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Record W2571445838 · doi:10.1182/blood.v118.21.265.265

Identification of Patient Subgroups Demonstrating Longer Progression-Free Survival (PFS) Benefit with Bortezomib-Rituximab Versus Rituximab in Patients with Relapsed or Refractory Follicular Lymphoma (FL): Biomarker Analyses of the Phase 3 LYM3001 Study

2011· article· en· W2571445838 on OpenAlexaff
Bertrand Coiffier, Weimin Li, Erin D. Henitz, Jayaprakash D. Karkera, Reyna Favis, Dana Gaffney, Alice Shapiro, Panteli Theocharous, Yusri Elsayed, Helgi van de Velde, Evgenii A. Osmanov, Xiaonan Hong, Adriana Scheliga, Fritz Offner, Simon Rule, Adriana Teixeira, Jan Walewski, Sven de Vos, Michael Crump, Ofer Shpilberg, Pier Luigi Zinzani, Andrew Cakana, Dixie-Lee Esseltine, George Mulligan, Deborah Ricci

Bibliographic record

VenueBlood · 2011
Typearticle
Languageen
FieldMedicine
TopicLymphoma Diagnosis and Treatment
Canadian institutionsPrincess Margaret Cancer CentreUniversity Health Network
Fundersnot available
KeywordsRituximabMedicineInternal medicineBortezomibOncologyFollicular lymphomaRefractory (planetary science)Progression-free survivalPhases of clinical researchBiomarkerPopulationLymphomaClinical trialMultiple myelomaChemotherapyBiology

