Introduction
Follicular lymphoma (FL) is a heterogenous disease historically divided into histological grades; grade (G) 1-2 have similar outcomes so are grouped together while G3 is further divided into G3'A' and G3'B' disease. Histologically, G3A has admixed centrocytes and centroblasts while G3B consists of centroblasts exclusively, with loss of follicular architecture. Thus, G3A and G3B are difficult to distinguish, and histological grading concordance remains poor at 60-70% (ILSG 1997, Rimsza 2018). Additionally, G3BFL is rare and controversies regarding G3B behavior exist with respect to whether it follows an indolent or aggressive clinical course with resultant major management implications. (Barraclough 2024)
Improved differentiation of FL grades via molecular characterization has been attempted by very small studies, limited by sample size and gene coverage, hence yielding conflicted results (Piccaluga 2008, Horn 2011). New molecular techniques enable extensive genetic exploratory analysis, with minimal RNA requirements, allowing more thorough interrogation of FL gene signatures. We assessed FL molecular characteristics via modern RNA sequencing and correlated with grade and outcomes.
Methods
Tumor samples from patients with nodal FL diagnosed between 2014- 2020 according to WHO criteria 4th Ed (Swerdlow 2008) with adequate paraffin-embedded tissue and clinical data were selected from tissue banks of two Australian sites. Diagnosis and grading was by an expert lymphoma haematopathologist. Patients were dichotomized into early progressors (progression of disease within 24 months of diagnosis) versus late/non-progressors (progression of disease > 24m after diagnosis or no progression event). RNA was extracted using Qiagen RNeasyA FFPE Kit according to manufacturer's guidelines and quantified by Qubit™ RNA High Sensitivity Assay Kit. Libraries for sequencing were prepared using the NEBNext Ultra™ II RNA Library Prep Kit and sequenced on the Nextseq 500 or Novaseq 6000 with 50 or 75bp paired-end reads. Sequenced reads were aligned to the human hg19 genome with HISAT2 and mapped to genes using HTSeq. The resulting gene count data were analyzed using the edgeR package in R.
Results
Forty-nine pt samples were analyzed: 13 G1/2FL, 24 G3AFL, 12 G3BFL. Median age was 67 years (range: 39-86) with a median follow-up of 39 months (range: 8-87). Early progressors accounted for 17 pts; of those 2 were G1/2 FL, 10 were G3AFL and 5 were G3BFL.
We performed differential gene expression analysis to compare the G1/2FL and the G3BFL samples and found that genes involved in DNA replication and mitosis were significantly up-regulated in G3BFL compared with G1/2FL. These genes, including MKI67, the cell division cycle (CDC) genes, cyclins and cyclin dependent kinase genes, are critical regulators of cell cycle progression. Unsupervised hierarchical clustering of samples using the G1/2FL and G3BFL differentially expressed genes delineated 2 distinct groups of G3AFL - those that clustered with G1/2FL and those that clustered with G3BFL. Further analysis of disease progression demonstrated odds of early progression were 6.3 times higher (95% C.I. 0.56-344, P= 0.17) in the G3AFL group that clustered with G3BFL compared with the group that clustered with G1/2 FL. Of the 7 G3AFL patients with a gene expression program clustering with G1/2FL, only 1 had an early progression, in contrast, 9 out of 17 patients with a gene expression program clustering with G3BFL had early progression.
Conclusions
In the largest study to date, with gene expression analysis we identified that compared to G1-3AFL, G3BFL differentially displays upregulation of genes involved in cell proliferation, which likely reflects a more aggressive disease phenotype. While G1/2FL and G3BFL display distinct gene expression profiles, G3AFL displays a molecular profile that overlaps both entities; with the majority of early G3AFL progressors clustering with G3BFL and late/non-progressors with G1/2FL. This may enable future clinical trial eligibility of G3AFL to reflect disease biology as opposed to grade.
Barraclough:Beigene: Honoraria; Novartis: Honoraria; Roche: Honoraria; Gilead: Honoraria. Hawkes:Janssen: Membership on an entity's Board of Directors or advisory committees, Speakers Bureau; Antengene: Membership on an entity's Board of Directors or advisory committees; Novartis: Membership on an entity's Board of Directors or advisory committees; Regeneron Pharmaceuticals, Inc.: Membership on an entity's Board of Directors or advisory committees, Other: Trial Steering Committee, Speakers Bureau; Gilead: Membership on an entity's Board of Directors or advisory committees, Speakers Bureau; Merck Sharpe and Dohme: Membership on an entity's Board of Directors or advisory committees; Sobi: Membership on an entity's Board of Directors or advisory committees; AstraZeneca: Membership on an entity's Board of Directors or advisory committees, Other: Trial Steering Committee, Research Funding, Speakers Bureau; Merck KGaA: Research Funding; Bristol Myer Squibb: Membership on an entity's Board of Directors or advisory committees, Other: Trial Steering Committee, Research Funding; Roche: Membership on an entity's Board of Directors or advisory committees, Research Funding. Lee:Roche: Honoraria; Gilead: Honoraria. Chong:Amgen, AstraZeneca, Bayer, Dizal Pharma, HUTCHMED, Incyte, Innate Pharma, Merck, Pfizer, Pharmacyclics, Roche: Research Funding; Bristol Myers Squibb, Regeneron Pharmaceuticals, Inc.: Consultancy, Research Funding; Takeda: Consultancy.
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