# Presentation: 420783

## caMTBAcaMidThroughputBiomarkerAnalysis Module

*Lyons-Weiler**1**, Shih**2**, Malehorn**3**, Bigbee**3**, Lokshin**3**, Hauskrecht**4** *
*1**PI*
*2**Developer*
*3**Data producers*
*4**Co-I*
*UPCI*

## caBIG
2004

- 2004

- Q5(PCA + LDA)

- Rproteomics

- ProtLIMS

- EDRN
- 2004

- UPCI/JPL

- UPCI

- JPL

- UAB

- FHCR

- VDB

- MDA

- etc.

## Luminex xMAP® Technology - 1

- Luminex xMAP® Technology - 1

- http://www.luminexcorp.com

## Luminex Multianalyte (xMAP®) Serum Profiling for Cancer Screening and Early Detection Anna E. Lokshin, Ph.D.Associate Member, NCI Early Detection Research Network Biomarkers Discovery Laboratory (EDRN/BDL)Director, Luminex FacilityUniversity of Pittsburgh Cancer Institute/Hillman Cancer Center

## Luminex xMAP® Technology - 2

- Luminex xMAP® Technology - 2

- http://www.luminexcorp.com

**Comparison with Conventional ELISA**

- Multiplexed quantitation of up to 100
-     analytes in a single sample
- Flexible - additional analytes are easily added to the panel
- Requires smaller sample volume (25 ml)
- Is more rapid. Equilibrium is reached
-     sooner in the near-liquid phase, so
-     incubation times are reduced
-     significantly, particularly if wash steps are
-     eliminated.
- Comparable sensitivity. Can detect cytokines
-     down to ~1 pg/ml.

## INITIAL SCREENING: LUMINEX ANALYTES

| Panel I
 | Cytokines:
IL-1, IL-2, IL-4, IL-5, IL-6, IL-8, IL-10, IL-12p40, IL-13, IL-15, IL-17, IL-18, TNF, TNFR I, TNFR II, IFN, GM-CSF, G-CSF, 
Chemokines:
RANTES, MIP-1a, MIP-1b, MCP-1, Eotaxin, MIG
Growth and angiogenic factors: 
EGF, EGFR, VEGF, bFGF, HGF, VEGF, NGF  |
| --- | --- |
| Panel II
 | Cancer antigens: CA 125, CA 15-3, CEA, AFP, CA 19-9
Apoptotic proteins: sFas and sFasL
Growth factors/oncogenes: EGFR, Her2/neu
Proteases: PSA free, kallikrein 8, MMP-2,3,7,9, TIMP-1, TIMP-2, 
Other markers: Cyfra 21-1, TPA, M-CSF, HMGB-1, S-100, LDH, CRP |
| Panel III
 | Circulating antibodies against:
Cytokines: IL-6, IL-8; Growth factors/receptors: EGF, EGFR, VEGF, Her2/neu, PDGF, PDGFR; Cancer antigens: CA 125, 15-3, 19-9, 72-4, CEA,MUC-1, PSA; Differentiation molecules: AFP, bhCG; Apoptotic molecules: survivin, Fas, FasL; transglutaminase; Oncogenes: c-myc, N-Ras, K-Ras, Akt1, p53; Cell cycle molecules: cyclin B, cyclin D |

## Luminex xMAP® serum levels of cytokines in early stage ovarian cancer patients and healthy controls

## Heterogeneity of Pathways to Cancer

- Prob(Cancer) ~ w1*m1+ w2*m2+ w3*m3+ wn*mn

## Common machine learning resources (caMTB + caPDAT)

- Ovarian set
- (100%)

- Learning set
- (70%)

- Test validation set (30%)

- Training set (70%)

- Learning validation set (30%)

## Generate 1000 cut point and AB/BA pattern vectors for each individual

- Divide the entire ovarian set into learning set (70%) and test validation set (30%)

- Divide the learning set into training set (70%) and learning validation set (30%)

- Run the same step as the k of m algorithm and get 1000 results, including the SN, SP, and ACE under different number of markers, *k, *for each one.

