Download development of omics-based profiling tests for toxicology and

DEVELOPMENT OF OMICS-BASED
PROFILING TESTS FOR TOXICOLOGY AND
CLINICAL TRIALS
Gilbert S. Omenn, MD, PhD
Center for Computational Medicine and Bioinformatics
University of Michigan
Ann Arbor, MI, USA
8th International Symposium on Recent Advances in
Environmental Health Research, Jackson State University
Jackson, MS, 19 September, 2011
Disclosures
Board of Directors, Amgen Inc (biotech leader)
Board of Directors, Armune Biosciences Inc
(Univ of Michigan spinoff, cancer diagnostics)
Boards of Scientific Advisers:
Arboretum Venture Partners (Ann Arbor)
Compendia Biosciences (UM spinoff, bioinformatics)
Galectin Therapeutics (early-stage; cancer and fibrosis)
Innocentive Innovation (Eli Lilly spinoff)
No conflicts relevant to this presentation.
Near-Completion of Human Genome Sequence, Feb 2001
Eric Lander
J. Craig Venter and Francis Collins
Ari Patrinos
Lee Hood
Protein
DNA
“Unlock the Secrets of the Laboratory”
In 1965, U.S. President Lyndon B. Johnson
swooped down on the campus of the National
Institutes of Health by helicopter from The
White House downtown in Washington DC.
He applauded the prowess of the biomedical
scientists at NIH and around the country.
He reminded us that the Nation is impatiently
waiting for results from research—translated
into better medicines and better health.
Vision of Biology as an Information Science:
Key Components
• An avalanche of ’omic information: validated SNPs, haplotype
blocks, candidate genes/alleles, sequences, proteins, &
metabolites—to be associated with disease risks
• Powerful computational methods and cyberinfrastructure
• Effective linkages with better environmental, dietary, and
behavioral datasets for eco-genetic analyses
• Credible privacy and confidentiality protections in research and
clinical care
• Breakthrough tests, vaccines, drugs, behaviors, and regulatory
actions to reduce health risks and cost-effectively treat patients
globally.
Integrating High-Throughput Measurements
with the Phenotype: from Science to Medicine
Theme: From Data to Knowledge
Further Disclosure
I currently chair the Institute of Medicine
(National Academy of Sciences) Committee
on “Omics-Based Predictive Tests for Clinical
Trials”.
The goal of these omics-based studies is more
effective, more specific, safer, more
“personalized” medical care.
Our Committee is charged with also making
the research more transparent and
reproducible.
Developing Omics Clinical Tests
• Molecular analyses of cancers can reveal information about
•
•
•
•
mechanisms of initiation and progression and provide the
foundation for clinical tests.
The aims of such tests include proper diagnosis, earlier
diagnosis, prognosis/risk of metastases, response to specific
therapies, and evidence of recurrence: “clinical utility”.
Cancers are very heterogeneous in causation, progression,
response to therapies, and risks of metastases and death.
Gene expression, genomic, epigenomic, proteomic, and
metabolomic studies are complementary “omics platforms” for
development of clinically useful tests.
It is a long path of discovery, confirmation, validation, clinical
trials, and FDA approval to establish test validity and utility.
Molecular Signatures in Medical Practice
Cancer Diagnosis and Prognosis
• Mammaprint®: 70-gene RNA sig from breast Ca (FDA-approved);
(Agendia) + BluePrint: 80-gene RNA sig that distinguishes basal,
luminal, and ERBB2 subgroups of breast cancer
• Oncotype DX®: 21-gene RNA sign from BRCA; 12-gene, colon
cancer (Genomic Health)
• Pathwork® Tissue of Origin Test: 2000 RNAs (PathworkDx)
• Ova1: 5 abundant proteins, for decision about pelvic surgery
Cardiovascular Disease Diagnosis and Prognosis
• AlloMap®: 11-gene RNA sig for rejection post-cardiac Tx (XDx)
• Corus CAD™: 23 gene blood RNA sig for CAD (CardioDx)
• Triage® Cardiac Panel: 3 blood protein sig for assessment of chest
pain and short of breath for potential AMI (Biosite)
• Genotype Panels: CYPs (drug metabolism); VKORC1 (Genelex)
Gene Expression Signatures to Guide
Treatment Decisions for Breast Cancer
• Mammaprint test to guide use of systemic
chemotherapy
• Based on early gene expression prognosis studies by
Van’t Veer et al (Nature 2002): node-negative, stage
I/II, age <55, distant metastases <5 yrs vs none in 5
yrs, distinguished with panel of 70 genes (transcripts)
• One of the few examples of a research test taken
through the entire process to FDA-approved clinical
In Vitro Diagnostic-Multigene Index Assay (IVDMIA) from Agendia, Inc (Amsterdam/Irvine)
Breast Cancer – MammaPrint Signature
Confirmation on Retrospective Consecutive Series
OncotypeDX--21Genes—to Guide Decision on
Chemotherapy in ER+, node-negative BRCA
Generates a risk score: <18, 18-30, > 31 = low,
intermediate, and high, with steep recurrence
rates (6.8, 14.3, 30.5% over 10 yrs) and
mortality rates (2.8, 10.7, 15.5%).
