The DOI matches its Crossref record; 243 of the 243 references it cites were found in a registry or an index.
Cell-state dynamics and therapeutic resistance in melanoma from the perspective of MITF and IFNγ pathways
Open accessAbstract
Targeted therapy and immunotherapy have greatly improved the prognosis of patients with metastatic melanoma, but resistance to these therapeutic modalities limits the percentage of patients with long-lasting responses. Accumulating evidence indicates that a persisting subpopulation of melanoma cells contributes to resistance to targeted therapy or immunotherapy, even in patients who initially have a therapeutic response; however, the root mechanism of resistance remains elusive. To address this problem, we propose a new model, in which dynamic fluctuations of protein expression at the single-cell level and longitudinal reshaping of the cellular state at the cell-population level explain the whole process of therapeutic resistance development. Conceptually, we focused on two different pivotal signalling pathways (mediated by microphthalmia-associated transcription factor (MITF) and IFNγ) to construct the evolving trajectories of melanoma and described each of the cell states. Accordingly, the development of therapeutic resistance could be divided into three main phases: early survival of cell populations, reversal of senescence, and the establishment of new homeostatic states and development of irreversible resistance. On the basis of existing data, we propose future directions in both translational research and the design of therapeutic strategies that incorporate this emerging understanding of resistance.
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“De-differentiation is exemplified by colon carcinogenesis (due to the loss of the differentiation-inducing transcription factor HOXA5 and SMAD4), 141,142 melanomagenesis (due to the loss of the transcription factor MITF), 143 or pancreatic neuroendocrine tumors (due to the loss of a differentiation-inducing miRNA in b cells).144”
From Meta-hallmarks of aging and cancer (2023) · cites this paper as [143]
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Cites
PD-1 and PD-L1 Immune Checkpoint Blockade to Treat Breast Cancer
Hartkopf, A. D., Taran, F., Wallwiener, M., Walter, C. B., Krämer, B., Grischke, E., & Brucker, S. Y. (2016) · Breast Care · doi:10.1159/000453569 · Cited by 30,989
Siegel, R. L., Miller, K. D., & Jemal, A. (2018) · CA A Cancer Journal for Clinicians · doi:10.3322/caac.21442 · Cited by 16,632
Siegel, R. L., Miller, K. D., & Jemal, A. (2016) · CA: A Cancer Journal for Clinicians · doi:10.3322/caac.21332 · Cited by 16,003
Adverse Renal Effects of Immune Checkpoint Inhibitors: A Narrative Review
Wanchoo, R., Karam, S., Uppal, N. N., Barta, V. S., Deray, G., Devoe, C., Launay‐Vacher, V., Jhaveri, K. D., & on behalf of Cancer and Kidney International Network Workgroup on Immune Checkpoint Inhibitors (2017) · American Journal of Nephrology · doi:10.1159/000455014 · Cited by 15,320
Improved Survival with Ipilimumab in Patients with Metastatic Melanoma
Hodi, F. S., O'Day, S. J., McDermott, D. F., Weber, R. W., Sosman, J. A., Haanen, J. B., Gonzalez, R., Robert, C., Schadendorf, D., Hassel, J. C., et al. (2010) · New England Journal of Medicine · doi:10.1056/nejmoa1003466 · Cited by 15,155
Siegel, R. L., Miller, K. D., & Jemal, A. (2017) · CA: A Cancer Journal for Clinicians · doi:10.3322/caac.21387 · Cited by 14,712
Siegel, R. L., Miller, K. D., & Jemal, A. (2015) · CA A Cancer Journal for Clinicians · doi:10.3322/caac.21254 · Cited by 11,854
Mutations of the BRAF gene in human cancer
