Treatment resistance to melanoma therapeutics on a single cell level.

Humans Melanoma / drug therapy Drug Resistance, Neoplasm / genetics Proto-Oncogene Proteins B-raf / genetics Single-Cell Analysis Cell Line, Tumor Phosphoglycerate Kinase / genetics Gene Expression Regulation, Neoplastic / drug effects Transcriptome Programmed Cell Death 1 Receptor / antagonists & inhibitors Mutation Metformin / pharmacology

Journal

Scientific reports
ISSN: 2045-2322
Titre abrégé: Sci Rep
Pays: England
ID NLM: 101563288

Informations de publication

Date de publication:
19 09 2024
Historique:
received: 28 01 2024
accepted: 05 09 2024
medline: 20 9 2024
pubmed: 20 9 2024
entrez: 19 9 2024
Statut: epublish

Résumé

Therapy targeting the BRAF-MEK cascade created a treatment revolution for patients with BRAF mutant advanced melanoma. Unfortunately, 80% patients treated will progress by 5 years follow-up. Thus, it is imperative we study mechanisms of melanoma progression and therapeutic resistance. We created a scRNA (single cell RNA) atlas of 128,230 cells from 18 tumors across the treatment spectrum, discovering melanoma cells clustered strongly by transcriptome profiles of patients of origins. Our cell-level investigation revealed gains of 1q and 7q as likely early clonal events in metastatic melanomas. By comparing patient tumors and their derivative cell lines, we observed that PD1 responsive tumor fraction disappears when cells are propagated in vitro. We further established three anti-BRAF-MEK treatment resistant cell lines using three BRAF mutant tumors. ALDOA and PGK1 were found to be highly expressed in treatment resistant cell populations and metformin was effective in targeting the resistant cells. Our study suggests that the investigation of patient tumors and their derivative lines is essential for understanding disease progression, treatment response and resistance.

Identifiants

DOI: 10.1038/s41598-024-72255-9 PMID: 39300183
pubmed: 39300183
doi: 10.1038/s41598-024-72255-9
pii: 10.1038/s41598-024-72255-9
doi:

Substances chimiques

Proto-Oncogene Proteins B-raf EC 2.7.11.1
BRAF protein, human EC 2.7.11.1
Phosphoglycerate Kinase EC 2.7.2.3
PGK1 protein, human EC 2.7.2.3
Programmed Cell Death 1 Receptor 0
Metformin 9100L32L2N
PDCD1 protein, human 0

Types de publication

Journal Article

Langues

eng

Sous-ensembles de citation

IM

Pagination

21915

Subventions

Organisme : National Cancer Institute, USA
ID : T32CA009621
Organisme : National Cancer Institute, USA
ID : U2CCA233303

Informations de copyright

© 2024. The Author(s).

