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Archive for the ‘BioIT: BioInformatics, NGS, Clinical & Translational, Pharmaceuticall R&D Informatics, Clinical Genomics, Cancer Informatics’ Category


SNP-based Study on high BMI exposure confirms CVD and DM Risks – no associations with Stroke

Reporter: Aviva Lev-Ari, PhD, RN

Genes Affirm: High BMI Carries Weighty Heart, Diabetes Risk – Mendelian randomization study adds to ‘burgeoning evidence’

by Crystal Phend, Senior Associate Editor, MedPage Today, July 05, 2017

 

The “genetically instrumented” measure of high BMI exposure — calculated based on 93 single-nucleotide polymorphisms associated with BMI in prior genome-wide association studies — was associated with the following risks (odds ratios given per standard deviation higher BMI):

  • Hypertension (OR 1.64, 95% CI 1.48-1.83)
  • Coronary heart disease (CHD; OR 1.35, 95% CI 1.09-1.69)
  • Type 2 diabetes (OR 2.53, 95% CI 2.04-3.13)
  • Systolic blood pressure (β 1.65 mm Hg, 95% CI 0.78-2.52 mm Hg)
  • Diastolic blood pressure (β 1.37 mm Hg, 95% CI 0.88-1.85 mm Hg)

However, there were no associations with stroke, Donald Lyall, PhD, of the University of Glasgow, and colleagues reported online in JAMA Cardiology.

The associations independent of age, sex, Townsend deprivation scores, alcohol intake, and smoking history were found in baseline data from 119,859 participants in the population-based U.K. Biobank who had complete medical, sociodemographic, and genetic data.

“The main advantage of an MR approach is that certain types of study bias can be minimized,” the team noted. “Because DNA is stable and randomly inherited, which helps to mitigate errors from reverse causality and confounding, genetic variation can be used as a proxy for lifetime BMI to overcome limitations such as reverse causality and confounding, a process that hampers observational analyses of obesity and its consequences.”

 

Other related articles published in this Open Access Online Scientific Journal include the following:

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Genomic Diagnostics: Three Techniques to Perform Single Cell Gene Expression and Genome Sequencing Single Molecule DNA Sequencing

Curator: Aviva Lev-Ari, PhD, RN

 

This article presents Three Techniques to Perform Single Cell Gene Expression and Genome Sequencing Single molecule DNA sequencing

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The BioPharma Industry’s Unrealized Wealth of Data, by Ben Szekely, Vice President, Cambridge Semantics

Reporter: Aviva Lev-Ari, PhD, RN

 

 

The BioPharma Industry’s Unrealized Wealth of Data

by Ben Szekely, Vice President of Solutions and Pre-sales, Cambridge Semantics

 

Solving the great medical challenges of our time reside within patient data. Clinical trial data, real-world evidence, patient feedback, genetic data, wearables data and adverse event reports contain signals to target medicines at the right patient populations, improve overall safety, and uncover the next blockbuster therapy for unmet medical needs.

However, data sources are large, diverse, multi-structured, messy and highly regulated presenting numerous challenges. As result, extracting value from data are slow to come and require manual work or long-poll dependencies on IT and Data Science teams.

Fortunately, there are new ways being adopted to take better advantage of the ever-growing volumes of patient data.  Called ‘Smart’ Patient Data Lakes (SPDL), these tools create an Enterprise Knowledge Graph built upon foundational and open Semantic Web technology standards, providing rich descriptions of data and flexibility end-to-end.  With the SPDL, biopharma researchers can:

  • Quickly on-board new data without requiring up-front modeling or mapping, ingesting data from any source versus months or weeks of preparation
  • Dynamically map and prepare data at analytics time
  • Horizontally scale in cloud or on-prem infrastructure to 100’s of nodes – allowing billions of facts to be analyzed, queried and explored in real-time   

The world’s BioPharma and research institutions are sitting on a wealth of highly differentiating and life-saving data and should begin to realize its value via Smart Patient Data Lakes (SPDL).

