Clinical Significance of Genetic Aberrations in Diffuse Large B Cell Lymphoma
Bibliographic record
Abstract
Abstract Background: Although R-CHOP has significantly improved outcome in diffuse large B-cell lymphoma (DLBCL), 40% of patients still experience relapsed/refractory disease. Further investigation into the genomic architecture of DLBCL is needed to determine the biological correlates that underlie treatment failure. Recent studies using next-generation sequencing strategies have described the landscape of recurrent mutations in DLBCL. However, with the exception of TP53 and FOXO1, little is known about the clinical relevance of recurrent mutations and importantly, the interactions of these genetic alterations in DLBCL. Moreover, an integrated analysis of copy number alterations and recurrent mutations annotated across cell-of-origin (COO) distinctions for a large cohort of DLBCL cases who have received uniform therapy is lacking. The present study examined the frequency and clinical impact of recurrent genetic aberrations of DLBCL using high-resolution technologies in a large population-based DLBCL cohort. Methods: We analyzed 348 newly diagnosed DLBCL cases that were uniformly treated with R-CHOP at the BC Cancer Agency (Vancouver) with available DNA extracted from fresh frozen biopsy material (tumor content >30%). Matched germ line DNA was available for 67 patients. Comprehensive clinical annotation was available through the BCCA Lymphoid Cancer Database. Targeted re-sequencing of the coding exons of 56 genes was performed using a Truseq Custom Amplicon assay. Gene selection was based on mutational frequencies that have been previously described in DLBCL mutational landscape publications. High-resolution copy number analyses were performed using Affymetrix SNP 6.0 arrays. Tissue microarrays were constructed using duplicate 0.6mm cores from 332 cases, and breakapart FISH assays for MYC, BCL2 and BCL6 and IHC staining for MYC, BCL2 and cell of origin proteins were performed. COO classification was available in 331 cases, according to gene expression profiling by the Lymph2Cx assay using the NanoString platform (Scott, Blood 2014;123) in 299 patients as well as Hans algorithm (Hans, Blood 2004;103) in 32 cases with low tumor content. 194 cases were assigned to GCB subtype, 107 cases, ABC/non-GCB and 30 were unclassifiable. Results: In the mutation analysis, we identified 2,757 SNVs and 245 small indels. The mean depth of coverage was 634. Recurrent mutation frequencies varied between 0 and 58, with a mean of 8.25 per case. 98% of cases harbored at least one mutation and 95% of cases multiple mutations. 10 mutated genes were detected significantly more frequently in the GCB subtype including CREBBP, GNA13, EZH2, TNFRSF14, IRF8,STAT3, BCL2, SGK1, MEF2B and CD83, and 4 mutated genes, MYD88, CD79B, PRDM1 and PIM1, in the ABC subtype. In the copy number analysis, 45 significant amplification peaks and 57 deletion peaks were revealed by the GISTIC algorithm. As previously reported, 9p21.3, including CDKN2A,were more frequently detected in the ABC subtypes. With a median follow up of 6.5 years for living patients, the 5 y disease specific survival (DSS) and time to progression (TTP) of all patients were 72% and 64%, respectively. The clinical cohort was representative of registry data from BC based on a comparison of patient characteristics and survival outcomes with 1,194 control DLBCL R-CHOP patients. The ABC subtype was significantly associated with an inferior DSS and TTP (both p<0.0001). In univariate analyses we identified several gene mutations and copy number aberrations significantly associated with survival in all patients. Of these genes, MYD88 and TP53 mutations were associated with significantly inferior TTP in the ABC subtype (p=0.04) and GCB subtype (p=0.002), respectively, while TMEM30A, CREBBP, PIM1 and BTG1 mutations were associated with prognosis in DLBCL. Our analyses confirm the poor prognosis conferred by TP53 mutations in DLBCL and, importantly, identified several novel genetic alterations associated with survival stratified by COO distinctions. Conclusions: Our approach using next generation sequencing and high resolution SNP array provides an accurate estimation of frequency and clinical significance of recurrent genetic alterations of DLBCL in a uniformly R-CHOP-treated large population-based cohort of patients. 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) | 0.001 | 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".