DNA Copy Number Gains of TCF4 (E2-2) Are Associated with Poor Outcome in Diffuse Large B-Cell Lymphoma
Bibliographic record
Abstract
Abstract The development of B-cells is a complex process that proceeds through multiple stages and is regulated by a hierarchy of transcription factors and other physiologic signals. Each unique B-cell malignancy can be aligned with a 'normal counterpart' at one or more of these discrete developmental stages. However, with the exception of translocations of transcription factor genes, the genetic basis for this is not well defined. We performed an analysis of high-resolution single nucleotide polymorphism (SNP) microarrays from 694 diffuse large B-cell (DLBCL) tumors to identify significant somatic copy number alterations (SCNA). Through integrative analysis of 249 tumors with matched gene expression profiling (GEP) data, we identified the likely targets of these alterations and found that genes that were targeted by DNA copy number gain were significantly enriched for DNA binding activity and transcription factor function. We extended upon this observation by analyzing SNP microarray data of a further 2,716 tumors from 7 additional subtypes of B-cell malignancy. Through this analysis, we identified patterns of transcription factor alterations that aligned with the differentiation state of the 'normal B-cell counterpart' of each malignancy. This provides evidence that SCNA of B-cell transcription factors may underlie the differentiation state of B-cell malignancies. DLBCL can be divided into two subtypes based upon gene expression profiles that align with either the germinal center B-cell differentiation state (GCB-like) or a post-GCB activated B-cell state (ABC-like). Having observed an enrichment for transcription factor SCNAs in DLBCL, and an alignment between transcription factor alterations and differentiation states in other B-cell malignancies, we hypothesized that SCNAs of transcription factors may also underlie the etiology of these molecular subtypes. By testing for associations between SCNAs and cell of origin subtype, we identified three co-segregating DNA copy number gains that were significantly enriched in the ABC-like subtype. These included gains of the BCL6 and SPIB genes that have been previously observed to be associated with the ABC-like subtype. In addition, we found gains of the TCF4 (E2-2) gene to be significantly enriched in ABC-like tumors. In line with this, TCF4 alterations were significantly associated with reduced overall survival in cohorts of patients treated with either CHOP (n=232, P=0.009) or R-CHOP (n=197, P=0.041). B-cell receptor (BCR) signaling is a key survival pathway in ABC-like DLBCL, and the TCF4 gene has a defined role in promoting the expression of immunoglobulin (Ig) genes that encode the B-cell receptor (BCR). The analysis of paired SCNA and GEP data revealed a significantly higher expression of Ig genes in tumors with TCF4 DNA copy number gain compared to those without, suggesting that normal BCR expression may be deregulated by this genetic alteration. In addition, chromatin-immunoprecipitation sequencing (ChIP-seq) for TCF4 in ABC-like DLBCL cell lines also revealed binding of TCF4 to an Ig gene enhancer region. As BCR signaling can be altered by somatic mutations in the CARD11, CD79B and MYD88 genes, we evaluated the relative representation of these mutations and TCF4 DNA copy number gains using targeted deep sequencing of 124 DLBCL tumors. This revealed that TCF4 DNA copy number gains largely mutually excluded CARD11 mutations, but significantly co-segregated with both MYD88 (FDR=0.005) and CD79B (FDR=0.053) mutations. In addition, we observed significant co-segregation between CD79B and MYD88 mutations (FDR<0.001). This is particularly notable due to the preliminary associations between combined CD79B and MYD88 mutation status and response to an inhibitor of BCR signaling, Ibrutinib. Together these data highlight an association between SCNA of B-cell transcription factors and the differentiation state of the 'normal counterpart' of the respective malignant B-cell. In line with this, we show that DNA copy number gains of the TCF4 transcription factor are associated with the ABC-like subtype of DLBCL, significantly worse overall survival, and increased Ig expression. These characteristics, in addition to the co-association between TCF4 DNA copy number gains and somatic mutations of CD79B and MYD88, suggest that TCF4 may be an important modifier of BCR signaling and contribute to the etiology of ABC-like DLBCL. Disclosures Rosenquist: Gilead Sciences: Speakers Bureau. Lunning:TG Therapeutics: Consultancy; AbbVie: Consultancy; Gilead: Consultancy; Bristol-Myer-Squibb: Consultancy; Juno: Consultancy; Genentech: Consultancy; Spectrum: Consultancy; Celgene: Consultancy; Pharmacyclics: Consultancy. Rodig:Bristol-Myers Squibb: Honoraria, Research Funding; Perkin Elmer: Membership on an entity's Board of Directors or advisory committees. Levy:Kite Pharma: Consultancy; Five Prime Therapeutics: Consultancy; Innate Pharma: Consultancy; Beigene: Consultancy; Corvus: Consultancy; Dynavax: Research Funding; Pharmacyclics: Research Funding.
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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) | 0.001 | 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".