1932

Abstract

Precision medicine can be simply defined as the identification of personalized treatment that matches patient-specific clinical and genomic characteristics. Since the completion of the Human Genome Project in 2003, significant advances have been made in our understanding of the genetic makeup of diseases, especially cancers. The identification of somatic mutations that can drive cancer has led to the development of therapies that specifically target the abnormal proteins derived from these mutations. This has led to a paradigm shift in our treatment methodology. Although some success has been achieved in targeting some genetic abnormalities, several challenges and limitations exist when applying precision-medicine concepts in leukemia and myelodysplastic syndromes. We review the current understanding of genomics in myelodysplastic syndromes (MDS) and leukemias and the limitations of precision-medicine concepts in MDS.

Loading

Article metrics loading...

/content/journals/10.1146/annurev-med-062915-095637
2017-01-14
2024-03-28
Loading full text...

Full text loading...

/deliver/fulltext/med/68/1/annurev-med-062915-095637.html?itemId=/content/journals/10.1146/annurev-med-062915-095637&mimeType=html&fmt=ahah

Literature Cited

  1. Sharma SV, Bell DW, Settleman J, Haber DA. 1.  2007. Epidermal growth factor receptor mutations in lung cancer. Nat. Rev. Cancer 7:3169–81 [Google Scholar]
  2. Kindler T, Lipka DB, Fischer T. 2.  2010. FLT3 as a therapeutic target in AML: still challenging after all these years. Blood 116:245089–102 [Google Scholar]
  3. Lander ES. 3.  2011. Initial impact of the sequencing of the human genome. Nature 470:7333187–97 [Google Scholar]
  4. Venter JC, Adams MD, Myers EW. 4.  et al. 2001. The sequence of the human genome. Science 291:55071304–51 [Google Scholar]
  5. Lander ES, Linton LM, Birren B. 5.  et al. 2001. Initial sequencing and analysis of the human genome. Nature 409:6822860–921 [Google Scholar]
  6. 6. ENCODE Project Consortium 2012. An integrated encyclopedia of DNA elements in the human genome. Nature 489:741457–74 [Google Scholar]
  7. Hansen TB, Jensen TI, Clausen BH. 7.  et al. 2013. Natural RNA circles function as efficient microRNA sponges. Nature 495:7441384–88 [Google Scholar]
  8. Haase D, Germing U, Schanz J. 8.  et al. 2007. New insights into the prognostic impact of the karyotype in MDS and correlation with subtypes: evidence from a core dataset of 2124 patients. Blood 110:134385–95 [Google Scholar]
  9. Schanz J, Steidl C, Fonatsch C. 9.  et al. 2011. Coalesced multicentric analysis of 2,351 patients with myelodysplastic syndromes indicates an underestimation of poor-risk cytogenetics of myelodysplastic syndromes in the international prognostic scoring system. J. Clin. Oncol. 29:151963–70 [Google Scholar]
  10. Schanz J, Tuchler H, Sole F. 10.  et al. 2012. New comprehensive cytogenetic scoring system for primary myelodysplastic syndromes (MDS) and oligoblastic acute myeloid leukemia after MDS derived from an international database merge. J. Clin. Oncol. 30:8820–29 [Google Scholar]
  11. Deeg HJ, Scott BL, Fang M. 11.  et al. 2012. Five-group cytogenetic risk classification, monosomal karyotype, and outcome after hematopoietic cell transplantation for MDS or acute leukemia evolving from MDS. Blood 120:71398–408 [Google Scholar]
  12. Vardiman JW, Thiele J, Arber DA. 12.  et al. 2009. The 2008 revision of the World Health Organization (WHO) classification of myeloid neoplasms and acute leukemia: rationale and important changes. Blood 114:5937–51 [Google Scholar]
  13. Coleman JF, Theil KS, Tubbs RR, Cook JR. 13.  2011. Diagnostic yield of bone marrow and peripheral blood FISH panel testing in clinically suspected myelodysplastic syndromes and/or acute myeloid leukemia: a prospective analysis of 433 cases. Am. J. Clin. Pathol. 135:6915–20 [Google Scholar]
  14. Afable MG 2nd, Wlodarski M, Makishima H. 14.  et al. 2011. SNP array-based karyotyping: differences and similarities between aplastic anemia and hypocellular myelodysplastic syndromes. Blood 117:256876–84 [Google Scholar]
  15. Bejar R, Stevenson K, Abdel-Wahab O. 15.  et al. 2011. Clinical effect of point mutations in myelodysplastic syndromes. N. Engl. J. Med. 364:262496–506 [Google Scholar]