Références

Larkin, J. et al. Combined vemurafenib and cobimetinib in BRAF-mutated melanoma. N. Engl. J. Med. 371, 1867–1876 (2014).
pubmed: 25265494 doi: 10.1056/NEJMoa1408868
Long, G. V. et al. Adjuvant Dabrafenib plus Trametinib in stage III BRAF-mutated melanoma. N. Engl. J. Med. 377, 1813–1823 (2017).
pubmed: 28891408 doi: 10.1056/NEJMoa1708539
Robert, C. et al. Five-year outcomes with Dabrafenib plus Trametinib in metastatic melanoma. N. Engl. J. Med. 381, 626–636 (2019).
pubmed: 31166680 doi: 10.1056/NEJMoa1904059
Ojha, R. et al. ER translocation of the MAPK pathway drives therapy resistance in BRAF-mutant melanoma. Cancer Discov. 9, 396–415 (2019).
pubmed: 30563872 doi: 10.1158/2159-8290.CD-18-0348
Johannessen, C. M. et al. A melanocyte lineage program confers resistance to MAP kinase pathway inhibition. Nature 504, 138–142 (2013).
pubmed: 24185007 pmcid: 4098832 doi: 10.1038/nature12688
Snyder, A. et al. Genetic basis for clinical response to CTLA-4 blockade in melanoma. N. Engl. J. Med. 371, 2189–2199 (2014).
pubmed: 25409260 pmcid: 4315319 doi: 10.1056/NEJMoa1406498
Kleffel, S. et al. Melanoma cell-intrinsic PD-1 receptor functions promote tumor growth. Cell 162, 1242–1256 (2015).
pubmed: 26359984 pmcid: 4700833 doi: 10.1016/j.cell.2015.08.052
Iwai, Y. et al. Involvement of PD-L1 on tumor cells in the escape from host immune system and tumor immunotherapy by PD-L1 blockade. Proc. Natl. Acad. Sci. U. S. A. 99, 12293–12297 (2002).
pubmed: 12218188 pmcid: 129438 doi: 10.1073/pnas.192461099
Network, C. G. A. Genomic classification of cutaneous melanoma. Cell 161, 1681–1696 (2015).
doi: 10.1016/j.cell.2015.05.044
Tirosh, I. et al. Dissecting the multicellular ecosystem of metastatic melanoma by single-cell RNA-seq. Science 352, 189–196 (2016).
pubmed: 27124452 pmcid: 4944528 doi: 10.1126/science.aad0501
Lawrence, M. S. et al. Mutational heterogeneity in cancer and the search for new cancer-associated genes. Nature 499, 214–218 (2013).
pubmed: 23770567 pmcid: 3919509 doi: 10.1038/nature12213
Hodis, E. et al. A landscape of driver mutations in melanoma. Cell 150, 251–263 (2012).
pubmed: 22817889 pmcid: 3600117 doi: 10.1016/j.cell.2012.06.024
Anna, B. et al. Mechanism of UV-related carcinogenesis and its contribution to nevi/melanoma. Exp. Rev. Dermatol. 2, 451–469 (2007).
doi: 10.1586/17469872.2.4.451
Vincent, K. M. & Postovit, L.-M. Investigating the utility of human melanoma cell lines as tumour models. Oncotarget 8, 10498–10509 (2017).
pubmed: 28060736 doi: 10.18632/oncotarget.14443
Luebker, S. A., Zhang, W. & Koepsell, S. A. Comparing the genomes of cutaneous melanoma tumors to commercially available cell lines. Oncotarget 8, 114877–114893 (2017).
pubmed: 29383127 pmcid: 5777739 doi: 10.18632/oncotarget.22928
Jerby-Arnon, L. et al. A cancer cell program promotes T cell exclusion and resistance to checkpoint blockade. Cell 175, 984-997.e24 (2018).
pubmed: 30388455 pmcid: 6410377 doi: 10.1016/j.cell.2018.09.006
Sade-Feldman, M. et al. Defining T cell states associated with response to checkpoint immunotherapy in melanoma. Cell 176, 404 (2019).
pubmed: 30633907 pmcid: 6647017 doi: 10.1016/j.cell.2018.12.034
El Marsafy, S., Bagot, M., Bensussan, A. & Mauviel, A. Dendritic cells in the skin–potential use for melanoma treatment. Pigment Cell Melanoma Res. 22, 30–41 (2009).
pubmed: 19040502 doi: 10.1111/j.1755-148X.2008.00532.x
North, J. P., Vemula, S. S. & Bastian, B. C. Chromosomal copy number analysis in melanoma diagnostics. Methods Mol. Biol. 1102, 199–226 (2014).
pubmed: 24258981 doi: 10.1007/978-1-62703-727-3_12
McPherson, L. A., Loktev, A. V. & Weigel, R. J. Tumor suppressor activity of AP2alpha mediated through a direct interaction with p53. J. Biol. Chem. 277, 45028–45033 (2002).
pubmed: 12226108 doi: 10.1074/jbc.M208924200
Sun, Y., Long, J. & Zhou, Y. Angiopoietin-like 4 promotes melanoma cell invasion and survival through aldolase A. Oncol. Lett. 8, 211–217 (2014).
pubmed: 24959248 pmcid: 4063564 doi: 10.3892/ol.2014.2071
Liu, R. et al. Silencing of PKG1 expression enhances the efficacy of vemurafenib against melanoma cell. Zhongguo Ying Yong Sheng Li Xue Za Zhi 33, 289–293 (2017).
pubmed: 29926631
Andrzejewski, S., Siegel, P. M. & St-Pierre, J. Metabolic profiles associated with metformin efficacy in cancer. Front. Endocrinol. 9, 372 (2018).
doi: 10.3389/fendo.2018.00372
Jaune, E. & Rocchi, S. Metformin: Focus on melanoma. Front. Endocrinol. 9, 472 (2018).
doi: 10.3389/fendo.2018.00472
Boudhraa, Z. et al. Annexin A1 in primary tumors promotes melanoma dissemination. Clin. Exp. Metastasis 31, 749–760 (2014).
pubmed: 24997993 doi: 10.1007/s10585-014-9665-2
Rohwer, N. et al. Annexin A1 sustains tumor metabolism and cellular proliferation upon stable loss of HIF1A. Oncotarget 7, 6693–6710 (2016).
pubmed: 26760764 doi: 10.18632/oncotarget.6793
Zhu, G.-H., Dai, H.-P., Shen, Q. & Zhang, Q. Downregulation of LPXN expression by siRNA decreases the malignant proliferation and transmembrane invasion of SHI-1 cells. Oncol. Lett. 17, 135–140 (2019).
pubmed: 30655748
Lin, X. et al. C-myc overexpression drives melanoma metastasis by promoting vasculogenic mimicry via c-myc/snail/Bax signaling. J. Mol. Med. 95, 53–67 (2017).
pubmed: 27543492 doi: 10.1007/s00109-016-1452-x
Rohde, M. et al. Members of the heat-shock protein 70 family promote cancer cell growth by distinct mechanisms. Genes Dev. 19, 570–582 (2005).
