Skip to main content
SpringerLink
Log in

Using Liquid Biopsy in the Treatment of Patient with OS

  • Chapter
  • First Online:
Current Advances in Osteosarcoma

Part of the book series: Advances in Experimental Medicine and Biology ((AEMB,volume 1257))

Abstract

Liquid biopsies encompass a number of new technologies designed to derive tumor data through the minimally invasive sampling of an accessible body fluid. These technologies remain early in their clinical development, and applications for patients with osteosarcoma are actively under investigation. In this chapter, we outline the current state of liquid biopsy technologies as they apply to cancer generally and osteosarcoma specifically, focusing on assays that detect and profile circulating tumor DNA (ctDNA), microRNAs (miRNA), and circulating tumor cells (CTCs). At present, ctDNA assays are the most mature, with multiple assays demonstrating the feasibility of detecting and quantifying ctDNA from blood samples of patients with osteosarcoma. Initial studies show that ctDNA can be detected in the majority of patients with osteosarcoma and that the detection and level of ctDNA correlates with a worse prognosis. Profiling of ctDNA can also identify specific somatic events that may have prognostic relevance, such as 8q gain in osteosarcoma. miRNAs are stable RNAs that regulate gene expression and are known to be dysregulated in cancer, and patterns of miRNA expression have been evaluated in multiple studies of patients with osteosarcoma. While studies have identified differential expression of many miRNAs in osteosarcomas compared to healthy controls, a consensus set of prognostic miRNAs has yet to be definitively validated. Recent studies have also demonstrated the feasibility of capturing CTCs in patients with osteosarcoma. The development of assays that quantify and profile CTCs for use as prognostic biomarkers or tools for biologic discovery is still in development. However, CTC technology holds incredible promise given the potential to perform multi-omic approaches in single cancer cells to understand osteosarcoma heterogeneity and tumor evolution. The next step required to move liquid biopsy technologies closer to helping patients will be wide-scale collection of patient samples from large prospective studies.

This is a preview of subscription content, log in via an institution to check access.

Access this chapter

Log in via an institution
Chapter
USD 29.95
Price excludes VAT (USA)
  • Available as PDF
  • Read on any device
  • Instant download
  • Own it forever
eBook
USD 139.00
Price excludes VAT (USA)
  • Available as EPUB and PDF
  • Read on any device
  • Instant download
  • Own it forever
Softcover Book
USD 179.99
Price excludes VAT (USA)
  • Compact, lightweight edition
  • Dispatched in 3 to 5 business days
  • Free shipping worldwide - see info
Hardcover Book
USD 179.99
Price excludes VAT (USA)
  • Durable hardcover edition
  • Dispatched in 3 to 5 business days
  • Free shipping worldwide - see info

Tax calculation will be finalised at checkout

Purchases are for personal use only

Institutional subscriptions

References

  1. Mandel P, Metais P (1948) Not Available. C R Seances Soc Biol Fil 142(3-4):241–243. http://www.ncbi.nlm.nih.gov/pubmed/18875018

    CAS  PubMed  Google Scholar 

  2. Leon SA, Shapiro B, Sklaroff DM, Yaros MJ (1977) Free DNA in the serum of cancer patients and the effect of therapy. Cancer Res 37(3):646–650

    CAS  PubMed  Google Scholar 

  3. Stroun M, Anker P, Lyautey J, Lederrey C, Maurice PA (1987) Isolation and characterization of DNA from the plasma of cancer patients. Eur J Cancer Clin Oncol 23(6):707–712. https://doi.org/10.1016/0277-5379(87)90266-5

    Article  CAS  PubMed  Google Scholar 

  4. Wan JCM, Massie C, Garcia-Corbacho J et al (2017) Liquid biopsies come of age: towards implementation of circulating tumour DNA. Nat Rev Cancer 17(4):223–238. https://doi.org/10.1038/nrc.2017.7

