Recent Contributions of Mass Spectrometry-Based "Omics" in the Studies of Breast Cancer

Breast cancer (BC) is one of the most heterogeneous groups of cancer. As every biotype of BC is unique and presents a particular "omic" signature, they are increasingly characterized nowadays with novel mass spectrometry (MS) strategies. BC therapeutic approaches are primarily based on the two features of human epidermal growth factor receptor 2 (HER2) and estrogen receptor (ER) positivity. Various strategic MS implementations are reported in studies of BC also involving data independent acquisitions (DIAs) of MS which report novel differential proteomic, lipidomic, proteogenomic, phosphoproteomic, and metabolomic characterizations associated with the disease and its therapeutics. Recently many "omic" studies have aimed to identify distinct subsidiary biotypes for diagnosis, prognosis, and targets of treatment. Along with these, drug-induced-resistance phenotypes are characterized by "omic" changes. These identifying aspects of the disease may influence treatment outcomes in the near future. Drug quantifications and characterizations are also done regularly and have implications in therapeutic monitoring and in drug efficacy assessments. We report these studies, mentioning their implications toward the understanding of BC. We briefly provide the MS instrumentation principles that are adopted in such studies as an overview with a brief outlook on DIA-MS strategies. In all of these, we have chosen a model cancer for its revelations through MS-based "omics".

Biomarker Characterization by MALDI-TOF/MS

Mass spectrometric techniques frequently used in clinical diagnosis, such as gas chromatography-mass spectrometry, liquid chromatography-mass spectrometry, ambient ionization mass spectrometry, and matrix-assisted laser desorption ionization/time-of-flight mass spectrometry (MALDI-TOF/MS), are discussed. Due to its ability to rapidly detect large biomolecules in trace amounts, MALDI-TOF/MS is an ideal tool for characterizing disease biomarkers in biologic samples. Clinical applications of MS for the identification and characterization of microorganisms, DNA fragments, tissues, and biofluids are introduced. Approaches for using MALDI-TOF/MS to detect various disease biomarkers including peptides, proteins, and lipids in biological fluids are further discussed. Finally, various sample pretreatment methods which improve the detection efficiency of disease biomarkers are introduced.

Proteomic profile and in silico analysis in metastatic melanoma with and without BRAF mutation

INTRODUCTION

Selective inhibitors of BRAF, vemurafenib and dabrafenib are the standard of care for metastatic melanoma patients with BRAF V600, while chemotherapy continued to be widely used in BRAF wild type patients.

MATERIALS AND METHODS

In order to discover novel candidate biomarkers predictive to treatment, serum of 39 metastatic melanoma vemurafenib (n = 19) or chemotherapy (n = 20) treated patients at baseline, at disease control and at progression, were analyzed using SELDI-TOF technology. In silico analysis was used to identify more significant peaks.

RESULTS

In patients with different BRAF status, we found 5 peptides significantly deregulated, with the down-regulation of the m/z 9176 peak strongly associated with BRAF mutation. At baseline as predictive biomarkers we identified 2 peptides - m/z 6411, 4075 - as significantly up-regulated in responders to chemotherapy and 4 peaks - m/z 5900, 12544, 49124 and 11724 - significantly up-regulated in longer vs shorter responders to vemurafenib. After response, 3 peptides (m/z 4658, 18639, and 9307) resulted significantly down regulated while 3 peptides m/z 9292, 7765 and 9176 appeared up-regulated respectively in chemotherapy and vemurafenib responder patients. In vemurafenib treated patients, 16 peaks appeared deregulated at progression compared to baseline time. In silico analysis identified proteins involved in invasiveness (SLAIN1) and resistance (ABCC12) as well as in the pathway of detoxification (NQO1) and apoptosis (RBM10, TOX3, MTEFD1, TSPO2). Proteins associated with the modulation of neuronal plasticity (RIN1) and regulatory activity factors of gene transcription (KLF17, ZBTB44) were also highlighted.

CONCLUSION

Our exploratory study highlighted some factors that deserve to be further investigated in order to provide a framework for improving melanoma treatment management through the development of biomarkers which could act as the strongest surrogates of the key biological events in stage IV melanoma.