 

 

CONTACT: Nadia Haidar

Global Results Communications ∙ 949-278-7328 ∙ nhaidar@globalresultspr.com

 

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Reporter and Curator: Dr. Sudipta Saha, Ph.D.

 

Low sperm count and motility are markers for male infertility, a condition that is actually a neglected health issue worldwide, according to the World Health Organization. Researchers at Harvard Medical School have developed a very low cost device that can attach to a cell phone and provides a quick and easy semen analysis. The device is still under development, but a study of the machine’s capabilities concludes that it is just as accurate as the elaborate high cost computer-assisted semen analysis machines costing tens of thousands of dollars in measuring sperm concentration, sperm motility, total sperm count and total motile cells.

 

The Harvard team isn’t the first to develop an at-home fertility test for men, but they are the first to be able to determine sperm concentration as well as motility. The scientists compared the smart phone sperm tracker to current lab equipment by analyzing the same semen samples side by side. They analyzed over 350 semen samples of both infertile and fertile men. The smart phone system was able to identify abnormal sperm samples with 98 percent accuracy. The results of the study were published in the journal named Science Translational Medicine.

 

The device uses an optical attachment for magnification and a disposable microchip for handling the semen sample. With two lenses that require no manual focusing and an inexpensive battery, it slides onto the smart phone’s camera. Total cost for manufacturing the equipment: $4.45, including $3.59 for the optical attachment and 86 cents for the disposable micro-fluidic chip that contains the semen sample.

 

The software of the app is designed with a simple interface that guides the user through the test with onscreen prompts. After the sample is inserted, the app can photograph it, create a video and report the results in less than five seconds. The test results are stored on the phone so that semen quality can be monitored over time. The device is under consideration for approval from the Food and Drug Administration within the next two years.

 

With this device at home, a man can avoid the embarrassment and stress of providing a sample in a doctor’s clinic. The device could also be useful for men who get vasectomies, who are supposed to return to the urologist for semen analysis twice in the six months after the procedure. Compliance is typically poor, but with this device, a man could perform his own semen analysis at home and email the result to the urologist. This will make sperm analysis available in the privacy of our home and as easy as a home pregnancy test or blood sugar test.

 

The device costs about $5 to make in the lab and can be made available in the market at lower than $50 initially. This low cost could help provide much-needed infertility care in developing or underdeveloped nations, which often lack the resources for currently available diagnostics.

 

References:

 

https://www.nytimes.com/2017/03/22/well/live/sperm-counts-via-your-cellphone.html?em_pos=small&emc=edit_hh_20170324&nl=well&nl_art=7&nlid=65713389&ref=headline&te=1&_r=1

 

http://www.npr.org/sections/health-shots/2017/03/22/520837557/a-smartphone-can-accurately-test-sperm-count

 

https://www.ncbi.nlm.nih.gov/pubmed/28330865

 

http://www.sciencealert.com/new-smartphone-microscope-lets-men-check-the-health-of-their-own-sperm

 

https://www.newscientist.com/article/2097618-are-your-sperm-up-to-scratch-phone-microscope-lets-you-check/

 

https://www.dezeen.com/2017/01/19/yo-fertility-kit-men-test-sperm-count-smartphone-design-technology-apps/

 

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2017 Agenda – BioInformatics: Track 6: BioIT World Conference & Expo ’17, May 23-35, 2017, Seaport World Trade Center, Boston, MA

Reporter: Aviva Lev-Ari, PhD, RN

2017bioit-bit-mini-logo

 

 bioinformatics

http://www.bio-itworldexpo.com/Bio-It_Expo_Content.aspx?id=140955

  #BioIT17

TUESDAY, MAY 23

7:00 am Workshop Registration and Morning Coffee

8:0011:30 Recommended Morning Pre-Conference Workshops*

(W4) Data Visualization to Accelerate Biological Discovery

12:304:00 pm Recommended Afternoon Pre-Conference Workshops*

(W13) Proteogenomics: Integration of Genomics and Proteomics Data

* Separate registration required.

