Journal of the Korean Society of Radiology (대한영상의학회지)

The Korean Society of Radiology (대한영상의학회)
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Correlation between MR Image-Based Radiomics Features and Risk Scores Associated with Gene Expression Profiles in Breast Cancer

유방암에서 자기공명영상 근거 영상표현형과 유전자 발현 프로파일 근거 위험도의 관계

  • Ga Ram Kim (Department of Radiology, Inha University Hospital, Inha University School of Medicine) ;
  • You Jin Ku (Department of Radiology, International St. Mary's Hospital, Catholic Kwandong University) ;
  • Jun Ho Kim (Department of Radiology, Inha University Hospital, Inha University School of Medicine) ;
  • Eun-Kyung Kim (Department of Radiology, Research Institute of Radiological Science, Severance Hospital, Yonsei University College of Medicine)
  • 김가람 (인하대학교 의과대학 부속병원 영상의학과) ;
  • 구유진 (가톨릭관동대학교 국제성모병원 영상의학과) ;
  • 김준호 (인하대학교 의과대학 부속병원 영상의학과) ;
  • 김은경 (연세대학교 의과대학 세브란스병원 방사선의과학연구소 영상의학교실)
  • Received : 2019.02.12
  • Accepted : 2019.09.14
  • Published : 2020.05.01
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Abstract

Purpose To investigate the correlation between magnetic resonance (MR) image-based radiomics features and the genomic features of breast cancer by focusing on biomolecular intrinsic subtypes and gene expression profiles based on risk scores. Materials and Methods We used the publicly available datasets from the Cancer Genome Atlas and the Cancer Imaging Archive to extract the radiomics features of 122 breast cancers on MR images. Furthermore, PAM50 intrinsic subtypes were classified and their risk scores were determined from gene expression profiles. The relationship between radiomics features and biomolecular characteristics was analyzed. A penalized generalized regression analysis was performed to build prediction models. Results The PAM50 subtype demonstrated a statistically significant association with the maximum 2D diameter (p = 0.0189), degree of correlation (p = 0.0386), and inverse difference moment normalized (p = 0.0337). Among risk score systems, GGI and GENE70 shared 8 correlated radiomic features (p = 0.0008-0.0492) that were statistically significant. Although the maximum 2D diameter was most significantly correlated to both score systems (p = 0.0139, and p = 0.0008), the overall degree of correlation of the prediction models was weak with the highest correlation coefficient of GENE70 being 0.2171. Conclusion Maximum 2D diameter, degree of correlation, and inverse difference moment normalized demonstrated significant relationships with the PAM50 intrinsic subtypes along with gene expression profile-based risk scores such as GENE70, despite weak correlations.

목적 자기공명영상 근거 영상표현형과 생체분자학적 아형, 유전자 발현 프로파일 근거 위험도 등 유방암 유전체 특징의 관계를 분석하고자 하였다. 대상과 방법 The Cancer Genome Atlas와 and the Cancer Imaging Archive에 공개된 자료를 이용하였다. 122개의 유방암의 자기공명영상에서 영상표현형이 추출되었다. 유전자 발현 프로파일에 따라 PAM50아형을 분류하고 위험도를 지정하였다. 영상표현형과 생체분자학적 특징의 관계를 분석하였다. 예측모델을 알아보기 위해 penalized generalized regression analysis를 이용하였다. 결과 PAM50아형은 maximum 2D diameter (p = 0.0189), degree of correlation (p = 0.0386), 그리고 inverse difference moment normalized (p = 0.0337)와 유의하게 관련이 있었다. 위험도 시스템 중에 GGI와 GENE70이 통계적으로 유의하게 8개의 영상표현형 특징을 서로 공유하였다(p = 0.0008~0.0492). Maximum 2D diameter가 두 위험도 시스템에서 가장 유의하게 관련있는 특징이었으나(p = 0.0139, p = 0.0008) 예측모델의 전반적인 연관 정도는 약했고 가장 높은 연관계수는 GENE70이 0.2171이었다. 결론 영상표현형 중에 maximum 2D diameter, degree of correlation, 그리고 inverse difference moment normalized가 PAM50 아형 그리고 GENE70과 같은 유전자 발현 프로파일 근거 위험도와 그 연관도는 약하였으나 유의한 관련을 보였다.

Keywords

Acknowledgement

This work was supported by an Inha University Hospital Research Grant.