Launched in 2004 by Genomic Health Inc.
Includes tests for estrogen and progesterone
receptors, which also informs the decision to
go ahead with Tamoxifen or Aromatase
inhibitor (endocrine preventive protocol).
Testimony by Richard Simon, DSc, Chief,
Biometrics, NCI, to IOM Committee
“Most cancer treatments benefit only a minority
of patients to whom they are administered .”
Prognostic, predictive, and effect-modifier
biomarkers could make a difference if
“actionable” in clinical decision-making.
“The one thing that different kinds of
biomarkers have in common is that they are
generally developed and validated poorly.”
Critical Evidence for Clinical Utility
Claims of medical utility for prognostic and predictive
biomarkers based on analysis of archived tissues can have
either a high or low level of evidence depending on:
analytical validation of the assay, nature of the study yielding
archived specimens, the number and condition of the
specimens, and prior development of a focused written plan
for analysis of a completely specified biomarker classifier.
Studies using archived tissues from prospective clinical
trials, conducted under ideal conditions and independently
confirmed, can provide the highest level of evidence.
Analyses of prognostic or predictive factors, using nonanalytically validated assays on a convenience sample of
tissues and conducted in an exploratory and unfocused
manner, provide poor evidence for clinical utility.
Prostate Cancer Diagnosis and Prognosis
The standard test for >30 yrs is PSA =
prostate-specific antigen. A single protein.
The test is quite good for monitoring treated
patients for response (drop in elevated PSA)
and recurrence (reappearance of PSA).
The test is poor for screening large numbers of
men for the diagnosis: Sn 0.6, Sp 0.6, very low
predictive value, i.e. many false-positives and
false-negatives.
Bioinformatics Approach Led to
the Discovery of Gene Fusions in
Prostate Cancers
Fusions of TMPRSS2 to the ETS Family
of Transcription Factors
Scott
Tomlins et al
Science
(2005)
Gene Fusions Reveal Molecular Subtypes of
Prostate Cancers: Personalized Oncology
ETS family fusions: 50-60%
ETS and PARP inhibitors
SPINK1:10-15%
SPINK1 mAb/EGFR inhibitors
B-RAF, K-RAS: 1-2% each
Raf kinase and Ras inhibitors
With the drugs in hand, seek the patients with the
corresponding target for specific therapy.
Making a Difference:
Asking Patient-Centric Questions
Especially among prostate cancers, a small
percentage of cancers account for the mortality:
those which are invasive and metastasize. What are
the molecular markers and mediators for such cellular
behaviors?
How can we tell apart the lethal cancers from the
relatively innocuous cancers that look the same by
histology and stage? This is more important than
earlier detection in most cases.
The story of sarcosine (N-methylglycine) follows.
Metabolomic Profiles Delineate Potential Role
for Sarcosine in Prostate Cancer Progression
Sreekumar et al (Nature 2009) combined highthroughput liquid-and-gas-chromatography-based mass
spectrometry to profile 1126 metabolites across 262
clinical samples related to prostate cancer (42 tissues;
110 urine, 110 plasma). Few differences in urine or
plasma; 60 of 626 identified in prostate tumor tissue but
not benign prostate. Six cpds showed increase from
benign to PCA to metastatic PCA: sarcosine, uracil,
kynurenine, glycerol-3-phosphate, leucine, and proline.
Oncomine Concept Maps showed amino acid
metabolism and methyltransferase activity increased.
Metabolomic Profiling of Cancer Progression
Sreekumar et al, Nature 2009
Sarcosine concentration is greatly increased in
metastatic prostate cancers, compared with
localized tumors and especially benign tissue.
