A biased look at Biomarkers

BioMarker

Definition:

Biomarker is a substance used as an indicator of a biologic state

Existence of living organisms or biological process. A particular disease state

Proteins

Nucleic acids

Metabolites: Carbohydrates

Lipids

Small molecules

Biomarker

Detection of biomarker

Detection of biomarkerQuantitative a link between quantity of the marker and disease Qualitative a link between exist of a marker and disease

Detection of biomarker – diagnosisSelf properties, e.g enzymatic activitiesAntibodies, IHC, ELISA

Biomarker & DiagnosisIdeal Marker for diagnosis

Should have great sensitivity, specificity, and accuracy in reflecting total disease burden. A tumor marker should also be prognostic of outcome and treatment

Biomarker for Screening

•The marker must be highly specific, minimize false positive and negative

•The marker must be easily detected without complicated medical procedures. The disease markers released to serum and urine are good targets for application of early screening.

•The method for screening should be cost effective.

•The marker must be able to clearly reflect the different stages of the disease (early)

Samples for biomarker detection

Blood, urine, or other body fluids samples

Tissue samples

Prostate Cancer marker PSA

PSA is a protein normally made in the prostate gland in ductal cells that make some of the semen. PSA helps to keep the semen liquid. PSA, also known as kallikrein III, seminin, semenogelase, γ-seminoprotein and P-30 antigen, is a glycoprotein, a serine protease

Prostate Cancer Diagnosis with PSA

Cancer of the prostate does not cause any symptoms until it is locally advanced or metastatic.

There is a correlation between elevated PSA and prostate cancer.

Detection of PSA is a surrogate for early detection of prostate cancer.

Large screening trials have shown that PSA nearly doubles the rate of detection when combined with other methods. Based on these data, PSA testing was approved by the US FDA for the screening and early detection of prostate cancer.

PSA is also found in the cytoplasm of benign prostate cells.

“I never dreamed that my discovery four decades ago would lead to such a profit-driven public health disaster." -Richard Ablin (inventor of the PSA test)

PSA screening generates ~$1.7 billion annually in the U.S. alone.

Sensitivity = the ability of the test to detect the disease (True positive rate)Specificity = the likelihood that your test will be normal if you are disease free (True Negative)

A brief aside about Statistics and Probability

-Statistics are the formalization of common sense-because they have to handle many different

situations, they can be really complicated-they should make you feel really good or really

bad about your data

-People are inherently bad at statisitics and probability Case Study: rate for being HIV positive: 1:10000 false positive rate of HIV test: 1:1000

If I test positive, what is the chance that I am really HIV negative?

-Statistics are the formalization of common sense-because they have to handle many different

situations, they can be really complicated-they should make you feel really good or really

bad about your data

-People are inherently bad at statisitics and probability Case Study: rate for being HIV positive: 1:10000 false positive rate of HIV test: 1:1000

What is the chance that I am HIV negative?

0.0001 0.001 0.01 0.1 0.9 0.99 0.9999

A brief aside about Statistics and Probability

-Statistics are the formalization of common sense-because they have to handle many different

situations, they can be really complicated-they should make you feel really good or really

bad about your data

-People are inherently bad at statisitics and probability Case Study: rate for being HIV positive: 1:10000 false positive rate of HIV test: 1:1000

What is the chance that I am HIV negative?

0.0001 0.001 0.01 0.1 0.9 0.99 0.9999

For every 1 True Positive there will be 10 false positives, so my chanceof being Negative is 10/11.

A brief aside about Statistics and Probability

Rate is 15:10000False Positive Rate is 60:1000

For every 15 True Positives, there will be 600 False Positives!Chance of being Negative 600/615 = .97Chance of being Positive = .03 (before test chance was 0.015)

-Is this true?

How about the PSA test?

Rate is 15:10000False Positive Rate is 60:1000

For every 15 True Positives, there will be 610 False Positives!Chance of being Negative 600/615 = .97Chance of being Positive = .03 (before test chance was 0.015)

-Is this true?The test will miss 80% of the true positives (sensitivity = 20%)

so there will only be 3 True Positives Detected so: Chance of being Negative 600/603 = 0.995 Chance of being True Positive = 0.005

Follow up for a +HIV test is another blood test.Follow up for +PSA test is tissue biopsy.

How about the PSA test?

By Age 65 the rate of Prostate Cancer climbs to 8:1000 and the test performsmuch better.For every 8 True Positives, there will be 60 False Positives!

Chance of being Negative 60/68 = .88Chance of being Positive = .12 (before test chance was 0.015)

How good does a Biomarker have to be?

