Proteomic Profiling of Primary Breast Cancer Predicts Axillary Lymph Node Metastasis

A radiogenomic multimodal and whole-transcriptome sequencing for preoperative prediction of axillary lymph node metastasis and drug therapeutic response in breast cancer: a retrospective, machine learning and international multicohort study

BACKGROUND

Axillary lymph nodes (ALN) status serves as a crucial prognostic indicator in breast cancer (BC). The aim of this study was to construct a radiogenomic multimodal model, based on machine learning and whole-transcriptome sequencing (WTS), to accurately evaluate the risk of ALN metastasis (ALNM), drug therapeutic response and avoid unnecessary axillary surgery in BC patients.

METHODS

In this study, conducted a retrospective analysis of 1078 BC patients from The Cancer Genome Atlas (TCGA), The Cancer Imaging Archive (TCIA), and Foshan cohort. These patients were divided into the TCIA cohort ( N =103), TCIA validation cohort ( N =51), Duke cohort ( N =138), Foshan cohort ( N =106), and TCGA cohort ( N =680). Radiological features were extracted from BC radiological images and differentially expressed gene expression was calibrated using technology. A support vector machine model was employed to screen radiological and genetic features, and a multimodal model was established based on radiogenomic and clinical pathological features to predict ALNM. The accuracy of the model predictions was assessed using the area under the curve (AUC) and the clinical benefit was measured using decision curve analysis. Risk stratification analysis of BC patients was performed by gene set enrichment analysis, differential comparison of immune checkpoint gene expression, and drug sensitivity testing.

RESULTS

For the prediction of ALNM, rad-score was able to significantly differentiate between ALN- and ALN+ patients in both the Duke and Foshan cohorts ( P <0.05). Similarly, the gene-score was able to significantly differentiate between ALN- and ALN+ patients in the TCGA cohort ( P <0.05). The radiogenomic multimodal nomogram demonstrated satisfactory performance in the TCIA cohort (AUC 0.82, 95% CI: 0.74-0.91) and the TCIA validation cohort (AUC 0.77, 95% CI: 0.63-0.91). In the risk sub-stratification analysis, there were significant differences in gene pathway enrichment between high and low-risk groups ( P <0.05). Additionally, different risk groups may exhibit varying treatment responses ( P <0.05).

CONCLUSION

Overall, the radiogenomic multimodal model employs multimodal data, including radiological images, genetic, and clinicopathological typing. The radiogenomic multimodal nomogram can precisely predict ALNM and drug therapeutic response in BC patients.

Toward Exempting from Sentinel Lymph Node Biopsy in T1 Breast Cancer Patients: A Retrospective Study

Background and Objective

Sentinel lymph node biopsy (SLNB) is used to assess the status of axillary lymph node (ALN), but it causes many adverse reactions. Considering the low rate of sentinel lymph node (SLN) metastasis in T1 breast cancer, this study aims to identify the characteristics of T1 breast cancer without SLN metastasis and to select T1 breast cancer patients who avoid SLNB through constructing a nomogram.

Methods

A total of 1,619 T1 breast cancer patients with SLNB in our hospital were enrolled in this study. Through univariate and multivariate logistic regression analysis, we analyzed the tumor anatomical and clinicopathological factors and constructed the Heilongjiang Medical University (HMU) nomogram. We selected the patients exempt from SLNB by using the nomogram.

Results

In the training cohort of 1,000 cases, the SLN metastasis rate was 23.8%. Tumor volume, swollen axillary lymph nodes, pathological types, and molecular subtypes were found to be independent predictors for SLN metastasis in multivariate regression analysis. Distance from nipple or surface and position of tumor have no effect on SLN metastasis. A regression model based on the results of the multivariate analysis was developed to predict the risk of SLN metastasis, indicating an AUC of 0.798. It showed excellent diagnostic performance (AUC = 0.773) in the validation cohort.