Sarcosine as Biomarker/Mediator
Sarcosine (N-methylglycine) was much higher in metastatic
tumors than localized, and nearly undetectable in benign prostate.
Its levels were also increased in invasive prostate cancer cell lines
relative to benign prostate epithelial cells. Knockdown of
glycine-N-methyl transferase attenuated prostate cancer invasion.
Exogenous sarcosine or knockdown of the enzyme that leads to
sarcosine degradation, sarcosine dehydrogenase, induced an
invasive phenotype in benign prostate epithelial cells.
Androgen receptor and the ERG gene fusion product coordinately
regulate components of the sarcosine pathway, binding to the
promoter of GNMT.
A test on urine sediment and supernatant is under development
by Metabolon after licensing from the Chinnaiyan Lab at U of M.
Schematic Representation of the
Sarcosine Pathway
O
O
SAM
H2N
GNMT
CH3
OH
Glycine
H
N
SAH
SARDH
CANCER
OH
Sarcosine
Invasion?
Migration?
Aggressivity?
PIPOX
SAM: S-Adenosyl-L-methionine; SAH: S-Adenosyl-L-homocysteine;
GNMT: Glycine-N-methyltransferase;
SARDH: Sarcosine dehydrogenase; PIPOX: L-Pipecolate oxidase
SARDH- overexpression reduces tumor growth and
decreases Sarcosine levels in mouse xenograft model
A. Tumor Growth
B. Sarcosine Levels
AUC of Individual Metabolites and
the Panel of the Training Set
Metabolites
AUC
Sarcosine
0.76
Glutamic Acid
0.74
Glycine
0.79
Cysteine
0.73
Multiplex Panel
0.88
Since the AUC of the panel (0.88) is higher than the AUC of each metabolites,
we expect the panel will outperform the individual markers
Metabolite Panel Construction
ROC curve of the training set
● The multiplex panel was developed using logistic regression on the training set of
70 urine sediments consisting of 4 metabolites.
● The performance of the panel was evaluated using leave-one-out cross
validation.
● The AUC (area under the ROC Curve) is 0.88 indicating high performance
Validation in an Independent Cohort
• The performance of the panel was evaluated using 88 urine sediments (28 biopsy negative, 28 biopsy
positive and 32 radical prostatectomy) in a blinded fashion. The AUC is 0.80.
• This data supports the utility of the multiplex metabolite marker panel in the non-invasive diagnosis of
prostate cancer.
Future Directions/Next Steps
Continue validation of the multivariate panel with independent
cohorts in Chinnaiyan Lab/UM Ctr for Translational
Pathology
Assist Metabolon in deployment/modification of the assay in
Metabolon CLIA lab; consider UM CLIA/CAP lab, too.
Facilitate EDRN validation trial of metabolite multiplex
Test additional metabolites for an expanded multiplex
Evaluate clinical utility for different use scenarios:
(a) diagnosis when PSA 4-10 ng/ml;
(b) aggressivity/risk that tumor is metastatic
Explore multiplexes with other classes of molecular
alterations, including TMPRSS2-ERG and PCA3.
Lifestage Exposures and
Adult Disease
UM NIEHS P30 Center (Howard Hu, PI)
Bioinformatics Core (BIC)
Launched May, 2011
The Bioinformatics Core
 The range of high-throughput technologies available for
studying the mechanisms of epigenetic modification is
rapidly expanding. Thus, the importance of epigenetics
researchers having access to advanced bioinformatics
collaborators is growing.
 The Bioinformatics Core (BIC) of the University of
Michigan NIEHS P30 Center aims to enhance the
interpretation of experimental and clinical results from a
broad range of epigenetic studies.
BIC Staff
Leader: Maureen
A. Sartor, PhD
Research Assistant Professor
Center for Computational Medicine and Bioinformatics
[email protected], 2044 Palmer Commons
Co-Leader: Gilbert
S. Omenn, MD PhD
Director, Center for Computational Medicine and Bioinformatics
Professor of Internal Medicine, Human Genetics, & Public
Health, [email protected]
Member:
Richard C. McEachin, PhD
Research Investigator
Center for Computational Medicine and Bioinformatics
[email protected], NCRC Bldg 10, Suite A121
Areas of Expertise
We offer guidance in using bioinformatics tools related to, but not limited to,
transcription factor binding motifs/modules, gene/toxin relationships,
functions and biological processes, public high-throughput data
repositories, genome visualization, regulatory prediction, and protein
interaction networks. Also, tools for natural language search and
processing of the biomedical literature.
