Cellular liquid biopsy provides unique chances for disease monitoring, preclinical model generation and therapy adjustment in rare salivary gland cancer patients.
CTC
liquid biopsy
personalized tumor therapy
salivary gland cancer
tumoroid culture
Journal
Molecular oncology
ISSN: 1878-0261
Titre abrégé: Mol Oncol
Pays: United States
ID NLM: 101308230
Informations de publication
Date de publication:
05 Oct 2024
05 Oct 2024
Historique:
revised:
02
08
2024
received:
30
01
2024
accepted:
15
08
2024
medline:
5
10
2024
pubmed:
5
10
2024
entrez:
5
10
2024
Statut:
aheadofprint
Résumé
While cell-free liquid biopsy (cfLB) approaches provide simple and inexpensive disease monitoring, cell-based liquid biopsy (cLB) may enable additional molecular genetic assessment of systemic disease heterogeneity and preclinical model development. We investigated 71 blood samples of 62 patients with various advanced cancer types and subjected enriched circulating tumor cells (CTCs) to organoid culture conditions. CTC-derived tumoroid models were characterized by DNA/RNA sequencing and immunohistochemistry, as well as functional drug testing. Results were linked to molecular features of primary tumors, metastases, and CTCs; CTC enumeration was linked to disease progression. Of 52 samples with positive CTC counts (≥1) from eight different cancer types, only CTCs from two salivary gland cancer (SGC) patients formed tumoroid cultures (P = 0.0005). Longitudinal CTC enumeration of one SGC patient closely reflected disease progression during treatment and revealed metastatic relapse earlier than clinical imaging. Multiomics analysis and functional in vitro drug testing identified potential resistance mechanisms and drug vulnerabilities. We conclude that cLB might add a functional dimension (to the genetic approaches) in the personalized management of rare, difficult-to-treat cancers such as SGC.
Identifiants
pubmed: 39367702
doi: 10.1002/1878-0261.13741
doi:
Types de publication
Journal Article
Langues
eng
Sous-ensembles de citation
IM
Subventions
Organisme : Deutsche Krebshilfe
ID : 70112504
Organisme : Bavarian ministry of economic affairs, energy and technology
ID : AZ 20-3410.1-1-1
Organisme : Deutsche Forschungsgemeinschaft
ID : SFB TRR 305 - A07
Organisme : Deutsche Forschungsgemeinschaft
ID : SFB TRR 305 - B13
Organisme : Deutsche Forschungsgemeinschaft
ID : SFB TRR 305 - Z02
Informations de copyright
© 2024 The Author(s). Molecular Oncology published by John Wiley & Sons Ltd on behalf of Federation of European Biochemical Societies.
Références
Zhou J, Wu Z, Hu J, Yang D, Chen X, Wang Q, et al. High‐throughput single‐EV liquid biopsy: rapid, simultaneous, and multiplexed detection of nucleic acids, proteins, and their combinations. Science Advances. 2020;6(47):eabc1204.
Phallen J, Sausen M, Adleff V, Leal A, Hruban C, White J, et al. Direct detection of early‐stage cancers using circulating tumor DNA. Sci Transl Med. 2017;9(403):eaan2415.
Luo H, Zhao Q, Wei W, Zheng L, Yi S, Li G, et al. Circulating tumor DNA methylation profiles enable early diagnosis, prognosis prediction, and screening for colorectal cancer. Sci Transl Med. 2020;12(524):eaax7533.
McDonald BR, Contente‐Cuomo T, Sammut SJ, Odenheimer‐Bergman A, Ernst B, Perdigones N, et al. Personalized circulating tumor DNA analysis to detect residual disease after neoadjuvant therapy in breast cancer. Sci Transl Med. 2019;11(504):eaax7392.
Markus H, Zhao J, Contente‐Cuomo T, Stephens MD, Raupach E, Odenheimer‐Bergman A, et al. Analysis of recurrently protected genomic regions in cell‐free DNA found in urine. Sci Transl Med. 2021;13(581):eaaz3088.
Zhang P, Wu X, Gardashova G, Yang Y, Zhang Y, Xu L, et al. Molecular and functional extracellular vesicle analysis using nanopatterned microchips monitors tumor progression and metastasis. Sci Transl Med. 2020;12(547):eaaz2878.
Lin D, Shen L, Luo M, Zhang K, Li J, Yang Q, et al. Circulating tumor cells: biology and clinical significance. Signal Transduct Target Ther. 2021;6(1):404.
Baccelli I, Schneeweiss A, Riethdorf S, Stenzinger A, Schillert A, Vogel V, et al. Identification of a population of blood circulating tumor cells from breast cancer patients that initiates metastasis in a xenograft assay. Nat Biotechnol. 2013;31(6):539–544.
