Low abundance protein enrichment for discovery of candidate plasma protein biomarkers for early detection of breast cancer

Advances in breast cancer control will be greatly aided by early detection so as to diagnose and treat breast cancer in its preinvasive state prior to metastasis. For breast cancer, the second leading cause of cancer-related death among women in the United States, early detection does allow for increased treatment options, including surgical resection, with a corresponding better patient response. Unfortunately, however, many patients' tumors are diagnosed following metastasis, thus making it more difficult to successfully treat the malignancy. There are, at present, no existing validated plasma/serum biomarkers for breast cancer. Only a few biomarkers (such as HER-2/neu, estrogen receptor, and progesterone receptor) have utility for diagnosis and prognosis. Thus, there is a great need for new biomarkers for breast cancer. This paper will focus on the identification of new serum protein biomarkers with utility for the early detection of breast cancer.

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Multiplex screening of 275 plasma protein biomarkers to identify a signature for early detection of colorectal cancer

Molecular Oncology ◽

10.1002/1878-0261.12591 ◽

2019 ◽

Vol 14 (1) ◽

pp. 8-21 ◽

Cited By ~ 2

Author(s):

Megha Bhardwaj ◽

Korbinian Weigl ◽

Kaja Tikk ◽

Axel Benner ◽

Petra Schrotz‐King ◽

...

Keyword(s):

Colorectal Cancer ◽

Early Detection ◽

Plasma Protein ◽

Protein Biomarkers

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Provista 002 prospective randomized trial utilizing Videssa in conjunction with imaging to detect breast cancer from patients with imaging findings age 25-75.

Journal of Clinical Oncology ◽

10.1200/jco.2015.33.28_suppl.31 ◽

2015 ◽

Vol 33 (28_suppl) ◽

pp. 31-31

Author(s):

David Emery Reese ◽

Rao Mulpuri ◽

Kasey Benson ◽

Elias Letsios ◽

Christa Corn ◽

...

Keyword(s):

Breast Cancer ◽

Early Detection ◽

Serum Protein ◽

Invasive Breast Cancer ◽

Breast Biopsy ◽

Dense Breast ◽

Protein Biomarkers ◽

Serum Samples ◽

Blood Draw ◽

Specific Patient

31 Background: An approach to detection that relies on biochemical markers of breast cancer would significantly contribute to more accurate detection in women with suspicious lesions. The combination of imaging, which identifies anatomical anomalies consistent with cancer with proteomic approaches promises to provide a powerful detection paradigm. A proteomic detection approach would provide a powerful tool for the detection of breast cancer in women with dense breast, a diagnosis that is difficult utilizing imaging alone. While protein signatures for the presence of breast cancer have remained elusive, we have developed a novel approach that combines serum protein biomarkers with tumor-associated autoantibodies. We utilized prospectively collected serum samples to develop novel algorithms for use in conjunction with imaging. We tested whether the assay was able to distinguish benign from invasive breast cancers in a prospective, randomized setting. Methods: Provista-002 enrolled 509 patients from multiple sites across the US and followed for 6 months after the first blood draw under IRB approval. Patients were consented after assessment of a BIRADS 3 or 4 and considered eligible if they were between 25 and 75 years of age, no history of cancer, no prior breast biopsy within the last six months, and were assessed as BIRADS 3 or 4 within 28 days. Upon enrollment, patients were randomized to either training or validation groups. Clinical truth was considered equal to or greater than 80% sensitivity and/or specificity. Serum protein biomarkers and tumor-associated autoantibodies identified in prior proteomic screens were measured prior to biopsy in a blinded and randomized fashion. Individual biomarker concentrations, together with specific patient data were evaluated using various logistic regression models developed from prior studies. Results: Provista-002 demonstrated a clear difference between women under the age of 50 from over the age of 50 in both markers required for early detection and the algorithm (models) used to distinguish benign from invasive breast cancer/DCIS. This is the first study that demonstrates clearly that modeling of proteomic patterns differs significantly in the BIRADS 3/4 setting and in the detection of early breast cancer lesions. As demonstrated in Provista – 001, we did not observe a statistical difference between early detection in women with dense breast and those with mostly fatty breast. The ability of the Videssa assay to distinguish between invasive breast cancer/DCIS from benign breast conditions was demonstrated as 85.7% sensitivity and 82.4% specificity for women under the age of 50 (although, unfortunately all lesions were pathologically confirmed to be CIS) and in women over the age of 50, the sensitivity was 86.4% and specificity was 83%. Conclusions: As above, both age groups of women needed distinct marker sets and linear regressions to distinguish benign (non-clinically significant) lesions from those that needed further evaluation (DCIS and IBC). Clinical trial information: NCT02078570.

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Mastocheck: Notable plasma protein biomarker for diagnosis of breast cancer in the real clinical practice by using multiple reaction monitoring-based mass spectrometry.

