Supplementary Material for:
Patterns of Genomic Loss of Heterozygosity Predict Homologous
Recombination Repair Defects in Epithelial Ovarian Cancer
Victor Abkevich1, *, Kirsten M. Timms1, *, Bryan T. Hennessy2, Jennifer Potter1, Mark S Carey3, Larissa A
Meyer4, Karen Smith-McCune5, Russell Broaddus6, Karen H Lu7, Jian Chen1, Thanh V Tran1, Deborah
Williams1, Diana Iliev1, Srikanth Jammulapati1, Lisa M FitzGerald1, Thomas Krivak8, Julie A. DeLoia9,
Alexander Gutin1, Gordon B. Mills3, Jerry S. Lanchbury1,
1
Myriad Genetics Inc., Salt Lake City, UT, USA
2
Beaumont Hospital and Royal College of Surgeons in Ireland (RCSI), Dublin, Ireland
3
Department of Obstetrics and Gynecology, University of British Colombia, Vancouver, Canada
4
Department of Obstetrics, Gynecology and Reproductive Sciences, The University of Texas Medical
School – Houston, TX, USA
5
Department of Obstetrics, Gynecology, and Reproductive Sciences, University of California San
Francisco, San Francisco, CA, USA
6
Department of Pathology, The University of Texas M.D. Anderson Cancer Center, Houston, TX, USA
7
Department of Gynecologic Oncology and Reproductive Medicine, The University of Texas M.D.
Anderson Cancer Center, Houston, TX, USA
8
Department of Obstetrics, Gynecology and Reproductive Sciences, University of Pittsburgh School of
Medicine, Pittsburgh, USA
9
School of Public Health and Health Services, The George Washington University, Washington DC, USA
*The first two authors contributed equally to this work
Corresponding author:
Kirsten Timms, Ph.D., Myriad Genetics, 320 Wakara Way, Salt Lake City, UT, 84108, USA. Phone: (801)
584-3759. Fax: (801) 584-3640. Email: ktimms@myriad.com
Supplementary Materials and Methods
Cell Line Growth
Cancer cell lines were grown in RPMI + 10% FBS + 1% penicillin/streptomycin media at 37 o C in T75
flasks until ~5x106 cell density. Exceptions were cell lines that required non-standard media, L-glutamine,
or insulin. Cells grown in suspension were centrifuged for 5 minutes at 1700 rpm in a 1.5 mL centrifuge
tube and the supernatant discarded. Cells grown in a monolayer had medium removed by aspiration, were
washed with PBS, and trypsin solution added. After the cells detached they were collected in medium,
transferred to a 1.5 mL microcentrifuge tube and centrifuged at 1700 rpm for 5 minutes. The supernatant
was discarded. Isolated cells were resuspended in 200 µL PBS.
Promoter Methylation qPCR Assays
DNA methylation-sensitive and methylation-dependent restriction enzymes were used to selectively digest
unmethylated or methylated genomic DNA, respectively. Post-digest DNA was quantified by real-time
PCR using primers flanking the regions of interest. The relative concentrations of differentially methylated
DNA are determined by comparing the amount of each digest with that of a mock digest. A cutoff of 0.10
was used to define samples as “methylated”.
Methylation Microarrays
The downloaded data from TCGA included the signal intensities from methylated (M) and unmethylated
(U) probes from Infinium HumanMethylation27 microarrays (Illumina, San Diego, CA). The fraction of
methylated probes was calculated (M/(I+M)). Data from this assay had higher noise than the qPCR assay
(data not shown), consequently a cutoff of 0.35 was used to define samples as “methylated”.
BRCA1 and CCP Signature Expression Assays
BRCA1 and Cell Cycle Progression Signature Expression Assays
RNA was treated with Amplification Grade Deoxyribonuclease I (Sigma-Aldrich Inc.) per manufacturer’s
protocol with an extended incubation time of 30 minutes. Reverse transcription was performed using a
High-Capacity cDNA Reverse Transcription Kit (Applied Biosystems, Foster City, CA) per manufacturer’s
instructions.
