Molecular Subtyping of DLBCL Patients from the Oncology Research Information Exchange Network

Background: Simplified LymphPlex algorithm recently described molecular subclassification of DLBCL based on mutations of 35 genes and rearrangements of BCL2, BCL6, and MYC. Seven distinct genetic subtypes are included: TP53^Mut, MCD-like (co-occurrence of MYD88^L265P and CD79^Mut), BN2-like (BCL6 fusions and NOTCH2^Mut), N1-like (NOTCH1^Mut), EZB-like with or without MYC rearrangement (EZH2, other chromatin modifier mutations and BCL2 fusions), and ST2-like (SGK1^Mut and TET2^Mut), with ~50% of patients (pts) assigned a molecular subtype (Shen et al, Nature 2023). Each group has potential therapeutic implications; for example, BTK inhibitors in MCD-like and N1-like subtype due to inherent NF-κB activation (Wilson et al, Cancer Cell 2021). EZH2 inhibitors in EZB-like subtype, etc. Additionally, MCD-like and TP53^Mut cohorts were identified to have relatively worse outcomes compared to their counterparts. This suggests subclassifying via LymphPlex for DLBCL pts provides key information on outcomes and may be used to guide therapeutic intervention.

Aim/Methods: Given the scarce application of this algorithm, our goal was to apply LymphPlex algorithm to the ORIEN Avatar cohort of 148 DLBCL pts with tumor whole exome sequencing (WES) and RNA sequencing or fluorescence in situ hybridization (FISH) data available to assign molecular subtype. We detected mutations from WES tumor samples using Haplotyper and curated known or predicted functional variants with a variant allele frequency (VAF) of ≥4% using Aster Insight's cBioPortal. Fusion calls were made from RNA-seq or clinical FISH testing records using cBioPortal. Variant and fusion data was input into LymphPlex v1 to generate DLBCL subclassifications. We described progression free survival (PFS) and overall survival (OS) stratified by molecular sub-classification.

Results: Subclassification was successful in 136/148 DLBCL pts from years 1994-2022. For these pts, median age at diagnosis was 63.5 years. For 122 pts with staging information available, the majority were advanced stage (70%). We could calculate IPI score in 69% of pts, of those 11.7% had IPI score of 0-1 vs 88.3% had IPI score of ≥2. COO origin data was available in 29% of pts; of those 20% were ABC and 80% were GCB subtype. 48.5% of the pts received RCHOP and the rest received other intensive regimens (RCODOX/M/RIVAC, REPOCH, etc) as their first-line treatment. 109/136 DLBCL samples were collected before front-line treatment.

LymphPlex yielded following subclassifications: 45(33%) TP53^Mut, 17(13%) EZB-like [11 negative, 4 positive, 2 unknown for MYCr], 11(8%) MCD-like, 5 (4%) N1-like, 4(3%) ST2-like, 3 (2%) were BN2-like, and 51(38%) “Other”. Median follow-up for the cohort was 52.8 months (1 - 256 months). The PFS showed significant differences among genetic subtypes assigned by the LymphPlex algorithm in our cohort (p=0.03 including “Other”; p=0.052 for only classified groups). Median PFS was worst in the MCD-like and TP53^Mut cohorts with 31.1 months (9.1-38.1) and 37.1 months (22.7-72.1), respectively, while median PFS was not reached for N1-like, ST2-like, BN-2 like, and EZB-like. “Other” pts had median PFS of 120 months [37-120]. PFS of the TP53^Mut subtype was inferior to the TP53^WT pts (p=0.04); PFS of the MCD-like subtype was inferior to all non-MCD-like subtypes (p=0.01). The OS did not show significant differences among genetic subtypes in this cohort (p=0.536). TP53^Mut pts showed unfavorable outcome as compared to TP53^WT pts with the median OS of 95 months [49-104 months; p=0.04].

