Evidence map›Paper›PMID 38002311›Full record

ReviewBiomolecules2023

Good Cop, Bad Cop: Profiling the Immune Landscape in Multiple Myeloma.

Niyati Seshagiri Sharma, Bibha Choudhary

Open access · goldAbstract readReview
In one paragraph

Review in Biomolecules, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 9 papers.

0numbers the graph read from it
0cells of the map it votes in
9citing papers in PubMed
2.3field-weighted citation impact, top 10% 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

9 citing papers in PubMed, 8 citations in OpenAlex.

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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

2 authors at 1 institution in 1 country.

Niyati Seshagiri SharmaInstitute of Bioinformatics and Applied Biotechnology (IBAB), Electronic City, Bengaluru 560100, India.
Bibha ChoudharyInstitute of Bioinformatics and Applied Biotechnology (IBAB), Electronic City, Bengaluru 560100, India.
Institute of Bioinformatics and Applied Biotechnology · IN

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Multiple myeloma (MM) is a dyscrasia of plasma cells (PCs) characterized by abnormal immunoglobulin (Ig) production. The disease remains incurable due to a multitude of mutations and structural abnormalities in MM cells, coupled with a favorable microenvironment and immune suppression that eventually contribute to the development of drug resistance. The bone marrow microenvironment (BMME) is composed of a cellular component comprising stromal cells, endothelial cells, osteoclasts, osteoblasts, and immune cells, and a non-cellular component made of the extracellular matrix (ECM) and the liquid milieu, which contains cytokines, growth factors, and chemokines. The bone marrow stromal cells (BMSCs) are involved in the adhesion of MM cells, promote the growth, proliferation, invasion, and drug resistance of MM cells, and are also crucial in angiogenesis and the formation of lytic bone lesions. Classical immunophenotyping in combination with advanced immune profiling using single-cell sequencing technologies has enabled immune cell-specific gene expression analysis in MM to further elucidate the roles of specific immune cell fractions from peripheral blood and bone marrow (BM) in myelomagenesis and progression, immune evasion and exhaustion mechanisms, and development of drug resistance and relapse. The review describes the role of BMME components in MM development and ongoing clinical trials using immunotherapeutic approaches.

Indexed as

Multiple MyelomaBone MarrowCytokinesEndothelial CellsHumansStromal CellsTumor MicroenvironmentCytokineshematopoiesisimmune profilingimmunotherapymultiple myelomatumor microenvironment

Identifiers

PMID38002311
PMCPMC10669790
OpenAlexW4388465564

What OpenQuestion holds

Textmetadata
LicenceCC BY
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.