Replicate preamplifications were run independently using the Taqman PreAmp Master Mix Kit (Applied
Biosystems) protocol in a 5ul reaction volume. Two preamplification replicates were run at 8 and 18 cycles
respectively for cell cycle gene assays. Three preamplification replicates were run at 18 cycles only for
BRCA1 assays. The post-amplification products were diluted 1:5 in low-EDTA Tris-EDTA (TE).
Quantitative Polymerase Chain Reaction (qPCR) was then performed and assessed on Gene Expression
M48 Dynamic Arrays (Fluidigm, South San Francisco, CA) per manufacturer’s protocol. The comparative
cycle threshold (CT) method was used to calculate relative gene expression. CTs from preamplification of
different numbers of cycles were centered by the average of the genes on the replicate that were in common
between all replicates. The resulting values were normalized first by the average C Ts of the housekeeper
genes then by the average of the normalized CTs of each assay on all samples from the first cohort to yield
∆∆CT. CCP score (Cuzick et al., 2011) and relative BRCA1 expression was calculated as the average of the
negative of the ∆∆CTs of the cell-cycle genes and BRCA1 assays, respectively.
Identification of Samples with Loss of BRCA1 Expression:
BRCA1 and BRCA2 mRNA levels are expected to correlate with the expression of cell cycle genes
(Whitfield et al., 2002), therefore any lack of correlation might suggest the presence of BRCA1 or BRCA2
mutations affecting the transcription of these genes. While there appears to be a linear relationship between
cell cycle expression and BRCA1 expression in a majority of samples, we observed a small group of
samples with elevated CCP score (Cuzick et al., 2011) and low BRCA1 expression. The threshold for
identifying patients with abnormal BRCA1 expression was defined using robust linear regression in a large
set of ovarian cancer samples (n = 234). BRCA1 expression was regressed on CCP score using iteratively
re-weighted least squares (IWLS), a method for linear regression that places lower weight on outliers. In
this application we used Huber weights. A 99% prediction interval for the resulting regression was
calculated. Points failing below the lower limit of the interval were considered abnormal. Under the
assumption that CCP score and BRCA1 expression are linearly correlated, the choice of a 99% interval
controls the false positive rate at 0.005.
Affymetrix 500K GeneChip arrays
The Affymetrix GeneChip Mapping NspI or StyI Assay Kit was used in the generation of biotinylated
DNA for Affymetrix Mapping 500K NspI or StyI microarray hybridizations (each assay was prepared
separately). Genomic DNA (250 ng) was digested with NspI or StyI restriction enzyme and adaptors were
added to restriction fragment ends with T4 DNA ligase. Adaptor-modified samples were PCR amplified
using Clontech Titanium Taq, which generated an amplified product of average size between 200 and 1,100
bp. Amplification products were purified using a Clontech DNA amplification cleanup kit. 90 µg of
purified DNA was fragmented using Affymetrix Fragmentation Reagent. Biotin-labeling of the fragmented
sample was accomplished using the GeneChip DNA Labeling Reagent. Biotin-labeled DNA was
hybridized on NspI or StyI Affymetrix microarrays at 49 oC for 16 to 18 hours in the Affymetrix rotation
oven. After hybridization, probe array wash and stain procedures were carried out on the automatic
Affymetrix Fluidics Stations as per manufacturer’s manual and microarrays were scanned and raw data was
collected by Affymetrix GeneChip Scanner 3000.
CN and LOH analysis of SNP microarray data
The algorithm is designed to determine the most likely allele specific copy number (ASCN) at each SNP
location. The corresponding likelihood explicitly takes into account contamination of a cancer DNA sample
with non-cancer stromal cell DNA. The algorithm reconstructs ASCN based on SNP data from tumor
samples; it is not designed to utilize SNP data from matching normal samples if available. The algorithm
infers probabilistically SNP genotypes in the normal DNA. The inference is based on presence of normal
DNA in a tumor sample as well as SNP allele frequencies in the population. It should be noted that if
contamination with normal DNA is low (for example, in cancer cell lines), the inference is based on allele
frequencies alone, and, therefore, the reconstructed ASCN may be affected by the ethnicity of a sample
donor. A similar algorithm for CN analysis is described in detail in Abkevich et al., 2010. The algorithm
used in this paper was implemented in two versions, one for analysis of Affymetrix 500K GeneChip array
data generated internally, and the other for analysis of GenomeWideSNP6 Affymetrix array data
downloaded
from
the
TCGA
web
site
(http://tcga-
data.nci.nih.gov/tcga/dataAccessMatrix.htm?diseaseType=OV). The latter array, in addition to SNP probes,
contains a number of probes for non-polymorphic locations across the human genome. These probes are
informative for CN analysis but are not directly informative for LOH analysis.
