Serum protein profiling for diagnosis of breast cancer using SELDI-TOF MS.

In search for novel markers for breast cancer, we aimed to identify and validate novel serum protein profiles specific for breast cancer, and assess the influence of clinical (subjects age) and pre-analytical (sample storage duration) variables on the constructed classifiers. To this end, sera of breast cancer patients (n=152) and healthy controls (n=129), randomly divided into a training and test set, were analysed by surface-enhanced laser desorption/ionisation time-of-flight mass spectrometry (SELDI-TOF MS). In the training set, 14 peak clusters were found to differ significantly in expression between cases and controls. None of the peak clusters were influenced by subjects age and sample storage duration. Ten peak clusters were also found significantly discriminative in the test set. Peak clusters were structurally identified as C3a des-arginine anaphylatoxin, (tentative) inter-alpha-trypsin inhibitor heavy chain 4 fragments and a fibrinogen fragment. Logistic regression analyses on the training set yielded a classification model with a moderate performance on the test set, corresponding to those reported in previously performed validation studies. Most likely originating from the highly heterogeneous nature of breast cancer, selection of breast cancer subgroups for comparison with healthy controls is expected to improve results of future diagnostic SELDI-TOF MS studies.

Detection of breast cancer by surface-enhanced laser desorption/ionization time-of-flight mass spectrometry tissue and serum protein profiling.

AIM: Novel diagnostic breast cancer markers have been extensively searched for in the proteome, using, among others, surface-enhanced laser desorption/ionization time-of-flight mass spectrometry (SELDI-TOF MS). Thus far, the majority of SELDI-TOF MS studies have investigated samples originating from biorepositories, which hampers biomarker discovery as they likely suffer from variable adherence to collection protocols. MATERIAL AND METHODS: We investigated breast cancer (n=75) and control (n=26) serum and tissue samples, collected prospectively by rigorous adherence to a strictly defined protocol. Sera were collected preoperatively and postoperatively, and serum and tissue samples were analyzed by SELDI-TOF MS using the IMAC30 Ni and Q10 pH 8 array. RESULTS: Three serum peaks were significantly associated with breast cancer, while in tissue, 27 discriminative peaks were detected. Several peak clusters gradually increased or decreased in intensity from healthy to benign to cancer, or with increasing cancer stage. The constructed classification trees had a tenfold cross-validated performance of 67% to 87%. Two tissue peaks were identified as N-terminal albumin fragments. These are likely to have been generated by (breast) cancer-specific proteolytic activity in the tumor microenvironment. CONCLUSIONS: These albumin fragment scan potentially provide insights into the pathophysiological mechanisms associated with, or underlying, breast cancer, and aid in improving breast cancer diagnosis.

Microarray gene-expression profiling to predict lymph node metastasis in penile carcinoma.

OBJECTIVE: To determine the value of gene-expression profiling as a predictor of the status of the regional nodes in patients with penile carcinoma. PATIENTS AND METHODS: Tumour samples of 56 patients with penile squamous cell carcinoma were analysed for the gene expression on 35 k oligoarrays; 32 were from patients with histopathologically confirmed lymph node metastases and 24 from those with no lymph node involvement. The 56 patients were divided into a training and validation set. For the training set, 15 patients with histopathologically confirmed nodal metastases and 15 without were selected. The validation set consisted of the remaining 26 patients, containing 17 node-positive and nine with no nodal metastases. RESULTS: A 44-probe classifier had the best performance within the training set; this classifier correctly assigned 29 of 30 specimens in the training set to the two outcome groups. In the validation set of 26 tumours, the classifier correctly assigned 14 of the 26 (54%) specimens to the two outcome groups. Of the 17 specimens with histologically confirmed nodal involvement, 12 were classified as node-positive and five as node-negative, resulting in a sensitivity of 71%. Of the nine specimens from node-negative patients, two were correctly classified as node-negative and seven as node positive, resulting in a specificity of 22%. CONCLUSIONS: In this series, gene expression profiling did not produce a useful classifier to predict nodal involvement in patients with penile carcinoma.

Multiple robust signatures for detecting lymph node metastasis in head and neck cancer.

