systems pathology: an introduction to omic approaches in...

Systems Pathology: An Introduction to Omic

Approaches in Personalized Medicine

Michael H. A. Roehrl, M.D., Ph.D.

Department of Pathology and Laboratory Medicine

Boston Medical Center

This session will introduce practicing pathologists, pathologists in training, and

laboratory professionals to the concepts of

• the basics of cutting-edge Omic technologies,

• the basics of genomics, transcriptomics, proteomics, and metabolomics,

• next generation DNA and transcriptome sequencing,

Our Goals for this Course


• the significance of cancer genome alterations for personalized treatment

decision making,

• mass spectrometry as an up and coming exciting enabling technology to look at

proteomes of diseased tissues, and

• the pathologist's key role in optimal large-scale biospecimen banking for

molecular pathology and research.

Examples will be included how to beneficially integrate novel Omic diagnostic

workflows into existing and future pathology workflows.

This course will equip participants with key tools for practicing pathology in the

future.


Don’t worry: No prior knowledge of molecular pathology is assumed.

We’ll cover the basics and equip you with the skills to evaluate, communicate, and

understand future trends in personalized pathology diagnostics.

This is a brand new course!

So please enjoy and ask lots of questions!

What do I have?

Why did I get it?

How will it behave?

Diagnostic

Etiologic

Predictive

The Fundamental Questions

How will it behave?

How will it respond?

Predictive

Therapeutic

Need to understand personalized pathophysiology.

Current Challenges in Cancer Diagnostics

• Diagnosis largely based on morphologic characteristics

• Limited number of diagnostic biomarkers

• Lack of prognostic and predictive markers• Lack of prognostic and predictive markers

• Lack of global personalized picture of molecular aberrations

• Difficulty to interpret tumor heterogeneity and tumor-stroma interplay

• Difficulty to extrapolate to individualized treatment recommendations

What is Omics?

• The suffix –ome (as used in molecular biology) refers to a totality of some sort

• Early uses: Biome (1916) and Genome (1920)

• Most common uses today: Genome, Transcriptome, Proteome, Metabolome

• “Explosion” of uses: Connectome, Cytome, Exposome, Exome, Glycome, Interferome,

Interactome, Ionome, Kinome, Lipidome, Mechanome, Membranome, Metagenome,

Metallome, ORFeome, Organome, Pharmacogenome, Phenome, Physiome, Regulome,

Secretome, etc.

What is Omics?

• Genomics: Global study of genomes of organisms; determination of the entire DNA

sequence; fine-scale genetic mapping; first DNA genome (bacteriophage) in 1977 (F.

Sanger); first free-living organism (H. influenzae) in 1995; Human Genome Project

(draft, 2001; “finished”, 2007, <1 error per 20,000 bases); technologies: classical

sequencing (Sanger), “next generation” DNA sequencing

• Transcriptomics: Global study of all RNA molecules (mRNA, rRNA, tRNA, other non-

coding RNA) in a cell or tissue type; quantification, distribution, time-dynamic coding RNA) in a cell or tissue type; quantification, distribution, time-dynamic

plasticity; technologies: RT-PCR, nucleotide arrays, “next generation” RNA-Seq

• Proteomics: Global study of all proteins in a cell or tissue type; quantification,

distribution, time-dynamic plasticity, chemical modification (phosphorylation,

methylation, glycosylation, etc.); term first introduced in 1994 (Marc Wilkins);

technologies: gel-based systems, antibody arrays, mass spectrometry

• Metabolomics: Global study of all “small molecules” in a cell or tissue type;

quantification, distribution, time-dynamic plasticity, chemical composition;

technologies: chromatography, mass spectrometry, NMR spectroscopy

The Link: Omics and Systems Biology

• Systems Biology: Comprehensive integration of genomic, transcriptomic, proteomic,

and metabolomic data to give a (more) complete picture of a cell/tissue/living organism

(this remains an unsolved problem and is an area of very active research)

• Systems Pathology: Application of Systems Biology concepts and tools to the study and • Systems Pathology: Application of Systems Biology concepts and tools to the study and

diagnosis of disease; key scientific concept for Personalized Disease Management

(outstanding opportunity for Pathology to take center stage in Molecular Medicine!)

