Cell Reports, Volume 19

Supplemental Information

Enhanced Rate of Acquisition of Point

Mutations in Mouse Intestinal Adenomas

Compared to Normal Tissue

Natalia Lugli, Vasilis S. Dionellis, Paloma Ordóñez-Morán, Irene Kamileri, Sotirios K.Sotiriou, Joerg Huelsken, and Thanos D. Halazonetis

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This PDF file contains:

Figure S1

Supplemental Figure Legend

Tables S1-S4

Experimental Procedures

Supplemental References

Figure S1

A

B

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SUPPLEMENTAL FIGURE LEGEND

Figure S1. Copy Number Alterations (CNAs), Related to Figure 4

(A) CNA in tumor-derived organoid II-T1B resulting in deletion of the telomeric 13

Mb of the q arm of chromosome 4.

(B) CNA in tumor-derived organoid II-T1B resulting in a focal deletion of 0.23 Mb

within the q arm of chromosome 18, involving the Apc locus.

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Table S1. Sequencing coverage of normal and tumor-derived organoids, Related

to Figure 2

See separate file.

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Table S2. Single nucleotide substitutions in the coding regions of the sequenced

normal and tumor-derived organoids, Related to Figure 3

See separate file.

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Table S3. Indels in the coding regions of the sequenced normal and tumor-

derived organoids, Related to Figure 4

See separate file.

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Table S4. Estimated mutation rates per cell division per base pair in normal and

precancerous/cancer cells, calculated from data derived from high throughput

sequencing studies, Related to Figure 2

Study Model Type Mutation rate per cell division per bp (for exomes)

Current study Mouse Normal small intestine 0.37×10!!"

Adenoma small intestine 4×10!!"

Alexandrov et al., 2015 Human Colon cancer 19×10!!"

Nikolaev et al, 2012 Human

Hyperplastic polyps (normal)

Dysplastic polyps

(adenomas)

0.1×10!!"

20×10!!"

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EXPERIMENTAL PROCEDURES

Mice

The C57BL/6J-ApcMin/J mice were purchased from the Jackson Laboratory (Moser

et al., 1990). All mice were kept on a 12-hour light/dark cycle in an SPF room. The

mice, all of which were males, were sacrificed at 4 months of age; all experiments

were authorized by the Canton of Lausanne and were performed according to

accepted guidelines for animal handling.

Organoid preparation and culture

Small intestine tissue was isolated from C57BL/6J-ApcMin/J mice. The tissue was

washed in cold PBS; healthy looking regions were separated from regions where

tumors were present; each tumor was treated separately. Each healthy part and each

tumor was cut into 2-3 mm pieces and incubated in PBS-EDTA for 30 minutes at 4°C.

Subsequently, the tissue slices were washed in PBS-FBS and dissociated crypts were

filtered through 70 µm cell strainers (BD Bioscience). After centrifugation, the pellets

were resuspended in media, and single crypts were picked both from the healthy part

and from each of the tumors. Each single healthy crypt or each single tumor crypt was

mixed with cold Matrigel® (Corning) and plated in 96-well plates. After the Matrigel®

formed a gel, tissue culture media was added. The tissue culture media was based on

AdDMEM/F12 (Life Technologies) supplemented with B27 and N2 (Life

Technologies) and 1.25 µM N-Acetylcysteine (Sigma-Aldrich). The following growth

factors were also added: 50 ng/mL murine recombinant EGF (Life Technologies) and

R-spondin 1 Fc fusion and noggin-6xHis (Ordóñez-Morán et al., 2015). The medium

was changed every 3 days and organoids were split every 4-5 days by mechanical

dissociation.

Immunofluorescence

Organoids were removed from Matrigel® using Cell Recovery Solution (Corning),

then embedded in OCT (Tissue-Tek); slices 10 µm thick were cut using a cryostat

(Leica CM 1850). Organoid sections were fixed for 1h in 4% paraformaldehyde at

room temperature and stained using standard immunofluorescence techniques and

commercially available antibody for γH2AX (Upstate). The nuclei were

counterstained with DAPI and images were acquired using a Zeiss 700 confocal

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microscope.

DNA extraction and exome sequencing

DNA was extracted from both healthy and tumor organoids and from the liver of each

mouse using the Qiagen DNeasy Blood & Tissue Kit (Qiagen), according to the

manufacturer’s instructions, and quantified using a Qubit Fluorometer (Thermo

Fisher). The fragmentation of the extracted DNA was conducted using a Covaris

instrument. The resultant DNA fragments (~200bp) were subjected to exome capture

using the SureSelect Mouse All Exon kit (Agilent), followed by preparation of paired-

end libraries and sequencing on an Illumina Hiseq2000 platform.