Prostate Cancer is one of the most frequent cancers (15:10000), mostcancers are much less frequent (1:10000: 1:50000) so a biomarker wouldhave to be much better than the PSA test. It is currently believed that a newbiomarker would need sensitivity and specificity better than 95%.

How good does a Biomarker have to be?

Early Proteomics Base Biomarker workwas based on SELDI

SELDI can detect 200-300 features in a sample. It has been used to findbiomarkers from everything from blood to tears.

Early Biomarker work has largely been discredited

-Biomarkers with similar masses kept being rediscovered-When the proteins were identified, they were abundant serum proteins and were from the same proteins-Multi-center studies failed to validate the biomarkers in “clinical”setting

-Realization that serum and other biofluids are incredibly complex.

-Realization that serum and other biofluids are incredibly variable and “fragile”

-some strong “biomarkers”-blood collection tube-# of freeze-thaw cycles-diet

Key Concept: Proteins vary widely in concentration

Typical Biomarker Discovery studywill take 50 samples per condition.Typically takes 10 samples per conditionto have a 90% chance of finding differencesof 2 times.Validation will take 1000s of samples. Finally the assay will have to be converted to something that can be done ina clinical lab.

2007

PCA or other Clustering is used for Biomarker discovery

Common Serum Markers for Cancer Diagnosis/prognosis

AFP CEA CA15-3 CA19-9 CA125 PSA PSAf PAP hTG HCGb Ferr NSE B2M A2M

Lung x x x x x x x

Pancreas x x x x x

Kidney x x x x

Breast x x x

Ovarian x x x x x x

Cervical x x

Uterine x x x x

Prostate x x x x x

Liver x x x x x

Gastro x x x

Colon x x x x x

Bladder x

Brain x

Leukemia x x x

Myeloma x

Thyroid x x

Testicular x x x x

Conclusions-Biomarker Discovery is difficult

-biofluids are complex-biofluids have a high dynamic range-biomarkers are usually low abundance

-even taking “proximal” fluids typically does not help

-the is a lot of person to person variability

-Most Biomarkers will never become clinically relevant-statistical standards for diagnostic tools is very high-the more prevalent the disease the “better” the biomarker will perform

-An MS based biomarker assay is unlikely due to the greater analytical performance of antibody based methods.

-For a biomarker workflow to be meaningful it must be quantitative!

Quantitative Approaches

Stable Isotope Labeling methods-adds heavy isotopes to one sample so chemically

identical compounds are mass shifted-added to the peptides/proteins using reactive groups-added to the proteins in vivo using heavy amino acids-can be multiplexed

Label free methods-extracted ion chromatograms-spectral counting

799.0 1441.8 2084.6 2727.4 3370.2 4013.0

Mass (m/z)

3348.0

0

10

20

30

40

50

60

70

80

90

100

% I

nte

ns

ity

4700 Reflector Spec #1 MC[BP = 863.4, 3348]

86

3.4

27

9

10

59

.53

33

17

37

.88

09

12

96

.67

97

96

3.5

27

1

12

10

.68

91

10

21

.55

20

14

25

.62

23

19

01

.88

27

13

53

.60

17

22

42

.16

63

99

5.5

37

5

88

1.2

42

8

10

79

.56

32

12

22

.62

18

17

20

.84

09

15

70

.67

59

112

5.4

92

3

117

4.5

80

4

20

30

.02

36

14

95

.68

21

24

65

.19

26

19

22

.87

02

18

44

.82

45

22

11.0

52

2

25

39

.43

24

1737.49425 1738.56954 1739.64483 1740.72011 1741.79540 1742.87069

Mass (m/z)

1941.2

0

10

20

30

40

50

60

70

80

90

100

% I

nte

ns

ity

4700 Reflector Spec #1 MC[BP = 863.4, 3348]

1738.8808

1739.8810

1740.8808

17

37

.88

09

ISOTOPE-CODED AFFINITY TAG (ICAT):

• Label protein samples with heavy and light reagent• Reagent contains affinity tag and heavy or light isotopes

Chemically reactive group: forms a covalent bond to the protein or peptide

Isotope-labeled linker: heavy or light, depending on which isotope is used

Affinity tag: enables the protein or peptide bearing an ICAT to be isolated by affinity chromatography in a single step

Example of an ICAT Reagent

S OI

NH

**

* *

O

OON

H

O

O

NH

NH

Biotin Affinity tag: Binds tightly to streptavidin-agarose resin

Linker: Heavy version will have deuteriums at *Light version will have hydrogens at *

Reactive group: Thiol-reactive group will bind to Cys

The ICAT Reagent

How ICAT works?