DNA

RNA

Transformative Pathology: Training Molecular Physicians of the Future

Data Source

RNA

Proteins

Metabolites

DNA

RNA


Data Source Technology

RNA

Proteins

Metabolites

DNA

RNA


Data Source Technology Data Analysis

Data InterpretationRNA

Proteins

Metabolites

Data Interpretation

Medical Action

Currently

Morphology Classification of diseases

DiagnosisStatic assessment

Specimen

Treatment

Assessment of response

(predominantly by imaging)

Functional assessment

Specimen

Morphologic assessment

Diagnosis

Classification of diseases

Pathophysiologic-dynamic assessment• Spatial heterogeneity (stem cells etc.)

• Omic parameters (WGS, transcriptome, proteome,

metabolome)

• Dynamic behavior

• Functional treatment response

TreatmentContinuous functional treatment

response monitoring

• Functional treatment response

Therapy adjustment

...

Transformative

Pathology

Genomics

DNA

RNA




Proteins

Metabolites

Data Interpretation

Medical Action

First Generation Sequencing

Conventional Sanger

sequencing workflow

Roehrl et al., 1996

Shendure, Li. Nat Biotech, 2008

Next Generation Sequencing

Conventional Sanger

sequencing workflow

Next gen sequencing

workflow



Applications

• De novo sequencing of whole genomes

• Resequencing (genome variations, mutations, etc.)

• “Tag counting” (gene expression, chromatin occupancy, ChIP-Seq, methylation

analysis) by short identifiable tag sequencing

Next Generation Technologies

Kahvejian et al. Nat Biotech, 2008


• Dideoxy sequencing (Sanger method)

• Solid phase sequencing

• Sequencing by hybridization

• Mass spectrometry

• Cyclic array sequencing

• Nanopore sequencing

• Other emerging platforms


Clonal amplification strategies

Emulsion PCR

(454, Polonator,

SOLiD)


Bridge/Cluster

PCR

(Solexa/Illumina)


454

Illumina/Solexa

Platform


SOLiD, Polonator

HeliScope


Non-Fluorescence Methods (Ion Torrent)

Life Technologies Corp.


Example workflow for Illumina sequencing

Target selection:

Whole genome

Exome

RNA

Custom

Next Generation Sequencing – Sequence Assembly

Sequence assembly from NGS reads (from data to finished sequence)

• Many algorithms available

• Under very active investigation

• De novo assembly: No reference genome required

• Mapping-based assembly: Uses a reference genome for assembly• Mapping-based assembly: Uses a reference genome for assembly

How does it work (in principle)?

Reference genome

Mapping algorithm

Next Generation Sequencing – De Novo Assembly

Overlap graph approach

De Bruijn graph approach

Next Generation Sequencing – De Novo Assembly

Exome Enrichment

Exome Enrichment

• ~30,000 targets (36.5 Mb)

• From various databases (ResSeq, CCDS,

miRBase)

• 2.1 million oligonucleotide probes

(covering 44.1 Mb)

Roche NimbleGen, Inc.

Cancer Genomics: Workflow

Myerson et al. Nat Rev Genetics, 2010

Cancer Genomics: Next Generation Sequencing


Cancer Genomics: Recent Examples


Cancer Genome Projects

• The Cancer Genome Atlas (TCGA) – comprehensive analysis of 20-

25 tumor types; data available on glioblastoma and serous ovarian

cancers

• The International Cancer Genome Consortium (ICGC) – large-scale

international cancer genome sequencing effort (50 tumor types)

• The Pediatric Cancer Genome Project (PCGP) – cancer genome

sequencing of 600 childhood cancer patients

Next Generation Sequencing – Why Did It Develop Just Now?

• Next gen sequencing (thus far mostly) borrows from original Sanger sequencing principles

(chain termination, sequencing by synthesis)

• Improvements in cyclic in-situ chemistry (polymerase, modified nucleotides, reversible

blocking moieties)

• Massive parallelization in space and time (by using 2-D immobilization or nanowells)

• Improvements in fluorescence chemistry

• Breakthroughs in nanofabrication and microfluidics

• Breakthroughs in high-resolution digital imaging

• Breakthroughs in high-performance computing, data storage, and analysis algorithms

(under very active development, “cloud” outsourcing)

Medical Impact of Exome Sequencing – An Example

June 11, 2011


Let’s look at the Methods section – now it is much clearer!!!

shotgun library

in-solution exome capture

Tiacci et al. NEJM, 2011

next gen sequencing

?


