Supplementary Information

Supplementary Methods

Transcriptome microarray assay

The transcriptome profiles of 198 samples, including 165 triple-negative

breast cancer (TNBC) tissues (patients diagnosed from January 1, 2011 to

December 31, 2012) and 33 paired adjacent normal breast tissues, were

determined using the Affymetrix Human Transcriptome Array 2.0 (HTA 2.0)

GeneChips (Affymetrix, Santa Clara, CA, USA) (1, 2). According to the

standard Affymetrix protocol, biotinylated cDNA was prepared from 250 ng

total RNA using the Ambion® WT Expression Kit. Subsequently, 5.5 μg of

labeled cDNA was hybridized to the HTA 2.0 microarray. Following

hybridization and washing, the GeneChips were scanned with the GeneChip®

Scanner 3000 7G using Affymetrix® GeneChip Command Console (AGCC)

software. The Affymetrix Expression Console (version 1.2.1) implementation

of the Robust Multichip Analysis (RMA) algorithm was used for quantile

normalization and background correction. Combat Software was applied to

adjust the normalized intensity to remove batch effects.

Quantitative real-time PCR (qRT-PCR)

We detected the expression of candidate mRNAs and lncRNAs using

qRT-PCR in all the 275 patients and another cohort of 82 TNBC patients

received neo-adjuvant chemotherapy. cDNA was synthesized using the

PrimeScriptTM RT reagent kit (Takara Bio Inc., Otsu, Japan), and the SYBR®

Premix Ex TaqTM kit (Takara Bio Inc., Otsu, Japan) and ABI PRISM 7900HT

Sequence Detection System (Applied Biosystems, Foster City, CA, USA) were

used for qRT-PCR analysis. All experiments were conducted following the 1

standard protocol provided by the manufacturer. The results were normalized

to U6 expression.

Identification of mRNAs and lncRNAs for signature construction

The detailed filtration process of filtration is illustrated in Supplementary

Figure S1. The random variance model (RVM) (3) corrected t test was applied

to select RNAs that were differentially expressed between 33 pairs of breast

tumor tissues and adjacent normal tissues. We selected the differentially

expressed mRNAs (fold change >2 or <0.33 with a false discovery rate [FDR]

<0.001). The thresholds for differentially expressed lncRNAs were as follows:

fold change >1.5 and FDR <0.001. All differently expressed RNAs were

included in pool A (Supplementary Figure S2). By combining the RNA

expression data from microarray and follow-up data obtained from 165 TNBC

patients, we obtained a set of RNAs correlated with recurrence-free survival

(RFS) that we placed in pool B. For the selection of mRNAs, a P value of less

than 0.1 (log rank test) was determined to be significant, and duplicated

mRNAs were excluded. For the selection of lncRNAs, a P value of less than

0.2 (log rank test) was determined to be significant, as the correlations were

less significant than those observed with the mRNAs. Additionally, only

intergenic lncRNAs were included. We selected the overlapped mRNAs and

lncRNAs between pool A and pool B. These RNAs were both tumor-specific

and correlated with RFS. For each of the selected RNAs, we designed three

different pairs of primers and performed qRT-PCR analysis on the 33 paired

TNBC and normal tissues mentioned previously. If all three primers for a given

RNA did not amplify successfully (i.e., the CT values were undetermined), we

designed three different primers. The qRT-PCR results were analyzed using a

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paired t test to validate the differential expression patterns, and P<0.1 was set

as significant. RNAs that failed to amplify with all six pairs of primers or that

showed expression patterns that were not concordant between the

transcriptome and qRT-PCR analyses were excluded. After this filtration step,

13 mRNAs and 6 lncRNAs were included. We performed qRT-PCR on all 137

samples in the training set to amplify the 19 RNAs. If there was discordance

between the expression trend of a given RNA and the results of survival

analysis, the RNA was excluded. After this exclusion step, the remaining

seven mRNAs and four lncRNAs were added to the prognostic signature one

by one until the model reached the highest area under curve (AUC) according

to the time-dependent receiver operating characteristic (ROC) curve (4-6).

