Archive for the ‘Pharmacogenomics’ Category

Milestones in Physiology & Discoveries in Medicine and Genomics: Request for Book Review Writing on


Milestones in Physiology

Discoveries in Medicine, Genomics and Therapeutics

Patient-centric Perspective 




Author, Curator and Editor

Larry H Bernstein, MD, FCAP

Chief Scientific Officer

Leaders in Pharmaceutical Business Intelligence



Chapter 1: Evolution of the Foundation for Diagnostics and Pharmaceuticals Industries

1.1  Outline of Medical Discoveries between 1880 and 1980

1.2 The History of Infectious Diseases and Epidemiology in the late 19th and 20th Century

1.3 The Classification of Microbiota

1.4 Selected Contributions to Chemistry from 1880 to 1980

1.5 The Evolution of Clinical Chemistry in the 20th Century

1.6 Milestones in the Evolution of Diagnostics in the US HealthCare System: 1920s to Pre-Genomics


Chapter 2. The search for the evolution of function of proteins, enzymes and metal catalysts in life processes

2.1 The life and work of Allan Wilson
2.2  The  evolution of myoglobin and hemoglobin
2.3  More complexity in proteins evolution
2.4  Life on earth is traced to oxygen binding
2.5  The colors of life function
2.6  The colors of respiration and electron transport
2.7  Highlights of a green evolution


Chapter 3. Evolution of New Relationships in Neuroendocrine States
3.1 Pituitary endocrine axis
3.2 Thyroid function
3.3 Sex hormones
3.4 Adrenal Cortex
3.5 Pancreatic Islets
3.6 Parathyroids
3.7 Gastointestinal hormones
3.8 Endocrine action on midbrain
3.9 Neural activity regulating endocrine response

3.10 Genomic Promise for Neurodegenerative Diseases, Dementias, Autism Spectrum, Schizophrenia, and Serious Depression


Chapter 4.  Problems of the Circulation, Altitude, and Immunity

4.1 Innervation of Heart and Heart Rate
4.2 Action of hormones on the circulation
4.3 Allogeneic Transfusion Reactions
4.4 Graft-versus Host reaction
4.5 Unique problems of perinatal period
4.6. High altitude sickness
4.7 Deep water adaptation
4.8 Heart-Lung-and Kidney
4.9 Acute Lung Injury

4.10 Reconstruction of Life Processes requires both Genomics and Metabolomics to explain Phenotypes and Phylogenetics


Chapter 5. Problems of Diets and Lifestyle Changes

5.1 Anorexia nervosa
5.2 Voluntary and Involuntary S-insufficiency
5.3 Diarrheas – bacterial and nonbacterial
5.4 Gluten-free diets
5.5 Diet and cholesterol
5.6 Diet and Type 2 diabetes mellitus
5.7 Diet and exercise
5.8 Anxiety and quality of Life
5.9 Nutritional Supplements


Chapter 6. Advances in Genomics, Therapeutics and Pharmacogenomics

6.1 Natural Products Chemistry

6.2 The Challenge of Antimicrobial Resistance

6.3 Viruses, Vaccines and immunotherapy

6.4 Genomics and Metabolomics Advances in Cancer

6.5 Proteomics – Protein Interaction

6.6 Pharmacogenomics

6.7 Biomarker Guided Therapy

6.8 The Emergence of a Pharmaceutical Industry in the 20th Century: Diagnostics Industry and Drug Development in the Genomics Era: Mid 80s to Present

6.09 The Union of Biomarkers and Drug Development

6.10 Proteomics and Biomarker Discovery

6.11 Epigenomics and Companion Diagnostics


Chapter  7

Integration of Physiology, Genomics and Pharmacotherapy

7.1 Richard Lifton, MD, PhD of Yale University and Howard Hughes Medical Institute: Recipient of 2014 Breakthrough Prizes Awarded in Life Sciences for the Discovery of Genes and Biochemical Mechanisms that cause Hypertension

7.2 Calcium Cycling (ATPase Pump) in Cardiac Gene Therapy: Inhalable Gene Therapy for Pulmonary Arterial Hypertension and Percutaneous Intra-coronary Artery Infusion for Heart Failure: Contributions by Roger J. Hajjar, MD

7.3 Diagnostics and Biomarkers: Novel Genomics Industry Trends vs Present Market Conditions and Historical Scientific Leaders Memoirs

7.4 Synthetic Biology: On Advanced Genome Interpretation for Gene Variants and Pathways: What is the Genetic Base of Atherosclerosis and Loss of Arterial Elasticity with Aging

7.5 Diagnosing Diseases & Gene Therapy: Precision Genome Editing and Cost-effective microRNA Profiling

7.6 Imaging Biomarker for Arterial Stiffness: Pathways in Pharmacotherapy for Hypertension and Hypercholesterolemia Management

7.7 Neuroprotective Therapies: Pharmacogenomics vs Psychotropic drugs and Cholinesterase Inhibitors

7.8 Metabolite Identification Combining Genetic and Metabolic Information: Genetic association links unknown metabolites to functionally related genes

7.9 Preserved vs Reduced Ejection Fraction: Available and Needed Therapies

7.10 Biosimilars: Intellectual Property Creation and Protection by Pioneer and by

7.11 Demonstrate Biosimilarity: New FDA Biosimilar Guidelines


Chapter 7.  Biopharma Today

8.1 A Great University engaged in Drug Discovery: University of Pittsburgh

8.2 Introduction – The Evolution of Cancer Therapy and Cancer Research: How We Got Here?

8.3 Predicting Tumor Response, Progression, and Time to Recurrence

8.4 Targeting Untargetable Proto-Oncogenes

8.5 Innovation: Drug Discovery, Medical Devices and Digital Health

8.6 Cardiotoxicity and Cardiomyopathy Related to Drugs Adverse Effects

8.7 Nanotechnology and Ocular Drug Delivery: Part I

8.8 Transdermal drug delivery (TDD) system and nanotechnology: Part II

8.9 The Delicate Connection: IDO (Indolamine 2, 3 dehydrogenase) and Cancer Immunology

8.10 Natural Drug Target Discovery and Translational Medicine in Human Microbiome

8.11 From Genomics of Microorganisms to Translational Medicine

8.12 Confined Indolamine 2, 3 dioxygenase (IDO) Controls the Homeostasis of Immune Responses for Good and Bad


Chapter 9. BioPharma – Future Trends

9.1 Artificial Intelligence Versus the Scientist: Who Will Win?

9.2 The Vibrant Philly Biotech Scene: Focus on KannaLife Sciences and the Discipline and Potential of Pharmacognosy

9.3 The Vibrant Philly Biotech Scene: Focus on Computer-Aided Drug Design and Gfree Bio, LLC

9.4 Heroes in Medical Research: The Postdoctoral Fellow

9.5 NIH Considers Guidelines for CAR-T therapy: Report from Recombinant DNA Advisory Committee

9.6 1st Pitch Life Science- Philadelphia- What VCs Really Think of your Pitch

9.7 Multiple Lung Cancer Genomic Projects Suggest New Targets, Research Directions for Non-Small Cell Lung Cancer

9.8 Heroes in Medical Research: Green Fluorescent Protein and the Rough Road in Science

9.9 Issues in Personalized Medicine in Cancer: Intratumor Heterogeneity and Branched Evolution Revealed by Multiregion Sequencing

9.10 The SCID Pig II: Researchers Develop Another SCID Pig, And Another Great Model For Cancer Research



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Content Consultant: Larry H Bernstein, MD, FCAP

Genomics Orientations for Personalized Medicine

Volume One

electronic Table of Contents

Chapter 1

1.1 Advances in the Understanding of the Human Genome The Initiation and Growth of Molecular Biology and Genomics – Part I

1.2 CRACKING THE CODE OF HUMAN LIFE: Milestones along the Way – Part IIA

1.3 DNA – The Next-Generation Storage Media for Digital Information

1.4 CRACKING THE CODE OF HUMAN LIFE: Recent Advances in Genomic Analysis and Disease – Part IIC

1.5 Advances in Separations Technology for the “OMICs” and Clarification of Therapeutic Targets

1.6 Genomic Analysis: FLUIDIGM Technology in the Life Science and Agricultural Biotechnology

Chapter 2

2.1 2013 Genomics: The Era Beyond the Sequencing of the Human Genome: Francis Collins, Craig Venter, Eric Lander, et al.

2.2 DNA structure and Oligonucleotides

2.3 Genome-Wide Detection of Single-Nucleotide and Copy-Number Variation of a Single Human Cell 

2.4 Genomics and Evolution

2.5 Protein-folding Simulation: Stanford’s Framework for Testing and Predicting Evolutionary Outcomes in Living Organisms – Work by Marcus Feldman

2.6 The Binding of Oligonucleotides in DNA and 3-D Lattice Structures

2.7 Finding the Genetic Links in Common Disease: Caveats of Whole Genome Sequencing Studies

Chapter 3

3.1 Big Data in Genomic Medicine

3.2 CRACKING THE CODE OF HUMAN LIFE: The Birth of Bioinformatics & Computational Genomics – Part IIB 

3.3 Expanding the Genetic Alphabet and linking the Genome to the Metabolome

3.4 Metabolite Identification Combining Genetic and Metabolic Information: Genetic Association Links Unknown Metabolites to Functionally Related Genes

3.5 MIT Scientists on Proteomics: All the Proteins in the Mitochondrial Matrix identified

3.6 Identification of Biomarkers that are Related to the Actin Cytoskeleton

3.7 Genetic basis of Complex Human Diseases: Dan Koboldt’s Advice to Next-Generation Sequencing Neophytes

3.8 MIT Team Researches Regulatory Motifs and Gene Expression of Erythroleukemia (K562) and Liver Carcinoma (HepG2) Cell Lines

Chapter 4

4.1 ENCODE Findings as Consortium

4.2 ENCODE: The Key to Unlocking the Secrets of Complex Genetic Diseases

4.3 Reveals from ENCODE Project will Invite High Synergistic Collaborations to Discover Specific Targets  

4.4 Human Variome Project: encyclopedic catalog of sequence variants indexed to the human genome sequence

4.5 Human Genome Project – 10th Anniversary: Interview with Kevin Davies, PhD – The $1000 Genome

4.6 Quantum Biology And Computational Medicine

4.7 The Underappreciated EpiGenome

4.8 Unraveling Retrograde Signaling Pathways

4.9  “The SILENCE of the Lambs” Introducing The Power of Uncoded RNA

4.10  DNA: One man’s trash is another man’s treasure, but there is no JUNK after all

Chapter 5

5.1 Paradigm Shift in Human Genomics – Predictive Biomarkers and Personalized Medicine – Part 1 

5.2 Computational Genomics Center: New Unification of Computational Technologies at Stanford

5.3 Personalized Medicine: An Institute Profile – Coriell Institute for Medical Research: Part 3

5.4 Cancer Genomics – Leading the Way by Cancer Genomics Program at UC Santa Cruz

5.5 Genome and Genetics: Resources @Stanford, @MIT, @NIH’s NCBCS

5.6 NGS Market: Trends and Development for Genotype-Phenotype Associations Research

5.7 Speeding Up Genome Analysis: MIT Algorithms for Direct Computation on Compressed Genomic Datasets

5.8  Modeling Targeted Therapy

5.9 Transphosphorylation of E-coli Proteins and Kinase Specificity

5.10 Genomics of Bacterial and Archaeal Viruses

Chapter 6

6.1  Directions for Genomics in Personalized Medicine

6.2 Ubiquinin-Proteosome pathway, Autophagy, the Mitochondrion, Proteolysis and Cell Apoptosis: Part III

6.3 Mitochondrial Damage and Repair under Oxidative Stress

6.4 Mitochondria: More than just the “Powerhouse of the Cell”

6.5 Mechanism of Variegation in Immutans

6.6 Impact of Evolutionary Selection on Functional Regions: The imprint of Evolutionary Selection on ENCODE Regulatory Elements is Manifested between Species and within Human Populations

6.7 Cardiac Ca2+ Signaling: Transcriptional Control

6.8 Unraveling Retrograde Signaling Pathways

6.9 Reprogramming Cell Fate

6.10 How Genes Function

6.11 TALENs and ZFNs

6.12 Zebrafish—Susceptible to Cancer

6.13 RNA Virus Genome as Bacterial Chromosome

6.14 Cloning the Vaccinia Virus Genome as a Bacterial Artificial Chromosome 

6.15 Telling NO to Cardiac Risk- DDAH Says NO to ADMA(1); The DDAH/ADMA/NOS Pathway(2)

6.16  Transphosphorylation of E-coli proteins and kinase specificity

6.17 Genomics of Bacterial and Archaeal Viruses

6.18  Diagnosing Diseases & Gene Therapy: Precision Genome Editing and Cost-effective microRNA Profiling

Chapter 7

7.1 Harnessing Personalized Medicine for Cancer Management, Prospects of Prevention and Cure: Opinions of Cancer Scientific Leaders @