Bioinformatics workflow

is key but not trivial!


Next Generation Sequencing – A “Software Zoo”


• The genomic basis of Hairy Cell Leukemia (HCL) was unknown

• Next gen exome sequencing of leukemic and normal cells from ONE (!) index patient

• Identification of 5 somatic mutations (including BRAF V600E)

• BRAF V600E found in 47 other HCL patients (100% of tested patients) by targeted Sanger

sequencingsequencing

• No BRAF V600E found in 195 patients with other leukemias/lymphomas by targeted

Sanger sequencing

• Functional demonstration of MEK and ERK phosphorylation as downstream targets of

BRAF activation

• BRAF inhibition reduces phosphorylation of MEK and ERK


Next Gen Sequencing – Open Questions

• Ethics and informed consent (GINA etc.)

• Next gen technologies in a CLIA setting (proficiency testing, QC, etc.)

• Who will perform the testing clinically and who will interpret the data? (Pathology!)

• How define “clinically actionable” mutations (e.g., if a clinical trial is not available for the

target)?

• Billing for Omic Testing?

Today: Fragmented billing, even for simple molecular tests... Tomorrow: Hopefully, task-based billing!

• Exome analysis for solid tumors

• Transcriptome analysis

• Proteome analysis (phosphorylation state)

• ...

• Pathologist’s data interpretation as key

billable component!

Transcriptomics

DNA

RNA




Proteins

Metabolites

Data Interpretation

Medical Action

Transcriptomics: DNA Arrays

Transcriptomics: DNA Arrays

Disadvantages of array-based transcriptomics:

• Limited linearity and dynamic range (no “digital” read-out as in NGS)

•No allele-specific read-out

•No discovery of alternative splicing or RNA editing•No discovery of alternative splicing or RNA editing

• Cross-hybridization noise

• Interrogation limited by immobilized probe population

•Hard to scale and parallelize (no sequence bar-coding as in NGS)

Forecast: DNA arrays will likely be (largely) replaced by sequencing technologies

RNA-Seq: Whole Transcriptome Shotgun Sequencing


Wang et al. Nat Rev Genetics, 2009


Example:

Discovery of gene fusions in prostate

and gastric cancers involving the RAF

kinase pathway by paired-end RNA-

Seq

Palanisamy et al. Nat Med, 2010

FISH verification

5 min break!

Proteomics

DNA

RNA




Proteins

Metabolites

Data Interpretation

Medical Action

3.2 ×××× 109 base pairs (haploid genome), 1.5% of which is the coding exome

Only ~20,500 protein-coding genes…

[+ non-coding ribonucleic acids and “ribozymes” (mostly ribosome)]

Diversity:

Why Study Proteins: Biologic Complexity in the Protein World

Diversity:

Polymorphisms (maybe 2××××)

Splice variants (maybe 3××××)

Post-translational modifications (phosphorylation, glycosylation,

transamination, alkylation, oxidation, etc.; maybe 3××××)

but ~400,000+ “different” proteins (!)…

and large dynamic complexity (~1012 – 1015)

Mass Spectrometry in Pathology

• Mass spectrometry (MS) is an analytical technology that measures mass-to-charge

ratios (“m/z “) of charged particles.

• MS is used to elucidate and quantify chemical compounds, including peptides,

carbohydrates, metabolites, and other chemical entities

• A typical MS procedure consists of multiple steps:

• Sample is loaded onto the instrument and is vaporized• Sample is loaded onto the instrument and is vaporized

• Sample is ionized (charged)

• Ions are separated by m/z ratios in electromagnetic fields

• Ions are detected quantitatively

• Ion signals are processed into mass spectra

• MS instruments have 3 components:

• Ion source (to convert gas phase sample into ions)

• Mass analyzer (electromagentic fields)

• Detector


• Different sample sources:

• Solid (MALDI, laser ionization)

• Liquid (chromatographic separation, electrospray ionization)

• Different ways of handling ions:

• Quadrupole

• Ion trap (linear, 3-D)

• Different types of ion fragmentation:• Different types of ion fragmentation:

• ETD

• PTR

• CID

• ECD

• Popular instruments in the life sciences:

• Time-of-flight

• Triple quadrupole (good quantification, fast)