Finally, three mRNAs and two lncRNAs were included in the final signature.

Continuing adding RNAs into the signature would only diminish the

signature’s performance (data not shown).

Development and validation of the integrated mRNA-lncRNA signature

based on qRT-PCR data

The detailed process of study design, patient selection and analytical

strategy is illustrated in Supplementary Figure S2. In addition to the 165

TNBC patients who provided samples for microarray experiments, we further

recruited another 110 TNBC patients who were diagnosed from January 1,

2010 to December 31, 2010 and randomly classified all 275 TNBC patients

into the training set (137 patients) and validation set (138 patients). All of the

275 TNBC tumor samples were tested using qRT-PCR, as stated before. We

selected an optimum cutoff score for the relative expression of each RNA

using X-tile plots (X-tile software version 3.6.1, Yale University School of

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Medicine, New Haven, CT, USA) based on the association with patient RFS in

the training set (5, 7) (Supplementary Table S3). Cox proportional hazard

regression modeling was applied to analyze correlation between RNA

expression and RFS. The regression coefficients of each of the RNA were

used to construct a recurrence score formula (5, 6, 8). The optimum cutoff for

the model was determined by the ROC curve by using the Youden Index (9,

10). The integrated mRNA-lncRNA signature was validated in a validation set

and a neoadjuvant cohort using the same coefficients derived from the

training cohort.

RNA interference

Each target sequence was designed using BLOCK-iT RNAi Designer (Life

Technologies, Wilmington, DE, USA) and filtered using NCBI BLAST to

reduce off-target effect. All the siRNA oligonucleotides used in the study were

synthesised by GenePharma Co. Ltd. (Shanghai). For cell ability-based siRNA

screening, the reverse transfection in 96-well plate was performed as follows:

Briefly, for each well in 96-well plate, 0.3 l Lipofectamine RNAiMAX was

dissolved in 25 l Opti-MEM medium and 7.5 pmol siRNA duplex in other 25

l Opti-MEM medium. Then the two reagents were mixed, incubated for 15

min and added to each well, together with 3×103 cells suspensed in 100 l

antibiotic-free growth medium. The transfection medium was changed with

fresh growth medium for 12 h post-transfection, and the cells were incubated

with another 72 h before cell viability detection by CCK-8 (Dojindo

Laboratories, Kumamoto, Japan). The sequence for siRNA negative control

was: 5-UUCUCCGAACGUGUCACGU-3. All raw data were collected at 450

nm wave length.

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Measurement of cell proliferation

Cells transfected with siRNA (5 × 103 per well) were seeded in 96-well

plates. Indicated concentrations of paclitaxel were added into the wells after 6 

h from seeding and incubation for the next 48 h, while in control group

paclitaxel was replaced with PBS. Cell viability was assessed by analyzing the

metabolic reduction of WST-8 (CCK-8 cell proliferation assay), as described

previously (12). A 6 parameter nonlinear regression was used to calculate IC50

values using Sigma Plot 2001 software (Systat Software, Chicago, IL, USA).

Cell invasion assay

The Boyden chamber invasion assay was used to assess the invasion

ability. 800 μl of medium (containing 0.1% bovine serum albumin) and cells

were added to the lower and upper compartment of the chamber, respectively.

After incubating for 24 h, non-migrated cells were removed, and cells that had

migrated through the Matrigel filter (BD Biosciences, Franklin Lakes, NJ,

USA) were counted.

Cell cycle arrest assay

For cycle assay, 2 × 105 cells per well were seeded in 6-well plates and

transfected with siRNA. After 48 h transfection, cells were treated with 5 nM

paclitaxel for 16 h. For cycle arrest assay, cells were stained with propidium

iodide and tested using flow cytometry according to the standard protocol

(13).

Co-expression analysis of lncRNAs and mRNAs

To identify interactions between mRNAs and lncRNAs, we constructed co-

expression networks. We pre-processed the data using the median

expression value of all transcripts and then screened for differentially

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expressed lncRNAs and mRNAs. For each pair of genes analyzed, we

calculated the Pearson correlation.