7.2 Consumer Market for Personal DNA Sequencing: Part 4

7.3 GSK for Personalized Medicine using Cancer Drugs Needs Alacris Systems Biology Model to Determine the In Silico Effect of the Inhibitor in its “Virtual Clinical Trial”

7.4 Drugging the Epigenome

7.5 Nation’s Biobanks: Academic institutions, Research institutes and Hospitals – vary by Collections Size, Types of Specimens and Applications: Regulations are Needed

7.6 Personalized Medicine: Clinical Aspiration of Microarrays

Chapter 8

8.1 Personalized Medicine as Key Area for Future Pharmaceutical Growth

8.2 Inaugural Genomics in Medicine – The Conference Program, 2/11-12/2013, San Francisco, CA

8.3 The Way With Personalized Medicine: Reporters’ Voice at the 8th Annual Personalized Medicine Conference, 11/28-29, 2012, Harvard Medical School, Boston, MA

8.4 Nanotechnology, Personalized Medicine and DNA Sequencing

8.5 Targeted Nucleases

8.6 Transcript Dynamics of Proinflammatory Genes

8.7 Helping Physicians identify Gene-Drug Interactions for Treatment Decisions: New ‘CLIPMERGE’ program – Personalized Medicine @ The Mount Sinai Medical Center

8.8 Intratumor Heterogeneity and Branched Evolution Revealed by Multiregion Sequencing[1]

8.9 Diagnosing Diseases & Gene Therapy: Precision Genome Editing and Cost-effective microRNA Profiling

Chapter 9

9.1 Personal Tale of JL’s Whole Genome Sequencing

9.2 Inspiration From Dr. Maureen Cronin’s Achievements in Applying Genomic Sequencing to Cancer Diagnostics

9.3 Inform Genomics Developing SNP Test to Predict Side Effects, Help MDs Choose among Chemo Regimens

9.4 SNAP: Predict Effect of Non-synonymous Polymorphisms: How Well Genome Interpretation Tools could Translate to the Clinic

9.5  LEADERS in Genome Sequencing of Genetic Mutations for Therapeutic Drug Selection in Cancer Personalized Treatment: Part 2

9.6 The Initiation and Growth of Molecular Biology and Genomics – Part I

9.7 Personalized Medicine-based Cure for Cancer Might Not Be Far Away

9.8 Personalized Medicine: Cancer Cell Biology and Minimally Invasive Surgery (MIS)

 Chapter 10

10.1 Pfizer’s Kidney Cancer Drug Sutent Effectively caused REMISSION to Adult Acute Lymphoblastic Leukemia (ALL)

10.2 Imatinib (Gleevec) May Help Treat Aggressive Lymphoma: Chronic Lymphocytic Leukemia (CLL)

10.3 Winning Over Cancer Progression: New Oncology Drugs to Suppress Passengers Mutations vs. Driver Mutations

10.4 Treatment for Metastatic HER2 Breast Cancer

10.5 Personalized Medicine in NSCLC

10.6 Gene Sequencing – to the Bedside

10.7 DNA Sequencing Technology

10.8 Nobel Laureate Jack Szostak Previews his Plenary Keynote for Drug Discovery Chemistry

Chapter 11

11.1 mRNA Interference with Cancer Expression

11.2 Angiogenic Disease Research Utilizing microRNA Technology: UCSD and Regulus Therapeutics

11.3 Sunitinib brings Adult acute lymphoblastic leukemia (ALL) to Remission – RNA Sequencing – FLT3 Receptor Blockade

11.4 A microRNA Prognostic Marker Identified in Acute Leukemia 

11.5 MIT Team: Microfluidic-based approach – A Vectorless delivery of Functional siRNAs into Cells.

11.6 Targeted Tumor-Penetrating siRNA Nanocomplexes for Credentialing the Ovarian Cancer Oncogene ID4

11.7 When Clinical Application of miRNAs?

11.8 How mobile elements in “Junk” DNA promote cancer. Part 1: Transposon-mediated tumorigenesis,

11.9 Potential Drug Target: Glycolysis Regulation – Oxidative Stress-responsive microRNA-320

11.10  MicroRNA Molecule May Serve as Biomarker

11.11 What about Circular RNAs?

Chapter 12

12.1 The “Cancer Establishments” Examined by James Watson, Co-discoverer of DNA w/Crick, 4/1953

12.2 Otto Warburg, A Giant of Modern Cellular Biology

12.3 Is the Warburg Effect the Cause or the Effect of Cancer: A 21st Century View?

12.4 Hypothesis – Following on James Watson

12.5 AMPK Is a Negative Regulator of the Warburg Effect and Suppresses Tumor Growth In Vivo

12.6 AKT signaling variable effects

12.7 Rewriting the Mathematics of Tumor Growth; Teams Use Math Models to Sort Drivers from Passengers

12.8 Phosphatidyl-5-Inositol signaling by Pin1

Chapter 13

13.1 Nanotech Therapy for Breast Cancer

13.2 BRCA1 a tumour suppressor in breast and ovarian cancer – functions in transcription, ubiquitination and DNA repair

13.3 Exome sequencing of serous endometrial tumors shows recurrent somatic mutations in chromatin-remodeling and ubiquitin ligase complex genes

13.4 Recurrent somatic mutations in chromatin-remodeling and ubiquitin ligase complex genes in serous endometrial tumors

13.5 Prostate Cancer: Androgen-driven “Pathomechanism” in Early onset Forms of the Disease

13.6 In focus: Melanoma Genetics

13.7 Head and Neck Cancer Studies Suggest Alternative Markers More Prognostically Useful than HPV DNA Testing

13.8 Breast Cancer and Mitochondrial Mutations

13.9  Long noncoding RNA network regulates PTEN transcription

Chapter 14

14.1 HBV and HCV-associated Liver Cancer: Important Insights from the Genome

14.2 Nanotechnology and HIV/AIDS treatment

14.3 IRF-1 Deficiency Skews the Differentiation of Dendritic Cells

14.4 Sepsis, Multi-organ Dysfunction Syndrome, and Septic Shock: A Conundrum of Signaling Pathways Cascading Out of Control

14.5  Five Malaria Genomes Sequenced

14.6 Rheumatoid Arthritis Risk

14.7 Approach to Controlling Pathogenic Inflammation in Arthritis

14.8 RNA Virus Genome as Bacterial Chromosome

14.9 Cloning the Vaccinia Virus Genome as a Bacterial Artificial Chromosome

Chapter 15

15.1 Personalized Cardiovascular Genetic Medicine at Partners HealthCare and Harvard Medical School

15.2 Congestive Heart Failure & Personalized Medicine: Two-gene Test predicts response to Beta Blocker Bucindolol

15.3 DDAH Says NO to ADMA(1); The DDAH/ADMA/NOS Pathway(2)

15.4 Peroxisome Proliferator-Activated Receptor (PPAR-gamma) Receptors Activation: PPARγ Transrepression for Angiogenesis in Cardiovascular Disease and PPARγ Transactivation for Treatment of Diabetes

15.5 BARI 2D Trial Outcomes

15.6 Gene Therapy Into Healthy Heart Muscle: Reprogramming Scar Tissue In Damaged Hearts

15.7 Obstructive coronary artery disease diagnosed by RNA levels of 23 genes – CardioDx, a Pioneer in the Field of Cardiovascular Genomic  Diagnostics

15.8 Ca2+ signaling: transcriptional control

15.9 Lp(a) Gene Variant Association

15.9.1 Two Mutations, in the PCSK9 Gene: Eliminates a Protein involved in Controlling LDL Cholesterol

15.9.2. Genomics & Genetics of Cardiovascular Disease Diagnoses: A Literature Survey of AHA’s Circulation Cardiovascular Genetics, 3/2010 – 3/2013

15.9.3 Synthetic Biology: On Advanced Genome Interpretation for Gene Variants and Pathways: What is the Genetic Base of Atherosclerosis and Loss of Arterial Elasticity with Aging

15.9.4 The Implications of a Newly Discovered CYP2J2 Gene Polymorphism Associated with Coronary Vascular Disease in the Uygur Chinese Population

15.9.5  Gene, Meis1, Regulates the Heart’s Ability to Regenerate after Injuries.

15.10 Genetics of Conduction Disease: Atrioventricular (AV) Conduction Disease (block): Gene Mutations – Transcription, Excitability, and Energy Homeostasis

15.11 How Might Sleep Apnea Lead to Serious Health Concerns like Cardiac and Cancers?

Chapter 16

16.1 Can Resolvins Suppress Acute Lung Injury?

16.2 Lipoxin A4 Regulates Natural Killer Cell in Asthma

16.3 Biological Therapeutics for Asthma

16.4 Genomics of Bronchial Epithelial Dysplasia

16.5 Progression in Bronchial Dysplasia

Chapter 17

17.1 Breakthrough Digestive Disorders Research: Conditions Affecting the Gastrointestinal Tract.

17.2 Liver Endoplasmic Reticulum Stress and Hepatosteatosis

17.3 Biomarkers-identified-for-recurrence-in-hbv-related-hcc-patients-post-surgery

17.4  Usp9x: Promising Therapeutic Target for Pancreatic Cancer

17.5 Battle of Steve Jobs and Ralph Steinman with Pancreatic cancer: How We Lost

Chapter 18

18.1 Ubiquitin Pathway Involved in Neurodegenerative Disease

18.2 Genomic Promise for Neurodegenerative Diseases, Dementias, Autism Spectrum, Schizophrenia, and Serious Depression

18.3 Neuroprotective Therapies: Pharmacogenomics vs Psychotropic Drugs and Cholinesterase Inhibitors

18.4 Ustekinumab New Drug Therapy for Cognitive Decline Resulting from Neuroinflammatory Cytokine Signaling and Alzheimer’s Disease

18.5 Cell Transplantation in Brain Repair

18.6 Alzheimer’s Disease Conundrum – Are We Near the End of the Puzzle?

Chapter 19

19.1 Genetics and Male Endocrinology

19.2 Genomic Endocrinology and its Future

19.3 Commentary on Dr. Baker’s post “Junk DNA Codes for Valuable miRNAs: Non-coding DNA Controls Diabetes”

19.4 Therapeutic Targets for Diabetes and Related Metabolic Disorders

19.5 Secondary Hypertension caused by Aldosterone-producing Adenomas caused by Somatic Mutations in ATP1A1 and ATP2B3 (adrenal cortical; medullary or Organ of Zuckerkandl is pheochromocytoma)

19.6 Personal Recombination Map from Individual’s Sperm Cell and its Importance

19.7 Gene Trap Mutagenesis in Reproductive Research

19.8 Pregnancy with a Leptin-Receptor Mutation

19.9 Whole-genome Sequencing in Probing the Meiotic Recombination and Aneuploidy of Single Sperm Cells

19.10 Reproductive Genetic Testing

Chapter 20

20.1 Genomics & Ethics: DNA Fragments are Products of Nature or Patentable Genes?

20.2 Understanding the Role of Personalized Medicine

20.3 Attitudes of Patients about Personalized Medicine

20.4  Genome Sequencing of the Healthy

20.5   Genomics in Medicine – Tomorrow’s Promise

20.6  The Promise of Personalized Medicine

20.7 Ethical Concerns in Personalized Medicine: BRCA1/2 Testing in Minors and Communication of Breast Cancer Risk

 20.8 Genomic Liberty of Ownership, Genome Medicine and Patenting the Human Genome

Chapter 21

Recent Advances in Gene Editing Technology Adds New Therapeutic Potential for the Genomic Era:  Medical Interpretation of the Genomics Frontier – CRISPR – Cas9


21.1 Introducing CRISPR/Cas9 Gene Editing Technology – Works by Jennifer A. Doudna

21.1.1 Ribozymes and RNA Machines – Work of Jennifer A. Doudna

21.1.2 Evaluate your Cas9 gene editing vectors: CRISPR/Cas Mediated Genome Engineering – Is your CRISPR gRNA optimized for your cell lines?

21.1.3 2:15 – 2:45, 6/13/2014, Jennifer Doudna “The biology of CRISPRs: from genome defense to genetic engineering”

21.1.4  Prediction of the Winner RNA Technology, the FRONTIER of SCIENCE on RNA Biology, Cancer and Therapeutics  & The Start Up Landscape in BostonGene Editing – New Technology The Missing link for Gene Therapy?