Journal of Clinical Oncology ◽

10.1200/jco.2019.37.15_suppl.3044 ◽

2019 ◽

Vol 37 (15_suppl) ◽

pp. 3044-3044

Author(s):

Yumi Kim ◽

Un-Beom Kang ◽

Sungsoo Kim ◽

Han-Byoel Lee ◽

Jigwang Jung ◽

...

Keyword(s):

Breast Cancer ◽

Clinical Practice ◽

Early Detection ◽

Plasma Protein ◽

Multiple Reaction Monitoring ◽

Diagnostic Model ◽

Protein Biomarker ◽

The Real ◽

Significant Difference ◽

Real Clinical Practice

3044 Background: Breast cancer is the most frequently diagnosed cancer and the most leading cause of cancer-related deaths among women worldwide. Although screening mammography is available, there is an ongoing interest in improved early detection and prognosis. And also, serum tumor marker levels, such as CA 15-3 and others, may reflect disease progression and recurrence, they have not proven to be sensitive for early disease detection. Research investigating biomarkers for early detection, prognosis and the prediction of treatment responses in breast cancer is rapidly expanding. However, no validated biomarker currently exists for use in routine clinical practice, and breast cancer detection and management remains dependent on invasive procedures We aimed to develop biomarker for diagnosis of breast cancer in the real clinical practice by using proteomics technology. Methods: Based on our previous studies, we performed verification and validation of 124 candidate proteins by using proteomics approach. Among these 124 candidate proteins, the three proteins (neural cell adhesion molecule L1-like protein, apolipoprotein C-1, carbonic anhydrase-1) with highest statistical significance were selected. We created the performance algorithm of the 3-protein diagnostic model to predict of the breast cancer. We performed several experiments for establishment and validation of cut-off value. Furthermore we conducted test for acquisition of sample stability and more experiments to achieve the reproducibility and level of evidence, compared with other cancers (colon, thyroid, ovary, pancreas and lung cancer) and established effect of anesthesia. Results: Total 1226 samples (532 patients of breast cancer, 562 healthy women and 100 sample of other cancers) was analyzed. The sensitivity, specificity and accuracy from confirmation experiment was 71.58%, 85.25% and AUC 0.8323, respectively. The result of comparison with other cancers, there are no statistical significant difference and no relevance with effects of anesthesia. With these results, we recently got permission it to use for in vitro diagnostic use from Korea Food and Drug Administration. Conclusions: In this study, we developed a plasma protein biomarker that may help to diagnosis of breast cancer in the real clinical practice. By using MRM approach, the 3-protein biomarker was validated in an independent cohort with acceptable accuracy for early diagnosis of breast cancer.

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Low abundance protein enrichment for discovery of plasma protein biomarkers for early detection of breast cancer.

Journal of Clinical Oncology ◽

10.1200/jco.2011.29.27_suppl.77 ◽

2011 ◽

Vol 29 (27_suppl) ◽

pp. 77-77

Author(s):

A. A. Quong ◽

M. Gormley ◽

R. Meng ◽

V. B. Bhat ◽

A. L. Rosenberg

Keyword(s):

Breast Cancer ◽

Plasma Proteins ◽

Biomarker Discovery ◽

Dynamic Range ◽

Statistical Significance ◽

Stage I ◽

Response Monitoring ◽

Protein Enrichment ◽

Protein Biomarkers ◽

Abundance Proteins

77 Background: Protein biomarkers for breast cancer are desired for early diagnosis, disease prognosis and drug response monitoring. Biomarkers in bodily fluids, such as plasma, allow for non-invasive monitoring and have additional value compared to tissue-based markers. Plasma-based biomarker discovery faces a challenge in that the wide dynamic range of protein concentrations prevents the detection of lower abundance proteins. In this study, we have investigated the use of a novel protein enrichment strategy combined with isobaric label-based LC-MS/MS as well as two experimental designs for the identification of biomarkers of early stage breast cancer. Methods: Plasma from 12 patients with benign breast lesions and 12 with stage I breast cancer were processed using ProteoMiner enrichment followed by on-bead digestion. Two types of standards were investigated: a pooled standard, consisting of equal portions from the 24 plasma digests and a universal standard. The samples were digested, labeled and analyzed using by HPLC-Chip/Q-TOF analysis. Proteins were identified and quantified using Spectrum Mill software. Results: Use of ProteoMiner beads resulted in extraction of sufficient protein for at least 10 technical replicates and cut down preparation time by 80%, as compared to MARS-based immunodepletion. A total of 414 plasma proteins were identified, 89% of which are low abundance plasma proteins and 14 of which were differentially expressed. Expression values normalized using the pooled vs. universal standards were significantly correlated. Conclusions: This study demonstrated use of the ProteoMiner technology for enrichment of low abundance proteins from plasma. Fourteen plasma-based biomarkers of stage I breast cancer were identified with statistical significance. A number of these proteins (e.g., protocadherin FAT2, flightless-1 homolog) have been linked to breast cancer relevant processes, such as cell migration, adhesion, estrogen receptor signaling and proliferation.

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