Toxicogenomic approaches for understanding molecular mechanisms of heavy metal mutagenicity and carcinogenicity

Heavy metals that are harmful to humans include arsenic, cadmium, chromium, lead, mercury, and nickel. Some metals or their related compounds may even cause cancer. However, the mechanism underlying heavy metal-induced cancer remains unclear. Increasing data show a link between heavy metal exposure and aberrant changes in both genetic and epigenetic factors via non-targeted multiple toxicogenomic technologies of the transcriptome, proteome, metabolome, and epigenome. These modifications due to heavy metal exposure might provide a better understanding of environmental disorders. Such informative changes following heavy metal exposure might also be useful for screening of biomarker-monitored exposure to environmental pollutants and/or predicting the risk of disease. We summarize advances in high-throughput toxicogenomic-based technologies and studies related to exposure to individual heavy metal and/or mixtures and propose the underlying mechanism of action and toxicant signatures. Integrative multi-level expression analysis of the toxicity of heavy metals via system toxicology-based methodologies combined with statistical and computational tools might clarify the biological pathways involved in carcinogenic processes. Although standard in vitro and in vivo endpoint testing of mutagenicity and carcinogenicity are considered a complementary approach linked to disease, we also suggest that further evaluation of prominent biomarkers reflecting effects, responses, and disease susceptibility might be diagnostic. Furthermore, we discuss challenges in toxicogenomic applications for toxicological studies of metal mixtures and epidemiological research. Taken together, this review presents toxicogenomic data that will be useful for improvement of the knowledge of carcinogenesis and the development of better strategies for health risk assessment.

Identification of novel breast cancer-associated transcripts by UniGene database mining and gene expression analysis in normal and malignant cells

Breast cancer is a heterogeneous and complex disease. Although the use of tumor biomarkers has improved individualized breast cancer care, i.e., assessment of risk, diagnosis, prognosis, and prediction of treatment outcome, new markers are required to further improve patient clinical management. In the present study, a search for novel breast cancer-associated genes was performed by mining the UniGene database for expressed sequence tags (ESTs) originating from human normal breast, breast cancer tissue, or breast cancer cell lines. Two hundred and twenty-eight distinct breast-associated UniGene Clusters (BUC1-228) matched the search criteria. Four BUC ESTs (BUC6, BUC9, BUC10, and BUC11) were subsequently selected for extensive in silico database searches, and in vitro analyses through sequencing and RT-PCR based assays on well-characterized cell lines and tissues of normal and cancerous origin. BUC6, BUC9, BUC10, and BUC11 are clustered on 10p11.21-12.1 and showed no homology to any known RNAs. Overall, expression of the four BUC transcripts was high in normal breast and testis tissue, and in some breast cancers; in contrast, BUC was low in other normal tissues, peripheral blood mononuclear cells (PBMCs), and other cancer cell lines. Results to-date suggest that BUC11 and BUC9 translate to protein and BUC11 cytoplasmic and nuclear protein expression was detected in a large cohort of breast cancer samples using immunohistochemistry. This study demonstrates the discovery and expression analysis of a tissue-restricted novel transcript set which is strongly expressed in breast tissue and their application as clinical cancer biomarkers clearly warrants further investigation.

Body mass index and serum proteomic profile in breast cancer and healthy women: a prospective study

Epidemiological studies suggest a possible association between BMI, diagnosis and clinical-pathological breast cancer characteristics but biological bases for this relationship still remain to be ascertained. Several biological mechanisms play a role in the genesis and progression of breast cancer. This study aimed to investigate relationships between BMI and breast cancer diagnosis/progression in a Southern Italian population and to try to interpret results according to the serum proteomic profile of healthy and breast cancer patients. BMI, presence or absence of breast cancer and its clinical-pathological characteristics were analyzed in a series of 300 breast cancer women and compared with those of 300 healthy women prospectively. To investigate whether obesity is associated with alterations in serum protein profile, SELDI-ToF approach was applied.Alcohol consumption (22.7% vs 11.3%; p<0.001) and postmenopausal status (65.7% vs 52%; p<0.001) but not BMI resulted significantly different in patients vs controls. Conversely, BMI was significantly associated with a larger-tumour size (BMI> = 30 respect to normal weight: OR = 2.49, 95% CI 1.25-4.99, p = 0.0098) and a higher probability of having positive axillary lymph node (OR = 3.67, CI 95% 2.16-6.23, p<0.0001). Multivariate analysis confirmed the association of breast cancer diagnosis with alcohol consumption (OR = 2.28;CI 1.36-3.83; p<0.0018). Serum protein profile revealed the presence of significant (p-value <0,01) differentially expressed peaks m/z 6934, m/z 5066 in high BMI breast cancer patients vs healthy subjects and m/z 6934, m/z 3346 in high vs low BMI breast cancer patients.The analysis of pathological features of cancer indicates that normal weight women have a significantly higher probability of having a smaller breast cancer at time of diagnosis and negative axillary lymph nodes while increased BMI is associated with an altered protein profile in breast cancer patients. Further studies to identify specific proteins found in the serum and their role in breast cancerogenesis and progression are in progress.

