Development and Optimization of a Machine-Learning Prediction Model for Acute Desquamation After Breast Radiation Therapy in the Multicenter REQUITE Cohort.

Purpose: Some patients with breast cancer treated by surgery and radiation therapy experience clinically significant toxicity, which may adversely affect cosmesis and quality of life. There is a paucity of validated clinical prediction models for radiation toxicity. We used machine learning (ML) algorithms to develop and optimise a clinical prediction model for acute breast desquamation after whole breast external beam radiation therapy in the prospective multicenter REQUITE cohort study. Methods and Materials: Using demographic and treatment-related features (m = 122) from patients (n = 2058) at 26 centers, we trained 8 ML algorithms with 10-fold cross-validation in a 50:50 random-split data set with class stratification to predict acute breast desquamation. Based on performance in the validation data set, the logistic model tree, random forest, and naïve Bayes models were taken forward to cost-sensitive learning optimisation. Results: One hundred and ninety-two patients experienced acute desquamation. Resampling and cost-sensitive learning optimisation facilitated an improvement in classification performance. Based on maximising sensitivity (true positives), the "hero" model was the cost-sensitive random forest algorithm with a false-negative: false-positive misclassification penalty of 90:1 containing m = 114 predictive features. Model sensitivity and specificity were 0.77 and 0.66, respectively, with an area under the curve of 0.77 in the validation cohort. Conclusions: ML algorithms with resampling and cost-sensitive learning generated clinically valid prediction models for acute desquamation using patient demographic and treatment features. Further external validation and inclusion of genomic markers in ML prediction models are worthwhile, to identify patients at increased risk of toxicity who may benefit from supportive intervention or even a change in treatment plan.

A data science approach for early-stage prediction of Patient's susceptibility to acute side effects of advanced radiotherapy.

The prediction by classification of side effects incidence in a given medical treatment is a common challenge in medical research. Machine Learning (ML) methods are widely used in the areas of risk prediction and classification. The primary objective of such algorithms is to use several features to predict dichotomous responses (e.g., disease positive/negative). Similar to statistical inference modelling, ML modelling is subject to the class imbalance problem and is affected by the majority class, increasing the false-negative rate. In this study, seventy-nine ML models were built and evaluated to classify approximately 2000 participants from 26 hospitals in eight different countries into two groups of radiotherapy (RT) side effects incidence based on recorded observations from the international study of RT related toxicity "REQUITE". We also examined the effect of sampling techniques and cost-sensitive learning methods on the models when dealing with class imbalance. The combinations of such techniques used had a significant impact on the classification. They resulted in an improvement in incidence status prediction by shifting classifiers' attention to the minority group. The best classification model for RT acute toxicity prediction was identified based on domain experts' success criteria. The Area Under Receiver Operator Characteristic curve of the models tested with an isolated dataset ranged from 0.50 to 0.77. The scale of improved results is promising and will guide further development of models to predict RT acute toxicities. One model was optimised and found to be beneficial to identify patients who are at risk of developing acute RT early-stage toxicities as a result of undergoing breast RT ensuring relevant treatment interventions can be appropriately targeted. The design of the approach presented in this paper resulted in producing a preclinical-valid prediction model. The study was developed by a multi-disciplinary collaboration of data scientists, medical physicists, oncologists and surgeons in the UK Radiotherapy Machine Learning Network.

Cancer cells grown in 3D under fluid flow exhibit an aggressive phenotype and reduced responsiveness to the anti-cancer treatment doxorubicin.

3D laboratory models of cancer are designed to recapitulate the biochemical and biophysical characteristics of the tumour microenvironment and aim to enable studies of cancer, and new therapeutic modalities, in a physiologically-relevant manner. We have developed an in vitro 3D model comprising a central high-density mass of breast cancer cells surrounded by collagen type-1 and we incorporated fluid flow and pressure. We noted significant changes in cancer cell behaviour using this system. MDA-MB231 and SKBR3 breast cancer cells grown in 3D downregulated the proliferative marker Ki67 (P < 0.05) and exhibited decreased response to the chemotherapeutic agent doxorubicin (DOX) (P < 0.01). Mesenchymal markers snail and MMP14 were upregulated in cancer cells maintained in 3D (P < 0.001), cadherin-11 was downregulated (P < 0.001) and HER2 increased (P < 0.05). Cells maintained in 3D under fluid flow exhibited a further reduction in response to DOX (P < 0.05); HER2 and Ki67 levels were also attenuated. Fluid flow and pressure was associated with reduced cell viability and decreased expression levels of vimentin. In summary, aggressive cancer cell behaviour and reduced drug responsiveness was observed when breast cancer cells were maintained in 3D under fluid flow and pressure. These observations are relevant for future developments of 3D in vitro cancer models and organ-on-a-chip initiatives.

