Clinical proteomics in breast cancer: a review

EpiBrCan-Lite: A lightweight deep learning model for breast cancer subtype classification using epigenomic data

BACKGROUND AND OBJECTIVES

Early breast cancer subtypes classification improves the survival rate as it facilitates prognosis of the patient. In literature this problem was prominently solved by various Machine Learning and Deep Learning techniques. However, these studies have three major shortcomings: huge Trainable Weight Parameters (TWP), suffer from low performance and class imbalance problem.

METHODS

This paper proposes a lightweight model named EpiBrCan-Lite for classifying breast cancer subtypes using DNA methylation data. This model encompasses three blocks namely Data Encoding, TransGRU, and Classification blocks. In Data Encoding block, the input features are encoded into equal sized chunks and then passed down to TransGRU block which is a modified version of traditional Transformer Encoder (TE). In TransGRU block, MLP module of traditional TE is replaced by GRU module, consisting of two GRU layers to reduce TWP and capture the long-range dependencies of input feature data. Furthermore, output of TransGRU block is passed to Classification block for classifying breast cancer into their subtypes.

RESULTS

The proposed model is validated using Accuracy, Precision, Recall, F1-score, FPR, and FNR metrics on TCGA breast cancer dataset. This dataset suffers from the class imbalance problem which is mitigated using Synthetic Minority Oversampling Technique (SMOTE). Experimentation results demonstrate that EpiBrCan-Lite model attained 95.85 % accuracy, 95.96 % recall, 95.85 % precision, 95.90 % F1-score, 1.03 % FPR, and 4.12 % FNR despite of utilizing only 1/1500 of TWP than other state-of-the-art models.

CONCLUSION

EpiBrCan-Lite model is efficiently classifying breast cancer subtypes, and being lightweight, it is suitable to be deployed on low computational powered devices.

OMIBONE: Omics-driven computer model of bone regeneration for personalized treatment

Treatment of bone fractures are standardized according to the AO classification, which mainly refers to the mechanical stabilization required in a given situation but neglect individual differences due to patient's healing potential or accompanying diseases. Specially in elderly or immune-compromised patients, the complexity of individual constrains on a biological as well as mechanical level are hard to account for. Here, we introduce a novel framework that allows to predict bone regeneration outcome using combined proteomic and mechanical analyses in a computer model. The framework uses Ingenuity Pathway Analysis (IPA) software to link protein changes to alterations in biological processes and integrates these in an Agent-Based Model (ABM) of bone regeneration. This combined framework allows to predict bone formation and the potential of an individual to heal a given fracture setting. The performance of the framework was evaluated by replicating the experimental setup of a mouse femur fracture stabilized with an intramedullary pin. The model was informed by serum derived proteomics data. The tissue formation patterns were compared against experimental data based on x-ray and histology images. The results indicate the framework potential in predicting an individual's bone formation potential and hold promise as a concept to enable personalized bone healing predictions for a chosen fracture fixation.

Decoding Hidden Messengers: Proteomic Profiling of Exosomes in Mammary Cancer Research

Cancer is a complex and heterogeneous disease, influenced by various factors that affect its progression and response to treatment. Although a histopathological diagnosis is crucial for identifying and classifying cancer, it may not accurately predict the disease's development and evolution in all cases. To address this limitation, liquid biopsy has emerged as a valuable tool, enabling a more precise and non-invasive analysis of cancer. Liquid biopsy can detect tumor DNA fragments, circulating tumor cells, and exosomes released by cancer cells into the bloodstream. Exosomes attracted significant attention in cancer research because of their specific protein composition, which can provide valuable insights into the disease. The protein profile of exosomes often differs from that of normal cells, reflecting the unique molecular characteristics of cancer. Analyzing these proteins can help identify cancer-associated markers that play important roles in tumor progression, invasion, and metastasis. Ongoing research and clinical validation are essential to advance and effectively utilize protein biomarkers in cancer. Nevertheless, their potential to improve diagnosis and treatment is highly promising. This review discusses several exosome proteins of interest in breast cancer, particularly focusing on studies conducted in mammary tissue and cell lines in humans and experimental animals. Unfortunately, studies conducted in canine species are scarce. This emphasis sheds light on the limited research available in this field. In addition, we present a curated selection of studies that explored exosomal proteins as potential biomarkers, aiming to achieve benefits in breast cancer diagnosis, prognosis, monitoring, and treatment.

An Inflection Point in Cancer Protein Biomarkers: What Was and What's Next

Biomarkers remain the highest value proposition in cancer medicine today - especially protein biomarkers. Yet despite decades of evolving regulatory frameworks to facilitate the review of emerging technologies, biomarkers have been mostly about promise with very little to show for improvements in human health. Cancer is an emergent property of a complex system and deconvoluting the integrative and dynamic nature of the overall system through biomarkers is a daunting proposition. The last two decades have seen an explosion of multi-omics profiling and a range of advanced technologies for precision medicine, including the emergence of liquid biopsy, exciting advances in single cell analysis, artificial intelligence (machine and deep learning) for data analysis and many other advanced technologies that promise to transform biomarker discovery. Combining multiple omics modalities to acquire a more comprehensive landscape of the disease state, we are increasingly developing biomarkers to support therapy selection and patient monitoring. Furthering precision medicine, especially in oncology, necessitates moving away from the lens of reductionist thinking towards viewing and understanding that complex diseases are, in fact, complex adaptive systems. As such, we believe it is necessary to re-define biomarkers as representations of biological system states at different hierarchical levels of biological order. This definition could include traditional molecular, histologic, radiographic, or physiological characteristics, as well as emerging classes of digital markers and complex algorithms. To succeed in the future, we must move past purely observational individual studies and instead start building a mechanistic framework to enable integrative analysis of new studies within the context of prior studies. Identifying information in complex systems and applying theoretical constructs, such as information theory, to study cancer as a disease of dysregulated communication could prove to be "game changing" for the clinical outcome of cancer patients.

