Preview

Медицинский алфавит

Расширенный поиск
Доступ открыт Открытый доступ  Доступ закрыт Доступ платный или только для Подписчиков

Радиогеномика рака молочной железы - новый вектор междисциплинарной интеграции лучевых и молекулярнобиологических технологий(обзор литературы)

https://doi.org/10.33667/2078-5631-2020-20-21-29

Полный текст:

Аннотация

В обзоре представлены последние данные о развитии нового направления междисциплинарной интеграции цифровых лучевых и молекулярно-биологических технологий omfcs, включающих высокие технологии в области геномики, транскриптомики, протеомики и метаболомики, которые являются основой системной биологии и будущего медицины. Интеграция медицинской визуализациии и достижений генетики породили новое направление научных исследований - радиогеномику, являющуюся ключевым шагом в развитии от/сэ-технологий. Радиогеномика - фенотип визуализации, компьютерное зрение - представляет междисциплинарную интеграцию визуальной радиологии и биологических систем, изучающих биомедицинские изображения, включающие фенотипические и генотипические параметры, отражающие молекулярную и генотипическую основу ткани, по которым можно предсказать риск РМЖ и результаты лечения пациентов. Связанные с современными аналитическими программными средствами количественные и качественные биомаркеры визуализации приносят беспрецедентное понимание сложной биологии опухоли и способствуют более глубокому знанию развития и прогрессирования рака. Используя последние достижения цифровых, информационных и молекулярно-биологических технологий, ведется активное сближение специальностей радиолога и генетика, давая возможность уже на этапе изучения медицинских изображений молочной железы получать информацию о биологической характеристике опухоли, молекулярном подтипе рака, определяющем прогноз заболевания, оценку степени риска рецидива, что является важным для выбора адекватной индивидуальной тактики мониторинга и выбора лечебного пособия. Разработка визуальных симптомокомплексов медицинских изображений молочной железы, характерных для разных молекулярных подтипов рака, будет способствовать уточненной диагностике разных проявлений рака, выбору адекватной лечебной тактики, способствующей увеличению продолжительности и сохранению высокого качества жизни женщины.

Об авторах

Н. И. Рожкова
МНИОИ им. П. А. Герцена - филиал ФГБУ «Национальный медицинский исследовательский радиологический центр» Минздрава России; Медицинский институт ФГАОУ ВО Российский университет дружбы народов
Россия

Доктор медицинских наук, проф., заслуженный деятель науки России, президент Российской ассоциации маммологов, рук. Национального центра онкологии репродуктивных органов МНИОИ им. П. А. Герцена,  проф. кафедры клинической маммологии, лучевой диагностики и лучевой терапии РУДН.

Москва



В. К. Боженко
ФГБУ «Российский научный центр рентгенорадиологии» Минздрава России
Россия

Доктор медицинских наук, проф., заслуженный врач России, зав. научно-исследовательским отделом молекулярной биологии и экспериментальной терапии опухолей.

Москва



И. И. Бурдина
Московский научно-исследовательский онкологический институт имени П. А. Герцена - филиал ФГБУ «Национальный медицинский исследовательский радиологический центр» Минздрава России
Россия

Кандидат медицинских наук, старший научный сотрудник Национального центра онкологии репродуктивных органов.

Москва



С. Б. Запирова
Московский научно-исследовательский онкологический институт имени П. А. Герцена - филиал ФГБУ «Национальный медицинский исследовательский радиологический центр» Минздрава России
Россия

Кандидат медицинских наук, старший научный сотрудник Национального центра онкологии репродуктивных органов.

Москва



Е. А. Кудинова
ФГБУ «Российский научный центр рентгенорадиологии» Минздрава России
Россия

Кандидат медицинских наук, зав. клинико-диагностической лаборатории.

Москва



П. Г. Лабазанова
Московский научно-исследовательский онкологический институт имени П. А. Герцена - филиал ФГБУ «Национальный медицинский исследовательский радиологический центр» Минздрава России
Россия

Младший научный сотрудник Национального центра онкологии репродуктивных органов.

Москва



М. Л. Мазо
МНИОИ им. П. А. Герцена - филиал ФГБУ «Национальный медицинский исследовательский радиологический центр» Минздрава России; Медицинский институт ФГАОУ ВО Российский университет дружбы народов
Россия

Кандидат медицинских наук, ген. секретарь Российской ассоциации маммологов, старший научный сотрудник Национального центра онкологии репродуктивных органов МНИОИ им. П. А. Герцена, доцент кафедры клинической маммологии, лучевой диагностики и лучевой терапии ФПК МР РУДН.

Москва



С. Ю. Микушин
Московский научно-исследовательский онкологический институт имени П. А. Герцена - филиал ФГБУ «Национальный медицинский исследовательский радиологический центр» Минздрава России
Россия

Кандидат медицинских наук, научный сотрудник Национального центра онкологии репродуктивных органов.

Москва


С. П. Прокопенко
МНИОИ им. П. А. Герцена - филиал ФГБУ «Национальный медицинский исследовательский радиологический центр» Минздрава России; Медицинский институт ФГАОУ ВО Российский университет дружбы народов
Россия

Кандидат медицинских наук, зав. отделением комплексной диагностики и интервенционной радиологии в маммологии Национального центра онкологии репродуктивных органов МНИОИ им. П. А. Герцена, зав. кафедрой клинической маммологии, лучевой диагностики и лучевой терапии ФПК МР РУДН.

Москва



О. Э. Якобс
Московский научно-исследовательский онкологический институт имени П. А. Герцена - филиал ФГБУ «Национальный медицинский исследовательский радиологический центр» Минздрава России
Россия

Доктор медицинских наук, старший научный сотрудник Национального центра онкологии репродуктивных органов МНИОИ им. П. А. Герцена, доцент кафедры клинической маммологии, лучевой диагностики и лучевой терапии ФПК М Медицинский институт ФГАОУ ВО РУДН.

