April 2015

New Method Enables Analysis of Preserved
Cancer Specimens With Exome Sequencing
A novel QC approach and the Nextera® Rapid Capture Exome Kit enable researchers to sequencedegraded DNA from FFPE tumor samples successfully.
Introduction
The specific causes of cancer progression have long baffled scientists
and physicians. Repositories are filled with historical tumor samples
collected from patients over the years that might hold the answers.
Unfortunately, while the process used to preserve tumor samples
is successful in saving them for decades, it often introduces DNAanalysis challenges. Formalin-fixed, paraffin-embedded (FFPE) tumor
samples usually contain degraded DNA, which is difficult to amplify fornext-generation sequencing (NGS).
Led by Annalisa Astolfi, PhD, researchers at the Centro
Interdipartimentale di Ricerche sul Cancro “Giorgio Prodi” (CIRC)
at the University of Bologna in Italy are developing new ways toretrieve the information preserved in FFPE tumors. They hope that
this information will provide a better understanding of the genetic
factors that cause cancer. Dr. Astolfi and her colleagues began using
microarrays to analyze gene expression in solid tumors and leukemia,
but later incorporated NGS into their analysis methods. “The advent ofNGS completely changed our studies,” Dr. Astolfi said. “We can now
investigate tumor samples more deeply with sequencing, enabling us
to search for novel somatic mutations in rare tumors. We don’t havemuch information about the somatic events that drive tumor onset andprogression in these cancers.”
While the CIRC team started sequencing the exomes of fresh-frozen(FF) tumor samples, their research on gastrointestinal stromal tumors(GIST) led them to explore FFPE samples. “Traditionally, GIST has
been characterized by mutually exclusive KIT or PDGFRA mutations,and recent findings have shown that inactivating SDH mutations arealso associated with GIST,”1,2 Dr. Astolfi added. “GIST studies focus
on the proportion of individuals with no known genetic marker for thedisease, and on the accompanying genetic lesions that characterizedisease progression and resistance to therapy. We’re investigatingother mutations that might contribute to GIST.”3

Valentina Indio, PhD; Milena Urbini, MSc; Annalisa Astolfi, PhD; andChiara Giusy Genovese, MSc (from left to right) at CIRC are developing new
methods for investigating and understanding cancer progression.

Sample Collection and DNA Extraction
The CIRC team collected clinical tumor samples, using establishedmethods to preserve a portion of the samples as FF. The remaining
portion of tumor specimens were fixed in 10% formalin solution for
6–72 hours, and then dehydrated and embedded in paraffin. FF
samples were extracted with the QIAamp DNA Mini Kit (QIAGEN).
FFPE slides were manually macrodissected by superimposing an
H and E stained slide, then extracted with the QIAamp DNA Micro
Kit (QIAGEN). DNA concentration was determined using both
spectrophotometric (NanoDrop, Thermo Scientific) and fluorometric(Quant-IT PicoGreen dsDNA Assay Kit, Life Technologies) methods.*“The advent of NGS completelychanged our studies. We can
now investigate FF and FFPE
tumor samples more deeply withsequencing, enabling us to search
for novel somatic mutations inrare tumors.”

To address the challenge of sequencing degraded DNA, Dr. Astolfi andcolleagues developed a quality check (QC) method for FFPE samples.
This method allows them to assess whether a particular sample will
yield high-quality data and whether to proceed with sequencing. Their
QC step uses a modification of the Random Amplified Polymorphic
DNA (RAPD) assay, a well-known method of DNA fingerprinting.4 It
produces an unbiased representation of DNA quality through random
amplification of multiple loci throughout the genome. “If we can selectsamples of high quality, then we can obtain high-quality data that arecomparable to FF samples,” Dr. Astolfi said.
This application note describes the QC method, DNA analysis of FF
and FFPE GIST, and data comparison with matched normal tissue.

*Illumina recommends using fluorometric quantification to determine DNA concentration
with Illumina products.

For Research Use Only. Not for use in diagnostic procedures.

April 2015

Quality Check on FFPE Samples
The CIRC team developed a QC step, using a modified RAPD method
to assess FFPE quality (Figure 1). Briefly, 5 ng of DNA from FF andFFPE DNA were amplified with RAPD primers, KAPA2G Fast HotStart
Taq Polymerase (Kapa Biosystems), MgCl2, and dNTPs. PCR wasperformed for 40 cycles.
To demonstrate concordance, the CIRC team also performed a QC
check using an established, gene-specific, quantitative PCR method(Kapa Biosystems). The results of the QC step were in concordancewith the QC results following the modified RAPD method.