Longitudinal Serum Protein Analysis of Women with a High Risk of Developing Breast Cancer Reveals Large Interpatient Versus Small Intrapatient Variations: First Results from the TESTBREAST Study

The prospective, multicenter TESTBREAST study was initiated with the aim of identifying a novel panel of blood-based protein biomarkers to enable early breast cancer detection for moderate-to-high-risk women. Serum samples were collected every (half) year up until diagnosis. Protein levels were longitudinally measured to determine intrapatient and interpatient variabilities. To this end, protein cluster patterns were evaluated to form a conceptual basis for further clinical analyses. Using a mass spectrometry-based bottom-up proteomics strategy, the protein abundance of 30 samples was analyzed: five sequential serum samples from six high-risk women; three who developed a breast malignancy (cases) and three who did not (controls). Serum samples were chromatographically fractionated and an in-depth serum proteome was acquired. Cluster analyses were applied to indicate differences between and within protein levels in serum samples of individuals. Statistical analyses were performed using ANOVA to select proteins with a high level of clustering. Cluster analyses on 30 serum samples revealed unique patterns of protein clustering for each patient, indicating a greater interpatient than intrapatient variability in protein levels of the longitudinally acquired samples. Moreover, the most distinctive proteins in the cluster analysis were identified. Strong clustering patterns within longitudinal intrapatient samples have demonstrated the importance of identifying small changes in protein levels for individuals over time. This underlines the significance of longitudinal serum measurements, that patients can serve as their own controls, and the relevance of the current study set-up for early detection. The TESTBREAST study will continue its pursuit toward establishing a protein panel for early breast cancer detection.

Molecular Histology Analysis of Cryopreserved Tissue Using Peptide/Protein MALDI-TOF Imaging Mass Spectrometry (MALDI-IMS)

Matrix-assisted laser desorption/ionization (MALDI) imaging mass spectrometry (IMS) has emerged as a powerful tool for analyzing the spatial distribution of peptides, small proteins, and other molecules within biological tissues. The obtained signals can be correlated with underlying tissue architecture, without any geometrical distortion, enabling the so-called molecular histology. Here, we analyzed cryopreserved tissue samples employing the MALDI-IMS for proteins and peptides. We used a nonstandard OCT-free cryo-slicing protocol, followed by Carnoy delipidation. Automated matrix spray was utilized to circumvent some of MALDI-IMS technology drawbacks in protein and peptide analysis.

Plasma Proteome Signature to Predict the Outcome of Breast Cancer Patients Receiving Neoadjuvant Chemotherapy

The plasma proteome of 51 non-metastatic breast cancer patients receiving neoadjuvant chemotherapy (NCT) was prospectively analyzed by high-resolution mass spectrometry coupled with nano-flow liquid chromatography using blood drawn at the time of diagnosis. Plasma proteins were identified as potential biomarkers, and their correlation with clinicopathological variables and survival outcomes was analyzed. Of 51 patients, 20 (39.2%) were HR+/HER2-, five (9.8%) were HR+/HER2+, five (9.8%) were HER2+, and 21 (41.2%) were triple-negative subtype. During a median follow-up of 52.0 months, there were 15 relapses (29.4%) and eight deaths (15.7%). Four potential biomarkers were identified among differentially expressed proteins: APOC3 had higher plasma concentrations in the pathological complete response (pCR) group, whereas MBL2, ENG, and P4HB were higher in the non-pCR group. Proteins statistically significantly associated with survival and capable of differentiating low- and high-risk groups were MBL2 and P4HB for disease-free survival, P4HB for overall survival, and MBL2 for distant metastasis-free survival (DMFS). In the multivariate analysis, only MBL2 was a consistent risk factor for DMFS (HR: 9.65, 95% CI 2.10-44.31). The results demonstrate that the proteomes from non-invasive sampling correlate with pCR and survival in breast cancer patients receiving NCT. Further investigation may clarify the role of these proteins in predicting prognosis and thus their therapeutic potential for the prevention of recurrence.

A comprehensive overview of proteomics approach for COVID 19: new perspectives in target therapy strategies

World Health Organisation declared COVID-19 a pandemic on March 11, 2020. It was temporarily named as 2019-nCoV then subsequently named as COVID-19 virus. A coronavirus is a group of viruses, known to be zoonotic, causing illness ranging from acute to mild respiratory infections. These are spherical or pleomorphic enveloped particles containing positive sense RNA. The virus enters host cells, its uncoated genetic material transcribes, and translates. Since it has started spreading rapidly, protective measures have been taken all over the world. However, its transmission has been proved to be unstoppable and the absence of an effective drug makes the situation worse. The scientific community has gone all-out to discover and develop a possible vaccine or a competent antiviral drug. Other domains of biological sciences that promise effective results and target somewhat stable entities that are proteins, could be very useful in this time of crisis. Proteomics and metabolomics are the vast fields that are equipped with sufficient technologies to face this challenge. Various protein separation and identification techniques are available which facilitates the analysis of various types of interactions among proteins and their evolutionary lineages. The presented review aims at confronting the question: 'how proteomics can help in tackling SARS-CoV-2?' It deals with the role of upcoming proteome technology in these pandemic situations and discusses the proteomics approach towards the COVID-19 dilemma.

Nanosensor networks for health-care applications

Functionalized transistors provide effective sensors for a variety of viruses (Zika, severe acute respiratory syndrome), toxins (botulinum), cancers (breast and prostate), and disease or injury biomarkers (troponin, cerebrospinal fluid). A hallmark of this approach is high specificity, rapid response (<5 minutes), and ability to be integrated with wireless data transmission capabilities. The ultimate goal is hand-held point-of-care detection that can streamline patient diagnosis.

Platelet Factor 4 as a Novel Exosome Marker in MALDI-MS Analysis of Exosomes from Human Serum

Exosomes are nanosized vesicles commonly found in biological fluids as a result of a secretion process involving endosomes and multivesicular bodies. The isolation and analysis of exosomes can be useful for noninvasive clinical diagnosis of a variety of human diseases. We investigated the utility of analyzing exosomal proteins, using matrix-assisted laser desorption/ionization combined with Fourier-transform ion cyclotron resonance mass spectrometry (MALDI-FTICR-MS), as a means of determining the presence of exosomes. MALDI-FTICR-MS analyses of exosomes enriched from human serum via centrifugation in a mass range of m/z 1000-20 000 yielded a distinctive protein around m/z 7766. The high mass accuracy and resolution of MALDI-FTICR-MS allowed for reliable comparisons against a protein database, through which the protein was identified as platelet factor 4 (PLF4), whose singly charged protein peak has an elemental composition of C₃₄₁H₅₇₇N₉₆O₁₀₁S₄⁺, with a theoretical most abundant isotopic peak at m/z 7765.194 and a theoretical average peak at m/z 7766. The MALDI-TOF MS analysis of exosomes from the serum of 27 patients with different states of liver diseases provided the most abundant PLF4 peak for each mass spectrum, along with several additional minor peaks. In conclusion, MALDI-MS is suitable as an alternative exosome detection method, serving as a valuable confirmation tool, greatly decreasing the time and workload associated with exosome identification.

