Somatic mutations and genetic polymorphisms of the PPP1R3 gene in patients with several types of cancers

CE-MS-based urinary biomarkers to distinguish non-significant from significant prostate cancer

Background

Prostate cancer progresses slowly when present in low risk forms but can be lethal when it progresses to metastatic disease. A non-invasive test that can detect significant prostate cancer is needed to guide patient management.

Methods

Capillary electrophoresis/mass spectrometry has been employed to identify urinary peptides that may accurately detect significant prostate cancer. Urine samples from 823 patients with PSA (<15 ng/ml) were collected prior to biopsy. A case–control comparison was performed in a training set of 543 patients (n_Sig = 98; n_non-Sig = 445) and a validation set of 280 patients (n_Sig = 48, n_non-Sig = 232). Totally, 19 significant peptides were subsequently combined by a support vector machine algorithm.

Results

Independent validation of the 19-biomarker model in 280 patients resulted in a 90% sensitivity and 59% specificity, with an AUC of 0.81, outperforming PSA (AUC = 0.58) and the ERSPC-3/4 risk calculator (AUC = 0.69) in the validation set.

Conclusions

This multi-parametric model holds promise to improve the current diagnosis of significant prostate cancer. This test as a guide to biopsy could help to decrease the number of biopsies and guide intervention. Nevertheless, further prospective validation in an external clinical cohort is required to assess the exact performance characteristics.

EPMA position paper in cancer: current overview and future perspectives

At present, a radical shift in cancer treatment is occurring in terms of predictive, preventive, and personalized medicine (PPPM). Individual patients will participate in more aspects of their healthcare. During the development of PPPM, many rapid, specific, and sensitive new methods for earlier detection of cancer will result in more efficient management of the patient and hence a better quality of life. Coordination of the various activities among different healthcare professionals in primary, secondary, and tertiary care requires well-defined competencies, implementation of training and educational programs, sharing of data, and harmonized guidelines. In this position paper, the current knowledge to understand cancer predisposition and risk factors, the cellular biology of cancer, predictive markers and treatment outcome, the improvement in technologies in screening and diagnosis, and provision of better drug development solutions are discussed in the context of a better implementation of personalized medicine. Recognition of the major risk factors for cancer initiation is the key for preventive strategies (EPMA J. 4(1):6, 2013). Of interest, cancer predisposing syndromes in particular the monogenic subtypes that lead to cancer progression are well defined and one should focus on implementation strategies to identify individuals at risk to allow preventive measures and early screening/diagnosis. Implementation of such measures is disturbed by improper use of the data, with breach of data protection as one of the risks to be heavily controlled. Population screening requires in depth cost-benefit analysis to justify healthcare costs, and the parameters screened should provide information that allow an actionable and deliverable solution, for better healthcare provision.

Molecular Screening of Cancer

The remarkable growth in our knowledge of the biology of cancer is leading to the identification of previously elusive pathways and networks involved in cancer causation. The development of technologies has played a pivotal role in furthering this understanding and appreciation of the complexity of tumorigenesis, and is advancing efforts to fully grasp the biology and exploit the knowledge for improvements in cancer detection, prevention, and therapy. The future of molecular screening, i.e. detection of risk or cancer via molecular determinants, has never been so close to a reality. Molecular assays employed in cancer detection and therapy are likely to revolutionize cancer treatment through individual-based diagnosis and treatment, i.e. personalized medicine. A number of detection techniques, such as the detection of aberrant DNA and RNA, the presence of auto-antibodies in serum or plasma, and protein profiling, are already in limited use for patient stratification for clinical trials and for predicting drug response.

Differential gene expression in premalignant human epidermis revealed by cluster analysis of serial analysis of gene expression (SAGE) libraries

Serial analysis of gene expression (SAGE) has been used for quantitative analysis of gene expression. We applied cluster analysis on multiple SAGE libraries derived from premalignant epidermal tissue (actinic keratosis), normal human epidermis, and cultured keratinocytes. The samples were obtained from skin biopsies without contamination by dermal tissue or blood. A total of 60,000 transcripts (tags) were analyzed. Two-way cluster analysis was applied to both the transcripts and the tissues, resulting in separation of the cultured cells from the epidermal samples, and clustering of many, presumably coregulated, genes. Two clusters of genes, strongly up-regulated in the tumor tissue compared with normal epidermis, were investigated in more detail. The differential expression of genes could be confirmed in actinic keratosis from four patients. Several of these genes have been previously associated with carcinogenesis or are likely to be important on the basis of their presumed function. Automated literature search tools show that a subgroup of these genes is coexpressed in other tissues and is part of an epidermal differentiation gene cluster on chromosome 1q21. We conclude that cluster analysis on large data sets uncovers clear partitions and correlations that could be confirmed by independent methods. We predict that these partitions will lead to biological interpretations that can be relevant for understanding the processes of carcinogenesis and tumor progression.

Trends in biomarker research for cancer detection

A key challenge in cancer control and prevention is detection of the disease as early as possible, enabling effective interventions and therapies to contribute to reduction in mortality and morbidity. Biomarkers are important as molecular signposts of the physiological state of a cell at a specific time. Active genes, their respective protein products, and other organic chemicals made by the cell create these signposts. As a normal cell progresses through the complex process of transformation to a cancerous state, biomarkers could prove vital for the identification of early cancer and people at risk of developing cancer. We discuss current research into the genetic and molecular signatures of cells, including microsatellite instability, hypermethylation and single-nucleotide polymorphisms. The use of genomic and proteomic high-throughput technology platforms to facilitate detection of early cancer by means of biomarkers, and issues on the analysis, validation, and predictive value of biomarkers based on these technologies are also discussed. We report on recent advances in identifying sources of biomarkers that can be accessed by noninvasive techniques, such as buccal-cell isolates, as well as traditional sources such as serum or plasma. We also focus on the work of the Early Detection Research Network at the National Cancer Institute, harnessing expertise from leading national and international institutions, to identify and validate biomarkers for the detection of precancerous and cancerous cells in assessing risk of cancer. The network also has a role in linking discovery to process development, resulting in early detection tests and clinical assessment.

Absence of PPP2R1A mutations in Wilms tumor

Evidence from genetic linkage analysis indicates that a gene located at 19q13.4, FWT2, is responsible for predisposition to Wilms tumor in many Wilms tumor families. This region has also been implicated in the etiology of sporadic Wilms tumor through loss of heterozygosity analyses. The PPP2R1A gene, encoding the alpha isoform of the heterotrimeric serine/threonine protein phosphatase 2A (PP2A), is located within the FWT2 candidate region and is altered in breast and lung carcinomas. PPP2R1B, encoding the beta isoform, is mutated in lung, colon, and breast cancers. These findings suggested that both PPP2R1A and PPP2R1B may be tumor suppressor genes. Additionally, PP2A is important in fetal kidney growth and differentiation and has an expression pattern similar to that of the Wilms tumor suppressor gene WT1. Since PPP2R1A was therefore a compelling candidate for the FWT2 gene, we analysed the coding region of PPP2R1A in DNA and RNA samples from affected members of four Wilms tumor families and 30 sporadic tumors and identified no mutations in PPP2R1A in any of these 34 samples. We conclude that PPP2R1A is not the 19q familial Wilms tumor gene and that mutation of PPP2R1A is not a common event in the etiology of sporadic Wilms tumor.