pubmed: 15741319 pmcid: 551577 doi: 10.1101/gad.305405
Budina-Kolomets, A. et al. HSP70 inhibition limits FAK-dependent invasion and enhances the response to melanoma treatment with BRAF inhibitors. Cancer Res. 76, 2720–2730 (2016).
pubmed: 26984758 pmcid: 4939897 doi: 10.1158/0008-5472.CAN-15-2137
Park, S.-Y. et al. DNAJB1 negatively regulates MIG6 to promote epidermal growth factor receptor signaling. Biochim. Biophys. Acta 1853, 2722–2730 (2015).
pubmed: 26239118 doi: 10.1016/j.bbamcr.2015.07.024
Yu, T. et al. The effect of tumor purity on next generation sequencing of colorectal cancer. J. Clin. Orthod. 40, e15557–e15557 (2022).
Birkeland, E. et al. Patterns of genomic evolution in advanced melanoma. Nat. Commun. 9, 2665 (2018).
pubmed: 29991680 pmcid: 6039447 doi: 10.1038/s41467-018-05063-1
Larkin, J. et al. Five-year survival with combined Nivolumab and Ipilimumab in advanced melanoma. N. Engl. J. Med. 381, 1535–1546 (2019).
pubmed: 31562797 doi: 10.1056/NEJMoa1910836
Rossi, A. et al. Drug resistance of BRAF-mutant melanoma: Review of up-to-date mechanisms of action and promising targeted agents. Eur. J. Pharmacol. 862, 172621 (2019).
pubmed: 31446019 doi: 10.1016/j.ejphar.2019.172621
Chen, H.-L. et al. Effect of metformin on proliferation capacity, apoptosis and glycolysis in K562 cells. Zhongguo Shi Yan Xue Ye Xue Za Zhi 27, 1387–1394 (2019).
pubmed: 31607288
Morales, D. R. & Morris, A. D. Metformin in cancer treatment and prevention. Annu. Rev. Med. 66, 17–29 (2015).
pubmed: 25386929 doi: 10.1146/annurev-med-062613-093128
Niehr, F. et al. Combination therapy with vemurafenib (PLX4032/RG7204) and metformin in melanoma cell lines with distinct driver mutations. J. Transl. Med. 9, 76 (2011).
pubmed: 21609436 pmcid: 3152784 doi: 10.1186/1479-5876-9-76
Ryabaya, O. et al. Metformin increases antitumor activity of MEK inhibitor binimetinib in 2D and 3D models of human metastatic melanoma cells. Biomed. Pharmacother. 109, 2548–2560 (2019).
pubmed: 30551515 doi: 10.1016/j.biopha.2018.11.109
Vujic, I. et al. Metformin and trametinib have synergistic effects on cell viability and tumor growth in NRAS mutant cancer. Oncotarget 6, 969–978 (2015).
pubmed: 25504439 doi: 10.18632/oncotarget.2824
Martin, M. J., Hayward, R., Viros, A. & Marais, R. Metformin accelerates the growth of BRAF V600E-driven melanoma by upregulating VEGF-A. Cancer Discov. 2, 344–355 (2012).
pubmed: 22576211 pmcid: 3364710 doi: 10.1158/2159-8290.CD-11-0280
Alexandrov, L. B. et al. Signatures of mutational processes in human cancer. Nature 500, 415–421 (2013).
pubmed: 23945592 pmcid: 3776390 doi: 10.1038/nature12477
Morita, K. et al. Clonal evolution of acute myeloid leukemia revealed by high-throughput single-cell genomics. Nat. Commun. 11, 5327 (2020).
pubmed: 33087716 pmcid: 7577981 doi: 10.1038/s41467-020-19119-8
Petti, A. A. et al. A general approach for detecting expressed mutations in AML cells using single cell RNA-sequencing. Nat. Commun. 10, 3660 (2019).
pubmed: 31413257 pmcid: 6694122 doi: 10.1038/s41467-019-11591-1
Lagonigro, M. S. et al. CTAB-urea method purifies RNA from melanin for cDNA microarray analysis. Pigment Cell Res. 17, 312–315 (2004).
pubmed: 15140079 doi: 10.1111/j.1600-0749.2004.00155.x
Butler, A., Hoffman, P., Smibert, P., Papalexi, E. & Satija, R. Integrating single-cell transcriptomic data across different conditions, technologies, and species. Nat. Biotechnol. 36, 411–420 (2018).
pubmed: 29608179 pmcid: 6700744 doi: 10.1038/nbt.4096
Hafemeister, C. & Satija, R. Normalization and variance stabilization of single-cell RNA-seq data using regularized negative binomial regression. Genome Biol. 20, 296 (2019).
pubmed: 31870423 pmcid: 6927181 doi: 10.1186/s13059-019-1874-1
Cibulskis, K. et al. Sensitive detection of somatic point mutations in impure and heterogeneous cancer samples. Nat. Biotechnol. 31, 213–219 (2013).
pubmed: 23396013 pmcid: 3833702 doi: 10.1038/nbt.2514
Koboldt, D. C. et al. VarScan 2: Somatic mutation and copy number alteration discovery in cancer by exome sequencing. Genome Res. 22, 568–576 (2012).
pubmed: 22300766 pmcid: 3290792 doi: 10.1101/gr.129684.111
Saunders, C. T. et al. Strelka: Accurate somatic small-variant calling from sequenced tumor-normal sample pairs. Bioinformatics 28, 1811–1817 (2012).
pubmed: 22581179 doi: 10.1093/bioinformatics/bts271
Ye, K., Schulz, M. H., Long, Q., Apweiler, R. & Ning, Z. Pindel: A pattern growth approach to detect break points of large deletions and medium sized insertions from paired-end short reads. Bioinformatics 25, 2865–2871 (2009).
pubmed: 19561018 pmcid: 2781750 doi: 10.1093/bioinformatics/btp394
Talevich, E., Shain, A. H., Botton, T. & Bastian, B. C. CNVkit: Genome-wide copy number detection and visualization from targeted DNA sequencing. PLoS Comput. Biol. 12, e1004873 (2016).
pubmed: 27100738 pmcid: 4839673 doi: 10.1371/journal.pcbi.1004873
Bray, N. L., Pimentel, H., Melsted, P. & Pachter, L. Near-optimal probabilistic RNA-seq quantification. Nat. Biotechnol. 34, 525–527 (2016).
pubmed: 27043002 doi: 10.1038/nbt.3519
Soneson, C., Love, M. I. & Robinson, M. D. Differential analyses for RNA-seq: Transcript-level estimates improve gene-level inferences. F1000Res 4, 1521 (2015).
pubmed: 26925227 doi: 10.12688/f1000research.7563.1
Qiu, X. et al. Reversed graph embedding resolves complex single-cell trajectories. Nat. Methods 14, 979–982 (2017).
pubmed: 28825705 pmcid: 5764547 doi: 10.1038/nmeth.4402