• Ion trap

• Variations thereof (e.g., qTOF)


Multiple Reaction Monitoring (MRM)

Pisitkun T et al. Physiology 2007;22:390-400


• In Laboratory Medicine:

• Vitamin D (superior to immunoassays)

• Steroid hormones

• Drug metabolites

• Immunosuppressant monitoring

• Biomarker discovery (blood, serum, plasma, urine, CSF, etc.) – research use• Biomarker discovery (blood, serum, plasma, urine, CSF, etc.) – research use

• In Anatomic Pathology (mostly research use):

• Biomarker discovery (tissues)

• MS imaging (“MALDI imaging”)

• Drug penetration


Predictable fragmentation patterns are key for identification!


1. Protein ID & PTM

2. TMT Quantitation

Easy nLC1000

Velos Pro

Nanospray Flex

Sample Preparation

Data Interpretation

REAGENTS & CONSUMABLES

Lysate Preparation

Protein Enrichment

Protein Clean-up

Protein Digestion

Peptide Enrichment

3. Absolute Quantitation

4. Glycan Analysis

SOFTWARE

Proteome Discoverer 1.3

Pinpoint 1.2

SimGlycan 2

Thermo Fisher Scientific


MALDI Imaging

(“Histology by MS”)

Schwamborn and Caprioli. Nat Rev Cancer, 2010


MALDI Imaging



Drug distribution

within a tumor

IEF & SDS-PAGE

:

Proteomic Analysis of Surgical Tissue

Tandem-MS/MS

Roehrl et al.

Proteomic Analysis of Colon Adenocarcinoma

Proteomic Analysis of Colon Adenocarcinoma

32/56 differentially regulated proteins (57%) were only discoverable after heparin enrichment.

Tandem Mass Spectrometry of Differentially Expressed Proteins

2-D Gel Spot

Tandem Mass Spectrometry of Differentially Expressed Proteins

×2.5 ×5.2 ×9.3 ×37.02-D Western

Differential Regulation of PRDX1 Protein Isoforms

1-D Western ×22.2

2-D Western captures isoform differences!

Tumor tissue is heterogeneous…

• Malignant cells

• Stroma

• Benign epithelial elements

• Inflammatory cells

• Blood and lymphatic vessels

• Nerve tissue

• Muscle• Muscle

• Extracellular matrix

Where is PSB7 up-regulated?

PSB7 Up-Regulation in Colon Cancer

Lymphatics

PSB7 Up-Regulation in Colon Cancer

Functional Significance of PSB7 – MHC Class I Escape?

↑↑↑↑ IFN-γ

α7β7β7α7 ↓↓↓↓ PSB7

↓↓↓↓ IFN-γ

Conventional proteasome Immunoproteasome

↑↑↑↑ PSB7

↑↑↑↑ MHC presentation↓↓↓↓ MHC presentation

Proteomic Alterations in Lung Cancer

Transgelin Cell motility?


Transgelin-2

PPIA

Cell motility?

Isomerase


TransgelinStroma

Transgelin-2 Carcinoma

“Spatial Mirror Images”

Transgelin

Transgelin-2


65% identical

87% similar !Needleman-Wunsch protein sequence alignment

Yet very different pattern of

overexpression!

Transgelin Transgelin-2

Transgelin


Transgelin-2

Metabolomics

DNA

RNA




Proteins

Metabolites

Data Interpretation

Medical Action

Mass spectrometry/NMR spectroscopy

Direct tissue extracts (soluble fraction,

membrane-bound, etc.)

Cancer Metabolomics

membrane-bound, etc.)

Metabolic ex vivo labeling (“bioreactor”)

using stable isotopes (13C glucose, 13C

amino acids, deuterated compounds, etc.)

Tissue Extracts (Aqueous,

Organic)NMR Spectra (1D-ND)

Pulse Sequences

B

y

z

NMR Signal

Coil

Principles of NMR-Based Tissue Metabolomics

NMR Spectrometer Resonance Assignment

Statistical Methods

(Principal Component

Analysis, etc.)