21.2 CRISPR in Other Labs

21.2.1 CRISPR @MIT – Genome Surgery

21.2.2 The CRISPR-Cas9 System: A Powerful Tool for Genome Engineering and Regulation

Yongmin Yan and Department of Gastroenterology, Hepatology & Nutrition, University of Texas M.D. Anderson Cancer, Houston, USADaoyan Wei*

21.2.3 New Frontiers in Gene Editing: Transitioning From the Lab to the Clinic, February 19-20, 2015 | The InterContinental San Francisco | San Francisco, CA

21.2.4 Gene Therapy and the Genetic Study of Disease: @Berkeley and @UCSF – New DNA-editing technology spawns bold UC initiative as Crispr Goes Global

21.2.5 CRISPR & MAGE @ George Church’s Lab @ Harvard

21.3 Patents Awarded and Pending for CRISPR

21.3.1 Litigation on the Way: Broad Institute Gets Patent on Revolutionary Gene-Editing Method

21.3.2 The Patents for CRISPR, the DNA editing technology as the Biggest Biotech Discovery of the Century

2.4 CRISPR/Cas9 Applications

21.4.1  Inactivation of the human papillomavirus E6 or E7 gene in cervical carcinoma cells using a bacterial CRISPR/Cas 

21.4.2 CRISPR: Applications for Autoimmune Diseases @UCSF

21.4.3 In vivo validated mRNAs

21.4.6 Level of Comfort with Making Changes to the DNA of an Organism

21.4.7 Who will be the the First to IPO: Novartis bought in to Intellia (UC, Berkeley) as well as Caribou (UC, Berkeley) vs Editas (MIT)??

21.4.8 CRISPR/Cas9 Finds Its Way As an Important Tool For Drug Discovery & Development


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mRNA data survival analysis

Curators: Larry H. Bernstein, MD, FCAP and Aviva Lev-Ari, PhD, RN



SURVIV for survival analysis of mRNA isoform variation

Shihao ShenYuanyuan WangChengyang WangYing Nian Wu & Yi Xing
Nature Communications7,Article number:11548
 Feb 2016      doi:10.1038/ncomms11548

The rapid accumulation of clinical RNA-seq data sets has provided the opportunity to associate mRNA isoform variations to clinical outcomes. Here we report a statistical method SURVIV (Survival analysis of mRNA Isoform Variation), designed for identifying mRNA isoform variation associated with patient survival time. A unique feature and major strength of SURVIV is that it models the measurement uncertainty of mRNA isoform ratio in RNA-seq data. Simulation studies suggest that SURVIV outperforms the conventional Cox regression survival analysis, especially for data sets with modest sequencing depth. We applied SURVIV to TCGA RNA-seq data of invasive ductal carcinoma as well as five additional cancer types. Alternative splicing-based survival predictors consistently outperform gene expression-based survival predictors, and the integration of clinical, gene expression and alternative splicing profiles leads to the best survival prediction. We anticipate that SURVIV will have broad utilities for analysing diverse types of mRNA isoform variation in large-scale clinical RNA-seq projects.

Eukaryotic cells generate remarkable regulatory and functional complexity from a finite set of genes. Production of mRNA isoforms through alternative processing and modification of RNA is essential for generating this complexity. A prevalent mechanism for producing mRNA isoforms is the alternative splicing of precursor mRNA1. Over 95% of the multi-exon human genes undergo alternative splicing2, 3, resulting in an enormous level of plasticity in the regulation of gene function and protein diversity. In the last decade, extensive genomic and functional studies have firmly established the critical role of alternative splicing in cancer4, 5, 6. Alternative splicing is involved in a full spectrum of oncogenic processes including cell proliferation, apoptosis, hypoxia, angiogenesis, immune escape and metastasis7, 8. These cancer-associated alternative splicing patterns are not merely the consequences of disrupted gene regulation in cancer but in numerous instances actively contribute to cancer development and progression. For example, alternative splicing of genes encoding the Bcl-2 family of apoptosis regulators generates both anti-apoptotic and pro-apoptotic protein isoforms9. Alternative splicing of the pyruvate kinase M (PKM) gene has a significant impact on cancer cell metabolism and tumour growth10. A transcriptome-wide switch of the alternative splicing programme during the epithelial–mesenchymal transition plays an important role in cancer cell invasion and metastasis11, 12.

RNA sequencing (RNA-seq) has become a popular and cost-effective technology to study transcriptome regulation and mRNA isoform variation13, 14. As the cost of RNA-seq continues to decline, it has been widely adopted in large-scale clinical transcriptome projects, especially for profiling transcriptome changes in cancer. For example, as of April 2015 The Cancer Genome Atlas (TCGA) consortium had generated RNA-seq data on over 11,000 cancer patient specimens from 34 different cancer types. Within the TCGA data, breast invasive carcinoma (BRCA) has the largest sample size of RNA-seq data covering over 1,000 patients, and clinical information such as survival times, tumour stages and histological subtypes is available for the majority of the BRCA patients15. Moreover, the median follow-up time of BRCA patients is ~400 days, and 25% of the patients have more than 1,200 days of follow-up. Collectively, the large sample size and long follow-up time of the TCGA BRCA data set allow us to correlate genomic and transcriptomic profiles to clinical outcomes and patient survival times.

To date, systematic analyses have been performed to reveal the association between copy number variation, DNA methylation, gene expression and microRNA expression profiles with cancer patient survival16, 17. By contrast, despite the importance of mRNA isoform variation and alternative splicing, there have been limited efforts in transcriptome-wide survival analysis of alternative splicing in cancer patients. Most RNA-seq studies of alternative splicing in cancer transcriptomes focus on identifying ‘cancer-specific’ alternative splicing events by comparing cancer tissues with normal controls (see refs 18, 19, 20, 21, 22, 23 for examples). A recent analysis of TCGA RNA-seq data identified 163 recurrent differential alternative splicing events between cancer and normal tissues of three cancer types, among which five were found to have suggestive survival signals for breast cancer at a nominal P-value cutoff of 0.05 (ref. 21). Some other studies reported a significant survival difference between cancer patient subgroups after stratifying patients with overall mRNA isoform expression profiles24, 25. However, systematic cancer survival analyses of alternative splicing at the individual exon resolution have been lacking. Two main challenges exist for survival analyses of mRNA isoform variation and alternative splicing using RNA-seq data. The first challenge is to account for the estimation uncertainty of mRNA isoform ratios inferred from RNA-seq read counts. The statistical confidence of mRNA isoform ratio estimation depends on the RNA-seq read coverage for the events of interest, with larger read coverage leading to a more reliable estimation14. Modelling the estimation uncertainty of mRNA isoform ratio is an essential component of RNA-seq analyses of alternative splicing, as shown by various statistical algorithms developed for detecting differential alternative splicing from multi-group RNA-seq data14, 26, 27, 28,29. The second challenge, which is a general issue in survival analysis, is to properly model the association of mRNA isoform ratio with survival time, while accounting for missing data in survival time because of censoring, that is, patients still alive at the end of the survival study, whose precise survival time would be uncertain. To date, no algorithm has been developed for survival analyses of mRNA isoform variation that accounts for these sources of uncertainty simultaneously.

Here we introduce SURVIV (Survival analysis of mRNA Isoform Variation), a statistical model for identifying mRNA isoform ratios associated with patient survival times in large-scale cancer RNA-seq data sets. SURVIV models the estimation uncertainty of mRNA isoform ratios in RNA-seq data and tests the survival effects of isoform variation in both censored and uncensored survival data. In simulation studies, SURVIV consistently outperforms the conventional Cox regression survival analysis that ignores the measurement uncertainty of mRNA isoform ratio. We used SURVIV to identify alternatively spliced exons whose exon-inclusion levels significantly correlated with the survival times of invasive ductal carcinoma (IDC) patients from the TCGA breast cancer cohort. Survival-associated alternative splicing events are identified in gene pathways associated with apoptosis, oxidative stress and DNA damage repair. Importantly, we show that alternative splicing-based survival predictors outperform gene expression-based survival predictors in the TCGA IDC RNA-seq data set, as well as in TCGA data of five additional cancer types. Moreover, the integration of clinical information, gene expression and alternative splicing profiles leads to the best prediction of survival time.

SURVIV statistical model

The statistical model of SURVIV assesses the association between mRNA isoform ratio and patient survival time. While the model is generic for many types of alternative isoform variation, here we use the exon-skipping type of alternative splicing to illustrate the model (Fig. 1a). For each alternative exon involved in exon-skipping, we can use the RNA-seq reads mapping to its exon-inclusion or -skipping isoform to estimate its exon-inclusion level (denoted as ψ, or PSI that is Per cent Spliced In14). A key feature of SURVIV is that it models the RNA-seq estimation uncertainty of exon-inclusion level as influenced by the sequencing coverage for the alternative splicing event of interest. This is a critical issue in accurate quantitative analyses of mRNA isoform ratio in large-scale RNA-seq data sets14, 26, 27, 28, 29. Therefore, SURVIV contains two major components: the first to model the association of mRNA isoform ratio with patient survival time across all patients, and the second to model the estimation uncertainty of mRNA isoform ratio in each individual patient (Fig. 1a).

Figure 1: The statistical framework of the SURVIV model.

(a) For each patient k, the patient’s hazard rate λk(t) is associated with the baseline hazard rate λ0(t) and this patient’s exon-inclusion level ψk. The association of exon-inclusion level with patient survival is estimated by the survival coefficient β. The exon-inclusion level ψk is estimated from the read counts for the exon-inclusion isoform ICk and the exon-skipping isoform SCk. The proportion of the inclusion and skipping reads is adjusted by a normalization function f that considers the lengths of the exon-inclusion and -skipping isoforms (see details in Results and Supplementary Methods). (b) A hypothetical example to illustrate the association of exon-inclusion level with patient survival probability over time Sk(t), with the survival coefficient β=−1 and a constant baseline hazard rate λ0(t)=1. In this example, patients with higher exon-inclusion levels have lower hazard rates and higher survival probabilities. (c) The schematic diagram of an exon-skipping event. The exon-inclusion reads ICk are the reads from the upstream splice junction, the alternative exon itself and the downstream splice junction. The exon-skipping reads SCk are the reads from the skipping splice junction that directly connects the upstream exon to the downstream exon.

Briefly, for any individual exon-skipping event, the first component of SURVIV uses a proportional hazards model to establish the relationship between patient k’s exon-inclusion level ψk and hazard rate λk(t).

For each exon, the association between the exon-inclusion level and patient survival time is reflected by the survival coefficient β. A positive β means increased exon inclusion is associated with higher hazard rate and poorer survival, while a negative β means increased exon inclusion is associated with lower hazard rate and better survival. λ0(t) is the baseline hazard rate estimated from the survival data of all patients (see Supplementary Methods for the detailed estimation procedure). A particular patient’s survival probability over time Sk(t) can be calculated from the patient-specific hazard rate λk(t) as . Figure 1b illustrates a simple example with a negative β=−1 and a constant baseline hazard rate λ0(t)=1, where higher exon-inclusion levels are associated with lower hazard rates and higher survival probabilities.

The second component of SURVIV models the exon-inclusion level and its estimation uncertainty in individual patient samples. As illustrated in Fig. 1c, the exon-inclusion level ψk of a given exon in a particular sample can be estimated by the RNA-seq read count specific to the exon inclusion isoform (ICk) and the exon-skipping isoform (SCk). Other types of alternative splicing and mRNA isoform variation can be similarly modelled by this framework29. Given the effective lengths (that is, the number of unique isoform-specific read positions) of the exon-inclusion isoform (lI) and the exon-skipping isoform (lS), the exon-inclusion level ψk can be estimated as . Assuming that the exon-inclusion read count ICk follows a binomial distribution with the total read count nk=ICk+SCk, we have:

The binomial distribution models the estimation uncertainty of ψk as influenced by the total read count nk, in which the parameter pk represents the proportion of reads from the exon-inclusion isoform, given the exon-inclusion level ψk adjusted by a length normalization function f(ψk) based on the effective lengths of the isoforms. The definitions of effective lengths for all basic types of alternative splicing patterns are described in ref. 29.

