Tumors are becoming recognized as genetically heterogeneous masses of cells with different clonal histories. Identifying the mutations present in these heterogeneous masses can lead to important insights into the future behavior of the tumor and possible intervention mechanisms. However, the rarity of pathogenic mutations in small subsets of cells can make identification of such alleles difficult. In this study, we demonstrate a complete workflow that facilitates the identification of rare and novel alleles from FFPE tumor sections. We collected small regions with different cellular morphologies from lung tumor samples using laser capture microdissection, extracted both DNA and RNA from these regions, and characterized mutations present and transcript abundances by using Ion AmpliSeq™ targeted sequencing. We show that LCM facilitates the detection of alleles that are not detectable in macrodissected tissue scrapes. We also show that different regions of a tumor have very different patterns of alleles detectable and have a great deal of genetic diversity. Finally, we show that RNA expression patterns are also clearly different in the different regions. Interestingly, dissected regions with similar gross tissue morphologies display differences in alleles present and RNA expression patterns. These results suggest how we may in the future use this method to analyze mutations present in a tumor is to microdissect different subregions of the tumor, and using Ion AmpliSeq™ panels to identify the alleles present in those subregions.

1. Identification of rare and novel alleles in FFPE tumor samples using laser
capture microdissection (LCM) and Ion AmpliSeq™ sequencing technologies
Stephen M. Jackson1, Paul Choppa2, Kulvinda Kaur2, Kristen Schmidt1, Kamini Varma1
1 Thermo Fisher Scientific, 200 Oyster Point Blvd, South San Francisco, CA; 2 Thermo Fisher Scientific LifeLabs, 910 Riverside Parkway, West Sacramento, CA
Thermo Fisher Scientific • 5791 Van Allen Way • Carlsbad, CA 92008 • lifetechnologies.com
ABSTRACT
Tumors are becoming recognized as genetically heterogeneous masses of cells with different
clonal histories. Identifying the mutations present in these heterogeneous masses can lead to
important insights into the future behavior of the tumor and possible intervention mechanisms.
However, the rarity of pathogenic mutations in small subsets of cells can make identification of
such alleles difficult. In this study, we demonstrate a complete workflow that facilitates the
identification of rare and novel alleles from FFPE tumor sections. We collected small regions with
different cellular morphologies from lung tumor samples using laser capture microdissection,
extracted both DNA and RNA from these regions, and characterized mutations present and
transcript abundances by using Ion AmpliSeq™ targeted sequencing. We show that LCM
facilitates the detection of alleles that are not detectable in macrodissected tissue scrapes. We
also show that different regions of a tumor have very different patterns of alleles detectable and
have a great deal of genetic diversity. Finally, we show that RNA expression patterns are also
clearly different in the different regions. Interestingly, dissected regions with similar gross tissue
morphologies display differences in alleles present and RNA expression patterns. These results
suggest how we may in the future use this method to analyze mutations present in a tumor is to
microdissect different subregions of the tumor, and using Ion AmpliSeq™ panels to identify the
alleles present in those subregions.
MATERIALS AND METHODS
Tissue staining and LCM
Human lung FFPE tissue blocks were acquired from a commercial vendor (Asterand, Detroit, MI). The
blocks were sectioned at 7 µm, mounted on slides, and stored at room temperature until use. Pathogenic
cells were identified by staining with hematoxylin and eosin; adjacent sections were left unstained. Tumor
cells in the hematoxylin and eosin–stained section were marked by a certified pathologist. Prior to laser
capture, an unstained section was stained with the Arcturus® Paradise® PLUS FFPE LCM Staining Kit
following the protocol indicated in the user manual. The stained slides and CapSure® Macro LCM Caps
were then loaded onto the ArcturusXT™ LCM system. Circles 2,000 µm in diameter were defined in
ArcturusXT™ LCM Software from the tumor regions and collected on the CapSure® Macro LCM Caps. For
comparison, whole tissue scrapes (WTS) from a slide, representing a mixed cell population sample, were
processed. Genomic DNA was extracted from the caps by following the protocol in the PicoPure® DNA
Extraction Kit with one minor modification: elution ws overnight at 65°C in 20 µL of extraction buffer with
periodic vortexing. Extracted DNA was quantified on a Qubit® Fluorometer. Typically, yields were between
25–60 ng from the LCM-captured specimens and around 2 µg from the WTS.
