# Presentation: 508801

## NCI ICBPIntegrative Cancer Biology Program -- MIT Grant, R Hynes PI 

-   **DNA damage signaling network (Michael Yaffe [Biol/BE], Leona Samson [BE/Biol], DL [BE/Biol/ChE])**
**  ****ErbB signaling network (Tyler Jacks [Biol], Peter Sorger [Biol/BE], Forest White [BE], DL [BE/Biol/ChE])**
**  ****Cell migration and invasion processes (Richard Hynes [Biol], Frank Gertler [Biol], DL [BE/Biol/ChE])**
**  ****Computational modeling (Bruce Tidor [BE/EECS], Jacob White [EECS])**
**  ****RNAi manipulation (Phil Sharp [Biol], Jianzhu Chen [Biol])**

## Integrative Systems Modeling

***Component***
***Properties:***
**molecular levels / states / locations / interactions / activities...  ****(arising from sequence & structure)**

***System***
***Operation***
***(‘phenotype’):***
**cell,**
**tissue,**
**organism,**
**...**

***Integrative Systems Modeling***

_**Objective:**_** **
**Predictive understanding**
**for effect of**
**component properties**
**-- in quantitative,**
**dynamic, multi-variate manner**

## Spectrum of Computational Modeling Methods

- differential equations

- clustering, PCA/ICA...

- Bayesian networks

**SPECIFIED**

**ABSTRACTED**

- Markov chains

- decision trees

***relationships***

***mechanisms***

***influences***
***and logic***

***(including***
***molecular***
***structure-based***
***computation)***

_**Appropriate approach depends on question and data**_

## Modeling Chain Needed for Prediction 

**Extracellular Cues**
**(*****e.g.*****, Ligand/Receptor**
**Interactions)**

**Biochemical**
**Signals**

**Biophysical**
**Processes**
**and Cell Behaviors**

***Modeling Chain Needed for Prediction ***
***of Effects of Gene Mutations and Drug Effects ***
***on Tumor Cells***

**Models for molecular reaction kinetics & transport processes (‘cue-signal’ relationships)**

**Models for cell-level functional behavior and/or underlying mechanisms**

**Models for biochemical regulation of biophysical processes and/or cell behavior (‘signal-response’ relationships)**

## Cell Migration

***Cell Migration***
***-- invasion,***
***metastasis,***
***angiogenesis***

## Cell Migration

**primary    **
**     ****tumor**

**matrix**

**ectopic tumor**
**growth**

***Cell Migration***
***In Tumor Invasion***

## Cell Migration “Cycle” of Biophysical Processes

## Regulation of Biophysical Processes by Biochemical Signals

***Regulation of Biophysical Processes by Biochemical Signals***

## Tumor Cell Migration in 3D Environments-- DU145 prostate tumor cells (single-cell tracking);Matrigel / Collagen-I / Fibronectin

***Tumor Cell Migration in 3D Environments******-- DU145 prostate tumor cells (single-cell tracking);******Matrigel / Collagen-I / Fibronectin***

## Biphasic Relationship is Found for 3D Tumor Cell Migration Speed vs Matrix Density-- Integrin-mediated adhesion? Matrix sterics and mechanics? 

***Biphasic Relationship is Found for ******3D Tumor Cell Migration Speed vs Matrix Density******-- Integrin-mediated adhesion? ******Matrix sterics and mechanics?****** ***

## “All of the Above” -- Complex Landscape of Cell/Matrix Adhesion and Matrix Steric & Mechanical Properties Governs 3D Migration

**Adhesion-inhibitory antibody decreases migration in some situations but increases it in others...**

## 3D Cell Migration Computational Model,Single-Cell Biophysical Simulation (Biophys. J. [2005])

## Model - Experiment Comparison

- Model Predictions

- Experimental Data

## Then, how are these biophysical motility processes and resulting migration 

***Then, how are these biophysical motility processes and resulting migration ***
***controlled by biochemical signals?***
***-- for prospective drug effects***

## *

***Example: ***
***How do signals downstream of EGF and Fibronectin integrate to influence cell migration speed?***

- 

*****

*****

*****

*****

*****

## Decision Tree Model for 5-Minute Signaling Data

**IF p-ERK is low: cell migration is slow; ~90% of the slowly-migrating cohort observations can be explained with this rule.**
**IF p-ERK is high AND p-MLC is intermediate: cell migration is fast; ~60% of the swiftly-moving cohort observations can be explained with this rule.**
**Another ~10% of the swiftly-moving cohort observations can be explained with the rule IF p-ERK is high AND p-MLC is low AND p-PLC******** is high AND p-PKC******** is high.**
***“******Network Logic” indicates predominant combined roles of MLC and ERK in ***_***regulating the critical balance between cell contractile force and cell/substratum adhesion for governing migration speed***_***.***

**(70% overall accuracy)**

- (_Bioinformatics_ [2005])

## Moving Forward -- “High-Throughput” Protein-Centric Quantitative Measurement -- Protein Levels, States, Activities, Locations, Interactions...