Conclusion

The HMU nomogram for predicting SLN metastasis incorporates four variables, including tumor volume, swollen axillary lymph nodes, pathological types, and molecular subtypes. The SLN metastasis rates of intraductal carcinoma and HER2 enriched are 2.05% and 6.67%. These patients could be included in trials investigating the SLNB exemption.

Proteomics of REPLICANT perfusate detects changes in the metastatic lymph node microenvironment

In breast cancer (BC), detecting low volumes of axillary lymph node (ALN) metastasis pre-operatively is difficult and novel biomarkers are needed. We recently showed that patient-derived ALNs can be sustained ex-vivo using normothermic perfusion. We now compare reactive (tumour-free; n = 5) and macrometastatic (containing tumour deposits >2 mm; n = 4) ALNs by combining whole section multiplex immunofluorescence with TMT-labelled LC-MS/MS of the circulating perfusate. Macrometastases contained significantly fewer B cells and T cells (CD4⁺/CD8⁺/regulatory) than reactive nodes (p = 0.02). Similarly, pathway analysis of the perfusate proteome (119/1453 proteins significantly differentially expressed) showed that immune function was diminished in macrometastases in favour of ‘extracellular matrix degradation’; only ‘neutrophil degranulation’ was preserved. Qualitative comparison of the perfusate proteome to that of node-positive pancreatic and prostatic adenocarcinoma also highlighted ‘neutrophil degranulation’ as a contributing factor to nodal metastasis. Thus, metastasis-induced changes in the REPLICANT perfusate proteome are detectable, and could facilitate biomarker discovery.

Studying Lymphatic Metastasis in Breast Cancer: Current Models, Strategies, and Clinical Perspectives

Breast cancer is the most commonly diagnosed cancer in women and the second most common cause of cancer-related deaths in the United States. Although early detection has significantly decreased breast cancer mortality, patients diagnosed with distant metastasis still have a very poor prognosis. The most common site that breast cancer spreads to are local lymph nodes. Therefore, the presence of lymph node metastasis remains one of most important prognostic factors in breast cancer patients. Given its significant clinical implications, increased efforts have been dedicated to better understand the molecular mechanism governing lymph node metastasis in breast cancer. The identification of lymphatic-specific biomarkers, including podoplanin and LYVE-1, has propelled the field of lymphatic metastasis forward. In addition, several animal models such as cell line-derived xenografts, patient-derived xenografts, and spontaneous tumor models have been developed to recreate the process of lymphatic metastasis. Moreover, the incorporation of various -omic platforms have provided further insight into the genetic drivers facilitating lymphatic metastasis, as well as potential biomarkers and therapeutic targets. Here, we highlight various models of lymphatic metastasis, their potential pitfalls, and other tools available to study lymphatic metastasis including imaging modalities and -omic studies.

Pathway-centric integrative analysis identifies RRM2 as a prognostic marker in breast cancer associated with poor survival and tamoxifen resistance

Breast cancer (BCa) molecular subtypes include luminal A, luminal B, normal-like, HER-2-enriched, and basal-like tumors, among which luminal B and basal-like cancers are highly aggressive. Biochemical pathways associated with patient survival or treatment response in these more aggressive subtypes are not well understood. With the limited availability of pathologically verified clinical specimens, cell line models are routinely used for pathway-centric studies. We measured the metabolome of luminal and basal-like BCa cell lines using mass spectrometry, linked metabolites to biochemical pathways using Gene Set Analysis, and developed a novel rank-based method to select pathways on the basis of their enrichment in patient-derived omics data sets and prognostic relevance. Key mediators of the pathway were then characterized for their role in disease progression. Pyrimidine metabolism was altered in luminal versus basal BCa, whereas the combined expression of its associated genes or expression of one key gene, ribonucleotide reductase subunit M2 (RRM2) alone, associated significantly with decreased survival across all BCa subtypes, as well as in luminal patients resistant to tamoxifen. Increased RRM2 expression in tamoxifen-resistant patients was verified using tissue microarrays, whereas the metabolic products of RRM2 were higher in tamoxifen-resistant cells and in xenograft tumors. Both genetic and pharmacological inhibition of this key enzyme in tamoxifen-resistant cells significantly decreased proliferation, reduced expression of cell cycle genes, and sensitized the cells to tamoxifen treatment. Our study suggests for evaluating RRM2-associated metabolites as noninvasive markers for tamoxifen resistance and its pharmacological inhibition as a novel approach to overcome tamoxifen resistance in BCa.

