www.sciencetranslationalmedicine.org/cgi/content/full/4/157/157ra143/DC1

Supplementary Materials for

Quantitative Image Analysis of Cellular Heterogeneity in Breast Tumors Complements Genomic Profiling

Yinyin Yuan,* Henrik Failmezger, Oscar M. Rueda, H. Raza Ali, Stefan Gräf, Suet-Feung Chin, Roland F. Schwarz, Christina Curtis, Mark J. Dunning, Helen Bardwell, Nicola Johnson, Sarah Doyle, Gulisa Turashvili, Elena Provenzano, Sam Aparicio,

Carlos Caldas, Florian Markowetz*

*To whom correspondence should be addressed. E-mail: florian.markowetz@cancer.org.uk (F.M.); yinyin.yuan@icr.ac.uk (Y.Y.)

Published 24 October 2012, Sci. Transl. Med. 4, 157ra143 (2012)

DOI: 10.1126/scitranslmed.3004330

This PDF file includes:

Materials and Methods Fig. S1. Visualization of cancer cell distribution in a tumor section. Fig. S2. Top genes expressed in different cell types and their enrichment. Fig. S3. Selecting cutoffs for determining the image-based low lymphocyte infiltration (LI) group in the discovery set of ER-negative samples. Fig. S4. A toy example of quantifying stromal cell spatial patterns with K statistics. Fig. S5. Stromal cell spatial pattern is not prognostic in ER-positive breast cancer in either the discovery or the validation cohort. Fig. S6. Patient stratification using combined image-based LI and stromal spatial pattern in ER-negative samples. Table S1. Classification accuracy by 10-fold cross-validation for artifacts, cancer cells, lymphocytes, and stromal cells based on the training set. Table S2. KEGG pathways enriched in the top 500 genes expressed in cancer cells, stromal cells, and lymphocytes. Table S3. Gene Ontology Biological Process enriched in the top 500 genes expressed in cancer cells, stromal cells, and lymphocytes.

Other Supplementary Material for this manuscript includes the following: (available at www.sciencetranslationalmedicine.org/cgi/content/full/4/157/157ra143/DC1)

Sweave file (PDF format)

METHODS AND MATERIALS

Sample collection and microarray profiling

The discovery set included 323 primary fresh frozen breast tumor samples with appropriate ethi-

cal approval from one of the five sites contributing to METABRIC (Molecular Taxonomy of

Breast Cancer International Consortium). The validation set contained 241 primary fresh frozen

breast tumor samples collected and stained at another METABRIC site. Samples were excluded

if the corresponding H&E image were deemed of too low of quality, such as out-of-focus or

fragmented tissues. Three 5-µm sections for H&E staining were taken during the sectioning for

nucleic acid extractions at three separate points: start, middle, and end, thereby generating a slide

containing three H&E sections per tumor. Some cases had fewer sections because the tissues

were too small. All H&E slides were scanned using the Aperio system at 20X.

DNA and RNA were extracted from ten 30-µm sections each from fresh frozen tumors

using the DNeasy Blood and Tissue Kit and the miRNeasy Kit (Qiagen, Crawley, UK) on the

QIAcube (Qiagen) according to the manufacturer’s instructions. DNA was hybridized to

Affymetrix SNP 6.0 arrays per the manufacturer’s instructions at AROS Applied Biotechnology

(Aarhus, Denmark). Samples that met the quality control criterion established by AROS and

suggested by the Affymetrix Genotyping Console v2.1 were subject to further in-house quality

assessment. For copy-number segmentation, Affymetrix SNP 6.0 arrays and Illumina expression

data were processed as described in (12).

Automated image-processing for quantifying tumor cellular contents

Our image-processing pipeline implemented as an R package, CRImage, is described in our

Sweave file, together with the codes and files for reproducing all of our results.

Image-processing validation

We describe image-processing results on our sample sets and technical details for validating

quantitative output from the pipeline using various methods including cross-validation, correla-

tion with categorical pathological scores, correlation with gene expression, and comparison with

pathological cell counts. We applied the image-processing pipeline on all samples in parallel on

a high-performance computer cluster. For each sample and each sub-image, the pipeline classi-

fied and calculated the total number of cancer cells, lymphocytes, and stromal cells, as well as

the image area. We took the median of sub-image scores as the summarizing cellular content

scores for a sample, as these were found to be the most robust.

