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Record W3212675168 · doi:10.1182/blood-2021-144820

Early Integration of High Dose Methotrexate to Frontline DLBCL Therapy Does Not Impact CNS Relapse Compared to End of Treatment Delivery: A Multicentre International Analysis of 1384 Patients

2021· article· en· W3212675168 on OpenAlexaff
Matthew R. Wilson, Toby A. Eyre, Amy A. Kirkwood, Nicole Wong Doo, Carole Soussain, Sylvain Choquet, Nicolas Martinex-Calle, Gavin Preston, Matthew J. Ahearne, Elisabeth Schorb, Marie‐Pierre Moles‐Moreau, Matthew Ku, Chiara Rusconi, Jahanzaib Khwaja, Mayur Narkhede, Katharine L. Lewis, Teresa Calimeri, Éric Durot, Loïc Renaud, Andreas Kiesbye Øvlisen, Graham McIlroy, Tim Ebsworth, Johnathon Elliot, Anna Santarsiere, Laure Ricard, Nimish Shah, Qin Liu, Adam Zayac, Francesco Vassallo, Laure Lebras, Louise Roulin, Naëlle Lombion, Kate Manos, Rubén Fernández, Nada Hamad, Alberto López‐García, Deirdre O’Mahony, Praveen Gounder, Nathalie Forgeard, Charlotte Lees, Kossi Agbétiafa, Tim Strüßmann, Thura Win Htut, Aline Clavert, Hamish W Scott, Anna Guidetti, Brett Barlow, Jeffrey W. Smith, Tarec Christoffer El‐Galaly, Chan Y. Cheah, Andrés JM Ferreri, Fiona Miall, Christopher P. Fox, Kate Cwynarski, Pam McKay

Bibliographic record

VenueBlood · 2021
Typearticle
Languageen
FieldMedicine
TopicCNS Lymphoma Diagnosis and Treatment
Canadian institutionsPrincess Margaret Cancer Centre
Fundersnot available
KeywordsMedicineMethotrexateInternal medicinePrimary central nervous system lymphomaOncologyDiffuse large B-cell lymphomaProportional hazards modelLymphomaSurgery

