The value of serum biomarkers (Bc1, Bc2, Bc3) in the diagnosis of early breast cancer

Serum Lipidomic Profiling in Breast Cancer to Identify Screening, Diagnostic, and Prognostic Biomarkers

Breast cancer is the major mortality cause of women worldwide. In the course of management of breast cancer, the identification of a biomarker is important in enhancing our knowledge on cancer pathology, predicting the response to treatment, and selecting the patients who are more favorable to receive certain treatments. These biomarkers have a prognostic value. In addition to traditional breast cancer prognosis factors such as the tumor size and grade, the axillary lymph node micrometastasis, and biomarkers such as HER2/neu, newly discovered biomarkers have been discovered. Some of these factors are genetic signature in tissue or in peripheral blood. Lipid profil, a simple and accessible biological examination, has been a novel path on the prediction of breast cancer risk of occurrence and recurrence in many studies. The main goal of our review is to evaluate lipid profile and breast cancer risk with an emphasis on the prognosis value of lipid profiles in breast cancer patient management.

Blood-based protein biomarkers in breast cancer

Breast cancer (BCa) is a significant healthcare problem on women worldwide. Thus, early detection is very important to reduce mortality. Furthermore, better BCa prognosis could improve selection of patients eligible for adjuvant therapy. New markers for early diagnosis, accurate prognosis and prediction of response to treatment are necessary to improve BCa care. The present review summarizes important aspects of the potential usefulness of modern technologies, strategies, and scientific findings in proteomic research for discovery of breast cancer-associated blood-based protein biomarkers in the clinic.

Proteomics analysis of human breast milk to assess breast cancer risk

Detection of breast cancer (BC) in young women is challenging because mammography, the most common tool for detecting BC, is not effective on the dense breast tissue characteristic of young women. In addition to the limited means for detecting their BC, young women face a transient increased risk of pregnancy-associated BC. As a consequence, reproductively active women could benefit significantly from a tool that provides them with accurate risk assessment and early detection of BC. One potential method for detection of BC is biochemical monitoring of proteins and other molecules in bodily fluids such as serum, nipple aspirate, ductal lavage, tear, urine, saliva and breast milk. Of all these fluids, only breast milk provides access to a large volume of breast tissue, in the form of exfoliated epithelial cells, and to the local breast environment, in the form of molecules in the milk. Thus, analysis of breast milk is a non-invasive method with significant potential for assessing BC risk. Here we analyzed human breast milk by mass spectrometry (MS)-based proteomics to build a biomarker signature for early detection of BC. Ten milk samples from eight women provided five paired-groups (cancer versus control) for analysis of dysregulatedproteins: two within woman comparisons (milk from a diseased breast versus a healthy breast of the same woman) and three across women comparisons (milk from a woman with cancer versus a woman without cancer). Despite a wide range in the time between milk donation and cancer diagnosis (cancer diagnosis occurred from 1 month before to 24 months after milk donation), the levels of some proteins differed significantly between cancer and control in several of the five comparison groups. These pilot data are supportive of the idea that molecular analysis of breast milk will identify proteins informative for early detection and accurate assessment of BC risk, and warrant further research. Data are available via ProteomeXchange with identifier PXD007066.

An efficient biomarker panel for diagnosis of breast cancer using surface-enhanced laser desorption ionization time-of-flight mass spectrometry

Breast cancer (BC) is the most frequently diagnosed cancer that affects women worldwide. Early detection of BC is important to improve survival rates and decrease mortality. The aim of the present study was to investigate serum biomarkers using surface-enhanced laser desorption ionization time-of-flight mass spectrometry (SELDI-TOF-MS) to distinguish patients with BC from the healthy population and patients with benign breast diseases (BBDs). A total of 62 patients with invasive ductal carcinoma, as confirmed by histopathology, and 47 non-cancerous individuals (NCIs) [16 healthy controls (HCs) and 31 patients with BBD] were enrolled in the present study. Serum protein profiles were determined by SELDI-TOF-MS using an immobilized metal affinity capture array. Serum from patients with BC were compared with that from the HC group using univariate and multivariate statistical analyses. A total of 118 clusters were generated from the individual serum. Univariate analysis revealed that 5 peaks were significantly downregulated (m/z 1,452, 2,670, 3,972, 5,354 and 5,523; P<0.001) and 4 were upregulated (m/z 6,850, 7,926, 8,115 and 8,143; P<0.001) in patients with BC compared with the HC group. A comparison of patients with BC and patients with BBD revealed an additional 9 protein peaks. Among these, 3 peaks (m/z 3,972, 5,336 and 11,185) were significantly downregulated and 6 peaks (m/z 4,062, 4,071, 4,609, 6,850, 8,115 and 8,133) were significantly upregulated. A total of 3 peaks [mass-to-change ratio (m/z) 3,972, 6,850 and 8,115 (BC2)] were common in both sets. The results of the present study suggest that a 4 protein peak set [m/z 3,972, 6,850 and 8,115 (BC2) and 8,949 (BC3)] could be used to distinguish patients with BC from NCI.

