Every year the American Cancer Society estimates the number of new cancer cases and deaths expected in the United States in the current year and compiles the most recent data on cancer incidence, mortality, and survival, using National Cancer Institute (NCI) incidence and National Center for Health Statistics (NCHS) mortality data. Incidence and death rates are age adjusted to the 1970 US standard population. It is estimated that 1,284,900 new cases of cancer will be diagnosed and 555,500 people will die from cancer in the United States in the year 2002. From 1992 to 1998, cancer death rates declined in males and females, while cancer incidence rates decreased among males and increased slightly among females. Most notably, African-American men showed the largest decline for both incidence and mortality. Nevertheless, African Americans still carry the highest burden of cancer with later-stage cancer diagnosis and poorer survival compared with whites. Despite the continued decline in cancer death rates, the total number of recorded cancer deaths in the United States continues to increase slightly due to the aging and expanding population.

https://onlinelibrary.wiley.com/doi/pdf/10.3322/canjclin.52.1.23

Cancer statistics, 2002.

The present invention is directed to secreted and transmembrane polypeptides and to nucleic acid molecules encoding those polypeptides. Also provided herein are vectors and host cells comprising those nucleic acid sequences, chimeric polypeptide molecules comprising the polypeptides of the present invention fused to heterologous polypeptide sequences, antibodies which bind to the polypeptides of the present invention and to methods for producing the polypeptides of the present invention.

Secreted and Transmembrane Polypeptides and Nucleic Acids Encoding the Same

The prostate-specific antigen test has been a major factor in increasing awareness and better patient management of prostate cancer (PCA), but its lack of specificity limits its use in diagnosis and makes for poor early detection of PCA. The objective of our studies is to identify better biomarkers for early detection of PCA using protein profiling technologies that can simultaneously resolve and analyze multiple proteins. Evaluating multiple proteins will be essential to establishing signature proteomic patterns that distinguish cancer from noncancer as well as identify all genetic subtypes of the cancer and their biological activity. In this study, we used a protein biochip surface enhanced laser desorption/ionization mass spectrometry approach coupled with an artificial intelligence learning algorithm to differentiate PCA from noncancer cohorts. Surface enhanced laser desorption/ionization mass spectrometry protein profiles of serum from 167 PCA patients, 77 patients with benign prostate hyperplasia, and 82 age-matched unaffected healthy men were used to train and develop a decision tree classification algorithm that used a nine-protein mass pattern that correctly classified 96% of the samples. A blinded test set, separated from the training set by a stratified random sampling before the analysis, was used to determine the sensitivity and specificity of the classification system. A sensitivity of 83%, a specificity of 97%, and a positive predictive value of 96% for the study population and 91% for the general population were obtained when comparing the PCA versus noncancer (benign prostate hyperplasia/healthy men) groups. This high-throughput proteomic classification system will provide a highly accurate and innovative approach for the early detection/diagnosis of PCA.

/pdf/serum-protein-fingerprinting-coupled-with-a-pattern-matching-3t6sn53nth.pdf

Serum Protein Fingerprinting Coupled with a Pattern-matching Algorithm Distinguishes Prostate Cancer from Benign Prostate Hyperplasia and Healthy Men

The prostate-specific antigen test has been a major factor in increasing awareness and better patient management of prostate cancer (PCA), but its lack of specificity limits its use in diagnosis and makes for poor early detection of PCA. The objective of our studies is to identify better biomarkers for early detection of PCA using protein profiling technologies that can simultaneously resolve and analyze multiple proteins. Evaluating multiple proteins will be essential to establishing signature proteomic patterns that distinguish cancer from noncancer as well as identify all genetic subtypes of the cancer and their biological activity. In this study, we used a protein biochip surface enhanced laser desorption/ionization mass spectrometry approach coupled with an artificial intelligence learning algorithm to differentiate PCA from noncancer cohorts. Surface enhanced laser desorption/ionization mass spectrometry protein profiles of serum from 167 PCA patients, 77 patients with benign prostate hyperplasia, and 82 age-matched unaffected healthy men were used to train and develop a decision tree classification algorithm that used a nine-protein mass pattern that correctly classified 96% of the samples. A blinded test set, separated from the training set by a stratified random sampling before the analysis, was used to determine the sensitivity and specificity of the classification system. A sensitivity of 83%, a specificity of 97%, and a positive predictive value of 96% for the study population and 91% for the general population were obtained when comparing the PCA versus noncancer (benign prostate hyperplasia/healthy men) groups. This highthroughput proteomic classification system will provide a highly accurate and innovative approach for the early detection/diagnosis of PCA.