Auteurs

Lijun Yao (L)

Department of Medicine, Washington University in St. Louis, St. Louis, MO, 63110, USA.
McDonnell Genome Institute, Washington University in St. Louis, St. Louis, MO, 63108, USA.

Bradley A Krasnick (BA)

Department of Surgery, Washington University School of Medicine, St. Louis, MO, USA.

Ye Bi (Y)

Department of Surgery, Washington University School of Medicine, St. Louis, MO, USA.

Sunantha Sethuraman (S)

Department of Medicine, Washington University in St. Louis, St. Louis, MO, 63110, USA.
McDonnell Genome Institute, Washington University in St. Louis, St. Louis, MO, 63108, USA.

Simon Goedegebuure (S)

Department of Surgery, Washington University School of Medicine, St. Louis, MO, USA.

Amila Weerasinghe (A)

Department of Medicine, Washington University in St. Louis, St. Louis, MO, 63110, USA.
McDonnell Genome Institute, Washington University in St. Louis, St. Louis, MO, 63108, USA.

Chris Wetzel (C)

Department of Surgery, Washington University School of Medicine, St. Louis, MO, USA.

Qingsong Gao (Q)

Department of Medicine, Washington University in St. Louis, St. Louis, MO, 63110, USA.
McDonnell Genome Institute, Washington University in St. Louis, St. Louis, MO, 63108, USA.

Abimbola Oyedeji (A)

Department of Surgery, Washington University School of Medicine, St. Louis, MO, USA.

Jacqueline Mudd (J)

Department of Surgery, Washington University School of Medicine, St. Louis, MO, USA.