Proteolysis (ie trypsin)

Lyse & Label

MIX

Affinity isolation on streptavidin beads

QuantificationMS

IdentificationMS/MS

100

m/z200 400 600

0

100

550 570 5900

m/z

Light

Heavy

NH2-EACDPLR-COOH

ICAT Quantitation

ICATICATAdvantages vs. DisadvantagesAdvantages vs. Disadvantages

• Estimates relative protein levels between samples with a reasonable level of accuracy (within 10%)

• Can be used on complex mixtures of proteins

• Cys-specific label reduces sample complexity

• Can set up the mass spectrometer to fragment only those peaks with a certain ratio

• Yield and non specificity

• Slight chromatography differences

• Expensive

• Tag fragmentation

• Meaning of relative quantification information

• No presence of cysteine residues or not accessible by ICAT reagent

Amine specific

PRG

Peptide Reactive Group

Charged Neutral loss

Isobaric Tag(Total mass = 145)

Reporter Balance PRG

Reporter(Mass = 114 thru 117)

Balance changes in concert with reporter mass to maintain total mass of 145Neutral loss in MS/MS

Balance(Mass = 31 thru 28)

Isobaric Tag(Total mass = 145)

Gives strong signature ion in MS/MS Gives good b- and y-ion series Maintains charge state Maintains ionization efficiency of peptide

= MS/MS Fragmentation Site

Isobaric TagTotal mass = 145

Reporter Group mass114 –117 (Retains Charge)

Balance GroupMass 31-28 (Neutral loss)

Amine specific peptidereactive group (NHS)

N

N

O

O

N

O

O

Multiplexed protein quantitation in saccharomyces cerevisiae using amine-reactive isobaric tagging reagents Ross, PL., et al, Mol Cell Proteomics 2004 3: 1154-1169.

iTRAQ™ Reagent Design

1347.0 1349.6 1352.2 1354.8 1357.4 1360.0

Mass (m/z)

1352.84

-Reporter-Balance-Peptide INTACT- 4 samples identical m/z

114

115

116

117

111.0 112.8 114.6 116.4 118.2

Mass (m/z)

114

116

115

117

- Reporter ions DIFFERENT

MS

Mix-N H114 31

-N H115 30

-N H116 29

-N H117 28

9.0 292.8 576.6 860.4 1144.2 1428.0

Mass (m/z)

0

10

20

30

40

50

60

70

80

90

100

% In

ten

sity y8

P

A b2

y1

0

q,H

72.1 b4

b1

45.1

b7

LT 112.

1

y3

b1

0

y5

b9

y2 b6

74.1 13

52.8

y6

142.

1

39.0 y4 b8

y9 y1

1

- Peptide fragments EQUAL

MS/MS b

y

b

yb

yb

y

+

+

+

+

-PRG114 31

-PRG115 30

-PRG116 29

-PRG117 28

S1

S2

S3

S4

Par

alle

l D

enat

ure

& D

iges

t

Isobaric Tagging - General Method (4-Plex)

Spotfire K-means Clustering of Protein-level Ratios

G1L S PM G1L S PM G1L S PM

9.0 292.8 576.6 860.4 1144.2 1428.0

Mass (m/z)

8396.7

0

10

20

30

40

50

60

70

80

90

100

% In

ten

sity

y8

P

A b2

y1

0

q,H

72.1 b4

b1

45.1

b7

LT 112.

1

y3

b1

0

y5

b9

y2 b6

74.1 13

52.8

y6

142.

1

39.0 y4 b8

y9 y1

1

869 871 873 875 877 879

Mass (m/z)

y8

757 759 761 763 765 767

Mass (m/z)

b7

*-TPHPALTEAK-*

111.0 112.8 114.6 116.4 118.2 120.0

Mass (m/z)

114.

1

116.

1

115.

1

117.