Statistical Analysis
The p-values presented in this paper were generated using the Kolmogorov-Smirnov test which is well
suited for this particular analysis. To confirm that the obtained p-values are reliable, they were recalculated
using an exact Fisher test. For example, for the first cohort out of 44 BRCA deficient samples 42 have an
HRD score above 10 (this cutoff was determine as optimal for separating BRCA deficient samples from
BRCA intact samples in all cohorts in the Kolmogorov-Smirnov test), while out of 88 BRCA intact
samples only 27 have HRD score above 10. The corresponding p-value according to Fisher test is 10-13,
while we obtained p-value=10-11 using the Kolmogorov-Smirnov test. For the second cohort out of 18
BRCA deficient samples 16 have an HRD score above 10, while out of 25 BRCA intact samples only 4
have an HRD score above 10. The corresponding p-value according to Fisher test is 2*10-6, while a pvalue=10-5 was obtained using the Kolmogorov-Smirnov test. For the third cohort out of 146 BRCA
deficient samples 129 have HRD score above 10, while out of 288 BRCA intact samples only 87 have
HRD score above 10. The corresponding p-value according to Fisher test is 10-32, while a p-value=10-29 was
obtained using the Kolmogorov-Smirnov test. In all of these comparisons an even more significant p-value
was obtained using Fisher test. This is because in the Kolmogorov-Smirnov the p-value is already adjusted
for the multiple testing required for determination of the optimal cutoff for separating BRCA deficient
samples from BRCA intact samples, while in Fisher test there is no such adjustment.
Supplementary Results
Effect of contamination with normal tissue on determination of HR deficiency and
HRD score
It is expected that at high levels of contamination of tumor samples with normal tissue, the molecular
assays used in this study to define HR deficiency (mutation detection, quantifying RNA expression and
promoter methylation) might be unreliable. In addition, HRD score estimation might be compromised at
high contamination. Therefore, we have investigated the dependence of the obtained results on the degree
of contamination. First,we examined whether the distribution of HRD scores depends on the degree of
contamination (Supplementary Figure S7). According to linear regression test this correlation was not
significant (p-value=0.94). However, at the highest contamination level of 90% there was an unusually
high number of samples with HRD score zero. This suggests that HRD score can be reliably estimated at
contamination of up to 85%.
Supplementary Figure S8 shows the distribution of contamination in carriers and non-carriers of deleterious
mutations in BRCA1 and BRCA2 genes. Mutations can be detected at contamination levels up to 90%.
Moreover, according to Kolmogorov-Smirnov test the difference in the degree of contamination among
carriers and non-carriers of deleterious mutations in BRCA1 and BRCA2 genes is only marginally
significant (p=0.03). This suggests that the results of mutation screening are reliable for all but highly
contaminated samples.
Supplementary Figure S9 shows the distribution of contamination in samples with methylated BRCA1 or
RAD51C genes as well as samples with low expression of BRCA1 gene and all other samples. It is clear
that detection of methylation and low RNA expression is strongly affected by contamination. Indeed, the
highest level of contamination in samples with methylated BRCA1 or RAD51C or low expression of
BRCA1 gene is 65%. This suggests that these assays are unreliable at contamination above 65%.
In conclusion, it appears that the detection of promoter methylation and low RNA expression are most
sensitive to high levels of contamination and become unreliable at contamination above 65%. Therefore, all
samples with contamination above 65% have been excluded from the statistical analysis.
Supplementary Tables
Supplementary Table S1. Patient and cancer characteristics. NA = not applicable.
