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<body>
<h1>Somatic Mutations</h1>
<p>The goal of this notebook is to introduce you to the Somatic Mutations BigQuery table.
This table is based on the open-access somatic mutation calls available in MAF files at the DCC. In addition to uploading all current MAF files from the DCC, the mutations were also annotated using Oncotator. A subset of the columns in the underlying MAF files and a subset of the Oncotator outputs were then assembled in this table.
In addition, the ETL process includes several data-cleaning steps because many tumor types actually have multiple current MAF files and therefore potentially duplicate mutation calls. In some cases, a tumor sample may have had mutations called relative to both a blood-normal and an adjacent-tissue sample, and in other cases MAF files may contain mutations called on more than one aliquot from the same sample. Every effort was made to include all of the available data at the DCC while avoiding having multiple rows in the mutation table describing the same somatic mutation. Note, however, that if the same mutation was called by multiple centers and appeared in different MAF files, it may be described on muliple rows (as you will see later in this notebook). Furthermore, in some cases, the underlying MAF file may have been based on a multi-center mutationa-calling exercise, in which case you may see a list of centers in the Center field, eg “bcgsc.ca;broad.mit.edu;hgsc.bcm.edu;mdanderson.org;ucsc.edu”.
In conclusion, if you are counting up the number of mutations observed in a sample or a patient or a tumor-type, be sure to include the necessary GROUP BY clause(s) in order to avoid double-counting!</p>
<p>As usual, in order to work with BigQuery, you need to import the bigquery module (gcp.bigquery) and you need to know the name(s) of the table(s) you are going to be working with:</p>
<pre><code class="r">require(bigrquery) || install.packages("bigrquery")
</code></pre>
<pre><code>## [1] TRUE
</code></pre>
<pre><code class="r">require(ggplot2) || install.packages("ggplot2")
</code></pre>
<pre><code>## [1] TRUE
</code></pre>
<pre><code class="r">library(ISBCGCExamples)
mutTable <- "[isb-cgc:tcga_201510_alpha.Somatic_Mutation_calls]"
</code></pre>
<p>Let's start by taking a look at the table schema:</p>
<pre><code class="r">querySql <- paste("SELECT * FROM ",mutTable," limit 1", sep="")
result <- query_exec(querySql, project=project)
</code></pre>
<pre><code>## Auto-refreshing stale OAuth token.
</code></pre>
<pre><code class="r">data.frame(Columns=colnames(result))
</code></pre>
<pre><code>## Columns