1

MS/MS Spectra of a Singly-charged Peptide

Summed Ion Intensity (~75,000 Spectra)

Reporter Group Placement: Selection of ‘Quiet Region’

Control

Example: Time course labeling

Test 1 Test 2 Test 3

Trypsin Digestion

MIX

LC MS/MS Analysis

Simplified Workflow: (One extra step)

SCXID and

MS/MS

Label with iTRAQ™ Reagents114 115 116 117

1 hr, RT,Single addition

Single 2D LC analysis for combined samples (4-plex)

Quant

Differential Expression using iTRAQ™ Reagent Approach

OverExpression of Chaperonin 10Non-Cysteine containing Protein

Quantitatio

n

100 200 300 400 500 600 700 800 900m/z, amu

0

5

10

15

20

25

30

35

40

45

50

54

b2

b3

b4 b5

b6 b7

y7 y6

y5

y4

y3

y2 y1

114 115 116 117m/z, amu

Normal

Normal

Cancer

Cancer

*VLQATVVAVGSGS*K* iTRAQ Labeled Residue

ITRAQITRAQAdvantages vs. DisadvantagesAdvantages vs. Disadvantages

• Estimates relative protein levels between samples with a reasonable level of accuracy (> 10%)

• Can be used on complex mixtures of proteins

• Isobaric so the tag is only visible in the MS/MS, keeping the precursor scans as clean as possible.

• The abundance of the peptides sums together. Making analysis of low abundance peptides easier.

• Replicates analyzed on the same LC-MS/MS run, minimizing run to run variability.

• Reagent not completely specific

• Expensive

• Does not work on ion trap instruments

• Reporters tend to dominate the spectra

• You have to fragment everything and sort out the ITRAQ reporters later. The mass spec spends a lot of time analyzing peptides with no quantitative differences.

Stable Isotope Labeling in Animal Culture

SILACSILACAdvantages vs. DisadvantagesAdvantages vs. Disadvantages

• Estimates relative protein levels between samples with a high level of accuracy ( <5%)

• Can be used on complex mixtures of proteins

• Can set up the mass spectrometer to fragment only those peaks with a certain ratio

• Extremely flexible and can be adapted to many systems.

• Labeling may be incomplete

• Urea Cycle may cause incorporation of heavy isotopes into other amino acids

• Expensive

• Works best on high resolution instruments.

Label-Free Quantitation

All approaches so far require purchase of isotopically labeled reagents (can be expensive).

•What if you want to compare large numbers of samples (10+)•What if you can’t afford lots of reagents?

•Peak/Spectral counting

•Peak area comparison (Extracted Ion Chromatograms)

Spectral Counting

•Count the number of peptides identified from a protein in each sample.•Typically do not count repeat identifications of the same peptide

•Not accurate at quantifying magnitude of change, but can be used to determine if there is a difference.

•In general, need a spectral count difference of about 4 peptides in order to be confident of a difference being real.

•Most proteins in complex mixtures are identified by less than 4 peptides.

EIC(Extracted Ion Chromatogram)

•Measure intensity of peak during its elution off HPLC column and into the mass spectrometer.•Measure area of peak in XIC.

•More accurate than selecting peak intensity for one given scan.

emPAI(Exponentially Modified Protein Abundance Index)

emPAI = 10PAI –1

Where PAI = Nobserved / Nobservable

What is an ‘observable’ peptide•Peptides with a precursor mass between 800-2400Da.

•There is a roughly linear relationship between log protein concentration and the ratio of ‘observable’ peptides observed in range of 3-500 fmoles.•If you know how much total protein you analyzed you can derive absolute abundancies.

Ishihama et al. Mol Cell Proteomics (2005) 4 9 1265-1272

MRM (Multiple Reaction Monitoring)

Look for a component of a specific mass that when fragmented forms a fragment of another specific mass.

Transition: precursor m/z 521.7 fragment m/z 757.6

•Very sensitive and specific.

MRM

•Best performed on a triple quadrupole instrument.•Scans are very fast, so can perform multiple transition scans on a chromatographic time-scale.•Requires a lot of optimization:

Verify transitions are reproducible, typically want 2-3 transitions/peptide, 3-4 peptides/protein.

Determine the retention time to maximize the number of peptides

that can be analyzed per run.

It is possible to analyze 100s of transition per hour•MRM coupled to isotopically labeled peptides allows for very high sensitivity and high accuracy analysis and can give absolute quantification.•Once optimized 1000s of samples can be run in a short time frame

•Not for discovery! You must already know what you are looking for, sometimes refered to as targeted proteomics

Issues with MS Quantitation Analysis

•Should you use all data for quantitation?•Minimum peak intensity?

•Peaks near to signal to noise will have much higher variability in quantitation accuracy.•Very intensive peaks may be saturated.

•Proteins identified by a single peptide are probably not accurately quantified?•It is best to ignore sequences with more than one form: PTMs, missed cleavages, etc.•Multiple charge states should be summed.

Results are normally reported with a mean and standard deviation

Conclusions

•There are many different ways to quantitate proteomics data•Quantitative studies need to be approached carefully, because it is easy to make mistakes•No one strategy is best

•MRM is the most sensitive and accurate, but requires the most optimization and cannot be used for discovery.