Total Number of Patients
Age at diagnosis
Range
Median
Unknown
Follow-up time (days)
Range
Median
Unknown
Stage
1
2
3
4
Unknown
Histology
Serous (high grade)
Serous (low grade)
Serous (unknown grade)
Non-serous
Mixed
Unknown
Grade
1
2
3
4
Unknown
Residual disease after surgery
0
<= 1 cm
> 1 cm
Unknown
Surgery
Yes
No
Unknown
Chemotherapy
Yes
Platinum (cis or carboplatin)-based (no taxane)
Platinum plus Taxane (paclitaxel or docetaxel)-based
No
Unknown
First cohort
152
Second cohort
53
Third cohort
435
37 - 88
59
4 (2.6%)
38 - 77
56
0
30 - 89
59
0
20 - 5570
1127
5 (3.3%)
213 - 3294
701
0
8 - 5480
874
2 (0.5%)
9 (5.9%)
14 (9.2%)
107 (70.4%)
21 (13.8%)
1 (0.7%)
0
0
46 (86.8%)
7 (13.2%)
0
6 (1.4%)
21 (4.8%)
338 (77.7%)
69 (15.9%)
1 (0.2%)
125 (87.2%)
8 (5.3%)
0
8 (5.3%)
10 (6.6%)
1 (0.7%)
39 (73.6%)
1 (1.9%)
0
4 (7.6%)
1 (1.9%)
8 (15.1%)
424 (97.5%)
3 (0.7%)
8 (1.8%)
0
0
0
8 (5.3%)
18 (11.8%)
126 (82.9%)
0
0
1 (1.9%)
12 (22.6%)
40 (75.5%)
0
0
2 (0.5%)
50 (11.5%)
373 (85.8%)
1 (0.2%)
8 (1.8%)
9 (5.9%)
95 (62.5%)
40 (26.3%)
8 (5.3%)
0
44 (83%)
9 (17%)
0
84 (19.3%)
200 (46.0%)
102 (23.5%)
49 (11.3%)
152 (100%)
0
0
53 (100%)
0
0
386 (88.7%)
0
49 (11.3%)
52 (98.1%)
1 (1.9%)
51 (96.2%)
0
1 (1.9%)
399 (91.7%)
NA
NA
23 (5.3%)
13 (3.0%)
139 (91.5%)
12 (7.9%)
127 (83.6%)
7 (4.6%)
6 (4%)
Supplementary Table S2. Number of samples used in each assay.
Assay
Affymetrix 500K SNP
arrays
BRCA1 and BRCA2 tumor
sequencing
BRCA1 and BRCA2
germline sequencing
CCP and BRCA1 qPCR
BRCA1 and BRCA2
methylation analysis
Other HR gene
methylation analysis
Cohort 1
Number of
samples
Reason assay was not
applied to all samples
Cohort 2
Number of
samples
Reason assay was not
applied to all samples
152
not applicable
53
not applicable
150
sequencing failed
normal tissue not
available or no
mutation detected in
tumor
insufficient tissue for
RNA extraction
insufficient DNA for
analysis
insufficient DNA for
analysis
52
sequencing failed
normal tissue not
available or no
mutation detected in
tumor
19
137
126
92
11
53
34
0
not applicable
insufficient DNA for
analysis
insufficient DNA for
analysis
Supplementary Table S3: Immortalized tumor cell lines used in this study.
Cell Line
Tissue
Cell Line
Tissue
CRL7482
Breast
ML46
Ovarian
HTB121
Breast
MPSCI
Ovarian
HTB27
Breast
HOC1
Ovarian
CRL2343
Breast
OAW42
Ovarian
HTB21
Breast
SW626
Ovarian
HTB127
Breast
OCC1
Ovarian
CRL2316
Breast
59M
Ovarian
CRL1897
Breast
OC316
Ovarian
CRL1902
Breast
IGROV1
Ovarian
CRL2321
Breast
EFO21
Ovarian
CRL2329
Breast
OVCAR8 Ovarian
HTB19
Breast
A2780
Ovarian
CRL2315
Breast
DOV13
Ovarian
CRL2865
Breast
EFO27
Ovarian
CRL2322
Breast
HEY
Ovarian
CRL2330
Breast
SKOV3
Ovarian
CRL2324
Breast
OVCAR3 Ovarian
CRL2338
Breast
OVCA420 Ovarian
HTB30
Breast
OVCA429 Ovarian
ACC308
Breast
FUOV1
Ovarian
CRL1504
Breast
UPN251
Ovarian
HTB122
Breast
CCL221
Colon
ACC589
Breast
CRL2102 Colon
CRL2326
Breast
CCL247
Colon
HTB22
Breast
HTB79
Pancreatic
HTB126
Breast
CRL7721
Breast
CRL2335
Breast
HTB20
Breast
CRL2336
Breast
HTB131
Breast
CRL2314
Breast
Supplementary Table S4. Promoter methylation assays used (SABiosciences).