Modified RAPD Method for FFPE QC
PCR Mix (25 µl total volume)
2.5 µl2 µl0.5 µl1.25 µl1.25 µl
0.1µl

10X buffer
25 mM MgCl2
10 mM dNTPs
10 µM forward primer (5’-AATCGGGCTG-3’)
10 µM reverse primer (5’-GAAACGGGTG-3’)
Taq polymerase

Thermal Cycler Protocol
95 oC		
95 oC
37 oC
72 oC
72 oC		-­‐	  	  500kb	  2 minutes
15 seconds
15 seconds
1 minute
30 seconds}

40 cycles

Library preparation yielded sufficient material for both FF and HQ-FFPEsamples, even though average library size was smaller (Figure 2).
For LQ-FFPE samples, the team pooled 2 different reactions to yield
500 ng of tagmented DNA.

“Our experience with the NRCE Kit
has been very positive.”
Figure 1: Amplification Results of QC Step—Gel electrophoresis ofRAPD-amplified DNA demonstrated that high-quality FFPE samples show afingerprint pattern that resembles the profile of FF samples, with the presence ofa prevalent, bright, 500 bp fragment as the highest amplicon size. Conversely,
low-quality FFPE samples (denoted by the blue arrow) amplify only shortfragments, with faint bands of less than 200 bp.

Library Preparation and Exome Sequencing
The team used the Nextera Rapid Capture Exome (NRCE) Kit toprepare DNA libraries from FF tumor tissue, FFPE tumor tissue, andmatched normal peripheral blood. FFPE samples were chosen torepresent 3 high-quality samples (HQ-FFPE) and 1 low-quality sample(LQ-FFPE). Starting with 100 ng of genomic DNA, they followed theNextera Rapid Capture protocol to tagment (tag and fragment) DNA,denature libraries to single-stranded DNA, hybridize to biotinylated
probes, and pool libraries from FF and FFPE samples.
“Our experience with the NRCE Kit has been very positive,” said Dr.
Astolfi. “We wanted to develop a protocol that was easy to implement
and that could be performed in any biomolecular laboratory. NRCE
possesses its own DNA tagmentation reaction and does not require
initial DNA fragmentation by Covaris or another method. The ability
to pool a different number of samples per reaction also makes
NRCE flexible for users with distinct needs. It’s important to notethat depending on the average size of the fragments, labs will needto determine the amount of input DNA and the concentration of thetransposon enzyme to use.”

Following library preparation, the team sequenced the libraries in a
2 × 100 bp whole-exome sequencing run on the HiScanSQTM System.
Sequencing generated an average of 57 million reads for FF and 56
million reads for HQ-FFPE samples. All other sequencing parameters
were comparable between FF and HQ-FFPE samples (Table 1).
Conversely, LQ-FFPE failed to meet sequencing thresholds and quality
parameters, with a much lower data yield (14 million reads) and low
average coverage.

Analysis and Results
Alignment and Variant Calling
After quality trimming and adapter removal, sequence reads were
mapped to the human reference genome hg19 with BWA software(Figure 3). Alignments were processed using SAMtools to remove
PCR duplicates and using GATK to perform local realignment aroundthe indel position, base quality score recalibration, and indel calling.
Variant calling was performed with MuTect to identify single nucleotide
variants (SNVs). Only variants that were covered at > 10× depth andmapped onto the 37 Mb NRCE target region were analyzed further.
“We observed that the quality of the variant call is lower in LQ-FFPE
samples,” said Valentina Indio, PhD, bioinformatics scientist at CIRC.
“In contrast, the number of variant calls and the quality of base calls in
HQ-FFPE samples are similar to FF samples.”

For Research Use Only. Not for use in diagnostic procedures.

April 2015

FF #1

FF #2

293 bp237 bp290 bp

HQ-FFPE #1230 bp

FF #3294 bp

HQ-FFPE #2

240 bpFF #4290 bp

HQ-FFPE #3

LQ-FFPE #4190 bp

Figure 2: Library Preparation Results—Library preparation yielded sufficient material for both FF and HQ-FFPE samples, even though average librarysize was slightly smaller for HQ-FFPE samples (235 bp vs 292 bp). In contrast, the LQ-FFPE sample yielded a significantly smaller library. Library profiles were generatedusing the Bioanalyzer (Agilent Technologies).

Raw Illumina data
BCL2FASTQ

GATK

Base recalibrator

Raw sequence data
Adapter removal
Quality and adapter trimming

MuTect

BWA

Mapping

Variant in NCRE
target region?

Local realignment

SNV calling

NoYes

Excluded

Variant coveredat > 10X?

No

Yes

Undetermined

Variant called inboth FF and FFPE?

No

Yes

False positive
Fasle negative

Shared variant

GATK

Indel calling

SAMtools
Duplicate removing
and sorting

Variant filtration

Somatic call
Somatic callFigure 3: Analysis Workflow—The CIRC researchers used an analysis workflow that includes alignment and variant calling, comparison of FF and FFPE results toidentify false positives and false negatives, and somatic variant identification.

