Double-Hit Signature with <i>TP53</i> Abnormalities Predicts Poor Survival in Patients with Germinal Center Type Diffuse Large B-Cell Lymphoma Treated with R-CHOP
Bibliographic record
Abstract
Background: In diffuse large B-cell lymphoma (DLBCL), the presence of MYC and BCL2 and/or BCL6 translocations, so-called double-hit lymphoma (DH), has been associated with an aggressive clinical course. Recently, it was reported that gene expression profiling (GEP) could also identify cases with the biological and clinical characteristics of DH lymphoma, including some without the requisite translocations (DHITsig-positive cases)1. The purpose of this study was to develop a molecular subtyping schema for germinal center B-cell type (GCB) DLBCL using genomic studies such as fluorescence in situ hybridization (FISH) cytogenetic analysis, GEP, and mutation analysis to risk-stratify patients with GCB DLBCL. Method and Results: We performed a detailed genomic analysis of 87 cases of de novo GCB DLBCL to identify characteristics that are associated with survival in those treated with R-CHOP. The cases were extensively characterized by combining the results of immunohistochemistry, cell-of-origin GEP (Nanostring), DH GEP (DLBCL90)1, FISH cytogenetic analysis for DH lymphoma, copy number analysis (CNA), and targeted deep sequencing using a custom mutation panel of 334 genes. These studies were used to divide the cases into four groups. GCB1: DHITsig-positive with TP53 inactivation (DHIT+TP53): DLBCL with TP53 mutations and/or deletions has a poor prognosis in patients treated with R-CHOP. We found 7 cases (8% of all cases) of GCB DLBCL that were DHITsig-pos with TP53 abnormalities. By FISH analysis, two cases had a triple-hit (TH), one was DH with MYC/BCL2, and 2 cases had a MYC translocation only. Cases in GCB1 had the worst overall survival (OS; Hazard Ratio (HR)=9.2, P=0.0018) and shortest progression-free survival (PFS; HR=6.1, P=0.002) compared to other groups (Figures 1 A/B). However, cases with TP53 abnormalities that were DHITsig-neg did not have the same poor survival. GCB2: DHITsig-positive (DHITsig-pos): The other 8 cases (9%) who were DHITsig-pos from the DBLCL90 GEP but lacked TP53 abnormalities showed a predilection (88%) for having an EZH2 mutation and/or BCL2 translocation (EZB of Schmitz et al2). These cases also had a high frequency of MYC mutations (63%) but lacked mutations in SGK1 and had a low frequency of mutations in linker histone genes (e.g. HIST1H1E). By FISH analysis, 3 cases were DH lymphoma with MYC/BCL2, 2 cases were TH lymphoma, and 1 case had a MYC translocation only. Typically DHITsig-pos cases have a poor OS when compared to DHITsig-neg cases1, however this group demonstrated good survival in our study, after removing the cases with TP53 abnormalities. GCB3: DHITsig-negative and EZH2 mutation and/or BCL2 translocation (EZB-like): We had 28 cases (32%) that were DHITsig-neg and had an EZH2 mutation and/or BCL2 translocation. These were categorized as EZB-like with some overlapping features with the DLBCL in Cluster 3 of Chapuy et al3. The survival of this group was intermediate compared to the other groups (Figures 1A/B). GCB4: DHITsig-negative and not EZB-like (GCB Other): The largest group of cases (51%) were DHITsig-neg and lacked EZH2 mutations and BCL2 translocations. These cases had frequent mutations in SGK1 (16%) and histone modifying genes (50%), as well as TET2 mutations (25%). These cases have similarities to Cluster 4 of Chapuy et al3 and the ST2 group from Wright et al4. The survival of this group was excellent (Figures 1 A/B). These groups were validated in an independent cohort of 188 cases of GCB DLBCL4 (Figures 1 C/D). Conclusions: We have identified four distinct biologic subgroups of GCB DLBCL with different survival rates, and with similarities to the genomic classifications from recent large retrospective studies of DLBCL. Patients with the DH signature but no abnormalities of TP53 (GCB2), and those lacking EZH2 mutation and BCL2 translocation (GCB4), had an excellent prognosis. However, patients with an EZB-like profile (GCB3) had an intermediate prognosis, whereas those with TP53 inactivation combined with the DH signature (GCB1) had an extremely poor prognosis. We propose this as a practical schema to risk-stratify patients with GCB DLBCL. This schema provides a promising new approach to identify high-risk patients for new and innovative therapies. Figure 1 Disclosures Herrera: AstraZeneca: Research Funding; Karyopharm: Consultancy; Genentech, Inc./F. Hoffmann-La Roche Ltd: Consultancy, Research Funding; Merck: Consultancy, Research Funding; Bristol Myers Squibb: Consultancy, Other: Travel, Accomodations, Expenses, Research Funding; Gilead Sciences: Consultancy, Research Funding; Seattle Genetics: Consultancy, Research Funding; Immune Design: Research Funding; Pharmacyclics: Research Funding. Zain:Kyowa Kirlin: Research Funding; Mundai Pharma: Research Funding; Seattle Genetics: Research Funding. Popplewell:Pfizer: Research Funding; Novartis: Research Funding; Roche: Research Funding. Kwak:Celltrion Healthcare: Membership on an entity's Board of Directors or advisory committees; CJ Healthcare: Consultancy; Sellas Life Sciences Grp: Consultancy; Enzychem Life Sciences: Membership on an entity's Board of Directors or advisory committees; Antigenics: Other: equity; InnoLifes, Inc: Consultancy, Membership on an entity's Board of Directors or advisory committees; Pepromene Bio: Consultancy, Membership on an entity's Board of Directors or advisory committees; Xeme Biopharma/Theratest: Other: equity; Celltrion, Inc.: Consultancy. Scott:NIH: Consultancy, Other: Co-inventor on a patent related to the MCL35 assay filed at the National Institutes of Health, United States of America.; Roche/Genentech: Research Funding; Celgene: Consultancy; NanoString: Patents & Royalties: Named inventor on a patent licensed to NanoString, Research Funding; Abbvie: Consultancy; AstraZeneca: Consultancy; Janssen: Consultancy, 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 | 0.000 |
| 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".