Serum IgA1 shows increased levels of α2,6-linked sialic acid in breast cancer.

The lectin Helix pomatia agglutinin (HPA) recognizes altered glycosylation in solid cancers and the identification of HPA binding partners in tumour tissue and serum is an important aim. Among the many HPA binding proteins, IgA1 has been reported to be the most abundant in liver metastases. In this study, the glycosylation of IgA1 was evaluated using serum samples from patients with breast cancer (BCa) and the utility of IgA1 glycosylation as a biomarker was assessed. Detailed mass spectrometric structural analysis showed an increase in disialo-biantennary N-linked glycans on IgA1 from BCa patients (p < 0.0001: non-core fucosylated; p = 0.0345: core fucosylated) and increased asialo-Thomsen-Friedenreich antigen (TF) and disialo-TF antigens in the O-linked glycan preparations from IgA1 of cancer patients compared with healthy control individuals. An increase in Sambucus nigra binding was observed, suggestive of increased α2,6-linked sialic acid on IgA1 in BCa. Logistic regression analysis showed HPA binding to IgA1 and tumour size to be significant independent predictors of distant metastases (χ 2 13.359; n = 114; p = 0.020) with positive and negative predictive values of 65.7% and 64.6%, respectively. Immunohistochemical analysis of tumour tissue samples showed IgA1 to be detectable in BCa tissue. This report provides a detailed analysis of serum IgA1 glycosylation in BCa and illustrates the potential utility of IgA1 glycosylation as a biomarker for BCa prognostication.

Cellular glycosylation affects Herceptin binding and sensitivity of breast cancer cells to doxorubicin and growth factors.

Alterations in protein glycosylation are a key feature of oncogenesis and have been shown to affect cancer cell behaviour perturbing cell adhesion, favouring cell migration and metastasis. This study investigated the effect of N-linked glycosylation on the binding of Herceptin to HER2 protein in breast cancer and on the sensitivity of cancer cells to the chemotherapeutic agent doxorubicin (DXR) and growth factors (EGF and IGF-1). The interaction between Herceptin and recombinant HER2 protein and cancer cell surfaces (on-rate/off-rate) was assessed using a quartz crystal microbalance biosensor revealing an increase in the accessibility of HER2 to Herceptin following deglycosylation of cell membrane proteins (deglycosylated cells Bmax: 6.83 Hz; glycosylated cells Bmax: 7.35 Hz). The sensitivity of cells to DXR and to growth factors was evaluated using an MTT assay. Maintenance of SKBR-3 cells in tunicamycin (an inhibitor of N-linked glycosylation) resulted in an increase in sensitivity to DXR (0.1 µM DXR P < 0.001) and a decrease in sensitivity to IGF-1 alone and to IGF-1 supplemented with EGF (P < 0.001). This report illustrates the importance of N-linked glycosylation in modulating the response of cancer cells to chemotherapeutic and biological treatments and highlights the potential of glycosylation inhibitors as future combination treatments for breast cancer.

Cadherin-5: a biomarker for metastatic breast cancer with optimum efficacy in oestrogen receptor-positive breast cancers with vascular invasion.

BACKGROUND: A glycoproteomic study has previously shown cadherin-5 (CDH5) to be a serological marker of metastatic breast cancer when both protein levels and glycosylation status were assessed. In this study we aimed to further validate the utility of CDH5 as a biomarker for breast cancer progression. METHODS: A nested case-control study of serum samples from breast cancer patients, of which n=52 had developed a distant metastatic recurrence within 5 years post-diagnosis and n=60 had remained recurrence-free. ELISAs were used to quantify patient serum CDH5 levels and assess glycosylation by Helix pomatia agglutinin (HPA) binding. Clinicopathological, treatment and lifestyle factors associated with metastasis and elevated biomarker levels were identified. RESULTS: Elevated CDH5 levels (P=0.028) and ratios of CDH5:HPA binding (P=0.007) distinguished patients with metastatic disease from those that remained metastasis-free. Multivariate analysis showed that the association between CDH5:HPA ratio and the formation of distant metastases was driven by patients with oestrogen receptor (ER+) positive cancer with vascular invasion (VI+). CONCLUSIONS: CDH5 levels and the CDH5 glycosylation represent biomarker tests that distinguish patients with metastatic breast cancer from those that remain metastasis-free. The test reached optimal sensitivity and specificity in ER-positive cancers with vascular invasion.