Biomarker Discovery

xy NMR Signal

NMR Pulse Sequences

m = -1/2

Bo

Precessional

orbit around

applied

magnetic

fieldSpinning

Nucleus

Relative

Energy

Bo

Bo

m = -1/2

I=1/2

Increasing Magnetic

Field Strength

o

m = +1/2

Cancer

Normal

The 1H NMR Spectrum Reveals Metabolic Signatures

Organic solvent

NMR

Aqueous solvent

LC-MS

Chemical shift

range (ppm)

Average

cancer/normalAssignment Student t test

2.64..2.62 1.45 aspartate 0.00176

2.84..2.78 1.68 aspartate 0.000148

3.04..3.00 0.71 creatine 0.000193

3.92..3.90 0.69 creatine/glucose 0.000204

5.18..5.16 0.16 GlcNAc 0.00715

4.64..4.62 0.12 glucose 5.07E-06

5.24..5.20 0.2 glucose 3.36E-05

3.50..3.44 0.38 glucose 2.35E-06

3.72..3.70 0.62 glucose 0.00031

3.84..3.82 0.64 glucose 0.00307

3.90..3.86 0.66 glucose 0.000362

2.38..2.32 1.46 glutamate/proline 1.85E-06

8.18..8.16 1.62 IMP 1.04E-05

Normalized metabolite NMR peaks in aqueous spectra differing

significantly to at least 99% confidence between normal and cancer

tissue

8.18..8.16 1.62 IMP 1.04E-05

4.50..4.48 1.79 IMP 0.00369

4.38..4.34 1.96 IMP 0.000517

6.12..6.10 2.25 IMP 4.00E-05

1.38..1.28 1.22 lactate 0.00144

4.12..4.06 1.26 lactate/proline 0.00117

3.54..3.50 0.55 myo-inositol 1.53E-13

4.06..4.04 0.58 myo-inositol 1.79E-05

3.64..3.58 0.6 myo-inositol 4.13E-10

4.02..3.98 1.58 O-phosphoethanolamine 0.00209

2.56..2.50 1.51 oxidized glutathione 0.000942

3.34..3.32 0.64 scyllo-inositol 0.0038

3.26..3.22 1.59 taurine 2.42E-08

3.44..3.40 2.23 taurine/proline 6.44E-09

6.00..5.92 1.65 UDP-GlcNAc 0.00424

7.96..7.92 1.99 UDP-GlcNAc 2.23E-05

5.52..5.48 2.23 UDP-GlcNAc 0.00245

4.26..4.18 2.51 UDP-GlcNAc 0.000127

5.80. 5.78 2.02 uracil 0.000247

7.52..7.50 3.24 uracil 5.83E-07

Cancer Normal

Unraveling Biomarkers By Principal Component Analysis (PCA)

Component

Co

mp

on

en

t

Principal Component Analysis

Center at mean

Empirical covariance matrix

Eigenvector and eigenvalue decomposition

Principal components

Eigenvector Eigenvalue

PC 1 vs. PC 2 plots for Pareto-

scaled and auto-scaled NMR

data. Borders were

constructed to optimally

separate between normal and

cancer.

Hierarchical clustering of aqueous samples sorted by PLS-DA

Biospecimen Banking

The Art and Science of Biospecimen Banking

A Key Topic for the Future of PathologyA Key Topic for the Future of Pathology

What do I have?

Why did I get it?

How will it behave?

Diagnostic

Etiologic

Prognostic

The Fundamental Questions

How will it behave?

How will it respond?

Prognostic

Predictive

Need to understand personalized pathophysiology

Current Challenges in Cancer Diagnostics

• Diagnosis largely based on morphologic characteristics

• Limited number of diagnostic biomarkers

• Lack of prognostic and predictive markers• Lack of prognostic and predictive markers

• Lack of global personalized picture of molecular aberrations

• Difficulty to interpret tumor heterogeneity and tumor-stroma interplay

• Difficulty to extrapolate to individualized treatment recommendations

1. The Central Role of Pathology in BioBanking

2. BioBanking Challenges

BioBanking in Pathology: Outline

3. Ultra-Rapid BioBanking at Boston Medical Center

4. Examples: Scientific Applications from My Lab

Pathology Takes

Center Stage

Transformative Pathology and Biospecimen Science

at Boston Medical Center

Diagnostics

Biospecimens

Center Stage

Pathology meets with the patient and

owns the informed consent process (>95%

consent rate)

Basic Biomedical Research

BankingBioinformatics and

Database Structures

Boston Medical Center BioBank Brochure for Patients





4. Examples: Scientific Applications from my Lab

BioBanking Legal Framework: HIPAA, GINA, CLIA

CLIA: Clinical Laboratory Improvement Amendments;

clinical laboratory performance standards

HIPAA: Health Insurance Portability and Accountability Act; HIPAA: Health Insurance Portability and Accountability Act;

defines protected health information (privacy); does

not explicitly cover privacy of genetic information

GINA: Genetic Information Nondiscrimination Act; privacy

of genetic information

Patient Consenting at Boston Medical Center: The Pathologist’s Job!