Москва



Список литературы

1. American College of Radiology (ACR): ACR BIRADS fifth edition: Breast imaging reporting and data system, Breast Imaging Atlas. Reston,-2013.

2. Latest global cancer data: Cancer burden rises to 18.1 million new cases and 9.6 million cancer deaths in 2018. International Agency for Research on Cancer. World Health Organization. 2018. Режим доступа: https://www.iarc.fr/en/media-centre/pr/2018/pdfs/pr263_E.pdf.

3. Mammography Quality Standards Act and Program [электронныйресурс] 2017. Режим доступа: https://www.fda.gov/radiation-emit-tingproducts/mammographyqualitystandards-actandprogram/default.htm.

4. WHO’s International Agency for Research on Cancer [Электронный ресурс]. 2018. Режимдоступа: http://gco.iarc.fr/

5. World Health Organization. Early diagnosis and screening of breast cancer [Электронный ресурс]. 2018. Режим доступа: http://www.who.int/cancer/prevention/diagnosis-screening/breast-cancer/en/.

6. Злокачественные новообразования в России в 2017 году (заболеваемость и смертность). Под ред. А.Д. Каприна, 6. 6. Старинского, Г. В. Петровой. М. 2018.-263 с.

7. Genetics Home Reference. Lister Hill National Center for Biomedical Communications, U. S. National Library of Medicine, National Institutes of Health, Department of Health & Human Services. What is precision medicine?https://ghr.nlm.nih.gov/primer/precisionmedicine/definition. Accessed September 8,2017. Google Scholar.

8. Collins FS, Varmus H. A new initiative on precision medicine. N Engl J Med 2015; 372 (9): 793-795. Crossref, Medline, Google Scholar.

9. Rahman M, Hasan MR. Cancer metabolism and drug resistance. Metabolites 2015; 5 (4): 571-600. Crossref, Medline, Google Scholar.

10. Tang J, Karhinen L, Xu T et al. Target inhibition networks: predicting selective combinations of drug-gable targets to block cancer survival pathways. PLOS Comput Biol 2013; 9 (9): e1003226. Crossref, Medline, Google Scholar.

11. Bettaieb A, Paul C, Plenchette S, Shan J, Chouch-ane L, Ghiringhelli F. Precision medicine in breast cancer, reality or utopia? J Transl Med 2017; 15 (1): 139. Crossref, Medline, Google Scholar.

12. Atkins MB, Larkin J. Immunotherapy combined or sequenced with targeted therapy in the treatment of solid tumors: current perspectives. J Natl Cancer Inst 2016; 108 (6): djv414. Crossref, Medline, Google Scholar.

13. Reuben A, Spencer CN, Prieto PA et al. Genomic and immune heterogeneity are associated with differential responses to therapy in melanoma. NPJ Genom Med. 2017; 2: 2. Medline, Google Scholar.

14. Foth M, Wouters J, de Chaumont C, Dynoodt P, Gallagher WM. Prognostic and predictive biomarkers in melanoma: an update. Expert Rev Mol Diagn 2016; 16 (2): 223-237. Crossref, Medline, Google Scholar.

15. Mari-Alexandre J, Diaz-Lagares A, Villalba M et al. Translating cancer epigenomics into the clinic: focus on lung cancer. Transl Res 2017; 189: 76-92. Crossref, Medline, Google Scholar.

16. Sundar R, Chenard-Poiner M, Collins DC, Yap TA. Imprecision in the era of precision medicine in non-small cell lung cancer.Front Med (Lausanne) 2017; 4: 39. Medline, Google Scholar.

17. Ruiz-Ceja KA, Chrino YI. Current FDA-approved treatments for non-small cell lung cancer and potential biomarkers for its detection. Biomed Pharmacother 2017; 90:24-37. Crossref, Medline, Google Scholar.

18. Rosenbaum JN, Weisman P. The evolving role of companion diagnostics for breast cancer in an era of next-generation omics.Am J Pathol 2017; 187 (10): 2185-2198. Crossref, Medline, Google Scholar.

19. Dey N, De P, Leyland-Jones B. PI3K-AKT-mTOR inhibitors in breast cancers: from tumor cell signaling to clinical trials. Pharmacol Ther2017; 175: 91-106. Crossref, Medline, Google Scholar.

20. Hobbs SK, Shi G, Homer R, Harsh G, Atlas SW, Bed-narski MD. Magnetic resonance image-guided proteomics of human glioblastoma multiforme. J Magn Reson Imaging 2003; 18 (5): 530-536. Crossref, Medline, Google Scholar.

21. Hodges TR, Ferguson SD, Heimberger AB. Immunotherapy in glioblastoma: emerging options in precision medicine. CNS Oncol2016; 5 (3): 175-186. Crossref, Medline, Google Scholar.

22. Chung C, Ma H. Driving toward precision medicine for acute leukemias: are we there yet? Pharmacotherapy 2017; 37 (9): 1052-1072. Crossref, Medline, Google Scholar.

23. Sikkema AH, den Dunnen WF, Diks SH, Peppelen-bosch MP, de Bont ES. Optimizing targeted cancer therapy: towards clinical application of systems biology approaches. Crit Rev Oncol Hematol2012; 82 (2): 171-186. Crossref, Medline, Google Scholar.

24. Kichko K, Marschall P, Flessa S. Personalized medicine in the U.S. and Germany: awareness, acceptance, use and preconditions for the wide implementation into the medical standard. J Pers Med 2016; 6 (2): E15. Crossref, Medline, Google Scholar.

25. Peng J. Meeting Report: EMBL Conference -Omics and Personalized Medicine: February 16-18,2012, Heidelberg, Germany. Biotechnol J2012; 7 (8): 943-945. Crossref, Medline, Google Scholar.