    Article  CAS  PubMed  Google Scholar 

  5. Merker JD, Oxnard GR, Compton C et al (2018) Circulating tumor DNA analysis in patients with cancer: American Society of Clinical Oncology and College of American Pathologists Joint Review. J Clin Oncol:JCO.2017.76.867. https://doi.org/10.1200/JCO.2017.76.8671

  6. Abbou SD, Shulman DS, DuBois SG, Crompton BD (2019, (October 2018) Assessment of circulating tumor DNA in pediatric solid tumors: the promise of liquid biopsies. Pediatr Blood Cancer:e27595. https://doi.org/10.1002/pbc.27595

  7. Sacher AG, Paweletz C, Dahlberg SE et al (2016) Prospective validation of rapid plasma genotyping for the detection of EGFR and KRAS mutations in advanced lung cancer. JAMA Oncol 2(8):1014–1022. https://doi.org/10.1001/jamaoncol.2016.0173

    Article  PubMed  PubMed Central  Google Scholar 

  8. Warren JD, Xiong W, Bunker AM et al (2011) Septin 9 methylated DNA is a sensitive and specific blood test for colorectal cancer. BMC Med 9(1):133. https://doi.org/10.1186/1741-7015-9-133

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  9. Heitzer E, Haque IS, Roberts CES, Speicher MR (2019) Current and future perspectives of liquid biopsies in genomics-driven oncology. Nat Rev Genet 20(2):71–88. https://doi.org/10.1038/s41576-018-0071-5

    Article  CAS  PubMed  Google Scholar 

  10. Oxnard GR, Paweletz CP, Kuang Y et al (2014) Noninvasive detection of response and resistance in egfrmutant lung cancer using quantitative next-generation genotyping of cell-free plasma DNA. Clin Cancer Res 20(6):1698–1705. https://doi.org/10.1158/1078-0432.CCR-13-2482

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  11. Remon J, Caramella C, Jovelet C et al (2017) Osimertinib benefit in EGFR-mutant NSCLC patients with T790M-mutation detected by circulating tumour DNA. Ann Oncol 28(4):784–790. https://doi.org/10.1093/annonc/mdx017

    Article  CAS  PubMed  Google Scholar 

  12. Jenkins S, Yang JCH, Ramalingam SS et al (2017) Plasma ctDNA analysis for detection of the EGFR T790M mutation in patients with advanced non-small cell lung cancer. J Thorac Oncol 12(7):1061–1070. https://doi.org/10.1016/j.jtho.2017.04.003

    Article  PubMed  Google Scholar 

  13. Schmiegel W, Scott RJ, Dooley S et al (2017) Blood-based detection of RAS mutations to guide anti-EGFR therapy in colorectal cancer patients: concordance of results from circulating tumor DNA and tissue-based RAS testing. Mol Oncol 11(2):208–219. https://doi.org/10.1002/1878-0261.12023

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  14. Gröbner SN, Worst BC, Weischenfeldt J et al (2018) The landscape of genomic alterations across childhood cancers. Nature 555(7696):321–327. https://doi.org/10.1038/nature25480

    Article  CAS  PubMed  Google Scholar 

  15. Huether R, Dong L, Chen X et al (2014) The landscape of somatic mutations in epigenetic regulators across 1,000 paediatric cancer genomes. Nat Commun 5:1–7. https://doi.org/10.1038/ncomms4630

    Article  CAS  Google Scholar 

  16. Lawrence MS, Stojanov P, Polak P et al (2013) Mutational heterogeneity in cancer and the search for new cancer-associated genes. Nature 499(7457):214–218. https://doi.org/10.1038/nature12213

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  17. Barris DM, Weiner SB, Dubin RA et al (2018) Detection of circulating tumor DNA in patients with osteosarcoma. Oncotarget 9(16):12695–12704. https://doi.org/10.18632/oncotarget.24268

    Article  PubMed  PubMed Central  Google Scholar 

  18. Crompton BD, Stewart C, Taylor-Weiner A et al (2014) The genomic landscape of pediatric Ewing sarcoma. Cancer Discov 4(11):1326–1341. https://doi.org/10.1158/2159-8290.CD-13-1037