2:006:00 Main Conference Registration Open

4:00 PLENARY KEYNOTE SESSION

Click here for detailed information

5:007:00 Welcome Reception in the Exhibit Hall with Poster Viewing

WEDNESDAY, MAY 24

7:00 am Registration Open and Morning Coffee

8:00 PLENARY KEYNOTE SESSION

Click here for detailed information

9:50 Coffee Break in the Exhibit Hall with Poster Viewing

APPLICATIONS & SOLUTIONS FOR DATA SHARING AND DECISION MAKING

10:50 Chairperson’s Remarks

Kevin Merlo, BioSafety Development Engineer, Dassault Systemes

11:00 Innovative Data Integration Applicable for Therapeutic Protein Development 2.0

Wolfgang Paul, Group Leader and Senior Scientist, Large Molecule Research, Roche

Therapeutic proteins are registered including sequence, structural and functional data and information. Millions of data points are captured during the development of Roche’s innovative therapeutic proteins in data warehouse used by DAMAS (data acquisition, management and analyses system). Fast access and visualization of relevant process and analytical data drive scientific discussion and decision making. Analyzing the stored big data is key towards process development of therapeutic proteins 2.0.

11:30 Informatics – A Silver Bullet for Pharmaceutical Sciences?

William Loging, Ph.D., Associate Professor of Genomics & Head, Production Bioinformatics, Genetics and Genomics Sciences, Icahn School of Medicine at Mount Sinai

The Pharmaceutical Sciences field is in constant search for the next big innovative push that will increase the success rate of drug programs. The fields of computational chemistry, structural bioinformatics – just to name a few – have changed the way drug researchers look for and identify novel drug candidates. Utilizing more than 15 years of Pharmaceutical experience, and using real world examples of high provide drug projects, this talk will provide practical steps for the merger of informatics and the strategic approaches needed for drug discovery success.

12:00 pm Big Data-Driven Bioinformatics

Frank Lee, Ph.D., Healthcare Life Sciences Industry Leader, Software Defined Infrastructure, IBM Systems, IBM

IBM will discuss the IBM Reference Architecture for Genomics, its new features, and case studies: hybrid cloud with integrated workload and data management for high performance genomics analytics; container technologies for migrating and sharing application and data; and application portal and metadata engine for global access to and searching of distributed resources. A demo of a hybrid cloud-based bioinformatics solution will follow.

12:30 Session Break

12:40 Luncheon Presentation I to be Announced

1:10 Luncheon Presentation II to be Announced

1:40 Session Break

STANDARDS FOR CHEMICAL STRUCTURES

1:50 Chairperson’s Remarks

1:55 PANEL DISCUSSION: Linking and Finding Information Using the IUPAC InChI Standard for Chemical Structures

Steve Heller, Ph.D., Project Director, InChI Trust; Scientific Information Consultant (Moderator)

Evan Bolton, Ph.D., Lead Scientist, National Center for Biotechnology Information (NCBI), National Library of Medicine (NLM), and National Institutes of Health (NIH)

Keith T. Taylor, BSc, Ph.D., MRSC, Principal, Ladera Consultancy

Tyler Peryea, Informatics Scientist, National Center for Advancing Translational Sciences (NCATS)

Lawrence Callahan, Ph.D., Chemist, Substance Registration System, Office of Critical Path Programs, Food and Drug Administration (FDA)

This session will highlight on-going efforts to strengthen and expand the non-proprietary IUPAC International Chemical Identifier (InChI) standard for chemical structures and its hashed-form, the InChIKey. Information standards are critical to enable effective communication of scientific content. Funding to maintain InChI comes from most major publishers and database providers as well as governmental agencies (NIH, FDA and NIST). The InChI is an open-source, widely adopted standard found in most chemical information containing databases, including those from Chemical Abstracts, Reaxys, ChEMBL, OpenPHACTS, PubChem, DrugBank, PDB, Sigma-Aldrich, and many others, such as internal Pharma corporate databases. InChI is an addition to a database, not a replacement. With the implementation of the ISO identification of medicinal products (IDMP) and the related ISO 11238 standards, adding and having an InChI will allow for an easier, effective, and more complete search for information on a particular drug.