Frequently Overlooked Issues in Biospecimen Science

Ethnic diversity: Are discovered biomarkers sensitive to ethnic

background; most current BioBanks are biased in their assets!

Specimen annotation: Co-morbidities, drug treatments,

surgical procedure specifics, anesthesia, intraoperative

complications/delays, clamp times, etc.

Future-proofing of the collection: Does the current workflow Future-proofing of the collection: Does the current workflow

allow for scientific investigation using technologies not

envisioned at the time of collection (example: chemical

additives as preservatives may invalidate metabolomic

workflow)

Integration of tissue and biofluid collection: How good is the

longitudinal follow-up protocol, IT infrastructure, collaboration

between Anatomic Pathology and Laboratory Medicine

Comparability across sites: How standardized are collection

and storage protocols aross various sites

African AmericanCaucasian

Ethnic Diversity in BioBanking: Key for Biomarker Discovery

Hispanic

Asian and other

Boston Medical Center BioBank

Academic BioBanking: Succeeding in Times of Tight Funding

Academic Medical Center

Public funding for BioBanks

NIH, states


Medical School

• High-complexity cases

• High volume

• Excellent clinical annotation

• IT infrastructure (EMR, BioBank)

• Active clinical trial site

Philanthropy for BioBanks


Public funding for BioBanks

NIH, states

Collaborative Agreements

with Biotech/Pharma

Academic BioBanking: Succeeding in Times of Tight Funding


Medical School

• High-complexity cases

• High volume

• Excellent clinical annotation

• IT infrastructure (EMR, BioBank)

• Active clinical trial site

Philanthropy for BioBanks

Codevelopment of biomarkers

Clinical trials

Infrastructure funding





4. Examples: Scientific Applications from my Lab

Why Ultra-Rapid BioBanking?

What Does It Take to Do It?

Tissue in FFPE Blocks: Harsh Chemical Exposure Limits Scientific Use!

Getting fixed tissue into paraffin

Dehydration (ethanol series)

Clearing (xylene)

Infiltration (molten paraffin)

Loss of:

Small molecules

Lipids

Peptides

Small RNAsInfiltration (molten paraffin)

Small RNAs

...

Science Platforms Drive Tissue Biospecimen Requirements

Formalin-fixed, paraffin-embedded (FFPE)

Tissue in “OCT”

(optimal cutting temperature; glycols/resins)

Genomics

Transcriptomics

ProteomicsTissue in some sort of “stabilizer”

(fixatives and/or degradation inhibitors;

e.g., RNAlater is ammonium sulfate)

Tissue at low temperature (LN)

Ultra-rapid procurement and low temperature

Proteomics

Proteomics with PTMs

Metabolomics

Lipidomics/Glycomics



Tissue in “OCT”


Genomics

Transcriptomics

Proteomics

Tissue in some sort of “stabilizer”





Proteomics


Metabolomics




Tissue in “OCT”


Genomics

Transcriptomics

ProteomicsTissue in some sort of “stabilizer”





Proteomics


Metabolomics


Integrated BioBanking Workflow at Boston Medical Center

Pre-OP visit

Clinical consenting

Pre-OP visit

BioBank consenting

(Pathology!)

IT Infrastructure (customized Freezerworks platform)


Operating RoomPre-OP visit

Clinical consenting

Pre-OP testing

Intraoperative Pathology Consult

(margins, diagnosis, molecular studies)


BioBank consenting

(Pathology!)

Pre-OP testing

Pre-OP BioBanking

(blood, urine, etc.)

Ultrarapid BioBanking

(tissue)




Clinical consenting

Pre-OP testing

Intraoperative Pathology Consult

(margins, diagnosis, molecular studies)

Post-OP hospital stay Outpatient follow-up

Clinic visits


BioBank consenting

(Pathology!)