    Article  CAS  PubMed  Google Scholar 

  19. Tirode F, Surdez D, Ma X et al (2014) Genomic landscape of Ewing sarcoma defines an aggressive subtype with co-association of STAG2 and TP53 mutations. Cancer Discov 4(11):1342–1353. https://doi.org/10.1158/2159-8290.CD-14-0622

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  20. Perry JA, Kiezun A, Tonzi P et al (2014) Complementary genomic approaches highlight the PI3K/mTOR pathway as a common vulnerability in osteosarcoma. Proc Natl Acad Sci U S A 111(51):E5564–E5573. https://doi.org/10.1073/pnas.1419260111

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  21. Brohl AS, Solomon DA, Chang W et al (2014) The genomic landscape of the Ewing Sarcoma family of tumors reveals recurrent STAG2 mutation. PLoS Genet 10(7):e1004475. https://doi.org/10.1371/journal.pgen.1004475

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  22. Shern JF, Chen L, Chmielecki J et al (2014) Comprehensive genomic analysis of rhabdomyosarcoma reveals a landscape of alterations affecting a common genetic axis in fusion-positive and fusion-negative tumors. Cancer Discov 4(2):216–231. https://doi.org/10.1158/2159-8290.CD-13-0639

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  23. Kovac M, Blattmann C, Ribi S et al (2015) Exome sequencing of osteosarcoma reveals mutation signatures reminiscent of BRCA deficiency. Nat Commun. https://doi.org/10.1038/ncomms9940

  24. Bousquet M, Noirot C, Accadbled F et al (2016) Whole-exome sequencing in osteosarcoma reveals important heterogeneity of genetic alterations. Ann Oncol 27(4):738–744. https://doi.org/10.1093/annonc/mdw009

    Article  CAS  PubMed  Google Scholar 

  25. Bayani J, Zielenska M, Pandita A et al (2003) Spectral karyotyping identifies recurrent complex rearrangements of chromosomes 8, 17, and 20 in osteosarcomas. Genes Chromosom Cancer 36(1):7–16. https://doi.org/10.1002/gcc.10132

    Article  CAS  PubMed  Google Scholar 

  26. Squire JA, Pei J, Marrano P et al (2003) High-resolution mapping of amplifications and deletions in pediatric osteosarcoma by use of CGH analysis of cDNA microarrays. Genes Chromosom Cancer 38(3):215–225. https://doi.org/10.1002/gcc.10273

    Article  CAS  PubMed  Google Scholar 

  27. Chen X, Bahrami A, Pappo A et al (2014) Recurrent somatic structural variations contribute to tumorigenesis in pediatric osteosarcoma. Cell Rep 7(1):104–112. https://doi.org/10.1016/j.celrep.2014.03.003

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  28. Pugh TJ, Morozova O, Attiyeh EF et al (2013) The genetic landscape of high-risk neuroblastoma. Nat Genet 45(3):279–284. https://doi.org/10.1038/ng.2529

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  29. Adalsteinsson VA, Ha G, Freeman SS et al (2017) Scalable whole-exome sequencing of cell-free DNA reveals high concordance with metastatic tumors. Nat Commun 8(1):1324. https://doi.org/10.1038/s41467-017-00965-y

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  30. Klega K, Imamovic-Tuco A, Ha G et al (2018) Detection of somatic structural variants enables quantification and characterization of circulating tumor DNA in children with solid tumors. JCO Precis Oncol 2018(2):1–13. https://doi.org/10.1200/PO.17.00285

    Article  Google Scholar 

  31. Koelsche C, Hartmann W, Schrimpf D et al (2018) Array-based DNA-methylation profiling in sarcomas with small blue round cell histology provides valuable diagnostic information. Mod Pathol 31(8):1246–1256. https://doi.org/10.1038/s41379-018-0045-3

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  32. Shen SY, Singhania R, Fehringer G et al (2018) Sensitive tumour detection and classification using plasma cell-free DNA methylomes. Nature 563(7732):579–583. https://doi.org/10.1038/s41586-018-0703-0