Matthew A Wyczalkowski (MA)

Department of Medicine, Washington University in St. Louis, St. Louis, MO, 63110, USA.
McDonnell Genome Institute, Washington University in St. Louis, St. Louis, MO, 63108, USA.

Michael Wendl (M)

Department of Medicine, Washington University in St. Louis, St. Louis, MO, 63110, USA.
McDonnell Genome Institute, Washington University in St. Louis, St. Louis, MO, 63108, USA.

Li Ding (L)

Department of Medicine, Washington University in St. Louis, St. Louis, MO, 63110, USA. lding@wustl.edu.
McDonnell Genome Institute, Washington University in St. Louis, St. Louis, MO, 63108, USA. lding@wustl.edu.
Siteman Cancer Center, Washington University in St. Louis, St. Louis, MO, 63110, USA. lding@wustl.edu.
Department of Genetics, Washington University in St. Louis, St. Louis, MO, 63110, USA. lding@wustl.edu.

Ryan C Fields (RC)

Department of Surgery, Washington University School of Medicine, St. Louis, MO, USA. rcfields@wustl.edu.
Siteman Cancer Center, Washington University in St. Louis, St. Louis, MO, 63110, USA. rcfields@wustl.edu.

Articles similaires

[Redispensing of expensive oral anticancer medicines: a practical application].

Lisanne N van Merendonk, Kübra Akgöl, Bastiaan Nuijen
1.00
Humans Antineoplastic Agents Administration, Oral Drug Costs Counterfeit Drugs

Smoking Cessation and Incident Cardiovascular Disease.

Jun Hwan Cho, Seung Yong Shin, Hoseob Kim et al.
1.00
Humans Male Smoking Cessation Cardiovascular Diseases Female

Evaluation of Low-Value Services Across Major Medicare Advantage Insurers and Traditional Medicare.

Ciara Duggan, Adam L Beckman, Ishani Ganguli et al.
1.00
Humans United States Aged Cross-Sectional Studies Medicare Part C

Effectiveness of Virtual Yoga for Chronic Low Back Pain: A Randomized Clinical Trial.

Hallie Tankha, Devyn Gaskins, Amanda Shallcross et al.
1.00
Humans Yoga Low Back Pain Female Male

Classifications MeSH

questionsmedicales.fr › Eucaryotes › Animaux › Chordés › Vertébrés › Mammifères › Eutheria › Primates › Haplorhini › Catarrhini › Hominidae › Humains › Treatment resistance to melanoma therapeutics...
questionsmedicales.fr › Tumeurs › Tumeurs par type histologique › Naevus et mélanomes › Mélanome › Treatment resistance to melanoma therapeutics...
questionsmedicales.fr › Phénomènes physiologiques › Phénomènes pharmacologiques et toxicologiques › Phénomènes pharmacologiques › Résistance aux substances › Résistance aux médicaments antinéoplasiques › Treatment resistance to melanoma therapeutics...
questionsmedicales.fr › Acides aminés, peptides et protéines › Protéines › Protéines tumorales › Protéines oncogènes › Protéines proto-oncogènes › Kinases raf › Protéines proto-oncogènes B-raf › Treatment resistance to melanoma therapeutics...
questionsmedicales.fr › Techniques d'investigation › Techniques cytologiques › Analyse sur cellule unique › Treatment resistance to melanoma therapeutics...
questionsmedicales.fr › Cellules › Cellules cultivées › Cellules cancéreuses en culture › Lignée cellulaire tumorale › Treatment resistance to melanoma therapeutics...
questionsmedicales.fr › Enzymes et coenzymes › Enzymes › Transferases › Phosphotransferases › Phosphotransferases (Carboxyl Group Acceptor) › Phosphoglycerate kinase › Treatment resistance to melanoma therapeutics...
questionsmedicales.fr › Phénomènes génétiques › Régulation de l'expression des gènes › Régulation de l'expression des gènes tumoraux › Treatment resistance to melanoma therapeutics...
questionsmedicales.fr › Phénomènes génétiques › Structures génétiques › Transcriptome › Treatment resistance to melanoma therapeutics...
questionsmedicales.fr › Facteurs biologiques › Marqueurs biologiques › Antigènes de différenciation › Antigènes de différenciation des lymphocytes T › Récepteur-1 de mort cellulaire programmée › Treatment resistance to melanoma therapeutics...
questionsmedicales.fr › Phénomènes génétiques › Variation génétique › Mutation › Treatment resistance to melanoma therapeutics...
questionsmedicales.fr › Composés chimiques organiques › Amidines › Guanidines › Biguanides › Metformine › Treatment resistance to melanoma therapeutics...