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Record W4379981918 · doi:10.1002/hon.3163_140

RITUXIMAB‐CONTAINING COMBINED MODALITY THERAPY IN LIMITED STAGE FOLLICULAR LYMPHOMA: MATURE FOLLOW UP AND DERIVATION OF A NOVEL PROGNOSTIC SCORE FROM THE TROG99.03 TRIAL

2023· article· en· W4379981918 on OpenAlexaff
Joshua W.D. Tobin, Vindi Jurinović, Hennes Tsang, Daniel Roos, Richard Tsang, Andrew Macann, Susan R. Davis, David Christie, Soi Cheng Law, Karthik Nath, Muhammed B. Sabdia, Ann‐Marie Patch, Jay Gunawardana, Lilia Merida de Long, Eva Hoster, Heike Horn, Mohamed Shanavas, Li Li, Chan Y. Cheah, Asa Ben‐Hur, Melody Y. Hou, Peter C. O’Brien, Anna Johnston, Tara Cochrane, Jason Butler, Ella R. Thompson, Piers Blombery, John F. Seymour, Maher K. Gandhi, Michael MacManus

Bibliographic record

VenueHematological Oncology · 2023
Typearticle
Languageen
FieldMedicine
TopicLymphoma Diagnosis and Treatment
Canadian institutionsPrincess Margaret Cancer Centre
Fundersnot available
KeywordsMedicineRituximabFollicular lymphomaInternal medicineRegimenOncologyRadiation therapyStage (stratigraphy)LymphomaProgression-free survivalGastroenterologySurgeryOverall survival

Abstract

fetched live from OpenAlex

Introduction: The TROG99.03 represents the only randomised phase III trial of combined modality therapy (CMT) in limited-stage follicular lymphoma ‘LSFL’, reporting a prolonged progression-free survival (PFS) in the CMT arm (MacManus, JCO, 2018). Here, we report extended follow up of this study providing mature data of patients treated with a rituximab-containing CMT regimen and the development of a new gene-expression based prognostic score. Methods: Patients with LSFL, grade 1–3a were randomised (1:1) to either involved-field radiotherapy alone (IFRT) (30–36Gy) or to CMT consisting of identical IFRT followed by 6 cycles of CVP. Reflecting evolving clinical practice, from 2006 onwards (i.e., ‘modern-era’), PET staging was increasingly utilised, and rituximab added to the CMT arm. Digital multiplex gene expression by Nanostring was performed on diagnostic biopsies based on genes previously identified to differentiate LSFL from advanced stage FL ‘ASFL’ (AM Staiger, Blood, 2020). Results: 150 patients were recruited between 2000 and 2012 with 31/75 patients in each arm recruited in the ‘modern-era’. At median follow-up 11.3 years, PFS remained superior for CMT compared to RT (HR 0.6; p = 0.043). Although no significant difference in OS was observed (HR 0.45, p = 0.11), compared with IFRT, patients in the CMT arm experienced fewer composite (deaths and histological transformation ‘HT’) events (HR 0.25; p = 0.045). With additional follow up no new non-malignant late toxicities were observed and incidence of secondary malignancies were similar between both arms (11 IFRT vs. 10 CMT, p = 0.99). Patients treated with a rituximab regimen (i.e., IFRT+R-CVP) had a markedly superior PFS compared to those treated without rituximab (i.e., IFRT, or IFRT+CVP), 8 year PFS rates 81% versus 52%, HR 0.42 p = 0.013 (Figure 1). Amongst PET staged patients the difference between R-CVP/IFRT versus IFRT increased (HR 0.35 p =0.027) suggesting this effect was not due to stage migration. No clinical factors were significantly associated with PFS on multivariate analysis. Nor were prognostic associations found for expression level of any individual genes. However, by penalised Cox regression an 8-gene Lasso-weighted prognosticator was identified, termed the ‘Bio-LSFL-score’. Genes (CACNA2D2, CD69, GZMB, IL7R, MYCT1, SLP1, TNFRSF14, TNFRSF25) reflected both B-cells and the microenvironment. The Bio-LSFL-score was highly significant for PFS (HR 0.25, p < 0.0001) with 100% patients in the high-risk group relapsing before 8 years. Conclusions: Particularly when incorporating rituximab, LSFL patients demonstrated significant increases in PFS and reduction in the rates of death and/or HT when treated with CMT compared with IFRT alone. A novel gene expression prognosticator was identified which in this trial cohort showed ‘ASFL-like’ behaviour by identifying LSFL patients unlikely to experience durable remissions. Keywords: diagnostic and prognostic biomarkers, indolent non-Hodgkin lymphoma, radiation therapy Conflicts of interests pertinent to the abstract C. Cheah Consultant or advisory role: Roche, Janssen, Gilead, Astra Zeneca, Lilly, TG Therapeutics, Beigene, Novartis, Menarini, Daizai, Abbvie, Genmab, BMS Research funding: BMS, Roche, Abbvie, MSD, Lilly J. F. Seymour Consultant or advisory role: Abbvie, Astra Zeneca, Celgene/BMS, Genentech, Genor Bio, Gilead, Janssen, Morphosys, Roche, Sunesis, TG Therapeutics Research funding: Celgene/BMS M. K. Gandhi Research funding: Beigene, Janssen

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 distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.001
Version: codex-gemma-dda1882f352aValidation 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 score0.181
Threshold uncertainty score0.519

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

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 head0.089
GPT teacher head0.330
Teacher spread0.241 · 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 teacher head, 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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Citations1
Published2023
Admission routes1
Has abstractyes

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