Gene Ontology (GO) and pathway analysis

GO analysis was applied to analyze the main function of genes co-

expressed with lncRNAs according to the GO database, which is the key

functional classification of the National Center for Biotechnology Information

(NCBI). The analysis can organize genes into hierarchical categories and

uncover the gene regulatory network based on biological process and

molecular function. Meanwhile, pathway analysis was used to determine the

significant pathways of the differential genes according to the Kyoto

Encyclopedia of Genes and Genomes database (KEGG). The Pearson Chi-

square test and Fisher’s exact test were used to select the significant

pathway.

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Supplementary Tables

Supplementary Table S1. Selected mRNAs and lncRNAs by comparing the expression profiles in 33 paired tumor and adjacent

normal tissues of triple-negative breast cancer.

Symbol ProbeSet Category Tumor/normal

Fold change(tumor/normal)

Fold changeP value FDR Log rank

P value

CHRDL1 TC0X001278.hg.1 mRNA down-regulation 0.32 4.70E-06 2.24E-05 7.59E-04

FCGR1A TC01001172.hg.1 mRNA up-regulation 2.17 2.00E-07 1.73E-06 0.0926

RSAD2 TC02000034.hg.1 mRNA up-regulation 2.19 1.34E-04 3.93E-04 0.0456HIF1A-

AS2 TC14002040.hg.1 lncRNA up-regulation 1.65 < 1E-07 < 1E-07 0.0783

AK124454a TC19002388.hg.1 lncRNA up-regulation 2.01 1.55E-05 7.17E-05 0.1681

aGene symbol are not available for the noncoding RNA, thus GenBank accession number was used to mark the RNA.Abbreviations: FDR, false discovery rate.

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Supplementary Table S2. Primers for the real-time RT-PCR analysis of

mRNAs and lncRNAs included in the signature.

Symbol Category

Primer

FCGR1A-ForwardFCGR1A-Reverse mRNA

TGGTGAATACAGGTGCCAGACCGTGAAGACTCTGCTGGA

RSAD2-ForwardRSAD2-Reverse mRNA

AGCATCGTGAGCAATGGAAGCGGCCAATAAGGACATTGAC

CHRDL1-ForwardCHRDL1-Reverse mRNA

ACAAGAAGTACAGAGTGGGTGAGGGCAGCACAGATGAGGAAT

HIF1A-AS2-ForwardHIF1A-AS2-Reverse lncRNA

CAACATACATTAAGGTGATGGCAGCTTCAACACCTCCAACTCA

AK124454-ForwardAK124454-Reverse lncRNA

TGTCTCTGCAGTCTCTTAAGCAGGGACAGCATGCACTTTGTT

U6-ForwardU6-Reverse

Internal control

CTCGCTTCGGCAGCACAAACGCTTCACGAATTTGCGT

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Supplementary Table S3. Cutoff value for each RNA.

CHRDL3 FCGR1A RSAD2 HIF1A-AS2 AK124454Δcta 3.74 6.93 4.85 3.54 11.62

a Δct value (U6 expression as reference) was used to relatively represent each

RNAs expression level. Cutoff values for the expression of RNAs were

decided by the X-tile software based on the association with the patients’

relapse free survival. For each patient, RNA expression level were marked as

high expression if the Δct less than the cutoff value and vice versa. In the risk

calculating formula, high expression status equals 1 while low expression

status equals 0.

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Supplementary Table S4. Univariate Cox proportional hazards regression

analysis of the integrated RNA signature and clinicopathological

characteristics with recurrence-free survival.