Identification of O-Linked Glycoproteins Binding to the Lectin Helix pomatia Agglutinin as Markers of Metastatic Colorectal Cancer.

BACKGROUND: Protein glycosylation is an important post-translational modification shown to be altered in all tumour types studied to date. Mucin glycoproteins have been established as important carriers of O-linked glycans but other glycoproteins exhibiting altered glycosylation repertoires have yet to be identified but offer potential as biomarkers for metastatic cancer. METHODOLOGY: In this study a glycoproteomic approach was used to identify glycoproteins exhibiting alterations in glycosylation in colorectal cancer and to evaluate the changes in O-linked glycosylation in the context of the p53 and KRAS (codon 12/13) mutation status. Affinity purification with the carbohydrate binding protein from Helix pomatia agglutinin (HPA) was coupled to 2-dimensional gel electrophoresis with mass spectrometry to enable the identification of low abundance O-linked glycoproteins from human colorectal cancer specimens. RESULTS: Aberrant O-linked glycosylation was observed to be an early event that occurred irrespective of the p53 and KRAS status and correlating with metastatic colorectal cancer. Affinity purification using the lectin HPA followed by proteomic analysis revealed annexin 4, annexin 5 and CLCA1 to be increased in the metastatic colorectal cancer specimens. The results were validated using a further independent set of specimens and this showed a significant association between the staining score for annexin 4 and HPA and the time to metastasis; independently (annexin A4: Chi square 11.45, P = 0.0007; HPA: Chi square 9.065, P = 0.0026) and in combination (annexin 4 and HPA combined: Chi square 13.47; P = 0.0002). CONCLUSION: Glycoproteins showing changes in O-linked glycosylation in metastatic colorectal cancer have been identified. The glycosylation changes were independent of p53 and KRAS status. These proteins offer potential for further exploration as biomarkers and potential targets for metastatic colorectal cancer.

Preferential lectin binding of cancer cells upon sialic acid treatment under nutrient deprivation.

The terminal monosaccharide of glycoconjugates on a eukaryotic cell surface is typically a sialic acid (Neu5Ac). Increased sialylation usually indicates progression and poor prognosis of most carcinomas. Here, we utilize two human mammary epithelial cell lines, HB4A (breast normal cells) and T47D (breast cancer cells), as a model system to demonstrate differential surface glycans when treated with sialic acid under nutrient deprivation. Under a starved condition, sialic acid treatment of both cells resulted in increased activities of α2→3/6 sialyltransferases as demonstrated by solid phase assay using lectin binding. However, a very strong Maackia amurensis agglutinin I (MAL-I) staining on the membrane of sialic acid-treated T47D cells was observed, indicating an increase of Neu5Acα2→3Gal on the cell surface. To our knowledge, this is a first report showing the utility of lectins, particularly MAL-I, as a means to discriminate between normal and cancer cells after sialic acid treatment under nutrient deprivation. This method is sensitive and allows selective detection of glycan sialylation on a cancer cell surface.

The DietCompLyf study: a prospective cohort study of breast cancer survival and phytoestrogen consumption.

DietCompLyf is a multi-centre prospective study designed to investigate associations between phytoestrogens - naturally occurring plant compounds with oestrogenic properties - and other diet and lifestyle factors with breast cancer recurrence and survival. 3159 women with grades I-III breast cancer were recruited 9-15 months post-diagnosis from 56 UK hospitals. Detailed information on clinico-pathological, diet, lifestyle and quality of life is collected annually up to 5 years. Biological samples have also been collected as a resource for subsequent evaluation. The characteristics of the patients and associations between pre-diagnosis intake of phytoestrogens (isoflavones and lignans; assessed using the EPIC-Norfolk UK 130 question food frequency questionnaire) and breast cancer (i) risk factors and (ii) prognostic factors are described for 1797 women who had complete data for all covariates and phytoestrogens of interest. Isoflavone intakes were higher in the patients who were younger at diagnosis, in the non-smokers, those who had breast-fed and those who took supplements. Lignan intakes were higher in patients with a higher age at diagnosis, in ex-smokers, those who had breast-fed, who took supplements, had a lower BMI at diagnosis, lower age at menarche and were nulliparous. No significant associations between pre-diagnosis phytoestrogen intake and factors associated with improved breast cancer prognosis were observed. The potential for further exploration of the relationship between phytoestrogens and breast cancer recurrence and survival, and for the establishment of evidence to improve dietary and lifestyle advice offered to patients following breast cancer diagnosis using DietCompLyf data is discussed.