26. Westerhoff HV, Palsson BO.The evolution of molecular biology into systems biology.Nat Biotechnol 2004; 22 (10): 1249-1252. Crossref, Medline, Google Scholar.

27. Peitsch MC, de Graaf D. A decade of systems biology: where are we and where are we going to? Drug Discov Today 2014; 19 (2): 105-107. Crossref, Medline, Google Scholar.

28. Chuang HY, Hofree M, Ideker T. A decade of systems biology. Annu Rev Cell Dev Biol2010; 26 (1): 721-744. Crossref, Medline, Google Scholar.

29. Institute for Systems Biology. What is systems biology. https://www.systemsbiology.org/about/what-is-systems-biology/. Accessed September 8, 2017. Google Scholar.

30. Kell DB, OliverSG. Here is the evidence, now what is the hypothesis? The complementary roles of inductive and hypothesis-driven science in the post-genomic era. Bio Essays 2004; 26 (1): 99-105. Crossref, Medline, Google Scholar.

31. Dey N, Williams C, Leyland-Jones B, De P. Mutation matters in precision medicine: a future to believe in. Cancer Treat Rev2017; 55: 136-149. Crossref, Medline, Google Scholar.

32. Bellazzi R. Big data and biomedical informatics: a challenging opportunity. Yearb Med Inform 2014; 9 (1): 8-13. Crossref, Medline, Google Scholar.

33. International Cancer Genome Consortium; Hudson TJ. Anderson W. et al. International network of cancer genome projects. Nature 2010; 464 (7291): 993-998. Crossref, Medline, Google Scholar.

34. Collins FS, Barker AD. Mapping the cancer genome: pinpointing the genes involved in cancer will help chart a new course across the complex landscape of human malignancies.Sci Am 2007; 296 (3): 50-57. Crossref, Medline, Google Scholar.

35. Carrasco-Ramiro F, Peiro-Pastor R, Aguado B. Human genomics projects and precision medicine. Gene Ther2017; 24 (9): 551-561. Crossref, Medline, Google Scholar.

36. Casamassimi A, Federico A, Rienzo M, Esposito S, Ciccodicola A. Transcriptome profiling in human diseases: new advances and perspectives. Int J Mol Sci2017; 18 (8): E 1652. Crossref, Medline, Google Scholar.

37. U. S. National Library of Medicine. Genomics: MeSH descriptor data 2017. https://meshb.nlm.nih.gov/ record/ui?ui=D 023281. Accessed September 8, 2017. Google Scholar.

38. U. S. National Library of Medicine. Genetics MeSH descriptor data 2017.https://meshb.nlm.nih.gov/record/ui?ui=D005823. Accessed September 8, 2017. Google Scholar.

39. Lander ES, Linton LM, Birren B et al. Initial sequencing and analysis of the human genome. Nature 2001; 409 (6822): 860-921. Crossref, Medline, Google Scholar.

40. Venter JC, Smith HO, Adams MD. The Sequence of the human genome. Clin Chem 2015; 61 (9): 1207-1208. Crossref, Medline, Google Scholar.

41. National Human Genome Research Institute. What is genomic medicine? https://www.genome.gov/27552451/what-is-genomic-medicine/. Accessed September8, 2017. Google Scholar.

42. Kamel HFM, Al-Amodi HSAB. Exploitation of gene expression and cancer biomarkers in paving the path to era of personalized medicine. Genomics Proteomics Bioinformatics 2017; 15 (4): 220-235. Crossref, Medline, Google Scholar.

43. Ritchie MD, Holzinger ER, Li R, Pendergrass SA, Kim D. Methods of tegrating data to uncover genotype-phenotype interactions. Nat Rev Genet2015; 16 (2): 85-97. Crossref, Medline, Google Scholar.

44. Evans DG, Shenton A, Woodward E, Lalloo F, Howell A, Maher ER. Penetrance estimates for BRCA1 and BRCA2 based on genetic testing in a Clinical Cancer Genetics service setting: risks of breast/ ovarian cancer quoted should reflect the cancer burden in the family. BMC Cancer2008; 8 (1): 155. Crossref, Medline, Google Scholar.

45. Riedl CC, Luff N, Bernhart C et al. Triple-modality screening trial for familial breast cancer underlines the importance of magnetic resonance imaging and questions the role of mammography and ultrasound regardless of patient mutation status, age, and breast density. J Clin Oncol 2015; 33 (10): JJ28-JJ35. Crossref, Medline, Google Scholar.

46. Lowe R, Shirley N, Bleackley M, Dolan S, Shafee T Tran-scriptomics technologies. PLOS Comput Biol 2017; 13 (5): e1005457. Crossref, Medline, Google Scholar.

47. Lee-Liu D, Almonacid LI, Faunes F, Melo F, Lar-rain J. Transcriptomics using next generation sequencing technologies. Methods Mol Biol 2012; 917: 293-317. Crossref, Medline, Google Scholar.

48. U. S. National Library of Medicine. Transcriptome MeSH Descriptor Data 2017. https://meshb.nlm.nih.gov/record/ui?ui=D059467. Accessed September 8, 2017. Google Scholar.

49. National Human Genome Research Institute. Transcriptome. https://www.genome.gov/13014330/transcriptome-fact-sheet/. Accessed September 8, 2017. Google Scholar.

50. HarbeckN, Thomssen C, Gnant M. St. Gallen 2013: brief preliminary summary of the consensus discussion. Breast Care (Basel) 2013; 8 (2): 102-109. Crossref, Medline, Google Scholar.