Molecular signatures of lymph node status by intrinsic subtype: gene expression analysis of primary breast tumors from patients with and without metastatic lymph nodes

Background

Identification of a gene expression signature in primary breast tumors that could classify patients by lymph node status would allow patients to avoid the morbidities of surgical disruption of the lymph nodes. Attempts to identify such a signature have, to date, been unsuccessful. Because breast tumor subtypes have unique molecular characteristics and different sites of metastasis, molecular signatures for lymph node involvement may vary by subtype.

Methods

Gene expression data was generated from HG U133A 2.0 arrays for 135 node positive and 210 node negative primary breast tumors. Intrinsic subtype was assigned using the BreastPRS. Differential gene expression analysis was performed using one-way ANOVA using lymph node status as the variable with a False-discovery rate <0.05, to define significance.

Results

Luminal A tumors were most common (51%) followed by basal-like (27%), HER2-enriched (14%) luminal B (7%) and normal-like (1%). Basal-like and luminal A tumors were less likely to have metastatic lymph nodes (35% and 37%, respectively) compared to luminal B or HER2-enriched (52% and 51%, respectively). No differentially expressed genes associated with lymph node status were detected when all tumors were considered together or within each subtype.

Conclusions

Gene expression patterns from the primary tumor are not able to stratify patients by lymph node status. Although the primary breast tumor may influence tumor cell dissemination, once metastatic cells enter the lymphatics, it is likely that characteristics of the lymph node microenvironment, such as establishment of a pre-metastatic niche and release of pro-survival factors, determine which cells are able to colonize. The inability to utilize molecular profiles from the primary tumor to determine lymph node status suggest that other avenues of investigation, such as how systemic factors including diminished immune response or genetic susceptibility contribute to metastasis, may be critical in the development of tools for non-surgical assessment of lymph node status with a corresponding reduction in downstream sequelae associated with disruption of the lymphatics.

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.

Proteomics for breast cancer urine biomarkers

Although the survival of breast cancer (BC) patients has increased over the last two decades due to improved screening programs and postoperative adjuvant systemic therapies, many patients die from metastatic relapse. Current biomarkers used in the clinic are not useful for the early detection of BC, or monitoring its progression, and have limited value in predicting response to treatment. The development of proteomic techniques has sparked new searches for novel protein markers for many diseases including BC. Proteomic techniques allow for a high-throughput analysis of samples with the visualization and quantification of thousands of potential protein and peptide markers. Human urine is one of the most interesting and useful biofluids for routine testing and provides an excellent resource for the discovery of novel biomarkers, with the advantage over tissue biopsy samples due to the ease and less invasive nature of collection. In this review, we summarize the results from studies where urine was used as a source for BC biomarker research and discuss urine sample preparation, its advantage, challenges, and limitation. We focus on the gel-based proteomic approaches as well as the recent development of quantitative techniques in BC urine biomarker detection. Finally, the future use of modern proteomic techniques in BC biomarker identification will be discussed.

Breast cancer biomarkers: proteomic discovery and translation to clinically relevant assays

Although the molecular classification and prognostic assessment of breast tumors based on gene expression profiling is well established, a number of proteomic studies that propose potential breast cancer biomarkers has not yet led to any new diagnostic, prognostic or predictive test in wide clinical use. This review examines the current status of breast cancer biomarkers, discusses sample types (including plasma, tumor tissue, nipple aspirate and ductal lavage, as well as cell culture models) and different electrophoretic and mass spectrometry methods that have been widely used for the discovery of proteomic biomarkers in breast cancer, and also considers several approaches to biomarker validation. The pathway leading from the initial proteomic discovery and validation process to translation into a clinically useful test is also discussed.

