Unsupervised clustering of Quantitative image phenotypes reveals breast cancer subtypes with distinct prognoses and Molecular pathways

Clinical Cancer Research

Volumn 23, Issue 13, 2017, Pages 3334-3342

  Wu, Jia ^a   Cui, Yi ^a,b   Sun, Xiaoli ^a,c   Cao, Guohong ^d   Li, Bailiang ^a   Ikeda, Debra M ^a,e   Kurian, Allison W ^a,e   Li, Ruijiang ^a,e  

^a STANFORD UNIVERSITY SCHOOL OF MEDICINE   (United States)

^b HOKKAIDO UNIVERSITY   (Japan)

^c ZHEJIANG UNIVERSITY   (China)

^d ZHEJIANG UNIVERSITY   (China)

^e STANFORD CANCER INSTITUTE   (United States)

Author keywords

[No Author keywords available]

Indexed keywords

ARTICLE; BREAST CANCER MOLECULAR SUBTYPE; CANCER PROGNOSIS; CELL HETEROGENEITY; CELL STRUCTURE; COHORT ANALYSIS; CONTRAST ENHANCEMENT; DYNAMIC CONTRAST-ENHANCED MAGNETIC RESONANCE IMAGING; GENE EXPRESSION; GENE MAPPING; HUMAN; IMAGE ANALYSIS; MAJOR CLINICAL STUDY; PHENOTYPE; PRIORITY JOURNAL; QUANTITATIVE DIAGNOSIS; RECURRENCE FREE SURVIVAL; RETROSPECTIVE STUDY; SIGNAL TRANSDUCTION; SURVIVAL PREDICTION; TUMOR CELL; TUMOR INVASION; ADULT; AGED; BREAST TUMOR; CLASSIFICATION; CLUSTER ANALYSIS; DIAGNOSTIC IMAGING; FEMALE; GENE EXPRESSION REGULATION; GENETICS; MIDDLE AGED; NUCLEAR MAGNETIC RESONANCE IMAGING; PATHOLOGY; PROGNOSIS;

CONTRAST MEDIUM; TUMOR MARKER;

ADULT; AGED; BIOMARKERS, TUMOR; BREAST NEOPLASMS; CLUSTER ANALYSIS; CONTRAST MEDIA; FEMALE; GENE EXPRESSION REGULATION, NEOPLASTIC; HUMANS; MAGNETIC RESONANCE IMAGING; MIDDLE AGED; PROGNOSIS; SIGNAL TRANSDUCTION;

EID: 85019861322     PISSN: 10780432     EISSN: 15573265     Source Type: Journal    
DOI: 10.1158/1078-0432.CCR-16-2415     Document Type: Article

References (56)