51. Goldhirsch A, Wood WC, Coates AS et al. Strategies for subtypes-dealing with the diversity of breast cancer: highlights of the St Gallen International Expert Consensus on the Primary Therapy of Early Breast Cancer 2011. Ann Oncol 2011; 22 (8): 1736-1747. Crossref, Medline, Google Scholar.

52. U. S. National Library of Medicine. Proteomics MeSH Descriptor Data 2017. https://meshb.nlm.nih.gov/record/ui?ui=D 04 0901. Accessed September 8, 2017. Google Scholar.

53. Weston AD, Hood L. Systems biology, proteomics, and the future of health care: toward predictive, preventative, and personalized medicine. J Pro-teome Res2004; 3 (2): 179-196. Crossref, Medline, Google Scholar.

54. Beck M, Claassen M, Aebersold R. Comprehensive proteomics. Curr Opin Biotechnol 2011; 22 (1): 3-8. Crossref, Medline, Google Scholar.

55. Gehlenborg N, O’Donoghue SI, Baliga NS et al. Visualization of omics data for systems biology. Nat Methods 2010; 7 (3 Suppl): S 56-S 68. Crossref, Medline, Google Scholar.

56. Geho DH, Lahar N, Ferrari M, Petricoin EF, Liotta LA. Opportunities for nanotechnology-based innovation in tissue proteomics. Biomed Microdevices 2004; 6 (3): 231-239. Crossref, Medline, Google Scholar.

57. Rodriguez M, Bajo-Santos C, HessvikNP et al. Identification of non-invasive miRNAs biomarkers for prostate cancer by deep sequencing analysis of urinary exosomes. Mol Cancer 2017; 16 (1): 156. Crossref, Medline, Google Scholar.

58. 0verbye A, Skotland T, Koehler CJ et al. Identification of prostate cancer biomarkers in urinary exosomes. Oncotarget2015; 6 (30): 30357-30376. Crossref, Medline, Google Scholar.

59. Kim Y, Jeon J, Mejia S et al. Targeted proteomics identifies liquid-biopsy signatures for extracapsular prostate cancer. Nat Commun 2016; 7: 11906. Crossref, Medline, Google Scholar.

60. Mardamshina M, Geiger T. Next-generation proteomics and its application to clinical breast cancer research. Am J Pathol2017; 187 (10): 2175-2184. Crossref, Medline, Google Scholar.

61. Riekeberg E, Powers R. New frontiers in metabolo-mics: from measurement to insight. F1000 Res2017; 6: 1148. Crossref, Medline, Google Scholar.

62. Pintus R, Bassareo PP, Dessi A, Deidda M, Mercuro G, Fanos V. Metabolomics and cardiology: toward the path of perinatal programming and personalized medicine. BioMed Res Int 2017; 2017: 6970631. Crossref, Medline, Google Scholar.

63. Li B, He X, Jia W, Li H. Novel applications of metabolomics in personalized medicine: a mini-review. Molecules 2017; 22 (7): E 1173. Crossref, Medline, Google Scholar.

64. Beger RD. A review of applications of metabolomics in cancer. Metabolites 2013; 3 (3): 552-574. Crossref, Medline, Google Scholar.

65. U. S. National Library of Medicine. Metabolome MeSH Descriptor Data 2017. https://meshb.nlm.nih.gov/record/ui?ui=D055442. Accessed DATE. Google Scholar.

66. Vander Heiden MG. Targeting cancer metabolism: a therapeutic window opens. Nat Rev Drug Discov 2011; 10 (9): 671-684. Crossref, Medline, Google Scholar.

67. Brown MV, McDunn JE, Gunst PR et al. Cancer detection and biopsy classification using concurrent histopathological and metabolomic analysis of core biopsies. Genome Med 2012; 4 (4): 33. Crossref, Medline, Google Scholar.

68. Ghasemi M, Nabipour I, Omrani A, Alipour Z, Assadi M. Precision medicine and molecular imaging: new targeted approaches toward cancer therapeutic and diagnosis. Am J Nucl Med Mol Imaging 2016; 6 (6): 310-327. Medline, Google Scholar.

69. Kalita-de Croft P, Al-Ejeh F, McCart Reed AE, Sau-nus JM, Lakhani SR. ‘Omics approaches in breast cancer research and clinical practice. Adv Anat Pathol 2016; 23 (6): 356-367. Crossref, Medline, Google Scholar.

70. Perou CM, S0rlie T, Eisen MB et al. Molecular portraits of human breast tumours. Nature 2000; 406 (6797): 747-752. Crossref, Medline, Google Scholar.

71. Curtis C. Genomic profiling of breast cancers.Curr Opin Obstet Gynecol 2015; 27 (1): 34-39. Crossref, Medline, Google Scholar.

72. Cancer Genome Atlas Network. Comprehensive molecular portraits of human breast tumours. Nature 2012; 490 (7418): 61-70. Crossref, Medline, Google Scholar.

73. Goldhirsch A, Winer EP, Coates AS et al. Personalizing the treatment of women with early breast cancer: highlights of the St Gallen International Expert Consensus on the Primary Therapy of Early Breast Cancer 2013. Ann Oncol 2013; 24 (9): 2206-2223. Crossref, Medline, Google Scholar.

74. MazurowskiMA, Zhang J, Grimm LJ, Yoon SC, Silber JI. Radiogenomic analysis of breast cancer: luminal B molecular subtype is associated with enhancement dynamics at MR imaging. Radiology 2014; 273 (2): 365-372. Link, Google Scholar.

75. Huber KE, Carey LA, Wazer DE. Breast cancer molecular subtypes in patients with locally advanced disease: impact on prognosis, patterns of recurrence, and response to therapy. Semin Radiat Oncol 2009; 19 (4): 204-210. Crossref, Medline, Google Scholar.

76. Lam SW, Jimenez CR, Boven E. Breast cancer classification by proteomic technologies: current state of knowledge. Cancer Treat Rev 2014; 40 (1): 129-138. Crossref, Medline, Google Scholar.

