MétaCan
Menu
← Back to cohort

Low Peripheral Blood NK Cell Count Is Associated with Worse Clinical Outcome in Patients with Follicular Lymphoma (FL) and Diffuse Large B-Cell Lymphoma (DLBCL) Treated with Immunochemotherapy: Results from the Frontline Phase 3 GALLIUM and GOYA Trials

2017· article· en· W2967137243 on OpenAlexaff
Magdalena Klánová, Mikkel Z. Oestergaard, Marek Trněný, Wolfgang Hiddemann, Robert Marcus, Laurie H. Sehn, Umberto Vitolo, Alexandra Bazeos, Valentin Goede, Harald Zeuner, Andrea Knapp, Denis Sahin, Nathalie Danesi, Christopher R. Bolen, Susan Robson, Jeffrey M. Venstrom, Tina Nielsen, Günter Fingerle‐Rowson

Bibliographic record

VenueBlood · 2017
Typearticle
Languageen
FieldImmunology and Microbiology
TopicImmune Cell Function and Interaction
Canadian institutionsUniversity of British ColumbiaBC Cancer Agency
Fundersnot available
KeywordsMedicineRituximabFollicular lymphomaCD20LymphomaCHOPInternal medicineDiffuse large B-cell lymphomaAntibody-dependent cell-mediated cytotoxicityImmunologyObinutuzumabOncologyCancer researchAntibodyMonoclonal antibody