Conjugation of quantum dots on carbon nanotubes for medical diagnosis and treatment.

Cancer is one of the leading causes of death worldwide and early detection provides the best possible prognosis for cancer patients. Nanotechnology is the branch of engineering that deals with the manipulation of individual atoms and molecules. This area of science has the potential to help identify cancerous cells and to destroy them by various methods such as drug delivery or thermal treatment of cancer. Carbon nanotubes (CNT) and quantum dots (QDs) are the two nanoparticles, which have received considerable interest in view of their application for diagnosis and treatment of cancer. Fluorescent nanoparticles known as QDs are gaining momentum as imaging molecules with life science and clinical applications. Clinically they can be used for localization of cancer cells due to their nano size and ability to penetrate individual cancer cells and high-resolution imaging derived from their narrow emission bands compared with organic dyes. CNTs are of interest to the medical community due to their unique properties such as the ability to deliver drugs to a site of action or convert optical energy into thermal energy. By attaching antibodies that bind specifically to tumor cells, CNTs can navigate to malignant tumors. Once at the tumor site, the CNTs enter into the cancer cells by penetration or endocytosis, allowing drug release, and resulting in specific cancer cell death. Alternatively, CNTs can be exposed to near-infrared light in order to thermally destroy the cancer cells. The amphiphilic nature of CNTs allows them to penetrate the cell membrane and their large surface area (in the order of 2600 m(2)/g) allows drugs to be loaded into the tube and released once inside the cancer cell. Many research laboratories, including our own, are investigating the conjugation of QDs to CNTs to allow localization of the cancer cells in the patient, by imaging with QDs, and subsequent cell killing, via drug release or thermal treatment. This is an area of huge interest and future research and therapy will focus on the multimodality of nanoparticles. In this review, we seek to explore the biomedical applications of QDs conjugated to CNTs, with a particular emphasis on their use as therapeutic platforms in oncology.

A targeted glycoproteomic approach identifies cadherin-5 as a novel biomarker of metastatic breast cancer.

Aberrant glycosylation has long been recognised as a hallmark of cancer, and is increasingly being exploited in biomarker discovery studies. Helix pomatia agglutinin (HPA) is known to bind aberrant glycans associated with metastatic breast cancer, and was used here to isolate glycoproteins from pooled breast cancer serum samples of (i) patients with recurrent breast cancer and (ii) patients with no sign of recurrence 5years after diagnosis of their primary tumour. Pregnancy zone protein, the polymeric immunoglobulin receptor and cadherin-5 emerged as potential markers of metastasis following proteomic identification of HPA binding glycoproteins. ELISAs were developed to verify these findings, and to assess protein glycosylation, in individual patient sera. The cadherin-5 ELISA discriminated serum samples of patients with recurrent breast cancer from those with no sign of recurrence, and analysis of cadherin-5 glycosylation by HPA also showed a significant difference between the two sample groups. The targeted glycoproteomic and validatory approach developed here has shown that when taking into account both the protein levels and HPA binding, serum cadherin-5 discriminated patients with recurrent breast cancer from those with no sign of recurrence with 90% specificity.

A novel approach to determining the affinity of protein-carbohydrate interactions employing adherent cancer cells grown on a biosensor surface.