Cross-validation. We assessed the classification accuracy by 10-fold cross-validation,

with or without the use of kernel smoothing (KS) (table S1). Only the initial training set created

by the pathologist was used to ensure all labels are correct.

Comparison with categorical pathological scores. The images were subject to histo-

pathological review to assess tumor cellularity and lymphocytic infiltration. Tumor cellularity

was scored visually by a pathologist (G.T.) in a semi-quantitative fashion on all the sections on

the slide, where cellularity values were binned such that “low cellularity” corresponded to sam-

ples with <40% tumor DNA, “moderate cellularity” corresponded to 40 to 70% tumor DNA, and

samples with >70% tumor DNA were considered to have “high cellularity.” Pathological lym-

phocytic infiltration was scored separately by three pathologists (H.R.A., G.T., E.P.) as absent,

mild, or severe: Absent if there were no lymphocytes, mild if there was a light scattering of lym-

phocytes, and severe if there was a prominent lymphocytic infiltrate.

Comparison with quantitative pathological cell counts. Each whole-tumor slide con-

tained large number of cells [median 61,090 ± 59,285 (SD)]. These numbers of cells cannot be

counted exhaustively by visual inspection; thus, 20 sub-images were randomly selected from the

discovery set to meet the following criteria: Relatively good quality as determined by an auto-

mated scoring scheme implemented in the package; containing at least 200 cells and at most

3,200 cells, and the images were from different tumors. Because of the high number of cells in

the sub-images [median 1483.5 ± 1424.7 (SD)], each of these selected sub-images was then di-

vided into four 1,000×1,000 pixel small images. From there, the one with the largest tissue area

was selected, resulting in 20 manageable images.

Correlation with gene expression data. With gene expression data, we can validate the

estimated cell proportions by cell-type–specific genes co-expressed with the cell abundance in

the discovery set. Expression heat map for 20 genes of highest positive correlations with cell

proportions are shown in fig. S2. We applied enrichment analysis in KEGG (15) and GO (16) to

the genes whose expression was highly correlated with the cellular proportions of each cell type.

Bioconductor R packages KEGG.db and GO.db were used as the pathway databases.

Quantitative cancer cell proportions enable microarray copy-number data correction

We describe validation experiments for our correction algorithm, which involved the use of the

validation set, fluorescence in situ hybridization (FISH), and the dilution series experiment. To

correct for the effects from normal cell contamination, we segmented the copy number log ratio

of the sum of the intensities (LR) using R packages DNAcopy (38) and mergeLevels (39). We

then computed signal-to-noise level of copy-number profiles as the ratio between the variance of

the segmented means (averaged by their lengths) and the mean of the variances of the LRs within

each of the segments. We then applied the following correction method and computed the same

ratio for the corrected LRs.

Algorithm for cellularity correction. We modeled the effect of contamination from

non-cancer cells in the LR and BAF observed estimators, as suggested for LR (37) and BAF (9):

(eqn S1)

(eqn S2)

where IA and IB are the intensity values for alleles A and B, respectively.

The algorithm we used to obtain corrected LR and BAF measures is:

1. Segment the observed LR using DNAcopy (38) and mergeLevels (39).

2. Compute the B Allele frequency (BAF) for each segment as the median of the BAF of its

SNPs if their 95th percentile is larger than 0.45 (corresponding to a BAF with a 3-banded

pattern profile), or as the median of the folded BAFs (that is, applying 1−BAF to all values

larger than 0.5) if there are at least 85% of the BAF values smaller than 0.15 (2-banded

pattern), or as the median of the folded BAFs larger than 0.15 (4-banded pattern).

3. Apply eqn. S3 and S4 to obtain corrected intensities for each segment:

(eqn S3)

LRobs = log2(c(IA + IB )− 2(1− c))− log2(2),

BAFobs = 2π

arctancIB + (1− c)cIA + (1− c)

IA = 2LROBS +1 + c −1− tan(BAFOBSπ / 2)(1− c)

c(tan(BAFOBSπ / 2)+1)

(eqn S4)

where the quantitative estimate of the cancer cell proportion, c, was obtained using the im-

age analysis pipeline. Observed LR and BAFs for each segment were obtained in steps 1

and 2. Negative corrected values were considered as 0 and the alleles were renamed to let

B be the major one.