Distinct from conventional survival analyses in which predictors do not have estimation uncertainty, the predictors in SURVIV are exon-inclusion levels ψk estimated from RNA-seq count data, and the confidence of ψk estimate for a given exon in a particular sample depends on the RNA-seq read coverage. We use the statistical framework of survival measurement error model30 to incorporate the estimation uncertainty of isoform ratio in the proportional hazards model. Using a likelihood ratio test, we test whether the exon-inclusion levels have a significant association with patient survival over the null hypothesis H0:β=0. The false discovery rate (FDR) is estimated using the Benjamini and Hochberg approach31. Details of the parameter estimation and likelihood ratio test in SURVIV are described in Supplementary Methods.


Figure 2: Simulation studies to assess the performance of SURVIV and the importance of modelling the estimation uncertainty of mRNA isoform ratio.

We compared our SURVIV model with Cox regression using point estimates of exon-inclusion levels, which does not consider the estimation uncertainty of the mRNA isoform ratio. (a) To study the effect of RNA-seq depth, we simulated the mean total splice junction read counts equal to 5, 10, 20, 50, 80 and 100 reads. We generated two sets of simulations with and without data-censoring. For each simulation, the true-positive rate (TPR) at 5% false-positive rate is plotted. The inset figure shows the empirical distribution of the mean total splice junction read counts in the TCGA IDC RNA-seq data (x axis in the log10 scale). (b) To faithfully represent the read count distribution in a real data set, we performed another simulation with read counts directly sampled from the TCGA IDC data. Sampled read counts were then multiplied by different factors ranging from 10 to 300% to simulate data sets with different RNA-seq read depth. Continuous and dashed lines represent the performance of SURVIV and Cox regression, respectively. Red lines represent the area under curve (AUC) of the ROC curve (TPR versus false-positive rate plot). Black lines represent the TPR at 5% false-positive rate.


Using these simulated data, we compared SURVIV with Cox regression in two settings, without or with censoring of the survival time. In the setting without censoring, the death and survival time of each individual is known. In the setting with censoring, certain individuals are still alive at the end of the survival study. Consequently, these patients have unknown death and survival time. Here, in the simulation with censoring, we assumed that 85% of the patients were still alive at the end of the study, similar to the censoring rate of the TCGA IDC data set. In both settings and with different depths of RNA-seq coverage, SURVIV consistently outperformed Cox regression in the true-positive rate at the same false-positive rate of 5% (Fig. 2a). As expected, we observed a more significant improvement in SURVIV over Cox regression when the RNA-seq read coverage was low (Fig. 2a).

To more faithfully recapitulate the read count distribution in a real cancer RNA-seq data set, we performed another simulation study with read counts directly sampled from the TCGA IDC data. To assess the influence of RNA-seq read depth on the performance of SURVIV and Cox regression, sampled read counts were then multiplied by different factors ranging from 10 to 300% to simulate data sets with different RNA-seq read depths (Fig. 2b). The TCGA IDC data set has an average RNA-seq depth of ~60 million paired-end reads per patient. Thus, the read depth of these simulated RNA-seq data sets ranged from ~6 million reads to 180 million reads per patient, representing low-coverage RNA-seq studies designed primarily for gene expression analysis32 up to high-coverage RNA-seq studies designed primarily for alternative isoform analysis29. At all levels of RNA-seq depth, SURVIV consistently outperformed Cox regression, as reflected by the area under curve of the receiver operating characteristic (ROC) curve as well as the true-positive rate at 5% false-positive rate (Fig. 2b). The improvement of SURVIV over Cox regression was particularly prominent when the read depth was low. For example, at 10% read depth, SURVIV had 7% improvement in area under curve (68% versus 61%) and 8% improvement in the true-positive rate at 5% false-positive rate (46% versus 38%). Collectively, these simulation results suggest that SURVIV achieves a higher accuracy by accounting for the estimation uncertainty of mRNA isoform ratio in RNA-seq data.

SURVIV analysis of TCGA IDC breast cancer data

To illustrate the practical utility of SURVIV, we used it to analyse the overall survival time of 682 IDC patients from the TCGA breast cancer (BRCA) RNA-seq data set (see Methods for details of the data source and processing pipeline). We chose to analyse IDC because it is the most frequent type of breast cancer33, comprising ~70% of patients in the TCGA breast cancer data set. To control for the effects of significant clinical parameters such as tumour stage and subtype and identify alternative splicing events associated with patient outcomes across multiple molecular and clinical subtypes, we followed the procedure of Croce and colleagues in analysing mRNA and microRNA prognostic signature of IDC33 and stratified the patients according to their clinical parameters. We then conducted SURVIV analysis in 26 clinical subgroups with at least 50 patients in each subgroup. We identified 229 exon-skipping events associated with patient survival in multiple clinical subgroups that met the criteria of SURVIV P-value≤0.01 in at least two subgroups of the same clinical parameter (cancer subtype, stage, lymph node, metastasis, tumour size, oestrogen receptor status, progesterone receptor status, HER2 status and age as shown in Fig. 3). DAVID (Database for Annotation, Visualization and Integrated Discovery) Gene Ontology analyses34 of the 229 alternative splicing events suggest an enrichment of genes in cancer-related functional categories such as intracellular signalling, apoptosis, oxidative stress and response to DNA damage (Supplementary Fig. 1). Table 1 shows a few selected examples of survival-associated alternative splicing events in cancer-related genes. Using two-means clustering of each individual exon’s inclusion levels, the 682 IDC patients can be segregated into two subgroups with significantly different survival times as illustrated by the Kaplan–Meier survival plot (Fig. 4). We also carried out hierarchical clustering of IDC patients using 176 survival-associated alternative exons (P≤0.01; SURVIV analysis of all IDC patients). Using the exon-inclusion levels of these 176 exons, we clustered IDC patients into three major subgroups, with 95, 194 and 389 patients, respectively. As illustrated by the Kaplan–Meier survival plots, the three subgroups had significantly different survival times (Supplementary Fig. 2).

Figure 3: SURVIV analysis of exon-skipping events in the TCGA IDC RNA-seq data set.

IDC patients are stratified into multiple clinical subgroups based on clinical parameters including cancer subtype, stage, lymph node status, metastasis, tumour size, oestrogen receptor status, progesterone receptor status, HER2 status and age. Only clinical subgroups with at least 50 patients are included in further analyses. Numbers of patients in the subgroups are indicated next to the names of the subgroups. Shown in the heatmap are the log10 SURVIV P-values of the 229 exons associated with patient survival (P≤0.01) in at least two subgroups of the same class of clinical parameters. Turquoise colour indicates positive correlation that higher exon-inclusion levels are associated with higher survival probabilities. Magenta colour indicates negative correlation that lower exon-inclusion levels are associated with higher survival probabilities.

TABLE 1 (not shown)

Figure 4: Kaplan–Meier survival plots of IDC patients stratified by two-means clustering of the exon-inclusion levels of four survival-associated alternative splicing events.

Clustering was generated for each of the four exons separately. Black lines represent patients with high exon-inclusion levels. Red lines represent patients with low exon-inclusion levels. The P-values are from SURVIV analysis of the TCGA IDC RNA-seq data. (a) ATRIP. (b) BCL2L11. (c) CD74. (d) PCBP4.


Figure 5: Alternative splicing of STAT5A exon 5 is significantly associated with IDC patient survival.

(a) The gene structure of the STAT5A full-length isoform compared to the ΔEx5 isoform skipping the 5th exon. (b) Kaplan–Meier survival plot of IDC patients stratified by two-means clustering using exon-inclusion levels of STAT5A exon 5. The 420 patients in Group 1 (average exon 5 inclusion level=95%) have significantly higher survival probabilities than the 262 patients in Group 2 (average exon 5 inclusion level=85%) (SURVIV P=6.8e−4). (c) Exon 5 inclusion levels of IDC patients stratified by two-means clustering using exon 5 inclusion levels. Group 1 has 420 patients with average exon-inclusion level at 95%. Group 2 has 262 patients with average exon-inclusion level at 85%. (d) STAT5A exon 5 inclusion levels in normal breast tissues versus breast cancer tumour samples. Exon-inclusion levels are extracted from 86 TCGA breast cancer patients with matched normal and tumour samples. Normal breast tissues have average exon 5 inclusion level at 95%, compared to 91% average exon-inclusion level in tumour samples. Error bars represent 95% confidence interval of the mean.

Network of survival-associated alternative splicing events


Figure 6: Splicing factor regulatory network of survival-associated alternative splicing events in IDC.

(ac) Kaplan–Meier survival plots of IDC patients stratified by the gene expression levels of three splicing factors: TRA2B (a, Cox regression P=1.8e−4), HNRNPH1 (b, P=3.4e−4) and SFRS3 (c, P=2.8e−3). Black lines represent patients with high gene expression levels. Red lines represent patients with low gene expression levels. (d) The exon-inclusion levels of a DHX30 alternative exon are negatively correlated with TRA2B gene expression levels (robust correlation coefficient r=−0.26, correlation P=1.2e−17). (e) The exon-inclusion levels of a MAP3K4 alternative exon are positively correlated withHNRNPH1 gene expression levels (robust correlation coefficient r=0.16, correlation P=2.6e−06). (f) A splicing co-expression network of the three splicing factors and their correlated survival-associated alternative exons. In total, 84 survival-associated alternative exons are significantly correlated with the three splicing factors. The positive/negative correlation between splicing factors and alternative exons is represented by blue/red lines, respectively. Exons whose inclusion levels are positively/negatively correlated with survival times are represented by blue/red dots, respectively. The size of the splicing factor circles is proportional to the number of correlated exons within the network.


Alternative splicing predictors of cancer patient survival


Figure 7: Cross-validation of different classes of IDC survival predictors measured by the C-index

A C-index of 1 indicates perfect prediction accuracy and a C-index of 0.5 indicates random guess. The plots indicate the distribution of C-indexes from 100 rounds of cross-validation. The centre value of the box plot is the median C-index from 100 rounds of cross-validation. The notch represents the 95%confidence interval of the median. The box represents the 25 and 75% quantiles. The whiskers extended out from the box represent the 5 and 95% quantiles. Two-sided Wilcoxon test was used to compare different survival predictors. The different classes of predictors are: (a) clinical information (median C-index 0.67). (b) Gene expression (median C-index 0.68). (c) Alternative splicing (median C-index 0.71). (d) Clinical information+gene expression (median C-index 0.69). (e) Clinical information+alternative splicing (median C-index 0.73). (f) Clinical information+gene expression+alternative splicing (median C-index 0.74). Note that ‘Gene’ refers to ‘Gene-level expression’ in these plots.

Next, we carried out the SURVIV analysis in five additional cancer types in TCGA, including GBM (glioblastoma multiforme), KIRC (kidney renal clear cell carcinoma), LGG (lower grade glioma), LUSC (lung squamous cell carcinoma) and OV (ovarian serous cystadenocarcinoma). As expected, the number of significant events at different FDR or P-value significance cutoffs varied across cancer types, with LGG having the strongest survival-associated alternative splicing signals with 660 significant exon-skipping events at FDR≤5% (Supplementary Data 3 and 4). Strikingly, regardless of the number of significant events, alternative splicing-based survival predictors outperformed gene expression-based survival predictors across all cancer types (Supplementary Fig. 3), consistent with our initial observation on the IDC data set.


Alternative processing and modification of mRNA, such as alternative splicing, allow cells to generate a large number of mRNA and protein isoforms with diverse regulatory and functional properties. The plasticity of alternative splicing is often exploited by cancer cells to produce isoform switches that promote cancer cell survival, proliferation and metastasis7, 8. The widespread use of RNA-seq in cancer transcriptome studies15, 47, 48 has provided the opportunity to comprehensively elucidate the landscape of alternative splicing in cancer tissues. While existing studies of alternative splicing in large-scale cancer transcriptome data largely focused on the comparison of splicing patterns between cancer and normal tissues or between different subtypes of cancer18, 21, 49, additional computational tools are needed to characterize the clinical relevance of alternative splicing using massive RNA-seq data sets, including the association of alternative splicing with phenotypes and patient outcomes.

We have developed SURVIV, a novel statistical model for survival analysis of alternative isoform variation using cancer RNA-seq data. SURVIV uses a survival measurement error model to simultaneously model the estimation uncertainty of mRNA isoform ratio in individual patients and the association of mRNA isoform ratio with survival time across patients. Compared with the conventional Cox regression model that uses each patient’s mRNA isoform ratio as a point estimate, SURVIV achieves a higher accuracy as indicated by simulation studies under a variety of settings. Of note, we observed a particularly marked improvement of SURVIV over Cox regression for low- and moderate-depth RNA-seq data (Fig. 2b). This has important practical value because many clinical RNA-seq data sets have large sample size but relatively modest sequencing depth.