## 1 ParticipantBarcode
## 2 Tumor_SampleBarcode
## 3 Tumor_AliquotBarcode
## 4 Tumor_SampleTypeLetterCode
## 5 Normal_SampleBarcode
## 6 Normal_AliquotBarcode
## 7 Normal_SampleTypeLetterCode
## 8 Study
## 9 Annotation_Transcript
## 10 CCLE_ONCOMAP_Total_Mutations_In_Gene
## 11 COSMIC_Total_Alterations_In_Gene
## 12 Center
## 13 Chromosome
## 14 DNARepairGenes_Role
## 15 DbSNP_RS
## 16 DbSNP_Val_Status
## 17 DrugBank
## 18 End_Position
## 19 Entrez_Gene_Id
## 20 GC_Content
## 21 GENCODE_Transcript_Name
## 22 GENCODE_Transcript_Status
## 23 GENCODE_Transcript_Type
## 24 GO_Biological_Process
## 25 GO_Cellular_Component
## 26 GO_Molecular_Function
## 27 Gene_Type
## 28 Genome_Change
## 29 HGNC_Accession_Numbers
## 30 HGNC_CCDS_IDs
## 31 HGNC_Ensembl_ID_Supplied_By_Ensembl
## 32 HGNC_HGNC_ID
## 33 HGNC_Locus_Group
## 34 HGNC_Locus_Type
## 35 HGNC_OMIM_ID_Supplied_By_NCBI
## 36 HGNC_RefSeq_Supplied_By_NCBI
## 37 HGNC_UCSC_ID_Supplied_By_UCSC
## 38 HGNC_UniProt_ID_Supplied_By_UniProt
## 39 Hugo_Symbol
## 40 Mutation_Status
## 41 NCBI_Build
## 42 Normal_Seq_Allele1
## 43 Normal_Seq_Allele2
## 44 Normal_Validation_Allele1
## 45 Normal_Validation_Allele2
## 46 OREGANNO_ID
## 47 Protein_Change
## 48 Ref_Context
## 49 Reference_Allele
## 50 Refseq_Prot_Id
## 51 Secondary_Variant_Classification
## 52 Sequence_Source
## 53 Sequencer
## 54 Start_Position
## 55 SwissProt_Acc_Id
## 56 SwissProt_Entry_Id
## 57 Transcript_Exon
## 58 Transcript_Position
## 59 Transcript_Strand
## 60 Tumor_Seq_Allele1
## 61 Tumor_Seq_Allele2
## 62 Tumor_Validation_Allele1
## 63 Tumor_Validation_Allele2
## 64 UniProt_AApos
## 65 UniProt_Region
## 66 Validation_Method
## 67 Variant_Classification
## 68 Variant_Type
## 69 cDNA_Change
</code></pre>
<p>That's a lot of fields! Let's dig in a bit further to see what is included in this table. For example let's count up the number of unique patients, tumor-samples, and normal-samples based on barcode identifiers:</p>
<pre><code class="r">for (x in c("ParticipantBarcode", "Tumor_SampleBarcode", "Normal_SampleBarcode")) {
querySql <- paste("SELECT COUNT(DISTINCT(",x,"), 25000) AS n ",
"FROM ",mutTable)
result <- query_exec(querySql, project=project)
cat(x, ": ", result[[1]], "\n")
}
</code></pre>
<pre><code>## ParticipantBarcode : 8373
## Tumor_SampleBarcode : 8435
## Normal_SampleBarcode : 8754
</code></pre>
<p>Now let's look at a few key fields and find the top-5 most frequent values in each field:</p>
<pre><code class="r">buildQuery <- function(x) {
paste("
SELECT ",x,", COUNT(*) AS n
FROM [isb-cgc:tcga_201510_alpha.Somatic_Mutation_calls]
WHERE ( ",x," IS NOT NULL )
GROUP BY ",x,"
ORDER BY n DESC
LIMIT 5", sep="")
}
query_exec(buildQuery("Hugo_Symbol"), project=project)
</code></pre>
<pre><code>## Hugo_Symbol n
## 1 Unknown 66354
## 2 TTN 24171
## 3 MUC16 15058
## 4 FLG 13068
## 5 OBSCN 7986
</code></pre>
<pre><code class="r">query_exec(buildQuery("Center"), project=project)
</code></pre>
<pre><code>## Center n
## 1 broad.mit.edu 3998891
## 2 bcgsc.ca 646933
## 3 hgsc.bcm.edu 485994
## 4 genome.wustl.edu 374934
## 5 ucsc.edu 190786
</code></pre>
<pre><code class="r">query_exec(buildQuery("Mutation_Status"), project=project)
</code></pre>
<pre><code>## Mutation_Status n
## 1 Somatic 5813176
</code></pre>