2:55 Sponsored Presentation (Opportunity Available)

3:10 Integrated Informatics for Biologics Discovery

Robert Brown, Ph.D., Vice President, Product Marketing, Dotmatics

3:25 Refreshment Break in the Exhibit Hall with Poster Viewing

MACHINE LEARNING TECHNIQUES AND APPLICATIONS TO PERFORM BIG DATA ANALYTICS ON –OMICS DATA

4:00 Building Disease Networks Using Text Mining and Machine Learning Techniques

Kamal Rawal, Ph.D., Assistant Professor, Biotech and Bioinformatics, Jaypee Institute of Information Technology

Obesity is a global epidemic affecting over 1.5 billion people and is one of the risk factors for several diseases such as type 2 diabetes mellitus and hypertension. We have constructed a comprehensive map of the molecules reported to be implicated in obesity. Using text mining & deep curation strategies combined with omics data, we have explained the therapeutics and side effects of several drugs (i.e., orlistat) at network level.

4:20 Big Data and Systems Biology: From Genome to Phenome (and Everything in Between)

Dan Jacobson, Ph.D., Computational Biologist, Oak Ridge National Laboratory

4:40 Novel Feature Selection Strategies for Enhanced Predictive Modeling and Deep Learning in the Biosciences

Tom Chittenden, Ph.D., D.Phil., Lecturer and Senior Biostatistics and Mathematical Biology Consultant, Harvard Medical School

We have built a robust AI approach that precisely assesses pathogenicity for all genomic missense variants. Coupled with our advanced deepCODE mathematical statistics feature selection strategy for constructing deep learning models, we are able to quantitatively integrate a priori pathway-based biological knowledge with multiple types of high-throughput omics data.

5:00 Network Analysis for Drug Discovery: Benchmarking Results and Best Practices Reported by CBDD Consortium

Marina Bessarabova, Ph.D., Senior Director, Discovery and Translational Science, Life Sciences Professional Services, Clarivate Analytics (Formerly the IP & Science Business of Thomson Reuters)

A large number of advanced approaches to network analysis of -omics data were developed by academia groups in the past 15 years. Adoption of these approaches in drug development requires thorough review of the published approaches, implementation of methods identified as potentially applicable to drug development and benchmarking of the methods with an aim to establish best practices for application of the methods to diseases and mechanism of action understanding, target identification, drug repositioning, patient stratification, biomarker discovery, and drug combination effect prediction. CBDD (Computational Biology Methods for Drug Discovery) is a precompetitive consortium between Novartis, Pfizer, Sanofi, Janssen, Regeneron, UCB, Roche, Takeda, Biogen, Boehringer Ingelheim, Bristol-Myers Squibb, Merck and Clarivate Analytics (formally Thomson Reuters) focused on adoption of network analysis approaches in drug development: literature review, method implementation and benchmarking. Benchmarking results and best practices for application of network analysis in drug development established by members of the program will be shared during the presentation.

5:30 15th Anniversary Celebration in the Exhibit Hall with Poster Viewing and Best of Show Awards

THURSDAY, MAY 25

7:00 am Registration Open and Morning Coffee

8:00 PLENARY KEYNOTE SESSION & AWARDS PROGRAM

8:05 Benjamin Franklin Awards and Laureate Presentation

8:35 Best Practices Awards Program

8:50 Plenary Keynote

Click here for detailed information

9:45 Coffee Break in the Exhibit Hall and Poster Competition Winners Announced

DATA COMPUTING AND BIOINFORMATICS IN AGRO CHEMICALS AND BIOTECHNOLOGY: CHALLENGES AND OPPORTUNITIES

10:30 Chairperson’s Remarks

Bino John, Ph.D., Computational Biology Group Leader, Dow AgroSciences LLC

10:40 How Biotech and Big Data Are Changing Agro Industry

Bino John, Ph.D., Computational Biology Group Leader, Dow AgroSciences LLC

More than 70% of the increase in food production in the next 50 years is expected to come from technological advances. Indeed, recent advances in genomics and phenomics are beginning to transform the Agro-industry, whereby creating new opportunities for informatics disciplines. While informatics needs in managing, analyzing, and visualizing big data share commonalties between Agro and the biomedical communities, Agro companies face unprecedented challenges in big biological data, generally larger than their peers in the biomedical community.