- Average all results in top *Ne*.
- Record and output the average result.

- 40 times

- Average 40 average learning results.
- Output the average result.

- Sort controls in the training set

- Select the top *Ne* (effective mating population size) – *p* results (based on *min_ACE* ) from the 1000 ones.
- Calculate the average and variance of the top *Ne* results.

- Crossover the cut point and AB/BA pattern vectors from the top *Ne* results and generate new 1000 vectors.
- Mutate these 1000 vectors by percentage of population and markers user specified

- Is the variance of *min_ACE* stable for 1000 time or time up?

- No

- Yes

- Keep *p* (proportion parents that are retained in the next generation ) parents in the last generation into the top *Ne*.

- Even generation?

- Yes

- No

*p* = 0

## Performance  with additive 

*Performance  with additive *
*models ACE ~ 0.45*

*Performance of  k of m classifiers*

## Slide 13

## Data and Metadata Components

- data_input_format
- meta_data_input_format
_**Analysis Components**_
- k_of_m_prediction_model
- cut_point_learner_GA
- binarized_linear_logReg_model
- variance_of_performance
- class_error_checking
- marker_tally
- random_resampling
- random_data_set_test
- pace_analysis
_**Visualization Components**_
- ROC_plot
- PACE_plot
- VOP_plot

- _Proposed Activity__	__               caBIG resource__	__                Action____________________________
- Requirements definition		n/a	                	close collaboration with data producers1
- UML creation			UML Modeler/EA	adopt + use UML modeler/EA
- Source code creation		caCORE SDK	submit UML modeler output
- Registered metadata generation	Semantic Connector	adopt caCORE 3.0; submit data models
- Harmonized
- CDE development/adoption	UML loader	participate in caDSR training
- Data Sharing			Data Sharing &	enable users to share data and metadata
- 				Intellectual Capital WS        at the recommended four levels

## Slide 15

## Fig. 1. The class diagram for the k of m classifier model. 

**Fig. 1. The class diagram for the k of m classifier model.**

## caProteo Use Case 1

- APPLICATION
- protLIMs

- ANALYSIS
- APPLICATION

- Rprot

- Q5

- caMTBA

- CENTRAL DATA
- REPOSITORY

- LOCAL
- DATA
- REPOSITORY

- Firewall

- Methods/
- Results Metadata

- Data/
- MetadataMining

- Data/
- Metadata
- Submission

- Data/
- Metadata

**caProteo Use Case 1**
- Low Granularity,
- Single Site View

- JLW: UPCI

- caPDA

- ?

- ?

## Peer-to-peer data sharing:

- APPLICATION

- ANALYSIS
- APPLICATION

- CENTRAL DATA
- REPOSITORY
- ?

- LOCAL
- DATA
- REPOSITORY

- LIMS
- APPLICATION

- ANALYSIS
- APPLICATION

- LOCAL
- DATA
- REPOSITORY

**Peer-to-peer data sharing:**
- Site A sends Site B a **data key**
- Site B can access a **‘study’**
- ‘Study’ = all de-identified
- data and metadata identified by
- Site A as a coherent whole.

**caProteo Use Case**
- Low Granularity,
- Peer-to-Peer View

**Data key** =
- Unique Identifier
- for all data and meta-data
- Associated with a study

- JLW: UPCI

- Firewall

- Firewall

**A**

**B**

- Data key

## Specific Aims

- Design and engineer an open-source component for caProteo for the analysis of low- to mid-dimensional proteomic data;
- Re-purpose a version of ProtLIMS/caLIMS to accept and deliver mid- to low-throughput data, specifically for LuminexTM and protein antibody arrays (and, incidentally, custom gene expression microarrays);
- Use NCI’s extensive caBIG development resources to make caMTBA and the unnamed, tailored ProtLIMS system a model for the development of additional proteomics modules for the expanding variety of flavors of proteomic data;
-  Continue to participate with the other Proteomics SIG and Data Analysis SIG members to evaluate/develop data and meta-data standards.