<pre><code class="r">query_exec(buildQuery("Protein_Change"), project=project)
</code></pre>
<pre><code>## Protein_Change n
## 1 p.M1I 2352
## 2 p.V600E 1243
## 3 p.R132H 1042
## 4 p.E545K 624
## 5 p.M1V 553
</code></pre>
<p>Everyone probably recognizes the V600E mutation in the previous result, so let's use that well-known BRAF mutation as a way to explore what other information is available in this table.</p>
<pre><code class="r">querySql <- "
SELECT
Tumor_SampleBarcode,
Study,
Hugo_Symbol,
Genome_Change,
Protein_Change
FROM
[isb-cgc:tcga_201510_alpha.Somatic_Mutation_calls]
WHERE
( Hugo_Symbol='BRAF'
AND Protein_Change='p.V600E' )
GROUP BY
Tumor_SampleBarcode,
Study,
Hugo_Symbol,
Genome_Change,
Protein_Change
ORDER BY
Study,
Tumor_SampleBarcode"
result <- query_exec(querySql, project=project)
head(result)
</code></pre>
<pre><code>## Tumor_SampleBarcode Study Hugo_Symbol Genome_Change Protein_Change
## 1 TCGA-YF-AA3L-01A BLCA BRAF g.chr7:140453136A>T p.V600E
## 2 TCGA-ZU-A8S4-01A CHOL BRAF g.chr7:140453136A>T p.V600E
## 3 TCGA-A6-5661-01A COAD BRAF g.chr7:140453136A>T p.V600E
## 4 TCGA-A6-5665-01A COAD BRAF g.chr7:140453136A>T p.V600E
## 5 TCGA-A6-6649-01A COAD BRAF g.chr7:140453136A>T p.V600E
## 6 TCGA-A6-6653-01A COAD BRAF g.chr7:140453136A>T p.V600E
</code></pre>
<p>Let's count these mutations up by study (tumor-type):</p>
<pre><code class="r">querySql <- "
SELECT
Study, COUNT(*) AS n
FROM
[isb-cgc:tcga_201510_alpha.Somatic_Mutation_calls]
WHERE
( Hugo_Symbol='BRAF'
AND Protein_Change='p.V600E' )
GROUP BY
Study
HAVING n > 1
ORDER BY n DESC
"
query_exec(querySql, project=project)
</code></pre>
<pre><code>## Study n
## 1 THCA 724
## 2 SKCM 441
## 3 LUAD 27
## 4 COAD 25
## 5 KIRP 6
## 6 CHOL 4
## 7 LGG 4
## 8 GBM 4
## 9 HNSC 2
## 10 BLCA 2
</code></pre>
<p>You may have noticed that in our earlier query, we did a GROUP BY to make sure that we didn't count the same mutation called on the same sample more than once. We might want to GROUP BY patient instead to see if that changes our counts – we may have multiple samples from some patients.</p>
<pre><code class="r">querySql <- "
SELECT
ParticipantBarcode,
Study,
Hugo_Symbol,
Genome_Change,
Protein_Change
FROM
[isb-cgc:tcga_201510_alpha.Somatic_Mutation_calls]
WHERE
( Hugo_Symbol='BRAF'
AND Protein_Change='p.V600E' )
GROUP BY
ParticipantBarcode,
Study,
Hugo_Symbol,
Genome_Change,
Protein_Change
ORDER BY
Study,
ParticipantBarcode"
result <- query_exec(querySql, project=project)
head(result)
</code></pre>
<pre><code>## ParticipantBarcode Study Hugo_Symbol Genome_Change Protein_Change
## 1 TCGA-YF-AA3L BLCA BRAF g.chr7:140453136A>T p.V600E
## 2 TCGA-ZU-A8S4 CHOL BRAF g.chr7:140453136A>T p.V600E
## 3 TCGA-A6-5661 COAD BRAF g.chr7:140453136A>T p.V600E
## 4 TCGA-A6-5665 COAD BRAF g.chr7:140453136A>T p.V600E
## 5 TCGA-A6-6649 COAD BRAF g.chr7:140453136A>T p.V600E
## 6 TCGA-A6-6653 COAD BRAF g.chr7:140453136A>T p.V600E
</code></pre>