Tissue biomarkers of breast cancer and their association with conventional pathologic features

Background:

Tissue protein expression profiling has the potential to detect new biomarkers to improve breast cancer (BC) diagnosis, staging, and prognostication. This study aimed to identify tissue proteins that differentiate breast cancer tissue from healthy breast tissue using protein chip mass spectrometry and to examine associations with conventional pathological features.

Methods:

To develop a training model, 82 BC and 82 adjacent unaffected tissue (AT) samples were analysed on cation-exchange protein chips by time-of-flight mass spectrometry. For validation, 89 independent BC and AT sample pairs were analysed.

Results:

From the protein peaks that were differentially expressed between BC and AT by univariate analysis, binary logistic regression yielded two peaks that together classified BC and AT with a ROC area under the curve of 0.92. Two proteins, ubiquitin and S100P (in a novel truncated form), were identified by liquid chromatography/tandem mass spectrometry and validated by immunoblotting and reactive-surface protein chip immunocapture. The combined marker panel was positively associated with high histologic grade, larger tumour size, lymphovascular invasion, ER and PR positivity, and HER2 overexpression, suggesting that it may be associated with a HER2-enriched molecular subtype of breast cancer.

Conclusion:

This independently validated protein panel may be valuable in the classification and prognostication of breast cancer patients.

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.

Proteomic-based biosignatures in breast cancer classification and prediction of therapeutic response

Protein-based markers that classify tumor subtypes and predict therapeutic response would be clinically useful in guiding patient treatment. We investigated the LC-MS/MS-identified protein biosignatures in 39 baseline breast cancer specimens including 28 HER2-positive and 11 triple-negative (TNBC) tumors. Twenty proteins were found to correctly classify all HER2 positive and 7 of the 11 TNBC tumors. Among them, galectin-3-binding protein and ALDH1A1 were found preferentially elevated in TNBC, whereas CK19, transferrin, transketolase, and thymosin β4 and β10 were elevated in HER2-positive cancers. In addition, several proteins such as enolase, vimentin, peroxiredoxin 5, Hsp 70, periostin precursor, RhoA, cathepsin D preproprotein, and annexin 1 were found to be associated with the tumor responses to treatment within each subtype. The MS-based proteomic findings appear promising in guiding tumor classification and predicting response. When sufficiently validated, some of these candidate protein markers could have great potential in improving breast cancer treatment.

Validation of tumor-associated macrophage ferritin light chain as a prognostic biomarker in node-negative breast cancer tumors: A multicentric 2004 national PHRC study

Novel prognostic biomarkers are imperatively needed to help direct treatment decisions by typing subgroups of node-negative breast cancer patients. Large screening of different biological compartments, such as the proteome, by means of high throughput techniques may greatly help scientists to find such markers. The present retrospective multicentric study included 268 node-negative breast cancer patients. We used a proteomic approach of SELDI-TOF-MS screening to identify differentially expressed cytosolic proteins with prognostic impact. The screening cohort was composed of 198 patients. Seventy supplementary patients were included for validation. Immunohistochemistry (IHC) and immunoassay (IA) were run to confirm the prognostic role of the marker identified by SELDI-TOF-MS screening. IHC was also used to explore links between selected marker and epithelial-mesenchymal transition (EMT)-like, proliferation and macrophage markers. Ferritin light chain (FTL) was identified as an independent prognostic marker (HR = 1.30-95% CI: 1.10-1.50, p = 0.001). Validation step by means of IHC and IA confirmed the prognostic value of FTL level. CD68 IHC showed that FTL was stored in tumor-associated macrophages (TAM), which exhibit an M2-like phenotype. We report here, first, the validation of FTL as a breast tumor prognostic biomarker in node-negative patients, and second, the fact that FTL is stored in TAM.