Werno C, Honarnejad K, Polzer B. Predicting therapy response by analysis of metastasis founder cells: emerging perspectives for personalized tumor therapy. Expert Rev Precis Med Drug Dev. 2020;5(6):413–420.
Gao H, Korn JM, Ferretti S, Monahan JE, Wang Y, Singh M, et al. High‐throughput screening using patient‐derived tumor xenografts to predict clinical trial drug response. Nat Med. 2015;21(11):1318–1325.
Izumchenko E, Paz K, Ciznadija D, Sloma I, Katz A, Vasquez‐Dunddel D, et al. Patient‐derived xenografts effectively capture responses to oncology therapy in a heterogeneous cohort of patients with solid tumors. Ann Oncol. 2017;28(10):2595–2605.
Pauli C, Hopkins BD, Prandi D, Shaw R, Fedrizzi T, Sboner A, et al. Personalized in vitro and in vivo cancer models to guide precision medicine. Cancer Discov. 2017;7(5):462–477.
Vlachogiannis G, Hedayat S, Vatsiou A, Jamin Y, Fernández‐Mateos J, Khan K, et al. Patient‐derived organoids model treatment response of metastatic gastrointestinal cancers. Science. 2018;359(6378):920–926.
Sachs N, de Ligt J, Kopper O, Gogola E, Bounova G, Weeber F, et al. A living biobank of breast cancer organoids captures disease heterogeneity. Cell. 2018;172(1):373–386.e10.
Klein CA, Seidl S, Petat‐Dutter K, Offner S, Geigl JB, Schmidt‐Kittler O, et al. Combined transcriptome and genome analysis of single micrometastatic cells. Nat Biotechnol. 2002;20(4):387–392.
Klein CA, Schmidt‐Kittler O, Schardt JA, Pantel K, Speicher MR, Riethmuller G. Comparative genomic hybridization, loss of heterozygosity, and DNA sequence analysis of single cells. Proc Natl Acad Sci USA. 1999;96(8):4494–4499.
Polzer B, Medoro G, Pasch S, Fontana F, Zorzino L, Pestka A, et al. Molecular profiling of single circulating tumor cells with diagnostic intention. EMBO Mol Med. 2014;6(11):1371–1386.
Dobin A, Davis CA, Schlesinger F, Drenkow J, Zaleski C, Jha S, et al. STAR: ultrafast universal RNA‐seq aligner. Bioinformatics. 2013;29(1):15–21.
Chu J, Sadeghi S, Raymond A, Jackman SD, Nip KM, Mar R, et al. BioBloom tools: fast, accurate and memory‐efficient host species sequence screening using bloom filters. Bioinformatics. 2014;30(23):3402–3404.
Deng C, Daley T, Smith AD. Applications of species accumulation curves in large‐scale biological data analysis. Quant Biol. 2015;3(3):135–144.
Ewels P, Magnusson M, Lundin S, Käller M. MultiQC: summarize analysis results for multiple tools and samples in a single report. Bioinformatics. 2016;32(19):3047–3048.
Li H. Aligning sequence reads, clone sequences and assembly contigs with BWA‐MEM. arXiv 13033997 [q‐bio]. 2013.
McCarthy DJ, Campbell KR, Lun ATL, Wills QF. Scater: pre‐processing, quality control, normalization and visualization of single‐cell RNA‐seq data in R. Bioinformatics. 2017;33(8):1179–1186.
Scheinin I, Sie D, Bengtsson H, van de Wiel MA, Olshen AB, van Thuijl HF, et al. DNA copy number analysis of fresh and formalin‐fixed specimens by shallow whole‐genome sequencing with identification and exclusion of problematic regions in the genome assembly. Genome Res. 2014;24(12):2022–2032.
Poell JB, Mendeville M, Sie D, Brink A, Brakenhoff RH, Ylstra B. ACE: absolute copy number estimation from low‐coverage whole‐genome sequencing data. Bioinformatics. 2019;35(16):2847–2849.
Liao Y, Smyth GK, Shi W. The subread aligner: fast, accurate and scalable read mapping by seed‐and‐vote. Nucleic Acids Res. 2013;41(10):e108.
Okonechnikov K, Conesa A, García‐Alcalde F. Qualimap 2: advanced multi‐sample quality control for high‐throughput sequencing data. Bioinformatics. 2016;32(2):292–294.
Maaten L. Accelerating t‐SNE using tree‐based algorithms. J Mach Learn Res. 2014;15:3221–3245.
Chen Y, Lun AT, Smyth GK. From reads to genes to pathways: differential expression analysis of RNA‐Seq experiments using Rsubread and the edgeR quasi‐likelihood pipeline. F1000Res. 2016;5:1438.