Abstract

fetched live from OpenAlex

Abstract Abstract 265 Background: Treatment goals in patients with relapsed FL are to prolong PFS and improve overall survival (OS). To optimize treatment for individual patients, identification of subgroups most likely to benefit from a specific therapy is important. The international, randomized, phase 3 LYM3001 study in patients with relapsed or refractory FL demonstrated improved PFS with bortezomib-rituximab vs rituximab alone (median 12.8 vs 11.0 months, HR 0.822, p=0.039), plus increased overall response rate (ORR; 63% vs 49%, p=0.0004), complete response rate (CR/CRu; 25% vs 18%, p=0.035), and durable (≥6 months) response rate (50% vs 38%, p=0.002) in an unselected patient population. Here we present exploratory biomarker analyses aimed at identifying patient subgroups deriving a longer PFS benefit with bortezomib-rituximab and showing a trend for better OS. Methods: Patients received five 5-week cycles of bortezomib-rituximab (N=336) or rituximab (N=340). Response was assessed using modified International Working Group response criteria. Archived tumor tissue was collected at baseline from 502 (74%) patients; whole blood samples for germ-line DNA were collected on day 1 of cycle 1 from 619 (92%) patients. Protocol-specified candidate biomarkers were based on associations with bortezomib (NF-κB p65, PSMA5, p27, PSMB1/5/8/9) or rituximab (CD68, FCGR2A/3A) activity. Immunohistochemistry assays were used for protein analysis. Taqman SNP assays and PCR/LDR were used for genotyping. Statistical analyses included single-marker analyses, pair-wise combination analyses (n=1140 comparisons), and multiple comparison analyses of all evaluable patients in LYM3001. Clinical covariates included in the analysis were baseline FLIPI score, prior rituximab, time since last anti-lymphoma therapy, region, age, gender, race, Ann Arbor stage, high tumor burden, and number of prior lines of therapy. Results: Single markers and biomarker pairs (n=102) highlighted patient subsets that had significantly improved outcomes with bortezomib-rituximab vs rituximab. For 14 of the pairs, the PFS benefit was ≥6 months. Using false discovery rate (FDR) to control for multiple comparison corrections, one biomarker pair was significant. This pair (presence of the PSMB1 P11A C/G heterozygote, and low CD68 expression [0–50 CD68-positive macrophages in the follicular space]) was associated with significantly improved PFS in patients receiving bortezomib-rituximab vs rituximab (median 16.6 vs 9.1 months, HR 0.407, p<0.0001, FDR=0.051) and had a population frequency of 33% (n=118) in biomarker-evaluable patients (N=356). Patients with high-risk features were represented in the biomarker-selected population (54% high tumor burden, 41% high FLIPI, 30% >2 prior lines of therapy). There was also a trend towards an OS benefit (medians not reached, HR 0.426, p=0.0550), as well as a significantly higher ORR (73.7% vs 47.5%, p=0.0077), a higher CR rate (33.3% vs 23%, p=0.3044), and a significantly longer time to next therapy (median 33.1 vs 14.8 months, p=0.0013). In patients lacking this biomarker pair (N=238) no significant efficacy differences were seen. No other similar studies were available to confirm the reproducibility of these analyses. Therefore, we split the LYM3001 dataset into discovery and confirmation cohorts (7:3 ratio of biomarker-evaluable patients) to enable evaluation and confirmation in independent cohorts of patients The significant biomarker pair of PSMB1 P11A C/G heterozygote and low CD68 was identified in the discovery cohort (N=198) with a PFS advantage with bortezomib-rituximab vs rituximab of 5.7 months (median 14.2 vs 8.4 months, p=0.0003) and an indication of longer OS (HR 0.47, p=0.1291). This biomarker pair also showed a clear PFS advantage in the confirmation cohort (N=108, 8.7-month PFS benefit; median 18.2 vs 9.5 months, HR 0.44, p=0.0817). Other significant biomarker combinations, including combinations of molecular and clinical variables (e.g. high tumor burden) were identified and will be presented. Conclusions: Analyses of the phase 3 LYM3001 trial identified biomarker combinations present in a third of patients offering a significant PFS benefit with bortezomib-rituximab vs rituximab. Use of such biomarker assays in patients with relapsed or refractory FL may aid identification of subgroups deriving maximal benefit from the addition of bortezomib to rituximab therapy. Disclosures: Coiffier: Janssen-Cilag: Consultancy; Roche: Consultancy; Amgen: Consultancy; Sanofi: Consultancy; Pfizer: Consultancy; Millennium Pharmaceuticals, Inc.: Consultancy; Celgene: Consultancy; Pharmacyclics: Consultancy; MedImmune: Consultancy; CTI: Consultancy. Off Label Use: Bortezomib used in combination with rituximab in patients with relapsed/refractory follicular lymphoma. Li:Janssen Research & Development: Employment; Johnson & Johnson: Equity Ownership. Henitz:Janssen Research & Development: Employment. Karkera:Janssen Research & Development: Employment; Johnson & Johnson: Equity Ownership. Favis:Janssen Research & Development: Employment; Johnson & Johnson: Equity Ownership. Gaffney:Janssen Research & Development: Employment; Johnson & Johnson: Equity Ownership. Shapiro:Janssen Research & Development: Employment; Johnson & Johnson: Equity Ownership. Theocharous:Janssen Research & Development: Employment; Johnson & Johnson: Equity Ownership. Elsayed:Janssen Research & Development: Employment; Johson & Johnson: Equity Ownership. de Velde:Janssen Research & Development: Employment; Johnson & Johnson: Equity Ownership. Rule:Johnson & Johnson: Advisory Board, Institutional grant, meeting attendance expenses, Honoraria. Walewski:Janssen-Cilag: Institutional/personal grants, advisory board; Hoffman La Roche: Honoraria, Institutional/personal grants, travel/accommodation expenses; Mundipharma: Honoraria; Celgene: Honoraria. de Vos:Millennium Pharmaceuticals, Inc: Consultancy. Crump:Janssen/Ortho-Biotech: Consultancy. Shpilberg:Janssen-Cilag: Consultancy, Honoraria. Cakana:Janssen Research & Development: Employment; Johnson & Johnson: Equity Ownership. Esseltine:Millennium Pharmaceuticals, Inc: Employment; Johnson & Johnson: Equity Ownership. Mulligan:Millennium Pharmaceuticals, Inc.: Employment. Ricci:Janssen Research & Development: Employment; Johnson & Johnson: Equity Ownership.

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)0.002
metaresearch head score (Gemma)0.001
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Non-randomized trial · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.002
Threshold uncertainty score0.008

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0020.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0010.000
Open science0.0000.000
Research integrity0.0000.001
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 head0.035
GPT teacher head0.300
Teacher spread0.265 · 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 designNon-randomized trial
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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Citations3
Published2011
Admission routes1
Has abstractyes

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