Ion AmpliSeq™ and Oncomine® panel library construction
We used an Ion AmpliSeq™ Colon and Lung Cancer Research Panel targeting 22 genes commonly
mutated in lung and colon tumors. To ensure complete coverage of these genes, the panel consists of 92
pairs of primers in a single pool, with an average amplicon length of 162 bp. We also used the
Oncomine® Cancer Research Panel targeting 143 genes (2,531 amplicons) known to be commonly
mutated in tumors. This larger panel allowed us to query many more genes and mutations, providing a
more complete catalog of the different alleles that may be present. Ion AmpliSeq™ DNA libraries were
constructed using 1–10 ng of DNA from each laser-captured isolate. The samples were processed using
the Ion AmpliSeq™ Library Kit 2.0 and the Ion Library Equalizer™ Kit according to the recommended
protocols. The template and enrichment steps were carried out on the Ion OneTouch™ 2 System. The
samples were applied to an Ion 318™ Chip v2 and processed for sequence information on an Ion PGM™
System.
Ion RNA AmpliSeq™ panel library construction
A custom Ion AmpliSeq™ RNA Panel was constructed consisting of 53 transcripts commonly mutated or
differentially expressed in human tumor samples. Tissue samples were collected onto CapSure® Macro
LCM Caps from 2,000 µm diameter circles. RNA was extracted from the caps following the protocol in the
Arcturus® Paradise® PLUS RNA Extraction and Isolation Kit and quantified on a Qubit® Fluorometer.
Total RNA (10 ng) was reverse transcribed using reagents in the Ion AmpliSeq™ RNA Library Kit as
described in the manual. The targets were amplified by PCR for 22 cycles, and libraries were prepared for
sequencing following the protocol described in the manual. Final library concentration was determined
using the Ion Library Quantification Kit and quality checked on a Bioanalyzer™ system (Agilent
Technologies). Libraries were adjusted to a final concentration of 40 pM. Sequencing templates were
prepared and loaded onto Ion 318™ Chips v2 BC using an Ion Chef™ instrument and sequenced on an
Ion PGM™ instrument. Mapped reads for each transcript were normalized to reads per million reads
(RPM) using the Ion AmpliSeq™ plugin for Torrent Suite™ Software.
Picopure® DNA
extraction
ArcturusXT™
Laser Capture
Microdissection
Ion AmpliSeq™
library
construction
Ion PGM™
Sequencer
Torrent Suite™
Software
Analysis
~1 hour 15 min30 min 4 hours 30 min
CONCLUSIONS
In this study, workflow for analyzing DNA variants and RNA transcript levels allows the efficient
analysis of many sequences from small amounts of FFPE tissue–extracted starting material.
Finally, we demonstrated that because tumors are heterogeneous, efficient cataloging of the
different variants in a tumor requires sampling several different regions of the tumor.
These data suggest we showed that the ArcturusXT™ LCM system can reveal the presence of
variant alleles that cannot be detected in whole tissue scrapes. In addition, we showed with the
Ion AmpliSeq™ that a combination of mutation detection and RNA expression analysis could
reveal clones of cells with similar histories or epigenetic modifications. Such clones may not be
evident by confining analyses to sequences obtained from a preparation of a whole tumor mass.
Some clones of cells might arise due to the activities of specific oncogenes or other targetable
molecules affecting entire pathways. By identifying these clones from a heterogeneous mixture,
it may be possible to design interventions targeted to specific cells. Therefore, understanding
tumor heterogeneity is extremely important, as it has been shown to affect responses to
molecularly targeted treatments of cancers [6].