11:00 Offering Outcomes: How Digital Farming Data Is Enabling New Business Models

Tobias Menne, Global Head of Digital Farming, Bayer

11:20 Building the Next-Generation R&D IT Infrastructure for Small Molecule Discovery

Paimun Amini, Chemistry IT Lead, R&D IT, Monsanto Company

Barrett Foat, Ph.D., Data Science Team Lead, Agricultural Productivity Innovations, Monsanto

The Pharma boom in the 90s & 2000s led to the emergence of a rich ecosystem of software companies focused on delivering the IT needs for small molecule discovery. Today, cloud data storage, IoT, and the growth of predictive analytics present new opportunities for the evolution of the R&D pipeline. New technologies allow for integrated software and hardware solutions that optimize productivity while removing the risk of technical debt.

11:40 Sponsored Presentation (Opportunity Available)

12:10 pm Session Break

12:20 Luncheon Presentation (Sponsorship Opportunity Available) or Enjoy Lunch on Your Own

1:20 Dessert Refreshment Break in the Exhibit Hall with Poster Viewing

LOOKING BEYOND THE GENOME OF THE PATIENT: DATA, ANALYSIS AND TOOLS TO IMPROVE BETTER DISEASE UNDERSTANDING FOR CURRENT TREATMENTS AND DRUG DEVELOPMENT

1:55 Chairperson’s Remarks

Michael N. Liebman, Ph.D., Managing Director, IPQ Analytics, LLC and Strategic Medicine, Inc.

2:00 Distinguishing between Precision Medicine and Accurate Medicine: Application to Heart Failure Patients and Clinical Practice

Michael N. Liebman, Ph.D., IPQ Analytics, LLC and Strategic Medicine, Inc.

Increasingly, patient stratification based on genomic analysis is being considered in disease management. Critically, the need to understand real world medical practice and real world patient complexities extends far beyond the genome of the patient. We have shown examples of this complexity in heart disease and how this impacts development of clinical guidelines, trial design, and development of new patient management approaches.

2:30 CARPEDIEM – Comorbidity and Risk Profiles Evaluation in Diabetes and Heart Morbidities

Sabrina Molinaro, Psy.D., Ph.D., Head, Department of Epidemiology and Health Services, Institute of Clinical Physiology, National Research Council of Italy

Our project uniquely develops a patient record that includes clinical and individual factors (EHR-driven phenotyping) that will be validated through the comparison of existing standards for building new risk algorithms. An understanding of the current limitations and biases of risk profiling in heart disease and diabetes and how an extended, integrated database and automatic rule-based classification system can be used to improve patient management.

3:00 PANEL DISCUSSION: Precision Medicine vs. Accurate Medicine: The Need to Understand Real World Medicine and Real World Patients

Michael N. Liebman, Ph.D., IPQ Analytics, LLC and Strategic Medicine, Inc. (Moderator)

Charles Barr, M.D., MPH, Group Medical Director and Head, Evidence Science and Innovation, Genentech

Hal Wolf, Director, National Leader of Information and Digital Health Strategy, The Chartis Group

4:00 Conference Adjourns

SOURCE

http://www.bio-itworldexpo.com/bioinformatics/

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Dr. Doudna: RNA synthesis capabilities of Synthego’s team represent a significant leap forward for Synthetic Biology

Reporter: Aviva Lev-Ari, PhD, RN

 

Synthego Raises $41 Million From Investors, Including a Top Biochemist

Synthego also drew in Dr. Doudna, who had crossed paths with the company’s head of synthetic biology at various industry conferences. According to Mr. Dabrowski, the money from her trust represents the single-biggest check from a non-institutional investor that the start-up has raised.