4. Compute corrected LR values for each SNP as the original LR values plus the difference

between corrected and original segmented means

5. Compute corrected BAF values for each heterozygous SNP with allelic imbalance as the

original value plus (or minus if the original BAF value was larger than 0.5) the difference

between corrected and original BAF for the segment.

FISH validation. We were interested in whether copy-number correction results in more

accurate detection of HER2 amplifications. We used FISH scores to confirm HER2-amplified

cases and tested its concordance with uncorrected and corrected HER2 microarray copy number.

To summarize copy-number changes in a gene, we used a method to account for both the length

and altitudes of copy-number segments in a gene. For one gene and one sample, when there are

different segments in the gene, the mean of segmented values weighted by the segment length is

taken as the copy-number profile. In our experience, this approach leads to more robust and ac-

curate results than other alternatives, such as taking the maximum absolute, the mean, or the me-

dian copy number.

In total there were 78 samples that are both in our cohort and on the tissue microarray

with valid FISH scores. Based on the FISH scores, we categorized the 78 samples into non-

amplified (<4 copies, HER2:CEN-17 ratio <2), low-level amplification (4−6 copies, ratio 2−3),

and high-level amplification (>6 copies, ratio >3). Based on the microarray SNP6.0 data in

HER2, we summarized the copy-number data into four levels: <3 [log2 ratio signal < log2(3/2)],

3-4 [log2(3/2)-log2(4/2)], 4-6 [log2(4/2)-log2(6/2)], >6 [>log2(6/2)]. Standard deviation of the

copy-number levels in the three categories are (0, 0.96, 0.76) before correction, and (0, 0.50,

0.98) after correction. Therefore, the correction does not lead to arbitrary signal inflation.

Dilution series experiment. To validate our cellularity correction algorithm, we diluted

the DNA from a breast cancer cell line HCC2218 (T) with its matched normal HCC2218BL

IB = 2LROBS +1 + 2c − 2− cIA

c

(MN) at a 10% stepwise (i.e. 90% T/10% MN, 80% T/20% MN, etc.). The diluted DNA was hy-

bridized onto Affymetrix SNP6.0 arrays. Samples were segmented and correction was applied as

detailed in the main text, using default values for the arguments of the DNAcopy and

mergeLevels algorithms. The normal MN cell line (0% tumor) was used for detecting CNVs; all

regions with a segmented mean outside the range [–0.35, 0.25] were considered somatic altera-

tions and they were removed. “Gold standard calls” were obtained from the 100% tumor sample

T using the thresholds 0.25 for gains and −0.35 for losses. Then, for the rest of the arrays, differ-

ent thresholds for gains and losses were applied and the sensitivity and specificity for detecting

the ’gold standard calls’ were computed.

Fluorescence in situ hybridization (FISH)

A breast tumor tissue microarray was constructed as described previously (40). Sections (5 µm)

were prepared from a tissue microarray block containing 172 cores and placed on positively

charged slides. The Abbott PathVysion HER2 DNA probe kit was used in accordance with the

manufacturer’s instructions with a modified pre-treatment method of 0.2 M HCL for 26 minutes,

10 mM citric buffer pH 6.0 at 80°C for 3 hours, and 5×10-3 mg/ml proteinase kinase for 32

minutes. Scoring was performed in each core on 30 non-overlapping tumor cell nuclei to deter-

mine mean HER2 and CEP17 copy number (centromere, control) and HER2/CEP17 ratio.

Quantitative scoring of stromal cell spatial patterns in ER-negative tumors

To avoid effects from varying cell densities in different samples and to lower computational cost,

we performed resampling on one type of cells at a time. For each resampling run, n cells were

sampled where n = 500×Area, so n is proportional to Area, which is defined as the number of

valid tissue pixels. Function Kest from the R package spatstat (41) was used to compute K(r).

The distance variable r was set to the range of [0−200] (the average distance from the center to

perimeter of cancer cells is 0.89). The physical meaning of this range is that the K-function is set

to detect spatial clustering of a radius of ~100 cancer cells lining up. We used the translation

edge correction function (42) for its feasibility and efficiency with complex window. The empir-

ical K-curve was then compared to the theoretical K-curve for Completely Random Point process

K(r)=πr2. Large area between the empirical and theoretical K-curves suggests spatial clustering,

while small area indicates spatial regularity. To summarize K for a resampling run, we took the

area between the empirical K-curve and the theoretical K-curve as the result. Finally, the mean of

all 500 resampling results was taken as the final K-score. This was performed independently for

a cell type and for an image.