77. Carey LA, Perou CM, Livasy CA et al. Race, breast cancer subtypes, and survival in the Carolina Breast Cancer Study. JAMA2006; 295 (21): 2492-2502. Crossref, Medline, Google Scholar.

78. Tsoutsou PG, Vozenin MC, Durham AD, Bourhis J. How could breast cancer molecular features contribute to locoregional treatment decision making? Crit Rev Oncol Hematol2017; 110: 43-48. Crossref, Medline, Google Scholar.

79. Iborra S, Stickeler E. HER2-orientated therapy in early and metastatic breast cancer. Breast Care (Basel) 2016; 11 (6): 392-397. Crossref, Medline, Google Scholar.

80. Jatoi I, Anderson WF, Jeong JH, Redmond CK. Breast cancer adjuvant therapy: time to consider its time-dependent effects. J Clin Oncol 2011; 29 (17): 2301-2304. Crossref, Medline, Google Scholar.

81. Metzger-Filho O, Sun 1, Viale G et al. Patterns of Recurrence and outcome according to breast cancer subtypes in lymph node-negative disease: results from international breast cancer study group trials VIII and IX. J Clin Oncol 2013; 31 (25): 30833090. Crossref, Medline, Google Scholar.

82. Romond EH, Perez EA, Bryant J et al. Trastuzum-ab plus adjuvant chemotherapy for operable HER 2-positive breast cancer. N Engl J Med2005; 353 (16): 1673-1684. Crossref, Medline, Google Scholar.

83. Haffty BG, Yang Q, Reiss M et al. Locoregional relapse and distant metastasis in conservatively managed triple negative early-stage breast cancer. J Clin Oncol 2006; 24 (36): 5652-5657. Crossref, Medline, Google Scholar.

84. Anderson WF, Jatoi I, Sherman ME. Qualitative age interactions in breast cancer studies: mind the gap. J Clin Oncol2009; 27 (32): 5308-5311. Crossref, Medline, Google Scholar.

85. Lal S, McCart Reed AE, de Luca XM, Simpson PT. Molecular signatures in breast cancer. Methods 2017; 131: 135-146. Crossref, Medline, Google Scholar.

86. Reis-Filho JS, Pusztai L. Gene expression profiling in breast cancer, classification, prognostication, and prediction. Lancet2011; 378 (9805): 1812-1823. Crossref, Medline, Google Scholar.

87. Weigelt B, Baehner FL, Reis-Filho JS. The contribution of gene expression profiling to breast cancer classification, prognostication and prediction: a retrospective of the last decade. J Pathol 2010; 220 (2): 263-280. Medline, Google Scholar.

88. Harris LN, Ismaila N, McShane LM et al. Use of biomarkers to guide decisions on adjuvant systemic therapy for women with early-stage invasive breast cancer: American Society of Clinical Oncology Clinical Practice Guideline. J Clin Oncol 2016; 34 (10): 1134-1150. Crossref, Medline, Google Scholar.

89. Gupta A, Mutebi M, Bardia A. Gene-expression-based predictors for breast cancer. Ann Surg Oncol2015; 22 (11): 3418-3432. Crossref, Medline, Google Scholar.

90. Coates AS, Winer EP, GoldhirschAet al. Tailoring therapies-improving the management of early breast cancer: St Gallen International Expert Consensus on the Primary Therapy of Early Breast Cancer2015. Ann Oncol 2015; 26 (8): 1533-1546. Crossref, Medline, Google Scholar.

91. Bertoli G, Cava C, Castiglioni I. MicroRNAs: new biomarkers for diagnosis, prognosis, therapy prediction and therapeutic tools for breast cancer. Theranostics 2015; 5 (10): 1122-1143. Crossref, Medline, Google Scholar.

92. Volinia S, Croce CM. Prognostic microRNA/mRNA signature from the integrated analysis of patients with invasive breast cancer. Proc Natl Acad Sci USA 2013; 110 (18): 7413-7417. Crossref, Medline, Google Scholar.

93. Blenkron C, Goldstein LD, Thorne NP et al. MicroRNA expression profiling of human breast cancer identifies new markers of tumor subtype. Genome Biol 2007; 8 (10): R214. Crossref, Medline, Google Scholar.

94. Mar-AguilarF, Mendoza-Ramrez JA, Malagon-San-tiago I et al. Serum crculating microRNA profiling for identification of potential breast cancer biomarkers. Dis Markers 2013; 34 (3): 163-169. Crossref, Medline, Google Scholar.

95. Reis-Filho JS. Next-generation sequencing. Breast Cancer Res 2009; 11 (Suppl 3): S12. Crossref, Medline, Google Scholar.

96. Behjati S, Tarpey PS. What is next generation sequencing? Arch Dis Child Educ Pract Ed 2013; 98 (6): 236-238. Crossref, Medline, Google Scholar.

97. Stephens PJ, Tarpey PS, Davies H et al. The landscape of cancer genes and mutational processes in breast cancer. Nature 2012; 486 (7403): 400-404. Crossref, Medline, Google Scholar.

98. Morganella S, Alexandrov LB, Glodzik D et al. The topography of mutational processes in breast cancer genomes. Nat Commun 2016; 7: 11383. Crossref, Medline, Google Scholar.

99. Yates LR, Gerstung M, Knappskog S et al. Subclonal diversification of primary breast cancer revealed by multiregion sequencing. Nat Med 2015; 21 (7): 751-759. Crossref, Medline, Google Scholar.

100. Ruggles KV, Krug K, Wang X et al. Methods, tools and current perspectives in proteogenomics. Mol Cell Proteomics 2017; 16 (6): 959-981. Crossref, Medline, Google Scholar.