Liquid chromatography-tandem and MALDI imaging mass spectrometry analyses of RCL2/CS100-fixed, paraffin-embedded tissues: proteomics evaluation of an alternate fixative for biomarker discovery

Human tissues are an important source of biological material for the discovery of novel biomarkers. Fresh-frozen tissue could represent an ideal supply of archival material for molecular investigations. However, immediate flash freezing is usually not possible, especially for rare or valuable tissue samples such as biopsies. Here, we investigated the compatibility of RCL2/CS100, a non-cross-linking, nontoxic, and nonvolatile organic fixative, with shotgun proteomic analyses. Several protein extraction protocols compatible with mass spectrometry were investigated from RCL2/CS100-fixed and fresh-frozen colonic mucosa, breast, and prostate tissues. The peptides and proteins identified from RCL2/CS100 tissue were then comprehensively compared with those identified from matched fresh-frozen tissues using a bottom-up strategy based on nano-reversed phase liquid chromatography coupled with tandem mass spectrometry (nanoRPLC-MS/MS). Results showed that similar peptides could be identified in both archival conditions and the proteome coverage was not obviously compromised by the RCL2/CS100 fixation process. NanoRPLC-MS/MS of laser capture microdissected RCL2/CS100-fixed tissues gave the same amount of biological information as that recovered from whole RCL2/CS100-fixed or frozen tissues. We next performed MALDI tissue profiling and imaging mass spectrometry and observed a high level of agreement in protein expression as well as excellent agreement between the images obtained from RCL2/CS100-fixed and fresh-frozen tissue samples. These results suggest that RCL2/CS100-fixed tissues are suitable for shotgun proteomic analyses and tissue imaging. More importantly, this alternate fixative opens the door to the analysis of small, valuable, and rare target lesions that are usually inaccessible to complementary biomarker-driven genomic and proteomic research.

A Comparison of the Proteomic Expression in Pooled Saliva Specimens from Individuals Diagnosed with Ductal Carcinoma of the Breast with and without Lymph Node Involvement

Purpose. The objective was to compare the salivary protein profiles of saliva specimens from individuals diagnosed with invasive ductal carcinoma of the breast (IDC) with and without lymph node involvement. Methods. Three pooled saliva specimens from women were analyzed. One pooled specimen was from healthy women; another was from women diagnosed with Stage IIa IDC and a specimen from women diagnosed with Stage IIb. The pooled samples were trypsinized and the peptide digests labeled with the appropriate iTRAQ reagent. Labeled peptides from each of the digests were combined and analyzed by reverse phase capillary chromatography on an LC-MS/MS mass spectrometer. Results. The results yielded approximately 174 differentially expressed proteins in the saliva specimens. There were 55 proteins that were common to both cancer stages in comparison to each other and healthy controls while there were 20 proteins unique to Stage IIa and 28 proteins that were unique to Stage IIb.