Here we showed two examples of different cell patterns in H&E sub-images (fig. S4, A and

D). One case had clustered stromal cells and the other had scattered stromal cells (fig. S4, B and

E). The empirical K-curves (black) showed large deviation from the theoretical curve (red) in the

clustered case, and small deviation in the scattered case, correctly capturing the difference be-

tween the spatial patterns of the corresponding images (fig. S4, C and F).

Cell spatial pattern is a compartment-specific, subtype-specific, and independent prog-

nostic factor. We stratified the ER-negative patients into two groups by the quantitative K-

scores for stromal cells. We used 25% and 75% quantiles to dichotomize the K-scores into low,

moderate, and high groups. The low and high groups were then merged into one ‘extreme’

group. To test whether cell spatial pattern is an independent prognostic factor in different sub-

types, we used the grouping based on K-scores as input to univariate and multivariate Cox re-

gression in Table 1.

Supplementary figures

Fig. S1: Visualization of cancer cell distribution in a tumor section. (A) The original H&E-stained image. (B) The section area. (C) Detected cancer cells are shown as black dots. (D) Can-cer cell–density map built by a quartic kernel where green indicates higher densities of cancer cells. (E) Lymphocyte density map is shown in dark blue. (F) Stromal cell density map is shown in pink. Scale bars, 1 mm.

Fig. S2: Top genes expressed in different cell types and their enrichment. (A to C) For cancer cells (A), lymphocytes (B), and stromal cells (C), the heat map on the left shows the expression of the top 20 genes highly correlated with the cell proportions. And on the right an enrichment map shows top 10 GO biological processes significantly enriched in the top 500 expressed genes. The sizes of the ontologies are correlated with the node sizes, and the overlaps among ontologies are indicated by the thickness of the edges. Color of the nodes indicates the significance of en-richment, the darker the more significant.

Fig. S3: Selecting cutoffs for determining the image-based low lymphocyte infiltration (LI) group in the discovery set of ER-negative samples. The test range 5-20% was used to give rea-sonable group sizes. Log-rank P-values were compared between the low and high LI groups, where the cut-off with lowest P-value was marked by a dotted line.

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Fig. S4: A toy example of quantifying stromal cell spatial patterns with K statistics. Two sets of sub-images exemplify how the K-score can differentiate between a case with clustered stromal cells (A to C) and a case with scattered stromal cells (D to F). (A) and (D) show two sub-images of H&E sections. Scale bars, 100 µm. (B) and (E) plot the detected stromal cells by our classifier (tissue area not shown). The large area between the empirical K-curve (black) and the theoretical K-curve for Completely Random Point Process (red) suggests spatial clustering in (C). The small area between the curves in (F) indicates spatial regularity. The theoretical curves are the same in (C) and (F).

Fig. S5: Stromal cell spatial pattern is not prognostic in ER-positive breast cancer in either the discovery or the validation cohort.

Fig. S6: Patient stratification using combined image-based LI and stromal spatial pattern in ER-negative samples.

Supplementary Tables

Table S1: Classification accuracy by 10-fold cross-validation for artifacts, cancer cells, lympho-cytes, and stromal cells based on the training set. The table shows the confusion matrix of the real classes of the cells in the columns and the predicted classes in the rows, where Precision was calculated by true positives/(true positives + false positives) and Recall was calculated by true positives/(true positives + false negatives).

Classes SVM prediction SVM+KS prediction

Artifact Cancer Lympho-cyte

Stro-mal

Recall (%)

Artifact Cancer Lympho-cyte

Stro-mal

Recall (%)

Artifact 311 2 0 2 98.7 307 5 0 3 95.7 Cancer 1 196 23 18 82.4 0 200 23 15 84 Lympho-cyte

0 9 92 1 90.2 0 10 92 0 90.2

Stromal 7 18 7 184 85.2 4 19 7 186 86.1 Precision (%)

97.5 87.1 75.4 89.8 98.7 85.5 75.4 91.2

Table S2. KEGG pathways enriched in the top 500 genes expressed in cancer cells, stromal cells, and lymphocytes. P-values were calculated using a hypergeometric test.

