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 min 30 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 ident i fy ing mutat ions 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

123

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

BRCA

2&chr13.32915246&T>C

MCL1&chr1.150551572&C>T

CCNE1&chr19.30303715&–&CC

ERBB

2&chr17.37879977&T>C

NF1&chr17.29663624&TT>A

A

MCL1&chr1.150547747&G>A

AKT1&chr14.105244803&GGC

T&>

Hotspo

t&chr2.212495343&–&T

MDM

2&chr12.69217911&AC&>

APC&chr5.112176756&T>A

ERBB

4&chr2.212812097&T>C

FGFR2&chr10.123284055&C>

T

BRCA

2&chr13.32906566&–&A

FBXW

7&chr4.153271309&A>T

MSH

2&chr2.47630550&C>

G

MSH

2&chr2.47702451&G>

T

Hotspo

t&chr2.25463483&G>A

IGF1R&chr15.99338368&T>G

NF1&chr17.29664829&T>C

BRCA

2&chr13.32906867&T>C

ATM&chr11.108151708&–&A

GATA

3&chr10.8115688&T>A

TSC2

&chr16.2112942&C>T

NF1&chr17.29663625&T>A

STK1

1&chr19.1207091&A>

C

APC&chr5.112176605&C>A

BRCA

2&chr13.32912477&A>G

NF1&chr17.29679248&T>A

JAK3

&chr19.17948031&G>

T

NOTC

H1&chr9.139391636&G>

A

FGFR4&chr5.176517985&A>G

FBXW

7&chr4.153303453&C>T

KRAS

%chr12.25398284%C>

G

KRAS

%chr12.25398284%C>

G

BRCA

2&chr13.32906729&A>C

SMAR

CB1&chr22.24135861&C>G

CCNE1&chr19.30305234&T>G

NOTC

H1&chr9.139417432&G>

A

NOTC

H1&chr9.139393427&G>

T

NF2&chr22.30069207&C>

T

IL6&chr7.22768707&T>G

DCUN1D

1&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

BRCA

1&chr17.41226394&C>T

TSC1

&chr9.135771689&G>

C

PTCH

1&chr9.98231361&G>

C

ATM$chr11

.108

1214

11$.$TTTT

Hotspo

t$chr11

.314

207$C>

GKD

R$chr4.559

7297

4$T>A

ATM$chr11

.108

1517

08$.$A

ATM$chr11

.108

1517

08$.$T

NOTC

H1$chr9.13

9409

952$C>

TFG

FR3$chr4.180

6187

$C>A

KDR$chr4.559

7294

6$A>

GNOTC

H1$chr9.13

9413

271$A>

GCC

ND1

$chr11

.694

5892

3$A>

GER

BB4$chr2.212

5783

80$.$AA

AAA

FBXW

7$chr4.153

2713

08$T>A

NPM

1$chr5.170

8375

14$.$TTTTTT

NF1$chr17

.296

7924

6$G>

ANF1$chr17

.296

7924

6$T>A

SMAR

CB1$chr22.24

1340

64$C>A

BRCA

1$chr17.41

2263

94$C>T

BRCA

2$chr13.32

9152

46$T>C

APC$chr5.112

1113

10$A>T

STK1

1$chr19.1221314$C$.

ERBB

2$chr17.37

8507

77$C>T

GAS6$chr13

.114

5595

35$T>C

JAK3

$chr19

.179

4803

1$G>

TKD

R$chr4.559

8023

9$C>

TRB

1$chr13.49

0339

19$C>G

PNP$chr14.20941309$.$T

MET$chr7.11

6322

791$A>

CCD

44$chr11

.351

8749

6$T>C

FBXW

7$chr4.153

3034

53$C>T

MDM

4$chr1.204

4970

54$T>C

Hotspo

t$chr1.11

5252

261$T>C

MSH

2$chr2.476

4146

9$A>

CNF1$chr17

.296

6362

4$TT>A

ATP

53%chr17

.757

4003

%G>A

TP53

%chr17

.757

4003

%G>A

GAS6$chr13

.114

5303

10$G>A

PIK3

R1'ch

r5.675

9110

6'A>

GNOTC

H1$chr9.13

9393

427$G>

TFG

FR2$chr10.12

3284

055$C>

TNOTC

H1$chr9.13

9391

438$G>

TFG

FR4$chr5.176

5235

62$C>A

NF1$chr17

.296

6482

9$T>C

ATM$chr11

.108

1517

08$T>A

FBXW

7$chr4.153

2682

42$G>A

IDH1

$chr2.20

9113

193$C>

ARB

1$chr13.49

0514

81$T>A

RB1$chr13.48

9542

47$.$AT

NOTC

H1$chr9.13

9417

356$CG

>GT

NKX

2.1$chr14.36

9860

23$.$AA

AABR

CA2$chr13.32

9124

77$A>G

FGFR4$chr5.176

5179

85$A>G

MSH

2$chr2.476

3055

0$C>

GIGF1R$chr15.99

3383

68$T>G

KDR$chr4.559

8023

9$C>

TSTK1

1$chr19.12

0709

1$A>

CEG

FR$chr7.55

2490

63$G>A

EGFR$chr7.55

1727

68$T>C

Hotspo

t$chr7.64

2694

1$C>

TBR

CA2$chr13.32

9533

88$T>C

BRCA

2$chr13.32

9036

85$C>T

FGFR4$chr5.176

5235

62$C>A

TP53

$chr17

.757

9472

$G>C

EGFR$chr7.55

2547

96$A>G

NF1$chr17

.296

6362

5$T>A

WT1$chr11

.324

5629

8$G>

ATET2$chr4.10

6156

163$G>

AHo

tspo

t$chr2.25

4675

08$T>C

FGFR1$chr8$382

9301

2$C>

TTSC2

$chr16

.211

2942

$C>T

STK1

1$chr19.12

0709

1$A>

CPTCH

1$chr9.982

7890

0$C>

TPIK3

R1$chr5.67

5228

51$A>C

TP53

$chr17

.757

8373

$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)

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