Abstract

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Abstract Introduction: Central nervous system (CNS) relapse in diffuse large B-cell lymphoma (DLBCL) is uncommon but is associated with poor outcomes. In selected high risk patients (pts), high dose methotrexate (HDMTX) is often used as CNS prophylaxis with frontline (1L) DLBCL therapy despite uncertain efficacy, optimum dose and timing of delivery. A recent UK study (Wilson et al 2020) showed that intercalated HDMTX (i-HDMTX) was associated with increased toxicity and R-CHOP delays compared to end of treatment (EOT) delivery. Although hypothesis generating, the study size was insufficient to determine whether EOT was non-inferior in terms of CNS relapse risk. Methods: We conducted an international, multicentre retrospective analysis of consecutive DLBCL or high grade BCL pts between 2007-20 from 47 centers in Europe, Australia and N. America. Pts were included if they received R-CHOP or R-CHOP-like 1L therapy with curative intent as well as HDMTX CNS prophylaxis (≥1 cycle). Concurrent intrathecal (IT) prophylaxis was permitted. Pts with known CNS involvement at baseline and those treated with more intensive protocols (e.g. R-DA-EPOCH) were excluded. i-HDMTX was defined as any pt receiving a HD-MTX cycle before the final R-CHOP cycle. CNS relapse events were excluded if occurring after first systemic lymphoma relapse/progression. Time to event endpoints were measured from diagnosis to first event or censor and analysed using Kaplan-Meier and Cox regression methods. Time to CNS relapse was analysed using competing risk Fine and Gray method (for death and non-synchronous systemic relapse). To mitigate for possible immortality bias in the EOT arm, a landmark analysis for pts alive and free from progression at 6 months was conducted. We aimed to exclude a 5% difference in 2-year (y) CNS relapse rates. Results: 1,384 pts were analysed. 750 received i-HDMTX and 634 received EOT HDMTX. Key baseline characteristics are summarised in Table 1. Median follow up was 37.9 months. 44.2% had high CNS IPI (4-6) with no significant difference between i-HDMTX and EOT groups (45.1% vs 43.1%, p=0.087). ≥2 cycles of HDMTX were used in 86.6% with no difference between groups (85.6% vs 87.9%, p=0.22). Concurrent IT prophylaxis use was higher for EOT pts (55.6% vs 38.1% p<0.0001). 78 CNS relapses (42 i-HDMTX, 36 EOT) were observed: parenchymal in 41 (53%), parenchymal and leptomeningeal in 16 (21%) and isolated leptomeningeal in 21 (27%). There was no significant difference in 2y CNS relapse rates between i-HDMTX and EOT in all pts: 5.2% vs 3.9%, adjusted hazard ratio (HR) 0.92 (95% CI 0.58-1.47), p=0.74, 2y difference -0.2% (-2.0-2.5) or landmark analysis: 2.8% vs 4.1%, HR: 0.93 (0.56-1.55), p=0.79, 2y difference: -0.3% (-1.8-2.2%) (Fig 1a/b). Exploratory analyses focusing on pts with isolated CNS relapse (n=57) demonstrated similar results (2y rates 3.6% vs 3.0%, p=0.99). On multivariable analysis (MVA) of risk factors for CNS relapse, renal/adrenal involvement was the only variable associated with increased CNS relapse risk (adjusted HR 1.74 (1.03-2.92), p=0.038). Notably, IT prophylaxis was not associated with reduction in CNS relapse. In 600 high CNS IPI (4-6) pts, there was no difference in CNS relapse risk between i-HD-MTX and EOT (3y rates 9.4% vs 8.6%, HR 0.92 (95% CI 0.52-1.62)). In a composite high risk group including CNS IPI 4-6 and/or any of the following: ≥3 extranodal sites, renal, adrenal, testicular or breast involvement (n=885) there was no difference in 3y CNS relapse rates between groups (i-HDMTX 7.6% vs EOT 7.4%, HR 0.94 (0.58-1.53)). Progression-free survival (PFS) and overall survival (OS) in the i-HDMTX and EOT groups were as follows: 3y PFS 70.7% vs 76.7% (p=0.098), 3y OS 79.9% vs 87.0% (p=0.0016). However, there were no PFS/OS differences between groups on landmark analysis (n=1259) (Fig 1c). On analysis of pts experiencing ≥1 R-CHOP delay of ≥7 days, use of i-HDMTX was the only factor on MVA associated with increased delays (p<0.0001). Discussion: We found no evidence that EOT delivery increases CNS relapse risk when compared to i-HDMTX in this large analysis of pts treated with 1L R-CHOP. Delays to R-CHOP cycles were increased with i-HDMTX. Findings in a high risk subgroup were unchanged and rates of CNS relapse in this HDMTX treated group were similar to published comparable high risk cohorts receiving infrequent CNS prophylaxis. Where HDMTX prophylaxis is used, delivery could be deferred until R-CHOP completion. Figure 1 Figure 1. Disclosures Wilson: Takeda: Other: Conference fees; Janssen: Other: Conference fees; Abbvie: Honoraria. Eyre: Janssen: Honoraria; Secura Bio: Consultancy, Honoraria; Gilead/KITE: Honoraria, Other: Travel support for conferences, Research Funding, Speakers Bureau; Roche: Consultancy, Honoraria; Abbvie: Consultancy, Honoraria, Other: Travel to conferences; AstraZeneca: Honoraria, Research Funding; Loxo Oncology: Consultancy, Honoraria, Membership on an entity's Board of Directors or advisory committees; Incyte: Consultancy; Beigene: Honoraria, Research Funding. Ahearne: Pfizer: Research Funding; Takeda: Honoraria; Roche: Honoraria. Schorb: Roche: Research Funding; Riemser Pharma GmbH: Honoraria, Research Funding; AbbVie: Research Funding. Ku: Antegene: Consultancy; Roche: Consultancy; Genor Biopharma: Consultancy. Narkhede: Genentech/Roche: Research Funding; Gilead: Research Funding; Genmab: Other: Medical writing support, Research Funding; TG Therapeautics: Research Funding. Lewis: AstraZeneca: Consultancy, Honoraria; Janssen: Honoraria, Patents & Royalties; Novartis: Patents & Royalties; Roche: Consultancy, Honoraria. Øvlisen: Abbvie: Other: Travel expenses. Santarsiere: Janssen: Honoraria. Shah: Abbvie, Janssen and Roche: Honoraria, Membership on an entity's Board of Directors or advisory committees. Roulin: Janssen: Other: Travel and meetings. Manos: Bristol-Myers Squibb: Other: Travel and meetings. Hamad: Novartis: Membership on an entity's Board of Directors or advisory committees, Speakers Bureau. Lopez-Garcia: Roche: Other: Speaker Honoraria, Travel and accommodation grants; Janssen: Other: Speaker Honoraria, Advisor, Travel and accommodation grants, Research Funding; Abbvie: Other: Speaker Honoraria, Advisor, Travel and accommodation grants; Celgene: Other: Speaker Honoraria; Fresenius: Other: Speaker Honoraria; Novonordisk: Other: Speaker Honoraria. El-Galaly: ROCHE Ltd: Ended employment in the past 24 months; Abbvie: Other: Speakers fee. Cheah: Beigene: Consultancy, Honoraria, Other: advisory; AbbVie: Research Funding; Celgene: Research Funding; AstraZeneca: Consultancy, Honoraria, Other: advisory; Loxo/Lilly: Consultancy, Honoraria, Other: advisory; TG Therapeutics: Consultancy, Honoraria, Other: advisory; Roche: Consultancy, Honoraria, Other: advisory and travel expenses, Research Funding; Janssen: Consultancy, Honoraria, Other: advisory; MSD: Consultancy, Honoraria, Other: advisory, Research Funding; Gilead: Consultancy, Honoraria, Other: advisory; Ascentage pharma: Consultancy, Honoraria, Other: advisory. Ferreri: Gilead, Novartis, Juno, PletixaPharm, Roche, Incyte: Membership on an entity's Board of Directors or advisory committees; BMS, Beigene, Pharmacyclics, Hutchison Medipharma, Amgen, Genmab, ADC Therapeutics, Gilead, Novartis, Pfizer: Research Funding. Fox: Roche: Honoraria, Membership on an entity's Board of Directors or advisory committees, Research Funding; Janssen: Other: speaker fees. Cwynarski: Gilead: Consultancy, Speakers Bureau; Incyte: Consultancy, Speakers Bureau; Atara: Consultancy; Celgene: Consultancy; Takeda: Consultancy, Other: travel to scientific conferences, Speakers Bureau; Kite, a Gilead Company: Consultancy, Other: travel to scientific conferences, Speakers Bureau; Janssen: Consultancy, Other: travel to scientific conferences; Roche: Consultancy, Other: travel to scientific conferences, Speakers Bureau; BMS/Celgene: Other: travel to scientific conferences. McKay: Roche: Honoraria, Membership on an entity's Board of Directors or advisory committees; Gilead: Honoraria, Other: Travel Support; KITE: Honoraria, Membership on an entity's Board of Directors or advisory committees; Takeda: Honoraria, Membership on an entity's Board of Directors or advisory committees, Other: Travel Support; Janssen: Honoraria, Other: Travel Support; Beigene: Honoraria, Membership on an entity's Board of Directors or advisory committees; BMS/Celgene: Honoraria, Membership on an entity's Board of Directors or advisory committees.

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)0.001
metaresearch head score (Gemma)0.003
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 score0.002
Threshold uncertainty score0.008

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.003
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0010.002
Science and technology studies0.0000.000
Scholarly communication0.0010.000
Open science0.0010.001
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0020.000

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 head0.020
GPT teacher head0.301
Teacher spread0.281 · 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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Citations2
Published2021
Admission routes1
Has abstractyes

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