Serum Protein Fingerprinting Coupled with a Pattern-matching Algorithm Distinguishes Prostate Cancer from Benign Prostate Hyperplasia and Healthy Men 1

The system-wide study of proteins presents an exciting challenge in this information-rich age of whole-genome biology. Although traditional investigations have yielded abundant information about individual proteins, they have been less successful at providing us with an integrated understanding of biological systems. The promise of proteomics is that, by studying many components simultaneously, we will learn how proteins interact with each other, as well as with non-proteinaceous molecules, to control complex processes in cells, tissues and even whole organisms. Here, I discuss the role of microarray technology in this burgeoning area.

http://www.cs.mun.ca/~harold/Courses/Old/CS6754.W04/Diary/ng1037.pdf

Protein microarrays and proteomics

Early detection is critical in cancer control and prevention. Biomarkers help in this process by providing valuable information about a the status of a cell at any given point in time. As a cell transforms from nondiseased to neoplastic, distinct changes occur that could be potentially detected through the identification of the appropriate biomarkers. Biomarker research has benefited from advances in technology such as proteomics. We discuss here ongoing research in this field, focusing on proteomic technologies. The advances in two-dimensional electrophoresis and mass spectrometry are discussed in light of their contribution to biomarker research. Chip-based techniques, such as surface-enhanced laser desorption, and ionization and emerging methods, such as tissue and antibody arrays, are also discussed. The development of bioinformatic tools that have and are being developed in parallel to proteomics is also addressed. This report brings into focus the efforts of the Early Detection Research Network at the National Cancer Institute in harnessing scientific expertise from leading institutions to identify and validate biomarkers for early detection and risk assessment.

Proteomics in Early Detection of Cancer

A novel proteomic approach for probing cell and tissue proteome, which combines liquid phase protein separations with microarray technology has been developed. Proteins in cell and tissue lysates or in cellular subfractions are separated using any one of a number of separation modes which may consist of ion exchange liquid chromatography (LC), reverse phase LC, carrier ampholyte based separations, e.g. the use of Rotofor, affinity based separations, or gel based separations. Each first-dimension fraction obtained using one separation mode can be further resolved using one or more of the other separation modes to yield either purified protein in solution or liquid fractions with substantially reduced protein complexity. The advantage of a liquid based separation system is that proteins in hundreds of individual fractions can be arrayed directly and used as targets for a variety of probes. Constituent proteins in reactive fractions are identified by mass spectrometry and may be further resolved to determine the nature of the reactive protein(s). We present in this report initial data based on microarray analysis of individual Rotofor fractions obtained from lung adenocarcinoma cell line A549 lysates which have been probed with antibodies against specific proteins.

Protein based microarrays: a tool for probing the proteome of cancer cells and tissues.

There is evidence that 8q amplification is associated with poor prognosis in hepatoblastoma. A previous comparative genomic hybridization analysis identified a critical region in chromosomal bands 8q11.2-q13. Using restriction landmark genomic scanning in combination with a virtual genome scan, we showed that this region is delineated by sequences within contig NT_008183 of chromosomal subbands 8q11.22-q11.23. A real-time PCR-based genomic copy number assay of 20 hepatoblastomas revealed gain or amplification in this critical chromosomal region in eight tumors. The expression of four genes and expressed sequence tags (ESTs) within this newly defined region was assayed by real-time reverse transcriptase polymerase chain reaction (RT-PCR) in four tumors with and six tumors without gain or amplification. The PLAG1 oncogene was found to be highly expressed in all but one tumor compared to normal liver tissue. Furthermore, quantitative RT-PCR revealed that the expression level of the developmentally regulated transcription factor PLAG1 was 3-12 times greater in hepatoblastoma tumors and cell lines compared to age-matched normal liver and comparable to the expression in fetal liver tissue. PLAG1 has been shown be a transcriptional activator of IGF2 in other tumor types. Using luciferase reporter assays, we demonstrated that PLAG1 transactivates transcription from the embryonic IGF2 promoter P3, also in hepatoblastoma cell lines. Thus, our results provide evidence that PLAG1 overexpression may be responsible for the frequently observed up-regulation of IGF2 in hepatoblastoma and therefore may be implicated in the molecular pathogenesis of this childhood neoplasia.

Amplification and Overexpression of the IGF2 Regulator PLAG1 in Hepatoblastoma

The display of a random peptide library on the surface of phage has been applied to the discovery of antigens specific to prostate cancer.

Sam M. Hanash

Papers

Proteomics in Early Detection of Cancer

Protein based microarrays: a tool for probing the proteome of cancer cells and tissues.

Amplification and Overexpression of the IGF2 Regulator PLAG1 in Hepatoblastoma

Harnessing immunity for cancer marker discovery.

The Human Proteome Organization A Mission to Advance Proteome Knowledge