 Epigenomics : DNA methylation (microarrays, Methyl-Seq, MeDIPSeq), histone modifications (ChIP-Seq), and microRNA analyses
 Genomics: microarrays, RNA-Seq, genome wide association,
linkage
 Proteomics
 Metabolomics
 Regulatory mechanisms and transcriptomics
 Integrative analyses and systems biology: pathways, annotation
 Phenotype definitions
 Data management
Assessment of Environmental Influences
 DNA methylation profile, from the lab of Dana
Dolinoy (NIEHS #1R01 ES017524-01)
 Sample throughput vs. genome coverage for
various DNA methylation techniques
 One of several mouse samples after exposure to
BPA. Samples were prepared using the
MethylPlex technique from Rubicon (Ann Arbor)
and deep sequenced using Illumina GAIIx
 Laird PW, Principles and challenges of
genomewide DNA methylation analysis, Nat
Rev Genet. 2010 Mar;11(3):191-203
Collaborations
Epigenomics Web Portal (Bisphenol A) - Genomics Portals is an
integrative, web-based computational platform for the analysis and
mining of genomics data, developed at the University of Cincinnati
by a BIC external advisor (http://eh3.uc.edu/GenomicsPortals).
Shinde K, Phatak M,
Freudenberg JM, Chen J,
Li Q, Joshi VK, Hu Z,
Ghosh K, Meller J,
Medvedovic M. Genomics
Portals: Integrative WebPlatform for Mining
Genomics Data. BMC
Genomics. Jan
13;11(1):27. 2010
High-Throughput Data Analysis
ConceptGen is a gene set enrichment and relationship mapping tool that can help you
identify, explore, and visualize relationships among gene sets (http://conceptgen.ncibi.org).
LRpath is an alternative gene set enrichment testing method for interpreting high-throughput
results, such as from DNA methylation experiments (http://lrpath.ncibi.org).
 ConceptGen graphic of related biological
concepts (p < 0.05) for genes with increased
methylation and decreased expression
level in HPV(-) relative to HPV(+) cell lines.
 Sartor MA, Dolinoy DC, Jones TR, Colacino JA,
Prince MEP, Carey TE, and Rozek LS,
Epigenetics June 2011; 6 (6): 777-787
LRpath graphic, methylation in 6 cancer datasets
Other Resources (https://portal.ncibi.org)
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MiMI - The MiMI database comprehensively includes protein interaction information that has
been integrated and merged from diverse protein interaction databases. (mimi.ncibi.org)
Gene2MeSH - Gene2MeSH uses a statistical approach to automatically annotate genes
with the concepts defined in Medical Subject Headings (MeSH). (gene2mesh.ncibi.org)
Comparative Toxicogenomic Database (CTD) - CTD advances understanding of the
effects of environmental chemicals on human health. (ctd.mdibl.org)
GeneGo – MetaCore and ToxHunter together provide an integrated knowledgebase and
software suite for pathway analysis and systems toxicology (www.genego.com)
GenePattern – GenePattern is a powerful genomic analysis platform developed at the
Broad Institute. (http://www.broadinstitute.org/cancer/software/genepattern)
Genomatix MatInspector identifies transcription factor binding sites (www.genomatix.de)
Cytoscape is an open source bioinformatics platform for visualizing molecular interaction
networks and biological pathways. (http://www.cytoscape.org)
Bioinformatics Training
Classes and/or one-on-one sessions are available. Recent sessions include training on
Cytoscape (hands-on and webinar formats), ConceptGen, Gene2MeSH, and MiMI. Suggestions
for future offerings are welcome. For more information on training sessions, contact Marci
Brandenberg ([email protected]), Maureen Sartor, or Rich McEachin.
Comparative Toxicogenomics Database
Manually-curated, public resource of the triad of chemical-gene, chemicaldisease, and gene-disease relationships, integrated to construct chemical-genedisease networks.
As of July 2010, CTD contained 1.4 million triad data points and analytical
tools like GeneComps and ChemComps to find comparable genes and
chemicals that share toxicogenomic profiles, enriched Gene Ontology terms,
and Venn diagram tools to discover overlapping and unique attributes of any
set of chemicals, genes, or disease, and enhance gene pathways data.
Indexed at numerous other databases, including PubChem, PharmGKB,
UniProt, T3DB, GAD, ChemID, and TOXNET. Link also with databases of
gene variants and eco-genetic relationships.
