Evidence map›Paper›PMID 28665416›Full record

ArticleBlood cancer journal2017

A gene expression signature distinguishes innate response and resistance to proteasome inhibitors in multiple myeloma.

A K Mitra, T Harding, U K Mukherjee, J S Jang, Y Li, R HongZheng, J Jen, P Sonneveld, S Kumar, W M Kuehl and 2 more

Abstract read
PubMed Full text at PMC Publisher
In one paragraph

Article in Blood cancer journal, 2017. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 29 papers, 1 of them a synthesis that pooled it.

0numbers the graph read from it
0cells of the map it votes in
29citing papers in PubMed, 1 pooled it
–field-weighted citation impact
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

29 citing papers in PubMed, 1 synthesis or guideline pooled it.

  1. Pooled it
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  16. Minimal Residual Disease in Myeloma: Application for Clinical Care and New Drug Registration.Clinical cancer research : an official journal of the American Association for Cancer Research · 2021
    Article
  17. Article
  18. Gene expression profiling as a prognostic tool in multiple myeloma.Cancer drug resistance (Alhambra, Calif.) · 2021
    Review
  19. Article
  20. Article
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

12 authors.

A K MitraDepartment of Genetics, Cell Biology and Development, University of Minnesota, Minneapolis, MN, USA.
T HardingDepartment of Genetics, Cell Biology and Development, University of Minnesota, Minneapolis, MN, USA.
U K MukherjeeSchool of Statistics, University of Minnesota, Minneapolis, MN, USA.
J S JangGenome Analysis Core, Center for Individualized Medicine, Mayo Clinic, Rochester, MN, USA.
Y LiDivision of Bioinformatics and Biostatistics, Department of Health Science Research, Mayo Clinic, Rochester, MN, USA.
R HongZhengBiomarker Discovery Program, Center for Individualized Medicine, Mayo Clinic, Rochester, MN, USA.
J JenGenome Analysis Core, Center for Individualized Medicine, Mayo Clinic, Rochester, MN, USA.
P SonneveldDepartment of Hematology, Erasmus MC Cancer Institute, Rotterdam, The Netherlands.
S KumarDivision of Hematology, Department of Internal Medicine, Mayo Clinic, Rochester, MN, USA.ORCID 0000-0001-5392-9284
W M KuehlGenetics Branch, Center for Cancer Research, National Cancer Institute, Bethesda, MD, USA.
V RajkumarDivision of Hematology, Department of Internal Medicine, Mayo Clinic, Rochester, MN, USA.
B Van NessDepartment of Genetics, Cell Biology and Development, University of Minnesota, Minneapolis, MN, USA.

Funding

Project 3 - Modeling Proteasome Inhibitor Response and Resistance in Cell Lines and Patient Samples with Single Cell Analysis of SubpopulationsU54CA224018 · NCI · MAYO CLINIC ARIZONA · PI BERGSAGEL, PETER LEIF · 2017 to 2021
$6.0M
6 · The paper itself

Abstract

Extensive interindividual variation in response to chemotherapy is a major stumbling block in achieving desirable efficacy in the treatment of cancers, including multiple myeloma (MM). In this study, our goal was to develop a gene expression signature that predicts response specific to proteasome inhibitor (PI) treatment in MM. Using a well-characterized panel of human myeloma cell lines (HMCLs) representing the biological and genetic heterogeneity of MM, we created an in vitro chemosensitivity profile in response to treatment with the four PIs bortezomib, carfilzomib, ixazomib and oprozomib as single agents. Gene expression profiling was performed using next-generation high-throughput RNA-sequencing. Applying machine learning-based computational approaches including the supervised ensemble learning methods Random forest and Random survival forest, we identified a 42-gene expression signature that could not only distinguish good and poor PI response in the HMCL panel, but could also be successfully applied to four different clinical data sets on MM patients undergoing PI-based chemotherapy to distinguish between extraordinary (good and poor) outcomes. Our results demonstrate the use of in vitro modeling and machine learning-based approaches to establish predictive biomarkers of response and resistance to drugs that may serve to better direct myeloma patient treatment options.

Indexed as

Gene ExpressionGene Expression ProfilingHumansMultiple MyelomaProteasome InhibitorsProteasome Inhibitors

Identifiers

PMID28665416
PMCPMC5520403

What OpenQuestion holds

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Registered trials

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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.