Explore the vast range of titles available.

Thyroid cancer poses a significant clinical challenge, and our understanding of its pathogenesis is incomplete. To gain insight into the pathogenesis of papillary thyroid carcinoma, transcriptional profiles of four normal thyroids and 51 papillary carcinomas (PCs) were generated using DNA microarrays. The tumors were genotyped for their common activating mutations: BRAF V600E point mutation, RET / PTC1 and 3 rearrangement and point mutations of KRAS , HRAS and NRAS . Principal component analysis based on the entire expression data set separated the PCs into three groups that were found to reflect tumor morphology and mutational status. By combining expression profiles with mutational status, we defined distinct expression profiles for the BRAF , RET / PTC and RAS mutation groups. Using small numbers of genes, a simple classifier was able to classify correctly the mutational status of all 40 tumors with known mutations. One tumor without a detectable mutation was predicted by the classifier to have a RET / PTC rearrangement and was shown to contain one by fluorescence in situ hybridization analysis. Among the mutation-specific expression signatures were genes whose differential expression was a direct consequence of the mutation, as well as genes involved in a variety of biological processes including immune response and signal transduction. Expression of one mutation-specific differentially expressed gene, TPO , was validated at the protein level using immunohistochemistry and tissue arrays containing an independent set of tumors. The results demonstrate that mutational status is the primary determinant of gene expression variation within these tumors, a finding that may have clinical and diagnostic significance and predicts success for therapies designed to prevent the consequences of these mutations.

The identification of circulating tumor antigens or their related autoantibodies provides a means for early cancer diagnosis as well as leads for therapy. The purpose of this study was to identify proteins that commonly induce a humoral response in lung cancer by using a proteomic approach and to investigate biological processes that may be associated with the development of autoantibodies. Aliquots of solubilized proteins from a lung adenocarcinoma cell line (A549) and from lung tumors were subjected to two-dimensional PAGE, followed by Western blot analysis in which individual sera were tested for primary antibodies. Sera from 54 newly diagnosed patients with lung cancer and 60 patients with other cancers and from 61 noncancer controls were analyzed. Sera from 60% of patients with lung adenocarcinoma and 33% of patients with squamous cell lung carcinoma but none of the noncancer controls exhibited IgG-based reactivity against proteins identified as glycosylated annexins I and/or II. Immunohistochemical analysis showed that annexin I was expressed diffusely in neoplastic cells in lung tumor tissues, whereas annexin II was predominant at the cell surface. Interestingly, IL-6 levels were significantly higher in sera of antibody-positive lung cancer patients compared with antibody-negative patients and controls. We conclude that an immune response manifested by annexins I and II autoantibodies occurs commonly in lung cancer and is associated with high circulating levels of an inflammatory cytokine. The proteomic approach we have implemented has utility for the development of serum-based assays for cancer diagnosis as we report in this paper on the discovery of antiannexins I and/or II in sera from patients with lung cancer.

Studies of gene expression in lung cancer have the potential to affect patient care, but the general applicability of the derived classifiers is unclear. David Beer and his colleagues now analyze more than 400 lung tumors from subjects at six institutions using eight different classifiers and show that the combination of molecular and clinical data best predicts patient survival ( pages 812–813 ). Although prognostic gene expression signatures for survival in early-stage lung cancer have been proposed, for clinical application, it is critical to establish their performance across different subject populations and in different laboratories. Here we report a large, training–testing, multi-site, blinded validation study to characterize the performance of several prognostic models based on gene expression for 442 lung adenocarcinomas. The hypotheses proposed examined whether microarray measurements of gene expression either alone or combined with basic clinical covariates (stage, age, sex) could be used to predict overall survival in lung cancer subjects. Several models examined produced risk scores that substantially correlated with actual subject outcome. Most methods performed better with clinical data, supporting the combined use of clinical and molecular information when building prognostic models for early-stage lung cancer. This study also provides the largest available set of microarray data with extensive pathological and clinical annotation for lung adenocarcinomas.