    Article  CAS  PubMed  Google Scholar 

  33. Li W, Zhang X, Lu X et al (2017) 5-Hydroxymethylcytosine signatures in circulating cell-free DNA as diagnostic biomarkers for human cancers. Cell Res 27(10):1243–1257. https://doi.org/10.1038/cr.2017.121

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  34. Cayrefourcq L, Mazard T, Joosse S et al (2015) Establishment and characterization of a cell line from human circulating colon cancer cells. Cancer Res 75(5):892–901. https://doi.org/10.1158/0008-5472.CAN-14-2613

    Article  CAS  PubMed  Google Scholar 

  35. Zahn H, Steif A, Laks E et al (2017) Scalable whole-genome single-cell library preparation without preamplification. Nat Methods 14(2):167–173. https://doi.org/10.1038/nmeth.4140

    Article  CAS  PubMed  Google Scholar 

  36. Clark SJ, Lee HJ, Smallwood SA, Kelsey G, Reik W (2016) Single-cell epigenomics: powerful new methods for understanding gene regulation and cell identity. Genome Biol 17(1):72. https://doi.org/10.1186/s13059-016-0944-x

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  37. Kalinich M, Bhan I, Kwan TT et al (2017) An RNA-based signature enables high specificity detection of circulating tumor cells in hepatocellular carcinoma. Proc Natl Acad Sci 114(5):1123–1128. https://doi.org/10.1073/pnas.1617032114

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  38. Allard WJ, Matera J, Miller MC et al (2004) Tumor cells circulate in the peripheral blood of all major carcinomas but not in healthy subjects or patients with nonmalignant diseases. Clin Cancer Res 10(20):6897–6904. https://doi.org/10.1158/1078-0432.CCR-04-0378

    Article  PubMed  Google Scholar 

  39. Vo KT, Edwards JV, Epling CL et al (2016) Impact of two measures of micrometastatic disease on clinical outcomes in patients with newly diagnosed Ewing Sarcoma: a report from the Children’s Oncology Group. Clin Cancer Res. https://doi.org/10.1158/1078-0432.CCR-15-2516

  40. Dubois SG, Epling CL, Teague J, Matthay KK, Sinclair E (2010) Flow cytometric detection of Ewing sarcoma cells in peripheral blood and bone marrow. Pediatr Blood Cancer 54(1):13–18. https://doi.org/10.1002/pbc.22245

    Article  PubMed  PubMed Central  Google Scholar 

  41. Satelli A, Brownlee Z, Mitra A, Meng QH, Li S (2015) Circulating tumor cell enumeration with a combination of epithelial cell adhesion molecule- and cell-surface vimentin-based methods for monitoring breast cancer therapeutic response. Clin Chem 61(1):259–266. https://doi.org/10.1373/clinchem.2014.228122

    Article  CAS  PubMed  Google Scholar 

  42. Roth M, Linkowski M, Tarim J et al (2014) Ganglioside GD2 as a therapeutic target for antibody-mediated therapy in patients with osteosarcoma. Cancer 120(4):548–554. https://doi.org/10.1002/cncr.28461

    Article  CAS  PubMed  Google Scholar 

  43. Heiner JP, Miraldi F, Kallick S et al (1987) Localization of GD2-specific monoclonal antibody 3F8 in human osteosarcoma. Cancer Res 47(20):5401–5406

    Google Scholar 

  44. Hayashi M, Zhu P, McCarty G et al (2017) Size-based detection of sarcoma circulating tumor cells and cell clusters. Oncotarget 8(45):78965–78977. https://doi.org/10.18632/oncotarget.20697

    Article  PubMed  PubMed Central  Google Scholar 

  45. Bartel DP (2004) MicroRNAs: genomics, biogenesis, mechanism, and function. Cell 116(2):281–297. https://doi.org/10.1016/s0092-8674(04)00045-5

    Article  CAS  PubMed  Google Scholar 

  46. Cortez MA, Bueso-Ramos C, Ferdin J, Lopez-Berestein G, Sood AK, Calin G (2011) a. MicroRNAs in body fluids--the mix of hormones and biomarkers. Nat Rev Clin Oncol 8(8):467–477. https://doi.org/10.1038/nrclinonc.2011.76