REFERENCES
1. Fidler IJ, Kripke ML (1977) Metastasis results from preexisting variant cells within a malignant tumor. Science
197:893–895.
3. Liegl B, Kepten I, Le C, et al. (2008) Heterogeneity of kinase inhibitor resistance mechanisms in GIST. J Pathol
216:64–74.
4. Maley CC, Galipeau PC, Finley JC, et al. (2006) Genetic clonal diversity predicts progression to esophageal
adenocarcinoma. Nat Genet 38:468–473.
5. Taniguchi K, Okami J, Kodama K, et al. (2008) Intratumor heterogeneity of epidermal growth
factor receptor mutations in lung cancer and its correlation to the response to gefitinib. Cancer Sci
99:929–935.
6. Yancovitz M, Litterman A, Yoon J, et al. (2012) Intra- and inter-tumor heterogeneity of BRAFV600E
mutations in primary and metastatic melanoma. PLoS ONE 7:e29336.
Figure 5. Gene expression analysis of lung tumor
samples. (Left) Transcript abundances in the different
regions of the 2182 sample. Although the WTS is a
reflection of transcript abundances in all regions, some
transcripts are differently expressed in the WTS when
compared to the different regions. (Right) Transcript
abundances in the 2162 sample. In this sample,
heterogeneity in the different regions is more
pronounced. Grey = transcript not detected in that
region.
Figure 8. Co-analysis of variant allele frequencies and transcript abundances in the 2182 tissue
sample. Subsets of the variants and transcripts show similar patterns of distribution. This might reflect a
common history of cells that contain those patterns. Grey = transcript or variant not detected in that region.
RESULTS
Current models of cancer progression postulate that
tumors arise from a single mutated cell, followed by
clonal expansion and acquisition of additional genetic
and genomic alterations. The continual accumulation of
new mutations can result in the appearance of different
tumor subclones with a variety of phenotypic
advantages. For example, these mutations could confer
the ability to proliferate uncontrollably, leave the primary
site where the original tumor mass occurred, and
colonize different organs [1]. Intra-tumor heterogeneity,
the presence of more than one clone of cancer cells
within a given tumor mass, and inter-tumor
heterogeneity, the presence of different genetic
alterations in different metastatic tumors from a single
source, have been identified in several tumor types
[2-5]. A complete understanding of the biology, and
ultimately the design of treatments, requires efficiently
identifying mutations present and transcript
abundances in subsets of cells in a tumor.
Example of laser capture microdissection. Images
are micrographs of intact tissue (left), tissue after
removal of region of interest (center), and cells
within region of interest (right). The ArcturusXT™
LCM facilitates LCM by offering the powerful
combination of laser capture and laser cutting for
microdissection applications. The solidstate
infrared (IR) laser, exclusive to the ArcturusXT™
system, delivers a gentle capture technique that
helps preserve biomolecular integrity and is ideal
for single cells and small numbers of cells. The
solid-state UV laser permits speed and precision
and is well suited for microdissecting dense tissue
structures and for capturing large numbers of
cells. Together, these two lasers provide the
flexibility to capture individual cells and large
regions from the same sample with minimal
damage to the molecules contained within those
regions.
Figure 2. Mutation analysis of lung tumor samples. (Left) Analysis of different subregions of sample
2162. Data from the Ion AmpliSeq™ custom lung cancer panel (dark blue bars) and Oncomine® Cancer
Research Panel (red bars) are shown. WTS: whole tissue scrape. (Right) Analysis of different subregions
of sample 2182. Data from the Ion AmpliSeq™ custom lung cancer panel (dark blue and red bars) and
Oncomine® Cancer Research Panel (OCRP, light blue and orange bars) are shown. Note that the
frequencies of alleles detected are very similar between the two panels.