Plasma lipidomics profiling identified lipid biomarkers in distinguishing early-stage breast cancer from benign lesions

BACKGROUND

Breast cancer is very common and highly fatal in women. Current non-invasive detection methods like mammograms are unsatisfactory. Lipidomics, a promising detection method, may serve as a novel prognostic approach for breast cancer in high-risk patients.

RESULTS

According the predictive model, the combination of 15 lipid species had high diagnostic value. In the training set, sensitivity, specificity, positive predictive value (PPV) and negative predictive value (NPV) of the combination of these 15 lipid species were 83.3%, 92.7%, 89.7%, and 87.9%, respectively. The AUC in the training set was 0.926 (95% CI 0.869-0.982). Similar results were found in the validation set, with the sensitivity, specificity, PPV and NPV at 81.0%, 94.5%, 91.9%, and 86.7%, respectively. The AUC was 0.938 (95% CI 0.889-0.986) in the validation set.

METHODS

Using triple quadrupole liquid chromatography electrospray ionization tandem mass spectrometry, this study was to detect global lipid profiling of a total of 194 plasma samples from 84 patients with early-stage breast cancer (stage 0-II) and 110 patients with benign breast disease included in a training set and a validation set. A binary logistic regression was used to build a predictive model for evaluating the lipid species as potential biomarkers in the diagnosis of breast cancer.

CONCLUSIONS

The combination of these 15 lipid species as a panel could be used as plasma biomarkers for the diagnosis of breast cancer.

Identification of a panel of complex autoantigens (LGALS3, PHB2, MUC1, and GK2) in combination with CA15-3 for the diagnosis of early-stage breast cancer

Currently, there is no effective single antigen and there are only a very limited number of complex antigens for the diagnosis of early-stage breast cancer (BC). In this study, we used serological analysis of recombinant cDNA expression libraries (SEREX) in combination with phage display technology to screen complex autoantigens from the sera of BC patients. The cDNA expression library was constructed using tissue samples of three patients with BC at as early as stage T1N0M0. The serum samples of ten patients, including the three patients who provided tissue samples, as well as five healthy human subjects as controls were used to screen the library. All seven autoantigens were identified from the library by four rounds of screening and matched the existing genes after a blast search using NCBI-BLAST. Then, the expression conditions of the autoantibodies of the seven autoantigens and anti-CA15-3 in the sera from 100 BC patients and 50 healthy donors were examined by gray values. The data were analyzed by the area under the receiver operating characteristic (ROC) curve and logistic regression diagnostic models. In the end, a panel of complex autoantigens consisting of B11 (LGALS3), B18 (PHB2), B119 (MUC1), B130 (GK2), and CA15-3, which had a sensitivity of 87 % and a specificity of 76 %, were identified. The area under the curve (AUC) of the complex antigens was 0.872, which is significantly greater than that of anti-CA15-3 alone (AUC = 0.634) for the diagnosis of BC. Thus, this panel of complex antigens provides a promising strategy for the diagnosis of early-stage BC.

SELDI-TOF-MS proteomic profiling of serum, urine, and amniotic fluid in neural tube defects

Neural tube defects (NTDs) are common birth defects, whose specific biomarkers are needed. The purpose of this pilot study is to determine whether protein profiling in NTD-mothers differ from normal controls using SELDI-TOF-MS. ProteinChip Biomarker System was used to evaluate 82 maternal serum samples, 78 urine samples and 76 amniotic fluid samples. The validity of classification tree was then challenged with a blind test set including another 20 NTD-mothers and 18 controls in serum samples, and another 19 NTD-mothers and 17 controls in urine samples, and another 20 NTD-mothers and 17 controls in amniotic fluid samples. Eight proteins detected in serum samples were up-regulated and four proteins were down-regulated in the NTD group. Four proteins detected in urine samples were up-regulated and one protein was down-regulated in the NTD group. Six proteins detected in amniotic fluid samples were up-regulated and one protein was down-regulated in the NTD group. The classification tree for serum samples separated NTDs from healthy individuals, achieving a sensitivity of 91% and a specificity of 97% in the training set, and achieving a sensitivity of 90% and a specificity of 97% and a positive predictive value of 95% in the test set. The classification tree for urine samples separated NTDs from controls, achieving a sensitivity of 95% and a specificity of 94% in the training set, and achieving a sensitivity of 89% and a specificity of 82% and a positive predictive value of 85% in the test set. The classification tree for amniotic fluid samples separated NTDs from controls, achieving a sensitivity of 93% and a specificity of 89% in the training set, and achieving a sensitivity of 90% and a specificity of 88% and a positive predictive value of 90% in the test set. These suggest that SELDI-TOF-MS is an additional method for NTDs pregnancies detection.