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  47. Montani F, Bianchi F (2016) Circulating cancer biomarkers: the macro-revolution of the micro-RNA. EBioMedicine 5:4–6. https://doi.org/10.1016/j.ebiom.2016.02.038

    Article  PubMed  PubMed Central  Google Scholar 

  48. Chen X, Ba Y, Ma L et al (2008) Characterization of microRNAs in serum: a novel class of biomarkers for diagnosis of cancer and other diseases. Cell Res 18(10):997–1006. https://doi.org/10.1038/cr.2008.282

    Article  CAS  PubMed  Google Scholar 

  49. Sozzi G, Boeri M, Rossi M et al (2014) Clinical utility of a plasma-based miRNA signature classifier within computed tomography lung cancer screening: a correlative MILD trial study. J Clin Oncol 32(8):768–773. https://doi.org/10.1200/JCO.2013.50.4357

    Article  PubMed  PubMed Central  Google Scholar 

  50. Montani F, Marzi MJ, Dezi F et al (2015) miR-test: a blood test for lung cancer early detection. J Natl Cancer Inst 107(6):djv063. https://doi.org/10.1093/jnci/djv063

    Article  CAS  PubMed  Google Scholar 

  51. Allen-Rhoades W, Kurenbekova L, Satterfield L et al (2015) Cross-species identification of a plasma microRNA signature for detection, therapeutic monitoring, and prognosis in osteosarcoma. Cancer Med 4(7):977–988. https://doi.org/10.1002/cam4.438

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  52. Shulman DS, Klega K, Imamovic-Tuco A et al (2018) Detection of circulating tumour DNA is associated with inferior outcomes in Ewing sarcoma and osteosarcoma: a report from the Children’s Oncology Group. Br J Cancer 119(5):615–621. https://doi.org/10.1038/s41416-018-0212-9

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  53. Henderson TO, Whitton J, Stovall M et al (2007) Secondary sarcomas in childhood cancer survivors: a report from the Childhood Cancer Survivor Study. J Natl Cancer Inst 99(4):300–308. https://doi.org/10.1093/jnci/djk052

    Article  PubMed  Google Scholar 

  54. Amant F, Verheecke M, Wlodarska I et al (2015) Presymptomatic identification of cancers in pregnant women during noninvasive prenatal testing. JAMA Oncol 1(6):814–819. https://doi.org/10.1001/jamaoncol.2015.1883

    Article  PubMed  Google Scholar 

  55. Bianchi DW, Chudova D, Sehnert AJ et al (2015) Noninvasive prenatal testing and incidental detection of occult maternal malignancies. JAMA 314(2):162–169. https://doi.org/10.1001/jama.2015.7120

    Article  CAS  PubMed  Google Scholar 

  56. Dong J, Liu Y, Liao W, Liu R, Shi P, Wang L (2016) miRNA-223 is a potential diagnostic and prognostic marker for osteosarcoma. J Bone Oncol 5(2):74–79. https://doi.org/10.1016/j.jbo.2016.05.001

    Article  PubMed  PubMed Central  Google Scholar 

  57. Ma W, Zhang X, Chai J, Chen P, Ren P, Gong M (2014) Circulating miR-148a is a significant diagnostic and prognostic biomarker for patients with osteosarcoma. Tumor Biol 35(12):12467–12472. https://doi.org/10.1007/s13277-014-2565-x

    Article  CAS  Google Scholar 

  58. Bielack SS, Kempf-Bielack B, Delling G et al (2002) Prognostic factors in high-grade osteosarcoma of the extremities or trunk: an analysis of 1,702 patients treated on neoadjuvant cooperative osteosarcoma study group protocols. J Clin Oncol 20(3):776–790. https://doi.org/10.1200/JCO.2002.20.3.776