In the “Other” unclassified group, we identified MSH6 (p=1.30E-03), RASGRF1 (p=3.71E-03), and IGF1R (p=9.53E-03) as the most significantly altered genes compared to all classified groups and upregulated expression of genes responsive to thyroglobulin triiodothyronine (T3; p=3.28E-07) and involved in glucose transport, including SLC2A1, NCOA1 and NCOA2.

Conclusion: Overall, we found the LymphPlex algorithm easy to use in DLBCL pts with WES/RNA/FISH data. We were able to classify 62% of pts into one of the seven subcategories. In keeping with previous literature, TP53^Mut and MCD-like had worst PFS compared to their counterparts and TP53^Mut had worse OS compared to their counterparts. Our analysis highlights the need for further sub-classification strategies to guide improved therapeutic options for over one-third of DLBCL cases that do not fit currently established molecular subtypes.

Shah:AbbVie, Seattle Genetics: Consultancy; Incyte, Epizyme, Seattle Genetics, Loxo Oncology, Acerta: Research Funding. Portell:Merck: Consultancy, Research Funding; Prelude: Research Funding; BeiGene: Consultancy, Research Funding; Jansen: Consultancy; AstraZeneca: Consultancy, Research Funding; AbbVie: Consultancy; SeaGen/Pfizer: Research Funding; Infinity: Research Funding; Genentech/Roche: Research Funding; Kite: Research Funding. Pinilla-Ibarz:Bristol Meyers Squibb: Consultancy, Speakers Bureau; Novartis: Honoraria; AbbVie: Consultancy, Speakers Bureau; Janssen: Consultancy, Speakers Bureau; Sanofi: Consultancy, Speakers Bureau; AstraZeneca: Consultancy, Speakers Bureau; Beigene: Consultancy, Speakers Bureau; Eli Lily: Consultancy, Speakers Bureau; Pfizer: Consultancy; Secura Bio: Consultancy, Speakers Bureau; Takeda: Consultancy, Speakers Bureau. Rhodes:Loxo Oncology: Research Funding; Janssen: Research Funding; Acerta: Research Funding; Verastem: Consultancy; TG Therapeutics: Consultancy; Seagen: Consultancy; Pharmacyclics: Consultancy, Research Funding; MorphoSys: Consultancy; Johnson and Johnson: Consultancy; Genentech: Consultancy; Epizyme: Consultancy; Beigene: Consultancy; ADC Therapeutics: Consultancy; AbbVie: Consultancy, Research Funding; Oncternal Therapeutics: Research Funding; VelosBio: Research Funding. Evens:Pfizer: Consultancy, Honoraria; Incyte: Consultancy, Honoraria; Daiichi Sankyo: Consultancy, Honoraria; Genentech: Consultancy, Honoraria; Novartis: Consultancy, Honoraria; Pharmacyclics: Consultancy, Honoraria. Sawalha:AbbVie: Research Funding; ADC: Consultancy; Genmab: Honoraria, Research Funding; Beigene: Research Funding. Bond:BMS: Research Funding; Incyte: Research Funding; GenMab: Research Funding; AstraZeneca: Research Funding; ADC Therapeutics: Consultancy; Accutar: Research Funding; Novartis: Consultancy, Research Funding; Kite/Gilead: Research Funding; Nurix Therapeutics: Consultancy, Research Funding. Maddocks:ADC Therapeutics: Consultancy; Incyte: Consultancy; BMS: Consultancy; AstraZeneca: Consultancy; Lilly: Consultancy; Genmab: Consultancy; Genentech: Consultancy; Janssen: Consultancy; Gilead/KITE: Consultancy; AbbVie: Consultancy. Link:Genentech: Research Funding. Cohen:Kite/Gilead: Consultancy; Hutchmed: Consultancy, Research Funding; Lilly: Consultancy, Research Funding; Genentech: Research Funding; Nurix: Research Funding; Takeda: Research Funding; Janssen: Consultancy; Astra Zeneca: Consultancy, Research Funding; Beigene: Consultancy.