# Presentation: 556750

## Update on Sequencing Analysis 

**Gaddy Getz**
**Representing the Broad Institute Genome Sequencing Center **
**TCGA meeting**
**July 23****rd****, 2008 **

## Main points

- An automatic pipeline for finding cancer-associated genes
- Power analysis
- A peek at phase II genes

**Notes:**
I would like to briefly touch 3 points:
The first one: Now that we’re close to submitting the first TCGA paper. We’re faced with the challenge to create a fully automated pipeline that identifies cancer associated genes and to make it available to the research community. This pipeline should reproduce the results from the paper.
The second point is about sample size and how it affects our power to detect mutation and future experimental design.
I will finish with interesting findings from a peek at phase || genes

## A fully automated sequencing analysis pipeline

- Baylor

- WashU

- Broad

-  Non-silent: verified/validation
-                    somatic mutation
- Silent: candidates after filtering            repetitive sites (artifacts)

- Identifying hypermutated samples

- Baylor

- WashU

- Broad

**MAF**

**verbose coverage**

- Database of
- sequenced
- transcripts

- Input Files

- List of samples toanalyze

- Next:
-  Calculation of background mutation rate
-  Significance analysis

**Notes:**
We are building a fully automated sequencing analysis pipeline (that we still need to name).
The inputs to the pipeline are:
-- MAF files from all centers and matched verbose coverage files that reflects the sequencing coverage at the time that the MAF files were generated
-- A database of sequenced transcripts with all their variants and indication of which of the variants are the most relevant.
-- A list of samples to analyze

We combine all the MAF files, verbose coverage files and transcript database to create a unified mutation table (for the list of analyzed samples) of silent and non-silent mutations and a coverage table that specifies the number of covered bases for each gene and sample. We also break the coverage to bases that are at risk for silent and non-silent mutations (and additional sequencing categories).

We then calculate for each sample its silent and non-silent mutation rates and identify outlier samples (hypermutated samples).
Based on the GBM phase I genes, 7 samples were identified as hypermutated – all are from previously treated patients.
We can then update the list of samples to remove the hypermutated ones (or as we did in the paper, remove all treated samples).

## A fully automated sequencing analysis pipeline

-  Estimate overall background mutation rate

- overall background mutation rate (x10-6)

- High

- Mid

- Low

- Calculate relative rates for mutation categories

- score

- p-value

- q-value

-    q<0.1
- significantly
- associated
- w/ cancer

- Soon to be a GenePattern pipeline

- 3.5

- 4.5

- 2.5

- CpG

- Other
- C+G

- A+T

- Indel

**Notes:**
Next, we use the SILENT mutations to estimate the background mutation rate. This estimation results with a distribution that reflects our uncertainty in the BMR. In the paper, we obtain a mid value of around 3.5x10-6 with a 95% confidence interval between 2.5 and 4.5. showing that we’re still not that accurate in determining the BMR with almost a 2 fold difference between the high and low estimates.

Since different bases are known to have different mutation rates such as C’s or G’s in CpGs we also calculate the relative rate for the different categories.

We then use these rates to score each gene according to how unlikely it is to obtain the observed mutations across all samples by chance.
Then we calculate a p-value for this score and correct for multiple hypothesis to obtain a q-value.

The output of the pipeline is a list of genes with q<0.1. These are designated as significantly associated with cancer.
In the paper, we identified 8 significant genes.

## Power to detect mutations (effect of sample size)

- Power analysis   500 samples we will have >90% power to detect 3% mutations

- BMR=3.48x10-6
- # of genes = 1,200
- Length = 1500 bp
- Missing data rate = 10%

**Notes:**
Now that we have a better estimate of the BMR for GBMs and the purity of the GBM samples (which affects the missing data rates)
We can more accurately calculate the power to detect mutations that occur at various rates in GBMs.
For example. PIK3R1 and PIKCA were the least frequent mutations at about 7-8%.
Here we can see that we had ~80% power to detect them.

Now that we’ve actually have data for 84 non-hypermutated samples
We can simulate different sample sizes by taking many random subsets of a given size and checking how often a gene would be called significant.
With 24 samples, we had less than 50% chance of calling PIK3CA significant and less than 60% for PIK3R1
With 12 samples, most likely we could have detected only p53  and PTEN.

scenarios 

## A peek at Phase II genes

-  Running the pipeline on Phase I (all centers) + Phase II (Broad only):       2 new genes make significance -- PIK3C2G, GSTM5

- new

- new

- PIK3C2G:  Another member of the PI3K family including PIK3CA and PIK3R1.
-                   Not previously reported in cancer - no mutations in COSMIC.
- 	    Mutually exclusive with PIK3CA and PIK3R1
- GSTM5:    Glutathione S-transferase
-                  Involved in detoxification of carcinogens and drugs
-                  Polymorphisms known to influence drug metabolism
-                  Mutations piling up in same region—artifact?

**Notes:**
Running the automated pipeline on Phase I from all centers and Phase II genes from Broad only
2 additional genes made significance.

PIK3C2G and GSTM5
PIK3C2G is another member the PI3K family which includes PIK3CA and PIK3R1

GSTM5 is a glutathione S-trasferase that is involved in detoxification of carcinogens and drugs.
Polymorphisms in it are known to influence drug metabolism.  However the mutations (there are additional unvalidated candidates) are piling up in the same region which may represent an artifact.

## Acknowledgements

- _Analysis Team_
- Mike Lawrence
- Wendy Winckler
- Michael O’Kelly
- Gaddy Getz
**Matthew Meyerson**
**Eric Lander**

- _Broad GSC_
- Carrie Sougnez
- Kristian Cibulskis
- Tim Fennell
- Huy Nguyen
- Stacey Gabriel
**Broad Sequencing Platform**
**Other GSCs**
**All TCGA **

**Notes:**
I would like to thank the Broad GSC and all other GSCs
And in particular the analysis team 
And Matthew and Eric of course.

## What about a two-tiered approach?

-  Two-tiered approach:
- Discovery set: 72 samples, 20,000 genes
- 6266 genes are expected to have 1 mutation in 72 samples (BMR=3.48x10-6)
- Validation set: 428 samples, 6266 genes
- 41% of total cost compared to single-tier design.
- Minimal drop in power to detect mutations that occur in3% of the tumors: power of 81% (single-tier)  75% (two-tier)

**Notes:**
Now we turn to the question “what should we do next?”
Should we continue to sequence the 1200 phase I and II genes in additional samples, or go deeper in say the 72 non-treated samples and use them as a discovery set and then sequence genes that pass a lenient threshold in the additional 428 samples.

For example, if we sequence all 20,000 genes and have a very lenient threshold of at least one mutation in the discovery set.
We could save 60% of the cost with only a slight drop in power to detect mutations that occur in 3% of samples.