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Gray Zone Lymphoma (GZL) with Features Intermediate Between Classical Hodgkin Lymphoma (cHL) and Diffuse Large B-Cell Lymphoma (DLBCL): Analysis of Tumor Immunophenotype (IP) and Critical Examination of Therapy with Associated Impact on Outcome

2014· article· en· W2979475210 on OpenAlexaff
Athena Kritharis, Jennifer A. Kanakry, Laurie H. Sehn, Tatyana Feldman, Aimee Kroll, Randy D. Gascoyne, Adam M. Petrich, Jeremy S. Abramson, Francisco J. Hernandez‐Ilizaliturri, Zeina Al‐Mansour, Camille Adeimy, Jessica Hemminger, Nancy L. Bartlett, Anthony R. Mato, Paolo F. Caimi, Ranjana H. Advani, Andreas K. Klein, Izidore S. Lossos, Oliver W. Press, Sonali M. Smith, Jesus C. Fabregas, Chadi Nabhan, Timothy S. Fenske, Jonathan W. Friedberg, Julie M. Vose, Kristie A. Blum, Andrew M. Evens

Bibliographic record

VenueBlood · 2014
Typearticle
Languageen
FieldMedicine
TopicLymphoma Diagnosis and Treatment
Canadian institutionsBC Cancer Agency
Fundersnot available
KeywordsMedicineB symptomsCD30Diffuse large B-cell lymphomaUnivariate analysisInternal medicineInternational Prognostic IndexImmunophenotypingRituximabLymphomaVincristineAnaplastic large-cell lymphomaOncologyCD20GastroenterologyChemotherapyMultivariate analysisImmunologyCyclophosphamide