Sample 2182
Stage is pT1b pN0.
Tumor content is 60%
1
2
3
1
2
3
4
5
Sample 2162
Stage is pT3
Tumor content unknown
INTRODUCTION
Figure 1. Hematoxylin and eosin–stained
sections and LCM-collected regions of lung
tumors used for this study. (Left) Sample 2182,
a stage pT1b pN0 tumor. (Right) Sample 2162, a
stage pT3 tumor. Numbered circles are regions
collected by LCM, and pen markings are
pathologist-supplied descriptions of morphology.
Adeno: adenoma-like, carcinoid: carcinoma-like,
Sq: squamous.
Figure 3. Allele frequency analysis of lung tumor samples. (Top) Analysis of allele frequency
heterogeneity in subregions of tumor sample 2182. Forty-nine allelic variants were detected in this tumor.
Each allele frequency was normalized to the average of frequencies in the different regions, log2
transformed, and plotted relative to the average. Red bars indicate frequencies greater than the mean,
and green bars indicate frequencies lower than the mean of all regions. The dotted line represents the
axis (equal to zero, or no difference from the mean) for each region and allele. For clarity, alleles that are
unique to a region are shown as maximal red bars, and no green bars. Note that most of these variants
are novel alleles and currently have no defined tumor relevance. Alleles that overlap in the Ion
AmpliSeq™ custom lung cancer panel and Oncomine® Cancer Research Panel are shown with bold
type. (Bottom) Analysis of allele frequency heterogeneity in subregions of sample 2162. Seventy-two
different variant alleles were found in this tumor sample. Frequencies are illustrated using the method
described above. Red italicized text highlights a pathogenic allele detected only by LCM.
WTS
Region 1
Region 2
Region 3
BRCA2&chr13.32915246&T>C
MCL1&chr1.150551572&C>T
CCNE1&chr19.30303715&–&CC
ERBB2&chr17.37879977&T>C
NF1&chr17.29663624&TT>AA
MCL1&chr1.150547747&G>A
AKT1&chr14.105244803&GGCT&>
Hotspot&chr2.212495343&–&T
MDM2&chr12.69217911&AC&>
APC&chr5.112176756&T>A
ERBB4&chr2.212812097&T>C
FGFR2&chr10.123284055&C>T
BRCA2&chr13.32906566&–&A
FBXW7&chr4.153271309&A>T
MSH2&chr2.47630550&C>G
MSH2&chr2.47702451&G>T
Hotspot&chr2.25463483&G>A
IGF1R&chr15.99338368&T>G
NF1&chr17.29664829&T>C
BRCA2&chr13.32906867&T>C
ATM&chr11.108151708&–&A
GATA3&chr10.8115688&T>A
TSC2&chr16.2112942&C>T
NF1&chr17.29663625&T>A
STK11&chr19.1207091&A>C
APC&chr5.112176605&C>A
BRCA2&chr13.32912477&A>G
NF1&chr17.29679248&T>A
JAK3&chr19.17948031&G>T
NOTCH1&chr9.139391636&G>A
FGFR4&chr5.176517985&A>G
FBXW7&chr4.153303453&C>T
KRAS%chr12.25398284%C>G
KRAS%chr12.25398284%C>G
BRCA2&chr13.32906729&A>C
SMARCB1&chr22.24135861&C>G
CCNE1&chr19.30305234&T>G
NOTCH1&chr9.139417432&G>A
NOTCH1&chr9.139393427&G>T
NF2&chr22.30069207&C>T
IL6&chr7.22768707&T>G
DCUN1D1&chr3.182672832&–&TTTTT
APC&chr5.112111309&T>A
ATM&chr11.108121411&–&TTTT
SOX2&chr3.181430890&G>T
NF1&chr17.29482988&C>T
TP53%chr17.7578413%C>A
TP53%chr17.7578413%C>A
BRCA1&chr17.41226394&C>T
TSC1&chr9.135771689&G>C
PTCH1&chr9.98231361&G>C
ATM$chr11.108121411$.$TTTT
Hotspot$chr11.314207$C>G
KDR$chr4.55972974$T>A
ATM$chr11.108151708$.$A
ATM$chr11.108151708$.$T
NOTCH1$chr9.139409952$C>T
FGFR3$chr4.1806187$C>A
KDR$chr4.55972946$A>G
NOTCH1$chr9.139413271$A>G
CCND1$chr11.69458923$A>G
ERBB4$chr2.212578380$.$AAAAA
FBXW7$chr4.153271308$T>A
NPM1$chr5.170837514$.$TTTTTT
NF1$chr17.29679246$G>A
NF1$chr17.29679246$T>A
SMARCB1$chr22.24134064$C>A
BRCA1$chr17.41226394$C>T
BRCA2$chr13.32915246$T>C
APC$chr5.112111310$A>T
STK11$chr19.1221314$C$.