Cytokeratin 8/18 as a new marker of mouse liver preneoplastic lesions

To search for a reliable biomarker of preneoplastic lesions arising early in mouse hepatocarcinogenesis the proteomes of microdissected basophilic foci, hepatocellular adenomas (HCAs), carcinomas (HCCs) and normal-appearing liver of B6C3F1 mice initiated with diethylnitrosamine (DEN) were analysed on anionic (Q10) surface-enhanced laser desorption/ionization time-of-flight mass spectrometry (SELDI-TOF-MS) ProteinChip arrays. Significant overexpression of cytokeratin 8 (CK8; m/z 54, 565), cytokeratin 18 (CK18; m/z 47,538) proteins was found in basophilic foci as well as in HCAs and HCCs. Furthermore, immunohistochemistry demonstrated profound overexpression of CK8 and CK18 proteins (CK8/18) in all basophilic foci, mixed cell type foci, HCAs and HCCs in B6C3F1 and C57BL/6J mice initiated with DEN. A strong correlation between CK8/18-positive foci development and multiplicity of liver tumors in B6C3F1 and C57Bl/6J mice was further observed. Moreover, formation of CK8 and CK18 complexes due to CK8 phosphorylation at Ser73 and Ser431 was found to be strongly associated with neoplastic transformation of mice liver basophilic foci. Elevation of CK8/18 was strongly correlated with induction of cell proliferation in basophilic foci and tumors. In conclusion, our data imply that CK8/18 is a novel reliable marker of preneoplastic lesions arising during mouse hepatocarcinogenesis which might be used for prediction of tumor development and evaluation of environmental agents as well as drugs and food additives using mouse liver tests.

Cytokeratin 8/18 overexpression and complex formation as an indicator of GST-P positive foci transformation into hepatocellular carcinomas

Screening of the proteome of microdissected glutathione S-transferase placental form (GST-P) positive foci and normal-appearing liver on anionic (Q10), and cationic (CM10) surface-enhanced laser desorption/ionization time-of-flight mass spectrometry (SELDI-TOF-MS) ProteinChip arrays demonstrated significant overexpression of cytokeratin 8 (CK8; m/z 54,020), cytokeratin 18 (CK18; m/z 47,760), microsomal cytochrome 5A (m/z 15,224) and histone type 2 H2aa3 (m/z 15,964) in the livers of rats initiated with diethylnitrosamine (DEN) followed by 10 weeks on phenobarbital (PB) at a dose of 500 ppm. Furthermore, formation of CK8 and CK18 complexes due to CK8 phosphorylation at Ser73 and Ser431 was found to be strongly associated with promotion of hepatocarcinogenesis by PB and the development of hepatocellular carcinomas. The data were confirmed by immunohistochemistry and real-time Q-PCR and profound overexpression of CK8 and CK18 (CK8/18) proteins and mRNAs were detected in several large size GST-P positive foci and liver tumors. A strong correlation between CK8/18 positive foci development and multiplicity of hepatocellular carcinomas was further observed. Moreover, elevation of CK8/18 was strongly associated with induction of cell proliferation in GST-P positive foci and tumors. In conclusion, our data imply that CK8/18 overexpression, those two cytokeratins complex formation associated with histone type 2 H2aa3 up-regulation and intermediate filament reorganization may drive neoplastic transformation of GST-P positive foci during rat hepatocarcinogenesis leading to the formation of hepatocellular carcinomas.

Biomarker discovery in low-grade breast cancer using isobaric stable isotope tags and two-dimensional liquid chromatography-tandem mass spectrometry (iTRAQ-2DLC-MS/MS) based quantitative proteomic analysis

The present pilot study constitutes a proof-of-principle in the use of a quantitative LC-MS/MS based proteomic method for the comparative analysis of representative low-grade breast primary tumor tissues with and without metastases and metastasis in lymph node relative to the nonmetastatic tumor type. The study method incorporated iTRAQ stable isotope labeling, two-dimensional liquid chromatography, nanoelectrospray ionization and high resolution tandem mass spectrometry using the hybrid QqTOF platform (iTRAQ-2DLC-MS/MS). The principal aims of this study were (1) to define the protein spectrum obtainable using this approach, and (2) to highlight potential candidates for verification and validation studies focused on biomarkers involved in metastatic processes in breast cancer. The study resulted in the reproducible identification of 605 nonredundant proteins (p < or = 0.05). A quantitative comparison revealed 3/3 proteins with significantly increased/decreased level in metastatic primary tumor and 13/6 proteins with increased/decreased level in lymph node metastasis compared to nonmetastatic primary tumor (p < 0.01). Changes in selected differentially expressed proteins were verified with qRT-PCR. Although our pilot scale study does not warrant general biological conclusions, the synergic regulation of some proteins with related function (e.g., heme binding proteins, proteins of energetic metabolism, interferon induced proteins, proteins with adhesive function) determined in our sample set reflects the ability of our method in providing biologically meaningful data. The main conclusion from this pilot study was that our quantitative proteomic method constitutes a novel way of analyzing cancerous breast tissue biopsy samples that can be extended as part of a larger scale biomarker discovery program.