1. Hammond ME, Hayes DF, Dowsett M, Allred DC, Hagerty KL, Badve S, et al. American Society of Clinical Oncology/College Of American Pathologists guideline recommendations for immunohistochemical testing of estrogen and progesterone receptors in breast cancer. J Clin Oncol 2010;28: 2784–95.
2. 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;34:1134–50.
3. Sotiriou C, Pusztai L. Gene-expression signatures in breast cancer. New Engl J Med 2009;360:790–800.
4. Voduc KD, Cheang MC, Tyldesley S, Gelmon K, Nielsen TO, Kennecke H. Breast cancer subtypes and the risk of local and regional relapse. J Clin Oncol 2010;28:1684–91.
5. Barry WT, Kernagis DN, Dressman HK, Griffis RJ, J'Vonne DH, Olson JA, et al. Intratumor heterogeneity and precision of microarray-based predictors of breast cancer biology and clinical outcome. J Clin Oncol 2010; 28:2198–206.
6. Zardavas D, Irrthum A, Swanton C, Piccart M. Clinical management of breast cancer heterogeneity. Nat Rev Clin Oncol 2015;12:381–94.
7. Hylton N.Dynamic contrast-enhanced magnetic resonance imaging as an imaging biomarker. J Clin Oncol 2006;24:3293–8.
8. Bhooshan N, Giger ML, Jansen SA, Li H, Lan L, Newstead GM. Cancerous breast lesions on dynamic contrast-enhanced MR images: computerized characterization for image-based prognostic markers. Radiology 2010; 254:680–90.
9. Hylton NM, Blume JD, Bernreuter WK, Pisano ED, Rosen MA, Morris EA, et al. Locally advanced breast cancer: MR imaging for prediction of response to neoadjuvant chemotherapy-results from ACRIN 6657/I-SPY TRIAL. Radiology 2012;263:663–72.
10. Li X, Abramson RG, Arlinghaus LR, Kang H, Chakravarthy AB, Abramson VG, et al. Multiparametric magnetic resonance imaging for predicting pathological response after the first cycle of neoadjuvant chemotherapy in breast cancer. Invest Radiol 2015;50:195–204.
11. Yankeelov TE, Mankoff DA, Schwartz LH, Lieberman FS, Buatti JM, Mountz JM, et al. Quantitative imaging in cancer clinical trials. Clin Cancer Res 2016;22:284–90.
12. Gillies RJ, Kinahan PE, Hricak H. Radiomics: images are more than pictures, they are data. Radiology 2016;278:563–77.
13. Hylton NM, Gatsonis CA, Rosen MA, Lehman CD, Newitt DC, Partridge SC, et al. Neoadjuvant chemotherapy for breast cancer: functional tumor volume by MR imaging predicts recurrence-free survival—results from the ACRIN 6657/CALGB 150007 I-SPY 1 TRIAL. Radiology 2016;279:44–55.
14. Aerts HJ, Velazquez ER, Leijenaar RT, Parmar C, Grossmann P, Cavalho S, et al. Decoding tumour phenotype by noninvasive imaging using a quantitative radiomics approach. Nat Commun 2014;5:4006.
15. O'Connor JP, Rose CJ, Waterton JC, Carano RA, Parker GJ, Jackson A. Imaging intratumor heterogeneity: role in therapy response, resistance, and clinical outcome. Clin Cancer Res 2015;21:249–57.
16. Fehr D, Veeraraghavan H, Wibmer A, Gondo T, Matsumoto K, Vargas HA, et al. Automatic classification of prostate cancer Gleason scores from multiparametric magnetic resonance images. Proc Natl Acad Sci 2015;112:E6265–E73.
17. 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;272:91–9.
18. Sutton EJ, Oh JH, Dashevsky BZ, Veeraraghavan H, Apte AP, Thakur SB, et al. Breast cancer subtype intertumor heterogeneity: MRIbased features predict results of a genomic assay. J Magn Reson Imaging 2015;42:1398–406.
19. Parikh J, Selmi M, Charles-Edwards G, Glendenning J, Ganeshan B, Verma H, et al. Changes in primary breast cancer heterogeneity may augment midtreatment mr imaging assessment of response to neoadjuvant chemotherapy. Radiology 2014;272:100–12.
20. Win T, Miles KA, Janes SM, Ganeshan B, Shastry M, Endozo R, et al. Tumor heterogeneity and permeability as measured on the CT component of PET/ CT predict survival in patients with non-small cell lung cancer. Clin Cancer Res 2013;19:3591–9.
21. Wu J, Gong G, Cui Y, Li R. Intratumor partitioning and texture analysis of dynamic contrastenhanced (DCE)MRI identifies relevant tumor subre-gions to predict pathological response of breast cancer to neoadjuvant chemotherapy. J Magn Reson Imaging 2016;44:1107–1115.
22. King V, Brooks JD, Bernstein JL, Reiner AS, Pike MC, Morris EA. Background parenchymal enhancement at breast MR imaging and breast cancer risk. Radiology 2011;260:50–60.
23. Dontchos BN, Rahbar H, Partridge SC, Korde LA, Lam DL, Scheel JR, et al. Are qualitative assessments of background parenchymal enhancement, amount of fibroglandular tissue on MR images, and mammographic density associated with breast cancer risk? Radiology 2015;276:371–80.
24. van der Velden BHM, Dmitriev I, Loo CE, Pijnappel RM, Gilhuijs KGA. Association between parenchymal enhancement of the contralateral breast in dynamic contrast-enhanced MR imaging and outcome of patients with unilateral invasive breast cancer. Radiology 2015;276:675–85.
25. Burnside ES, Drukker K, Li H, Bonaccio E, Zuley M, Ganott M, et al. Using computer-extracted image phenotypes from tumors on breast magnetic resonance imaging to predict breast cancer pathologic stage. Cancer 2016;122:748–57.
26. Li H, Zhu Y, Burnside ES, Drukker K, Hoadley KA, Fan C, et al. MR imaging radiomics signatures for predicting the risk of breast cancer recurrence as given by research versions of mammaprint, oncotype DX, and PAM50 gene assays. Radiology 2016;281:382–91.
27. Segal E, Sirlin CB, Ooi C, Adler AS, Gollub J, Chen X, et al. Decoding global gene expression programs in liver cancer by noninvasive imaging. Nat Biotechnol 2007;25:675–80.