Pre-OP testing

Pre-OP BioBanking


Ultrarapid BioBanking

(tissue)

Post-OP hospital stay

Post-OP BioBanking


Outpatient follow-up

Follow-up BioBanking

(re-biopsies, blood, urine, etc.)


Consultation Ultra-Fast BioBanking

Intraoperative Pathology Consultation:

The Gateway To High-Quality Tissue Proteomics

0 min

1 min

<5 min5 min

10 min

0 min

1 min

Intraoperative Pathology Consultation:

The Gateway To High-Quality Tissue Proteomics

- Intraoperative Pathology (“Frozen Section Pathology”) provides high-value, real-time

diagnostic decision making information during surgery or other interventional procedures

(e.g., image-guided biopsies) and is a springboard for moving the future of pathology

forward

- Essential for BioBanking (speed of sample procurement, quality of material, achieving

highest banking rates)

5 min

10 min

highest banking rates)

- Critical for the future of molecular diagnostic medicine (functional assay development,

triaging, real-time assay development – genomic/transcriptomic/proteomic etc., therapy

response prediction and monitoring)

- Critical for patient care (real-time feedback)

- Critical for real-time personalized therapeutic/diagnostic decision making within the health

care team (pathologists, surgeons, medical oncologists, radiation oncologists)

Logistics for Ultra-Rapid BioBanking

Frozen Section Laboratory

(Pathology)

Operating Room

Active communication

(rapid alert system)

BioBank

LN freezing

Barcoding

Initial database annotation

Intra-OR freezing

Pre-resection biopsy

Clinical Laboratory

(blood, serum, plasma, urine, CSF, etc.)



(Pathology)

Operating Room



BioBank

LN freezing

Barcoding


Intra-OR freezing


Clinical Laboratory


Close time monitoring


1. Establish rapid communication between OR, Pathology, and BioBank

2. Establish Intraoperative Pathology Consultation as a focus point2. Establish Intraoperative Pathology Consultation as a focus point

3. Collaborate with a good pathologist!

Pathology Takes

Center Stage


at Boston Medical Center

Diagnostics

Biospecimens

Center Stage



consent rate)



Database Structures


1. Establish rapid communication between OR, Pathology, and BioBank

2. Establish Intraoperative Pathology Consultation as a focus point2. Establish Intraoperative Pathology Consultation as a focus point

3. Collaborate with a good pathologist!



(Pathology)

Operating Room



BioBank

LN freezing

Barcoding


Intra-OR freezing


Clinical Laboratory


5 min break!

Systems PathologyMaking Diagnostic Medicine Quantitative and PredictiveMaking Diagnostic Medicine Quantitative and Predictive

DNA mRNA Proteins

Metabolites

Substrates

Life

RNA Biosynthesis

Normal Disease

What is Systems Pathology?

• Study of the interactions between the components of a biological

system (e.g., enzymes and metabolites)

• How these interactions give rise to the function and behavior of that • How these interactions give rise to the function and behavior of that

system

• Quantitatively describe biological processes

Bands on a gel?Pathway pictorials?

Really Not Good Enough!(No offense, all data shown here by Roehrl et al.)


More bands on a gel? Fluorescence tracking?

Even more bands on a gel?

(No offense, all data shown here by Roehrl et al.)

Also Not Good Enough!


TheoryComputer

Modeling

What is Systems Biology?

Experiment

What is Systems Biology?

TheoryComputer

Modeling

ExperimentGenomics

Transcriptomics

Proteomics Metabolomics

Physiomics

Glycomics

Interactomics

130K Processors

280 TFlops

Approaches to Modeling

• Quantum Mechanics (1-10 × 10-10 m)

• Newtonian Molecular Dynamics (10-500 × 10-10 m)

• Spatial (1-3 D) Convection, Diffusion, and Active Transport

• Chemical Kinetics (kon, koff, kcat, etc.)

• Deterministic (N Large) vs. Stochastic (N Small) Simulation

Scaled Approaches to Modeling

0.1 nm 10 nm 1 µµµµm 1 mm 1 m0.1 nm 10 nm 1 µµµµm 1 mm 1 m

0.1 nm 10 nm 1 µµµµm 1 mm 1 m

Scaled Approaches to Modeling


Mesobiology

Systems Biology: Quantitative Modeling

Monte Carlo Stochastic Simulation

Monte Carlo Stochastic Simulation

This is somewhat analogous to the Heisenberg uncertainty principle of QM…

Nanofluidics will have to deal with this to be

useful in the clinical lab of the future!