The development of biological agents for the treatment of solid tumours is an area of considerable activity. We are pursuing carbohydrate-binding proteins (lectins) in a strategy aimed at targeting cancer-associated changes in glycosylation. To evaluate lectin-cancer cell interactions we developed a novel cell biosensor in which binding events take place at the cell surface, more closely mimicking an in vivo system. Metastatic, SW620, and non-metastatic, SW480, colorectal cancer cells were grown on the surface of a tissue-culture compatible polystyrene coated biosensor chip and housed in a quartz crystal microbalance (QCM) apparatus, the kinetics of binding of a diverse range of lectins was evaluated. The lectin Helix pomatia agglutinin (HPA) has been shown to bind aggressive metastatic cancer and was produced in recombinant form (His- and RFP-tagged). The affinity of HPA was in the nanomolar range to the metastatic SW620 cells but was only in the micromolar range to the non-metastatic SW480. Overall, the dissociation constant (K(D)) of the lectins tested in the new cell biosensor system was an order of magnitude lower (nanomolar range) than has generally been reported with systems such as QCM/SPR. This new cell-biosensor enables molecular interactions to be studied in a more relevant environment. An intrinsic problem with developing new biological therapies is the difficulty in determining the affinity with which proteins will interact with intact cell surfaces. This methodology will be of interest to researchers developing new biological approaches for targeting cell surfaces in a wide range of diseases, including cancer.

The lectin Helix pomatia agglutinin recognizes O-GlcNAc containing glycoproteins in human breast cancer.

There has been considerable interest in understanding the epitopes that bind the lectin Helix pomatia agglutinin (HPA) in breast cancer as the lectin has been shown to identify glycosylation changes associated with the development of metastatic disease. HPA has previously been shown to recognize aberrant O-linked α-N-acetylgalactosamine (GalNAcα)/mucin glycosylation in cancer, including exposed Tn epitopes. However, recent glycan-array analysis reported that diverse epitopes are also recognized by the lectin, e.g. consortium for functional glycomics (CFG) data: GalNAcα1,3Gal; ß-GalNAc; GlcNAcß1,4Gal. The intriguing observations from the CFG array led to this study, in which HPA-binding epitopes were localized and characterized in an in vitro model of breast cancer metastasis. HMT3522 (benign disease), BT474 (primary cancer) and T47D/MCF7 (metastatic cancer) cells were assessed in confocal microscopy-based co-localization studies and a glycoproteomic analysis based on 2-dimensional electrophoresis (2DE), western blotting and mass spectrometry was adopted. HPA binding correlated with levels of integrin α6, transcription factors heterogeneous nuclear ribonuclear protein (HnRNP) H1, HnRNP D-like, HnRNP A2/B1 as well as heat shock protein 27 (Hsp27), glial fibrillary acidic protein and enolase 1 (ENO1). These glycoproteins were non-detectable in the non-metastatic breast cancer cell lines. The recognition of HnRNPs, Hsp27 and ENO1 by HPA correlated with O-GlcNAcylation of these proteins. Integrin α6 was the most abundant HPA glycoprotein in the breast cancer cells with a metastatic phenotype; this concurred with previous findings in colorectal cancer. This is the first report in which HPA has been shown to bind O-GlcNAcylated transcription factors. This class of proteins represents a new means by which HPA differentiates cancer cells with an aggressive metastatic phenotype.

Cell surface glycan-lectin interactions in tumor metastasis.

The development of secondary cancers, metastases, requires that a multitude of events are completed in an ordered and sequential manner. This review focuses on the role of cell surface glycans and their binding partners in the metastatic process. A common feature of metastasis is that the steps require adhesive interactions; many of these are mediated by cell surface glycans and their interactions with endogenous carbohydrate binding proteins (lectins). Aberrant glycosylation is a key feature of malignant transformation and the glycans involved influence the adhesive interactions of cancer cells often providing favorable conditions for tumor dissemination. This review focuses on glycans on the cancer cell surface and their association with endogenous lectins. In particular, E-cadherin and siglec-mediated disaggregation of tumor cells from the primary tumor mass; integrins, laminin and CD44-mediated invasion and migration of tumor cells through the connective tissue; the involvement of heparan sulphate in tumor angiogenesis and C-/S-type lectin interactions with the vasculature. The potential role of glycans in cancer cell evasion of immune surveillance is considered.

Identification, cloning, and characterization of two N-acetylgalactosamine-binding lectins from the albumen gland of Helix pomatia.

