Five malignant B cell states (S1–S5) were identified, each reflecting distinct differentiation and transcriptional programs with prognostic relevance, S1 and S5 representing the most favorable and adverse subtypes, respectively.

EcoTyper-defined Lymphoma Ecotypes (LE1–LE9), capturing co-associations between malignant and microenvironmental cell states, further refined prognostic classification, with LE1/LE2 and LE9 associated with the worst and most favorable prognosis, respectively. Both B-cell states and LE retained independent prognostic value in multivariable analyses after adjusting for the Cell-of-Origin and the revised International Prognostic Index.

Read the rest (2 more)

Mutational profiling revealed state-specific patterns: S1 harbored mutations in genes involved in epigenetic regulation, cytoskeletal organization, and tumor microenvironment (TME) interactions (CREBBP, EZH2, ACTB), whereas S5 was enriched for mutations promoting survival signaling, immune evasion, and differentiation blockade (MYD88, CD79B, TP53). Pathway analysis indicated that S1 was linked to cell adhesion, extracellular matrix remodeling, and inflammatory signaling, while S5 displayed enrichment of cell cycle deregulation, metabolic reprogramming, and immune evasion.

Accordingly, S1 tumors appear reliant on TME interactions, whereas S5 tumors adopt a predominantly TME-independent, intrinsically aggressive phenotype. Collectively, these findings delineate a biologically coherent framework linking pathway deregulation with cell state and ecotype specific mutational landscapes, refining DLBCL risk classification and adding an additional dimension to conventional prognostic models.

Links

Topics

From keywords
Life Sciences
Inferred from text
Cancer 65% · Disease 75% · RNA sequencing 75% · Sequencing 75%
Provenance · 1 source records, 11 field assertions
SourceKeyLast seenRaw
NCBI GEOGSE33753114 d agoJSON v1
FieldAssertionExtractorEvidence
access_levelsource · NCBI GEOconnector:ncbi_geo@1.0.0
concepts[disease].local:disease:cancerenrichment · NCBI GEOkeyword-concept-rules@1.0.0title+description (65%)
concepts[disease].local:disease:diseaseenrichment · NCBI GEOkeyword-concept-rules@1.0.0title+description (75%)
concepts[field].local:field:life-sciencesmapping · NCBI GEOconnector:ncbi_geo@1.0.0
concepts[method].geo_series_type:expression-profiling-by-high-throughput-sequencingsource · NCBI GEOconnector:ncbi_geo@1.0.0/gdstype
concepts[modality].local:modality:rna-seqenrichment · NCBI GEOkeyword-concept-rules@1.0.0title+description (75%)
concepts[modality].local:modality:sequencingenrichment · NCBI GEOkeyword-concept-rules@1.0.0title+description (75%)
concepts[organism].NCBITaxon:9606source · NCBI GEOconnector:ncbi_geo@1.0.0/taxon
descriptionsource · NCBI GEOconnector:ncbi_geo@1.0.0/summary
publication_datesource · NCBI GEOconnector:ncbi_geo@1.0.0
titlesource · NCBI GEOconnector:ncbi_geo@1.0.0/title