# Presentation: 627049

## The Cancer Genome Atlas Pilot Projectcancergenome.nih.gov

**The Cancer Genome Atlas Research Network **

## The Cancer Genome Atlas Pilot Projectcancergenome.nih.gov

**Overview and description of project**
**Preliminary analyses of ovarian specimens**

## TCGA Pilot Project:  A Collaboration in Translational Genomics 

**The Cancer Genome Atlas (TCGA)** **is a three-year pilot project of the ****National Cancer Institute and ****the National Human Genome Research Institute ****to increase our understanding of the**  _**genomic changes associated with individual cancers.**__ _

**It is anticipated that TCGA’s integrated database of molecular and clinical information will provide scientists unprecedented opportunities to discover and develop a new generation of targeted diagnostics, therapies, and preventives for cancer. **** **

## TCGA Network

## TCGA Network

**	**_**SITE**_**	****	**_**PI**_
**Cedars-Sinai****	****Beth Karlan**
**Fox Chase****	****Andy Godwin**
**Mayo****	****	****Jeremy Chien / Lynn Hartman**
**MD Anderson ****	****Anil Sood**
**MSKCC ****	****	****Doug Levine**
**UCSF****	****	****Karen Smith-McCune**
**Washington U****	****Janet Rader**
**GOG****	****	****Mike Birrer**

- n=264

- 64 (22%)

- 76 (27%)

- 144 (51%)

## TCGA Pilot Project Network

**TCGA Pilot Project Network**

- Biospecimens-related data storage
- Histopathology confirmation performed
- Biomolecules isolated, QC'ed and distributed

**Human Cancer Biospecimen****Core Resource**

- Data Coordinating Center, DCC
- Analyses of data

**Data Management, Bioinformatics, ****and Computational Analysis**

***Communication***

## TCGA Data Access

**Open-Access Data (Publicly available)**
- Minimal clinical data set (tumor type, age at diagnosis, sex, outcome)
- Gene expression
- Raw and normalized for gene-based oligonucleotide platforms
- DNA methylation
- Chromosomal regions copy number alterations
- Interpretation from SNP-based platforms
- Raw data for oligonucleotide-based platforms
- Trace files with NCBI-required annotation for each amplicon
**Controlled-Access Data (Data Access Request is required)**
- Complete clinical data set
- “Linking” sequence information to identify all sequencing for patients
- Gene expression
- Raw and normalized for human exon-based oligonucleotide platforms
- Chromosomal regions copy number alterations
- Raw data for SNP-based platforms

## The Cancer Genome Atlas Pilot Project

**Overview and description of project**
**Preliminary analyses of ovarian specimens**

## aCGH on Agilent 244K

**Ovarian - TCGA**

**GBM - TCGA**

**Lung - TSP**

## CNV profiles of various cancers

**CNV profiles of various cancers**

- Chin et al. *Genome Bio *2007, TCGA Research Network, *Nature* 2008, Jones et al. *Science *2008

**Chromosomes**

- David Haussler, Ph.D., M.S.
- University of California, Santa Cruz

**Notes:**
All CNV plots have identical y-axis scale (-2.25  2.25).  No gain applied.  

Difference in noise has various possible causes:  purity of sample, array platform characteristics, biological heterogeneity of tumor CNV profile in different tissues...

To generate the PCA plot:
	Sampled genome, averaging CNV data for each study into 45 equally sized bins across each chromosome, ignoring chromosome X and Y.  A total of 990 bins.

	Took the 40 samples that had the most variance across the 990 bins for the 4 tissue types on the CNV profiles (Breast, Ovarian, Pancreatic and TCGA GBM).  A total of ~160 samples. I did this to avoid biasing the PCA towards a dataset with LOTS of samples like Harvard GBM CGH (258 samples).

	Ran PCA on these 160 samples and 990 variables (160 x 990 matrix).  Extracted the first two components.