Gene Symbol
Description
Assay catalog ID
MDC1
Mediator or DNA damage checkpoint 1
MePH08721-2A
PARP1
Poly(ADP-ribose) polymerase 1
MePH02379-2A
BRCA1
Breast Cancer 1, early onset
MePH28472-1A
BRCA2
Breast Cancer 2, early onset
MePH28473-1A
RAD50
RAD50 homolog
MePH28350-1A
RAD51C
RAD51 homolog C
MePH22389-1A
PALB2
Partner and localizer of BRCA2
MePH28516-1A
CHEK2
CHK2 checkpoint homolog
MePH28264-1A
ATM
Ataxia telangiectasia mutated
MePH28470-1A
RAD51
RAD51 homolog
MePH19071-2A
Supplementary Table S5. qPCR assays used (Applied Biosystems).
Gene Symbol
Assay Catalog ID
Assay Function
ASF1B
Hs00216780_m1
CCP
ASPM
Hs00411505_m1
CCP
BIRC5
Hs00153353_m1
CCP
BUB1B
Hs01084828_m1
CCP
C18orf24
Hs00536843_m1
CCP
CDC20
Hs03004916_g1
CCP
CDC2
Hs00364293_m1
CCP
CDCA3
Hs00229905_m1
CCP
CDCA8
Hs00983655_m1
CCP
CDKN3
Hs00193192_m1
CCP
CENPF
Hs00193201_m1
CCP
CENPM
Hs00608780_m1
CCP
CEP55
Hs00216688_m1
CCP
DLGAP5
Hs00207323_m1
CCP
DTL
Hs00978565_m1
CCP
FOXM1
Hs01073586_m1
CCP
KIAA0101
Hs00207134_m1
CCP
KIF11
Hs00189698_m1
CCP
KIF20A
Hs00993573_m1
CCP
KIF4A
Hs01020169_m1
CCP
MCM10
Hs00960349_m1
CCP
NUSAP1
Hs01006195_m1
CCP
ORC6L
Hs00204876_m1
CCP
PBK
Hs00218544_m1
CCP
PLK1
Hs00153444_m1
CCP
PRC1
Hs00187740_m1
CCP
PTTG1
Hs00851754_u1
CCP
RAD51
Hs00153418_m1
CCP
RAD54L
Hs00269177_m1
CCP
RRM2
Hs00357247_g1
CCP
TK1
Hs01062125_m1
CCP
TOP2A
Hs00172214_m1
CCP
CLTC
Hs00191535_m1
CCP Housekeeper
MMADHC
Hs00739517_g1
CCP Housekeeper
MRFAP1
Hs00738144_g1
CCP Housekeeper
PPP2CA
Hs00427259_m1
CCP Housekeeper
PSMA1
Hs00267631_m1
CCP Housekeeper
PSMC1
Hs02386942_g1
CCP Housekeeper
RPL13A
Hs03043885_g1
CCP Housekeeper
RPL37
Hs02340038_g1
CCP Housekeeper
RPL38
Hs00605263_g1
CCP Housekeeper
RPL4
Hs03044647_g1
CCP Housekeeper
RPL8
Hs00361285_g1
CCP Housekeeper
RPS29
Hs03004310_g1
CCP Housekeeper
SLC25A3
Hs00358082_m1
CCP Housekeeper
TXNL1
Hs00355488_m1
CCP Housekeeper
UBA52
Hs03004332_g1
CCP Housekeeper
BRCA1
Hs00173233_m1
BRCA1
BRCA1
Hs00173237_m1
BRCA1
BRCA1
Hs01556190_m1
BRCA1
BRCA1
Hs01556191_m1
BRCA1
GUSB
Hs99999908_m1
BRCA1 Housekeeper
HMBS
Hs00609297_m1
BRCA1 Housekeeper
SDHA
Hs00188166_m1
BRCA1 Housekeeper
UBC
Hs00824723_m1
BRCA1 Housekeeper
YWHAZ
Hs00237047_m1
BRCA1 Housekeeper
Supplementary Table S6. BRCA1, BRCA2, and RAD51C defects detected in the study cohorts. 1 – Two of
these mutations were excluded from the analysis because one copy of BRCA2 remained intact.