Datasets from microarray and proteomics studies in various species are
available at the Chemical Effects in Biological Systems Knowledgebase
((Waters et al., 2008); http://cebs.niehs.nih.gov/cebs).
Aims for Proteomics of Cancers
1. Profile tumor specimens
a) for diagnosis and stratification of patients
b) for prognosis with particular therapies
c) for clues to circulating biomarkers
2. Profile circulating proteins
a) to discover and validate biomarkers for earlier
diagnosis
b) to apply such biomarkers to predict/monitor
response to treatment and recurrence.
OVA1 Test for Ovarian Cancers
Based on empirical results from mass spectrometry-based
proteomics—not tied to cancer pathways or mechanisms
Five abundant plasma proteins:
beta-2 microglobulin and CA125 (MUC16): up
transthyretin, apolipoprotein A1, transferrin: down
Approved by FDA for narrow indication of testing for
cancer prior to surgery in women who have pelvic masses
(80-90% benign). Licensed by Johns Hopkins (Dan Chan)
to Vermillion, Inc. (Sn 0.92, Sp 0.45).
Aptamer-based Proteomics, Lung Cancer
Biomarker Panel (Ostroff/Gold, 2010)
Aptamer 12-Biomarker Performance in
Distinguishing NSCLC from Controls
Performance of Aptamer Classifier on
Serum Samples from 4 Sites
Barriers for Proteomic Cancer Biomarker
Discovery in Plasma
Human cancers are very heterogeneous
Tumor proteins are in low abundance for early detection of
cancers
Tumor proteins are greatly diluted upon release to
extracellular fluid and blood
Plasma is an extraordinarily complex specimen dominated
by high abundance proteins
Knowledge of the plasma proteome is still limited
(latest, least-redundant Human Plasma Peptide Atlas has
1929 canonical proteins: Farrah et al, Mol Cell Proteomics
June 2011; www.peptideatlas.org)
Biomarker Discovery from Tumor
Tissues and Plasma: Strategies
1.
2.
3.
4.
Start with microarray or next-gen sequencing evidence
for carcinogenic pathway mechanisms in tumor and
track corresponding protein biomarker candidates to the
plasma; e.g., TMPRSS2/ETS fusion and sarcosine in
prostate cancers.
Perform targeted proteomics with SRM/MRM to
identify and quantitate these candidates.
Detect auto-antibodies in plasma as a biological
amplification of tumor protein signals, then confirm in
tumor tissue.
Identify alternative splice isoforms of biologically
meaningful proteins in cancers and in plasma of humans
and mouse models: exciting new work from my lab.
A New Class of Biomarker Candidates,
from Alternative Splicing
Alternative splicing generates protein
diversity without increasing genome size
Most genes produce alternative transcripts
Greatly improved MS/MS instrumentation
enables confident identification of peptides
from proteins coded by mRNA transcript
sequences expressed at quite low levels.
Alternative Splice Isoforms
– Contribute to diseases, especially cancers
– Potentially useful as biomarkers for cancer
Alternative Splicing Events
Rajan, P. et al., Nature Reviews Urology, 2009, 6, 454-460
Mouse Models and Human Cancers
used in Splice Variant Studies
Pancreatic Cancer
– mutations: Kras G12D activation and INK4a/ARF
deletion
– Menon et al, Cancer Res 2009;69:300-309
Breast Cancer
– mutation: Her2/Neu amplification
– Menon & Omenn, Cancer Res 2010; 70:3440-49
Human Prostate Cancer and VCaP/RWPE Cell Lines
-- Yocum et al (2011, submitted)
Different Types of Alternative Splicing
Events among Novel Peptides
Protein
Novel peptide
Mgi
symbol
Sample
type
Probable splicing
mechanism
M14C435_25_s53_e
5231_1_rf1_c1_n0|
ITFDDHKNGSCGVSYI
AQEPDAP
flnb
tumor
intron retention
M19C1480_7_s383_
e953_1_rf1_c1_n0|
ATETARLLPGTALAEA
QSPLRRLTLTQAPPR
fth1
tumor
located in the 5'UTR region;
alternate translation start site
M15C7603_11_s118
1_e1415_1_rf1_c1_
n0|
QTSSRPAMGGGTARW
QR
gapdh1
normal
different frame; frame 2
MXC910_48_s179_
e2030_1_rf0_c1_n0|
LLEELAAARPGEPALM
SSSPLSKKRR
uba1
normal
novel peptide from Ensembl
exon 2 and 3 junction; the
currently annotated Ensembl
cDNA starts from exon 3
M7C13466_20_s2_e
518_1_rf2_c1_n0|
EARSLSDGGPADSVEA
AK
nap1l4
tumor
exon skipping;
exon 2 skipped
Protein Interaction Network Displayed
by MiMI-Cytoscape Plugin
(Only the direct interactions between the input genes are shown)
The parent gene
symbols of the
alternative splice
variants found
only in the tumor
sample of the
breast cancer
dataset were used
as the input gene
list.