Synthego’s new funds will help the company take its products to a more global customer base, as well as broaden its offerings. The longer-term goal, Mr. Dabrowski said, is to help fully automate biotech research and take care of much of the laboratory work that scientists currently handle themselves.

The model is cloud technology, where companies rent out powerful remote server farms to handle their computing needs rather than rely on their own hardware.

“We’ll be able to do their full research workflow,” he said. “If you look at how cloud computing developed, it used to be that every company handled their server farm. Now it’s all handled in the cloud.”

SOURCE

Other related articles published in this Open Access Online Scientific Journal include the following:

UPDATED – Status “Interference — Initial memorandum” – CRISPR/Cas9 – The Biotech Patent Fight of the Century: UC, Berkeley and Broad Institute @MIT

Reporter: Aviva Lev-Ari, PhD, RN

https://pharmaceuticalintelligence.com/2016/01/06/status-interference-initial-memorandum-crisprcas9-the-biotech-patent-fight-of-the-century/

 

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A novel 5-gene pancreatic adenocarcinoma classifier: Meta-analysis of transcriptome data – Clinical Genomics Research @BIDMC

Curator: Tilda Barliya, PhD

Analysis of  Bhasin et al paper and Literature search

Table 1: 5-genes classifiers as biomarkers for PDAC:

Gene symbol Gene name Subcellular localization
ECT2 Epithelial cell transforming sequence 2 oncogene Nucleus, cytoplasm
AHNAK2 AHNAKE nucleoprotein 2 Plasma membrane, cytoplasm
POSTN Periostin, osteoblast specific factor Extracellular space
TMPRSS4 Transmembrane protease, serine 4 Plasma membrane

 

SERPINB5 Serpin peptidase inhibitor, clade B (ovalbumin) member 5 Extracellular space


Introduction
:

  • Bhasin et al, conducted a beautiful study using a powerful meta-analysis from different sources to identify the “important/classifier” genes associated with Pancreatic Cancer (PDAC).
  • The authors identified 5 genes that were considered as good classifiers (table 1).
  • It is important to note that the meta-analysis was performed on tissue and microdissection samples.
  • In their summary, the authors aim to validate these genes in blood/urine samples.
  • While these genes might be over expressed in tissue samples it may not be true to their existence in blood and careful examination and validation is required.
  • Liquid biopsies are emerging as the go-to use tools for disease detection, mostly aimed for early diagnosis.
  • Liquid biopsies are non-invasive biopsies of blood, urine (potentially saliva) and their “exotic” components, i.e miRNA, exosomes etc.
  • Since Liquid biopsies are non-invasive, they are painless and patients are more complied.
  • It is important to note that there is a gap between the expression of a gene or a protein in tissue section and their expression in the blood and may not necessarily correlate.
  • It will be very interesting to follow their research and future outcomes.

Additional References:

  • TMPRSS4: an emerging potential therapeutic target in cancer.

https://www.ncbi.nlm.nih.gov/pmc/articles/PMC4453593/

  • The tumour trail left in blood

http://www.nature.com/nature/journal/v532/n7598/full/532269a.html

Aashir Awan, PhD, wrote on 12/28/2016

I was wondering if these same 5 genes were upregulated in the pancreatic ductal adenocarcinoma cell lines that are available out there.  In doing cell biology work, there is always a dilemma whether cancer cell lines correctly re-capitulate in vivo tumors or not.  Personally, I prefer primary cell lines to do analysis but this finding can be used to test primary vs cell line.  In addition, I’ve attached the gene network for Ect2.  If you look carefully, the two big proteins that jump out are RACGAP1 and KIF23.  I think in designing therapies, combinatorial targets can yield the best outcomes.  Drugs directed towards two or more targets would seem ideal in my opinion.

ect2

Gene Network for Ect2

Original Research
Oncotarget. 2016 Apr 26;7(17):23263-81. doi: 10.18632/oncotarget.8139.

