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Record W3097024120 · doi:10.1182/blood-2020-134985

The Burkitt Lymphoma International Prognostic Index (BL-IPI)

2020· article· en· W3097024120 on OpenAlexaffabout
Adam J. Olszewski, Lasse Hjort Jakobsen, Graham P. Collins, Kate Cwynarski, Veronika Bachanová, Kristie A. Blum, Kirsten M Boughan, Mark Bower, Alessia Dalla Pria, Alexey V. Danilov, Kevin A. David, Catherine Diefenbach, Fredrik Ellin, Narendranath Epperla, Umar Farooq, Tatyana Feldman, Alina S. Gerrie, Deepa Jagadeesh, Manali Kamdar, Reem Karmali, Shireen Kassam, Vaishalee P. Kenkre, Nadia Khan, Andreas K. Klein, Izidore S. Lossos, Matthew A. Lunning, Peter Martin, Nicolas Martinex-Calle, Silvia Montoto, Seema Naik, Neil Palmisiano, David Peace, Elizabeth H. Phillips, Tycel Phillips, Craig A. Portell, Nishitha Reddy, Anna Santarsieri, Maryam Sarraf Yazdy, Knut B. Smeland, Scott E. Smith, Stephen D. Smith, Suchitra Sundaram, Parameswaran Venugopal, Adam Zayac, Xiaoyin Zhang, Catherine Zhu, Chan Y. Cheah, Tarec Christoffer El‐Galaly, Andrew M. Evens

Bibliographic record

VenueBlood · 2020
Typearticle
Languageen
FieldMedicine
TopicLymphoma Diagnosis and Treatment
Canadian institutionsSpinal Cord Injury BCUniversity of British Columbia
Fundersnot available
KeywordsInternational Prognostic IndexMedicineHazard ratioInternal medicineProportional hazards modelConfidence intervalOncologyLactate dehydrogenaseCensoring (clinical trials)Diffuse large B-cell lymphomaGastroenterologyLymphomaPathologyBiology