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.

Serum amyloid a protein in clinical cancer diagnosis

The serum amyloid A (SAA) protein is an acute phase protein that is synthesized under the regulation of inflammatory cytokines during both acute and chronic inflammation. It is suggested that the SAA increases correlate with many types of carcinogenesis and neoplastic diseases. Th changes in SAA in serum could therefore indicate the progress and malignancy of the disease, as well as the host responses. The present paper reviewed the rationale of using SAA as potential cancer biomarker in clinical diagnosis, including the contribution and involvement of SAA in cancer growth and development. Then we discussed the current applications of SAA in diagnosis and tracing of different types of cancers. Finally the proteomics techniques, especially the SELDI-TOF MS to identify SAA in serum from patients were appreciated as an important manner in clinical diagnosis.

The application of SELDI-TOF-MS in clinical diagnosis of cancers

Cancer diagnosis is important, and the early diagnosis of cancers could predict a more successful treatment. The proteomic studies emerged to be useful in combined analyses of samples from patients and provide more accurate diagnosis when compared to the single-factor-based diagnosis. In recent years, cancer detection with surface-enhanced laser desorption/ionization time of flight mass spectrometry (SELDI-TOF MS) is flourishing and brought significant progress in this area. This paper summarizes some recent results with this technique for cancer diagnosis.

Identification of serum protein markers for breast cancer relapse with SELDI-TOF MS

Surface-enhanced laser desorption/ionization time-of-flight mass spectrometry (SELDI-TOF MS) was used to screen serum samples to identify protein markers for early breast cancer relapse. We collected 67 serum samples from patients with breast cancer (24 preoperative; 23 postoperative without breast cancer relapse; 20 postoperative with breast cancer relapse). Eight protein peaks varied between the presurgical group and the postsurgical group without breast cancer relapse; 4 protein peaks were differentially expressed between the postsurgical patients without relapse and patients with relapse. The peak at 3964 m/z dropped after surgery and rebounded after relapse (P < 0.01). These results indicate that there are differences in serum protein expression among the three different groups of patients. SELDI-TOF MS could be used to screen blood samples for the early detection of relapse in primary breast cancer patients. Specifically, protein peak at 3964 m/z is a potential biomarker for the detection of early breast cancer relapse.

Up-regulated proteins in the fluid bathing the tumour cell microenvironment as potential serological markers for early detection of cancer of the breast

Breast cancer is by far the most common diagnosed form of cancer and the leading cause of cancer death in women today. Clinically useful biomarkers for early detection of breast cancer could lead to a significant reduction in mortality. Here we describe a detailed analysis using gel-based proteomics in combination with mass spectrometry and immunohistochemistry (IHC) of the tumour interstitial fluids (TIF) and normal interstitial fluids (NIF) collected from 69 prospective breast cancer patients. The goal of this study was to identify abundant cancer up-regulated proteins that are externalised by cells in the tumour microenvironment of most if not all these lesions. To this end, we applied a phased biomarker discovery research strategy to the analysis of these samples rather than comparing all samples among each other, with inherent inter and intra-sample variability problems. To this end, we chose to use samples derived from a single tumour/benign tissue pair (patient 46, triple negative tumour), for which we had well-matched samples in terms of epithelial cell numbers, to generate the initial dataset. In this first phase we found 110 proteins that were up-regulated by a factor of 2 or more in the TIF, some of which were confirmed by IHC. In the second phase, we carried out a systematic computer assisted analysis of the 2D gels of the remaining 68 TIF samples in order to identify TIF 46 up-regulated proteins that were deregulated in 90% or more of all the available TIFs, thus representing common breast cancer markers. This second phase singled out a set of 26 breast cancer markers, most of which were also identified by a complementary analysis using LC-MS/MS. The expression of calreticulin, cellular retinoic acid-binding protein II, chloride intracellular channel protein 1, EF-1-beta, galectin 1, peroxiredoxin-2, platelet-derived endothelial cell growth factor, protein disulfide isomerase and ubiquitin carboxyl-terminal hydrolase 5 were further validated using a tissue microarray containing 70 malignant breast carcinomas of various grades of atypia. A significant number of these proteins have already been detected in the blood/plasma/secretome by others. The next steps, which include biomarker prioritization based on the hierarchal evaluation of these markers, antibody and antigen development, assay development, analytical validation, and preliminary testing in the blood of healthy and breast cancer patients, are discussed.