Cell type Rank Expected Hits Size P-value KEGG pathway

Cancer

1 1.70 18 117 1.1e-12 Cell cycle

2 14.00 47 974 2.2e-11 Metabolic pathways

3 0.53 9 36 4.8e-09 DNA replication

4 1.50 13 103 2.0e-08 Oxidative phosphorylation

5 1.30 11 88 2.5e-07 Pyrimidine metabolism

6 1.60 12 107 2.5e-07 Oocyte meiosis

7 1.40 10 96 4.9e-06 Parkinson’s disease

8 2.10 12 143 5.8e-06 Alzheimer’s disease

9 2.10 12 147 7.4e-06 Huntington’s disease

10 1.50 10 104 9.7e-06 Spliceosome

Stromal

1 2.70 31 187 2.7e-22 Focal adhesion

2 1.20 19 79 3.0e-17 ECM-receptor interaction

3 1.40 12 96 7.7e-08 Amoebiasis

4 4.40 20 301 2.0e-07 Pathways in cancer

5 1.80 12 124 1.4e-06 Cell adhesion molecules (CAMs)

6 1.50 11 104 1.5e-06 Leukocyte transendothelial migration

7 1.20 9 79 6.7e-06 Hypertrophic cardiomyopathy (HCM)

8 1.20 9 85 1.1e-05 Dilated cardiomyopathy

9 1.60 10 109 1.4e-05 Vascular smooth muscle contraction

10 1.10 8 72 2.2e-05 Adherens junction

1 0.47 9 32 3.7e-09 Primary immunodeficiency

2 1.40 13 97 2.5e-08 T cell receptor signaling pathway

Lymphocyte

3 0.98 11 67 3.4e-08 B cell receptor signaling pathway

4 1.70 13 116 1.8e-07 Natural killer cell mediated cytotoxicity

5 3.50 17 238 1.5e-06 Cytokine-cytokine receptor interaction

6 1.40 10 95 1.1e-05 Chagas disease

7 1.10 8 75 9.4e-05 Hematopoietic cell lineage

8 2.50 12 173 1.0e-04 Chemokine signaling pathway

9 0.67 6 46 2.1e-04 Malaria

10 1.10 7 75 5.4e-04 Apoptosis

Table S3: Gene Ontology Biological Process enriched in the top 500 genes ex-pressed in cancer cells, stromal cells, and lymphocytes. P-values were calculated us-ing a hypergeometric test.

Cell type Rank Expected Hits Size P-value GO Biological Process

Cancer

1 3.30 39 224 1.2e-28 Cell division

2 6.30 49 431 3.2e-27 Cell cycle

3 2.60 28 177 1.0e-19 Mitosis

4 0.57 12 39 9.5e-13 Chromosome segregation

5 2.00 19 138 4.8e-09 DNA replication

6 2.50 20 173 1.3e-11 DNA repair

7 1.80 15 124 3.0e-09 Protein folding

8 3.20 19 217 5.3e-09 RNA splicing

9 0.22 6 15 3.9e-08 Double-strand break repair via homolo-gous recombination

10 0.74 9 51 1.4e-07 DNA recombination

Stromal

1 6.50 60 448 2.9e-37 Cell adhesion

2 12.00 47 816 5.2e-14 Multicellular organismal development

3 0.37 11 25 5.2e-14 Collagen fibril organization

4 1.80 19 122 1.6e-13 Skeletal system development

5 4.10 27 278 1.6e-13 Negative regulation of cell proliferation

6 0.82 14 56 2.3e-13 Extracellular matrix organization

7 1.60 16 108 3.1e-11 Angiogenesis

8 0.82 12 56 9.4e-11 Lung development

9 0.51 10 35 1.2e-10 Blood vessel development

10 15.00 46 1016 3.0e-10 Signal transduction

1 4.20 23 287 2.0e-09 Immune response

2 2.20 17 148 2.0e-09 Cell surface receptor–linked signaling

Lymphocyte

pathway

3 0.47 8 32 6.6e-08 T cell activation

4 15.00 41 1016 1.8e-07 Signal transduction

5 13.00 34 872 7.5e-06 Regulation

6 12.00 31 790 1.9e-05 Regulation of transcription

7 3.90 16 269 3.7e-05 Interspecies interaction between organ-isms

8 0.22 4 15 1.0e-04 Regulation of immune response

9 3.00 13 207 1.3e-04 Inflammatory response

10 0.70 6 48 2.7e-04 Response to cytokine stimulus