Davies, H., Bignell, G. R., Cox, C., Stephens, P., Edkins, S., Clegg, S., Teague, J., Woffendin, H., Garnett, M. J., Bottomley, W., et al. (2002) · Nature · doi:10.1038/nature00766 · Cited by 10,674
Stem cells, cancer, and cancer stem cells
Reya, T., Morrison, S. J., Clarke, M. F., & Weissman, I. L. (2001) · Nature · doi:10.1038/35102167 · Cited by 9,738
The Epithelial-Mesenchymal Transition Generates Cells with Properties of Stem Cells
Mani, S. A., Guo, W., Liao, M.-J., Eaton, E. N., Ayyanan, A., Zhou, A. Y., Brooks, M. W., Reinhard, F., Zhang, C. C., Shipitsin, M. V., et al. (2008) · Cell · doi:10.1016/j.cell.2008.03.027 · Cited by 8,839
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Items 101–107 of 107
Zheng, Q., Yang, Q., Zhou, J., Gu, X., Zhou, H., Dong, X., Zhu, H., & Chen, Z. (2021) · Figshare · doi:10.6084/m9.figshare.14889916
Identification et modulation des voies moléculaires impliquées dans l’agressivité du mélanome cutané
Gautron, A. (2019) · theses.fr (ABES) · doi:10.70675/b9c75939z7407z49abzafcbze6e92703d459
Rapid Transcription Dynamics Confers Cytarabine Resistance in Acute Myeloid Leukemia
Tatsumi, G., Kumari, R., Suzuki, Y., Li, S., Scharf, R., Taylor, S. J., Sundaravel, S., Verma, A. K., Wheat, J. C., & Steidl, U. (2026) · Blood Cancer Discovery · doi:10.1158/2643-3230.bcd-25-0317
“This knowledge gap underscores the need for further investigation into nongenetic mechanisms that drive early-stage resistance after conventional chemotherapy, as these mechanisms may act as a prelude to more long-term and stable mechanisms such as genetic changes (37).”
Nakamura, M., Nakamura, J., Shiohama, Y., Haruta, T., Fukunaga, K.-I., Murayama, H., Igase, M., Mizuno, T., & Yonezawa, T. (2026) · Research Square · doi:10.21203/rs.3.rs-9987003/v1
Makki, R., Kim, H., Aoun, M., & Shekhar, M. P. V. (2026) · Molecular and Digital Medicine · doi:10.1016/j.mdmed.2026.100009
Closing the adaptive window: immune–metabolic strategies to constrain melanoma therapy resistance
Riccardo, F., Conti, L., Defilippi, P., Arina, P., Audrito, V., & Calautti, E. (2026) · Frontiers in Immunology · doi:10.3389/fimmu.2026.1954638
Metadata
- Authors
- Bai, X., Fisher, D. E., & Flaherty, K. T.
- Affiliations
- Harvard University (US)
- Corresponding author
- Flaherty, Keith T.
- Venue
- Nature Reviews Clinical Oncology
- Published
- 2019-04-09
- Volume
- 16
- Issue
- 9
- Pages
- 549-562
- ISSN
- 1759-4774 (ISSN-L)
- 1759-4782
- PMC
- PMC7185899
- OpenAlex
- W2936119023
- Cited by
- 107
- References
- 243
- Citations by year
- 2026: 7 · 2025: 20 · 2024: 7 · 2023: 10 · 2022: 9 · 2021: 24 · 2020: 20 · 2019: 10
- FWCI
- 1.39
- Type
- journal article
- Language
- en
- Open access
- green
- www.ncbi.nlm.nih.gov
- Licence
- www.springer.com/tdm (tdm)
- Locations
- Nature Reviews Clinical Oncology (publishedVersion)
- PubMed (publishedVersion)
- PubMed Central (acceptedVersion)
- Funders
- National Cancer Institute (U54CA224068, P01CA163222)
- National Institute of Arthritis and Musculoskeletal and Skin Diseases (R01AR043369)
- Topics
- Life Sciences › Biochemistry, Genetics and Molecular Biology › Molecular Biology › Melanoma and MAPK Pathways
- Health Sciences › Medicine › Oncology › Cancer Immunotherapy and Biomarkers
- Health Sciences › Medicine › Oncology › Cutaneous Melanoma Detection and Management
- Keywords
- melanoma, treatment resistance, MITF, interferon-gamma, cell state dynamics, targeted therapy resistance, immunotherapy resistance, metastatic melanoma
- MeSH terms
- Humans, Interferon-gamma, Melanoma, Models, Biological, Signal Transduction, Drug Resistance, Neoplasm, Microphthalmia-Associated Transcription Factor
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