# Presentation: 432444

## the biomarker companyTM

**the biomarker company****TM**

**Howard Schulman, Ph.D.**
- Vice President Research and Development
- 650-230-1577
- _[hschulman@surromed.com](mailto:hschulman@surromed.com)_
- www.surromed.com

## Presentation Overview

**Biomarker Discovery – By Opportunity or by Design? **
**Technology Platform Capabilities**
**Immunophenotyping (hypothesis-directed)**
**Microvolume Laser Scanning Cytometry (& Immunoassay)**
**Differential proteomics & metabolomics (discovery-directed)**
**State-of-the-art mass spectrometry capability**
**Informatics – Overview**
**Applications of Biomarker/Target Discovery & Validation Platform**
**Biomarker Discovery Laboratory – It’s the process**
**Quality System Approach**
**Reproducibility and sensitivity**

## Biological Marker DiscoveryA Window into Systems Biology

**Simple molecules**

**   ****CD4+ **
**T-cells**

**Insulin**

**PSA**

**HbA****1****C**

**Glucose**

**Cholesterol**

- steroids

- lipids

- peptides             proteins

- mRNA/genes

- cells

- patients

**Clinical **
**Phenotype**

**Complex **
**Systems**

**CRP**

**Homocysteine**

**TG**

**Her2 **
**Cells**

**Notes:**
Biomarkers range in chemical structure and size from metabolite to peptide, protein and blood cell and thus biomarker discovery requires comprehensive phenotyping with a range of technologies

## Comprehensive Phenotyping & Biological Marker Discovery:The SurroMed Process

**Comprehensive Phenotyping & Biological Marker Discovery:****The SurroMed Process**

**Notes:**
The process of  biomarker discovery requires attention to details from sample acquisition to data processing. Advanced bioanalysis needs to be performed by a quality system approach to assure consistency that deliver on accuracy and reproducibility needed for detection of small differences between samples

## SurroScanTM Cytometry System:

**Profile >200 Cell Populations from ********1 ml Whole Blood**

**• ****SurroMed’s proprietary microvolume laser cytometry platform**
**• ****Combinations of fluorophore-tagged antibodies to cell surface markers**
**•**** ****64 3-color assays / 10 ********L each > 200 populations**
**• ****Absolute cell counts, antigen density**
**•**** ****Instrument control, data processing, uploading completely automated**
**Operating in SurroMed’s Biomarker****Discovery Lab and at two collaborator sites**
**• ****> 80,000 clinical study assays performed (16 instruments)**

- Capillary array cartridge

**Notes:**
Cellular phenotyping is done by a novel cytometry platform. Each assay consists of 10 ul blood mixed with 3 fluorophore-tagged antibodies to cell surface markers. There is no need for removal of unbound antibodies. The entire sample is loaded into one capillary of a 32 capillary cartridge; typically 2 such cartridges (64 assays) are used in a study. The laser in the SurroScanTM rasters across each capillary to generate quantitative data on the absolute cell count and antigen density. In this way data is generated on 200 blood cell types from 1 ml of whole blood at high throughput (appx. 2.5 hr per 64 assays).

## Multi-dimensional Proteomic and Metabolomic Analysis

**Multi-dimensional Proteomic and Metabolomic Analysis**

**LOW MW**

**Tryptic Digestion**

**HPLC**
**Nano ESI-MS**

**DeepLook****TM**** MS**
**2D LC**
**Nano ESI-MS**

**High-throughput**

**GC-MS**

**HPLC**
** ****ESI-MS**

**SERUM**

**Differential quantification and molecular identification (MS/MS)**

**Remove HSA & IgG**

**HIGH MW**

** ****Tracking >4000 molecular species         ****>30,000 molec. species**

- Metabolome

- Proteome

**High-throughput**

**Notes:**
Proteomic and metabolomic analysis is performed on a variety of biological fluids and tissues. Separation and analysis of serum is shown.
An initial separation based on size results in a metabolome (<5000 daltons) and proteome (>5000 daltons). Three basic analysis are performed on each sample, taking approximately 2 hr for each high-throughput analysis: The metabolome is analyzed by gas chromatography-mass spec (derivatized volatile matabolites) and HPLC-MS (endogenous peptides and non-volatile metabolites); the proteome is depleted of the two most abundant proteins (HAS and IgG) by a single affinity step, digested into tryptic peptides and analyzed by HPLC-MS. Collectively, the three analyses enable us to track over 4000 molecular species (metabolites and peptides). Those species demonstrating a differential expression between sample groups being compared are identified by tandem MS (MS/MS) so that peptides and their originating proteins can be identified by relatively easily with the use of protein sequence databases.