101. Gonzalez-Angulo AM, Hennessy BT, Meric-Bernstam F et al. Functional proteomics can define prognosis and predict pathologic complete response in patients with breast cancer. Clin Proteomics 2011; 8 (1): 11. Crossref, Medline, Google Scholar.

102. Al-Ejeh F, Miranda M, Shi W et al. Kinome profiling reveals breast cancer heterogeneity and identifies targeted therapeutic opportunities for triple negative breast cancer. Oncotarget2014; 5 (10): 3145-3158. Crossref, Medline, Google Scholar.

103. Chung L, Moore K, Phillips L, Boyle FM, Marsh DJ, Baxter RC. Novel serum protein biomarker panel revealed by mass spectrometry and its prognostic value in breast cancer. Breast Cancer Res 2014; 16 (3): R63. Crossref, Medline, Google Scholar.

104. Huang JH, Han D, Ruggles ME, Jayaraman A, Ugaz VM. Characterization of enzymatic micromachining for construction of variable cross-section microchannel topologies. Biomicrofluidics 2016; 10 (3): 033102. Crossref, Medline, Google Scholar.

105. Mertins P, Mani DR, Ruggles KV et al. Proteogenomics connects somatic mutations to signalling in breast cancer. Nature 2016; 534 (7605): 55-62. Crossref, Medline, Google Scholar.

106. Ruggles KV, Tang 1, Wang X et al. An analysis of the sensitivity of proteogenomic mapping of somatic mutations and novel splicing events in cancer. Mol Cell Proteomics 2016; 15 (3): 1060-1071. Crossref, Medline, Google Scholar.

107. Gunther UL. Metabolomics biomarkers for breast cancer. Pathobiology 2015; 82 (3-4): 153-165. Crossref, Medline, Google Scholar.

108. Cao MD, Sitter B, Bathen TF et al. Predicting longterm survival and treatment response in breast cancer patients receiving neoadjuvant chemotherapy by MR metabolic profiling. NMR Biomed 2012; 25 (2): 369-378. Crossref, Medline, Google Scholar.

109. Mazurowski MA. Radiogenomics: what it is and why it is important. J Am Coll Radiol 2015; 12 (8): 862-866. Crossref, Medline, Google Scholar.

110. Bai HX, Lee AM, Yang L et al. Imaging genomics in cancer research: limitations and promises. Br J Radiol2016; 89 (1061): 20151030. Crossref, Medline, Google Scholar.

111. Gillies RJ, Kinahan PE, Hricak H. Radiomics: images are more than pictures, they are data. Radiology 2016; 278 (2): 563-577. Link, Google Scholar.

112. Herold CJ, Lewin JS, Wibmer AG et al. Imaging in the age of precision medicine: summary of the Proceedings of the 10th Biannual Symposium of the International Society for Strategic Studies in Radiology. Radiology 2016; 279 (1): 226-238. Link, Google Scholar.

113. Thrall JH. Moreton lecture: imaging in the age of precision medicine. J Am Coll Radiol 2015; 12 (10): 1106-1111. Crossref, Medline, Google Scholar.

114. Lambin P, Rios-Velazquez E, Leijenaar R et al. Radiomics: extracting more information from medical images using advanced feature analysis. Eur J Cancer2012; 48 (4): 441-446. Crossref, Medline, Google Scholar.

115. Kuo MD, Jamshidi N. Behind the numbers: decoding molecular phenotypes with radiogenomics-guiding principles and technical considerations. Radiology 2014; 270 (2): 320-325. Link, Google Scholar.

116. Kumar V, Gu Y, Basu S et al. Radiomics: the process and the challenges. Magn Reson Imaging 2012; 30 (9): 1234-1248. Crossref, Medline, Google Scholar.

117. Sala E, Mema E, Himoto Y et al. Unravelling tumour heterogeneity using next-generation imaging: radiomics, radiogenomics, and habitat imaging. Clin Radiol2017; 72 (1): 3-10. Crossref, Medline, Google Scholar.

118. European Society of Radiology (ESR). Medical imaging in personalised medicine: a white paper of the research committee of the European Society of Radiology (ESR). Insights Imaging 2015; 6 (2): 141-155. Crossref, Medline, Google Scholar.

119. D'Orsi CJ, Sickles EA, Mendelson EB et al. ACR BIRADS Atlas, Breast Imaging Reporting and Data System. 5th ed. Reston, Va: American College of Radiology, 2013. Google Scholar.

120. Szczypihski PM, Strzelecki M, Materka A, Klepaczko A. MaZda: a software package for image texture analysis. Comput Methods Programs Biomed2009; 94 (1): 66-76. Crossref, Medline, Google Scholar.

121. Materka A. Texture analysis methodologies for magnetic resonance imaging. Dialogues Clin Neu-rosci 2004; 6 (2): 243-250. Medline, Google Scholar.

122. Patil SS, Junnarkar AA, Gore DV. Study of texture representation techniques. Int J Emerg Trends Technol Comput Sci 2014; 3 (3). Google Scholar.

123. Haralick RM, Shanmugam M, Dinstein IH. Textural features for image classification. IEEE Trans Syst Man Cybern1973; SMC-3 (6): 610-621. Crossref, Google Scholar.

124. Wengert GJ, Helbich TH, Vogl WD et al. Introduction of an automated, user-independent, quantitative, volumetric magnetic resonance imaging breast density measurement system using the Dixon sequence: comparison with mammographic breast density assessment. Invest Radiol 2015; 50 (2): 73-80. Crossref, Medline, Google Scholar.

125. Grimm LJ. Breast MRI radiogenomics: current status and research implications. J Magn Reson Imaging 2016; 43 (6): 1269-1278. Crossref, Medline, Google Scholar.