Training Set

HR (95%CI) P

Validation Set

HR (95%CI) P

Age(≤50y vs >50y) 0.33 (0.14-0.77) 0.011 0.73 (0.31-1.72) 0.467

Menopause(No vs Yes) 0.52 (0.22-1.20) 0.125 0.58 (0.24-1.37) 0.213

Tumor grade(≤II vs >II) 0.88 (0.36-2.16) 0.787 2.53 (0.73-8.76) 0.144

Tumor size(≤2cm vs >2cm) 1.46 (0.56-3.81) 0.436 3.01 (0.89-10.24) 0.077

Positive LNs(≤3 vs >3) 5.69 (2.33-13.90) <0.001 3.61 (1.50-8.71) 0.004

Ki67(≤20% vs >20%) 2.37 (0.76-7.43) 0.139 1.12 (0.445-2.81) 0.812

Radiotherapy(no vs yes) 5.40 (1.98-14.76) 0.001 1.74 (0.71-4.24) 0.222

Chemotherapy(non-taxane vs taxane) 1.00 (0.42-2.35) 0.997 0.97 (0.38-2.43) 0.940

Integrated RNA signature (low risk vs. high risk) 2.45 (1.29-4.65) 0.006 2.97 (1.52-5.79) 0.001

Abbreviations: CI, confidence interval; HR, hazard ratio; LN, lymph node.

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Supplementary Table S5. Clinicopathologic characteristics of patients with

triple-negative breast cancer who received neoadjuvant chemotherapy.

NCT set (n=82)Characteristics No. Low risk (%) High risk (%)Age (y)

MedianIQR

4842-52

5048-52

4641-52

≤50 51 (62.2) 27 (56.2) 24 (70.6) >50 31 (37.8) 21 (43.8) 10 (29.4)Menopausal status Premenopausal 52 (63.4) 25 (52.1) 27 (79.4) Postmenopausal 30 (36.6) 23 (47.9) 7 (20.6)Pre-NCT tumor size (cm)

≤2 2 (2.4) 2 (4.2) 0 (0) >2, ≤5 26 (31.7) 17 (35.4) 9 (26.5)

>5 54 (65.9) 29 (60.4) 25 (73.5)Pre-NCT tumor grade I-II 32 (39.0) 20 (41.7) 12 (35.3) III 50 (61.0) 28 (58.3) 22 (64.7)Pre-NCT LN status Negative

Positive19 (23.2)63 (76.8)

13 (27.1)35 (72.9)

6 (17.6)28 (82.4)

Pathologic response pCR 29 (35.4) 23 (47.9) 6 (17.6) Non-pCR 53 (64.6) 25 (52.1) 28 (82.4)Follow-up time (mo)

MedianIQR

43.519.0-66.0

51.524.5-66.0

32.513.0-73.0

RFS event 28 11 17Abbreviations: IQR, interquartile range; LN, lymph node; NCT, neoadjuvant chemotherapy; pCR, pathological complete remission.

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Supplementary Table S6. Multivariate logistic regression analysis of the

integrated RNA signature and clinicopathological characteristics with

pathological complete remission.

NCT set

Variablea ORb (95%CI) Pc

Age(≤50y vs >50y) 0.85 (0.31-2.39) 0.764

Menopausal status(pre vs post) 0.73 (0.26-2.07) 0.555

Pre-NCT tumor size(≤2cm vs >2cm) 0.54 (0.20-1.48) 0.227

Pre-NCT tumor grade(≤II vs >II) 0.71 (0.26-1.90) 0.494

Pre-NCT LN status(negative vs positive) 0.76 (0.24-2.34) 0.626

Integrated RNA signature(low risk vs high risk) 0.23 (0.07-0.71) 0.011

Abbreviations: LN, lymph node; NCT, neoadjuvant chemotherapy; OR, odds ratio.aAdjusted by multivariate logistic regression models including age, menopausal status, pre-NCT tumor size, pre-NCT tumor grade,pre-NCT LN status and integrated RNA signature.bOdds ratio for the likelihood of having pathological complete remission, for an increment in expression of one unit based on logistic regression.cP value for likelihood ratio test derived from probit regression.

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Supplementary Table S7. Top ten mRNAs co-expressed with lncRNAs

AK124454 and HIF1A-AS2.