One application of DeepLooKTM is an initial separation into several (5-10) fractions by strong cation exchange followed by separation of each fraction by LC-MS. This 2D LC/LC-MS uses appx. 10 types as much protein (e.g. 200 ug) but provides quantitative information of >30,000 peptides.

## High Throughput LC-MS of Human Serum

- 44-45min

- only 20 μg of digested protein

**> 2000 molecular species**
**> 600 proteins identified**
**~ 2 hr total cycle time**

**LC**

**MS**

**Notes:**
A typical separation of serum involves only 20 ug of digested protein loaded onto the LC. Effluent from the gradient on the LC is electrosprayed online into the MS and a spectrum is taken every second or so of the 90 min LC run. Bottom panel shows a composite of approximately 60 spectra taken between minute 44 and 45 of the LC run. The high accuracy MS can readily resolve the high density of peptides.

## A DeepLookTM at the Proteome: Enhanced Profiling

**+**

**+**

**Mass Spec**

**Specificity of Capture Chemistry **

**High**

**Low**

**Charge-based**
**Hydrophobic/hydrophilic**
**Acidic/basic**

**MAbs**
**Ligands**

**Class specific**
**[e.g. IMAC (P-proteome), **
**Lectins, ATP,Calmodulin, etc.] **

**Affinity**
**Chemistry**

**Notes:**
In addition to 1D LC-MS, we also examine subproteomes by an initial affinity capture of protein isoforms or protein classes or biophysical classes (e.g. anionic) prior to LC-MS.

## The Informatics of Biomarker Discovery

**Database**

**B**
**I**
**O**
**M**
**A**
**R**
**K**
**E**
**R**
**S**

**Statistics Data Mining**
**Visualization**

**Stored Data**

**Cytometry**

**Clinical**

**Mass Spectrometry**
**Proteomics / Metabolomics**

**ELISA**

**IP**

**LIMS**

**Notes:**
Each screen shot represent a piece of software used to process the data
Intranet enabled tools
A variety of statistical and data mining software are used to characterize statistically significant differences between normal and cancer samples and to produce signatures of cancer which can consist of just mass spec peaks (which are then readily identified by tantem MS) or a combination of data from different platforms.

##  Descriptive and Predictive Data Mining

***Describe data statistically***
-  Means; standard deviations
-  Paired and unpaired t-tests
-  GLM & linear mixed models
***Reduce dimensionality***
-  Principle component analysis
-  Clustering (hierarchical, K-means, ...)

***Supervised learning***
** ***Classification and regression, linear and non-linear*
- Neural networks
- Discriminant analysis
- SVMs

**Access w/ browser,**
**     ****local intranet**

**All classes **
**of data**

***Clinical Data Analysis***
- Text mining CRFs
- Classification trees

**Notes:**
Intranet based tools for statistical analysis and data mining.

## Cytometry
Mass spectrometry

**Cytometry**
**Mass spectrometry**

**Rheumatoid Arthritis Studies**

**January 23, 2002 To SurroMed BOD, Confidential**

**Notes:**
An example from a rheumatoid arthritis study for which we recently performed a pilot analysis of the first set of samples.

## Proteome
2452 Components

- 2452 Components

|  | LC-MS |
| --- | --- |
|  | Obs. |
| P <0.0001 | 17 [<1] |
| P <0.001 | 68 [2] |
| P <0.01 | 220 [25] |
| P <0.05 | 508 [123] |

| LC-MS | GC-MS |
| --- | --- |
| Obs. | Obs. |
| 0 | 0 |
| 4 [1] | 0 [<1] |

**Metabolome**
- 1382 LC-MS Components
-  320 GC-MS Components

- [  ] Expected by chance

- 4154 molecular components  tracked in 1D LC/MS
- Median within group CV ~ 30%

**Summary Mass Spec Statistics: RA Study**

- DeepLookTM MS analysis ~ 35,000 components tracked

**Notes:**
In an actual study with human serum we were able to differentially quantify more than 4000 molecular components and identify significant differences in a subset of these. For example, at p<0.001 we were able to identify 68 peptides that were differentially expressed. Only 2 peptides would be expected to change based on chance alone.
Also impressive is that our 2D LC/LC-MS analysis of these samples was able to compare the differential level of 35,000 peptides in these samples, derived from several thousand proteins.

