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Record W2935816391 · doi:10.1186/s40425-019-0538-8

Follicular lymphoma patients with KIR2DL2 and KIR3DL1 and their ligands (HLA-C1 and HLA-Bw4) show improved outcome when receiving rituximab

2019· article· en· W2935816391 on OpenAlexaff
Amy K. Erbe, Wei Wang, Lakeesha Carmichael, Anna Hoefges, Bartosz Grzywacz, Patrick K. Reville, Erik A. Ranheim, Jacquelyn A. Hank, KyungMann Kim, Songwon Seo, Eneida A. Mendonça, Yiqiang Song, Vaishalee P. Kenkre, Fangxin Hong, Randy D. Gascoyne, Elisabeth Paietta, Sandra J. Horning, Jeffrey S. Miller, Brad S. Kahl, Paul M. Sondel

Bibliographic record

VenueJournal for ImmunoTherapy of Cancer · 2019
Typearticle
Languageen
FieldImmunology and Microbiology
TopicImmune Cell Function and Interaction
Canadian institutionsBC Cancer Agency
FundersHyundai Hope On WheelsNational Cancer InstituteStand Up To CancerU.S. Department of Health and Human ServicesNational Institutes of HealthUniversity of Wisconsin Carbone Cancer CenterECOG-ACRIN Cancer Research GroupMidwest Athletes Against Childhood CancerAmerican College of Radiology Imaging NetworkU.S. Public Health ServiceSt. Baldrick's FoundationAmerican Association for Cancer ResearchUniversity of Wisconsin-Madison
KeywordsRituximabFollicular lymphomaHuman leukocyte antigenFollicular phaseMedicineImmunologyLymphomaInternal medicineAntigen

Abstract

fetched live from OpenAlex

BACKGROUND: The ECOG-ACRIN Cancer Research Group evaluated rituximab treatment schedules for patients with newly-diagnosed low-tumor-burden follicular-lymphoma (FL). All patients received 4-weekly rituximab treatments as induction therapy. Clinically-responding patients were randomized to receive rituximab every 13 weeks ("maintenance") vs. no additional rituximab until progression ("non-maintenance"). Based on "time-to-rituximab-failure (TTRF)", the study-committee reported there was no overall-benefit for maintenance rituximab in this setting. Tumor-reactive mAbs, like rituximab, trigger natural killer (NK) cells. NK-cell responses are regulated, in part, by interactions between killer immunoglobulin-like receptors (KIRs) on NK cells and their interactions with KIR-ligands. In a separate study of children with neuroblastoma treated with a different mAb, we found certain KIR/KIR-ligand genotypes associated with improved outcome. Here, we assessed whether a subset of FL patients show improved outcome from the maintenance rituximab based on these same KIR/KIR-ligand genotypes. METHODS: Genotypes for KIR/KIR-ligand were determined and assessed for associations with outcome [duration of response, TTRF and % tumor shrinkage] as a post-hoc analysis of this phase III trial. Our primary objective was to assess specific KIR/KIR-ligand genotype associations, followed by separate prespecified KIR/KIR-ligand genotype associations in follow-up analyses. Statistical analyses for association of genotype with clinical outcome included: Log-rank tests and Cox proportional hazards regression models to assess duration of response and TTRF; analysis of variance (ANOVA) was used for assessment of % tumor shrinkage. RESULTS: We found that patients inheriting KIR2DL2 and its ligand (HLA-C1) along with KIR3DL1 and its ligand (HLA-Bw4) had improved outcome over patients without this genotype. In addition, patients with KIR2DL2 and HLA-C1 along with KIR3DL1 and HLA-Bw4 also showed improved duration of response and tumor shrinkage if they received maintenance, while patients without this genotype showed no such improvement when receiving maintenance. CONCLUSIONS: The data presented here indicate that a subset of FL patients, identified by certain KIRs/KIR-ligands, have improved outcome and may benefit from additional rituximab treatment. Taken together, this suggests that the efficacy of tumor-reactive mAb treatment for some patients is influenced by KIRs on NK cells. However, prior to considering these genotypes in a clinically-actionable manner, these findings need independent validation in other studies.

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)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Bench or experimental · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.460
Threshold uncertainty score0.656

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
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 head0.007
GPT teacher head0.236
Teacher spread0.229 · 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 designBench or experimental
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
Published2019
Admission routes1
Has abstractyes

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