Molecular classification of tumors based on their gene expression profiles promises to significantly refine diagnosis and management of cancer patients. The establishment of organ-specific gene expression patterns represents a crucial first step in the clinical application of the molecular approach. Here, we report on the gene expression profiles of 154 primary adenocarcinomas of the lung, colon, and ovary. Using high-density oligonucleotide arrays with 7129 gene probe sets, comprehensive gene expression profiles of 57 lung, 51 colon, and 46 ovary adenocarcinomas were generated and subjected to principle component analysis and to a cross-validated prediction analysis using nearest neighbor classification. These statistical analyses resulted in the classification of 152 of 154 of the adenocarcinomas in an organ-specific manner and identified genes expressed in a putative tissue-specific manner for each tumor type. Furthermore, two tumors were identified, one in the colon group and another in the ovarian group, that did not conform to their respective organ-specific cohorts. Investigation of these outlier tumors by immunohistochemical profiling revealed the ovarian tumor was consistent with a metastatic adenocarcinoma of colonic origin and the colonic tumor was a pleomorphic mesenchymal tumor, probably a leiomyosarcoma, rather than an epithelial tumor. Our results demonstrate the ability of gene expression profiles to classify tumors and suggest that determination of organ-specific gene expression profiles will play a significant role in a wide variety of clinical settings, including molecular diagnosis and classification.

Histopathology is insufficient to predict disease progression and clinical outcome in lung adenocarcinoma. Here we show that gene-expression profiles based on microarray analysis can be used to predict patient survival in early-stage lung adenocarcinomas. Genes most related to survival were identified with univariate Cox analysis. Using either two equivalent but independent training and testing sets, or 'leave-one-out' cross-validation analysis with all tumors, a risk index based on the top 50 genes identified low-risk and high-risk stage I lung adenocarcinomas, which differed significantly with respect to survival. This risk index was then validated using an independent sample of lung adenocarcinomas that predicted high- and low-risk groups. This index included genes not previously associated with survival. The identification of a set of genes that predict survival in early-stage lung adenocarcinoma allows delineation of a high-risk group that may benefit from adjuvant therapy.

Morphologic assessment of lung tumors is informative but insufficient to adequately predict patient outcome. We previously identified transcriptional profiles that predict patient survival, and here we identify proteins associated with patient survival in lung adenocarcinoma. A total of 682 individual protein spots were quantified in 90 lung adenocarcinomas by using quantitative two-dimensional polyacrylamide gel electrophoresis analysis. A leave-one-out cross-validation procedure using the top 20 survival-associated proteins identified by Cox modeling indicated that protein profiles as a whole can predict survival in stage I tumor patients (P = 0.01). Thirty-three of 46 survival-associated proteins were identified by using mass spectrometry. Expression of 12 candidate proteins was confirmed as tumor-derived with immunohistochemical analysis and tissue microarrays. Oligonucleotide microarray results from both the same tumors and from an independent study showed mRNAs associated with survival for 11 of 27 encoded genes. Combined analysis of protein and mRNA data revealed 11 components of the glycolysis pathway as associated with poor survival. Among these candidates, phosphoglycerate kinase 1 was associated with survival in the protein study, in both mRNA studies and in an independent validation set of 117 adenocarcinomas and squamous lung tumors using tissue microarrays. Elevated levels of phosphoglycerate kinase 1 in the serum were also significantly correlated with poor outcome in a validation set of 107 patients with lung adenocarcinomas using ELISA analysis. These studies identify new prognostic biomarkers and indicate that protein expression profiles can predict the outcome of patients with early-stage lung cancer.

Pancreatic cancer has a poor prognosis, in part due to lack of early detection. The identification of circulating tumor antigens or their related autoantibodies provides a means for early cancer diagnosis. We have used a proteomic approach to identify proteins that commonly induce a humoral response in pancreatic cancer. Proteins from a pancreatic adenocarcinoma cell line (Panc-1) were subjected to two-dimensional PAGE, followed by Western blot analysis in which individual sera were tested for autoantibodies. Sera from 36 newly diagnosed patients with pancreatic cancer, 18 patients with chronic pancreatitis and 15 healthy subjects were analyzed. Autoantibodies were detected against a protein identified by mass spectrometry as vimentin, in sera from 16/36 patients with pancreatic cancer (44.4%). Only one of 18 chronic pancreatitis patients and none of the healthy controls exhibited reactivity against this vimentin isoform. Interestingly, none of several other isoforms of vimentin detectable in 2-D gels exhibited reactivity with patient sera. Vimentin protein expression levels were investigated by comparing the integrated intensity of spots visualized in 2-D PAGE gels of various cancers. Pancreatic tumor tissues showed greater than a 3-fold higher expression of total vimentin protein than did the lung, colon, and ovarian tumors that were analyzed. The specific antigenic isoform was found at 5-10 fold higher levels. The detection of autoantibodies to this specific isoform of vimentin may have utility for the early diagnosis of pancreatic cancer.

