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Test Could Identify Which Prostate Cancers Require Treatment

The level of expression of three genes associated with aging can be used to predict whether seemingly low-risk prostate cancer will remain slow-growing, according to researchers at the Herbert Irving Comprehensive Cancer Center at Columbia University Medical Center. Use of this three-gene biomarker, in conjunction with existing cancer-staging tests, could help physicians better determine which men with early prostate cancer can be safely followed with “active surveillance” and spared the risks of prostate removal or other invasive treatment. The findings were published recently in the online edition of Science Translational Medicine.

Berg Receives Frost & Sullivan 2013 Drug Discovery Technology Innovation Award

Berg, a biopharmaceutical company committed to uncovering health solutions through a data-driven, biological research approach, was recently recognized with the 2013 North American Drug Discovery Technology Innovation Award at the Frost & Sullivan Growth, Innovation and Leadership Awards Gala, the annual event by the global research organization that highlights the most promising new innovative technology across sectors.

Biomarker May Predict Prostate Cancers Requiring Treatment

Not all early-stage prostate cancer diagnoses are alike. While some patients have aggressive tumors, others have slow-growing, low Gleason score tumors that may not require treatment, but instead can be monitored with regular clinical evaluations. But distinguishing between prostate cancers that require treatment and those that do not is still a major challenge.

Researchers at Columbia University in New York City have now identified a 3-gene signature that could indicate whether a particular early-stage prostate cancer is indolent. The test relies on a tissue sample, and along with a prostate-specific antigen (PSA) test and a histology assessment, could help clinicians make an accurate diagnosis. The early results, including a blinded retrospective analysis of 43 patients, show that the signature can accurately predict which patients with low-risk disease would develop metastatic prostate cancer and which patients would not progress. The study is published in Science Translational Medicine.

“These types of markers will, for the first time, give us the opportunity to measure biological features of cancer in the same patient, with multiple biopsies spread out over many years,” said Eric Klein, MD, chairman, Glickman Urological and Kidney Institute at the Cleveland Clinic in Ohio.
Cory Abate-Shen, PhD, professor of urological oncology at Columbia University; Andrea Califano, PhD, professor of systems biology at Columbia University; and colleagues used a computational approach that identified three genes—FGFR1, PMP22, and CDKN1A—all associated with aging, that could accurately predict outcomes of low-risk, low Gleason score prostate tumors. Protein and mRNA levels of all three genes were high in those patients who had non-aggressive, indolent disease and low in those who had aggressive tumors.

Clinicians still rely on the Gleason score, a histology and pathology evaluation that does not incorporate any molecular assessment. Those patients with a Gleason score of 8 or higher are candidates for immediate treatment, but whether men with a score of 6 or 7 require treatment is difficult to assess—no test exists to identify the small percentage of patients who have early-stage prostate cancer that is more likely to metastasize.

The 3-gene signature was validated using an independent prostate cancer cohort. According to the study authors, the signature was prognostic and improved prognosis compared with the use of PSA and clinical assessment.

“We would predict that the test would be beneficial for patients with low Gleason score prostate tumors,” said Abate-Shen. “These patients are now typically monitored on active surveillance protocols, and the patients get a biopsy periodically. The test would be conducted on the biopsy.”

Rather than focusing on the entire genome, the researchers focused on 377 genes involved in aging, predicting that genes involved in aging and senescence are critical for tumor suppression. Cellular senescence is known to play a role in tumor suppression and is associated with benign prostate tumors both in the clinic and in mouse models, according to the researchers. Using a computational analysis called gene set enrichment analysis (GSEA), they narrowed the long gene list to 19 genes, and then to a set of 3 genes that could identify indolent tumors.

“To focus on senescence genes is intellectually interesting,” said Klein. “There is already a body of work supporting the role of these genes in prostate cancer, but to my knowledge no one has looked at them in early-stage disease before.”

Forty-three patients, who had been under active surveillance for 10 years at Columbia University Medical School, were used for the blinded retrospective analysis to assess the predictive value of the gene signature. Each patient had been diagnosed with low-risk prostate cancer, with a Gleason score of 6 or less. The test was correctly able to identify all 14 patients who eventually developed advanced prostate cancer.

CDKN1A has been shown to be linked to senescence and to regulate the cell cycle. Previous studies have shown that downregulation of the gene is linked to cancer progression. The correlation of FGFR1 (fibroblast growth factor receptor 1) with indolent tumors was surprising, as fibroblast growth factors have been shown to play a role in prostate cancer development. But, as the authors highlight in their discussion, FGFR1 signaling in prostate cancer is likely complex. The third gene in the signature, PMP22, encodes a glycoprotein expressed in neurons and has not been previously associated with prostate cancer.

This 3-gene signature is different from previously identified biomarkers, which have largely focused on advanced tumors. The potential biomarker test could complement other approaches in development, such as urine or blood tests, according to the authors.

A trial to validate the genetic signature is underway at Columbia University, and a national trial is being planned.

“It is really important to find novel ways to help to define early-stage tumors that may or may not progress to aggressive disease,” said Abate-Shen. “This will ultimately really help to minimize overtreatment, while capitalizing on the benefits of cancer screening.”

Other genomic approaches to distinguish indolent and aggressive disease are also underway. The first-generation expression-based tests, including Oncotype DX prostate and Prolaris, can facilitate clinical decisions based on the molecular characteristics of a prostate tumor. Both the available tests and the new ones “promise to reduce overtreatment and help men make the right decisions based on biology rather than uncertainty,” said Klein. 

