CONCORDANCE BETWEEN IMMUNOHISTOCHEMISTRY AND GENE EXPRESSION PROFILING SUBTYPING FOR DIFFUSE LARGE B‐CELL LYMPHOMA IN THE PHASE 3 PHOENIX TRIAL
Bibliographic record
Abstract
Introduction: Diffuse large B-cell lymphoma (DLBCL) can be classified based on cell-of-origin (COO) into germinal center B-cell–like (GCB), activated B-cell–like (ABC), and unclassified (UNC) subtypes by gene expression profiling (GEP), and GCB and non-GCB subtypes by immunohistochemistry (IHC). In the phase 3 PHOENIX trial (NCT01855750) that enrolled untreated patients (pts) with non-GCB DLBCL by IHC, ibrutinib (ibr) + R-CHOP did not improve event-free survival (EFS) vs placebo (pbo) + R-CHOP in the intent-to-treat (ITT, non-GCB by IHC) or ABC (by GEP) populations; however, an increase in EFS and overall survival with ibr was seen in pts < 60 years (yrs), but not in pts ≥ 60 yrs due to increased toxicity in elderly pts. This work aimed to determine the concordance between IHC and GEP for DLBCL subtyping and outcomes related to subtypes. Methods: Baseline paraffin-embedded, formalin-fixed tissue samples were used to confirm non-GCB DLBCL by Hans-based IHC (Dako pharmDx™ kit) at a central laboratory. Available tumor samples were retrospectively analyzed for ABC subtype by GEP (HTG EdgeSeq DLBCL COO Assay). The concordance was evaluated by comparing non-GCB calls by IHC with ABC + UNC by GEP or GCB calls between IHC and GEP. Survival outcomes were compared between GEP subtypes in each arm and across study arms. Results: In all screened pts, 1111/1336 (83.2%) samples also provided evaluable GEP results; the concordance between GEP and IHC was 80.2% for non-GCB and 61.3% for GCB calls, resulting in an overall concordance of 76.9% (Figure), with 73.7% of non-GCB samples (by IHC) being identified as ABC by GEP. In pts < 60 yrs (n = 506), the concordance for non-GCB, GCB, and overall was 76.1%, 67.6%, and 74.3% respectively. In 747 evaluable samples from 838 enrolled non-GCB pts, 75.9% were ABC by GEP; 17.2% were GCB and 6.8% UNC. In both ITT and age < 60 yrs populations, EFS rate in GCB DLBCL by GEP was higher vs ABC in either arm, although the difference was not statistically significant and even smaller in the ibr arm. When comparing the two arms in the ITT population, EFS was similar between arms regardless of COO. In pts < 60 yrs, EFS was better with the addition of ibr to R-CHOP in ABC pts (HR 0.56 [95% CI, 0.33-0.98]; p = 0.0348; Figure); the difference between arms was not statistically significant in GCB (HR 0.64 [95% CI, 0.22-1.86; p = 0.4119) or UNC (HR 1.12 [95% CI, 0.22-5.97]) subtypes as numbers were small. Keywords: gene expression profile (GEP); ibrutinib; immunohistochemistry (IHC). Disclosures: Balasubramanian, S: Employment Leadership Position: Janssen, Pharmacyclics; Stock Ownership: Pharmacyclics, Johnson & Johnson, Gilead Sciences, Celgene, Vertex, AbbVie. Wang, S: Employment Leadership Position: Janssen; Stock Ownership: Janssen. Major, C: Employment Leadership Position: Janssen; Stock Ownership: Janssen. Hodkinson, B: Employment Leadership Position: Janssen R&D. Schaffer, M: Employment Leadership Position: Janssen; Stock Ownership: Janssen. Sehn, L: Consultant Advisory Role: Celgene, AbbVie, Seattle Genetics, TG Therapeutics, Janssen, Amgen, Roche/Genentech, Gilead Sciences, Lundbeck, Apobiologix, Karyopharm Therapeutics, Kite Pharma, Merck, Takeda Pharmaceuticals, TEVA Pharmaceuticals Industries, TG Therapeutics; Honoraria: Amgen, Apobiologix, AbbVie, Celgene, Gilead Sciences, Janssen-Ortho, Karyopharm Therapeutics, Kite Pharma, Lundbeck, Merck, Roche/Genentech, Seattle Genetics, Takeda Pharmaceuticals, TEVA Pharmaceuticals Industries, TG Therapeutics; Research Funding: Roche/Genentech (Inst). Johnson, P: Consultant Advisory Role: Janssen, Epizyme, Boehringer Ingelheim; Honoraria: Takeda Pharmaceuticals, Bristol-Myers Squibb, Novartis, Celgene, Kite Pharma, Genmab, Incyte, MorphoSys; Research Funding: Janssen, Epizyme; Other Remuneration: Combined use of Fc gamma RIIb (CD32b) and CD20-specific antibodies; WO patent, PCT/GB2011/051572; EU11760819.0. Zinzani, P: Honoraria: Servier, Bristol-Myers Squibb, Gilead, Jansen, Merck Sharp & Dohme, Celltrion, Celgene, Roche; Other Remuneration: Verastem, Servier, Bristol-Myers Squibb, Gilead, Janssen, Merck Sharp & Dohme, Celltrion, Celgene, Roche. Carey, J: Employment Leadership Position: Janssen Research and Development; Stock Ownership: Johnson & Johnson. Liu, G: Employment Leadership Position: Janssen. Loefgren, C: Employment Leadership Position: Janssen; Stock Ownership: Janssen. Shreeve, M: Employment Leadership Position: Janssen; Stock Ownership: Johnson & Johnson, Pfizer. Sun, S: Employment Leadership Position: Johnson & Johnson; Stock Ownership: Johnson & Johnson. Zhuang, S: Employment Leadership Position: Janssen Research & Development; Stock Ownership: Johnson & Johnson. Vermeulen, J: Employment Leadership Position: Janssen; Stock Ownership: Janssen. Staudt, L: Other Remuneration: Patents and patents pending regarding gene expression profiling in lymphoma that have been licensed by Nanostring and for which I receive royalties. Younes, A: Consultant Advisory Role: BMS, Incyte, Janssen, Genentech and Merck; Honoraria: Merck, Roche, Takeda Pharmaceuticals, Janssen, AbbVie; Research Funding: Janssen (Inst), Curis (Inst), Pharmacyclics (Inst), Roche (Inst), AstraZeneca (Inst), Genentech (Inst).
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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 | 0.009 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".