<pre><code class="r">table(result$Study)
</code></pre>
<pre><code>##
## BLCA CHOL COAD GBM HNSC KIRP LGG LUAD READ SKCM THCA
## 1 1 25 4 1 1 2 8 1 170 258
</code></pre>
<p>When we counted the number of mutated samples, we found 261 THCA samples, but when we counted the number of patients, we found 258 THCA patients, so let's see what's going on there.</p>
<pre><code class="r">querySql <- "
SELECT
ParticipantBarcode,
COUNT(*) AS m
FROM (
SELECT
ParticipantBarcode,
Tumor_SampleBarcode,
COUNT(*) AS n
FROM
[isb-cgc:tcga_201510_alpha.Somatic_Mutation_calls]
WHERE
( Hugo_Symbol='BRAF'
AND Protein_Change='p.V600E'
AND Study='THCA' )
GROUP BY
ParticipantBarcode,
Tumor_SampleBarcode,
)
GROUP BY
ParticipantBarcode
HAVING
m > 1
ORDER BY
m DESC"
result <- query_exec(querySql, project=project)
head(result)
</code></pre>
<pre><code>## ParticipantBarcode m
## 1 TCGA-J8-A3YH 2
## 2 TCGA-EM-A2CS 2
## 3 TCGA-EM-A2P1 2
</code></pre>
<p>Sure enough, we see that the same mutation is reported twice for each of these three patients. Let's look at why:</p>
<pre><code class="r">querySql <- "
SELECT
ParticipantBarcode,
Tumor_SampleBarcode,
Tumor_SampleTypeLetterCode,
Normal_SampleBarcode,
Normal_SampleTypeLetterCode,
Center,
FROM
[isb-cgc:tcga_201510_alpha.Somatic_Mutation_calls]
WHERE
( Hugo_Symbol='BRAF'
AND Protein_Change='p.V600E'
AND Study='THCA'
AND ParticipantBarcode='TCGA-EM-A2P1' )
ORDER BY
Tumor_SampleBarcode,
Normal_SampleBarcode,
Center"
result <- query_exec(querySql, project=project)
head(result)
</code></pre>
<pre><code>## ParticipantBarcode Tumor_SampleBarcode Tumor_SampleTypeLetterCode
## 1 TCGA-EM-A2P1 TCGA-EM-A2P1-01A TP
## 2 TCGA-EM-A2P1 TCGA-EM-A2P1-01A TP
## 3 TCGA-EM-A2P1 TCGA-EM-A2P1-01A TP
## 4 TCGA-EM-A2P1 TCGA-EM-A2P1-06A TM
## 5 TCGA-EM-A2P1 TCGA-EM-A2P1-06A TM
## 6 TCGA-EM-A2P1 TCGA-EM-A2P1-06A TM
## Normal_SampleBarcode Normal_SampleTypeLetterCode Center
## 1 TCGA-EM-A2P1-10A NB bcgsc.ca
## 2 TCGA-EM-A2P1-10A NB broad.mit.edu
## 3 TCGA-EM-A2P1-10A NB hgsc.bcm.edu
## 4 TCGA-EM-A2P1-10A NB bcgsc.ca
## 5 TCGA-EM-A2P1-10A NB broad.mit.edu
## 6 TCGA-EM-A2P1-10A NB hgsc.bcm.edu
</code></pre>
<p>Aha! not only did this patient provide both a primary tumor (TP) and a metastatic ™ sample, but we have mutation calls from three different centers.</p>
<p>Finally, let's pick out one of these mutations and see what some of the other fields in this table can tell us:</p>
<pre><code class="r">querySql <- "
SELECT
ParticipantBarcode,
Tumor_SampleTypeLetterCode,
Normal_SampleTypeLetterCode,
Study,
Center,
Variant_Type,
Variant_Classification,
Genome_Change,
cDNA_Change,
Protein_Change,
UniProt_Region,
COSMIC_Total_Alterations_In_Gene,
DrugBank
FROM
[isb-cgc:tcga_201510_alpha.Somatic_Mutation_calls]
WHERE
( Hugo_Symbol='BRAF'
AND Protein_Change='p.V600E'
AND Study='THCA'
AND ParticipantBarcode='TCGA-EM-A2P1'
AND Tumor_SampleTypeLetterCode='TP'
AND Center='broad.mit.edu' )"
result <- query_exec(querySql, project=project)
head(result)
