Research Papers: Pathology:
Molecular classification of non-invasive breast lesions for personalised therapy and chemoprevention
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Niamh Buckley1,*, David Boyle1,*, Darragh McArt1, Gareth Irwin1, D. Paul Harkin1, Tong Lioe2, Stephen McQuaid1, Jacqueline A. James1, Perry Maxwell1, Peter Hamilton1, Paul B. Mullan1 and Manuel Salto-Tellez1
1 Centre for Cancer Research and Cell Biology, Queen’s University Belfast, Belfast, United Kingdom
2 Department of Histopathology, Belfast City Hospital, Belfast, United Kingdom
* These authors have contributed equally to this work
Manuel Salto-Tellez, email:
Keywords: pre-invasive breast cancer, DCIS, personalised medicine, biomarker, molecular pathology, Pathology Section
Received: November 09, 2015 Accepted: November 27, 2015 Published: December 09, 2015
Breast cancer screening has led to a dramatic increase in the detection of pre-invasive breast lesions. While mastectomy is almost guaranteed to treat the disease, more conservative approaches could be as effective if patients can be stratified based on risk of co-existing or recurrent invasive disease.
Here we use a range of biomarkers to interrogate and classify purely non-invasive lesions (PNL) and those with co-existing invasive breast cancer (CEIN). Apart from Ductal Carcinoma in situ (DCIS), relative homogeneity is observed. DCIS contained a greater spread of molecular subtypes. Interestingly, high expression of p-mTOR was observed in all PNL with lower expression in DCIS and invasive carcinoma while the opposite expression pattern was observed for TOP2A.
Comparing PNL with CEIN, we have identified p53 and Ki67 as predictors of CEIN with a combined PPV and NPV of 90.48% and 43.3% respectively. Furthermore, HER2 expression showed the best concordance between DCIS and its invasive counterpart.
We propose that these biomarkers can be used to improve the management of patients with pre-invasive breast lesions following further validation and clinical trials. p53 and Ki67 could be used to stratify patients into low and high-risk groups for co-existing disease. Knowledge of expression of more actionable targets such as HER2 or TOP2A can be used to design chemoprevention or neo-adjuvant strategies. Increased knowledge of the molecular profile of pre-invasive lesions can only serve to enhance our understanding of the disease and, in the era of personalised medicine, bring us closer to improving breast cancer care.
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