Evidence map›Paper›PMID 41681883›Full record

ReviewCancers2026

Ex Vivo Treatment Response Prediction in Multiple Myeloma: Assay Formats, Clinical Correlation, and Future Directions.

Gavin R Oliver, Carlton C Barnett, Kendra E Hightower, Yubin Kang, Muhamed Baljevic

Abstract readReview
In one paragraph

Review in Cancers, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 1 paper.

0numbers the graph read from it
0cells of the map it votes in
1citing papers in PubMed
–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

1 citing paper in PubMed.

  1. 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

5 authors.

Gavin R OliverXilis, Inc., Durham, NC 27703, USA.ORCID 0000-0002-9948-3799
Carlton C BarnettXilis, Inc., Durham, NC 27703, USA.
Kendra E HightowerXilis, Inc., Durham, NC 27703, USA.
Yubin KangDepartment of Medicine, Duke University, Durham, NC 27708, USA.ORCID 0000-0001-6678-8732
Muhamed BaljevicVanderbilt-Ingram Cancer Center, Vanderbilt University Medical Center, Nashville, TN 37240, USA.ORCID 0000-0002-0630-8458

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Ex vivo functional testing for multiple myeloma is rapidly evolving, yet no single assay has reached the level of reliability and clinical utility needed for routine decision-making. Existing approaches generally fall into three categories: 2D cultures, 3D models, and dynamic systems. Each contributes valuable but incomplete insight into therapeutic response. Among these, 2D assays remain the most mature, with the most extensive clinical correlations to date, though their simplified architecture limits their ability to reflect the full complexity of the bone marrow microenvironment. However, 3D systems, including spheroids and matrix-based organoids, offer improved preservation of tumor heterogeneity and microenvironmental cues. These platforms show emerging clinical relevance and may hold advantages over traditional 2D formats, and validation efforts are developing. Dynamic systems, including microfluidic models and perfused bone-marrow mimetics, represent the most physiologically ambitious category, yet their technical intricacy and early stage of development have so far limited broad clinical correlation. Altogether, the current landscape highlights substantial progress but lacks an optimal assay. In this review, we take the unique approach of examining published ex vivo tests that have demonstrated a level of clinical correlation. We evaluate their respective formats, strengths and limitations, and discuss considerations for what an ideal future assay may encompass.

Indexed as

2D models3D modelsclinical correlationdynamic systemsex vivo testmultiple myelomareview

Identifiers

PMID41681883
PMCPMC12896484

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.