Targeted Sequencing Reveals Novel Gene Mutations Associated with Transformation and Early Progression in Follicular Lymphoma
Bibliographic record
Abstract
Abstract Introduction : Follicular lymphoma (FL) remains an incurable malignancy as patients eventually experience progressive disease. A subset of patients is at risk of early lymphoma-related mortality due to histological transformation (TFL) to aggressive lymphoma (2-3% of patients per year) or early progression after immuno-chemotherapy, each of which leads to shortened survival. Mutations associated with transformation and/or early progression have been reported in the literature (e.g. CDKN2A, TP53, B2M) and the m7-FLIPI clinico-genetic risk model defines a high-risk patient group with poor failure-free survival after first-line treatment. However, the current knowledge of genetic alterations underlying transformation and/or early progression is inadequate to explain the majority of transformed cases and accurately predict early progressive disease. Materials and Methods: We performed targeted capture-based sequencing of 94 genes in a large cohort of fresh-frozen and formalin-fixed and paraffin-embedded patient specimens (402 samples in total). This cohort comprised 277 samples from 159 patients that experienced transformation (including 128 samples at T1 (the time of FL) and 149 samples at T2 (the time of transformation) with 118 paired biopsies) and 125 samples from 125 patients (pre-treatment samples only) presenting with either early progression within 2.5 years after starting immunochemotherapy (n=41) or late or never progression for at least 5 years after starting immunochemotherapy or observation (n=84). Mutations were called using MutationSeq and putative single nucleotide polymorphisms were filtered out using either matching germline samples (n=84) or dbSNP (v147). Bayesian proportion tests were used to compare the prevalence of gene mutations between groups. Results : We first compared T1 (n=128) and T2 (n=149) samples from 159 patients experiencing transformation. Eleven genes were more commonly altered in transformed lymphoma. These included genes that had previously been associated with transformation, such as TP53, B2M, MYC and EBF1, as well as genes that have not yet been implicated as contributing to this process, for example EZH2, CCND3, PIM1 and ITPKB. Moreover, mutations in GNA13, S1PR2 and P2RY8, that have been implicated in dissemination of germinal centre B cells, were enriched in T2 samples. In addition, cell-of-origin classification was available for 108 of the TFL cases with DLBCL histology, 18 and 90 of which were of the ABC and GCB subtype, respectively. Although the number of ABC-TFL cases was small, we observed higher percentages of BCL10 (16% versus 1%, Fisher P=0.004), CD79B (22% versus 4%, Fisher P=0.005) and MYD88 mutations (28% versus 9%, P=0.006) in ABC-TFL than in GCB-TFL, suggesting that B-cell receptor signaling and NF-κB signaling are important contributors to the ABC phenotype in TFL. Next, we assessed the association of gene mutations with patient outcomes contrasting early progressers (n=41) and late or non-progressers (n=84). Eleven genes were mutated more commonly in early progressers than in late progressers, including KMT2C, TP53, BTG1, MKI67, XBP1 and SOCS1. Overall, 32 out of 41 early progressers (78%) had mutations in at least one of the 11 early progression-associated genes, but none of the individual early progression-associated genes were mutated at a frequency > 22%. Thus, early progression appears to be related to relatively infrequent genetic alterations. None of the early progression-associated gene mutations form part of the m7-FLIPI outcome predictor. Furthermore, in our cohort that was enriched for clinical extremes, the m7-FLIPI was similarly associated with early progression when compared to the FLIPI but it was not superior, having higher specificity (89% versus 76%), but lower sensitivity (36% versus 63%). Conclusions: We found novel associations of gene mutations with transformation and showed that TFL is molecularly heterogeneous, with the ABC subtype being characterized by differential gene mutation when compared to the GCB subtype. With regards to progressive disease after immuno-chemotherapy, our approach identified early progression as a distinct clinico-genetic disease category that is imperfectly captured by traditional prognostic tools. Disclosures Connors: Seattle Genetics: Research Funding; Bristol Myers Squib: Research Funding; F Hoffmann-La Roche: Research Funding; NanoString Technologies: Research Funding; Millennium Takeda: Research Funding. Scott:NanoString Technologies: Patents & Royalties: named inventor on a patent for molecular subtyping of DLBCL that has been licensed to NanoString Technologies.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".