126. Peterson CB, Bogomolov M, Benjamini Y, Sabatti C. Many phenotypes without many false discoveries: error controlling strategies for multitrait association studies. Genet Epidemiol 2016; 40 (1): 45-56. Crossref, Medline, Google Scholar.

127. Reiner A, Yekutieli D, Benjamini Y. Identifying differentially expressed genes using false discovery rate controlling procedures. Bioinformatics 2003; 19 (3): 368-375. Crossref, Medline, Google Scholar.

128. Benjamini Y, Hochberg Y. Controlling the false discovery rate: a practical and powerful approach to multiple testing. J R StatSoc Series B StatMethodol 1995; 57 (1): 289-300. Google Scholar.

129. Sadot E, Simpson AL, Do RK et al. Cholangiocar-cinoma: correlation between molecular profiling and imaging phenotypes. PLoS One 2015; 10 (7): e0132953. Crossref, Medline, Google Scholar.

130. Yamamoto S, Han W, Kim Y et al. Breast cancer: radiogenomic biomarker reveals associations among dynamic contrast-enhanced MR imaging, long noncoding RNA, and metastasis. Radiology 2015; 275 (2): 384-392. Link, Google Scholar.

131. Ashraf AB, Daye D, Gavenonis S et al. Identification of intrinsic imaging phenotypes for breast cancer tumors: preliminary associations with gene expression profiles. Radiology 2014; 272 (2): 374-384. Link, Google Scholar.

132. Yamamoto S, Maki DD, Korn RL, Kuo MD. Radiogenomic analysis of breast cancer using MRI: a preliminary study to define the landscape. AJR Am J Roentgenol2012; 199 (3): 654-663. Crossref, Medline, Google Scholar.

133. Grimm LJ. Breast MRI radiogenomics: current status and research implications. J Magn Reson Imaging 2016; 43 (6): 1269-1278. Crossref, Medline, Google Scholar.

134. Pinker K, Shitano F, Sala E et al. Background, current role, and potential applications of radiogenomics. J Magn Reson Imaging 2017 Nov2. [Epub ahead of print]. Google Scholar.

135. Hu Z, Fan C, Oh DS et al. The molecular portraits of breast tumors are conserved across microarray platforms. BMC Genomics 2006; 7 (1): 96. Crossref, Medline, Google Scholar.

136. Teschendorff AE, Miremadi A, Pinder SE, Ellis IO, Caldas C. An immune response gene expression module identifies a good prognosis subtype in estrogen receptor negative breast cancer. Genome Biol 2007; 8 (8): R 157. Crossref, Medline, Google Scholar.

137. Zhu Y, Li H, Guo W et al. Deciphering genomic underpinnings of quantitative MRI-based radiom-ic phenotypes of invasive breast carcinoma. Sci Rep 2015; 5 (1): 17787. Crossref, Medline, Google Scholar.

138. Li H, Zhu Y, Burnside ES et al. MR Imaging radiomics signatures for predicting the risk of breast cancer recurrence as given by research versions of Mam-maPrint, Oncotype DX, and PAM50 gene assays. Radiology 2016; 281 (2): 382-391. Link, Google Scholar.

139. Li H, Zhu Y, Burnside ES et al. Quantitative MRI radiomics in the prediction of molecular classifications of breast cancer subtypes in the TCGA/TCIA data set. NPJ Breast Cancer2016; 2. pii: 16012. Crossref, Medline, Google Scholar.

140. Sutton EJ, Dashevsky BZ, Oh JH et al. Breast cancer molecular subtype classifier that incorporates MRI features. J Magn Reson Imaging 2016; 44 (1): 122-129. Crossref, Medline, Google Scholar.

141. Sutton EJ, Oh JH, Dashevsky BZ et al. Breast cancer subtype intertumor heterogeneity: MRI-based features predict results of a genomic assay. J Magn Reson Imaging 2015; 42 (5): 1398-1406. Crossref, Medline, Google Scholar.

142. Mahrooghy M, Ashraf AB, Daye D et al. Pharmacokinetic tumor heterogeneity as a prognostic biomarker for classifying breast cancer recurrence risk. IEEE Trans Biomed Eng 2015; 62 (6): 1585-1594. Crossref, Medline, Google Scholar.

143. Mahrooghy M, Ashraf AB, Daye D et al. Heterogeneity wavelet kinetics from DCE-MRI for classifying gene expression based breast cancer recurrence risk. Med Image Comput Assist Interv 2013; 16 (Pt 2): 295-302. Medline, Google Scholar.

144. Yamaguchi K, Abe H, Newstead GM et al. Intratumoral heterogeneity of the distribution of kinetic parameters in breast cancer, comparison based on the molecular subtypes of invasive breast cancer. Breast Cancer2015; 22 (5): 496-502. Crossref, Medline, Google Scholar.

145. Blaschke E, Abe H. MRI phenotype of breast cancer kinetic assessment for molecular subtypes. J Magn Reson Imaging 2015; 42 (4): 920-924. Crossref, Medline, Google Scholar.

146. Li H, Zhu Y, Burnside ES et al. MR Imaging radiomics signatures for predicting the risk of breast cancer recurrence as given by research versions of Mam-maPrint, Oncotype DX, and PAM50 gene assays. Radiology 2016; 281 (2): 382-391. Link, Google Scholar.

147. Waugh SA, Purdie CA, Jordan LB et al. Magnetic resonance imaging texture analysis classification of primary breast cancer. Eur Radiol 2016; 26 (2): 322-330. Crossref, Medline, Google Scholar.

148. Grimm LJ, Zhang J, Mazurowski MA. Computational approach to radiogenomics of breast cancer. Luminal A and luminal B molecular subtypes are associated with imaging features on routine breast MRI extracted using computer vision algorithms. J Magn Reson Imaging 2015; 42 (4): 902-907. Crossref, Medline, Google Scholar.