## Some Changes in the Proteome in Rheumatoid Arthritis

**Expected**

**Inflammatory**
** ****markers**
**Previously**
**associated**
**with RA**

**Not expected**

**&****DOWN in RA  ****	****       ****%****UP in RA**

*********	****p < 0.0001**
**** ****	****p <0.001**
*** ****	****p < 0.01**

**Notes:**
Upon identification of the differentially expressed peptides we found that previously identified changes in named proteins (both increases and decreases) were correctly identified by our discovery based proteomic approach. In addition, numerous other proteins identified by this approach have not previously been found to be associated with RA or inflammation.

## Proof of Principle Experiments: ErbB2 is one of the 

- ErbB2 peptide GLQSLPTHDPSPLQR

- FACS Analysis

- LC-MS Analysis

**ErbB2**

**ErbB2**

**Differential Profiling of Membranes Identifies/Validates Cancer Targets**

- Proof of Principle Experiments: ErbB2 is one of the
- membrane proteins that is differentially expressed

**Notes:**
Membrane proteomics was used to assess prospective candidate cancer targets for a pharma partner. In a proof of principle experiment, we were able to use membrane protomics to show that ErbB2 is overexpressed in SKOV-3 cells.

## Proteomic Analysis of a Prospective Cancer Target

*New Target* in Membrane Fraction

**Notes:**
Membrane proteomics was used to validate a prospective cancer target. It was initially identified by genomic approaches that could not assure that the protein was actually overexpressed nor could determine whether it was expressed on the membrane. The proteomic analysis on right indicate that it is overexpressed in membranes. Antibody was generated and indeed verified the proteomic finding.

## Application: Improving and Understanding Preclinical Models (for Pharma)

**Application: Improving and Understanding Preclinical Models (for Pharma)**

- MLS Cytometry
- Immunoassays
- Proteomics
- Metabolomics

- Mouse Model A:

**A   F     O         **

- Mouse Model B:

**A D F   J O P T   X**

*In vitro* Model 1:

**A D   G J O   T**

- Human Disease:

**A D F G J O P R T X**

**A     G   O       X**

*In vitro* Model 2:

**Notes:**
We generally do not know how well the bioanalytical phenotype of an animal or in vitro model matches the human disease.
Comprehensive phenotyping can be used to assess which biomolecules in the human disease most closely match which model.
One application of this is to examine a drug response in an animal model in comparison to the human and an example of the first stage of this analysis is shown in the pharmacodynamic profiling in a rat model on the next slide.

## Two Examples:  Pharmacodynamic Profiling for PharmaMany Molecules Are Differentially Expressed in Rat

| Drug vs Placebo (10 each; <0.5 ml sample) |  |  |
| --- | --- | --- |
| P-value | Metabolome 1449 molecules | Proteome 3700 molecules |
| <0.00001 | 1 | 47 (1) |
| <0.001 | 42 (2) | 460  (4) |

**A**

| Drug vs Placebo (10 each; <0.5 ml sample) |  |  |
| --- | --- | --- |
| P-value | Metabolome 1060 molecules | Proteome 2919 molecules |
| <0.00001 | 1 | 43 (1) |
| <0.001 | 15 (1) | 121  (3) |

- (  ) Expected by chance

**B**

- Study-wide CVs (30,000 peaks) = 26%

**Miniaturization enables comprehensive analysis from small sample volumes **

**Notes:**
Another example of the power of discovery mode LC-MS to track thousands of metabolomic and proteomic components and identify differentially components that are differentially expressed in response to drug application.

## The Phenotyping Revolution

**The Phenotyping Revolution**
**Hypothesis- and Discovery-directed Approaches**