Using the TCGA IDC breast cancer RNA-seq data of 682 patients, SURVIV identified 229 alternative splicing events associated with patient survival time, which met the criteria of SURVIVP-values≤0.01 in multiple clinical subgroups. While the statistical threshold seemed loose, several lines of evidence suggest the functional and clinical relevance of these survival-associated alternative splicing events. These alternative splicing events were frequently identified and enriched in the gene functional groups important for cancer development and progression, including apoptosis, DNA damage response and oxidative stress. While some of these events may simply reflect correlation but not causal effect on cancer patient survival, other events may play an active role in regulating cancer cell phenotypes. For example, a survival-associated alternative splicing event involving exon 5 of STAT5A is known to regulate the activity of this transcription factor with important roles in epithelial cell growth and apoptosis37. Using a co-expression network analysis of splicing factor to exon correlation across all patients, we identified three splicing factors (TRA2B, HNRNPH1 and SFRS3) as potential hubs of the survival-associated alternative splicing network of IDC. The expression levels of all three splicing factors were negatively associated with patient survival times (Fig. 6a–c), and both TRA2B and HNRNPH1 were previously reported to have an impact on cancer-related molecular pathways40, 41, 42, 43, 44, 45. Finally, despite the limited power in detecting individual events, we show that the survival-associated alternative splicing events can be used to construct a predictor for patient survival, with an accuracy higher than predictors based on clinical parameters or gene expression profiles (Fig. 7). This further demonstrates the potential biological relevance and clinical utility of the identified alternative splicing events.

We performed cross-validation analyses to evaluate and compare the prognostic value of alternative splicing, gene expression and clinical information for predicting patient survival, either independently or in combination. As expected, the combined use of all three types of information led to the best prediction accuracy. Because we used penalized regression to build the prediction model, combining information from multiple layers of data did not necessarily increase the number of predictors in the model. The perhaps more surprising and intriguing result is that alternative splicing-based predictors appear to outperform gene expression-based predictors when used alone and when either type of data was combined with clinical information (Fig. 7). We observed the same trend in five additional cancer types (Supplementary Fig. 3). We note that this finding was consistent with a previous report that cancer subtype classification based on splicing isoform expression performed better than gene expression-based classification25. While this trend seems counterintuitive because accurate estimation of gene expression requires much lower RNA-seq depth than accurate estimation of alternative splicing29, one possible explanation may be the inherent characteristic of isoform ratio data. By definition, mRNA isoform ratio is estimated as the ratio of multiple mRNA isoforms from a single gene. Therefore, mRNA isoform ratio data have a ‘built-in’ internal control that could be more robust against certain artefacts and confounding issues that influence gene expression estimates across large clinical RNA-seq data sets, such as poor sample quality and RNA degradation12. Regardless of the reasons, our data call for further studies to fully explore the utility of mRNA isoform ratio data for various clinical research applications.

The SURVIV source code is available for download at SURVIV is a general statistical model for survival analysis of mRNA isoform ratio using RNA-seq data. The current statistical framework of SURVIV is applicable to RNA-seq based count data for all basic types of alternative splicing patterns involving two isoform choices from an alternatively spliced region, such as exon-skipping, alternative 5′ splice sites, alternative 3′ splice sites, mutually exclusive exons and retained introns, as well as other forms of alternative isoform variation such as RNA editing. With the rapid accumulation of clinical RNA-seq data sets, SURVIV will be a useful tool for elucidating the clinical relevance and potential functional significance of alternative isoform variation in cancer and other diseases.


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Announcement from LPBI Group: key code LPBI16 for Exclusive Discount to attend Boston’s Discovery on Target (September 19-22, 2016, CRISPR: Mechanisms to Applications on 9/19/2016)


Leaders in Pharmaceutical Business Intelligence (LPBI) Group is a Media Partner of CHI for CHI’s 14th Annual Discovery on Target taking place September 19 – 22, 2016 in Boston.

As a proud partner of this event, Leaders in Pharmaceutical Business Intelligence Group has secured a special discounted price for you to attend, resulting in a $200 discount on a commercial registration and $100 discount on an academic registration!

*This offer is valid for new registrants only, does not apply to previously registered attendees or short courses, and cannot be combined with any other offer. You must mention key code LPBI16 to receive this discount.

Don’t miss your opportunity to network with 1,100+ of your peers at this year’s event. Special early registration savings are currently available through Friday, August 12.

Preliminary AGENDA and Registration Link

For sponsorship & exhibit information, please contact: Jon Stroup, Sr Business Development Manager,
(+1) 781-972-5483,


See us in CHI’s Media Partners section online:

Contact: 617-244-4024,




Leaders in Pharmaceutical Business Intelligence (LPBI) Group, Boston


will cover in REAL TIME

Cambridge Healthtech Institute’s

Discovery on Target

September 19-22, 2016,

CRISPR: Mechanisms to Applications 

September 19, 2016

Westin Boston Waterfront, Boston, MA

In Attendance, streaming LIVE using Social Media

Aviva Lev-Ari, PhD, RN



Stephen J Williams, PhD

Senior Editor



Leaders in Pharmaceutical Business Intelligence (LPBI) Group is a Media Partner of CHI for CHI’s 14th Annual Discovery on Target taking place September 19 – 22, 2016 in Boston.


As a proud partner of this event, Leaders in Pharmaceutical Business Intelligence Group has secured a special discounted price for you to attend, resulting in a $200 discount on a commercial registration and $100 discount on an academic registration!

*This offer is valid for new registrants only, does not apply to previously registered attendees or short courses, and cannot be combined with any other offer. You must mention key code LPBI16 to receive this discount.

Don’t miss your opportunity to network with 1,100+ of your peers at this year’s event. Special early registration savings are currently available through Friday, June 3.


Preliminary AGENDA and Registration Link

For sponsorship & exhibit information, please contact: Jon Stroup, Sr Business Development Manager,
(+1) 781-972-5483,


See us in CHI’s Media Partners section online:

Contact: 617-244-4024,



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Molecular basis for schizophrenia

Larry H. Bernstein, mD, FCAP, Curator




The proteome of schizophrenia

Juliana M Nascimento  & Daniel Martins-de-Souza
npj Schizophrenia 1, Article number: 14003 (2015)

On observing schizophrenia from a clinical point of view up to its molecular basis, one may conclude that this is likely to be one of the most complex human disorders to be characterized in all aspects. Such complexity is the reflex of an intricate combination of genetic and environmental components that influence brain functions since pre-natal neurodevelopment, passing by brain maturation, up to the onset of disease and disease establishment. The perfect function of tissues, organs, systems, and finally the organism depends heavily on the proper functioning of cells. Several lines of evidence, including genetics, genomics, transcriptomics, neuropathology, and pharmacology, have supported the idea that dysfunctional cells are causative to schizophrenia. Together with the above-mentioned techniques, proteomics have been contributing to understanding the biochemical basis of schizophrenia at the cellular and tissue level through the identification of differentially expressed proteins and consequently their biochemical pathways, mostly in the brain tissue but also in other cells. In addition, mass spectrometry-based proteomics have identified and precisely quantified proteins that may serve as biomarker candidates to prognosis, diagnosis, and medication monitoring in peripheral tissue. Here, we review all data produced by proteomic investigation in the last 5 years using tissue and/or cells from schizophrenic patients, focusing on postmortem brain tissue and peripheral blood serum and plasma. This information has provided integrated pictures of the biochemical systems involved in the pathobiology, and has suggested potential biomarkers, and warrant potential targets to alternative treatment therapies to schizophrenia.

Schizophrenia is a complex neuropsychiatric disorder that produces severe symptoms and significant lifelong disability, causing massive personal and societal burden.1,2 About 1% of the world’s population is affected by schizophrenia.3 Despite the strong genetic component, showing increasing risks for those related to schizophrenic patients,4 and the known role of environment as a trigger, schizophrenia signs and symptoms have unknown etiology. Currently, the disease diagnosis is essentially clinically defined by observed signs of psychosis, which often include paranoid delusions and auditory hallucinations,5 with onset during late adolescence and/or early adulthood.

Pharmacological treatments are available for schizophrenia; yet, most of the currently used antipsychotic medications were discovered in the 1950s, or are a variation of those medications, and since then no new major drug class has been introduced to the clinic. In addition, efficacy of medication is poor, and only about 40% of schizophrenic patients respond effectively to initial treatment with antipsychotics.6,7 Unfortunately, comprehensive studies on molecular mechanisms of schizophrenia have been scant; hence, current treatments are only partly beneficial to a subset of symptoms. The response to drugs is heterogenous, mainly because of individual variations of the disease, in addition to scarce knowledge on its pathophysiology, impairing both diagnosis and adequate treatment selection.8,9

Heterogenic and multifactorial aspects of schizophrenia have always hindered biochemical characterization studies and delayed the establishment of preclinical models of the disease.10 Several studies, including postmortem, imaging, pharmacological, and genetic studies, reported common traces of the disease, such as synaptic deficits, abnormal neural network, and changes in neurotransmission, involving dopamine, glutamate, and gamma-aminobutyric acid.2,11,12,13 Additional abnormalities, such as aberrant inflammatory responses, oligodendrocyte alterations, epigenetic changes, mitochondrial dysfunction, and reactive oxygen species (ROS) imbalance, are often described in schizophrenia.14,15,16

A complex cross talk between genetic and environmental factors during neurogenesis is responsible for promoting differences of gene and protein expression in schizophrenia, causing abnormal processes during neurodevelopment.2 Recent studies found reinforcement of genes associated with the major hypotheses of glutamatergic neurotransmission, such as DRD2 (dopamine receptor D2)—the main target of antipsychotic drugs17—among other potential targets, involving perturbation of specific neurotransmitter systems or pathways, which are yet to be studied. The complexity of schizophrenia reinforces the need to unravel molecular mechanisms, as those insights have been shown to be essential in identifying and validating drug targets and biomarkers.9 Therefore, unraveling models with relevance to the cause and onset of schizophrenia is essential toward improving treatments and outcomes for those with the disorder.

Here we review the advances of proteomics on schizophrenia research, toward a better understanding of disease mechanisms and response to treatment, and the efforts toward the discovery of biomarkers for diagnosis and disease evolution.

The role of proteomics in schizophrenia research

In the past century, psychiatric research was dedicated to understanding the nature of several disorders, including action of psychotherapeutics. It was also shown that schizophrenia is a highly heritable disease, indicating a strong genetic influence and an estimated heritability of 80–85%,18,19 more likely with a polygenic basis.20 Since the beginning of the twenty-first century, revolution of genomic technologies has allowed a deeper understanding of the genetic basis of diseases, and several genetic findings on psychiatric disorders have been reported,21 unraveling candidate genes linked to risk factors of psychiatric disorders, such as DISC1 (disrupted in schizophrenia 1),22 involved in neuronal development and synapse formation.23,24 In fact, the International Schizophrenia Consortium (ISC) found indication for a polygenic contribution to schizophrenia.25 While candidate gene studies are beneficial, in cases with a not yet well-understood biology, such as schizophrenia, a single gene only adds a small phenotype effect to the multifactorial etiology of the disease.20,26,27

Since 2008, genomic technology innovations have led to a better understanding of psychiatric disorders, providing information about numerous genes that have a role in brain development.21 Recent advances of next-generation sequencing have facilitated a higher coverage and sample throughput of schizophrenia studies.28,29,30Furthermore, international collaborations, which increased the number of participant subjects and samples, have combined efforts to provide deeper insight from comprehensive biological data sets, such as the Psychiatric GWAS (genome-wide association studies) Consortium (PGC; Most recently, two main studies, reporting comprehensive GWAS analysis, were able to identify 13 (ref. 27) and 108 schizophrenia-associated risk loci,17 the latter being the largest GWAS study on schizophrenia to date, with up to 36,989 cases and 113,075 controls. Unbiased GWAS,17,27,32,33 indicating genetic regions (loci) that contribute to disease susceptibility, and structural variation studies, such as copy number variants,30,34 are the main identification sources of gene variants with small effects on disease phenotype.35 For instance, copy number variants, including deletions and duplications of several DNA segments, confer significant risk increase in alleles of schizophrenia genome up to 10–25-fold.9,34,36Several of those findings support the leading etiological hypothesis of the disorder, and point to functionally related targets, such as DRD2, miRNA-137, N-methyl-D-aspartate receptor (NMDAR) complex, or calcium channel subunits.17,30,36,37 Information on genetic variations as a base will increase knowledge on mechanisms of schizophrenia and other psychiatric disorders.