Meta-analysis of transcriptome data identifies a novel 5-gene pancreatic adenocarcinoma classifier.

Abstract

PURPOSE:

Pancreatic ductal adenocarcinoma (PDAC) is largely incurable due to late diagnosis. Superior early detection biomarkers are critical to improving PDAC survival and risk stratification.

EXPERIMENTAL DESIGN:

Optimized meta-analysis of PDAC transcriptome datasets identified and validated key PDAC biomarkers. PDAC-specific expression of a 5-gene biomarker panel was measured by qRT-PCR in microdissected patient-derived FFPE tissues. Cell-based assays assessed impact of two of these biomarkers, TMPRSS4 and ECT2, on PDAC cells.

RESULTS:

A 5-gene PDAC classifier (TMPRSS4, AHNAK2, POSTN, ECT2, SERPINB5) achieved on average 95% sensitivity and 89% specificity in discriminating PDAC from non-tumor samples in four training sets and similar performance (sensitivity = 94%, specificity = 89.6%) in five independent validation datasets. This classifier accurately discriminated PDAC from chronic pancreatitis (AUC = 0.83), other cancers (AUC = 0.89), and non-tumor from PDAC precursors (AUC = 0.92) in three independent datasets. Importantly, the classifier distinguished PanIN from healthy pancreas in the PDX1-Cre;LSL-KrasG12D PDAC mouse model. Discriminatory expression of the PDAC classifier genes was confirmed in microdissected FFPE samples of PDAC and matched surrounding non-tumor pancreas or pancreatitis. Notably, knock-down of TMPRSS4 and ECT2 reduced PDAC soft agar growth and cell viability and TMPRSS4 knockdown also blocked PDAC migration and invasion.

CONCLUSIONS:

This study identified and validated a highly accurate 5-gene PDAC classifier for discriminating PDAC and early precursor lesions from non-malignant tissue that may facilitate early diagnosis and risk stratification upon validation in prospective clinical trials. Cell-based experiments of two overexpressed proteins encoded by the panel, TMPRSS4 and ECT2, suggest a causal link to PDAC development and progression, confirming them as potential therapeutic targets.

KEYWORDS:

bioinformatics; biomarkers; meta-analysis; pancreatic cancer; transcriptome

PMID:
26993610
PMCID:
PMC5029625
DOI:
10.18632/oncotarget.8139

SOURCE

Oncotarget, Vol. 7, No. 17 – Referred as PDF, above

 

BIDMC Researchers Discover Early Indicators of Pancreatic Cancer

LibermannBhasin_PancreasCancerStudy

Markers may help doctors detect pancreatic cancer before it becomes deadly

In photo: First author Manoj Bhasin, PhD, and co-senior author Towia Libermann, PhD, Co-Director and Director of BIDMC’s Genomics, Proteomics, Bioinformatics and Systems Biology Center.

SOURCE

http://www.bidmc.org/News/PRLandingPage/2016/March/Libermann-Pancreatic-Cancer-Research-2016.aspx

BOSTON – Pancreatic cancer, the fourth leading cause of cancer death in the United States, is often diagnosed at a late stage, when curative treatment is no longer possible. A team led by investigators at Beth Israel Deaconess Medical Center (BIDMC) has now identified and validated an accurate 5-gene classifier for discriminating early pancreatic cancer from non-malignant tissue. Described online in the journal Oncotarget, the finding is a promising advance in the fight against this typically fatal disease.

“Pancreatic cancer is a devastating disease with a death rate close to the incidence rate,” said co-senior author Towia Libermann, PhD, Director of the Genomics, Proteomics, Bioinformatics and Systems Biology Center at BIDMC and Associate Professor of Medicine at Harvard Medical School (HMS). “Because more than 90 percent of pancreatic cancer cases are diagnosed at the metastatic stage, when there are only limited therapeutic options, earlier diagnosis is anticipated to have a major impact on extending life expectancy for patients. There has been a lack of reliable markers, early indicators and risk factors associated with pancreatic cancer, but this new way of differentiating between healthy and malignant tissue offers hope for earlier diagnosis and treatment.”