Cohort
N
BRCA1
BRCA1
BRCA2
BRCA1
+
mutation
mutation
methylation
Total
N
RAD51C
RAD51C
methylation
methylation
BRCA2
and/or low
+ BRCA1
mutation
expression
mutation
1
152
1
23
8
13
45
89
2
1
2
53
0
11
3
5
19
ND
ND
ND
3
435
0
51
341
64
1491
435
11
0
Supplementary Table S7. BRCA1 and BRCA2 Mutations in Cancer Cell Lines.
Cell line
Tissue
Gene
Variant
Classification
Comment
Reversion
IGROV1
Ovarian
BRCA1
IVS21+1G>C
Suspected
Heterozygous
No
Yes
deleterious
UPN251
Ovarian
BRCA1
1199del29
Deleterious
Homozygous
1246delA
Deleterious
Homozygous
OC316
Ovarian
BRCA1
5579delA
Deleterious
Heterozygous
No
CRL2324
Breast
BRCA1
R1751X
Deleterious
Homozygous
No
BRCA2
E1593X
Deleterious
Heterozygous
No
CRL2336
Breast
BRCA1
5385insC
Deleterious
Homozygous
No
CRL2330
Breast
BRCA2
5811delA
Deleterious
Heterozygous
No
HTB20
Breast
BRCA2
S3094X
Deleterious
Heterozygous
No
CCL247
Colon
BRCA2
8249insA
Deleterious
Heterozygous
No
CCL221
Colon
BRCA2
3827DelGT
Deleterious
Heterozygous
No
5579delA
Deleterious
Heterozygous
HTB79
Pancreas
BRCA2
6174delT
Deleterious
Homozygous
No
OVCAR8
Ovarian
BRCA1
Methylation
Suspected
Homozygous
No
Homozygous
No
Homozygous
No
deleterious
EFO21
Ovarian
BRCA1
Methylation
Suspected
deleterious
CRL2314
Ovarian
BRCA1
Methylation
Suspected
deleterious
Supplementary Figure Legends
Supplementary Figure S1. Percent methylation for BRCA1 for the first cohort. Methylation of the BRCA1
promoter was measured for 126 samples from the first cohort. The distribution, which can be roughly
described as Poisson distribution in combination with a long tail, was used to define a cut-off for HR
deficiency for methylation somewhere between 10 and 15%.
Supplementary Figure S2. Comparison between CCP score and expression of BRCA1. The line corresponds
to the cut-off for low expression, samples below this line are considered BRCA1 deficient due to loss of
expression. Only samples without mutations in BRCA1 or BRCA2 or methylation of BRCA1 are shown.
Expression of BRCA1 and BRCA2 was measured for 137 out of 152 samples from the first cohort, and for
all samples from the second cohort. Expression outliers were not observed for BRCA2, or for other genes
from the HR pathway (MDC1, PARP1, RAD50, RAD51, RAD51C, PALB2, CHEK2, ATM). However there
were a significant number of outliers with very low expression of BRCA1.
Supplementary Figure S3. Whole chromosome LOH in tumor samples. Blue circles correspond to BRCA1
or BRCA2 deficient samples. Red circles correspond to BRCA1 and BRCA2 intact samples. Combined area
under blue and red circles is the same. The area under each individual circle is proportional to the number
of samples with the corresponding number of LOH regions.
Figure 3a. Number of LOH regions covering whole chromosomes for the first cohort.
Figure 3b. Number of LOH regions covering whole chromosomes for the third cohort.