Abstract

fetched live from OpenAlex

Abstract Introduction: Natural killer (NK) cells are key elements of the innate immune system. Obinutuzumab (GA101; G) is an anti-CD20 monoclonal antibody with enhanced direct cell death activity and antibody-dependent cellular cytotoxicity (ADCC) vs rituximab (R). Upon binding CD20-bound G/R, NK cells are activated and attack the target cell. Therefore, low pre-treatment NK cell count (NKCC) may be associated with worse outcomes in G/R-treated patients (pts). This exploratory post hoc analysis evaluated the prognostic impact of baseline (BL) NKCC in FL pts treated with G/R plus chemo in the Phase 3 GALLIUM trial (NCT01332968) and DLBCL pts treated with G/R plus CHOP in the Phase 3 GOYA trial (NCT01287741). Methods: BL peripheral blood (PB) NKCCs, assessed by flow cytometry (CD3-CD56+CD16+ cells; normal range [central lab]: 95-640 cells/µL), were available in 1064/1202 (88.6%) FL pts and 1287/1418 (90.8%) DLBCL pts. Cell-of-origin (COO) was determined in 933/1418 DLBCL pts using the Nanostring Research Use Only Lymphoma Subtyping Test (LST). COO and BL NKCC were available in 857/1418 pts. Whole transcriptome gene expression was analyzed using TruSeq RNA sequencing in tumor tissue from 552/1418 DLBCL pts. A 57-gene signature designed to reflect NK cell tumor infiltration was applied to RNA sequencing data from 552 pts; median score was used to define high/low subgroups. Kaplan-Meier methodology was used to estimate progression-free (PFS) and overall survival (OS), and a Cox regression univariate model was used to estimate corresponding HR and CI. The relative importance of BL variables (NKCC, gender, geographic region, treatment arm, chemo backbone/no. of planned chemo cycles, FLIPI/IPI, extranodal/bone marrow involvement, sum of products of diameter, Ann Arbor stage) was evaluated using multivariate (MV) Cox regression models with a stepwise approach. Results: Median (range) BL NKCCs were 220 cells/μL (0-3300) in FL and 200 cells/μL (0-1900) in DLBCL pts. Overall, 108/1064 (10.2%) FL pts and 255/1287 (19.8%) DLBCL pts had low BL NKCC (<100 cells/μL). By COO subtype, 83/485 (17.1%) germinal center B-cell-like (GCB), 37/140 (26.4%) unclassified, and 54/232 (23.3%) activated B-cell-like (ABC) DLBCL pts had low BL NKCC. BL disease characteristics of pts with low vs normal NKCCs are shown in Table 1. Low BL NKCC was associated with advanced disease. On univariate analysis low BL NKCC was associated with shorter PFS in FL (HR 1.57, 95% CI 1.10-2.25, p=0.01; 3-yr PFS 71.6% vs 80.1%) and DLBCL (HR 1.36, 95% CI 1.07-1.72, p=0.01; 3-yr PFS 62.8% vs 70.0%; Figure 1), and shorter OS in FL (HR 2.58, 95% CI 1.51-4.42, p=0.0003; 3-yr OS 87.6% vs 94.3%). DLBCL pts with low NKCC had a trend to worse OS vs pts with normal NKCC (HR 1.35, 95% CI 1.00-1.82, p=0.052; 3-yr OS 77.6% vs 82.3%). On MV analysis, low BL NKCC was independently associated with PFS in FL (HR 1.48, 95% CI 1.02-2.14, p=0.04) and DLBCL (HR 1.36, 95% CI 1.01-1.83, p=0.04). The DLBCL result appeared to be driven by COO subtype, with the highest estimated HR in GCB (HR 1.58, 95% CI 1.0-2.5, p=0.05), and no effect in unclassified and ABC. Interestingly, in line with the enhanced ADCC of G, the impact of low BL NKCC on PFS was stronger in G-treated FL pts (HR 2.06, 95% CI 1.24-3.41, p<0.01) vs R-treated pts (HR 1.19, 95% CI 0.71-1.99, p=0.5), while less pronounced in the GCB DLBCL subgroup (G-treated pts: HR 1.25, 95% CI 0.91-0.77, p=0.182; R-treated pts: HR 1.47, 95% CI 1.06-2.06, p=0.021; Figure 2). Although there was no correlation between PB NKCC and tumor NK cell gene expression among biomarker-evaluable pts in GOYA, low tumor NK cell gene expression was associated with shorter PFS in G-treated DLBCL pts (all COO subtypes; HR 1.95, 95% CI 1.2-3.1, p<0.01), with a trend for low CD56 mRNA expression alone (as continuous variable) correlating with shorter PFS (p=0.043). Conclusions: In this largest prospective collection of PB NK cells to date in FL and DLBCL, substantial numbers of pts had reduced NKCCs at BL, which was associated with advanced disease. Univariate and MV analyses may suggest that low PB NKCC is independently associated with shorter PFS in FL and DLBCL and shorter OS in FL. Likewise, low NK cell gene expression in tumor tissue was associated with shorter PFS in G-treated DLBCL pts. Collectively, our results from this exploratory analysis indicate that the number of NK cells in PB and tumor tissue may impact clinical outcome of non-Hodgkin lymphoma pts treated with anti-CD20 antibodies. Disclosures Klanova: F. Hoffmann-La Roche Ltd: Employment, Other: GALLIUM and GOYA are sponsored by F. Hoffmann-La Roche Ltd. Third-party editorial support, under the direction of Magdalena Klanova, was provided by Lynda McEvoy of Gardiner-Caldwell Communications, and was funded by F. Hoffmann-La Roche Ltd. Oestergaard: F. Hoffmann-La Roche Ltd: Employment. Trněný: Takeda: Consultancy, Honoraria; BMS: Consultancy, Honoraria; Abbvie: Consultancy, Honoraria; Celgene: Consultancy, Honoraria, Research Funding; Roche: Consultancy, Honoraria, Research Funding; Janssen: Consultancy, Honoraria; Gilead: Consultancy, Honoraria. Marcus: Celgene: Other: Support for meeting attendance ; Roche: Consultancy, Honoraria, Other: Travel support, Speakers Bureau. Sehn: Roche/Genentech: Consultancy, Honoraria; Amgen: Consultancy, Honoraria; Celgene: Consultancy, Honoraria; Abbvie: Consultancy, Honoraria; Janssen: Consultancy, Honoraria; Seattle Genetics: Consultancy, Honoraria. Vitolo: Gilead: Honoraria; Janssen: Honoraria, Membership on an entity's Board of Directors or advisory committees; Takeda: Honoraria; Celgene: Honoraria, Membership on an entity's Board of Directors or advisory committees; Mundipharma: Honoraria; Roche: Honoraria, Membership on an entity's Board of Directors or advisory committees, Research Funding. Bazeos: Roche: Other: • A year-long academic collaboration contract with Roche (no financial gain).. Goede: Gilead: Membership on an entity's Board of Directors or advisory committees; Roche: Honoraria, Membership on an entity's Board of Directors or advisory committees, Other: travel grants, Speakers Bureau; Janssen: Honoraria, Membership on an entity's Board of Directors or advisory committees, Other: travel grants, Speakers Bureau. Zeuner: Roche: Employment. Knapp: Roche: Employment. Sahin: Roche: Employment, Equity Ownership. Danesi: Roche: Employment. Bolen: Genentech: Employment, Equity Ownership. Robson: F. Hoffmann la Roche: Employment. Venstrom: Genentech, Inc.: Employment. Nielsen: F. Hoffmann-La Roche Ltd: Employment, Equity Ownership. Fingerle-Rowson: F. Hoffmann-La Roche Ltd: Employment, Equity Ownership.

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: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.002
Threshold uncertainty score0.005

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0010.000
Open science0.0000.000
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 head0.016
GPT teacher head0.262
Teacher spread0.245 · 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".

Quick stats

Citations2
Published2017
Admission routes1
Has abstractyes

Explore more

Same venueBlood→Same topicImmune Cell Function and Interaction→French-language works237,207→