The C-CRK gene, cellular homolog of the avian v- crk oncogene, encodes two alternatively spliced adaptor signaling proteins, CRKI (28 kDa) and CRKII (40 kDa). Both CRKI and CRKII have been shown to activate kinase signaling and anchorage-independent growth in vitro and CRKI transformed cells readily form tumors in nude mice. Affymetrix oligonucleotide arrays were used to analyse 86 lung adenocarcinomas and 10 uninvolved lung tissues. C-CRK mRNA expression was increased in more advanced (stage III versus stage I), larger (T 2–4 versus T 1 ), and poorly differentiated tumors and in tumors from patients demonstrating poor survival ( P =0.00034). An overlapping series of 93 lung adenocarcinomas (64 stage I and 29 stage III) and 10 uninvolved lung specimens were measured for quantitative differences in CRKI and CRKII protein levels using 2-D PAGE. CRK protein spots were identified using mass spectrometry and 2-D Western blotting. A significant increase in levels of the CRKI oncoprotein and the phosphorylated isoform of CRKII was observed in tumors ( P <0.05). No difference in protein level was evident between stages. Concordant with mRNA expression, CRKI and CRKII were increased in poorly differentiated tumors ( P <0.05). CRK immunohistochemical analysis of tumor tissue arrays using the same tumor series also demonstrated increased abundance of nuclear and cytoplasmic CRK in more proliferative tumors ( P <0.05). This study provides the first quantitative analysis of discrete CRKI and CRKII protein isoforms in human lung tumors and provides evidence that the C-CRK proto-oncogene may foment a more aggressive phenotype in lung cancers.

Pancreatic cancer has a poor prognosis, in part due to lack of early detection. The identification of circulating tumor antigens or their related autoantibodies provides a means for early cancer diagnosis. We have used a proteomic approach to identify proteins that commonly induce a humoral response in pancreatic cancer. Proteins from a pancreatic adenocarcinoma cell line (Panc-1) were subjected to two-dimensional PAGE, followed by Western blot analysis in which individual sera were tested for autoantibodies. Sera from 36 newly diagnosed patients with pancreatic cancer, 18 patients with chronic pancreatitis and 15 healthy subjects were analyzed. Autoantibodies were detected against a protein identified by mass spectrometry as vimentin, in sera from 16/36 patients with pancreatic cancer (44.4%). Only one of 18 chronic pancreatitis patients and none of the healthy controls exhibited reactivity against this vimentin isoform. Interestingly, none of several other isoforms of vimentin detectable in 2-D gels exhibited reactivity with patient sera. Vimentin protein expression levels were investigated by comparing the integrated intensity of spots visualized in 2-D PAGE gels of various cancers. Pancreatic tumor tissues showed greater than a 3-fold higher expression of total vimentin protein than did the lung, colon, and ovarian tumors that were analyzed. The specific antigenic isoform was found at 5–10 fold higher levels. The detection of autoantibodies to this specific isoform of vimentin may have utility for the early diagnosis of pancreatic cancer.

The sequencing of the human genome has had an enormous impact on the proteomic analysis of cancer by providing a sequence-based framework for understanding the human proteome of tumor cells, tissues, and biological fluids. There is intense interest in applying proteomic technologies to uncover, at the protein level, processes involved in neoplastic transformation and new biomarkers that correlate with early diagnosis, as well as to accelerate the development of new therapeutic targets. To that effect, new technologies are being developed in order to meet the needs for the high throughput and high sensitivity that is required for cancer-related applications of proteomics. These innovative technologies have greatly enhanced our ability to separate and characterize complex protein mixtures, and have aided our ability to identify proteins with greater sensitivity, thereby providing the groundwork for future scientific breakthroughs and possibly providing impetus for the development of personalized cancer therapy.