Study: A Molecular Signature Predictive of Indolent Prostate Cancer [Science Translational Medicine]

Source: CancerNetwork

Nodality, Inc. Reports Promising Rheumatoid Arthritis Study Results to Predict Patient Treatment Response to TNF Inhibitors

Nodality, Inc., an innovative biotechnology company advancing discovery, development and use of transformative therapies by revealing functional systems biology, recently announced results of the Company’s comprehensive research study to identify cell markers (biomarkers) of disease activity and treatment success in rheumatoid arthritis (RA) patients. The study findings demonstrated that Nodality’s SCNP technology, which measures functional pathways at the single cell level, can be used to identify biomarkers of responsiveness to treatment with tumor necrosis factor inhibitors (TNFIs). RA affects an estimated two million Americans, and TNFIs constitute the most commonly prescribed therapy. Approximately half of patients respond to treatments such as TNFIs, leaving a substantial unmet need to identify which patients are more likely to respond to current therapies. Optimizing use of currently available therapies could potentially delay tissue damage and progression of disease.

SCNP provides the core technology foundation for Nodality’s programs dedicated to improving clinical medicine by increasing the efficiency of therapeutic R&D programs, enhancing life cycle management for commercialized drugs, and introducing new predictive diagnostics. The study results were featured in an oral presentation titled, Comparison of functional immune signaling profiles in peripheral blood mononuclear cells (PBMC) from rheumatoid arthritis (RA) patients versus healthy donors (HD) using Single Cell Network Profiling (SCNP) (Abstract W7.02.04), at the 15th International Congress of Immunology (ICI) in Milan, Italy, taking place August 22 to 27, 2013. The findings were presented by S. Louis Bridges, Jr., M.D., Ph.D., Marguerite Jones Harbert-Gene V. Ball, MD Professor of Medicine, Director, Division of Clinical Immunology and Rheumatology, University of Alabama School of Medicine.

“Nodality’s research program demonstrates the great promise and potential in gaining a better understanding of disease biology and applying this to the development of prognostic and predictive biomarkers for autoimmune diseases such as RA,” commented Alessandra Cesano, M.D., Ph.D., Chief Medical Officer of Nodality. “I look forward to the final results of this program, one of the most comprehensive of its kind. Our technology, based on immune-biology, can predict which RA patients will respond to specific therapies and reveal the mechanisms of drug resistance, thus informing alternative therapeutic strategies.”

The Nodality research program compares healthy and diseased peripheral blood cells at the single cell level, studying samples obtained through the national Treatment Efficacy and Toxicity in Rheumatoid Arthritis Database and Repository (TETRAD). Nodality anticipates completing its research program and announcing the key findings later this year.

Laura Brege, Nodality’s President and Chief Executive Officer, stated, “ICI has provided an important opportunity to showcase one of our key programs in immunology, further validating our broadly enabling SCNP platform. This platform has led to major collaborations in immunology addressing significant unmet needs among patients, as well as new predictive diagnostic modalities in blood cancers. Ultimately, Nodality’s goal is to accelerate and make more efficient the development of new therapeutic agents for serious diseases affecting large patient populations within immunology and oncology, two areas of continuing significant unmet clinical need.”

Additional program results were featured in a second oral presentation at the ICI Congress in a presentation titled, Functional proteomic interrogation of immune cell crosstalk and the effects of cytokine-targeted inhibitors using Single Cell Network Profiling (SCNP) (Abstract W7.02.03).

Source: Nodality, Inc

Entelos and ISB Announce Collaborative Gene Expression Breakthrough

Entelos Holding Corp. (“Entelos” or “the Company”), a premier provider of physiologicalsystemmodeling and services, and Seattle-based Institute for System Biology (ISB), the nonprofit pioneers of the systems approach to study the molecular causes of diseases, today announced the successful integration of gene expression data into quantitative physiological simulations. This proprietary capability improves understanding of the gene expression and disease outcomes to radically improve the predictive discernment of the complex nature of disease, yielding insights into novel therapeutic targets, biomarkers, and patient selection that should support a new era of precision medicine.

Entelos and ISB worked together to define a scientifically sound and scalable methodology to provide breakthrough capabilities for both the modeling and systems pharmacology communities. It addresses business-critical problems in both pharmaceutical research and healthcare. “This workflow is transformative for understanding the role of molecular interactions and their impact on pharmaceutical R&D and healthcare decision making,” stated Entelos Founder and CTO, Tom Paterson. “By utilizing our computer models, we are able to use all identified correlations across gene network studies to decipher genetic influence on the disruptions identified as disease. As an example, the new capabilities were able to help us clearly define from a pool of 51 potential biomarkers, and which biomarkers identified non-responders and responders for anti-IL1 therapies for rheumatoid arthritis.”

“The mapping and application of clinical gene expression data sets a new standard and role for quantitative physiological modeling within the drug discovery and development process,” stated Entelos President and CEO, Shawn O’Connor. “It’s only due to the unique depth and breadth of the Entelos quantitative physiological models that these sorts of mappings and analyses can be carried out across the entire pathophysiology of a disease. This is the beginning of truly understanding and leveraging the human genome for therapeutic success”

“As the interconnected features of the disease space become increasingly more visible, we are continuing to look for new ways to decipher the elaborate data that hides therapeutic success“ said Dr. Lee Hood, co-founder and president of Institute for Systems Biology and recipient of the National Medal of Science. “This approach represents a breakthrough capability for deriving insights from those data sets.”

This demonstrated convergence of top-down functional systems biology and bottom-up molecular systems biology provides an approach for using clinical gene expression data to investigate a wide diversity of diseases, to decipher disease complexity, and to understand variability and reduce uncertainty in populations and sub populations. Entelos and ISB are now seeking commercial partners to advance additional existing disease models (Atherosclerosis, Type 2 Diabetes, Hypertension, Rheumatoid Arthritis, etc.) and generate new in silico applications.

Source: Entelos