Helix pomatia agglutinin (HPA), the lectin from the albumen gland of the Roman snail, has been used in histochemical studies relating glycosylation changes to the metastatic potential of solid tumors. To facilitate the use of HPA in a clinical (diagnostic) setting, detailed analysis of the lectin, including cloning and recombinant production of HPA, is required. A combination of isoelectric focusing, amino acid sequence analysis, and cloning revealed two polypeptides in native HPA preparations (HPAI and HPAII), both consistent with GalNAc-binding lectins of the H-type family. Pairwise sequence alignment showed that HPAI and HPAII share 54% sequence identity whereas molecular modeling using SWISS-MODEL suggests they are likely to adopt similar tertiary structure. The inherent heterogeneity of native HPA highlighted the need for production of functional recombinant protein; this was addressed by preparing His-thioredoxin-tagged fusion products in Escherichia coli Rosetta-gami B (DE3) cells. The recombinant lectins agglutinated human blood group A erythrocytes whereas their oligosaccharide specificity, evaluated using glycan microarrays, showed that they predominantly bind glycans with terminal α-GalNAc residues. Surface plasmon resonance with immobilized GalNAc-BSA confirmed that recombinant HPAI and HPAII bind strongly with this ligand (K(d) = 0.60 nm and 2.00 nm, respectively) with a somewhat higher affinity to native HPA (K(d) = 7.67 nm). Recombinant HPAII also bound the breast cancer cells of breast cancer tissue specimens in a manner similar to native lectin. The recombinant HPA described here shows important potential for future studies of cancer cell glycosylation and as a reagent for cancer prognostication.

A sensitive assay to measure biomarker glycosylation demonstrates increased fucosylation of prostate specific antigen (PSA) in patients with prostate cancer compared with benign prostatic hyperplasia.

BACKGROUND: Prostate specific antigen (PSA) measurement is used for the diagnosis of prostate cancer (PCa) but the test lacks specificity due to the number of false positive readings. The glycosylation of PSA is altered in PCa but studies in this area have been limited to few clinical samples and/or require advanced laboratory facilities. An assay to assess PSA glycosylation was established using equipment available in most routine biomedical testing laboratories. METHODS: Serum samples from patients with PCa or benign prostatic hyperplasia (BPH) were used. PSA (range 4-10 ng/ml) was affinity purified, separated and probed with the lectin Ulex europaeus (UEA-1; specific for α1,2 linked fucose). An enzyme-linked immunosorbent lectin assay (ELLA) with colorimetric detection was devised and PSA fucosylation assessed in a further independent set of 26 samples. RESULTS: Free PSA (fPSA) from PCa patients showed a significant increase in fucosylation compared with fPSA from patients with BPH. The ELLA was 92% specific and 69% sensitive for PCa over BPH. In comparison, fPSA measurement was 70% specific and 56% sensitive (threshold set to 25% tPSA) for PCa over BPH. CONCLUSIONS: Changes in glycosylation of PSA were identified using 50 µl of serum with PSA in the range of 4-10 ng/ml, this represents a more specific and sensitive test for PCa based on fucosylation changes of fPSA.

Identification and elimination of false-positives in an ELISA-based system for qualitative assessment of glycoconjugate binding using a selection of plant lectins.

Systematic optimization of a lectin-based enzyme-linked immunosorbent assay (ELISA) procedure using a panel of 21 biotinylated plant lectins and a glycoprotein with defined glycosylation (i) identified blocking as a limiting step in solid phase sugar binding analysis and (ii) found Synblock to be a better alternative to bovine serum albumin (BSA) as blocking agent.

Breast cancer proteomics using two-dimensional electrophoresis: studying the breast cancer proteome.

Proteomics has emerged as a powerful approach for studying disease-associated changes in protein levels. The most commonly used method in proteomics studies remains two-dimensional gel electrophoresis, but the methodology, standardization, and interpretation of the results obtained require considerable expertise. In this chapter, we describe the approaches we have taken to studying the breast cancer proteome, using cells grown in vitro and cancer specimens as the starting materials.

Current perspectives in cancer proteomics.

Proteome technology has been used widely in cancer research and is a useful tool for the identification of new cancer markers and treatment-related changes in cancer. This article details the use of proteome technology in cancer research, and laboratory-based and clinical cancer research studies are described. New developments in proteome technology that enable higher sample-throughput are evaluated and methods for enhancing conventional proteome analysis (based on two-dimensional electrophoresis) discussed. The need to couple laboratory-based proteomics research with clinically relevant models of the disease is also considered, as this remains the next main challenge of cancer-related proteome research.