Abstract

fetched live from OpenAlex

Background. BL is a rare, high-grade B-cell lymphoma that is often studied in trials with small sample sizes. Historical definitions of "low-risk BL" vary between studies, use arbitrary cutoffs for lactate dehydrogenase (LDH), and identify a small favorable group, leaving >80-90% of patients (pts) in an undifferentiated "high-risk" category. A validated prognostic index will help compare study cohorts and better define good-prognosis pts for whom reduced treatment would be appropriate vs a poor-prognosis group in need of new approaches. Herein, we constructed and validated a simplified prognostic model for BL applicable to diverse clinical settings across the world. Methods. We derived the BL-IPI from a large real-world evidence cohort of US adults treated for BL in 2009-2018 (Evens A, Blood 2020). Progression-free survival (PFS) from diagnosis until BL recurrence, progression, death, or censoring was the primary outcome. We first determined the best prognostic cutoffs for age, LDH (normalized to local upper limit normal, ULN), hemoglobin (Hgb), and albumin. Independent risk factors were ascertained by forward stepwise selection into Cox regression from candidate variables: age, sex, HIV+ status, ECOG performance status (PS) ≥2, advanced stage (3/4), involvement of >1 extranodal site, bone marrow, central nervous system (CNS), values of LDH, Hgb, and albumin. Derivation models used multiple imputation to mitigate bias from missing data and reported hazard ratios (HR) with 95% confidence interval (CI). BL-IPI groups, defined by inspection of survival curves, were compared using log-rank test for trend. We validated performance of the BL-IPI in an external retrospective dataset of BL pts treated contemporaneously in centers from the United Kingdom, Scandinavia, Canada, and Australia. Results. Characteristics of pts in the derivation (N= 633) and validation (N=457) cohorts are shown in the Table. Age ≥40 years (yr), LDH >3xULN, Hgb <11.5 g/dL, and albumin <3.5 g/dL were determined as optimal prognostic cutoffs. Age ≥40 yr, PS ≥2, stage 3/4, involvement of marrow, CNS, LDH >3xULN, low Hgb, and low albumin were associated with inferior PFS in univariate tests. In the multivariable model age ≥40 yr, LDH >3xULN, PS ≥2, and CNS involvement were selected as 4 independent prognostic factors; adding stage did not enhance the model. The model was simplified to 3 groups with 0 (low risk; 18% of pts), 1 (intermediate risk; 36% of pts; HR=3.14; 95%CI, 1.61-6.14), or 2-4 factors (high risk; 46% of pts; HR=6.52; 95%CI, 3.48-12.20; Fig A) with 3 yr PFS of 92%, 72%, and 53%, respectively (P<.001, Fig. B); median PFS was reached only in the high-risk group (46 months, 95%CI, 19-53). BL-IPI was similarly prognostic for overall survival (OS, P<.001; Fig. C). Among pts with stage III/IV (historically classified as "high-risk" and constituting 78% of all pts in the cohort), the BL-IPI further discriminated subgroups with 3 yr PFS of 87%, 71%, and 52%, respectively (P<.001; Fig. D), and OS of 95%, 75%, and 57%, respectively (P<.001; Fig. E). In addition, BL-IPI was prognostic regardless of HIV status, in the subcohort treated with rituximab (3 yr PFS: 92%, 73%, and 55%, respectively, P<.001), and among pts treated with specific regimens: CODOX-M/IVAC±R (3 yr PFS: 88%, 67%, 61%, respectively, P=.004), DA-EPOCH-R (3 yr PFS, 87%, 73%, 51%, respectively, P<.001), or hyperCVAD/MA±R (3yr PFS: 100%, 80%, 54%, respectively, P<.001). In the international validation cohort, fewer pts had CNS involvement; most received CODOX-M/IVAC+R; and PFS/OS estimates at 3 yr were higher. BL-IPI categories were of similar size (low-risk 15%, intermediate-risk 35%, high-risk 50%), and provided similar risk discrimination (Harrell's C=.65 in both datasets). PFS at 3 yr was 96%, 82%, and 63%, respectively (P<.001; Fig. F), and OS was 99%, 85%, and 64%, respectively (P<.001; Fig. G). In the validation cohort, BL-IPI remained prognostic in the subsets receiving rituximab (P<.001) and in advanced stage (P<.001). Conclusions. BL-IPI is a novel prognostic index specific to BL, which was validated to allow for simplified stratification and comparison of risk distribution in geographically diverse cohorts. The index identified a low-risk group with PFS >90-95%, which could be targeted with future strategies for treatment de-escalation. Conversely, only about 55-60% of pts in the high-risk group achieved cure with currently available immunochemotherapy. Disclosures Olszewski: Spectrum Pharmaceuticals: Research Funding; Genentech, Inc.: Research Funding; TG Therapeutics: Research Funding; Adaptive Biotechnologies: Research Funding. Jakobsen:Takeda: Honoraria. Collins:ADC Therapeutics: Consultancy, Honoraria; Celleron: Consultancy, Honoraria, Research Funding; Novartis: Consultancy, Honoraria, Speakers Bureau; Amgen: Research Funding; BeiGene: Consultancy; BMS: Consultancy, Honoraria, Research Funding, Speakers Bureau; Gilead: Consultancy, Honoraria, Speakers Bureau; MSD: Consultancy, Honoraria, Research Funding; Taekda: Consultancy, Honoraria, Other: travel, accommodations, expenses, Speakers Bureau; Roche: Consultancy, Honoraria, Other: travel, accommodations, expenses , Speakers Bureau; Pfizer: Honoraria; Celgene: Research Funding. Cwynarski:Janssen: Consultancy, Membership on an entity's Board of Directors or advisory committees, Other: Travel Support; Atara: Consultancy, Membership on an entity's Board of Directors or advisory committees; Gilead: Consultancy, Membership on an entity's Board of Directors or advisory committees, Speakers Bureau; KITE: Consultancy, Membership on an entity's Board of Directors or advisory committees, Other: Travel Support, Speakers Bureau; Takeda: Consultancy, Membership on an entity's Board of Directors or advisory committees, Other: Travel Support, Speakers Bureau; Celgene: Consultancy, Membership on an entity's Board of Directors or advisory committees; Roche: Consultancy, Membership on an entity's Board of Directors or advisory committees, Other: Travel Support, Speakers Bureau. Bachanova:Incyte: Research Funding; Karyopharma: Membership on an entity's Board of Directors or advisory committees; BMS: Research Funding; FATE: Research Funding; Kite: Membership on an entity's Board of Directors or advisory committees; Gamida Cell: Membership on an entity's Board of Directors or advisory committees, Research Funding. Danilov:Abbvie: Consultancy; BeiGene: Consultancy; Nurix: Consultancy; Celgene: Consultancy; Gilead Sciences: Research Funding; Takeda Oncology: Research Funding; Pharmacyclics: Consultancy; Bayer Oncology: Consultancy, Research Funding; Genentech: Consultancy, Research Funding; TG Therapeutics: Consultancy; Astra Zeneca: Consultancy, Research Funding; Verastem Oncology: Consultancy, Research Funding; Karyopharm: Consultancy; Aptose Biosciences: Research Funding; Bristol-Myers Squibb: Research Funding; Rigel Pharmaceuticals: Consultancy. Diefenbach:Trillium: Research Funding; Millenium/Takeda: Research Funding; MEI: Research Funding; Merck: Consultancy, Research Funding; Seattle Genetics: Consultancy, Research Funding; Bristol-Myers Squibb: Consultancy, Research Funding; Genentech, Inc.: Consultancy, Research Funding; Incyte: Research Funding; LAM Therapeutics: Research Funding; Denovo: Research Funding. Epperla:Pharmacyclics: Honoraria; Verastem Oncology: Speakers Bureau. Farooq:Kite, a Gilead Company: Honoraria. Feldman:Pfizer: Research Funding; Portola: Research Funding; Janssen: Speakers Bureau; AstraZeneca: Consultancy; Cell Medica: Research Funding; Seattle Genetics, Inc.: Consultancy, Honoraria, Other: Travel expenses, Research Funding, Speakers Bureau; Viracta: Research Funding; Trillium: Research Funding; Rhizen: Research Funding; Corvus: Research Funding; BMS: Consultancy, Honoraria, Research Funding; Kite: Honoraria, Other: Travel expenses, Speakers Bureau; Celgene: Honoraria, Research Funding; Takeda: Honoraria, Other: Travel expenses; Amgen: Research Funding; Pharmacyclics: Honoraria, Other, Speakers Bureau; Abbvie: Honoraria; Bayer: Consultancy, Honoraria; Eisai: Research Funding; Kyowa Kirin: Consultancy, Research Funding. Gerrie:AbbVie: Consultancy, Honoraria, Research Funding; Astrazeneca: Consultancy, Research Funding; Janssen: Consultancy, Honoraria, Research Funding; Roche: Research Funding; Sandoz: Consultancy. Jagadeesh:Regeneron: Research Funding; Seattle Genetics: Membership on an entity's Board of Directors or advisory committees, Research Funding; Debiopharm Group: Research Funding; MEI Pharma: Research Funding; Verastem: Membership on an entity's Board of Directors or advisory committees. Kamdar:BMS: Consultancy; Abbvie: Consultancy; Karyopharm: Consultancy; Celgene: Consultancy; AstraZeneca: Consultancy; Pharmacyclics: Consultancy; Seattle Genetics: Speakers Bureau. Karmali:Takeda: Research Funding; AstraZeneca: Speakers Bureau; BeiGene: Speakers Bureau; Karyopharm: Honoraria; BMS/Celgene/Juno: Honoraria, Other, Research Funding, Speakers Bureau; Gilead/Kite: Honoraria, Other, Research Funding, Speakers Bureau. Khan:Seattle Genetics: Research Funding; Janssen: Honoraria; Pharmacyclics: Honoraria; Bristol Myers Squibb: Research Funding; Celgene: Research Funding. Klein:Takeda: Membership on an entity's Board of Directors or advisory committees. Lossos:Verastem: Consultancy, Honoraria; Stanford University: Patents & Royalties; Seattle Genetics: Consultancy, Other; Janssen Biotech: Honoraria; NCI: Research Funding; Janssen Scientific: Consultancy, O

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)0.008
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 score0.006
Threshold uncertainty score0.012

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0020.008
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0030.002
Science and technology studies0.0000.000
Scholarly communication0.0010.001
Open science0.0010.001
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0030.001

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 head0.014
GPT teacher head0.241
Teacher spread0.227 · 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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Citations1
Published2020
Admission routes2
Has abstractyes

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