Abstract
Despite significant advances in breast imaging, the ability to accurately detect Breast Cancer (BC) remains a challenge. With the discovery of key biomarkers and protein signatures for BC, proteomic technologies are currently poised to serve as an ideal diagnostic adjunct to imaging. Research studies have shown that breast tumors are associated with systemic changes in levels of both serum protein biomarkers (SPB) and tumor associated autoantibodies (TAAb). However, the independent contribution of SPB and TAAb expression data for identifying BC relative to a combinatorial SPB and TAAb approach has not been fully investigated. This study evaluates these contributions using a retrospective cohort of pre-biopsy serum samples with known clinical outcomes collected from a single site, thus minimizing potential site-to-site variation and enabling direct assessment of SPB and TAAb contributions to identify BC. All serum samples (n = 210) were collected prior to biopsy. These specimens were obtained from 18 participants with no evidence of breast disease (ND), 92 participants diagnosed with Benign Breast Disease (BBD) and 100 participants diagnosed with BC, including DCIS. All BBD and BC diagnoses were based on pathology results from biopsy. Statistical models were developed to differentiate BC from non-BC (i.e., BBD and ND) using expression data from SPB alone, TAAb alone, and a combination of SPB and TAAb. When SPB data was independently used for modeling, clinical sensitivity and specificity for detection of BC were 74.7% and 77.0%, respectively. When TAAb data was independently used, clinical sensitivity and specificity for detection of BC were 72.2% and 70.8%, respectively. When modeling integrated data from both SPB and TAAb, the clinical sensitivity and specificity for detection of BC improved to 81.0% and 78.8%, respectively. These data demonstrate the benefit of the integration of SPB and TAAb data and strongly support the further development of combinatorial proteomic approaches for detecting BC.
Highlights
Breast cancer (BC) is the most commonly diagnosed malignancy and is the leading cause of cancer mortality among women [1]
All serum samples (n = 210) were collected prior to biopsy. These specimens were obtained from 18 participants with no evidence of breast disease (ND), 92 participants diagnosed with Benign Breast Disease (BBD) and 100 participants diagnosed with Breast Cancer (BC), including DCIS
When modeling integrated data from both serum protein biomarkers (SPB) and tumor associated autoantibodies (TAAb), the clinical sensitivity and specificity for detection of BC improved to 81.0% and 78.8%, respectively
Summary
Breast cancer (BC) is the most commonly diagnosed malignancy and is the leading cause of cancer mortality among women [1]. Detection of early-stage BC is widely recognized as being associated with a high cure rate and less morbid treatment. Even after decades of widespread mammographic screening, the rate at which women present at a later stage of BC has been only marginally reduced [3,4,5]. Multi-modality screening (using whole breast ultrasound or breast magnetic resonance imaging, MRI) has demonstrated significant improvement in cancer detection [6], but these approaches are limited to a minority of patients who are at high risk and/or have high mammographic density, with additional restrictions dictated by cost and feasibility. Critics have pointed out that multi-modality screening will increase the number of unnecessary biopsies and could add to the issue of over-diagnosis [7,8,9]
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