Automated image analysis of cyclin D1 protein expression in invasive lobular breast carcinoma provides independent prognostic information.
The emergence of automated image analysis algorithms has aided the enumeration, quantification, and immunohistochemical analyses of tumor cells in both whole section and tissue microarray samples. To date, the focus of such algorithms in the breast cancer setting has been on traditional markers in the common invasive ductal carcinoma subtype. Here, we aimed to optimize and validate an automated analysis of the cell cycle regulator cyclin D1 in a large collection of invasive lobular carcinoma and relate its expression to clinicopathologic data. The image analysis algorithm was trained to optimally match manual scoring of cyclin D1 protein expression in a subset of invasive lobular carcinoma tissue microarray cores. The algorithm was capable of distinguishing cyclin D1-positive cells and illustrated high correlation with traditional manual scoring (κ=0.63). It was then applied to our entire cohort of 483 patients, with subsequent statistical comparisons to clinical data. We found no correlation between cyclin D1 expression and tumor size, grade, and lymph node status. However, overexpression of the protein was associated with reduced recurrence-free survival (P=.029), as was positive nodal status (P<.001) in patients with invasive lobular carcinoma. Finally, high cyclin D1 expression was associated with increased hazard ratio in multivariate analysis (hazard ratio, 1.75; 95% confidence interval, 1.05-2.89). In conclusion, we describe an image analysis algorithm capable of reliably analyzing cyclin D1 staining in invasive lobular carcinoma and have linked overexpression of the protein to increased recurrence risk. Our findings support the use of cyclin D1 as a clinically informative biomarker for invasive lobular breast cancer.
Breakthrough Breast Cancer Research Unit, School of Cancer, Enabling Sciences and Technology, University of Manchester, Manchester Academic Health Science Centre, Paterson Institute for Cancer Research, The Christie NHS Foundation Trust, Wilmslow Road, Manchester, M20 4BX, UK.
Nicholas P
NP
Tobin
Katja L
KL
Lundgren
Catherine
C
Conway
Lola
L
Anagnostaki
Sean
S
Costello
Göran
G
Landberg
D000328:Adult; D000368:Aged; D000369:Aged, 80 and over; D000465:Algorithms; D014408:Biomarkers, Tumor / Q000378:metabolism*; D001943:Breast Neoplasms / Q000175:diagnosis / Q000378:metabolism* / Q000401:mortality; D018275:Carcinoma, Lobular / Q000175:diagnosis / Q000378:metabolism* / Q000401:mortality; D002467:Cell Nucleus / Q000378:metabolism / Q000473:pathology; D019938:Cyclin D1 / Q000378:metabolism*; D018572:Disease-Free Survival; D005260:Female; D005784:Gene Amplification; D006801:Humans; D007091:Image Processing, Computer-Assisted / Q000379:methods*; D008408:Mastectomy; D008875:Middle Aged; D009361:Neoplasm Invasiveness; D009367:Neoplasm Staging; D011379:Prognosis; D015996:Survival Rate; D013548:Sweden / Q000453:epidemiology
D014408:Biomarkers, Tumor; C530104:CCND1 protein, human; D019938:Cyclin D1