Supplement – Materials and methods
Sample collection and DNA preparation
Tumor samples and matched germline blood were collected under an
Institutional Review Board (IRB) approved protocol; banked frozen specimens were
reviewed by a single genitourinary pathologist (H.A.A.) for tumor content and viability.
When blood was not available as a source of germline DNA, matched frozen tissue from
uninvolved lymph nodes or bladder was used. In the minority of cases in which normal
bladder was used as a source of germline DNA, we used the fibromuscular tissue of the
bladder rather than the urothelium in order to avoid the potential that histologically
normal urothelium contained any “field effect” genomic alterations. Clinical and
pathologic data were obtained from a prospectively maintained institutional database.
DNA was extracted using the Qiagen DNeasy and PAXgene Blood DNA kits.
Targeted capture and sequencing
Tumor and germline DNA was sequenced using the MSK-IMPACT assay
(Integrated Mutation Profiling of Actionable Cancer Targets) as previously described [1,
2]. Custom oligonucleotides were designed to capture all protein-coding exons of up to
300 genes. Barcoded sequencing libraries were prepared, subjected to exon capture,
and sequenced on an Illumina HiSeq 2000/2500. The initial 19 tumors were analyzed
using a version of MSK-IMPACT that included 230 genes (version 2). Subsequent
samples were analyzed on versions including additional genes (version 3: 275 genes,
version 4: 279 genes, version 5: 300 genes; Supplementary Table S4).
2
Validation of mutations in STAG2, KDM6A, ARID1A and MLL2 was performed
using a second assay/platform. Specifically, an equimolar pool of barcoded libraries was
subjected to exon capture (Nimblegen SeqCap EZ, Roche) and sequenced on an
Illumina MiSeq.
Sequence analysis
Sequence reads were aligned to the reference human genome hg19 and
analyzed to identify single-nucleotide variants, small insertions and deletions (indels),
and copy number alterations as previously described [2]. All candidate mutations and
indels were reviewed manually. The mean sequence coverage for each target region
was used to compute copy number. Sequence coverage for each exon was compared
in tumor and matched germline samples, after performing sample-wide LOESS
normalization for GC percentage across exons and normalizing for global differences in
“on-target” sequence coverage. Increases and decreases in the tumor-to-germline
coverage ratios were used to infer copy number changes; ratios ≥3.0 were defined as
amplifications and ratios ≤0.3 as deletions.
Statistical analysis
We assessed the relationships between genomic alterations and
clinicopathologic outcomes in patients for whom tumor tissue was obtained at radical
cystectomy. For these analyses, we included alterations deemed to have functional
significance (for oncogenes, recurrent missense mutations and amplifications; for tumor
suppressors, nonsense mutations, frameshift indels and deletions).
3
Associations between pathologic variables and alterations within individual genes
or cancer-related pathways were analyzed using Fisher’s exact test. The Kaplan-Meier
method and log-rank test were used for estimations and univariable comparisons of
survival. Multivariable Cox regression models tested associations between alterations
and survival outcomes, adjusted for pT stage and nodal involvement. Statistical
significance was defined as p-values <0.05. Analyses were conducted using SAS v9.2
and R v2.13.1 including the ‘survival’ package.
Data availability
Genomic and associated clinical data are publically available through the cBioPortal for
Cancer Genomics [3].
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References for Supplementary Materials and Methods
[1] Wagle N, Berger MF, Davis MJ, Blumenstiel B, Defelice M, Pochanard P, et al. Highthroughput detection of actionable genomic alterations in clinical tumor samples by targeted,
massively parallel sequencing. Cancer Discov. 2012;2:82-93.
[2] Won HH, Scott SN, Brannon AR, Shah RH, Berger MF. Detecting somatic genetic alterations
in tumor specimens by exon capture and massively parallel sequencing. J Vis Exp.
2013:e50710.
[3] Cerami E, Gao J, Dogrusoz U, Gross BE, Sumer SO, Aksoy BA, et al. The cBio cancer
genomics portal: an open platform for exploring multidimensional cancer genomics data. Cancer
Discov. 2012;2:401-4.