Clinical proteomics in breast cancer: a review

Breast cancer imposes a significant healthcare burden on women worldwide. Early detection is of paramount importance in reducing mortality, yet the diagnosis of breast cancer is hampered by the lack of an adequate detection method. In addition, better breast cancer prognostication may improve selection of patients eligible for adjuvant therapy. Hence, new markers for early diagnosis, accurate prognosis and prediction of response to treatment are warranted to improve breast cancer care. Since proteomics can bridge the gap between the genetic alterations underlying cancer and cellular physiology, much is expected from proteome analyses for the detection of better protein biomarkers. Recent technical advances in mass spectrometry, such as matrix-assisted laser desorption/ionisation time-of-flight mass spectrometry (MALDI-TOF MS) and its variant surface-enhanced laser desorption/ionisation (SELDI-) TOF MS, have enabled high-throughput proteome analysis. In the current review, we give a comprehensive overview of the results of expression proteomics (i.e. protein profiling) research performed in breast cancer using these two platforms. Many protein peaks have been reported to bear significant diagnostic, prognostic or predictive value, however, only few candidate markers have been structurally identified yet. In addition, although of pivotal importance in preventing overfitting of data and systematic bias by pre-analytical parameters, validation of biomarker candidates by other, quantitative, methods and/or in new populations is very limited. Moreover, none of the identified candidate biomarkers has been investigated for their utility as breast cancer markers in large, prospective, clinical settings. As such, the candidate biomarkers discussed in this overview have not been validated sufficiently to be used for clinical patient care. Nonetheless, regarding the promising results up to now, MALDI- and SELDI-TOF MS protein profiling studies could eventually fulfil the great promise that protein biomarkers have for improving cancer patient outcome, provided that these studies are performed with adequate statistical power and analytical rigour.

Surface-enhanced laser desorption/ionization time-of-flight proteomic profiling of breast carcinomas identifies clinicopathologically relevant groups of patients similar to previously defined clusters from cDNA expression

Introduction

Microarray-based gene expression profiling represents a major breakthrough for understanding the molecular complexity of breast cancer. cDNA expression profiles cannot detect changes in activities that arise from post-translational modifications, however, and therefore do not provide a complete picture of all biologically important changes that occur in tumors. Additional opportunities to identify and/or validate molecular signatures of breast carcinomas are provided by proteomic approaches. Surface-enhanced laser desorption/ionization time-of-flight mass spectrometry (SELDI-TOF MS) offers high-throughput protein profiling, leading to extraction of protein array data, calling for effective and appropriate use of bioinformatics and statistical tools.

Methods

Whole tissue lysates of 105 breast carcinomas were analyzed on IMAC 30 ProteinChip Arrays (Bio-Rad, Hercules, CA, USA) using the ProteinChip Reader Model PBS IIc (Bio-Rad) and Ciphergen ProteinChip software (Bio-Rad, Hercules, CA, USA). Cluster analysis of protein spectra was performed to identify protein patterns potentially related to established clinicopathological variables and/or tumor markers.

Results

Unsupervised hierarchical clustering of 130 peaks detected in spectra from breast cancer tissue lysates provided six clusters of peaks and five groups of patients differing significantly in tumor type, nuclear grade, presence of hormonal receptors, mucin 1 and cytokeratin 5/6 or cytokeratin 14. These tumor groups resembled closely luminal types A and B, basal and HER2-like carcinomas.

