Evidence map›Paper›PMID 27831501›Full record

ArticleAnnals of oncology : official journal of the European Society for Medical Oncology2017

Characterizing CD137 upregulation on NK cells in patients receiving monoclonal antibody therapy.

A Makkouk, V Sundaram, C Chester, S Chang, A D Colevas, J B Sunwoo, H Maecker, M Desai, H E Kohrt

Registry-linked trialOpen access · bronzeAbstract read
In one paragraph

Article in Annals of oncology : official journal of the European Society for Medical Oncology, 2017. The graph could read no effect estimate from its abstract, so it casts no vote on the map. It is linked to trial NCT01114256 (FNA Tumor Sampling for CD137 Modulation), which is not on this map. Cited by 12 papers.

0numbers the graph read from it
0cells of the map it votes in
12citing papers in PubMed
0.5field-weighted citation impact, top 32% of its field
1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

The trial behind it

Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.

NCT01114256 terminatednot on this map

FNA Tumor Sampling for CD137 Modulation: A Pilot Study

TypeobservationalSponsorStanford UniversityRan2010 to 2016Enrolled83ConditionsHead and Neck Cancer, Breast Cancer, Head and Neck Cancers, Non-Hodgkin LymphomaArmsFNA Biopsy
3 · Its place in the literature

Who cites it

12 citing papers in PubMed, 15 citations in OpenAlex.

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  12. Systematic evaluation of immune regulation and modulation.Journal for immunotherapy of cancer · 2017
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4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

9 authors at 3 institutions in 2 countries.

A MakkoukDepartment of Medicine, Division of Oncology, Stanford University , Stanford, CA, USA.
V SundaramQuantitative Sciences Unit, Department of Medicine, Stanford University School of Medicine, CA, USA.
C ChesterDepartment of Medicine, Division of Oncology, Stanford University , Stanford, CA, USA.
S ChangInstitute for Immunity, Transplantation and Infection, Stanford University School of Medicine, Stanford, CA, USA.
A D ColevasDepartment of Medicine, Division of Oncology, Stanford University , Stanford, CA, USA.
J B SunwooDepartment of Otorhinolaryngology, Stanford University, Stanford, USA.
H MaeckerInstitute for Immunity, Transplantation and Infection, Stanford University School of Medicine, Stanford, CA, USA.
M DesaiQuantitative Sciences Unit, Department of Medicine, Stanford University School of Medicine, CA, USA.
H E KohrtDepartment of Medicine, Division of Oncology, Stanford University , Stanford, CA, USA.
Stanford Medicine · USStanford University · USInstitute of Infection and Immunity · CA

Funding

Translational Oncology Research Program (Project-005)P30CA124435 · NCI · STANFORD UNIVERSITY · PI Vittorio Sebastiano · 2007 to 2026
$71.4M
NCI NIH HHS P30 CA124435
6 · The paper itself

Abstract

Background: In the era of personalized cancer medicine, identifying techniques for effectively matching patients to efficacious treatments is a critical step in the treatment process. The advent of anti-cancer immunotherapies necessitates novel approaches to biomarker identification beyond traditional genomic profiling. One promising approach is incorporation of nomograms into treatment decisions. Nomograms are prediction tools, based on statistical modeling, designed to predict treatment outcomes. As a first step toward developing a nomogram, we conducted analyses to predict CD137 expression of natural killer cells after monoclonal antibody (mAb) treatment. Patients and methods: Patient, tumor and immune characteristics were collected from 199 patients with breast cancer (N = 62), head/neck cancers (N = 46) and non-Hodgkin's lymphoma (NHL) (N = 91), who were receiving mAb therapy as part of clinical trials. The difference in CD137 expression before and after mAb therapy was assessed by flow cytometry. To evaluate those who respond to mAb therapy via increased CD137 expression, we applied classification and regression trees (CART), multivariable lasso regression tools and Random Forest. Results: The CD137 expression was significantly different for each cancer type [mean (SD): Breast: 6.6 (6.5); Head/Neck: 11.0 (7.0); NHL: 7.5 (7.1), P < 0.0001]. For breast cancer and NHL, FcR polymorphism and baseline CD137 expression were significant predictors of increased CD137 expression; for head/neck cancer, FcR polymorphism and age were significant predictors of increased expression. Conclusions: Our preliminary results suggest that FcR polymorphism, pre-treatment CD137 expression and age are significant predictors of CD137 upregulation in patients. This study demonstrates that the development of a nomogram for therapy response is feasible. Further work validating our models in an independent cohort will provide the next steps in developing a nomogram that may be used to individualize this therapeutic approach for patients (NCT01114256).

Indexed as

AgedAntineoplastic Agents, ImmunologicalBiomarkers, TumorBreast NeoplasmsFemaleHead and Neck NeoplasmsHumansKiller Cells, NaturalLymphoma, Non-HodgkinMaleTumor Necrosis Factor Receptor Superfamily, Member 9Up-RegulationAntineoplastic Agents, ImmunologicalBiomarkers, TumorTumor Necrosis Factor Receptor Superfamily, Member 9biomarkercancerCD137monoclonal antibodynatural killer cellsregression tree

Identifiers

PMID27831501
PMCPMC6246233
OpenAlexW2555459688

What OpenQuestion holds

Textmetadata
Read underepoch 390

Registered trials

Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.