References

  1. Parker JS, Mullins M, Cheang MC, Leung S, Voduc D, Vickery T, et al. Supervised risk predictor of breast cancer based on intrinsic subtypes. J Clin Oncol 2009;27:1160-1167 
  2. Haibe-Kains B, Desmedt C, Rothe F, Piccart M, Sotiriou C, Bontempi G. A fuzzy gene expression-based computational approach improves breast cancer prognostication. Genome Biol 2010;11:R18 
  3. Kim GR, Ku YJ, Cho SG, Kim SJ, Min BS. Associations between gene expression profiles of invasive breast cancer and Breast Imaging Reporting and Data System MRI lexicon. Ann Surg Treat Res 2017;93:18-26 
  4. Loi S, Haibe-Kains B, Desmedt C, Wirapati P, Lallemand F, Tutt AM, et al. Predicting prognosis using molecular profiling in estrogen receptor-positive breast cancer treated with tamoxifen. BMC Genomics 2008;9:239 
  5. Loi S, Haibe-Kains B, Majjaj S, Lallemand F, Durbecq V, Larsimont D, et al. PIK3CA mutations associated with gene signature of low mTORC1 signaling and better outcomes in estrogen receptor-positive breast cancer. Proc Natl Acad Sci U S A 2010;107:10208-10213 
  6. Sotiriou C, Wirapati P, Loi S, Harris A, Fox S, Smeds J, et al. Gene expression profiling in breast cancer: understanding the molecular basis of histologic grade to improve prognosis. J Natl Cancer Inst 2006;98:262-272 
  7. Van't Veer LJ, Dai H, Van de Vijver MJ, He YD, Hart AA, Mao M, et al. Gene expression profiling predicts clinical outcome of breast cancer. Nature 2002;415:530-536 
  8. Paik S, Shak S, Tang G, Kim C, Baker J, Cronin M, et al. A multigene assay to predict recurrence of tamoxifen-treated, node-negative breast cancer. N Engl J Med 2004;351:2817-2826 
  9. Kim JH. Imaging informatics: a new horizon for radiology in the era of artificial interlligence, big data, and data science. J Korean Soc Radiol 2019;80:176-201 
  10. Cancer Genome Atlas Network. Comprehensive molecular portraits of human breast tumours. Nature 2012;490:61-70 
  11. Clark K, Vendt B, Smith K, Freymann J, Kirby J, Koppel P, et al. The Cancer Imaging Archive (TCIA): maintaining and operating a public information repository. J Digit Imaging 2013;26:1045-1057 
  12. Harris LN, Ismaila N, McShane LM, Andre F, Collyar DE, Gonzalez-Angulo AM, 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;34:1134-1150 
  13. Dowsett M, Sestak I, Lopez-Knowles E, Sidhu K, Dunbier AK, Cowens JW, et al. Comparison of PAM50 risk of recurrence score with oncotype DX and IHC4 for predicting risk of distant recurrence after endocrine therapy. J Clin Oncol 2013;31:2783-2790 
  14. Caan BJ, Sweeney C, Habel LA, Kwan ML, Kroenke CH, Weltzien EK, et al. Intrinsic subtypes from the PAM50 gene expression assay in a population-based breast cancer survivor cohort: prognostication of short- and long-term outcomes. Cancer Epidemiol Biomarkers Prev 2014;23:725-734 
  15. Sestak I, Cuzick J, Dowsett M, Lopez-Knowles E, Filipits M, Dubsky P, et al. Prediction of late distant recurrence after 5 years of endocrine treatment: a combined analysis of patients from the Austrian breast and colorectal cancer study group 8 and arimidex, tamoxifen alone or in combination randomized trials using the PAM50 risk of recurrence score. J Clin Oncol 2015;33:916-922 
  16. Desmedt C, Haibe-Kains B, Wirapati P, Buyse M, Larsimont D, Bontempi G, et al. Biological processes associated with breast cancer clinical outcome depend on the molecular subtypes. Clin Cancer Res 2008;14:5158-5165 
  17. Filipits M, Rudas M, Jakesz R, Dubsky P, Fitzal F, Singer CF, et al. A new molecular predictor of distant recurrence in ER-positive, HER2-negative breast cancer adds independent information to conventional clinical risk factors. Clin Cancer Res 2011;17:6012-6020 
  18. Song SE, Shin SU, Moon HG, Ryu HS, Kim K, Moon WK. MR imaging features associated with distant metastasis-free survival of patients with invasive breast cancer: a case-control study. Breast Cancer Res Treat 2017;162:559-569 
  19. Lee SE, Han K, Kwak JY, Lee E, Kim EK. Radiomics of US texture features in differential diagnosis between triple-negative breast cancer and fibroadenoma. Sci Rep 2018;8:13546 
  20. Agner SC, Rosen MA, Englander S, Tomaszewski JE, Feldman MD, Zhang P, et al. Computerized image analysis for identifying triple-negative breast cancers and differentiating them from other molecular subtypes of breast cancer on dynamic contrast-enhanced MR images: a feasibility study. Radiology 2014;272:91-99 
  21. Youk JH, Son EJ, Chung J, Kim JA, Kim EK. Triple-negative invasive breast cancer on dynamic contrast-enhanced and diffusion-weighted MR imaging: comparison with other breast cancer subtypes. Eur Radiol 2012;22:1724-1734 
  22. Badve S, Dabbs DJ, Schnitt SJ, Baehner FL, Decker T, Eusebi V, et al. Basal-like and triple-negative breast cancers: a critical review with an emphasis on the implications for pathologists and oncologists. Mod Pathol 2011;24:157-167 
  23. Fuckar D, Dekani'c A, Stifter S, Musta'c E, Krstulja M, Dobrila F, et al. VEGF expression is associated with negative estrogen receptor status in patients with breast cancer. Int J Surg Pathol 2006;14:49-55 
  24. Koukourakis MI, Manolas C, Minopoulos G, Giatromanolaki A, Sivridis E. Angiogenesis relates to estrogen receptor negativity, c-erbB-2 overexpression and early relapse in node-negative ductal carcinoma of the breast. Int J Surg Pathol 2003;11:29-34 
  25. Waugh SA, Purdie CA, Jordan LB, Vinnicombe S, Lerski RA, Martin P, et al. Magnetic resonance imaging texture analysis classification of primary breast cancer. Eur Radiol 2016;26:322-330 
  26. Koren S, Bentires-Alj M. Breast tumor heterogeneity: source of fitness, hurdle for therapy. Mol Cell 2015;60:537-546 
  27. Zhu Y, Li H, Guo W, Drukker K, Lan L, Giger ML, et al. Deciphering genomic underpinnings of quantitative MRI-based radiomic phenotypes of invasive breast carcinoma. Sci Rep 2015;5:17787