    Article  PubMed  Google Scholar 

  59. Bacci G, Longhi A, Versari M, Mercuri M, Briccoli A, Picci P (2006) Prognostic factors for osteosarcoma of the extremity trerated with neoadjuvant chemotherapy: 15-year experience in 789 patients treated at a single institution. Cancer 106(5):1154–1161. https://doi.org/10.1002/cncr.21724

    Article  PubMed  Google Scholar 

  60. Parkinson CA, Gale D, Piskorz AM et al (2016) Exploratory analysis of TP53 mutations in circulating tumour DNA as biomarkers of treatment response for patients with relapsed high-grade serous ovarian carcinoma: a retrospective study. PLoS Med 13(12):e1002198. https://doi.org/10.1371/journal.pmed.1002198

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  61. Thierry AR, Mouliere F, Gongora C et al (2010) Origin and quantification of circulating DNA in mice with human colorectal cancer xenografts. Nucleic Acids Res 38(18):6159–6175. https://doi.org/10.1093/nar/gkq421

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  62. Bettegowda C, Sausen M, Leary RJ et al (2014) Detection of circulating tumor DNA in early- and late-stage human malignancies. Sci Transl Med 6(224):224ra24. https://doi.org/10.1126/scitranslmed.3007094

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  63. Lecomte T, Berger A, Zinzindohoué F et al (2002) Detection of free-circulating tumor-associated DNA in plasma of colorectal cancer patients and its association with prognosis. Int J Cancer 100(5):542–548. https://doi.org/10.1002/ijc.10526

    Article  CAS  PubMed  Google Scholar 

  64. Ziyan W, Shuhua Y, Xiufang W, Xiaoyun L (2011) MicroRNA-21 is involved in osteosarcoma cell invasion and migration. Med Oncol 28(4):1469–1474. https://doi.org/10.1007/s12032-010-9563-7

    Article  CAS  PubMed  Google Scholar 

  65. Duan Z, Choy E, Harmon D et al (2011) MicroRNA-199a-3p is downregulated in human osteosarcoma and regulates cell proliferation and migration. Mol Cancer Ther 10(8):1337–1345. https://doi.org/10.1158/1535-7163.MCT-11-0096

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  66. Osaki M, Takeshita F, Sugimoto Y et al (2011) MicroRNA-143 regulates human osteosarcoma metastasis by regulating matrix metalloprotease-13 expression. Mol Ther 19(6):1123–1130. https://doi.org/10.1038/mt.2011.53

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  67. Zhou G, Lu M, Chen J et al (2015) Identification of miR-199a-5p in serum as noninvasive biomarkers for detecting and monitoring osteosarcoma. Tumor Biol 36(11):8845–8852. https://doi.org/10.1007/s13277-015-3421-3

    Article  CAS  Google Scholar 

  68. Lian F, Cui Y, Zhou C, Gao K, Wu L (2015) Identification of a plasma four-microRNA panel as potential noninvasive biomarker for osteosarcoma. PLoS One 10(3):e0121499. https://doi.org/10.1371/journal.pone.0121499

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  69. Tian Q, Jia J, Ling S, Liu Y, Yang S, Shao Z (2014) A causal role for circulating miR-34b in osteosarcoma. Eur J Surg Oncol 40(1):67–72. https://doi.org/10.1016/j.ejso.2013.08.024

    Article  CAS  PubMed  Google Scholar 

  70. Ouyang L, Liu P, Yang S, Ye S, Xu W, Liu X (2013) A three-plasma miRNA signature serves as novel biomarkers for osteosarcoma. Med Oncol 30(1):340. https://doi.org/10.1007/s12032-012-0340-7

    Article  CAS  PubMed  Google Scholar 

  71. Guenther LM, Rowe RG, Acharya PT et al (2017) Response Evaluation Criteria in Solid Tumors (RECIST) following neoadjuvant chemotherapy in osteosarcoma. Pediatr Blood Cancer:e26896. https://doi.org/10.1002/pbc.26896

  72. Pearce MS, Salotti JA, Little MP et al (2012) Radiation exposure from CT scans in childhood and subsequent risk of leukaemia and brain tumours: a retrospective cohort study. Lancet 380(9840):499–505. https://doi.org/10.1016/S0140-6736(12)60815-0