Current advances in biomarkers for targeted therapy in triple-negative breast cancer

Triple-negative breast cancer (TNBC) is a complex heterogeneous disease characterized by the absence of three hallmark receptors: human epidermal growth factor receptor 2, estrogen receptor, and progesterone receptor. Compared to other breast cancer subtypes, TNBC is more aggressive, has a higher prevalence in African-Americans, and more frequently affects younger patients. Currently, TNBC lacks clinically accepted targets for tailored therapy, warranting the need for candidate biomarkers. BiomarkerBase, an online platform used to find biomarkers reported in clinical trials, was utilized to screen all potential biomarkers for TNBC and select only the ones registered in completed TNBC trials through clinicaltrials.gov. The selected candidate biomarkers were classified as surrogate, prognostic, predictive, or pharmacodynamic (PD) and organized by location in the blood, on the cell surface, in the cytoplasm, or in the nucleus. Blood biomarkers include vascular endothelial growth factor/vascular endothelial growth factor receptor and interleukin-8 (IL-8); cell surface biomarkers include EGFR, insulin-like growth factor binding protein, c-Kit, c-Met, and PD-L1; cytoplasm biomarkers include PIK3CA, pAKT/S6/p4E-BP1, PTEN, ALDH1, and the PIK3CA/AKT/mTOR-related metabolites; and nucleus biomarkers include BRCA1, the gluco-corticoid receptor, TP53, and Ki67. Candidate biomarkers were further organized into a "cellular protein network" that demonstrates potential connectivity. This review provides an inventory and reference point for promising biomarkers for breakthrough targeted therapies in TNBC.

Identification of Gene-Expression Signatures and Protein Markers for Breast Cancer Grading and Staging

The grade of a cancer is a measure of the cancer's malignancy level, and the stage of a cancer refers to the size and the extent that the cancer has spread. Here we present a computational method for prediction of gene signatures and blood/urine protein markers for breast cancer grades and stages based on RNA-seq data, which are retrieved from the TCGA breast cancer dataset and cover 111 pairs of disease and matching adjacent noncancerous tissues with pathologists-assigned stages and grades. By applying a differential expression and an SVM-based classification approach, we found that 324 and 227 genes in cancer have their expression levels consistently up-regulated vs. their matching controls in a grade- and stage-dependent manner, respectively. By using these genes, we predicted a 9-gene panel as a gene signature for distinguishing poorly differentiated from moderately and well differentiated breast cancers, and a 19-gene panel as a gene signature for discriminating between the moderately and well differentiated breast cancers. Similarly, a 30-gene panel and a 21-gene panel are predicted as gene signatures for distinguishing advanced stage (stages III-IV) from early stage (stages I-II) cancer samples and for distinguishing stage II from stage I samples, respectively. We expect these gene panels can be used as gene-expression signatures for cancer grade and stage classification. In addition, of the 324 grade-dependent genes, 188 and 66 encode proteins that are predicted to be blood-secretory and urine-excretory, respectively; and of the 227 stage-dependent genes, 123 and 51 encode proteins predicted to be blood-secretory and urine-excretory, respectively. We anticipate that some combinations of these blood and urine proteins could serve as markers for monitoring breast cancer at specific grades and stages through blood and urine tests.

Enhancing MALDI time-of-flight mass spectrometer performance through spectrum averaging

Matrix-assisted laser desorption ionization time-of-flight (MALDI-TOF) mass spectrometers are simple and robust mass spectrometers used for analysis of biologically relevant molecules in diverse fields including pathogen identification, imaging mass spectrometry, and natural products chemistry. Despite high nominal resolution and accuracy, we have observed significant variability where 30–50% of individual replicate measurements have errors in excess of 5 parts-per-million, even when using 5-point internal calibration. Increasing the number of laser shots for each spectrum did not resolve this observed variability. What is responsible for our observed variation? Using a modern MALDI-TOF/TOF instrument, we evaluated contributions to variability. Our data suggest a major component of variability is binning of the raw flight time data by the electronics and clock speed of the analog-to-digital (AD) detection system, which requires interpolation by automated peak fitting algorithms and impacts both calibration and the observed mass spectrum. Importantly, the variation observed is predominantly normal in distribution, which implies multiple components contribute to the observed variation and suggests a method to mitigate this variability through spectrum averaging. Restarting the acquisition impacts each spectrum within the electronic error of the AD detector system and defines a new calibration function. Therefore, averaging multiple independent spectra and not a larger number of laser shots leverages this inherent binning error to mitigate variability in accurate MALDI-TOF mass measurements.

Search for breast cancer biomarkers in fractionated serum samples by protein profiling with SELDI-TOF MS

BACKGROUND

Many high-abundant acute phase reactants have been previously detected as potential breast cancer biomar-kers. However, they are unlikely to be specific for breast cancer. Cancer-specific biomarkers are thought to be among the lower abundant proteins.

METHODS

We aimed to detect lower abundant discriminating proteins by performing serum fractionation by strong anion exchange chromatography preceding protein profiling with SELDI-TOF MS. In a pilot study, we tested the different fractions resulting from fractionation, on several array types. Fraction 3 on IMAC30 and Fraction 6 on Q10 yielded the most discriminative proteins and were used for serum protein profiling of 73 incident breast cancer cases and 73 matched controls.

RESULTS

Eight peaks showed statistically significantly different intensities between cases and controls (P⧁0.05), and had less than 10% chance to be a false-positive finding. Seven of these were tentatively identified as apolipoprotein C-II (m/z 8,909), oxidized apolipoprotein C-II (m/z 8,925), apolipoprotein C-III (m/z 8,746), fragment of coagulation factor XIIIa (m/z 3,959), heterodimer of apolipoprotein A-I and apolipoprotein A-II (m/z 45,435), hemoglobin B-chain (m/z 15,915), and post-translational modified hemoglobin (m/z 15,346).

CONCLUSION

By extensive serum fractionation, we detected many more proteins than in previous studies without fractionation. However, discriminating proteins were still high abundant. Results indicate that either lower abundant proteins are less distinctive, or more rigorous fractionation and selective protein depletion, or a more sensitive assay, are needed to detect lower abundant discriminative proteins.

