Dataset Information

Dendritic Cell-Based Immunotherapy in Multiple Myeloma: Challenges, Opportunities, and Future Directions.

ABSTRACT: Immunotherapeutic approaches, including adoptive cell therapy, revolutionized treatment in multiple myeloma (MM). As dendritic cells (DCs) are professional antigen-presenting cells and key initiators of tumor-specific immune responses, DC-based immunotherapy represents an attractive therapeutic approach in cancer. The past years, various DC-based approaches, using particularly ex-vivo-generated monocyte-derived DCs, have been tested in preclinical and clinical MM studies. However, long-term and durable responses in MM patients were limited, potentially attributed to the source of monocyte-derived DCs and the immunosuppressive bone marrow microenvironment. In this review, we briefly summarize the DC development in the bone marrow niche and the phenotypical and functional characteristics of the major DC subsets. We address the known DC deficiencies in MM and give an overview of the DC-based vaccination protocols that were tested in MM patients. Lastly, we also provide strategies to improve the efficacy of DC vaccines using new, improved DC-based approaches and combination therapies for MM patients.

SUBMITTER: Verheye E

PROVIDER: S-EPMC8778019 | biostudies-literature | 2022 Jan

REPOSITORIES: biostudies-literature

ACCESS DATA

Publications

Dendritic Cell-Based Immunotherapy in Multiple Myeloma: Challenges, Opportunities, and Future Directions.

Verheye Emma E Bravo Melgar Jesús J Deschoemaeker Sofie S Raes Geert G Maes Anke A De Bruyne Elke E Menu Eline E Vanderkerken Karin K Laoui Damya D De Veirman Kim K

International journal of molecular sciences 20220114 2

Immunotherapeutic approaches, including adoptive cell therapy, revolutionized treatment in multiple myeloma (MM). As dendritic cells (DCs) are professional antigen-presenting cells and key initiators of tumor-specific immune responses, DC-based immunotherapy represents an attractive therapeutic approach in cancer. The past years, various DC-based approaches, using particularly ex-vivo-generated monocyte-derived DCs, have been tested in preclinical and clinical MM studies. However, long-term and ...[more]

PMID: 35055096

Similar Datasets

Project description:Clustered regularly interspaced short palindromic repeat (CRISPR)-based genome editing (GED) technologies have unlocked exciting possibilities for understanding genes and improving medical treatments. On the other hand, Artificial intelligence (AI) helps genome editing achieve more precision, efficiency, and affordability in tackling various diseases, like Sickle cell anemia or Thalassemia. AI models have been in use for designing guide RNAs (gRNAs) for CRISPR-Cas systems. Tools like DeepCRISPR, CRISTA, and DeepHF have the capability to predict optimal guide RNAs (gRNAs) for a specified target sequence. These predictions take into account multiple factors, including genomic context, Cas protein type, desired mutation type, on-target/off-target scores, potential off-target sites, and the potential impacts of genome editing on gene function and cell phenotype. These models aid in optimizing different genome editing technologies, such as base, prime, and epigenome editing, which are advanced techniques to introduce precise and programmable changes to DNA sequences without relying on the homology-directed repair pathway or donor DNA templates. Furthermore, AI, in collaboration with genome editing and precision medicine, enables personalized treatments based on genetic profiles. AI analyzes patients' genomic data to identify mutations, variations, and biomarkers associated with different diseases like Cancer, Diabetes, Alzheimer's, etc. However, several challenges persist, including high costs, off-target editing, suitable delivery methods for CRISPR cargoes, improving editing efficiency, and ensuring safety in clinical applications. This review explores AI's contribution to improving CRISPR-based genome editing technologies and addresses existing challenges. It also discusses potential areas for future research in AI-driven CRISPR-based genome editing technologies. The integration of AI and genome editing opens up new possibilities for genetics, biomedicine, and healthcare, with significant implications for human health.

Project description:Whereas the treatment of MM was dependent solely on alkylating agents and corticosteroids during the prior three decades, the landscape of therapeutic measures to treat the disease began to expand enormously early in the current century. The introduction of new classes of small-molecule drugs, such as proteasome blockers (bortezomib and carfilzomib), immunomodulators (lenalidomide and pomalidomide), nuclear export inhibitors (selinexor), and histone deacetylase blockers (panobinostat), as well as the application of autologous stem cell transplantation (ASCT), resulted in a seismic shift in how the disease is treated. The picture changed dramatically once again starting with the 2015 FDA approval of two monoclonal antibodies (mAbs) - the anti-CD38 daratumumab and the anti-SLAMF7 elotuzumab. Daratumumab, in particular, has had a great impact on MM therapy and today is often included in various regimens to treat the disease, both in newly diagnosed cases and in the relapse/refractory setting. Recently, other immunotherapies have been added to the arsenal of drugs available to fight this malignancy. These include isatuximab (also anti-CD38) and, in the past year, the antibody-drug conjugate (ADC) belantamab mafodotin and the chimeric antigen receptor (CAR) T-cell product idecabtagene vicleucel (ide-cel). While the accumulated benefits of these newer agents have resulted in a doubling of the disease's five-year survival rate to more than 5 years and improved quality of life, the disease remains incurable. Almost without exception patients experience relapse and/or become refractory to the drugs used, making the search for innovative therapies all the more essential. This review covers the current scope of anti-myeloma immunotherapeutic agents, both those in clinical use and on the horizon, including naked mAbs, ADCs, bi- and multi-targeted mAbs, and CAR T-cells. Emphasis is placed on the benefits of each along with the challenges that need to be overcome if MM is to be considered curable in the future.

Dataset Information

Dendritic Cell-Based Immunotherapy in Multiple Myeloma: Challenges, Opportunities, and Future Directions.

Publications

Dendritic Cell-Based Immunotherapy in Multiple Myeloma: Challenges, Opportunities, and Future Directions.

Similar Datasets

OmicsDI is part of the ELIXIR infrastructure

Tweets