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Record W4400428906 · doi:10.1038/s41408-024-01084-w

Autologous transplant vs. CAR-T therapy in patients with DLBCL treated while in complete remission

2024· article· en· W4400428906 on OpenAlexfundno aff
Mazyar Shadman, Kwang Woo Ahn, Manmeet Kaur, Lazaros J. Lekakis, Amer Beitinjaneh, Madiha Iqbal, Nausheen Ahmed, Brian T. Hill, Nasheed Hossain, Peter A. Riedell, Ajay K. Gopal, Natalie S. Grover, Matthew J. Frigault, Jonathan E. Brammer, Nilanjan Ghosh, Reid W. Merryman, Aleksandr Lazaryan, Ron Ram, Mark Hertzberg, Bipin N. Savani, Farrukh T. Awan, Farhad Khimani, Sairah Ahmed, Vaishalee P. Kenkre, Matthew L. Ulrickson, Nirav N. Shah, Mohamed A. Kharfan‐Dabaja, Alex F. Herrera, Craig S. Sauter, Mehdi Hamadani

Bibliographic record

VenueBlood Cancer Journal · 2024
Typearticle
Languageen
FieldMedicine
TopicCAR-T cell therapy research
Canadian institutionsnot available
FundersNational Institute of Environmental Health SciencesNational Institute of Allergy and Infectious DiseasesOffice of Naval ResearchLegend BiotechPharmacyclicsTakeda OncologyHealth Resources and Services AdministrationMorphoSysSwedish Orphan BiovitrumVertex PharmaceuticalsOmeros CorporationAstellas PharmaAdaptive BiotechnologiesPfizerIncyteKiadis Pharmabluebird bioJazz PharmaceuticalsStemCyteActinium PharmaceuticalsCareDxNational Heart, Lung, and Blood InstituteNovartis Pharmaceuticals CorporationBristol-Myers SquibbCSL BehringBeiGeneHistoGeneticsAtara BiotherapeuticsNational Cancer InstituteGilead SciencesSanofiGlaxoSmithKlineGateway for Cancer ResearchMallinckrodt PharmaceuticalsAstellas Pharma USAmgen
KeywordsMedicineCumulative incidenceInternal medicineUnivariate analysisIncidence (geometry)Progression-free survivalHematopoietic cellSurgeryRetrospective cohort studyHematopoietic stem cell transplantationTransplantationMultivariate analysisChemotherapyHaematopoiesis

Abstract

fetched live from OpenAlex

In patients with relapsed DLBCL in complete remission (CR), autologous hematopoietic cell transplantation (auto-HCT) and CAR-T therapy are both effective, but it is unknown which modality provides superior outcomes. We compared the efficacy of auto-HCT vs. CAR-T in patients with DLBCL in a CR. A retrospective observational study comparing auto-HCT (2015-2021) vs. CAR-T (2018-2021) using the Center for International Blood & Marrow Transplant Research registry. Median follow-up was 49.7 months for the auto-HCT and 24.7 months for the CAR-T cohort. Patients ages 18 and 75 with a diagnosis of DLBCL were included if they received auto-HCT (n = 281) or commercial CAR-T (n = 79) while in a CR. Patients undergoing auto-HCT with only one prior therapy line and CAR-T patients with a previous history of auto-HCT treatment were excluded. Endpoints included Progression-free survival (PFS), relapse rate, non-relapse mortality (NRM) and overall survival (OS). In univariate analysis, treatment with auto-HCT was associated with a higher rate of 2-year PFS (66.2% vs. 47.8%; p < 0.001), a lower 2-year cumulative incidence of relapse (27.8% vs. 48% ; p < 0.001), and a superior 2-year OS (78.9% vs. 65.6%; p = 0.037). In patients with early (within 12 months) treatment failure, auto-HCT was associated with a superior 2-year PFS (70.9% vs. 48.3% ; p < 0.001), lower 2-year cumulative incidence of relapse (22.8% vs. 45.9% ; p < 0.001) and trend for higher 2-year OS (82.4% vs. 66.1% ; p = 0.076). In the multivariable analysis, treatment with auto-HCT was associated with a superior PFS (hazard ratio 1.83; p = 0.0011) and lower incidence of relapse (hazard ratio 2.18; p < 0.0001) compared to CAR-T. In patients with relapsed LBCL who achieve a CR, treatment with auto-HCT is associated with improved clinical outcomes compared to CAR-T. These data support the consideration of auto-HCT in select patients with LBCL achieving a CR in the relapsed setting.

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)0.001
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 score0.001
Threshold uncertainty score0.003

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.001
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)0.0010.000

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 head0.028
GPT teacher head0.297
Teacher spread0.269 · 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".

Quick stats

Citations23
Published2024
Admission routes1
Has abstractyes

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