Comparison of Sequencing Results for FF andFFPE Samples
The CIRC team analyzed the resulting data set to estimate theagreement between FFPE and FF data. Variants that appeared in both
FF and FFPE samples were designated “shared variants.”“HQ-FFPE samples showed a shared variant concordance of 94–96%,
while LQ-FFPE samples have a shared variant concordance of about50%,” Dr. Indio noted.

Somatic Variant Identification“To highlight somatic variants, we filtered the concordant variants
against databases of human variability, such as dbSNP5 and 1000
Genomes,”6 explained Dr. Indio. “We excluded the variants that aredefined as polymorphisms (1% incidence in the normal population),defining the remainder as somatic variants if they were absent in thenormal counterpart. We then selected the nonsynonymous mutations
and indels that lead to frameshift mutations. To prioritize the variants
correlated to disease, we used software to predict the effects of themutation on protein structure.”

For Research Use Only. Not for use in diagnostic procedures.

April 2015
Table 1: Sequencing Results
SampleBases > Q30

Trimmed BasesPCR Duplicates

Mapped Reads

Off-Target Reads

FF #1

83.6%

8.1%

7.8%

91.1%

51.5%

Average Coverage
71×

FF #2

84.0%

7.7%

7.6%

91.4%

52.7%

59×

FF #3

84.4%

9.3%

7.5%

91.2%

52.0%

61×

FF #4

84.0%

8.3%

7.1%

91.9%

50.8%

55×

HQ-FFPE #1

84.7%

9.4%

10.3%

88.4%

45.7%

65×

HQ-FFPE #2

85.0%

9.3%

10.4%

88.4%

47.1%

58×

HQ-FFPE #3

85.9%

9.1%

10.0%

89.0%

45.5%

77×

LQ-FFPE #4

82.0%

20.6%

12.0%

86.7%

38.8%

17×

“From this analysis, we identified 4, 13, 25, and 27 somatic mutationsin the 4 FF GIST samples. We defined the somatic mutations asshared variants if they were also present in FFPE samples (3, 13, 24,and 14, respectively),” noted Dr. Indio. “False-positive variants (presentonly in the FFPE samples) were negligible in HQ-FFPE. In contrast, in
LQ-FFPE, false positives represented > 40% of the identified variants.
Most of the false-positive calls in the LQ-FFPE were C to T and G
to A substitutions, an expected result of the cytosine deamination
due to formalin fixation. In the HQ-FFPE samples, the distribution ofsubstitution type is uniform and comparable to FF samples.”“We were surprised to perform
exome sequencing on FFPE
samples so successfully, withcomparable data for FF andFFPE samples.”Conclusions

The CIRC researchers plan to apply their FFPE exome sequencing
method to more tumor samples from different cancer types. “Wewant to expand this analysis to more cohorts of patients with detailed
clinical data,” Dr. Astolfi said. “We’re trying to understand how thegenetic basis of tumors might influence reactions to therapeutic drugs,
and how the molecular background of rare tumors differs from morecommon tumors.”

References
1.

2.

3.

4.

5.
6.

Pantaleo MA, Astolfi A, Indio V, et al. SDHA loss-of-function mutationsin KIT-PDGFRA wild-type gastrointestinal stromal tumors identified bymassively parallel sequencing. J Natl Cancer Inst. 2011;103:983-987.
Pantaleo MA, Astolfi A, Urbini M, et al. Analysis of all subunits, SDHA,SDHB, SDHC, SDHD, of the succinate dehydrogenase complex in KIT/PDGFRA wild-type GIST. Eur J Hum Genet. 2014;22:32-39.
Nannini M, Astolfi A, Urbini M, et al. Integrated genomic study of quadrupleWT GIST (KIT/PDGFRA/SDH/RAS pathway wild-type GIST). BMC Cancer.
2014;14:685.
Siwoski A, Ishkanian A, Garnis C, Zhang L, Rosin M, Lam WL. An efficientmethod for the assessment of DNA quality of archival microdissected
specimens. Mod Pathol. 2002;15:889-892.
dbSNP (www.ncbi.nlm.nih.gov/SNP)
1000 Genomes (www.1000genomes.org)

By assessing FFPE sample quality and only choosing high-quality
samples for sequencing, Dr. Astolfi’s team achieved exome
sequencing results with FFPE samples that were comparable to FF
samples. “We recommend that researchers not proceed with exome
enrichment and sequencing of LQ-FFPE samples, and instead selecthigh-quality samples to generate more useful data,” Dr. Astolfi said.
“For example, in our GIST database, we estimated that around 40%of FFPE specimens are of high quality. We were surprised to perform
exome sequencing on FFPE samples so successfully, with the data
generated comparable to that achieved with FF samples. By using
this method, we were able to include more GIST samples for which
the underlying tumorigenic event was not known. We hope to identify
at least 1 gene that seems to be related to the pathogenesis ofthese tumors.”

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For Research Use Only. Not for use in diagnostic procedures.
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Pub. No. 370-2015-001 Current as of 16 April 2015