Implications of functional proteomics in breast cancer

Breast cancer is one of the major public health problems of the Western world. Recent advances in genomics and gene expression-profiling approaches have enriched our understanding of this heterogeneous disease. However, progress in functional proteomics in breast cancer research has been relatively slow. Allied with genomics, the functional proteomics approach will be important in improving diagnosis through better classification of breast cancer and in predicting prognosis and response to different therapies, including chemotherapy, hormonal therapy, and targeted therapy. In this review, we will present functional proteomic approaches with a focus on the recent clinical implications of utilizing the reverse-phase protein array platform in breast cancer research.

Endobiogeny: a global approach to systems biology (part 1 of 2)

Endobiogeny is a global systems approach to human biology that may offer an advancement in clinical medicine based in scientific principles of rigor and experimentation and the humanistic principles of individualization of care and alleviation of suffering with minimization of harm. Endobiogeny is neither a movement away from modern science nor an uncritical embracing of pre-rational methods of inquiry but a synthesis of quantitative and qualitative relationships reflected in a systems-approach to life and based on new mathematical paradigms of pattern recognition.

Rapid peptidomic profiling of peritoneal fluid by MALDI-TOF mass spectrometry for the identification of biomarkers of endometriosis

Peptidomic profiling of peritoneal fluid by Matrix Assisted Laser Desorption Ionization-Time of Flight Mass Spectrometry (MALDI-TOF-MS) may represent a promising, suitable, rapid method for early diagnosis and staging of endometriosis. In a case-control study, peritoneal fluid was collected from 23 patients affected by endometriosis (eight minimal/mild endometriosis and 15 moderate/severe endometriosis) and six "endometriosis free" women undergoing laparoscopy. MALDI-TOF mass spectra of the peptide fraction extracted from peritoneal fluid samples lead to identify biomarkers potentially suitable for discriminating between peritoneal fluid samples from women affected by minimal/mild endometriosis and those from women affected by moderate/severe endometriosis. Peptidomic analysis of peritoneal fluid samples may define putative peptide biomarkers suitable for staging endometriosis and improve our understanding of the pathogenesis of endometriosis.

Modern approach of breast cancer diagnostics

In the review have been classified literature data concerning modern instrumental, microscopic and molecular (metabolomics, proteomics, genetics and epigenetics) approaches for early breast cancer diagnostics. The analytical performance and perspectives of their application in clinical practice also have been evaluated.

Proteomic demonstration of the recurrent presence of inter-alpha-inhibitor H4 heavy-chain during aspergillosis induced in an animal model

Invasive pulmonary aspergillosis remains a matter of great concern in oncology/haematology, intensive care units and organ transplantation departments. Despite the availability of various diagnostic tools with attractive features, new markers of infection are required for better medical care. We therefore looked for potential pulmonary biomarkers of aspergillosis, by carrying out two-dimensional (2D) gel electrophoresis comparing the proteomes of bronchial-alveolar lavage fluids (BALF) from infected rats and from control rats presenting non-specific inflammation, both immunocompromised. A bioinformatic analysis of the 2D-maps revealed significant differences in the abundance of 20 protein spots (ANOVA P-value<0.01; q-value<0.03; power>0.8). One of these proteins, identified by mass spectrometry, was considered of potential interest: inter-alpha-inhibitor H4 heavy-chain (ITIH4), characterised for the first time in this infectious context. Western blotting confirmed its overabundance in all infected BALF, particularly at early stages of murine aspergillosis. Further investigations were carried on rat serum, and confirmed that ITIH4 levels increased during experimental aspergillosis. Preliminary results in human samples strengthened this trend. To our knowledge, this is the first description of the involvement of ITIH4 in aspergillosis.

Fluopsin C induces oncosis of human breast adenocarcinoma cells

AIM

Fluopsin C, an antibiotic isolated from Pseudomonas jinanesis, has shown antitumor effects on several cancer cell lines. In the current study, the oncotic cell death induced by fluopsin C was investigated in human breast adenocarcinoma cells in vitro.

METHODS

Human breast adenocarcinoma cell lines MCF-7 and MD-MBA-231 were used. The cytotoxicity was evaluated using MTT assay. Time-lapse microscopy and transmission electron microscopy were used to observe the morphological changes. Cell membrane integrity was assessed with propidium iodide (PI) uptake and lactate dehydrogenase (LDH) assay. Flow cytometry was used to measure reactive oxygen species (ROS) level and mitochondrial membrane potential (Δψm). A multimode microplate reader was used to analyze the intracellular ATP level. The changes in cytoskeletal system were investigated with Western blotting and immunostaining.

RESULTS

Fluopsin C (0.5-8 μmol/L) reduced the cell viability in dose- and time-dependent manners. Its IC50 values in MCF-7 and MD-MBA-231 cells at 24 h were 0.9 and 1.03 μmol/L, respectively. Fluopsin C (2 μmol/L) induced oncosis in both the breast adenocarcinoma cells characterized by membrane blebbing and swelling, which was blocked by pretreatment with the pan-caspase inhibitor Z-VAD-fmk. In MCF-7 cells, fluopsin C caused PI uptake into the cells, significantly increased LDH release, induced cytoskeletal system degradation and ROS accumulation, decreased the intracellular ATP level and Δψm. Noticeably, fluopsin C exerted comparable cytotoxicity against the normal human hepatocytes (HL7702) and human mammary epithelial cells with the IC50 values at 24 h of 2.7 and 2.4 μmol/L, respectively.

CONCLUSION

Oncotic cell death was involved in the anticancer effects of fluopsin C on human breast adenocarcinoma cells in vitro. The hepatoxicity of fluopsin C should not be ignored.

Application of surface-enhanced laser desorption/ionization time-of-flight mass spectrometry technology for the diagnosis of colorectal adenoma

The aim of the present study was to identify a specific biological marker for the diagnosis of colorectal adenomas through the analysis of variations in serum protein profiling in colorectal adenoma patients. The study was conducted at the Renmin Hospital of Wuhan University (Wuhan, China) between September 2011 and May 2012. Surface-enhanced laser desorption/ionization time-of-flight mass spectrometry (SELDI-TOF-MS) was performed to compare the serum protein profiles of 50 patients with colorectal adenoma and 50 healthy individuals. The obtained protein profiles were analyzed using Biomarker Wizard software. Twenty protein peaks were identified to exhibit differences in average intensity between colorectal adenomas compared with normal controls, including peaks 8,565.84, 8,694.51 and 5,910.50 Da, in which the intensity between the patients and control individuals was significantly different. Two peaks, 8,565.84 and 8,694.51 Da, were observed to be highly expressed in the colorectal adenomas, however, expression was low in the control samples. By contrast, 5,910.50 Da expression was low in the colorectal adenomas and high in the controls. The results of the current study indicate that the three protein peaks may represent specific biomarkers for colorectal adenomas.