Deciphering the human genome was a revolution in genetics, and the anticipated next step was to decode RNA complexity to understand how information was delivered, and its variety between individuals. Development of large-scale transcriptome analyses, such as cDNA microarrays, Serial Analysis of Gene Expression, and the analyses of Expressed Sequence Tag, and more recently the advance of whole transcriptome shotgun sequencing (or RNA-Seq), providing the presence and quantification of RNA at a given time in a genome, allowed a deeper insight into the dynamics of an organism. Transcriptome analyses revealed RNA implication in psychiatric diseases,38,39 including abnormalities resulting from alternative splicing, in addition to messenger RNA transcripts, such as total RNA and small RNA, including micro-RNA.40 Those abnormalities were observed in several biological processes, such as synaptic and mitochondrial/energetic function,41,42,43 cytoskeleton,44 immune and inflammation response,45,46,47 and the myelination pathway.48 Although not yet fully understood, the more the pieces of the puzzle discovered, the more comprehensive the pathology network becomes.

Genomic and transcriptomic studies generated significant data, although these changes cannot yet be translated into biomarkers. The main limitation of genetic approaches in schizophrenia is extrapolation to functional protein expression, as proteins undergo several modifications from transcription to posttranslation, and transcript abundance cannot really predict protein levels either in normal conditions or in response to stress, such as diseases.49,50 Therefore, proteomic techniques are being increasingly used in screening for identification of biomarkers in schizophrenia,51,52 providing several insights into the pathophysiology of the disease. Proteomics can show global expression of proteins or protein groups, and is more complex than genomics as it can change from each cell type at any given time or state.49 Also a high-throughput method, proteomic studies detect fewer expressed proteins than a transcriptomic detects expressed genes, but protein expression provides a precise functional profile and presents an unbiased current physiological state as a reflex of the complex interaction of gene versus environment. The importance of those interactions has been increasing in the research of schizophrenia and other neurological diseases.10,41,53

Regarding research into schizophrenia, numerous studies have investigated the proteome of postmortem brain tissue, including several brain regions such as the dorsolateral prefrontal cortex,41,54,55 frontal cortex,56thalamus,57 anterior cingulate cortex,58,59,60hippocampus,61,62 corpus callosum,63 and insular cortex.64 Postmortem brain tissue has yielded many valuable insights into the pathophysiology of schizophrenia, but less information on disease onset and development. Thus, other tissues and cells have been tested, providing data from naive patients as well, such as from cerebrospinal fluid (CSF),65,66,67,68blood serum and plasma,69,70,71,72,73 liver,68,74 and fibroblasts,75 which can be biopsied from living patients, among others,76,77aiming to reveal more about potential biomarkers of discovery and monitoring of the disease.

Proteomic methodologies used in schizophrenia research

A proteome comprises the entire set of proteins in a biological system (cell, tissue, or organism) in a particular state, at a given time.78 The need to understand all proteins derived from almost 20,000 genes identified by the Human Genome Project turns molecular biology studies toward proteomics. Because of the progress of mass spectrometry techniques, more fine and high-throughput methods are available, supporting the identification of hundreds (or thousands) of proteins in a single biological sample. In 2014, two major consortiums have delivered a draft of the human proteome,79,80 with a large-scale data set covering 84–92% of the protein-coded genes annotated for the human genome. The more information annotated on protein knowledge databases, the more unknown causes of diseases and biomarker identification can be performed.

In the first decade of proteomics, the main quantitative methods used were gel-based, such as two-dimensional gel electrophoresis (2DE), including the fluorescent two-dimensional differential gel electrophoresis (2D-DIGE). Despite its recognized usefulness,81 gel-based techniques have been consistently replaced by gel-free techniques with the introduction of the concept of shotgun proteomics, which employs basically liquid chromatography followed by mass spectrometry (LC/MS).82 The large scale was possible only because of the development of proteomics based on mass spectrometry, which offers insights into protein abundance, expression profiles according to cell type, posttranslational modifications, and protein–protein interactions, and the possibility to study modifications at the protein level.83

2DE was first described in 1975,84 and after intense enhancements in the 1980s85,86 it became widely used in the separation of complex protein mixtures according to their isoelectric point, in the first dimension by isoelectric focusing, and according to their molecular weight (MW), in the second dimension, by sodium dodecyl sulfate-polyacrylamide gel electrophoresis. This separation leads to a protein profile comprising several spots, each of which, in theory, represents a single protein, providing information about intact proteins and isoforms. Protein visualization techniques include common post-run methods, such as Coomassie blue or silver staining, and also pre-labeling of samples with fluorescent dies, such as in the 2D-DIGE.87 Image analysis of the latter provides a more sensitive quantification method, as up to 10-fold lower amount of samples can be applied. In addition, 2D-DIGE allows co-running of different samples in the same gel, labeled with distinct fluorescent dies (i.e., Cy3, Cy2, Cy5), and might also include an internal control for cross-gel comparison purposes. Those techniques have significantly improved in the previous years with respect to reproducibility and robustness, allowing better comparison between samples and across different laboratories.

Furthermore, mass spectrometry (MS) revolutionized proteomic studies when combined with the 2DE/2D-DIGE workflow, improving sensitivity for identification of differentially expressed proteins, by measuring molecular mass-to-charge ratio of ions (m/z).88 Protein spots, excised from the gel, are digested (i.e., trypsin) and masses of these peptides measured on MS instruments, providing a peptide mass fingerprint of each protein, which is then compared with an in silico-digested database. Further fragmentation of each peptide, performed on an MS/MS instrument, provides the sequence of that peptide, assisting in protein identification. Disadvantages of 2DE/MS combination include the incompatibility to very low or very high molecular weight or isoelectric point, in addition to those proteins with low abundance, which will not be spotted.89 Nevertheless, several proteome studies in schizophrenia were performed using proteomic screening approaches such as 2DE/2D-DIGE, providing large-scale data on the pathophysiology of the disease.41,55,56,58,61,66,90

Hence, schizophrenia and other psychiatric disorder studies have intensively used shotgun proteomics for the analysis of peptides and proteins for profiling, and for quantification of protein modification analysis.54,59,72,75,77,91 For shotgun proteomics, proteins are first digested into peptides (i.e., using trypsin, as previously), which are next separated by high-performance liquid chromatography online-connected to a hybrid MS, providing a gel-free proteomic system (LC-MS/MS). Shotgun proteomics lead to the possibility of identifying more proteins, increasing sampling of low abundant and extreme-sized proteins.82 Most proteomic studies on schizophrenia have used label-free methods for quantification,59,75,92 which assume chromatographic peak areas correlated to the concentration of peptides,93and is one of the simplest ways to compare proteomics, allowing comparison among several samples at once.94Nevertheless, both in vitro and in vivo stable isotopic labeling methods are available in shotgun proteomics, for quantification accuracy of protein concentrations simultaneously in several biological samples. In vitroapproaches include isobaric tags for relative and absolute quantification95 and isotope-coded protein labeling, whereas in vivo metabolic methods, such as stable isotope labeling by amino acids in cell culture96 and stable isotope labeling in mammals, have been used in proteomic quantification.97 Some have been applied to neuropsychiatric disorders, in postmortem brains and CSF,54,57,98 and in animal and cell studies99,100 on proteomic research.

The power to identify and quantify proteins and protein sets at high resolution, among multiple samples, is essential to understand large case studies in biomedical research. Recent advances have been made in MS-based techniques, such as selected reaction monitoring/multiple reaction monitoring, which has just emerged as a promising technology for a more precise MS-based quantification of targeted protein,101,102 and was awarded Nature’s Method of the Year in 2012 on biological research methods.103 Selected reaction monitoring is specific, accurate, and sensitive, as it selects proteotypic peptides—those that uniquely identify the targeted protein—for its analysis, which might overcome several current validation issues, such as semiquantitative western blotting techniques, availability, and specificity.104 Furthermore, this ability to quantify specific proteins across several samples is particularly interesting with regard to biomarkers, as clinical validation of biomarker signatures for a given disease must be tested over a large sample set to achieve satisfactory statistical power. Indeed, proteomic studies in psychiatric disorders slowly start to validate pathways and biological functions that were found differentially expressed by selected reaction monitoring.105,106,107,108

Likewise, other proteomic methodologies have been extensively applied to schizophrenia research in order to discover and validate biomarkers, such as multiplex immunoassays,69 which use multiplexed dye-coded microspheres of selected protein sets, thus providing profile studies of cytokines, growth factors, or metabolic pathways, from blood serum or CSF samples.70,109,110,111Aiming to reach the broader spectrum of protein visualization, concerns regarding the possibility to obtain sub-proteomes (using fractionation methods)112 by depleting high-abundant proteins or enriching a group of proteins in a sample should be part of the design and technique choice. Protein separation and quantification using SELDI-TOF-MS ProteinChip analysis or metal ion affinity chromatography to select proteins from a mixture have been used in schizophrenia research lately.65,68,72 Regardless of the protein analysis method, study design and sample preparation choice are crucial steps in proteomic studies. Platforms using reduced number of analytes, but a broader number of clinical samples, provide a precise statistical interpretation.

Indeed, statistics and bioinformatics are of extreme importance for proteomic studies, as different types of assays (2DE, shotgun-MS, or multiplex immunoassays) are required to precisely quantify changes in expression of hundreds (or thousands) of proteins. Therefore, those fields are improving, together with the development of new tools and methods for proteomic analysis, offering better algorithms and image analysis tools, in order to provide a more robust analysis from the growing number of data generated.


What do proteomics tell us about schizophrenia?

Proteomic technologies, mostly focusing on mass spectrometric analysis, are a valuable tool in psychiatric research. A simple search on PubMed using the terms ‘proteomics or proteome and schizophrenia’ provides a total of 218 articles since the first article on proteomics of schizophrenia in the beginning of the 2000s.56 Out of them, 124 articles (and growing) were published within the last 5 years (2010–2014) on human and animal studies, including some reviews, showing considerable increase in awareness of the importance of proteomics in the study of schizophrenia. We have focused, for the purpose of the review, on proteomic studies on human samples of schizophrenia patients compared with controls, from the last 5 years.

These studies, which are summarized in Table 1, have been using proteomic screening approaches such as shotgun-MS (10/23), 2DE/DIGE (7/23), and multiplex immunoassays (10/23), alone or combined. Although postmortem brains are the main studied tissue in schizophrenia research,57,58,59,61,113 influences of chronic medication or sample heterogeneity and age have impaired some interpretation of the molecular differences found in postmortem brain tissue of schizophrenia patients compared with control subjects.114Thus, current studies have been mostly focusing on more accessible peripheral tissues, with a preference for blood serum and plasma,72,73,109,115 and CSF,65,66 although there are studies on skin fibroblasts75 and saliva as well.76Those have become the main tissues used in proteomic studies of schizophrenia because of the possibility of multiple sampling, thus providing better characterization of disease onset, development, and response to treatment. This broader characterization could lead to a more complete understanding of the disease and to development of diagnostic/prognostic biomarkers. Indeed, an analysis of proteins that are common to brain, CSF, and blood samples from at least two studies presented in Table 1, using Ingenuity Pathway Analysis (IPA, Ingenuity Systems, Qiagen, Redwood, CA, USA; www.ingenuity.comFigure 1), shows biomarker candidates of psychiatric disorders and their interactions, and is further discussed.

Table 1: Human proteomic studies from the last 5 years of different tissues and cells in schizophrenic patients

Figure 1 Protein network of regulated proteins in schizophrenia brain, CSF, and blood samples, analyzed by ingenuity pathways knowledge database. ALDOC, aldolase C; CSF, cerebrospinal fluid; GAPDH, glyceraldehyde-3-phosphate dehydrogenase.

Figure 1


Neuronal transmission and synaptic function

Differentially expressed proteins in schizophrenia proteomic studies have been found to be involved in neuronal transmission, synaptic plasticity, and neurites outgrowth, including several cytoskeletal constituents. Most significant proteomic changes included downregulation of neuroreceptors such as NMDA receptors and alpha-amino-3-hydroxy-5-methyl-4-isoxazole propionate (AMPA), in addition to glutamatergic signaling molecules, such as neurofilaments (NEFL and NEFM), glutamate-ammonia ligase (GLUL), and guanine nucleotide-binding proteins (G proteins) (GNB1), or dihydropyrimidinase-related protein 2 (DPYSL2), which are involved in synaptic function, axon guidance, and signal transduction impairment in schizophrenia.59,113NEFL, in addition to its role in neuronal morphogenesis, is directly associated with NMDA receptors. NMDAR hypofunction was associated with neurotransmitter dysfunction in NR1 transgenic mice,105 with variations in bioactive peptides and proteins. As GLUL is responsible for removing glutamate from neuronal synapses, it is most likely involved in glutamate imbalance in schizophrenia.2

Other proteins related to NMDA functionality and synaptic plasticity, such as MAPK3, SYNPO, CYFIP2, VDAC, CAMK2B, PRDX1, and ESYT, were also observed differentially expressed in postsynaptic density-enriched samples of postmortem brain tissues.59 Data corresponding to a genomic study of schizophrenia34found an excess of copy number variants in schizophrenia, confirming several of the proteins differentially regulated with functions in the postsynaptic membrane.