</code></pre>
<pre><code>## ParticipantBarcode Tumor_SampleTypeLetterCode
## 1 TCGA-EM-A2P1 TP
## Normal_SampleTypeLetterCode Study Center Variant_Type
## 1 NB THCA broad.mit.edu SNP
## Variant_Classification Genome_Change cDNA_Change Protein_Change
## 1 Missense_Mutation g.chr7:140453136A>T c.1799T>A p.V600E
## UniProt_Region
## 1 Protein kinase. {ECO:0000255|PROSITE- ProRule:PRU00159}.
## COSMIC_Total_Alterations_In_Gene
## 1 27380
## DrugBank
## 1 Dabrafenib(DB08912)|Regorafenib(DB08896)|Sorafenib(DB00398)|Vemurafenib(DB08881)
</code></pre>
<p>When working with variants or mutations, there is another public BigQuery table that you might find useful. Developed by Tute Genomics, this comprehensive, publicly-available database of over 8.5 billion known variants was announced earlier this year. This table includes several types of annotations and scores, such ase Polyphen2 and MutationAssessor, and a proprietary “Tute score” which estimates whether a SNP or indel is likely to be associate with Mendelian phenotypes.</p>
<p>For example, you can look up all exonic BRAF mutations in the TuteTable in less than 20 seconds:</p>
<pre><code class="r">querySql <- "
SELECT
Chr,
Start,
Func,
Gene,
AA,
Polyphen2_HDIV_score,
Polyphen2_HVAR_score,
MutationAssessor_score,
TUTE
FROM
[silver-wall-555:TuteTable.hg19]
WHERE
( Gene='BRAF'
AND Func='exonic' )
ORDER BY
Start ASC"
tuteBRAFscores <- query_exec(querySql, project=project)
summary(tuteBRAFscores)
</code></pre>
<pre><code>## Chr Start Func
## Length:6903 Min. :140434396 Length:6903
## Class :character 1st Qu.:140454001 Class :character
## Mode :character Median :140487373 Mode :character
## Mean :140494524
## 3rd Qu.:140508723
## Max. :140624502
##
## Gene AA Polyphen2_HDIV_score
## Length:6903 Length:6903 Min. :0.0000
## Class :character Class :character 1st Qu.:0.0730
## Mode :character Mode :character Median :0.6640
## Mean :0.5562
## 3rd Qu.:0.9900
## Max. :1.0000
## NA's :1918
## Polyphen2_HVAR_score MutationAssessor_score TUTE
## Min. :0.0000 Min. :-2.770 Min. :0.0000
## 1st Qu.:0.0310 1st Qu.: 0.345 1st Qu.:0.1080
## Median :0.2630 Median : 1.150 Median :0.1967
## Mean :0.4139 Mean : 1.248 Mean :0.3322
## 3rd Qu.:0.8700 3rd Qu.: 1.895 3rd Qu.:0.5681
## Max. :1.0000 Max. : 4.505 Max. :0.9317
## NA's :1918 NA's :1927
</code></pre>
<p>Let's go back to the TCGA somatic mutations table and pull out all BRAF mutations and then join them with the matching mutations in the Tute Table so that we can compare the distribution of scores (eg MutationAssessor and TUTE) between the somatic mutations seen in TCGA and the larger set of variants contained in the Tute Table.</p>
<pre><code class="r">querySql <- "
SELECT
Hugo_Symbol,
Protein_Change,
MutationAssessor_score,
TUTE
FROM (
SELECT
Hugo_Symbol,
Protein_Change
FROM
[isb-cgc:tcga_201510_alpha.Somatic_Mutation_calls]
WHERE
( Hugo_Symbol='BRAF' )
GROUP BY
Hugo_Symbol,
Protein_Change ) AS tcga
JOIN (
SELECT
Gene,
AA,
MutationAssessor_score,
TUTE
FROM
[silver-wall-555:TuteTable.hg19]
WHERE
( Gene='BRAF' ) ) AS tute
ON
tcga.Hugo_Symbol=tute.Gene
AND tcga.Protein_Change=tute.AA"
tcgaBRAFscores <- query_exec(querySql, project=project)
summary(tcgaBRAFscores)
</code></pre>
<pre><code>## Hugo_Symbol Protein_Change MutationAssessor_score
## Length:212 Length:212 Min. :-1.005
## Class :character Class :character 1st Qu.: 0.345
## Mode :character Mode :character Median : 1.320
## Mean : 1.566
## 3rd Qu.: 2.455
## Max. : 4.475
## NA's :79
## TUTE
## Min. :0.0000
## 1st Qu.:0.0940
## Median :0.1860
## Mean :0.3246
## 3rd Qu.:0.5940
## Max. :0.9264
##
</code></pre>
<pre><code class="r">tcgaBRAFscores$Source <- "TCGA"
tcgaScores <- tcgaBRAFscores[,c("MutationAssessor_score", "TUTE", "Source")]
tuteBRAFscores$Source <- "TUTE"
tuteScores <- tuteBRAFscores[,c("MutationAssessor_score", "TUTE", "Source")]
brafScores <- rbind(tcgaScores, tuteScores)
qplot(brafScores$TUTE, color=Source, data=brafScores, geom="density", xlab="TUTE scores")
</code></pre>
<p><img 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" alt="plot of chunk som_mut_fig1"/></p>
<pre><code class="r">qplot(brafScores$MutationAssessor_score, color=Source, data=brafScores, geom="density", xlab="MutationAssessor score")
</code></pre>
<pre><code>## Warning: Removed 2006 rows containing non-finite values (stat_density).
</code></pre>
<p><img 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" alt="plot of chunk som_mut_fig1"/></p>
<p>Both of these plots suggest that some of the somatic BRAF mutations observed in TCGA tumor samples are scored as more deleterious by both TUTE and MutationAssessor. In the TUTE histogram, a larger fraction of somatic mutations also get a score of 0.
Note that in these histograms, each count represents a single variant, ie a specific protein change. Mutations that are seen across multiple patients are not being counted multiple times.</p>
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