The consumption of seaweed as a protective factor in the etiology of breast cancer: proof of principle

Daily consumption of seaweed has been proposed as a factor in explaining lower postmenopausal breast cancer (BC) incidence and mortality rates in Japan. This clinical trial assessed the impact of introducing seaweed- to non-seaweed-consuming American postmenopausal women. Fifteen healthy postmenopausal women were recruited for a 3-month single-blinded placebo controlled clinical trial; five had no history of BC (controls) and ten were BC survivors. Participants ingested ten capsules daily (5 g day-1) of placebo for 4 weeks, seaweed (Undaria) for 4 weeks, then placebo for another 4 weeks. Blood and urine samples were collected after each treatment period. Urinary human urokinase-type plasminogen activator receptor concentrations (uPAR) were analyzed by ELISA, and urine and serum were analyzed for protein expression using surface-enhanced laser desorption/ionization-time-of-flight mass spectrometry (SELDI-TOF-MS). Urinary creatinine standardized uPAR (in pg mL μg-1 creatinine) changed significantly between groups, decreasing by about half following seaweed supplementation (placebo 1, 1.5 (95 % CI, 0.9-2.1) and seaweed, 0.9 (95 % CI, 0.6-1.1) while placebo 2 returned to pre-seaweed concentration (1.7 (95 % CI, 1.2-2.2); p = 0.01, ANOVA). One SELDI-TOF-MS-identified urinary protein (m/z 9,776) showed a similar reversible decrease with seaweed and is reported to be associated with cell attachment. One serum protein (m/z 8,928) reversibly increased with seaweed and may be the immunostimulatory complement activation C3a des-arginine. uPAR is higher among postmenopausal women generally, and for BC patients, it is associated with unfavorable BC prognosis. By lowering uPAR, dietary seaweed may help explain lower BC incidence and mortality among postmenopausal women in Japan.

Comparative proteomic analysis of ductal breast carcinoma demonstrates an altered expression of chaperonins and cytoskeletal proteins

The aim of the present study was to analyze the protein composition of ductal breast carcinoma and the surrounding normal tissue in individual patients using comparative 2D proteomics and mass spectrometry to detect candidate disease biomarkers for diagnosis and prognosis. Samples of normal and cancerous tissue obtained form 28 patients were analyzed. Chaperonins and cytoskeletal proteins predominated among the 11 proteins for which major changes in abundance were detected. Of these 11 proteins with an altered expression, 2 had a decreased expression and 9 had an increased expression. In addition, the abundance of a few cytokeratins was also altered; however, they were not capable of serving as specific circulatory biomarkers. The proteins which we observed to exhibit an altered expression in infiltrating ductal breast carcinoma may be exploited as novel targets for therapeutic interventions or represent novel diagnostic/prognostic markers for the early detection of aggressive tumors, particularly those with multridrug-resistant phenotypes during the earlier stages of the disease.

Sensitive and specific peak detection for SELDI-TOF mass spectrometry using a wavelet/neural-network based approach

SELDI-TOF mass spectrometer's compact size and automated, high throughput design have been attractive to clinical researchers, and the platform has seen steady-use in biomarker studies. Despite new algorithms and preprocessing pipelines that have been developed to address reproducibility issues, visual inspection of the results of SELDI spectra preprocessing by the best algorithms still shows miscalled peaks and systematic sources of error. This suggests that there continues to be problems with SELDI preprocessing. In this work, we study the preprocessing of SELDI in detail and introduce improvements. While many algorithms, including the vendor supplied software, can identify peak clusters of specific mass (or m/z) in groups of spectra with high specificity and low false discover rate (FDR), the algorithms tend to underperform estimating the exact prevalence and intensity of peaks in those clusters. Thus group differences that at first appear very strong are shown, after careful and laborious hand inspection of the spectra, to be less than significant. Here we introduce a wavelet/neural network based algorithm which mimics what a team of expert, human users would call for peaks in each of several hundred spectra in a typical SELDI clinical study. The wavelet denoising part of the algorithm optimally smoothes the signal in each spectrum according to an improved suite of signal processing algorithms previously reported (the LibSELDI toolbox under development). The neural network part of the algorithm combines those results with the raw signal and a training dataset of expertly called peaks, to call peaks in a test set of spectra with approximately 95% accuracy. The new method was applied to data collected from a study of cervical mucus for the early detection of cervical cancer in HPV infected women. The method shows promise in addressing the ongoing SELDI reproducibility issues.

Photonic crystal enhanced fluorescence for early breast cancer biomarker detection

Photonic crystal surfaces offer a compelling platform for improving the sensitivity of surface-based fluorescent assays used in disease diagnostics. Through the complementary processes of photonic crystal enhanced excitation and enhanced extraction, a periodic dielectric-based nanostructured surface can simultaneously increase the electric field intensity experienced by surface-bound fluorophores and increase the collection efficiency of emitted fluorescent photons. Through the ability to inexpensively fabricate photonic crystal surfaces over substantial surface areas, they are amenable to single-use applications in biological sensing, such as disease biomarker detection in serum. In this review, we will describe the motivation for implementing high-sensitivity, multiplexed biomarker detection in the context of breast cancer diagnosis. We will summarize recent efforts to improve the detection limits of such assays though the use of photonic crystal surfaces. Reduction of detection limits is driven by low autofluorescent substrates for photonic crystal fabrication, and detection instruments that take advantage of their unique features.

Proteomic biomarkers predicting lymph node involvement in serum of cervical cancer patients. Limitations of SELDI-TOF MS

BACKGROUND

Lymph node status is not part of the staging system for cervical cancer, but provides important information for prognosis and treatment. We investigated whether lymph node status can be predicted with proteomic profiling.

