Evidence map›Paper›PMID 42240912›Full record

ReviewMolecular biology reports2026

Harnessing CAR-NK cells against multiple myeloma: current landscape and future directions.

Samira Anvari, Jalal Naghinezhad, Ameneh Shokati, Asma Sobhani, Behnam Mortazavi, Mahshid Akhavan Rahnama, Mohammad Ahmadvand

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In one paragraph

Review in Molecular biology reports, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.

0numbers the graph read from it
0cells of the map it votes in
0citing 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

0 citing papers in PubMed.

No citing paper in PubMed yet.

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

7 authors.

Samira Anvari *Blood Transfusion Research Center, High Institute for Research and Education in Transfusion Medicine, Iranian Blood Transfusion Organization Building, Next to the Milad Tower, Hemmat Exp. Way, P.O.Box:14665-1157, Tehran, Iran.
Jalal Naghinezhad *Blood Transfusion Research Center, High Institute for Research and Education in Transfusion Medicine, Iranian Blood Transfusion Organization Building, Next to the Milad Tower, Hemmat Exp. Way, P.O.Box:14665-1157, Tehran, Iran.
Ameneh ShokatiDepartment of Applied Cell Sciences, School of Advanced Technologies in Medicine, Tehran University of Medical Sciences, Tehran, Iran.
Asma SobhaniDepartment of Genetics, Faculty of Biological Science, Tarbiat Modares University, Tehran, Iran.
Behnam MortazaviDepartment of systems Biotechnology, Faculty of Industrial and Environmental Biotechnology, National Institute of Genetic Engineering and Biotechnology (NIGEB), Tehran, Iran.
Mahshid Akhavan RahnamaCell Therapy and Hematopoietic Stem Cell Transplantation Research Center, Research Institute for Oncology, Hematology and Cell Therapy, Tehran University of Medical Sciences, Tehran, Iran. m-akhavanrahnama@farabi.tums.ac.ir.ORCID http://orcid.org/0000-0001-8226-3290
Mohammad AhmadvandCell Therapy and Hematopoietic Stem Cell Transplantation Research Center, Research Institute for Oncology, Hematology and Cell Therapy, Tehran University of Medical Sciences, Tehran, Iran. mahmadvand@sina.tums.ac.ir.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

backgroundMultiple myeloma (MM) remains an aggressive and largely incurable plasma cell malignancy, with relapse common despite therapeutic advances. Although CAR-T cells have transformed the field, their clinical use is constrained by toxicity, complex manufacturing, and limited accessibility. These limitations have accelerated interest in CAR-engineered natural killer (CAR-NK) cells as a safer, more scalable, and potentially off-the-shelf immunotherapeutic platform.

objectiveThis review goes beyond a descriptive summary of CAR-NK research in MM by providing an integrated framework that connects antigen targeting, synthetic engineering, tumor microenvironment adaptation, biomarker-guided response monitoring, and translational manufacturing barriers. In doing so, it highlights not only what has been achieved, but also what currently limits clinical implementation and where the field is most likely to advance next.

methodsWe performed a comprehensive literature analysis of preclinical and clinical studies on CAR-NK cell therapy in MM, focusing on target selection, persistence-enhancing strategies, immune evasion, biomarker development, combination approaches, and GMP-compatible manufacturing platforms.

resultsCAR-NK cells offer several advantages over CAR-T therapy, including lower risks of cytokine release syndrome, neurotoxicity, and graft-versus-host disease. Beyond summarizing currently explored targets such as BCMA, CD138, SLAMF7, and GPRC5D, this review identifies the main design principles driving next-generation CAR-NK development: cytokine armoring, genome editing, dual-targeting strategies, and nanotechnology-enabled delivery. Importantly, we also synthesize emerging translational priorities, including predictive biomarkers for patient stratification, serial monitoring of treatment response, and scalable closed-system manufacturing approaches that may determine clinical feasibility.

conclusionCAR-NK therapy is evolving from a promising concept into a realistic therapeutic platform for MM. This review contributes a forward-looking translational roadmap by integrating engineering innovation, biomarker-based precision medicine, and manufacturing scalability, thereby defining the key steps needed to move CAR-NK cells toward durable and clinically meaningful impact in refractory MM.

Indexed as

Immunotherapy, AdoptiveKiller Cells, NaturalMultiple MyelomaReceptors, Chimeric AntigenAnimalsHumansTumor MicroenvironmentReceptors, Chimeric AntigenCAR-NK cellsHematologic MalignancyImmunotherapyMultiple MyelomaNext-generation Cellular TherapyTumor Microenvironment

Identifiers

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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.