Mapping and somatic SNS calling

The Burrows-Wheeler Alignment tool (v. 0.7.12) was used for the alignment of

sequenced reads on the mouse reference genome NCBI build GRCm38/mm10 and the

resultant sam files were processed by SAMtools v1.3:sam, in order to perform bam

transformation, sorting, removal of PCR duplicates and indexing. Base quality

recalibration and INDELs realignment was performed using the Genome Analysis

Tool Kit (GATK v.3.5.0). Somatic SNS calling was conducted using the default

parameters of MuTect2 algorithm, which is integrated in the GATK toolkit. In

addition to the embedded filtering process of MuTect (Cibulskis et al., 2013), we

applied some extra criteria regarding the somatic SNS calling: discard variants located

in snp loci, according to NCBI mouse dbsnp142; discard variants with depth less than

14x; shortlist variants with allele fraction of at least 20%; apply 10% tolerance to

strand bias, with the variant present in at least two times in forward (read 1) and

reverse (read 2) read respectively. Moreover, a panel of normal samples, containing

variants from all liver and normal intestine tissues of our mice, was used in order to

discard germline mutations with lower allele frequencies in the general population. In

order to test the sensitivity and the specificity of the algorithm and avoid possible

false positive calls, the aforementioned pipeline was repeated, switching the labels of

normal and tumor samples. Mutation rates were calculated using the following

formula:

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Identification of LOH events

To detect LOH events, we used the HaplotypeCaller algorithm of the GATK toolkit in

order to call all the heterozygous SNSs variants in liver and all the homozygous SNSs

variants in normal and tumor organoids, and then look for possible overlaps. Raw

variants were first called using GATK v.3.5.0 Haplotypecaller algorithm with default

settings and additional parameters: stand_emit_conf 10 and stand_call_conf 30. Low

quality variants were eliminated using GATK VariantFiltration v.3.5.0 with the

options --filterExpression "QD < 2.0 || FS > 60.0 || MQ < 40.0 || MQRankSum < -12.5

|| ReadPosRankSum < -8.0" --filterName "snv_filter" and --Description="Low

quality". The shortlist of variants was further filtered by applying extra criteria:

discard variants with depth less than 14x; shortlist variants with allele fraction of at

least 20%; apply 10% tolerance to strand bias, with the variant present in at least two

times in forward (read 1) and reverse (read 2) read respectively.

Somatic small indels calling

Raw variants were called using GATK Haplotypecaller v.3.5.0 as previously

described. The quality was assessed by discarding variants using GATK

VariantFiltration v.3.5.0 with the following parameters: --filterExpression "QD < 2.0

|| FS > 200.0 || MQRankSum < -20", --filterName "indel_filter" and --

Description="Low quality". Since this algorithm is not designed for the direct

detection of somatic mutations, we first called all the small indels in the exome of

liver samples and then excluded the same variants detected in both normal and tumor

organoids. Additional filter processes with the same extra criteria as in SNSs calling,

as well as the usage of a panel of normal samples, secured the elimination of false

positives calls.

Somatic mutation signatures

The mutational spectra of detected somatic SNSs were examined using the

SomaticSignature v.2.10.0 R package for the analysis of all the 96 possible

trinucleotide changes.

!"#$#%&' !"#$ =!!"#$%& !" !"#$%&' !"!#!"#$%& !" !"#$%!&'( ! /!"#$%& !"#$%% !"#"$"%&$

!"#$%&' !"#$%& !"#!"# !"#$%ℎ × 2

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Copy number alteration calling

For the detection of CNAs, bam files were analyzed by VarScan2 v.2.2.4 using the

recommended workflow (Koboldt et al., 2013). VarScan2 copy number mode run

with the parameters --min-coverage 50 --min-segment-size 400 --max-segment-size

500. The parameter --min-coverage 50 was also used during VarScan copyCaller step.

Circular binary segmentation (CBS) and plotting were handled by DNAcopy library

v.1.48.0 using the underneath script:

library(DNAcopy)

cn <- read.table("output.copynumber.called.center",header=T)

CNA.object <-CNA( genomdat = cn[,7], chrom = cn[,1], maploc = cn[,2], data.type

= "logratio", sampleid = "sample_name")

CNA.smoothed <- smooth.CNA(CNA.object)

segment <- segment(CNA.smoothed, verbose=0, min.width=2, undo.SD=3)

seg.pvalue <- segments.p(segment, ngrid=100, tol=1e-6, alpha=0.05,

search.range=100, nperm=1000)

write.table (seg.pvalue, file="DNAcopy.out.file", sep="\t")

plot(segment, plot.type = "p")

plot(segment, plot.type = "c")

In order to detect somatic CNA events, we excluded CNAs that were present in the

liver tissue of the mouse from which the organoids were prepared.

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SUPPLEMENTAL REFERENCES

Cibulskis, K., Lawrence, M.S., Carter, S.L., Sivachenko, A., Jaffe, D., Sougnez, C.,

Gabriel, S., Meyerson, M., Lander, E.S., and Getz, G. (2013). Sensitive detection of

somatic point mutations in impure and heterogeneous cancer samples. Nat Biotechnol.

31, 213-219.

Koboldt, D.C., Larson, D.E., and Wilson, R.K. (2013). Using VarScan 2 for Germline

Variant Calling and Somatic Mutation Detection. Curr. Protoc. Bioinformatics 44,

15.4.1-17.

Ordóñez-Morán, P., Dafflon, C., Imajo, M., Nishida, E., and Huelsken, J. (2015).

HOXA5 Counteracts Stem Cell Traits by Inhibiting Wnt Signaling in Colorectal

Cancer. Cancer Cell 28, 815-829.