MATERIAL & METHODS

Serum samples of 60 cervical cancer patients (FIGO I/II) were obtained before primary treatment. Samples were run through a HPLC depletion column, eliminating the 14 most abundant proteins ubiquitously present in serum. Unbound fractions were concentrated with spin filters. Fractions were spotted onto CM10 and IMAC30 surfaces and analyzed with surface-enhanced laser desorption time of flight (SELDI-TOF) mass spectrometry (MS). Unsupervised peak detection and peak clustering was performed using MASDA software. Leave-one-out (LOO) validation for weighted Least Squares Support Vector Machines (LSSVM) was used for prediction of lymph node involvement. Other outcomes were histological type, lymphvascular space involvement (LVSI) and recurrent disease.

RESULTS

LSSVM models were able to determine LN status with a LOO area under the receiver operating characteristics curve (AUC) of 0.95, based on peaks with m/z values 2,698.9, 3,953.2, and 15,254.8. Furthermore, we were able to predict LVSI (AUC 0.81), to predict recurrence (AUC 0.92), and to differentiate between squamous carcinomas and adenocarcinomas (AUC 0.88), between squamous and adenosquamous carcinomas (AUC 0.85), and between adenocarcinomas and adenosquamous carcinomas (AUC 0.94).

CONCLUSIONS

Potential markers related with lymph node involvement were detected, and protein/peptide profiling support differentiation between various subtypes of cervical cancer. However, identification of the potential biomarkers was hampered by the technical limitations of SELDI-TOF MS.

Comparison of tear protein levels in breast cancer patients and healthy controls using a de novo proteomic approach

Noninvasive biomarkers are urgently needed for early detection of breast cancer since the risk of recurrence, morbidity and mortality are closely related to disease stage at the time of primary surgery. In the past decade, many proteomics-based approaches were developed that utilize the protein profiling of human body fluids or identification of putative biomarkers to obtain more knowledge on the effects of cancer emergence and progression. Herein, we report on an analysis of proteins in the tear fluid from breast carcinoma patients and healthy women using a de novo proteomic approach and 25 mixed samples from each group. This study included 25 patients with primary invasive breast carcinoma and 25 age-matched healthy controls. We performed a MALDI-TOF-TOF-driven semi-quantitative comparison of tear protein levels in cancer (CA) and control (CTRL) using a de novo approach in pooled samples. Over 150 proteins in the tear fluid of CTRL and CA were identified. Using an in-house-developed algorithm we found more than 20 proteins distinctly upregulated or downregulated in the CTRL and CA groups. We identified several proteins that had modified expression in breast cancer patients. These proteins are involved in host immune system pathways (e.g., C1Q1 or S100A8) and different metabolic cascades (ALDH3A or TPI). Further validation of the results in an independent population combined with individual protein profiling of participants is needed to confirm the specificity of our findings and may lead to a better understanding of the pathological mechanism of breast cancer.

Changes in the expression of plasma proteins associated with thrombosis in BRCA1 mutation carriers

PURPOSE

Although BRCA1 gene mutations have been associated with breast cancer, BRCA1 mutations have been also involved in other functions. Thrombosis and coagulation are novel mechanisms recently associated with cancer. The aims of the present study were (a) to evaluate, using proteomics, if BRCA1 mutation carriers have a different plasma proteins expression related to thrombosis and coagulation profile than non-mutant BRCA1 women and (b) to analyze if the expression of these proteins may be different among BRCA1 mutation carriers with and without breast cancer.

METHODS

Proteomic study was based on 2-dimensional electrophoresis and mass spectrometry. The study was performed in 10 BRCA1 non-mutant controls and 21 women with BRCA1 mutations (with breast cancer (n = 8) and breast cancer-free (n = 13)), all of them free of family history or diagnosis of ovarian cancer.

RESULTS

Proteomic study showed that fibrinogen gamma chain isotypes 2 and 3, serotransferrin isotype 4, and convertase C3/C5 isotypes 1-5 were significantly increased in plasma from BRCA1 mutation carriers with respect to BRCA1 non-mutant controls. Plasma levels of alpha-1 antitrypsin isotypes 2-5, apolipoprotein A-IV, and vitamin D-binding protein isotypes 1 and 2 were significantly reduced in BRCA1 mutation carriers with respect to non-mutant controls. Only apolipoprotein A-IV plasma levels were significantly higher in cancer-free BRCA1 mutations carriers compared with BRCA1 mutations carriers who developed breast cancer.

CONCLUSION

It is suggested that independently of breast cancer generation, BRCA1-encoded gene alterations are associated with changes in the expression of circulating proteins associated with thrombosis and coagulation.

Proteomic studies in breast cancer (Review)

Breast cancer is one of the most common types of invasive cancer in females worldwide. Despite major advances in early cancer detection and emerging therapeutic strategies, further improvement has to be achieved for precise diagnosis to reduce the chance of metastasis and relapses. Recent proteomic technologies have offered a promising opportunity for the identification of new breast cancer biomarkers. Matrix-assisted laser desorption/ionization, time-of-flight mass spectrometry (MALDI-TOF MS) and the derived surface-enhanced laser desorption/ionization mass spectrometry (SELDI-TOF MS) enable the development of high-throughput proteome analysis based on comprehensive reliable biomarkers. In this review, we examined proteomic technologies and their applications, and provided focus on the proteomics-based profiling analyses of tumor tissues/cells in order to identify and confirm novel biomarkers of breast cancer.

Comparison of functional proteomic analyses of human breast cancer cell lines T47D and MCF7

T47D and MCF7 are two human hormone-dependent breast cancer cell lines which are widely used as experimental models for in vitro and in vivo (tumor xenografts) breast cancer studies. Several proteins involved in cancer development were identified in these cell lines by proteomic analyses. Although these studies reported the proteomic profiles of each cell line, until now, their differential protein expression profiles have not been established. Here, we used two-dimensional gel and mass spectrometry analyses to compare the proteomic profiles of the two cell lines, T47D and MCF7. Our data revealed that more than 164 proteins are differentially expressed between them. According to their biological functions, the results showed that proteins involved in cell growth stimulation, anti-apoptosis mechanisms and cancerogenesis are more strongly expressed in T47D than in MCF7. These proteins include G1/S-specific cyclin-D3 and prohibitin. Proteins implicated in transcription repression and apoptosis regulation, including transcriptional repressor NF-X1, nitrilase homolog 2 and interleukin-10, are, on the contrary, more strongly expressed in MCF7 as compared to T47D. Five proteins that were previously described as breast cancer biomarkers, namely cathepsin D, cathepsin B, protein S100-A14, heat shock protein beta-1 (HSP27) and proliferating cell nuclear antigen (PCNA), are found to be differentially expressed in the two cell lines. A list of differentially expressed proteins between T47D and MCF7 was generated, providing useful information for further studies of breast cancer mechanisms with these cell lines as models.