Supplementary Figure S4. Correlation between HRD scores and HR deficiency calculated for different
LOH region length cut-offs for the first cohort. Corresponding log10(p-value) are on the y-axis. The
relationship between the cut-off of the size of LOH regions and the significance of correlation of the HRD
score with HR deficiency was investigated, this dependence was found to be relatively weak in the interval
of the sizes of LOH regions from 11 to 21 Mb. The cut-off of 15 Mb approximately in the middle of the
interval was arbitrarily selected for further analysis. The rational for this selection rather than selecting the
cut-off with the lowest p-value is that the latter cut-off is more sensitive to statistical noise present in the
data.
Supplementary Figure S5. Comparison of HRD scores in three groups of BRCA1 and BRCA2 deficient
samples for the combined data from all three cohorts. Row A: 46 carriers of germline mutations in BRCA1;
B: 24 carriers of somatic mutations in BRCA1; C: 82 samples with either methylation or low expression of
BRCA1; D: 27 carriers of germline mutations in BRCA2; E: 9 carriers of somatic mutations in BRCA2.
Three samples were excluded from the analysis: one with both methylation of BRCA1 and a germline
mutation in BRCA1, one with both methylation of RAD51C and a germline mutation in BRCA1, and one
with both germline and somatic mutations in BRCA2. Green circles correspond to carriers of somatic
mutations, red circles correspond to carriers of germline mutations, and blue circles correspond to samples
with either methylation or low expression of the BRCA1 gene. The combined area under the green, red, and
blue circles is the same. The area under each individual circle is proportional to the number of samples with
the corresponding number of LOH regions.
Supplementary Figure S6. Comparison of HRD scores of BRCA1, BRCA2, and RAD51C deficient samples.
Blue circles correspond to BRCA1 deficient samples, red circles correspond to BRCA2 deficient samples,
and green circles correspond to RAD51C deficient samples. The combined area under red, blue, and green
circles is the same. The area under each individual circle is proportional to the number of samples with the
corresponding number of LOH regions.
Supplementary Figure S7. Comparison of HRD scores of samples with different degree of contamination
with benign tissue. The area under each individual circle is proportional to the number of samples with the
corresponding number of HRD scores and degree of contamination.
Supplementary Figure S8. Correlation between contamination of tumor samples with benign tissue and
results of BRCA1 and BRCA2 sequencing. Blue circles correspond to samples with deleterious mutations in
BRCA1 and/or BRCA2 genes. Red circles correspond to the remaining samples. Combined area under blue
and red circles is the same. The area under each individual circle is proportional to the number of samples
with the corresponding contamination.
Supplementary Figure S9. Correlation between contamination of tumor samples with benign tissue and
results of methylation and expression analysis of BRCA1 and RAD51C genes. Blue circles correspond to
samples with either methylated BRCA1 or RAD51C genes and/or samples with low BRCA1 expression. Red
circles correspond to the remaining samples. Combined area under blue and red circles is the same. The
area under each individual circle is proportional to the number of samples with the corresponding
contamination.
Supplementary References
Abkevich V, Iliev D, Timms KM, Tran T, Skolnick M, Lanchbury JS, Gutin A. (2010) Computational
method for estimating copy numbers in normal samples, cancer cell lines, and solid tumors using array
comparative genomic hybridization. Journal of Biomedicine and Biotechnology Epub 2010 Jul 8.
Cuzick J, Swanson GP, Fisher G, Brothman A, Berney DM, Reid JE, Mesher D, Speights VO, Stankiewicz
E, Foster CS, Møller H, Scardino P, Warren JD, Park J, Younus A, Flake DD 2 nd, Wagner S, Gutin A,
Lanchbury JS, Stone S, Transatlantic Prostate Group. (2011) Prognostic value of an RNA expression
signature derived from cell cycle proliferation genes in patients with prostate cancer: a retrospective study.
Lancet Oncology 12: 245-55.
Whitfield ML, Sherlock G, Saldanha AJ, Murray JI, Ball CA, Alexander KE, Matese JC, Perou CM, Hurt
MM, Brown PO, Botstein D. (2002) Identification of genes periodically expressed in the human cell cycle
and their expression in tumors. Mol Biol Cell 13: 1977-2000.