Genome-wide mRNA expression measurements can identify molecular signatures of cancer and are anticipated to improve patient management. Such expression profiles are currently being critically evaluated based on an apparent instability in gene composition and the limited overlap between signatures from different studies. We have recently identified a primary tumor signature for detection of lymph node metastasis in head and neck squamous cell carcinomas. Before starting a large multicenter prospective validation, we have thoroughly evaluated the composition of this signature. A multiple training approach was used for validating the original set of predictive genes. Based on different combinations of training samples, multiple signatures were assessed for predictive accuracy and gene composition. The initial set of predictive genes is a subset of a larger group of 825 genes with predictive power. Many of the predictive genes are interchangeable because of a similar expression pattern across the tumor samples. The head and neck metastasis signature has a more stable gene composition than previous predictors. Exclusion of the strongest predictive genes could be compensated by raising the number of genes included in the signature. Multiple accurate predictive signatures can be designed using various subsets of predictive genes. The absence of genes with strong predictive power can be compensated by including more genes with lower predictive power. Lack of overlap between predictive signatures from different studies with the same goal may be explained by the fact that there are more predictive genes than required to design an accurate predictor.

Gene expression profiling to predict outcome after chemoradiation in head and neck cancer.

PURPOSE: The goal of the present study was to improve prediction of outcome after chemoradiation in advanced head and neck cancer using gene expression analysis. MATERIALS AND METHODS: We collected 92 biopsies from untreated head and neck cancer patients subsequently given cisplatin-based chemoradiation (RADPLAT) for advanced squamous cell carcinomas (HNSCC). After RNA extraction and labeling, we performed dye swap experiments using 35k oligo-microarrays. Supervised analyses were performed to create classifiers to predict locoregional control and disease recurrence. Published gene sets with prognostic value in other studies were also tested. RESULTS: Using supervised classification on the whole series, gene sets separating good and poor outcome could be found for all end points. However, when splitting tumors into training and validation groups, no robust classifiers could be found. Using Gene Set Enrichment analysis, several gene sets were found to be enriched in locoregional recurrences, although with high false-discovery rates. Previously published signatures for radiosensitivity, hypoxia, proliferation, "wound," stem cells, and chromosomal instability were not significantly correlated with outcome. However, a recently published signature for HNSCC defining a "high-risk" group was shown to be predictive for locoregional control in our dataset. CONCLUSION: Gene sets can be found with predictive potential for locoregional control after combined radiation and chemotherapy in HNSCC. How treatment-specific these gene sets are needs further study.

Influence of sample storage duration on serum protein profiles assessed by surface-enhanced laser desorption/ionisation time-of-flight mass spectrometry (SELDI-TOF MS).

BACKGROUND: Issues have been raised concerning the robustness and validity of alleged serum markers discovered by surface-enhanced laser desorption/ionisation time-of-flight mass spectrometry (SELDI-TOF MS). Pre-analytical variables have been shown to exert a profound effect on protein profiles, irrespective of true biological variation. However, little is known about the possible effects of sample storage duration on protein profiles. We, therefore, aimed to investigate the effects of extended storage duration on the serum protein profile. METHODS: Archival sera from 140 breast cancer patients, stored at -30 degrees C for 1-11 years, were profiled by SELDI-TOF MS using immobilised metal affinity capture (IMAC) arrays, a condition applied in the majority of breast cancer biomarker discovery studies. RESULTS: Fourteen peak clusters, structurally identified as C3a des-arginine anaphylatoxin and multiple fragments of albumin and fibrinogen, were found to be significantly associated with sample storage duration by five distinct patterns. These proteins have been described previously as potential cancer markers, rendering them specific to both disease and sample handling issues. CONCLUSIONS: To prevent experimental variation being interpreted erroneously as disease associated variation, assessment of potential confounding pre-analytical parameters is a pre-requisite in biomarker discovery and validation studies. In addition, with respect to the different (non-)linear patterns observed in the current study, simply performing linear corrections to account for sample storage duration will not necessarily suffice.

Heterogeneity of gene expression profiles in head and neck cancer.

BACKGROUND: Results of gene expression profiling studies from different institutes often lack consistency. This could be due to the use of different microarray platforms and protocols, or to intratumoral heterogeneity in mRNA expression. The aim of our study was to quantify intratumoral heterogeneity in head and neck cancer. METHODS: Forty-four fresh frozen biopsies were taken from 22 patients, 2 per tumor. RNA was extracted, tested for quality, amplified, labeled, and hybridized to a 35k oligoarray. RESULTS: Unsupervised clustering analyses using all genes, the most variable genes, or random gene sets showed that 80% to 90% of biopsy pairs clustered together. A within-pair-between-pair scatter ratio analysis showed that the similarity between matching pairs was significantly greater than that between random pairs (p <.00001). CONCLUSIONS: Two biopsies from the same tumor show far greater similarity in gene expression than biopsies from different tumors, supporting the use of 1 biopsy for expression profiling.