The gene symbols
in bold are
differentially
expressed
proteins
3-UTRs, 3-UTRs,
Summary
We have identified many biologically interesting novel and
known Alternative Splice Variants
Many were over-expressed in the cancer samples versus
the normal samples—either by labeling or by spectral
counting
Some cancer-associated splice variants have more CK-2
and PKC phosphorylation sites
Predictive structural studies can show the effects of
splicing on phosphorylation
Alternative Splice Variants could be used as biomarker
candidates
Ongoing Special Studies
Structural analyses of conformational and
functional features of the differentially-expressed
splice variants may help us understand the
underlying mechanisms in different types of
cancers
We are combining next-generation mRNA
sequencing and proteomics-based identification of
splice variants with targeted (MRM) proteomics to
develop biomarker assays.
We have studies in progress on human prostate,
lung, and colon tumors or cell lines.
Human Plasma PeptideAtlas – 91 expts; 3,172,759
peptide-spectrum matches; 20,679 distinct peptides
at FDR 0.0016; 1929 canonical proteins at FDR 0.01
The HUPO Human Plasma Project (HPP)
Major Goals of the Human Proteome Project
Identify and characterize proteins from all of the
20,300 protein-coding genes.
Identify and quantify protein isoforms from
post-translational modifications, splicing, SNPs,
tissue-specific expression in health & disease
Lay foundation for biomarker discovery,
confirmation, validation, and development of
clinically useful multiplex assays
Contribution to the HPP by
Antibody-based Proteomics
Mathias Uhlén, HumanProtein Atlas
V7.0, 2010, with immunohistochemistry for 10,000 proteins
www.mrmatlas.org
Picotti et al Nature Methods 2007; Picotti et al , Nature Methods, 2010
Peptide/Protein SRM Coverage by Chromosome
Chromosome-Based HPP (9/2010)
Pipeline
1
1
15
ERBB2
PSMD3
2
2
1
1
GRB7
3
ZPBP2
IKZF3
LOC72812
9
4
MED24
RARA
1
1
GSDMA
CSF3
5
1
1
2
2
2
C17orf37
3
1
6
1
9
5
CDC6
PTMs
Genes found in Ensembl
Gene
with MS information
(GPMDB)
Number of pseudogenes
1
1
THRA
1
ORMDL3
1
1
5
1
NR1D1
RAPGEFL
1
Ser/Thr Acetyl GPM
Met Acetyl GPM
Lys Acetyl GPM
1
MSL1
6
2
WIPF2
GSDMB
5
CASC3
Ser/Thr Phospho Unipro
Ser/Thr Phospho GPM
Tyr Phospho Uniprot
Tyr Phospho GPMDB
N-glyco Unipep
New
1
ERBB2
C17orf37
GRB7
IKZF3
ZPBP2
PSMD3
GSDMA
LOC72812
9
ORMDL3
GSDMB
1
CSF3
MED24
THRA
NR1D1
MSL1
RARA
CDC6
WIPF2
RAPGEFL
1
CASC3
Gene
1
Antibody found in HPA or
Antibodypedia
Number of pseudogenes
No Antibody in HPA and
Antibodypedia
Gene
1
Number of hypothetical
genes
Integrating High-Throughput Measurements
with the Phenotype: from Science to Medicine
Acknowledgements
Prostate Cancer Studies
Arul Chinnaiyan (UM), Arun Sreekumar (Baylor)
Epigenomics
Maureen Sartor, Dana Dolinoy, Laura Rozek (UM)
Peptide Atlas
Terry Farrah, Eric Deutsch, Ruedi Aebersold, Rob
Moritz (ISB/ETHZ)
Protein Alternative Splice Variants
Rajasree Menon, Anastasia Yocum, Yang Zhang (UM)
Chromosome 17, Human Proteome Project (HPP)
Bill Hancock, Michael Snyder, Ron Beavis