149. Grimm LJ, Zhang J, Baker JA, Soo MS, Johnson KS, Mazurowski MA. Relationships between MRI Breast Imaging-Reporting and Data System (BI-RADS) lexicon descriptors and breast cancer molecular subtypes: internal enhancement is associated with luminal B subtype. Breast J 2017; 23 (5): 579-582. Crossref, Medline, Google Scholar.

150. Woodard GA, Ray KM, Joe BN, Price ER. Qualitative radiogenomics: association between Oncotype DX test recurrence score and BI-RADS mammographic and breast MR imaging features. Radiology 2018; 286 (1): 60-70. Link, Google Scholar.

151. Zaric O, Pinker K, Zbyn S et al. Quantitative sodium MR imaging at 7 T: initial results and comparison with diffusion-weighted imaging in patients with breast tumors. Radiology 2016; 280 (1): 39-48. Link, Google Scholar.

152. Schmitt B, Trattnig S, Schlemmer HP. CEST-imaging: a new contrast in MR-mammography by means of chemical exchange saturation transfer. Eur J Radiol 2012; 81 (Suppl 1): S 144-S 146. Crossref, Medline, Google Scholar.

153. Schmitt B, Zamecnik P, Zaiss M et al. A new contrast in MR mammography by means of chemical exchange saturation transfer (CEST) imaging at 3 Tesla: preliminary results. Rofo 2011; 183 (11): 1030-1036. Crossref, Medline, Google Scholar.

154. Rakow-Penner R, Daniel B, Glover GH. Detecting blood oxygen level-dependent (BOLD) contrast in the breast. J Magn Reson Imaging 2010; 32 (1): 120-129. Crossref, Medline, Google Scholar.

155. Pinker K, Bogner W, Baltzer P et al. Improved differentiation of benign and malignant breast tumors with multiparametric 18 fluorodeoxyglucose positron emission tomography magnetic resonance imaging: a feasibility study. Clin Cancer Res 2014; 20 (13): 3540-3549. Crossref, Medline, Google Scholar.

156. Djekidel M. Radiogenomics and Radioproteomics. OMICS J Radiol 2013; 2: 115. Google Scholar.

157. Noor AM, Holmberg L, Gillett C, Grigoriadis A. Big data: the challenge for small research groups in the era of cancer genomics. Br J Cancer 2015; 113 (10): 1405-1412. Crossref, Medline, Google Scholar.

158. Alyass A, Turcotte M, Meyre D. From big data analysis to personalized medicine for all: challenges and opportunities.BMC Med Genomics 2015; 8 (1): 33. Crossref, Medline, Google Scholar.

159. Clark K, Vendt B, Smith K et al. The Cancer Imaging Archive (TCIA): maintaining and operating a public information repository. J Digit Imaging 2013; 26 (6): 1045-1057. Crossref, Medline, Google Scholar.

160. European Society of Radiology (ESR). White paper on imaging biomarkers. Insights Imaging 2010; 1 (2): 42-45. Crossref, Medline, Google Scholar.

161. Prediction of Low versus High Recurrence Scores in Estrogen Receptor-Positive, Lymph Node-Negative Invasive Breast Cancer on the Basis of Radiologic-Pathologic Features: Comparison with Oncotype DX Test Recurrence Scores, Radiology 2016.Volume: 280 Issue: 2 pp. 370-378.

162. Molecular Classification of Infitrating Breast Cancer: Toward Personalized Therapy, Radio Graphics 2014, Volume: 34 Issue: 5 pp. 1178-1195.

163. Lipid and Metabolite Deregulation in the Breast Tissue of Women Carrying BRCA1 and BRCA2 Genetic Mutations, Radiology 2015.Volume: 275 Issue: 3 pp. 675-682, Vol. 287, No. 3.

164. Katja Pinker1, Joanne Chin, Amy N. Melsaether, Elizabeth A. Morris, Linda Moy. Published Online: May 212018 https://doi.org/10.1148/radiol.2018172171.

165. Радиологические технологии и биогенетические маркеры в дифференциальной диагностике заболеваний молочной железы, сопровождающихся скоплением микрокальцинатов. Якобс О.Э., Кудинова Е. А., Рожкова Н. И., Боженко В. К. Вестник Российского научного центра рентгенорадиологии Минздрава России. 2017. Т. 17. № 1. С. 6. http://vestnik.rncrr.ru/vestnik/v17/docs/yakobs.pdf.

166. Маммология. Национальное руководство.2-е издание. Ред. Каприна А. Д., Рожковой Н. И. // М.. ГЭОТАР Медиа, 2016, 496.

167. Каприн А.Д., Рожкова Н.И. Рак молочной железы / М.: ГЭОТАР-Медиа, 2018.-456 с.


Для цитирования:


Рожкова Н.И., Боженко В.К., Бурдина И.И., Запирова С.Б., Кудинова Е.А., Лабазанова П.Г., Мазо М.Л., Микушин С.Ю., Прокопенко С.П., Якобс О.Э. Радиогеномика рака молочной железы - новый вектор междисциплинарной интеграции лучевых и молекулярнобиологических технологий(обзор литературы). Медицинский алфавит. 2020;(20):21-29. https://doi.org/10.33667/2078-5631-2020-20-21-29

For citation:


Rozhkova N.I., Bozhenko V.K., Burdina I.I., Zapirova S.B., Kudinova E.A., Labazanova P.G., Mazo M.L., Mikushin S.Yu., Prokopenko S.P., Yakobs O.E. Radiogenomics of breast cancer as new vector of interdisciplinary integration of radiation and molecular biological technologies (literature review). Medical alphabet. 2020;(20):21-29. (In Russ.) https://doi.org/10.33667/2078-5631-2020-20-21-29

Просмотров: 224


ISSN 2078-5631 (Print)