Reproducibility of mass spectrometry based protein profiles for diagnosis of ovarian cancer across clinical studies: A systematic review

The focus of this systematic review is to give an overview of the current status of clinical protein profiling studies using MALDI and SELDI MS platforms in the search for ovarian cancer biomarkers. A total of 34 profiling studies were qualified for inclusion in the review. Comparative analysis of published discriminatory peaks to peaks found in an original MALDI MS protein profiling study was made to address the key question of reproducibility across studies. An overlap was found despite substantial heterogeneity between studies relating to study design, biological material, pre-analytical treatment, and data analysis. About 47% of the peaks reported to be associated to ovarian cancer were also represented in our experimental study, and 34% of these redetected peaks also showed a significant difference between cases and controls in our study. Thus, despite known problems related to reproducibility an overlap in peaks between clinical studies was demonstrated, which indicate convergence toward a set of common discriminating, reproducible peaks for ovarian cancer. The potential of the discriminating protein peaks for clinical use as ovarian cancer biomarkers will be discussed and evaluated. This article is part of a Special Issue entitled: Proteomics: The clinical link.

Salivary Proteomic Signatures of Oral Squamous Cell Carcinoma

Delay in diagnosing oral squamous cell carcinoma (OSCC) can be still identified as a major cause of its high morbidity and mortality. To date, the early diagnosis for OSCC is mainly based on clinical oral examination and there is an urgent need for reliable markers; thus, advancements in molecular technologies has set the stage for investigating new markers, as well as new diagnostic matrices. The aim of the present study is to investigate the presence of proteomic signatures of OSCC in saliva and their use as potential biomarkers for early and non-invasive diagnosis. Saliva from 45 OSCC patients and 30 healthy controls was analysed by SELDI-TOF mass spectrometry and ProteinChip® technology. A supervised multivariate statistical analysis (Classification and Regression Tree - CART) was used to build models for discriminating between OSCC and controls, and between early (ES-OSCC) and late stage (LS-OSCC) cancers. The peptide with 8041 Da mass was 22-fold more expressed in OSCC, thus being a suitable potential biomarker. Classification and regression analysis allowed to build a model that was capable of correctly classifying all cancers and controls in an independent testing set, using the 8041 m/z peak as splitter. Eleven peaks were also differently expressed between ES-OSCCand LS-OSCC, but, basing on these differences, it was not possible to build an algorithm to predict tumour staging. These findings confirm that saliva proteome in OSCC patients is different from healthy controls and these variations might reflect different stages of disease progression and are worthy of further validation as diagnostic and prognostic biomarkers.

Early diagnostic protein biomarkers for breast cancer: how far have we come?

Many studies have used surface-enhanced laser desorption/ionization time-of-flight mass spectrometry or matrix-assisted laser desorption/ionization time-of-flight mass spectrometry to search for blood-based proteins that are related to the presence of breast cancer. We review the biomarkers discovered or targeted measured by these methods and discuss the strengths and weaknesses of these studies. We highlight two proteins that were most often related to breast cancer: C3a des-arginine anaphylatoxin (C3adesArg) (molecular weight: 8,938 Da) and fragments of inter-alpha trypsin inhibitor heavy chain H4 (ITIH4). In addition, we elaborate on three important methodological aspects related to these studies: protein identification, specificity of the markers, and disease heterogeneity. Finally, we propose some points to be addressed in future studies. These include the use of other analytical measurement techniques, need of protein identification, the importance of identical sample handling protocols for cases and controls, and the stratification of the results according to molecular subtypes and stages of breast cancer. Ultimately this may lead to the discovery of new and valid breast cancer specific biomarkers.

Breast cancer proteomics: a review for clinicians

PURPOSE

Breast cancer is one of the major health problems of the Western world. Although the survival rate has improved with progress in screening and adjuvant systemic therapies, one-third of the patients with initial breast tumor have recurrence of the disease 10 years after the diagnosis, demonstrating the presence of micrometastasis. The underlying molecular mechanism of the disease needs to be better understood. Allied to genomics, proteomics technologies promise to be valuable for identifying new markers that improve screening, early diagnosis, prognosis and prediction of therapeutic response or toxicity, as well as the identification of new therapeutic targets. In this review, we present features of proteomic technology and its main implications, focusing on the protein profile in tumor tissues/cells through MALDI/SELDI, as well as on the current proteomic challenges in the breast cancer study.

METHODS

We performed a research of protein profiling studies using mass spectrometry in breast cancer to identify potential biomarkers.

RESULTS

Many protein peaks have been reported to bear significant diagnostic, prognostic or predictive value; however, the candidate biomarkers have not been validated for use in clinical patient care.

CONCLUSIONS

Proteomics is under development and, despite technical barriers that precede the use of proteomics analysis in clinical practice and breast cancer complexity, MALDI-TOF/SELDI-TOF MS proteomic platforms with their innovations are powerful analytical tools for the detection of better protein biomarkers, since the studies are conducted with adequate statistical power and analytical rigor. In the near future, they will be able to fulfill their role in personalized medicine.

Plasma biomarker profiles differ depending on breast cancer subtype but RANTES is consistently increased

BACKGROUND

Current biomarkers for breast cancer have little potential for detection. We determined whether breast cancer subtypes influence circulating protein biomarkers.

METHODS

A sandwich ELISA microarray platform was used to evaluate 23 candidate biomarkers in plasma samples that were obtained from subjects with either benign breast disease or invasive breast cancer. All plasma samples were collected at the time of biopsy, after a referral due to a suspicious screen (e.g., mammography). Cancer samples were evaluated on the basis of breast cancer subtypes, as defined by the HER2 and estrogen receptor statuses.

RESULTS

Ten proteins were statistically altered in at least one breast cancer subtype, including four epidermal growth factor receptor ligands, two matrix metalloproteases, two cytokines, and two angiogenic factors. Only one cytokine, RANTES, was significantly increased (P < 0.01 for each analysis) in all four subtypes, with areas under the curve (AUC) for receiver operating characteristic values that ranged from 0.76 to 0.82, depending on cancer subtype. The best AUC values were observed for analyses that combined data from multiple biomarkers, with values ranging from 0.70 to 0.99, depending on the cancer subtype. Although the results for RANTES are consistent with previous publications, the multi-assay results need to be validated in independent sample sets.