    Article  PubMed  PubMed Central  Google Scholar 

  73. McBride DJ, Orpana AK, Sotiriou C et al (2010) Use of cancer-specific genomic rearrangements to quantify disease burden in plasma from patients with solid tumors. Genes Chromosomes Cancer 49(11):1062–1069. https://doi.org/10.1002/gcc.20815

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  74. McDonald BR, Contente-Cuomo T, Sammut S-J et al (2019) Personalized circulating tumor DNA analysis to detect residual disease after neoadjuvant therapy in breast cancer. Sci Transl Med 11(504):eaax7392. https://doi.org/10.1126/scitranslmed.aax7392

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  75. Ozaki T, Schaefer KL, Wai D et al (2002) Genetic imbalances revealed by comparative genomic hybridization in osteosarcomas. Int J Cancer 102(4):355–365. https://doi.org/10.1002/ijc.10709

    Article  CAS  PubMed  Google Scholar 

  76. Roberts RD, Lizardo MM, Reed DR et al (2019) Provocative questions in osteosarcoma basic and translational biology: a report from the Children’s Oncology Group. Cancer 36(16):1631–1641. https://doi.org/10.1002/cncr.32351

    Article  Google Scholar 

  77. Stover DG, Parsons HA, Ha G et al (2018) Association of cell-free DNA tumor fraction and somatic copy number alterations with survival in metastatic triple-negative breast cancer. J Clin Oncol 36(6):543–553. https://doi.org/10.1200/JCO.2017.76.0033

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  78. Chicard M, Boyault S, Daage LC et al (2016) Genomic copy number profiling using circulating free tumor DNA highlights heterogeneity in Neuroblastoma. Clin Cancer Res 22(22):5564–5573. https://doi.org/10.1158/1078-0432.CCR-16-0500

    Article  CAS  PubMed  Google Scholar 

  79. Chicard M, Colmet Daage L, Clement N et al (2017) Whole exome sequencing of cell-free DNA reveals temporo-spatial heterogeneity and identifies treatment-resistant clones in neuroblastoma. Clin Cancer Res. clincanres.1586.2017. https://doi.org/10.1158/1078-0432.CCR-17-1586

  80. Cobas EGFR Mutation test V2 - PMA Number: P150044. https://www.accessdata.fda.gov/scripts/cdrh/cfdocs/cfpma/pma.cfm?id=P150044. Published 2016

  81. EMA. European Medicines Agency. Iressa: public assessment report—product information. https://www.ema.europa.eu/en/documents/product-information/iressa-epar-product-information_en.pdf. Published 2016

  82. EMA. European Medicines Agency. Tagrisso: public assessment report—product information

    Google Scholar 

Download references

Author information

Authors and Affiliations

  1. Pediatric Hematology/Oncology, Dana-Farber/Boston Children’s Cancer and Blood Disorders Center, Boston, MA, USA

    David S. Shulman & Brian D. Crompton

Authors
  1. David S. Shulman
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Brian D. Crompton
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Brian D. Crompton .

Editor information

Editors and Affiliations

  1. Department of Pediatric Research, Department of Cancer Biology, University of Texas MD Anderson Cancer Center, Houston, TX, USA

    Eugenie S. Kleinerman

  2. Division of Pediatrics, University of Texas MD Anderson Cancer Center, Houston, TX, USA

    Richard Gorlick

Rights and permissions

Reprints and permissions

Copyright information

© 2020 Springer Nature Switzerland AG

About this chapter

Check for updates. Verify currency and authenticity via CrossMark

Cite this chapter

Shulman, D.S., Crompton, B.D. (2020). Using Liquid Biopsy in the Treatment of Patient with OS. In: Kleinerman, E.S., Gorlick, R. (eds) Current Advances in Osteosarcoma . Advances in Experimental Medicine and Biology, vol 1257. Springer, Cham. https://doi.org/10.1007/978-3-030-43032-0_9

Download citation

Publish with us

Policies and ethics

Search

Navigation