Calcium homeostasis and signaling

Calcium signaling has also been found to be differentially regulated in schizophrenia proteomic studies.54,76,113,116Calcium is a pivotal metabolite for the dopaminergic hypothesis in schizophrenia, mainly because it has a central role in the function of dopamine receptors D1 and D2.117 Proteins such as calmodulin (CALM1, CALM2), calcium/calmodulin-dependent protein kinase II (CAMK2B, CAMK2D, CAMK2G), voltage-dependent anion channels (VDAC1, VDAC2), and the plasma membrane calcium-transporting ATPase 4 (PMCA-4) are some of the calcium-related proteins found downregulated in the brains of schizophrenia patients.59,116 Some proteins were found differentially expressed in secretion fluids of schizophrenic patients—for example, calmodulin-like proteins and the S100 family of calcium-binding proteins (S100A6, S100A12)—such as in eccrine sweat118 and saliva.76 Complementing these findings, S100B was found downregulated in the nuclear proteome of schizophrenia corpus callosum.119 In addition, calcium activated differential expression of calmodulin-dependent protein kinase II (CAMK2), and calcineurin A in phencyclidine-treated rats.113  

Energy metabolism

The brain has a high glucose uptake to supply its major metabolic activity rate. Thus, one of the most consistent dysfunctions underlying the pathophysiology of schizophrenia is in energy metabolism pathways, along with mitochondrial dysfunction and oxidative stress.120,121Glucose metabolism is confirmed by hyperglycemia, impaired glucose tolerance, and/or insulin resistance in first-onset, antipsychotic, naive schizophrenic patients.110,122 Numerous proteomic studies have identified the glycolysis–gluconeogenesis pathway as being consistently disrupted both in brain and CSF,41,57,58,60,123 and is followed by peripheral tissues.77,90,113,120 The expression of proteins associated with the energy metabolism pathway, such as aldolase C (ALDOC), enolase 1 (ENO1), neuronal enolase 2 (ENO2), lactate dehydrogenase B (LDHB), phosphoglycerate mutase 1 (brain) (PGAM1), phosphoglycerate kinase 1 (PGK1), pyruvate kinase isozyme R/L [PKLR], and glyceraldehyde-3-phosphate dehydrogenase (GAPDH), are often significantly deregulated in schizophrenic patients compared with controls.55,77,120 The most consistent differentially expressed enzyme is aldolase C (ALDOC), which was found altered in several brain samples58,61,66 and also as a marker on blood serum samples.77 Likewise, pyruvate, the final product of glycolysis, and NADPH have been quantified in lower amounts in schizophrenic samples compared with controls, in the thalamus,57 and is replicated in phencyclidine-treated rats, a model of schizophrenia research.113 Whereas schizophrenia seems to be more associated with glycolysis, major depressive disorders are likely to be more associated with oxidative phosphorylation.124

DISC1, a major risk factor of the schizophrenia-susceptibility gene candidate,22 can affect mitochondrial morphology and axonal trafficking.125,126 Alterations of mitochondria morphology were reinforced by the imbalance of the oxidative phosphorilation system, including proteins such as NADH dehydrogenases (i.e., NDUFA1, NDUFV2, NDUFS3, NDUFB5), and ATPases (ATP5B, ATP6V1B2, ATP6V1A1), which have been previously shown altered in animal models of schizoprenia,113,127,128,129 but also had significant regulation in human brains.57,59 Other molecules such as dopamine have been shown to inhibit electron transport chain complex I (NADH dehydrogenase).130     

Oxidative stress

This overall imbalance of mitochondrial energy metabolism, associated with elevated calcium, leads to hazardous ROS concentrations and oxidative stress events in brain cells.131 The resultant ROS may cause oxidative damage in cellular DNA, RNA, proteins, and membrane lipids. Proteomics of the brain have shown several enzymes involved in redox activities (responsible for removing ROS and protecting cells against oxidative injury) to be differentially expressed in schizophrenia brain tissues. Proteins such as superoxide dismutase (which catalyze the dismutation of superoxide (O2) into oxygen and hydrogen peroxide), peroxiredoxins (PRDX1, PRDX2, and PRDX3) (which are responsible for reducing hydrogen peroxide), glutathione S-transferases (i.e., GSTM3, GSTTLp28, and GSTP1) (which are a family of multifunctional enzymes involved in cellular detoxification, glutathione reduction, and neutralization of ROS), and NADPH-dependent oxidoreductases such as carbonyl reductases (CBR1 and CBR2) and quinoid dihydropteridine reductase (QDPR) (which might be involved in the NADP/NADPH imbalance observed in the thalamus) were often found regulated in brain tissue,41,57,58,59,61,113 but could also be detected to be differentially regulated in peripheral tissues such as blood and fibroblast samples .70,73,75,132 Proteomics and combined metabolomics support evidence that slight imbalance in energy glucose metabolism, disrupting mitochondria and the oxidative phosphorylation system, results in compromised ATP production and oxidative stress, which is central in the pathophysiology of schizophrenia.41,113,133  


Cytoskeleton constituents are proteins that have shown broad differential expressions in schizophrenia—namely, microtubules such as tubulins (TBA1B, TUBB2A), microfilaments such as actins (ACTG1, ACTB) and actin-binding proteins such as tropomyosins (TPM1, TPM2, TPM3, TPM4), and intermediate filaments (i.e., GFAP, vimetin) and endocytosis proteins, such as dynamin (DNM1), a protein involved in clathrin-mediated endocytosis and other vesicular trafficking processes.55,58,59,113,134,135 Such modifications impact the cellular structure, axonal function, and neurite outgrowth, influencing synaptic plasticity and metabolism, all significantly influencing disturbed cytoskeleton arrangement in schizophrenia.44 Protein components of the cytoskeleton, such as the above-mentioned neurofilaments M and L (NEFL, NEFM) and DPYSL2, a regulator of cytoskeleton remodeling, have a role in axon guidance, neuronal growth, and cell migration. Glial fibrillary acidic protein (GFAP), the major intermediate filament of astrocytes, was found to be strongly regulated in brain tissues, both up- and downexpressed, indicating a precise protein expression across the brain.55,59,61,135,136 In addition, actin was often found downregulated in brain tissues,41,61,62,75 but was upregulated in fibroblasts75 or liver74 of schizophrenic patients.

Immune system and inflammation

Several abnormalities were found in schizophrenia proteomics, including changes in immune- and inflammation-related pathways in first-onset schizophrenic patients compared with controls. Molecules such as α-defensins (DEFA1, DEFA2, DEFA3, DEFA4), migration inhibitory factor, and several interleukins (IL-1ra, IL-8, IL-10, IL-15, IL-16, IL-17, and IL-18), including growth factors such as brain-derived neurotrophic factor, have been differentially regulated in blood samples from schizophrenic patients compared with controls.70,73,76,109,115,132 In addition, extracellular calcium-binding S100A12 exhibits cytokine-like characteristics, recruiting inflammatory cells to the sites of tissue damage. Indeed, anti-inflammatory treatment with cyclooxygenase-2 (COX-2) inhibitors has shown diminished schizophrenic behavior by blocking the synthesis of proinflammatory prostaglandins.137 Multiplex immunoassay profiling studies of blood serum have found numerous components of inflammation signaling pathways.109,111,115 Levels of anti-inflammatory cytokines IL-1ra and IL-10 were decreased after treatment with atypical antipsychotics, which correlated with symptom improvement.109 In addition, profiling studies using a subset of cytokines found increased levels of interleukins (i.e., IL-1β) in the cerebrospinal fluid of first-episode schizophrenic patients, indicative of immune system activation in the brain of some patients.138 Therefore, a proper subset of those altered molecular inflammatory molecules could be included in a sensitive and specific biomarker panel, both for diagnosis and treatment follow-up response.

An overview

Diverse proteomic techniques provided non-biased screening analyses of postmortem brain tissue from schizophrenic patients, and insights into pathways affected in the disease.10,57 In addition, more accessible tissues, such as cerebrospinal fluid, blood serum and plasma, and others such as fibroblasts, liver, and urine,57,66,70,74,75,139 have complemented those findings, suggesting several proteins that could be used as biomarkers to improve diagnosis. We have not gone through the details of the role of oligodendrocytes in schizophrenia, as these were recently tackled somewhere else,140,141,142 although these are as important as all that are listed here.

Proteomic insights from naive first-onset patients’ impaired protein pathways confirm patterns of disease onset, which along with genetic predisposition could be used as biomarkers for stratification of patients, improving the diagnosis and treatment classification. Also, this valuable information can lead to a more individualized medication, selected according to specific molecular dysfunctions and phenotype observed in schizophrenic individuals. Understanding the pathways affected by medication might also lead to reliable analytical platforms to evaluate individual response to treatment in a personalized-medicine mode. Moreover, the ability to monitor levels of molecules in noninvasive body fluids, such as saliva, urine, or blood serum or plasma, is a great advance. In addition, knowledge of gene–protein pathway networks affected and impaired by the disease can give clues for the development of new and more efficient targeted drugs to those relevant pathways.51,121     


Psychiatric disorders are one of the biggest burdens to society,3 and consequently one of the most challenging fields of medical research, with complex and multifactorial characteristics, along with genetic, neurodevelopmental, environmental, and molecular components. Hence, proteomics can add valuable insights into revealing psychiatric disorder connections, as it is closely linked to phenotype, and, by definition, proteomics constitute one of the most suitable approaches for this purpose.143

In 2010, the Human Proteome Organization (HUPO) started a project aiming to map the entire human proteome, the Human Proteome Project (HPP) initiative, with joint initiatives such as the Chromosome-centric Human Proteome Project.144,145,146 Thus, at the beginning of 2014, two extensive drafts of this map were released,79,80 showing progress in the identification of proteins from high-quality proteomic data to complement genomic annotation. The Human Brain Proteome Project (HBPP) initiative, specifically addressing the proteomic landscape of the human brain, aims to study individuals affected by neurodegenerative diseases, understanding its many different cell types and their particular structure at the cellular and tissue level.147,148 Another main focus is to untangle the human plasma proteome149 on health and disease, to support biomarker validation and development of new tools for diagnosis, disease progression, and medication efficiency, considering the confounding factors present in those body fluids.

From the schizophrenia research point of view, this are exciting news, because of the potential of information that can be extracted, as, regardless of efforts in the search for biomarkers, by investigating the transcriptome and proteome in the post-genomic era, schizophrenia is one more psychiatric disorder without a reliable marker. Those recent advances in ‘omics’ technologies, such as genomics, transcriptomics, proteomics, and metabolomics, which are not only expanding coverage and resolution but also becoming cheaper and more accessible, present new prospects for a global comprehension of biological characteristics of disease mechanisms.150

While genomic and transcriptomic technologies have achieved single-nucleotide resolution, the protein coverage of the amino-acid sequence is still restricted. State-of-the-art shotgun mass spectrometry has improved immensely, such as targeted proteomic measurements, and is useful for biomarker identification. Although the detection of some protein variants, such as differential splice products and posttranslational modifications, remains a challenge for proteomics to get a more comprehensive picture of the whole proteome using a systematic approach. This high-throughput investigation of nucleic acids, proteins, and metabolites from particular tissues and cells provides essential data, which is basic to system biology studies, in order to create integral models of cellular processes.151 Therefore, integrating biological data from omics studies to the expertise of complementary disciplines such as mathematics, physics, and computational sciences, toward better conceptual analysis and predictive models, provides new tools for understanding biological systems at different levels. Hence, we can analyze the cellular space-time and hierarchical organization,152 aiming for complete understanding of psychiatric diseases and identifying candidate biomarkers, especially before and after the onset of clinical manifestations, as well as target metabolic pathways impaired and/or affected by antipsychotics, in order to distinguish subgroups of patients who respond to medication on the basis of their molecular profiles.51

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Heterochronic microRNAs in temporal specification of neural stem cells: application toward rejuvenation

Takuya Shimazaki  & Hideyuki Okano

npj Aging and Mechanisms of Disease 2, Article number: 15014 (2016)

Plasticity is a critical factor enabling stem cells to contribute to the development and regeneration of tissues. In the mammalian central nervous system (CNS), neural stem cells (NSCs) that are defined by their capability for self-renewal and differentiation into neurons and glia, are present in the ventricular neuroaxis throughout life. However, the differentiation potential of NSCs changes in a spatiotemporally regulated manner and these cells progressively lose plasticity during development. One of the major alterations in this process is the switch from neurogenesis to gliogenesis. NSCs initiate neurogenesis immediately after neural tube closure and then turn to gliogenesis from midgestation, which requires an irreversible competence transition that enforces a progressive reduction of neuropotency. A growing body of evidence indicates that the neurogenesis-to-gliogenesis transition is governed by multiple layers of regulatory networks consisting of multiple factors, including epigenetic regulators, transcription factors, and non-coding RNA (ncRNA). In this review, we focus on critical roles of microRNAs (miRNAs), a class of small ncRNA that regulate gene expression at the post-transcriptional level, in the regulation of the switch from neurogenesis to gliogenesis in NSCs in the developing CNS. Unraveling the regulatory interactions of miRNAs and target genes will provide insights into the regulation of plasticity of NSCs, and the development of new strategies for the regeneration of damaged CNS.