	Then took ALL samples for the 4 tissue types plus all samples from three additional studies Lung (Weir 2007), GBM (Parsons 2008), and Pancreatic Data (Jones et al 2008).  A total of 918 samples across 5 tissue types. Multiplied the sample data by the PCA loading vectors computed earlier to calculate the first two components for each sample. 

	Plotted these PCA components and colored the dots according to tissue type for what you see above. 

--------------------- 

Breast (Chin 2007) -- 220 samples, VUMC Microarray Core Facility Human 30K 60mer oligochip
Ovarian (TCGA Harvard) -- 38 samples, Agilent 244K CGH
Pancreatic (Jones 2008, Johns Hopkins) -- 24 samples, Illumina 1M SNP array 
Brain (TCGA Harvard) -- 258 samples, Agilent 244K CGH
Brain (Parsons 2008, Johns Hopkins) – 23 samples, Illumina 1M SNP array 
Lung must be TSP lung andeocarcimona, Rick Wilson ,,,, Nature, 2008

## CNV profiles of various cancers

- Chin et al. *Genome Bio *2007, TCGA Research Network, *Nature* 2008, Jones et al. *Science *2008

- David Haussler, Ph.D., M.S.
- University of California, Santa Cruz

**Notes:**
All CNV plots have identical y-axis scale (-2.25  2.25).  No gain applied.  

Difference in noise has various possible causes:  purity of sample, array platform characteristics, biological heterogeneity of tumor CNV profile in different tissues...

To generate the PCA plot:
	Sampled genome, averaging CNV data for each study into 45 equally sized bins across each chromosome, ignoring chromosome X and Y.  A total of 990 bins.

	Took the 40 samples that had the most variance across the 990 bins for the 4 tissue types on the CNV profiles (Breast, Ovarian, Pancreatic and TCGA GBM).  A total of ~160 samples. I did this to avoid biasing the PCA towards a dataset with LOTS of samples like Harvard GBM CGH (258 samples).

	Ran PCA on these 160 samples and 990 variables (160 x 990 matrix).  Extracted the first two components.

	Then took ALL samples for the 4 tissue types plus all samples from three additional studies Lung (Weir 2007), GBM (Parsons 2008), and Pancreatic Data (Jones et al 2008).  A total of 918 samples across 5 tissue types. Multiplied the sample data by the PCA loading vectors computed earlier to calculate the first two components for each sample. 

	Plotted these PCA components and colored the dots according to tissue type for what you see above. 

--------------------- 

Breast (Chin 2007) -- 220 samples, VUMC Microarray Core Facility Human 30K 60mer oligochip
Ovarian (TCGA Harvard) -- 38 samples, Agilent 244K CGH
Pancreatic (Jones 2008, Johns Hopkins) -- 24 samples, Illumina 1M SNP array 
Brain (TCGA Harvard) -- 258 samples, Agilent 244K CGH
Brain (Parsons 2008, Johns Hopkins) – 23 samples, Illumina 1M SNP array 
Lung must be TSP lung andeocarcimona, Rick Wilson ,,,, Nature, 2008

## Cancer Genome Workbench

**17p**

## PTEN

**Holder**

## CCNE1

## Mutated genes in >1 sample

**TP53**

## Mutated genes in >1 sample

**COSMIC**

**Sanger CGP**

## Gene expression

- Normal

- Tumor

## Summary

**A new book on genomics and genetics**
**A parts list to identify new targets and test hypotheses**
**A toolbox to engage the larger ovarian community**
**Move genomics from the lab into the clinic with direct measurable patient benefit**
**Multidimensional perspective of the genome**

- The Cancer Genome Atlas Pilot Projectcancergenome.nih.gov

## Acknowledgements

_**Memorial Sloan-Kettering**_
**Marc Ladanyi**
**Cameron Brennan**
**Nick Socci**
**Agnes Viale**

_***The Cancer Genome Atlas Research Network***_
_***Ovarian Tissue Source Sites***_

- cancergenome.nih.gov