***WBs, FACS,***
***mass spectrometry***

***Multi-well kinase activity assays***

***Protein microarrays***

***Moving Forward -- “High-Throughput” Protein-Centric Quantitative Measurement -- Protein Levels, States, Activities, Locations, Interactions...***

***Live-cell***
***imaging***

## Example Problem:HER2 Over-Expression (e.g., breast cancer)

**hMEC Parental**
**(High EGFR)**

**hMEC-24H**
**(High EGFR & ****HER2****)**

**EGF**

- Peptide1: 0,5,10,30
- Peptide2: 0,5,10,30
- Peptide3: 0,5,10,30
- Peptide4: 0,5,10,30
- ......

***Example Problem:******HER2 Over-Expression (e.g., breast cancer)***

**Notes:**
We run the 4 point time courses in duplicate under the 4 conditions.
We took 5 the 5 min time point from each condition and made a 4 conditons 5min run in duplicate.
The results of this run allow us to compare the siganl intensity of a given peptide at 5 minutes for the 4 conditions and thus act us a proxy to compare the time courses of a given peptide acroos the 4 conditions.

## HER2 Over-Expression Effect on EGF-Induced Signaling - A

- Y974

- Y1148

- Y1068

- Y1173

- Y704

- S302/Y313

- PKC

- Y313

- Y204

- ERK2

- T202 /Y204

- Y187

- ERK1

- T185 /Y187

- PLC-

- Y771

- Y1253

- Y1045

- Y659

- GAB1

- Y259

-  Y580

- P110

- P85

- Y607

- Y464

- Y467

- Y783

- SRC

- Y418

- Y1005

- Y1248

- Y877

- Y1127/Y1139

***HER2 Over-Expression Effect on EGF-Induced Signaling - A***

## HER2 Over-Expression Effect on EGF-Induced Signaling - B

- Catenin-

- Y20

- Y22

- FAK

- Y576

- Y317

- PXN

- Y118

- S84/Y88

- Y88

- Y96

- Y213

-  Y217

- Y221

- Y228

- Y296

- Y291

- Y280

- Y334

- Catenin-

- Y234

- Y249

- Y387

- Y327

- p130

- SHC

- Y317

- Y239

- Y239/Y240

- Y204

- ERK2

- T202 /Y204

- Y19/Y22

- Y22

- Y6/Y14

- Y14

- Y132

- Y251

- Y207

- Y221

- Y781 (1)

- Y1189 (4)

- Y1207 (4)

- Y1680

***HER2 Over-Expression Effect on EGF-Induced Signaling - B***

## Neuronal Self-Organizing Maps (4 cell states, 4 time-points)-- elucidates consistent dynamic modules

***Neuronal Self-Organizing Maps (4 cell states, 4 time-points)******-- elucidates consistent dynamic modules***

**EGFR-EGFR**

**HRG**

**EGF & HRG**

**HER2-EGFR**

## Principal Component / Partial Least-Square Regression-- elucidates key signal combinations governing responses

**EGF**

**Proliferation or Migration**

**HRG**

**Signal**
**#1**

**#1**

**#2**

**#3**

**#4**

**#5**

***Principal Component / Partial Least-Square Regression******-- elucidates key signal combinations governing responses***

## Determination of Key Signals Governing Enhanced Proliferation and Migration Arising from HER2 Overexpression-- Prospective Prediction of Drug Effects  

**migration**

**proliferation**

***Determination of Key Signals Governing Enhanced Proliferation and Migration Arising from HER2 Overexpression******-- Prospective Prediction of Drug Effects******  ***

**PLSR ‘Loadings’ Plot**

## Modeling Chain Underway for Prediction 

**Extracellular Cues**
**(*****e.g.*****, Ligand/Receptor**
**Interactions)**

**Biochemical**
**Signals**

**Biophysical**
**Processes**
**and Cell Behaviors**

***Modeling Chain Underway for Prediction ***
***of Effects of Gene Mutations and Drug Effects ***
***on Tumor Cells***

**Models for molecular reaction kinetics & transport processes (‘cue-signal’ relationships)**

**Models for cell-level functional behavior and/or underlying mechanisms**

**Models for biochemical regulation of biophysical processes and/or cell behavior (‘signal-response’ relationships)**