At What Scale Does Stochastic Behavior Live?


Mesobiology

Systems Pathology of Signaling: Modeling a Complex Network

77 differential equations

112 rate constants

76 species

Modeling a Complex Network: Blood Coagulation

22 fluid phase factors

10 fluid phase complexes

19 lipid-bound factors

25 lipid-bound complexes

Model Validation

Thrombin (M)

3e-8

4e-8

Normal

Hemophilia A (50%)

Modeling Hemophilia

Time (s)

50 100 150 200 250 300

Thrombin (M)

0

1e-8

2e-8

Hemophilia A (1%)

Thrombin (M)

8.0e-8

1.0e-7

1.2e-7

1.4e-7

Factor V Leiden(APC Resistance)

Modeling Hypercoagulable States

Time (s)

50 100 150 200 250 300

Thrombin (M)

0.0

2.0e-8

4.0e-8

6.0e-8

8.0e-8

Normal

Protein C Deficiency

Modeling Therapeutic Intervention: Warfarin Treatment

Thrombin (M)

3e-8

4e-8

No Warfarin

Early Warfarin Treatment

Time (s)

50 100 150 200 250 300

Thrombin (M)

0

1e-8

2e-8

Warfarin Steady-State

Network Architecture Determines Drug of Choice

X11

X1

With Feedback

No Feedback

Modeling Temporo-Spatial Cellular Processes

Summary

This session will introduce practicing pathologists, pathologists in training, and

laboratory professionals to the concepts of

• the basics of cutting-edge Omic technologies,

• the basics of genomics, transcriptomics, proteomics, and metabolomics,




• the significance of cancer genome alterations for personalized treatment

decision making,

• mass spectrometry as an up and coming exciting enabling technology to look at

proteomes of diseased tissues, and

• the pathologist's key role in optimal large-scale biospecimen banking for

molecular pathology and research.

DNA

RNA




Proteins

Metabolites

Data Interpretation

Medical Action


Example workflow for Illumina sequencing

Target selection:

Whole genome

Exome

RNA

Custom


Let’s look at the Methods section – now it is much clearer!!!

shotgun library

in-solution exome capture


next gen sequencing

yes


Wang et al. Nat Rev Genetics, 2009


MALDI Imaging



IEF & SDS-PAGE

:

Proteomic Analysis of Surgical Tissue

Tandem-MS/MS

Roehrl et al.

Metabolomics in Pathology

Pathology Takes

Center Stage


Diagnostics

Biospecimens

Center Stage



consent rate)



Database Structures

Outlook

Integrating Personalized Cancer Genomes

Exome sequence (>30× coverage)

Personalized database representing all

patient-specific aberrations

Integrating Personalized Cancer Genomes

Exome sequence (>30× coverage)

Personalized database representing all

patient-specific aberrations

Personalized functional characterization

Morphologic snapshot(LM, EM, IHC, MP)

Omic snapshot

Genome sequencing

Transcriptome

Proteome

Metabolome

Transformative Pathology

• Dynamics

• Tumor heterogeneity

• Treatment response monitoring

• Differential treatment sensitivity

Ex vivo bioreactor

Stem cell isolation

Systems perturbation

(small molecules, siRNA)

Quantitative model building and data integration

Systems Pathology

Acknowledgements

Julia Y. Wang

Dan Remick

Jung-hyun Rho

Sidney Wang

BMC Pathology and Laboratory Medicine

Channing Laboratory, Brigham and Women’s

Hospital

MIT, Francis Bitter Magnet LabSidney Wang

Robert Pistey

Brian Japp

Cheryl Spencer

Kathy Tilton

MIT, Francis Bitter Magnet Lab

National Institutes of Health

American Cancer Society

Karin Grunebaum Cancer Research Foundation

Thank you for attending this brand new course!

I am looking forward to your thoughts, comments,

and suggestions!

Michael H. A. Roehrl, M.D., Ph.D.

[email protected]

Curious for more? NEW Systems Pathology Short Course:

USCAP Meeting, Vancouver, March 2012

systems pathology: an introduction to omic approaches in...

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