CONCLUSIONS

Different breast cancer subtypes produce distinct biomarker profiles, and circulating protein biomarkers have potential to differentiate between true- and false-positive screens for breast cancer.

IMPACT

Subtype-specific biomarker panels may be useful for detecting breast cancer or as an adjunct assay to improve the accuracy of current screening methods.

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.

Optimal management of bone metastases in breast cancer patients

Bone metastasis in breast cancer is a significant clinical problem. It not only indicates incurable disease with a guarded prognosis, but is also associated with skeletal-related morbidities including bone pain, pathological fractures, spinal cord compression, and hypercalcemia. In recent years, the mechanism of bone metastasis has been further elucidated. Bone metastasis involves a vicious cycle of close interaction between the tumor and the bone microenvironment. In patients with bone metastases, the goal of management is to prevent further skeletal-related events, manage complications, reduce bone pain, and improve quality of life. Bisphosphonates are a proven therapy for the above indications. Recently, a drug of a different class, the RANK ligand antibody, denosumab, has been shown to reduce skeletal-related events more than the bisphosphonate, zoledronic acid. Other strategies of clinical value may include surgery, radiotherapy, radiopharmaceuticals, and, of course, effective systemic therapy. In early breast cancer, bisphosphonates may have an antitumor effect and prevent both bone and non-bone metastases. Whilst two important Phase III trials with conflicting results have led to controversy in this topic, final results from these and other key Phase III trials must still be awaited before a firm conclusion can be drawn about the use of bisphosphonates in this setting. Advances in bone markers, predictive biomarkers, multi-imaging modalities, and the introduction of novel agents have ushered in a new era of proactive management for bone metastases in breast cancer.

A new method for isolation of interstitial fluid from human solid tumors applied to proteomic analysis of ovarian carcinoma tissue

Major efforts have been invested in the identification of cancer biomarkers in plasma, but the extraordinary dynamic range in protein composition, and the dilution of disease specific proteins make discovery in plasma challenging. Focus is shifting towards using proximal fluids for biomarker discovery, but methods to verify the isolated sample's origin are missing. We therefore aimed to develop a technique to search for potential candidate proteins in the proximal proteome, i.e. in the tumor interstitial fluid, since the biomarkers are likely to be excreted or derive from the tumor microenvironment. Since tumor interstitial fluid is not readily accessible, we applied a centrifugation method developed in experimental animals and asked whether interstitial fluid from human tissue could be isolated, using ovarian carcinoma as a model. Exposure of extirpated tissue to 106 g enabled tumor fluid isolation. The fluid was verified as interstitial by an isolated fluid:plasma ratio not significantly different from 1.0 for both creatinine and Na(+), two substances predominantly present in interstitial fluid. The isolated fluid had a colloid osmotic pressure 79% of that in plasma, suggesting that there was some sieving of proteins at the capillary wall. Using a proteomic approach we detected 769 proteins in the isolated interstitial fluid, sixfold higher than in patient plasma. We conclude that the isolated fluid represents undiluted interstitial fluid and thus a subproteome with high concentration of locally secreted proteins that may be detected in plasma for diagnostic, therapeutic and prognostic monitoring by targeted methods.

Serum Ferritin in Healthy Women and Breast Cancer Patients

Serum tumor markers are important tools in managing patients with breast cancer. Currently used CA 15-3 and CEA have found their clinical application particularly in the follow-up of patients with advanced disease. Ferritin belongs to a group of other molecules of potential interest to clinicians whose concentration is also altered in sera of patients with breast tumors. In this study the serum ferritin concentration was estimated in the sera of breast cancer patients before initial surgical treatment or those with advanced disease, and compared to healthy women as control. Ferritin level was measured by an immunoradiometric assay. The aim was to asses whether the serum ferritin concentration was altered in breast cancer and whether it could be related to progression of the disease. In healthy women, a statistically significant difference (p<0.05) in ferritin concentration was observed between premenopausal and postmenopausal women. In both breast cancer groups ferritin levels were higher than in healthy premenopausal women (both p<0.05). In patients with advanced disease, ferritin was further elevated (p<0.05) compared to preoperative levels in the patient group undergoing initial surgical treatment. These results indicate that an elevated ferritin concentration in the serum of younger women could serve as an additional parameter in breast cancer diagnosis and staging.

Discovery and preclinical validation of salivary transcriptomic and proteomic biomarkers for the non-invasive detection of breast cancer

Background

A sensitive assay to identify biomarkers using non-invasively collected clinical specimens is ideal for breast cancer detection. While there are other studies showing disease biomarkers in saliva for breast cancer, our study tests the hypothesis that there are breast cancer discriminatory biomarkers in saliva using de novo discovery and validation approaches. This is the first study of this kind and no other study has engaged a de novo biomarker discovery approach in saliva for breast cancer detection. In this study, a case-control discovery and independent preclinical validations were conducted to evaluate the performance and translational utilities of salivary transcriptomic and proteomic biomarkers for breast cancer detection.

Methodology/Principal Findings

Salivary transcriptomes and proteomes of 10 breast cancer patients and 10 matched controls were profiled using Affymetrix HG-U133-Plus-2.0 Array and two-dimensional difference gel electrophoresis (2D-DIGE), respectively. Preclinical validations were performed to evaluate the discovered biomarkers in an independent sample cohort of 30 breast cancer patients and 63 controls using RT-qPCR (transcriptomic biomarkers) and quantitative protein immunoblot (proteomic biomarkers). Transcriptomic and proteomic profiling revealed significant variations in salivary molecular biomarkers between breast cancer patients and matched controls. Eight mRNA biomarkers and one protein biomarker, which were not affected by the confounding factors, were pre-validated, yielding an accuracy of 92% (83% sensitive, 97% specific) on the preclinical validation sample set.

Conclusions

Our findings support that transcriptomic and proteomic signatures in saliva can serve as biomarkers for the non-invasive detection of breast cancer. The salivary biomarkers possess discriminatory power for the detection of breast cancer, with high specificity and sensitivity, which paves the way for prediction model validation study followed by pivotal clinical validation.