  16. Papaemmanuil E, Gerstung M, Malcovati L. 16.  et al. 2013. Clinical and biological implications of driver mutations in myelodysplastic syndromes. Blood 122:223616–27 [Google Scholar]
  17. Haferlach T, Nagata Y, Grossmann V. 17.  et al. 2014. Landscape of genetic lesions in 944 patients with myelodysplastic syndromes. Leukemia 28:2241–47 [Google Scholar]
  18. Jaiswal S, Fontanillas P, Flannick J. 18.  et al. 2014. Age-related clonal hematopoiesis associated with adverse outcomes. N. Engl. J. Med. 371:262488–98 [Google Scholar]
  19. Genovese G, Kahler AK, Handsaker RE. 19.  et al. 2014. Clonal hematopoiesis and blood-cancer risk inferred from blood DNA sequence. N. Engl. J. Med. 371:262477–87 [Google Scholar]
  20. Xie M, Lu C, Wang J. 20.  et al. 2014. Age-related mutations associated with clonal hematopoietic expansion and malignancies. Nat. Med. 20:121472–78 [Google Scholar]
  21. Cargo CA, Rowbotham N, Evans PA. 21.  et al. 2015. Targeted sequencing identifies patients with pre-clinical MDS at high risk of disease progression. Blood 126:2362–65 [Google Scholar]
  22. Kwok B, Hall JM, Witte JS. 22.  et al. 2015. MDS-associated somatic mutations and clonal hematopoiesis are common in idiopathic cytopenias of undetermined significance. Blood 126:2355–61 [Google Scholar]
  23. Greenberg P, Cox C, LeBeau MM. 23.  et al. 1997. International scoring system for evaluating prognosis in myelodysplastic syndromes. Blood 89:62079–88 [Google Scholar]
  24. Malcovati L, Germing U, Kuendgen A. 24.  et al. 2007. Time-dependent prognostic scoring system for predicting survival and leukemic evolution in myelodysplastic syndromes. J. Clin. Oncol. 25:233503–10 [Google Scholar]
  25. Kantarjian H, O'Brien S, Ravandi F. 25.  et al. 2008. Proposal for a new risk model in myelodysplastic syndrome that accounts for events not considered in the original International Prognostic Scoring System. Cancer 113:61351–61 [Google Scholar]
  26. Greenberg PL, Attar E, Bennett JM. 26.  et al. 2013. Myelodysplastic syndromes: clinical practice guidelines in oncology. J. Natl. Compr. Cancer Netw. 11:7838–74 [Google Scholar]
  27. Nazha A, Seastone D, Keng M. 27.  et al. 2015. The Revised International Prognostic Scoring System (IPSS-R) is not predictive of survival in patients with secondary myelodysplastic syndromes. Leuk. Lymphoma 56:3437–39 [Google Scholar]
  28. Nazha A, Komrokji RS, Garcia-Manero G. 28.  et al. 2016. The efficacy of current prognostic models in predicting outcome of patients with myelodysplastic syndromes at the time of hypomethylating agent failure. Haematologica 101:e224–e227 [Google Scholar]
  29. Nazha A, Narkhede M, Radivoyevitch T. 29.  et al. 2016. Incorporation of molecular data into the Revised International Prognostic Scoring System in treated patients with myelodysplastic syndromes. Leukemia 30:2214–20 [Google Scholar]
  30. Garcia-Manero G. 30.  2014. Myelodysplastic syndromes: 2014 update on diagnosis, risk-stratification, and management. Am. J. Hematol. 89:197–108 [Google Scholar]
  31. Sekeres MA, Cutler C. 31.  2014. How we treat higher-risk myelodysplastic syndromes. Blood 123:6829–36 [Google Scholar]
  32. Fenaux P, Ades L. 32.  2009. Review of azacitidine trials in Intermediate-2 and high-risk myelodysplastic syndromes. Leuk. Res. 33:Suppl. 2S7–11 [Google Scholar]
  33. List AF, Fenoux P, Mufti GJ. 33.  et al. 2008. Azacitidine (AZA) extends overall survival in higher-risk myelodysplastic syndromes (MDS) without necessity for complete remission. J. Clin. Oncol. 26:1557006 (Abstr.) [Google Scholar]
  34. Fenaux P, Mufti GJ, Hellstrom-Lindberg E. 34.  et al. 2009. Efficacy of azacitidine compared with that of conventional care regimens in the treatment of higher-risk myelodysplastic syndromes: a randomised, open-label, phase III study. Lancet Oncol 10:3223–32 [Google Scholar]
  35. Kantarjian H, Issa JP, Rosenfeld CS. 35.  et al. 2006. Decitabine improves patient outcomes in myelodysplastic syndromes: results of a phase III randomized study. Cancer 106:81794–803 [Google Scholar]
  36. Kantarjian HM, O'Brien S, Huang X. 36.  et al. 2007. Survival advantage with decitabine versus intensive chemotherapy in patients with higher risk myelodysplastic syndrome: comparison with historical experience. Cancer 109:61133–37 [Google Scholar]