Yu G, Wang LG, Han Y, He QY. clusterProfiler: an R package for comparing biological themes among gene clusters. OMICS. 2012;16(5):284–287.
Wickham H. ggplot2: elegant graphics for data analysis. New York, NY: Springer; 2016.
Driehuis E, Kretzschmar K, Clevers H. Establishment of patient‐derived cancer organoids for drug‐screening applications. Nat Protoc. 2020;15(10):3380–3409.
Hientz K, Mohr A, Bhakta‐Guha D, Efferth T. The role of p53 in cancer drug resistance and targeted chemotherapy. Oncotarget. 2017;8(5):8921–8946.
Liu R, Chen Y, Liu G, Li C, Song Y, Cao Z, et al. PI3K/AKT pathway as a key link modulates the multidrug resistance of cancers. Cell Death Dis. 2020;11(9):797.
Zhou Y, Wu C, Lu G, Hu Z, Chen Q, Du X. FGF/FGFR signaling pathway involved resistance in various cancer types. J Cancer. 2020;11(8):2000–2007.
Repetto M, Crimini E, Giugliano F, Morganti S, Belli C, Curigliano G. Selective FGFR/FGF pathway inhibitors: inhibition strategies, clinical activities, resistance mutations, and future directions. Expert Rev Clin Pharmacol. 2021;14(10):1233–1252.
Drapkin BJ, George J, Christensen CL, Mino‐Kenudson M, Dries R, Sundaresan T, et al. Genomic and functional fidelity of small cell lung cancer patient‐derived xenografts. Cancer Discov. 2018;8(5):600–615.
Faugeroux V, Pailler E, Oulhen M, Deas O, Brulle‐Soumare L, Hervieu C, et al. Genetic characterization of a unique neuroendocrine transdifferentiation prostate circulating tumor cell‐derived eXplant model. Nat Commun. 2020;11(1):1884.
Gao D, Vela I, Sboner A, Iaquinta PJ, Karthaus WR, Gopalan A, et al. Organoid cultures derived from patients with advanced prostate cancer. Cell. 2014;159(1):176–187.
Hodgkinson CL, Morrow CJ, Li Y, Metcalf RL, Rothwell DG, Trapani F, et al. Tumorigenicity and genetic profiling of circulating tumor cells in small‐cell lung cancer. Nat Med. 2014;20(8):897–903.
Koch C, Kuske A, Joosse SA, Yigit G, Sflomos G, Thaler S, et al. Characterization of circulating breast cancer cells with tumorigenic and metastatic capacity. EMBO Mol Med. 2020;12(9):e11908.
Pereira‐Veiga T, Abreu M, Robledo D, Matias‐Guiu X, Santacana M, Sánchez L, et al. CTCs‐derived xenograft development in a triple negative breast cancer case. Int J Cancer. 2019;144(9):2254–2265.
Vishnoi M, Liu NH, Yin W, Boral D, Scamardo A, Hong D, et al. The identification of a TNBC liver metastasis gene signature by sequential CTC‐xenograft modeling. Mol Oncol. 2019;13(9):1913–1926.
Yu M, Bardia A, Aceto N, Bersani F, Madden MW, Donaldson MC, et al. Cancer therapy. Ex vivo culture of circulating breast tumor cells for individualized testing of drug susceptibility. Science. 2014;345(6193):216–220.
Zhang L, Ridgway LD, Wetzel MD, Ngo J, Yin W, Kumar D, et al. The identification and characterization of breast cancer CTCs competent for brain metastasis. Sci Transl Med. 2013;5(180):180ra48.
Fisher BM, Tang KD, Warkiani ME, Punyadeera C, Batstone MD. A pilot study for presence of circulating tumour cells in adenoid cystic carcinoma. Int J Oral Maxillofac Surg. 2021;50(8):994–998.
Metcalf R, Mohan S, Hilton S, Pierce J, Hudson J, Betts G, et al. The application of liquid biopsies in metastatic salivary gland cancer to identify candidate therapeutic targets. Ann Oncol. 2017;28:vii8.
Crosbie PA, Shah R, Krysiak P, Zhou C, Morris K, Tugwood J, et al. Circulating tumor cells detected in the tumor‐draining pulmonary vein are associated with disease recurrence after surgical resection of NSCLC. J Thoracic Oncol. 2016;11(10):1793–1797.
de Melo GD, Jardim DL, Marchesi MS, Hortobagyi GN. Mechanisms of resistance and sensitivity to anti‐HER2 therapies in HER2+ breast cancer. Oncotarget. 2016;7(39):64431–64446.
Robey RW, Pluchino KM, Hall MD, Fojo AT, Bates SE, Gottesman MM. Revisiting the role of ABC transporters in multidrug‐resistant cancer. Nat Rev Cancer. 2018;18(7):452–464.