Genetic Abnormalities in Follicular Lymphoma and Transformed Follicular Lymphoma.
Bibliographic record
Abstract
Abstract Abstract 2648 Follicular lymphoma (FL) is an indolent lymphoma and the second most common type of non-Hodgkin lymphoma in the Western world. It is characterized by the t(14;18) chromosomal translocation, which is present in up to 90% of cases. About 40% of FL cases eventually transform into a more aggressive lymphoma (tFL), most commonly diffuse large B-cell lymphoma (DLBCL). To identify the secondary chromosomal abnormalities that contribute to the development of FL, and to its transformation, we undertook a large study using the Affymetrix 250k NspI SNP array to identify copy number abnormalities (CNAs) in 198 FL and 79 tFL samples, 75% of which have concurrent gene expression profiling studies using Affymetrix U133+2 or A+B arrays for correlative analysis. There were 22 recurrent chromosomal abnormalities that were present in over 10% of FL cases, including gains of 7, 12, 18, 21, X, 1q, 2q, 5p, 6p, 8q, 17q and loss of 6q. We also identified 20 smaller CNAs that occurred in over 5% of FL cases, the most frequent being loss of chromosome 1p36.33-p36.31 including TNFRSF14, loss of chromosome 10q23.1-q25.1 encompassing several possible cancer-related genes such as PTEN, gain of chromosome 2p16.1-p15 including REL, and gain of chromosome 8q24.13-q24.3 including MYC. Univariate Cox regression models were used to analyze the CNA regions that occurred in at least 10 FL cases as predictors of overall survival. Four recurrent CNAs were predictive of survival in univariate analysis below the p=0.05 significance level, and two were found to be borderline significant. A gain of X or the p arm of X was predictive of poor survival. Additionally, two losses on 6q (6q13–15 and 6q23.3–24.1) were associated with poor survival. The 6q23.3–24.1 loss contains TNFAIP3, which encodes a negative regulator of the NF-kB pathway, and is a frequent site of homozygous loss. Additionally, a gain of chromosome 8 that includes the MYC gene, and a loss of chromosome 9 that includes CDKN2A, were borderline predictors of poor survival. Patients with FLs that have 7 or more abnormalities had worse survival than those with fewer abnormalities. We also compared the CNAs found in tFL samples to FL samples and identified 26 abnormalities that were at least 5 times more frequent in tFL and present in at least 5% of tFLs. A gain of 3q27.3-q28 containing 5 genes including BCL6 and LPP, for example, was found in 11% of tFL case, but only 2% of FL cases. We also found differences in the deletion of Beta-2 Microglobulin (B2M) between FL and tFL. The B2M locus is deleted in 8% of FLs, but in 21% of tFLs. B2M, a subunit of the MHC class I molecule, is known to be repressed by mechanisms such as mutation and deletion in de novo DLBCL, as a way for the tumor to evade immune surveillance. HLA-A- B, and/or -C were deleted in 5% of FLs and almost 9% of tFLs. CD58, which plays a role in T- and NK-cell immune responses, was deleted in 3% of FLs and 11% of tFLs. Overall, 19% of FLs and 37% of tFLs had an abnormality in CD58, B2M, and/or HLA class I, indicating that evasion of immune surveillance is important in transformation to a more aggressive disease. We also compared CNAs from tFL cases to those found in de novo GCB-DLBCL cases and identified several that differed markedly between the 2 diseases, such as a gain of chromosome 21 which was present in 21% of tFL cases but only 3% of DLBCL cases. In conclusion, FL, tFL, and de novo GCB-DLBCL share common CNAs, but the prevalence of the individual lesions differ among the 3 entities. Functional validation of potential candidate genes will determine important pathways in the development and progression of FL, and identify possible targets for therapeutic intervention. Disclosures: No relevant conflicts of interest to declare.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| 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) | 0.003 | 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".