Abstract

fetched live from OpenAlex

Abstract Background: The WHO recognizes a category of B-cell lymphoma unclassifiable with features intermediate between DLBCL and cHL, also known as GZL. This is a challenging disease entity to treat due to disease heterogeneity and lack of pathologic or clinical prognostic indicators as well as absence of standard management guidelines for untreated or relapsed/refractory patients (pts). Methods: We performed a multicenter retrospective analysis of newly diagnosed GZL pts treated from 2001-2012 across 19 North American academic centers. Diagnosis was established by institutional expert pathology review. This work is an expansion and update of prior reported data (Evens AM et al, ASH 2013, #847) with 16 cases added to the original report. Additionally, new data were examined including histopathologic and IP analysis and detailed frontline and salvage therapy. Prognostic factors associated with survival on univariate and multivariate Cox regression analyses were examined. Results: Characteristics of 112 GZL pts included: median age 39 years (19-86); M:F 1.8:1; ECOG PS 0-1 87%; B symptoms 40%; anemia 59%; hypoalbuminemia 30%; bulky disease (≥10cm) 24%; non-mediastinal presentation 57%; bone marrow pos 11%; Stage III/IV 52%; IPI 0-2 77%; and IPS 0-2 in 81%. The most prevalent tumor IPs were: 93% CD20+ (100/108), 91% CD30+ (98/108), 78% CD79+ (43/55), 97% Pax5+ (67/69), 97% Oct2+ (27/28), and 94% MUM1+ (32/32). CD15 (44%, 45/101) and CD45 (69%, 48/70) were variable. Only 13% and 24% of pts were CD10+ (4/30) and EBV+ (13/55), respectively. Notably, IP did not differ based on clinical presentation (ie, mediastinal vs non-mediastinal). The most common frontline treatments were R-CHOP n=52, ABVD +/- R n=34, and R-EPOCH n=11. 71% of CD20+ pts were treated with rituximab as part of frontline therapy. At 31-month median follow-up, 2-year PFS and OS for all pts were 40% and 88%, respectively. The only pathologic factor correlating with outcome was CD20 positivity (PFS: HR 0.34, 95% CI 0.16-0.73, P=0.006). Characteristics correlating with PFS were anemia (HR 0.51, 95% CI 0.29-0.91, P=0.022), low albumin (HR 0.57, 95% CI 0.32-1.00, P=0.05), and IPI (continuous: HR 1.48, 95% CI 1.19-1.82, P=0.0003). For therapy, 2-year PFS and OS for R-CHOP were 46% and 84%, respectively; ABVD+/-R 25% and 96%, respectively; and R-EPOCH 68% and 83%, respectively (Fig. 1). R-EPOCH predicted improved PFS (0.047), however this effect was abrogated after controlling for IPI, anemia and hypoalbuminemia (P=0.2). Pts who received rituximab with frontline therapy had improved 2-year PFS (51% vs 19%, respectively, P=0.012). Interestingly, the significance of CD20 persisted on Cox regression controlling for rituximab (rituximab HR 0.55, 95% 0.33-0.93, P=0.025; CD20 0.35, 95% CI 0.16-0.75, P=0.007). Furthermore, the effect of rituximab remained significant after controlling for IPI, anemia, and hypoalbuminemia (HR 0.35, 95% CI 0.18-0.69, P=0.002). Overall, 58% of pts relapsed with median time to relapse of 7 months (0-64); the mean number of salvage therapies was 3. Regimens at 1st relapse included: R-ICE (n=33), R-ESHAP (n=6), R-EPOCH (n=7), ABVD (n=1) and brentuximab vedotin (n=4). Beyond first relapse, the most common treatments were: brentuximab vedotin (n=7) and radiation (n=5). 61% of relapsed pts proceeded to stem cell transplantation (SCT) (38% allogeneic, 62% autologous). 20/27 (74%), 20/32 (63%), and 3/3 (100%) of pts who relapsed after frontline ABVD, R-CHOP and R-EPOCH, respectively, had SCT at relapse. 2-year OS was superior for pts who had SCT (88% vs 67%, P=0.014; Fig. 2), which persisted on multivariable regression (SCT: HR0.14, 95% CI 0.02-0.95, P=0.044; IPI continuous: HR 2.04, 95% CI 1.00-4.16, P=0.05; anemia: HR 2.40, 95% CI 0.32-18.25, P=0.4; low albumin: HR 5.54, 95% CI 1.08-28.44, P=0.04). Conclusions: To the best of our knowledge, this represents the largest series of GZL reported to date. Presence of CD20 appeared to be an independent prognostic factor and treatment with a rituximab-based DLBCL-specific regimen for frontline therapy was associated with the most optimal PFS. In addition, pts who underwent SCT at relapse appeared to have superior OS, however caution should be given to this finding given likely selection bias. Continued examination of this unique lymphoma is warranted. Figure 1. PFS comparison of frontline therapeutic regimens for GZL. Figure 1. PFS comparison of frontline therapeutic regimens for GZL. Figure 2. Impact of SCT on OS for patients with relapsed/refractory GZL Figure 2. Impact of SCT on OS for patients with relapsed/refractory GZL Disclosures Bartlett: Seattle Genetics, Inc.: Other, Research Funding; Takeda Pharmaceuticals International Co.: Research Funding; Pfizer: Research Funding; Pharmacyclics: Research Funding; Novartis: Research Funding; MedImmune: Research Funding; Celgene: Research Funding; ImaginAb: Research Funding; Genentech: Research Funding; Janssen: Research Funding; AstraZeneca: Research Funding. Mato:Genentech, Celgene, Millenium: Speakers Bureau. Advani:Seattle Genetics, Inc.: Research Funding, Travel expenses Other; Genentech: Research Funding; Janssen Pharmaceuticals: Research Funding; Pharmacyclics: Research Funding; Celgene: Research Funding; Takeda International Pharmaceuticals Co.: Research Funding. Blum:Janssen, Pharmacyclics : Research Funding.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

How this classification was reachedexpand

Full frame machine prediction

Teacher imitation

Not 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.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.000
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.001
Threshold uncertainty score0.003

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0010.000

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.

Opus teacher head0.010
GPT teacher head0.262
Teacher spread0.253 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designObservational
Domainnot available
GenreEmpirical

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".

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Published2014
Admission routes1
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