ERBB2$chr17.37850777$C>T
GAS6$chr13.114559535$T>C
JAK3$chr19.17948031$G>T
KDR$chr4.55980239$C>T
RB1$chr13.49033919$C>G
PNP$chr14.20941309$.$T
MET$chr7.116322791$A>C
CD44$chr11.35187496$T>C
FBXW7$chr4.153303453$C>T
MDM4$chr1.204497054$T>C
Hotspot$chr1.115252261$T>C
MSH2$chr2.47641469$A>C
NF1$chr17.29663624$TT>AA
TP53%chr17.7574003%G>A
TP53%chr17.7574003%G>A
GAS6$chr13.114530310$G>A
PIK3R1'chr5.67591106'A>G
NOTCH1$chr9.139393427$G>T
FGFR2$chr10.123284055$C>T
NOTCH1$chr9.139391438$G>T
FGFR4$chr5.176523562$C>A
NF1$chr17.29664829$T>C
ATM$chr11.108151708$T>A
FBXW7$chr4.153268242$G>A
IDH1$chr2.209113193$C>A
RB1$chr13.49051481$T>A
RB1$chr13.48954247$.$AT
NOTCH1$chr9.139417356$CG>GT
NKX2.1$chr14.36986023$.$AAAA
BRCA2$chr13.32912477$A>G
FGFR4$chr5.176517985$A>G
MSH2$chr2.47630550$C>G
IGF1R$chr15.99338368$T>G
KDR$chr4.55980239$C>T
STK11$chr19.1207091$A>C
EGFR$chr7.55249063$G>A
EGFR$chr7.55172768$T>C
Hotspot$chr7.6426941$C>T
BRCA2$chr13.32953388$T>C
BRCA2$chr13.32903685$C>T
FGFR4$chr5.176523562$C>A
TP53$chr17.7579472$G>C
EGFR$chr7.55254796$A>G
NF1$chr17.29663625$T>A
WT1$chr11.32456298$G>A
TET2$chr4.106156163$G>A
Hotspot$chr2.25467508$T>C
FGFR1$chr8$38293012$C>T
TSC2$chr16.2112942$C>T
STK11$chr19.1207091$A>C
PTCH1$chr9.98278900$C>T
PIK3R1$chr5.67522851$A>C
TP53$chr17.7578373$T>C
WTS
Region 1
Region 2
Region 3
Region 4
Region 5
Figure 4. Fraction of unique alleles detected in
WTS specimens or LCM regions. Analyzing
individual LCM regions identified more unique alleles
than the WTS. Notably, there were alleles identified
in the WTS that were not detected in any of the
regions. This might reflect the fact that not all of the
tumor area was sampled by LCM.
Table 1. Frequencies of alleles detected only in
microdissected regions
Kleppe & Levine, Nature
Medicine 20, 342–344 (2014)
For research use only. Not for use in diagnostic procedures
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