  37. Silverman LR, Fenaux P, Mufti GJ. 37.  et al. 2011. Continued azacitidine therapy beyond time of first response improves quality of response in patients with higher-risk myelodysplastic syndromes. Cancer 117:122697–702 [Google Scholar]
  38. Gore SD, Hermes-DeSantis ER. 38.  2009. Enhancing survival outcomes in the management of patients with higher-risk myelodysplastic syndromes. Cancer Control. J. Moffitt Cancer Cent. 16:Suppl.2–10 [Google Scholar]
  39. Gore SD, Fenaux P, Santini V. 39.  et al. 2013. A multivariate analysis of the relationship between response and survival among patients with higher-risk myelodysplastic syndromes treated within azacitidine or conventional care regimens in the randomized AZA-001 trial. Haematologica 98:71067–72 [Google Scholar]
  40. Zeidan AM, Sekeres MA, Garcia-Manero G. 40.  et al. 2015. Comparison of risk stratification tools in predicting outcomes of patients with higher-risk myelodysplastic syndromes treated with azanucleosides. Leukemia 30:649–57 [Google Scholar]
  41. Itzykson R, Kosmider O, Cluzeau T. 41.  et al. 2011. Impact of TET2 mutations on response rate to azacitidine in myelodysplastic syndromes and low blast count acute myeloid leukemias. Leukemia 25:71147–52 [Google Scholar]
  42. Bejar R, Lord A, Stevenson K. 42.  et al. 2014. TET2 mutations predict response to hypomethylating agents in myelodysplastic syndrome patients. Blood 124:172705–12 [Google Scholar]
  43. Traina F, Visconte V, Elson P. 43.  et al. 2014. Impact of molecular mutations on treatment response to DNMT inhibitors in myelodysplasia and related neoplasms. Leukemia 28:178–87 [Google Scholar]
  44. Bejar R, Stevenson KE, Caughey B. 44.  et al. 2014. Somatic mutations predict poor outcome in patients with myelodysplastic syndrome after hematopoietic stem-cell transplantation. J. Clin. Oncol. 32:252691–98 [Google Scholar]
  45. Kulasekararaj AG, Smith AE, Mian SA. 45.  et al. 2013. TP53 mutations in myelodysplastic syndrome are strongly correlated with aberrations of chromosome 5, and correlate with adverse prognosis. Br. J. Haematol. 160:5660–72 [Google Scholar]
  46. Saft LSL, Greenberg P, Shiansong J. 46.  et al. 2014. p53 mutant independently impacts risk: analysis of deletion 5q, lower-risk myelodysplastic syndromes (MDS) patients treated with lenalidomide (LEN) in the MDS-004 Study. Blood 124:414Abstr. 414 [Google Scholar]
  47. Druker BJ, Talpaz M, Resta DJ. 47.  et al. 2001. Efficacy and safety of a specific inhibitor of the BCR-ABL tyrosine kinase in chronic myeloid leukemia. N. Engl. J. Med. 344:141031–37 [Google Scholar]
  48. Leyland-Jones B. 48.  2002. Trastuzumab: hopes and realities. Lancet Oncol 3:3137–44 [Google Scholar]
  49. Sohal DP, Rini BI, Khorana AA. 49.  et al. 2016. Prospective clinical study of precision oncology in solid tumors. J. Natl. Cancer Inst. 108 In press doi: 10.1093/jnci/djv332
  50. Molenaar RJ, Thota S, Nagata Y. 50.  et al. 2015. Clinical and biological implications of ancestral and non-ancestral IDH1 and IDH2 mutations in myeloid neoplasms. Leukemia 29:112134–42 [Google Scholar]
  51. 51. The Cancer Genome Atlas. 2013. Genomic and epigenomic landscapes of adult de novo acute myeloid leukemia. N. Engl. J. Med. 368:222059–74 [Google Scholar]
  52. Molenaar RJ, Radivoyevitch T, Maciejewski JP. 52.  et al. 2014. The driver and passenger effects of isocitrate dehydrogenase 1 and 2 mutations in oncogenesis and survival prolongation. Biochim. Biophys. Acta 1846:2326–41 [Google Scholar]
  53. DiNardo C, Stein EM, Altman JK. 53.  et al. 2015. AG-221, an oral, selective, first-in-class, potent inhibitor of the IDH2 mutant enzyme, induced durable responses in a phase 1 study of IDH2 mutation-positive advanced hematologic malignancies. Proc. Haematol. Eur. Hematol. Assoc. Annu. Meet. 100:Suppl. 1569 (Abstr.) [Google Scholar]
  54. Buonamici S, Perino S, Lim KH. 54.  et al. 2014. SF3B1 mutations induce aberrant mRNA splicing in cancer and confer sensitivity to spliceosome inhibition Presented at Annu. Meet. Am. Assoc. Cancer Res., Apr. 5–9, San Diego, CA
/content/journals/10.1146/annurev-med-062915-095637
Loading
/content/journals/10.1146/annurev-med-062915-095637
Loading

Data & Media loading...

  • Article Type: Review Article
This is a required field
Please enter a valid email address
Approval was a Success
Invalid data
An Error Occurred
Approval was partially successful, following selected items could not be processed due to error