Evidence map›Paper›PMID 27006493›Full record

ArticleClinical cancer research : an official journal of the American Association for Cancer Research2016

Myeloma Cell Dynamics in Response to Treatment Supports a Model of Hierarchical Differentiation and Clonal Evolution.

Min Tang, Rui Zhao, Helgi van de Velde, Jennifer G Tross, Constantine Mitsiades, Suzanne Viselli, Rachel Neuwirth, Dixie-Lee Esseltine, Kenneth Anderson, Irene M Ghobrial and 4 more

Open access · bronzeAbstract read
In one paragraph

Article in Clinical cancer research : an official journal of the American Association for Cancer Research, 2016. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 12 papers.

0numbers the graph read from it
0cells of the map it votes in
12citing papers in PubMed
3.2field-weighted citation impact, top 8% 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.

Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

12 citing papers in PubMed, 28 citations in OpenAlex.

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  9. Extinction rates in tumour public goods games.Journal of the Royal Society, Interface · 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

14 authors at 2 institutions in 1 country.

Min Tang *Department of Biostatistics and Computational Biology, Dana-Farber Cancer Institute, and Department of Biostatistics, Harvard T. H. Chan School of Public Health, Boston, MA 02115, USA.
Rui Zhao *Department of Biostatistics and Computational Biology, Dana-Farber Cancer Institute, and Department of Biostatistics, Harvard T. H. Chan School of Public Health, Boston, MA 02115, USA.
Helgi van de VeldeTakeda Pharmaceuticals, Inc., Cambridge, MA.
Jennifer G TrossDepartment of Biostatistics and Computational Biology, Dana-Farber Cancer Institute, and Department of Biostatistics, Harvard T. H. Chan School of Public Health, Boston, MA 02115, USA.
Constantine MitsiadesDana-Farber Cancer Institute, Boston, USA.
Suzanne ViselliOncology R&D, Janssen Research & Development LLC, Raritan, USA.
Rachel NeuwirthTakeda Pharmaceuticals, Inc., Cambridge, MA.
Dixie-Lee EsseltineTakeda Pharmaceuticals, Inc., Cambridge, MA.
Kenneth AndersonDana-Farber Cancer Institute, Boston, USA.
Irene M GhobrialDana-Farber Cancer Institute, Boston, USA.
Jesús F San MiguelHospital Universitario Salamanca, CIC, IBMCC (USAL-CSIC), Salamanca, Spain.
Paul G RichardsonDana-Farber Cancer Institute, Boston, USA.
Michael H TomassonDivision of Oncology, School of Medicine, Washington University in St. Louis.
Franziska MichorDepartment of Biostatistics and Computational Biology, Dana-Farber Cancer Institute, and Department of Biostatistics, Harvard T. H. Chan School of Public Health, Boston, MA 02115, USA.
Dana-Farber Cancer Institute · USHarvard University · US

Funding

Viral VectorP30CA086862 · NCI · UNIVERSITY OF IOWA · PI Douglas Robert Spitz · 2000 to 2026
$70.0M
Project 3: Single Cell Measures of Intratumor Diversity for Optimal Breast Cancer TherapyU54CA193461 · NCI · DANA-FARBER CANCER INST · PI POLYAK, KORNELIA · 2015 to 2020
$11.2M
NCI NIH HHS U54 CA193461
6 · The paper itself

Abstract

purposeSince the pioneering work of Salmon and Durie, quantitative measures of tumor burden in multiple myeloma have been used to make clinical predictions and model tumor growth. However, such quantitative analyses have not yet been performed on large datasets from trials using modern chemotherapy regimens. EXPERIMENTAL

designWe analyzed a large set of tumor response data from three randomized controlled trials of bortezomib-based chemotherapy regimens (total sample size n = 1,469 patients) to establish and validate a novel mathematical model of multiple myeloma cell dynamics.

resultsTreatment dynamics in newly diagnosed patients were most consistent with a model postulating two tumor cell subpopulations, "progenitor cells" and "differentiated cells." Differential treatment responses were observed with significant tumoricidal effects on differentiated cells and less clear effects on progenitor cells. We validated this model using a second trial of newly diagnosed patients and a third trial of refractory patients. When applying our model to data of relapsed patients, we found that a hybrid model incorporating both a differentiation hierarchy and clonal evolution best explains the response patterns.

conclusionsThe clinical data, together with mathematical modeling, suggest that bortezomib-based therapy exerts a selection pressure on myeloma cells that can shape the disease phenotype, thereby generating further inter-patient variability. This model may be a useful tool for improving our understanding of disease biology and the response to chemotherapy regimens. Clin Cancer Res; 22(16); 4206-14. ©2016 AACR.

Indexed as

Cell DifferentiationClonal EvolutionModels, TheoreticalClinical Trials as TopicHumansModels, StatisticalMultiple MyelomaReproducibility of Results

Identifiers

PMID27006493
PMCPMC4987182
OpenAlexW2302632356

What OpenQuestion holds

Textmetadata
LicenceTDM
Read underepoch 390

Registered trials

None linked

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.