Conclusion

Our results show similar clustering of tumors to those provided by cDNA expression profiles of breast carcinomas. This fact testifies the validity of the SELDI-TOF MS proteomic approach in such a type of study. As SELDI-TOF MS provides different information from cDNA expression profiles, the results suggest the technique's potential to supplement and expand our knowledge of breast cancer, to identify novel biomarkers and to produce clinically useful classifications of breast carcinomas.

Clinical proteomics and breast cancer: strategies for diagnostic and therapeutic biomarker discovery

A major challenge of breast cancer research is the identification of accurate biomarkers that improve screening, early diagnosis, prediction of aggressiveness, and prediction of therapeutic response or toxicity, as well as the identification of new molecular therapeutic targets. The new proteomic techniques promise to be valuable for identifying such tissue and serum markers. The different techniques currently applied to clinical samples of breast cancer and the most important results obtained are summarized in this review.

Implications of applied research for prognosis and therapy of breast cancer

Breast cancer is the one of leading causes of cancer-related deaths in women within economically developed regions of the world. The heterogeneity of the natural history of breast cancer complicates patient management in that there is tremendous variability in response to treatment and for survival. More recently, several biomarkers (hormone receptor status and HER2 expression) have been added to the risk evaluation and therapeutic assessments. Evolving knowledge of molecular biology and newer techniques, such as genomics and proteomics, offer the potential to better define the biologic nature of the disease process, both for risk and therapy. This review discusses classical as well as new prognostic and predictive techniques. These are leading to a paradigm shift from empirical treatment to an individually tailored approach, which may soon become a realistic option for patients, based on specific molecular profiles.

Molecular mechanisms and treatment of bone metastasis

The metastasis of cancer cells to bone alters bone architecture and mineral homeostasis. As described by the 'seed and soil' hypothesis, bone represents a fertile ground for cancer cells to flourish. A 'vicious cycle' of reciprocal bone-cancer cellular signals occurs with osteolytic (bone-resorbing) metastases, and a similar mechanism likely modulates osteoblastic (bone-forming) metastatic lesions as well. The development of targeted therapies either to block initial cancer cell chemotaxis, invasion and adhesion or to break the 'vicious cycle' is dependent on a more complete understanding of bone metastases. Although bisphosphonates delay progression of skeletal metastases, it is clear that more-effective therapies are needed. Cancer-associated bone morbidity remains a major public health problem, and to improve therapy and prevention it is important to understand the pathophysiology of the effects of cancer on bone. This review details scientific advances in this area.

Applications of SELDI-MS technology in oncology

Considerable interest, speculation and controversy have been generated utilising surface-enhanced laser desorption/ionization in conjunction with mass spectrometry (SELDI-MS) for the diagnosis, prognosis and therapeutic monitoring of cancer and offers an attractive approach to cancer biomarker discovery from tissues and biological fluids. This technology utilises a combination of mass spectrometry and chromatography to facilitate protein profiling of complex biological mixtures. Compared to some other more traditional proteomic platforms, such as 2D polyacrylamide gel electrophoresis, it has a high-throughput capability and can resolve low-mass proteins. However, a considerable number of challenging issues related to the design of studies, including reproducibility, sensitivity, specificity, variation in sample collection, processing and storage, have been reported as problematic with this technology; albeit some of these concerns could perhaps also be lauded against other proteomic approaches that have attempted to address complex protein mixtures, such as plasma. Applications, successes and limitations of SELDI-MS in both clinical and basic science arenas will be reviewed in this article.

Microdissecting the proteome

The complexity of the proteome is extremely high, because every organ or even a part of it can differ considerably in its protein composition. Performing proteomic studies therefore means to separate these functional different tissue areas before analysis. Otherwise all gained results will be depending on the question whether they are incorrect or at least dubious and do they reflect the different functions of tissues at all. The separation of functional tissue areas can be achieved by laser-based microdissection. In this review we will discuss the compatibly of microdissected formalin or cryofixed tissue with different proteomic techniques like 2-DE, MS and protein arrays.