AK124454 HIF1A-AS2mRNA Coefficient mRNA CoefficientCHEK1 0.5928561 HIF1A 0.7392446

C11orf82 0.5818136 IL8 0.564712DEPDC1 0.5562664 ERO1L 0.5548231SPC25 0.5526421 CLEC5A 0.5244528XRCC2 0.5495506 PGK1 0.5846322DNA2 0.5482257 P4HA1 0.5783645KIF11 0.535331106 PLAUR 0.5484504FLRT2 -0.536526 PGAM1 0.5479989

GPR124 -0.545691 PLOD2 0.5102739MAF -0.555115 HLF -0.526427

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Supplementary Table S8. Top ten gene-ontology terms and pathways in which the co-expressed mRNAs involved.

AK124454 HIF1A-AS2GO terms P Pathway P GO terms P Pathway P

mitotic prometaphase 5.87E-8 ECM-receptor

interaction 0.001 regulation of glycolysis 4.80E-6 Glycolysis /

Gluconeogenesis 4.86E-4

mitotic cell cycle 1.36E-6 Focal adhesion 0.045response to endoplasmic reticulum stress

7.41E-5 Biosynthesis of amino acids 0.001

M phase of mitotic cell cycle 1.78E-6 Mismatch repair 0.071 cellular response to

interleukin-1 9.91E-5 HIF-1 signaling pathway 0.001

cell adhesion 5.00E-6 Homologous recombination 0.086 gluconeogenesis 2.15E-4 Proteoglycans in

cancer 0.006

cell division 1.26E-5 DNA replication 0.110 glycolysis 2.25E-4 Metabolic pathways 0.011

mitotic anaphase 3.20E-5 PI3K-Akt signaling pathway 0.119 signal transduction 3.50E-4 Pathways in

cancer 0.012

DNA repair 2.45E-4 ABC transporters 0.134endoplasmic reticulum unfolded protein response

7.69E-4Glycine, serine and threonine metabolism

0.037

mitotic spindle organization 3.15E-4

Intestinal immune network for IgA production

0.155 cellular protein metabolic process 0.001 Bladder cancer 0.038

spindle organization 3.96E-4 p53 signaling

pathway 0.206vascular endothelial growth factor production

0.001 Lysine degradation 0.049

meiosis 0.003Complement and coagulation cascades

0.209 neural fold elevation formation 0.001 Malaria 0.049

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Supplementary Figures

Supplementary Figure S1

Supplementary Figure S1. Flowchart of RNA filtration. Abbreviations: FDR, false discovery

rate; HTA, Human Transcriptome Array; TNBC, triple-negative breast cancer; qRT-PCR:

quantitative real-time PCR.15

Supplementary Figure S2

Supplementary Figure S2. Flowchart of study design, patient selection and analytical strategy. Abbreviations: HTA, Human

Transcriptome Array; RFS, recurrence-free survival; TNBC, triple-negative breast cancer.

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Supplementary Figure S3

Supplementary Figure S3. Hierarchical clustering of 33 paired tumor and adjacent normal

breast tissues with the 5 differentially expressed RNAs using Euclidean distance and average

linkage clustering. Every row represents an individual mRNA/lncRNA, and each column

represents an individual sample. Pseudocolors indicate transcript levels from low to high on a

log 2 scale from -2 to 2, ranging from a low association strength (dark, black) to high (bright,

red, or green).

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Supplementary Figure S4

Supplementary Figure S4. Validation of the expression of each mRNA/lncRNA incorporated in

the integrated signature in the 33 paired tumor and adjacent normal tissues from the training set

using quantitative real-time polymerase chain reaction (qRT-PCR). Expression of these mRNAs

and lncRNAs measured by qRT-PCR was notably different between tumor and non-cancer

breast tissues and were significantly correlated with their microarray data.

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Supplementary Figure S5

Supplementary Figure S5. Predictive values of taxane benefit using the integrated signature.

(A) Estimates of recurrence-free survival (RFS) according to the scores calculated by the

integrated mRNA-lncRNA signature in the neoadjuvant chemotherapy cohort (n=82). (B) Time-

dependent ROC curves were plotted to assess the efficacy of the signature in predicting three-

year recurrence-free survival, with area under curve (AUC) reported.

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