The investigators used a number of publicly available gene expression datasets for pancreatic cancer and developed a strategy to reanalyze these datasets together, applying rigorous statistical criteria to compare different datasets from different laboratories and different platforms with each other. The team then selected a subset of data for developing a panel for differentiating between pancreatic cancer and healthy pancreas tissue and thereafter applied this “Pancreatic Cancer Predictor” to the remaining datasets for independent validation to confirm the accuracy of the markers.

After demonstrating and independently validating that a 5-gene pancreatic cancer predictor discriminated between cancerous and healthy tissue, the researchers applied the predictor to datasets that also included benign lesions of the pancreas, including pancreatitis and early stage cancer. The predictor accurately differentiated pancreatic cancer, benign pancreatic lesions, early stage pancreatic cancer and healthy tissue. The predictor achieved on average 95 percent sensitivity and 89 percent specificity in discriminating pancreatic cancer from non-tumor samples in four training sets and similar performance (94 percent sensitivity, 90 percent specificity) in five independent validation datasets.

“Using innovative data normalization and gene selection approaches, we combined the statistical power of multiple genomic studies and masked their variability and batch effects to identify robust early diagnostic biomarkers of pancreatic cancer,” said first author Manoj Bhasin, PhD, Co-Director of BIDMC’s Genomics, Proteomics, Bioinformatics and Systems Biology Center and Assistant Professor of Medicine at HMS.

“The identification and initial validation of a highly accurate 5-gene pancreatic cancer biomarker panel that can discriminate late and early stages of pancreatic cancer from normal pancreas and benign pancreatic lesions could facilitate early diagnosis of pancreatic cancer,” said co-senior author Roya Khosravi-Far, PhD, Associate Professor of Pathology at BIDMC. “Our findings may open a window of opportunity for earlier diagnosis and, consequently, earlier intervention and more effective treatment of this deadly cancer, leading to higher survival rates.”

The first diagnostic application of the panel may be for analyses of fine needle biopsies routinely used for diagnosing pancreatic cancer and for determining the malignant potential of mostly benign pancreatic cysts that can sometimes be precursors of pancreatic cancer. In addition to providing a new tool for diagnoses, the research may also lead to new insights into how pancreatic cancer arises.

“Because these five genes are ‘turned on’ so early in the development of pancreatic cancer, they may play roles as drivers of this disease and may be exciting targets for therapies,” said Libermann. Most of the five genes—named TMPRSS4, AHNAK2, POSTN, ECT2 and SERPINB5—have been linked to migration, invasion, adhesion, and metastasis of pancreatic or other cancers.

The scientists next plan to evaluate the precise roles of the five genes and to validate the accuracy of their diagnostic assay in a prospective clinical study. “Moving forward, we will explore the potential to convert this tissue-based diagnostic into a noninvasive blood or urine test,” Libermann said.

“To further enhance the diagnostic power of this biomarker, we plan to expand it by including non-coding RNAs, proteins, metabolites and mutations associated with pancreatic cancer. This will result in development of the first of its kind biomarker that gauges pancreatic cancer alterations from multiple genomic angles for making highly accurate diagnoses,” added Bhasin. Such an inexpensive and simple test could help transform the landscape of pancreatic cancer and help prevent many of the estimated 330,000 deaths that it causes worldwide each year.

Study coauthors include BIDMC investigators Kenneth Ndebele, Octavian Bucur, Eric Yee, Jessica Plati, Andrea Bullock, Xuesong Gu, Eduardo Castan, Peng Zhang, Robert Najarian, Maria Muraru and Rebecca Miksad, and the University of Nebraska-Lincoln’s Hasan H. Otu. The work was supported by the National Institutes of Health, National Cancer Institute and Ben and Rose Cole Charitable Pria Foundation.

SOURCE

http://www.bidmc.org/News/PRLandingPage/2016/March/Libermann-Pancreatic-Cancer-Research-2016.aspx

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