Biomarkers in the diagnosis of primary and recurrent breast cancer

Despite the widespread use of mammography for breast cancer screening, breast cancer remains the most common cause of cancer-related mortality among women worldwide. The identification of biomarkers that identify cancers when they are small, localized and most treatable is an important aim of current breast cancer research. Biomarkers need to be sensitive, specific, reproducible and easily collected from patients from readily accessible tissue or body fluids. While conventional biomarker research has focused on soluble proteins, cell markers, proteomics and DNA methylation, much progress has also been made in the field of immunobiomarkers and multiparameter gene arrays. Currently, no one biomarker has demonstrated sufficient sensitivity and reproducibility for independent clinical and commercial use. This review summarizes the current state of breast cancer biomarker research and anticipated future directions.

Proteomic analysis of the abdominal aortic aneurysm wall

PURPOSES

A ruptured AAA (rAAA) is a common cause of death in males over 60 years of age, and the global mortality from rAAA exceeds 80 %. The pathological processes occurring in the wall of the developing AAA are still unclear. The potential pathophysiological mechanisms underlying aortic aneurysms have been examined by many studies using immunohistochemistry and were, therefore, targeted at specific, preselected protein antigens.

METHODS

We collected samples of tissue from anterior wall of an aneurysm sac from 15 patients indicated for AAA resection (group A) during the period from 2010 to 2011. These samples were subjected to a proteomic analysis. In addition, we collected control samples of identical aortic tissue from 10 heart-beating deceased organ donors (group B).

RESULTS

A total of 417 differentially expressed protein fractions were identified, 18 of which were only detected in the healthy controls, while 85 were specific for aneurysm tissue and 314 were detectable in both groups. In 175 protein fractions, the gel-derived spot volumes differed significantly between aneurismal and healthy aortic tissue.

CONCLUSIONS

We found a significant difference in the proteome of the AAA tissue and non-dilated aortic tissue. We demonstrated that the AAA proteome is considerably richer and more varied than the healthy and atherosclerotic aorta. We believe that our results clearly demonstrate a completely different etiopathogenesis of atherosclerosis and aneurismal disease.

Proteomic profile in familial breast cancer patients

OBJECTIVES

Breast cancer is the most common form of cancer affecting women, and the strongest risk factor remains family history. Although screening in asymptomatic women seems able to reduce breast-cancer related mortality, it is of limited usefulness in young women and patients with familial breast cancer syndrome. New diagnostic tools useful for breast cancer management are urgently needed. The aim of the present paper is to look for new candidate tumor markers useful for diagnosis in these patients.

DESIGN AND METHODS

In this prospective study 292 serum samples (100 from healthy people, 100 from sporadic breast cancer patients and 92 from familial breast cancer patients) were analyzed by SELDI-TOF-MS. All samples both from cancer patients and healthy subjects were run in duplicate and randomly spotted on CM10 and IMAC30 protein chip array. Data were analyzed using the expression differential mapping (EDM) tool, decisional tree and multivariate analysis. A further in silico investigation was performed in order to hypothesize the identity of evidenced peptides.

RESULTS

EDM highlighted thirteen and sixteen significant differentially expressed peaks by CM10 and IMAC30 protein chip respectively. Subsequent analysis showed that two peaks at m/z 11730 and 5066 were differentially expressed in sporadic and familial breast cancer patients respectively, while a peak at m/z 8127 was overexpressed only in familial breast cancer patients. The diagnostic power of protein peaks was tested by decisional tree; sensitivity and specificity ranged from 17% to 91.67%.

CONCLUSIONS

We show that the serum profile of familial breast cancer patients was different when compared with that of sporadic breast cancer patients. We hypothesized the identity of the most significant peaks, and further studies are now planned in order to definitively establish the identity.

Advancements in the use of blood tests for cancer screening in women at high risk for endometrial and breast cancer

Several years ago, it was argued that the identification of serum biomarkers is one of the most promising approaches for the detection of early-stage malignant or even premalignant lesions. In this review, the need to establish better monitoring protocols is described for obese women who are at higher risk for the development of malignancies commonly associated with excess weight; specifically endometrial and postmenopausal breast cancer. These cancers have been chosen for this review article as our aim was to focus on female cancers that have been linked with obesity. Cancer screening is essential in detecting disease in its earliest stage in order to reduce morbidity and mortality; however, effective screening is not available for many cancer types. Even for cancers that have effective screening protocols available, there are barriers to screening in obese individuals, such as reduced mobility and embarrassment. These barriers often delay screening in these vulnerable population groups, leading to detection of the disease at a more advanced stage and ultimately leading to a poorer prognosis. As of today, biomarkers do not replace but augment imaging and other existing screening approaches. Future development of blood- or urine-based biomarkers as a way to screen individuals at high risk for certain cancers may prove to be an excellent method for overcoming the barriers that individuals at high risk are facing today. The overall purpose of this manuscript is to provide an overview of screening techniques and to identified barriers and alternate biomarker-based approaches for improvement of endometrial and breast cancer screening in obese women.