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Larry H. Bernstein, MD, FCAP, Curator



The New Landscape of Pharmacogenetics  

Standardized Assays Are Driving Preemptive Genotyping and Personalized Drug Therapies

  • For decades, genotyping has promised to serve as a practical means of relating genetic make-up and pharmacological efficiency—first at the level of patient groups and more recently at the level of individuals. Genotyping, however, still has a fairly limited role in determining which drug therapies, and which doses, should be used in specific circumstances.

    If genotyping is to find widespread adoption, it will have to overcome several barriers, most notably variation in assays and delay in reporting, difficulty in translating genotype into specific actions, and a perceived lack of economic and/or clinical value. Technological advances coupled with changes in the availability of genetic information will dramatically change the landscape of pharmacogenetics.

    The efficacy of any given drug therapy is dependent on a number of factors, most commonly described through the pharmacokinetic parameters of absorption, distribution, metabolism, and elimination (ADME). Together, these factors determine whether a patient will need increased or decreased dosages, or whether a given therapy will work at all in that patient. Additionally, these factors can determine drug-drug interactions for patients on polypharmacy.

  • Genotype Variants

    Although a detailed description of specific genotype variants is beyond the scope of this article, a brief survey of the diversity of genotypes is helpful to provide a sense of the complexity that is inherent in genotyping, which has, in some ways, slowed the adoption of pharmacogenetics. As an example, human leukocyte antigen (HLA) genes are among the most highly polymorphic genes; more than 3,600 HLA class II alleles have been described.

    More than 50 human cytochromes P450 (CYPs) have been identified, and most have at least several single nucleotide polymorphisms (SNPs), with CYP2D6 having over 100 identified SNPs. Specific combinations of polymorphisms are translated into star alleles, which are used to predict the impact on therapeutic response.

    As might be expected, any individual enzyme can metabolize multiple drugs, and most drugs can be metabolized by multiple enzymes. Drugs can also inhibit metabolizing enzymes, while metabolizing enzymes can activate drugs by converting prodrugs into active metabolites. Generally, changes in functional activity of the enzyme are translated clinically by categorizing patients as poor, intermediate, extensive, or ultrarapid metabolizers.

    The FDA has now included pharmacogenomics information in the labeling of 166 approved drugs, some of which include specific action to be taken based on biomarker information. Table 1summarizes the biomarkers and indications for the pharmacogenomics labels. The FDA labels rangefrom dosage and pharmacokinetics information to precautions and, in nine of the labels, boxed warnings to highlight potentially serious adverse reactions.

    Most pharmacogenetics assays are currently offered as laboratory-developed tests; therefore, there is a wide range in the specific variants that are reported for any given target. As noted above, CYP2D6 has over 100 identified SNPs, and laboratories report various numbers of star alleles. Historically, this is because most genotyping assays involve methods based on the multiplex polymerase chain reaction (PCR). Accordingly, in these assays, the cost or effort to perform the genotyping is approximately proportional to the size of the panel.

    Additionally, because some of the functional variants are copy number changes, multiple assays may be required (for example, quantitative PCR for copy number, plus PCR for genotyping). More recent advances in microarray technology make it possible to perform more complete genotyping and copy number analysis of known star alleles simultaneously across multiple genes, thus reducing the cost and increasing the efficiency of pharmacogenomics. For example, the Affymetrix DMET Axiom Assay can analyze over 4,000 genotypes across 900 genes along with copy number in a single assay.

    From a regulatory perspective, it is likely that the disparate technologies laboratories use to generate their pharmacogenetics results will coalesce into a few, defined FDA-cleared devices. Because arrays can reproducibly provide comprehensive genotyping and copy number information at low cost, analytical and clinical validity can be readily demonstrated in a regulatory submission.

    The translation of specific genotype combinations into actionable clinical utility is hampered by difficulties in interpretation. Part of this relates to the somewhat ambiguous notation of the impact of a given star allele; the designation “ultrametabolizer,” for example, does not obviously translate to a specific dose for a given individual.

    Additionally, parameters such as ethnicity, age, body mass index, and gender can influence the pharmacokinetics in any specific individual. The establishment of guidelines can assist the practitioner in utilizing pharmacogenetics information to make therapeutic selections. At the forefront of establishing guidelines is the Clinical Pharmacogenetics Implementation Consortium (CPIC), which provides guidelines centered around specific genes as well as for specific drugs.

  • Preemptive Genotyping

    Click Image To Enlarge +
    Physicians, who need to make therapeutic decisions quickly and cannot wait for genotype results, are increasingly looking at preemptive genotyping as a potential solution to improve treatment options. [iStock/D3Damon]

    In most cases, physicians need to make treatment decisions immediately and cannot wait for genotype results. The obvious solution to this is preemptive genotyping, which is being deployed at five academic medical institutions (Mayo Clinic, Mount Sinai, St. Jude Children’s Research Hospital, University of Florida and Shands Hospital, and Vanderbilt University Medical Center) as part of the Translational Pharmacogenetics Program.

    For preemptive genotyping to be widely deployed, the structure of electronic health records (EHRs) will need to evolve so that they enable the retrieval, storage, and reporting of complex genotyping data. Moreover, they will need to be able to provide the translation of star alleles with metabolizing status for specific drugs, dosing guidelines or suggestions for alternative drugs, and links to guidelines and other supporting information.

    The most sophisticated embodiments of EHRs will also take into account other information that can influence dosing contained within the EHR, such as the patient’s ethnicity, weight, sex, and other medications. Most EHRs lack such capabilities, but two trends will substantially alter this landscape.

    First, there is an increasing recognition of the role medical informatics plays in healthcare and an increased emphasis on this role at medical institutions, both academic and community-based. Second, the entry of high-tech giants such as Google and Apple into the medical informatics and large-scale genotyping/genetic analysis arena will accelerate the development of these tools.

    Third-party payers have generally been reluctant to pay for most pharmacogenetics tests. The paucity of prospective randomized clinical studies showing either clinical or economic utility remains a fundamental hurdle for widespread adoption of pharmacogenetics. A likely path for the generation of clinical data will be through large, publicly funded genotyping initiatives in combination with investigator-initiated studies that rely primarily on mining EHRs for dosing, adverse reaction, and outcome information.

    One such initiative is tied to the Million Veterans Program. It is mining data to explore the pharmacogenetics of metformin response in diabetics with renal disease.

    Another push may come from consumers who choose to proactively obtain their pharmacogenetics information. Such activity will heavily depend on the appropriate EHR and bioinformatics infrastructure at primary care centers as well as harmonization of analytical test methods. These requirements suggest that consumer-driven work will lag the efforts at academic medical centers.

  • Future Perspectives

    Future Perspectives

    The pace at which pharmacogenetics is incorporated into healthcare will increase due to factors such as the decreasing cost of genotyping, the installation of a medical informatics infrastructure, and increased consumer demand for personal genotyping information. Moreover, these factors will reinforce each other and help preemptive genotyping become the norm rather than the exception.

    As this trend gathers momentum, it will begin contributing to a virtuous cycle in which the increased availability of genotyping data associated with outcome information will permit the development of additional and more precise treatment algorithms. Technological advances in genotyping, most notably high-density genotyping at low cost with high reproducibility, and medical informatics will be key to making this a reality.






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Roche/Genentech’s Late-Stage Pipeline beyond Cancer: Ocrelizumab, against primary progressive MS & relapsing/remitting MS – $2.7 billion peak sales forecast


Reporter: Aviva Lev-Ari, PhD, RN




Beyond Cancer


1. ocrelizumab, $2.7 billion peak sales forecast

What has the multiple sclerosis market excited about ocrelizumab is its success against primary progressive MS. Until orcrelizumab, no treatment in history has succeeded in a Phase III trial against this extremely debilitating form of MS.

Ocrelizumab is also being positioned for relapsing/remitting MS. Clinical trial data released in October showed that the treatment cut MS relapses by almost half compared with Merck’s competing drug, Rebif.

On a commercial basis, ocrelizumab’s expanded label (to include both forms of MS) should greatly increase its revenue potential. While a conservative estimate of ocrelizumab’s peak sales puts it at $2.7 billion, some see a peak sales potential for ocrelizumab in the neighborhood of $6 billion. That’s certainly a long shot, but not out of the question, since it is based on a MS market that is now worth $19 billion growing at 5% annually, with ocrelizumab eventually reaching a 30% market share.

Roche has stated plans for applying for regulatory approval for ocrelizumab in the first half of 2016. The drug’s accelerated approval status means an expedited review, with the FDA likely to take action on the application within 6 months. While ocrelizumab’s timeline depends on many variables, there is potential for sales to begin by year-end 2016.


Cancer Indications


2. Atezolizumab: $2.5 billion peak sales projected

Roche’s immuno-oncology drug atezolizumab follows ocrelizumab in blockbuster potential. Drugs such as atezolizumab (atezo) work by turning off cancer’s ability to remain undetected by the immune system, and atezo has put up some impressive data in its clinical trials. For example, in its POPLAR trial against advanced non-small-cell lung cancer, atezo doubled the likelihood of survival in patients taking the drug relative to placebo.

Being first matters, however. The market already has powerful competitors for atezo in Merck’s Keytruda and Bristol-Myers Squibb‘s (NYSE:BMY) Opdivo. On the other hand, both Keytruda and Opdivo are PD-1 treatments, and atezo works through another mechanism, PD-L1.

Genentech researchers believe PD-L1 is a more significant engine in cancer than PD-1. If they are correct, atezo will have a more long-lasting effect on stopping cancer growth, which would make the drug a potential first choice. Roche is driving some 36 studies  toward making a broad case for atezo with the FDA. Encouraging data keeps coming in. But investors should realize that how this drug will perform against competition from Keytruda and Opdivo is still very much an open question.

A more immediate commercial advantage for atezo is that Roche has a powerful in-house diagnostic division providing tools that can tag patients likely to respond to the drug. Many cancer therapies are ineffective with a large percentage of patients, and by specifically identifying those cancer patients who should benefit, Roche can personalize cancer treatment. That’s a big plus with payers, who naturally want to conserve their money for therapies more likely to be effective. As personalized medicine becomes steadily more widespread, full-year sales for Roche’s diagnostic division have grown–increasing 6% in 2015 to $10.7 billion.

Atezo’s breakthrough therapy designation gives it a solid chance of rolling out this year, but some industry watchers are deferring atezo’s projected launch date until 2017. Calculating a launch date is an inexact science, so that’s certainly possible.

3. Venetoclax: $1.4 billion projected for Roche

Roche’s third blockbuster speeding toward FDA approval is AbbVie partnered venetoclax. The drug is targeted to treat a highly virulent form of leukemia (chronic lymphocytic leukemia), specifically in those patients with a mutation that makes the cancer more aggressive and often results in shortened survival. Late-stage trials are also ongoing in non-Hodgkin’s lymphoma, acute myeloid leukemia, and multiple myeloma.

Roche has U.S. marketing rights  to the drug, and FiercePharma estimates Roche’s share of peak sales at $1.4 billion by 2020. The drug, which has already been fast-tracked for approval under the agency’s breakthrough designation last May, scored a priority review from the FDA in January. Roche expects FDA clearance in 2016.




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

Immune-Oncology Molecules In Development & Articles on Topic in

Curators: Stephen J Williams, PhD and Aviva Lev-Ari, PhD, RN

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