28. Diehn M, Nardini C, Wang DS, McGovern S, Jayaraman M, Liang Y, et al. Identification of noninvasive imaging surrogates for brain tumor gene-expression modules. P Natl Acad Sci USA 2008;105:5213–8.
29. Gevaert O, Xu JJ, Hoang CD, Leung AN, Xu Y, Quon A, et al. Non-Small cell lung cancer: identifying prognostic imaging biomarkers by leveraging public gene expression microarray data-methods and preliminary results. Radiology 2012;264:387–96.
30. NairVS,GevaertO,DavidzonG,NapelS,GravesEE,HoangCD.Prognostic PET F-18-FDG uptake imaging features are associated with major onco-genomic alterations in patients with resected non-small cell lung cancer (vol 72, pg 3725, 2012). Cancer Res 2012;72:4870–1.
31. Yamamoto S, Han W, Kim Y, Du LT, Jamshidi N, Huang DS, et al. Breast cancer: radiogenomic biomarker reveals associations among dynamic contrast-enhanced MR imaging, long noncoding RNA, and metastasis. Radiology 2015;275:384–92.
32. 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.
33. Yamamoto S, Huang D, Du L, Korn RL, Jamshidi N, Burnette BL, et al. Radiogenomic analysis demonstrates associations between 18F-fluoro-2-deoxyglucose PET, prognosis, and epithelial-mesenchymal transition in non–small cell lung cancer. Radiology 2016;280:261–70.
34. Yamamoto S, Maki DD, Korn RL, Kuo MD. Radiogenomic analysis of breast cancer using MRI: a preliminary study to define the landscape. Am J Roentgenol 2012;199:654–63.
35. Chen W, Giger ML, Bick U. A fuzzy C-means (FCM)-based approach for computerized segmentation of breast lesions in dynamic contrastenhanced MR images 1. Acad Radiol 2006;13:63–72.
36. Tustison NJ, Avants BB, Cook PA, Zheng Y, Egan A, Yushkevich PA, et al. N4ITK: improved N3 bias correction. IEEE Transact Med Imaging 2010;29:1310–20.
37. Monti S, Tamayo P, Mesirov J, Golub T. Consensus clustering: a resampling-based method for class discovery and visualization of gene expression microarray data. Machine learning 2003;52:91–118.
38. Reynolds AP, Richards G, de la Iglesia B, Rayward-Smith VJ. Clustering rules: a comparison of partitioning and hierarchical clustering algorithms. J Math Model Algorithms 2006;5:475–504.
39. Tusher VG, Tibshirani R, Chu G. Significance analysis of microarrays applied to the ionizing radiation response. Proc Natl Acad Sci 2001;98:5116–21.
40. Kapp AV, Tibshirani R. Are clusters found in one dataset present in another dataset? Biostatistics 2007;8:9–31.
41. Wilcox RR.Adjusting for unequal variances when comparing means in one-way and two-way fixed effects ANOVA models. J Educ Behav Stat 1989; 14:269–78.
42. Tibshirani R, Hastie T, Narasimhan B, Chu G. Diagnosis of multiple cancer types by shrunken centroids of gene expression. Proc Natl Acad Sci 2002; 99:6567–72.
43. Liu H, Lafferty J, Wasserman L. The nonparanormal: Semiparametric estimation of high dimensional undirected graphs. J Mach Learn Res 2009;10:2295–328.
44. Vaske CJ, Benz SC, Sanborn JZ, Earl D, Szeto C, Zhu J, et al. Inference of patient-specific pathway activities from multi-dimen-sional cancer genomics data using PARADIGM. Bioinformatics 2010;26:i237–ii45.
45. Hanahan D, Weinberg RA. Hallmarks of cancer: the next generation. Cell 2011;144:646–74.
46. Foekens JA, Romain S, Look MP, Martin P-M, Klijn JG. Thymidine kinase and thymidylate synthase in advanced breast cancer: response to tamoxifen and chemotherapy. Cancer Res 2001;61:1421–5.
47. Longley DB, Harkin DP, Johnston PG. 5-fluorouracil: mechanisms of action and clinical strategies. Nat Rev Cancer 2003;3:330–8.
48. Andre F, Michiels S, Dessen P, Scott V, Suciu V, Uzan C, et al. Exonic expression profiling of breast cancer and benign lesions: a retrospective analysis. Lancet Oncol 2009;10:381–90.
49. Barbera-Guillem E, Nyhus JK, Wolford CC, Friece CR, Sampsel JW. Vascular endothelial growth factor secretion by tumor-infiltrating macrophages essentially supports tumor angiogenesis, and IgG immune complexes potentiate the process. Cancer Res 2002;62:7042–9.
50. Wang M, Kaufman RJ. The impact of the endoplasmic reticulum protein-folding environment on cancer development. Nat Rev Cancer 2014;14: 581–97.
51. Baselga J, Campone M, Piccart M, Burris HA III, Rugo HS, Sahmoud T, et al. Everolimus in postmenopausal hormone-receptor–positive advanced breast cancer. New Engl J Med 2012;366:520–9.
52. Bachelot T, Bourgier C, Cropet C, Ray-Coquard I, Ferrero J-M, Freyer G, et al. Randomized phase II trial of everolimus in combination with tamoxifen in patients with hormone receptor–positive, human epidermal growth factor receptor 2–negative metastatic breast cancer with prior exposure to aromatase inhibitors: A GINECO study. J Clin Oncol 2012;30:2718–24.
53. Gherardi E, Birchmeier W, Birchmeier C, Woude GV. Targeting MET in cancer: rationale and progress. Nat Rev Cancer 2012;12:89–103.
54. Martelotto LG, Ng CK, Piscuoglio S, Weigelt B, Reis-Filho JS. Breast cancer intra-tumor heterogeneity. Breast Cancer Res 2014;16:1.
55. Shipitsin M, Campbell LL, Argani P, Weremowicz S, Bloushtain-Qimron N, Yao J, et al. Molecular definition of breast tumor heterogeneity. Cancer Cell 2007;11:259–73.
56. Itakura H, Achrol AS, Mitchell LA, Loya JJ, Liu T, Westbroek EM, et al. Magnetic resonance image features identify glioblastoma phenotypic subtypes with distinct molecular pathway activities. Sci Transl Med 2015;7:303ra138.