A 41 gene signature derived from breast cancer stem cells as a predictor of survival

A 41-gene signature derived from breast cancer stem cells as a predictor of survival

Zhi-Qiang Yin 1?,Jian-Jun Liu 1?,Ying-Chun Xu 1,Jian Yu 2,Guo-Hui Ding 2,Feng Yang 1,Lei Tang 1,Bao-Hong Liu 2,Yue Ma 1,Yu-Wei Xia 1,Xiao-Lin Lin 1and Hong-Xia Wang 1,3*

Introduction

Personalized medicine,the selection of therapy based on a patient ’s individual characteristics,may result in better outcomes than the use of generalized medicine [1-4].Prognostic factors commonly applied in breast cancer include age,tumor size,lymph node involvement,patho-logical grade,and status of HER-2,Ki-67,and several hormone receptors,including both estrogen receptor (ER)and progesterone receptor (PR)[5,6].Although sev-eral guidelines have been developed to assist clinicians in selecting patients who are at high risk of recurrence,it still remains a challenge to distinguish patients who

have poor prognosis and require demanding adjuvant systemic therapy from those who could be spared such treatment.Due to the complexity of the disease,several other factors have been investigated for their potential to predict breast cancer outcome.However,most have only limited predictive power [7,8].

Recent findings support the concept that a rare popu-lation of cells,termed cancer stem-like cells (CSCs),is the cellular origin of cancer [9,10].Such findings imply that it is these CSCs that are responsible for tumor initi-ation,progression,and response to therapy [11,12].Therefore,an advance in our knowledge of the proper-ties of CSCs has become a topic of considerable interest.We previously identified a rare population of breast cancer stem cells (BCSCs)from tissue [13,14].Human cancer is characterized by high heterogeneity in gene ex-pression and phenotype,both of which influence tumor growth rate and drug sensitivity .We performed expression

*Correspondence:whx365@https://www.wendangku.net/doc/e51030487.html, ?

Equal contributors 1

Shanghai Renji Hospital,Shanghai Jiaotong University School of Medicine,Shanghai 200127,China 3

Department of Oncology,Renji Hospital,School of Medicine,Shanghai Jiaotong University,Shanghai 200127,China

Full list of author information is available at the end of the

article

?2014Yin et al.;licensee BioMed Central Ltd.This is an Open Access article distributed under the terms of the Creative Commons Attribution License (https://www.wendangku.net/doc/e51030487.html,/licenses/by/2.0),which permits unrestricted use,distribution,and reproduction in any medium,provided the original work is properly credited.The Creative Commons Public Domain Dedication waiver (https://www.wendangku.net/doc/e51030487.html,/publicdomain/zero/1.0/)applies to the data made available in this article,unless otherwise stated.

Yin et al.Journal of Experimental &Clinical Cancer Research 2014,33:49https://www.wendangku.net/doc/e51030487.html,/content/33/1/49

profiling to identify signaling pathways enriched in BCSCs. According to the gene expression profile,we found that sixty-three probe sets corresponding to forty-one genes showed greater than a four-fold difference in BCSCs com-pared to non-BCSCs.We hypothesized that this BCSC signature might be useful as a classification system since it outperformed most other clinical variables in predicting the likelihood of distant metastases and overall survival (OS)in breast cancer patients.

A more accurate means of prognostication in breast cancer will improve the selection of patients for adjuvant systemic therapy and will improve clinical decisions and strategies used to treat patients with this disease.There-fore,the present study was conducted to further evaluate the forty-one gene signature as a tool to accurately esti-mate the risks of metastases and survival in breast can-cer patients.

Methods

Database of patients

Normalized gene expression data,together with the pa-tient’s characteristics,were retrieved from the public GEO database(https://www.wendangku.net/doc/e51030487.html,/geo;acces-sion number GSE7390).For each patient,the informa-tion generated from the dataset included surgery type, angioinvasion(lymph vascular invasion),histopatho-logical grading,ER status,OS,distant metastasis-free survival(DMFS),clinical risk group according to St.Gallen criteria,National Provider Identifier(NPI)criteria, Adjuvant online(AOL)(https://www.wendangku.net/doc/e51030487.html,), Veridex signature,MammaPrint,and Oncotype Dx. Study design

The41DEGs(differential expressed genes)correspond to63probe sets.Based on these probe sets,we obtained relevant expression values of patients from GSE7390. The centroid expression of these probe sets was applied as the patient classification threshold.Based on the threshold of the prognostic signatures,breast cancer pa-tients in the dataset can be classified into two separate groups—patients with high expression(high-EL)of the prognostic signature and patients with low expression (low-EL)of the prognostic signature.

Statistical analysis

To assess the prognostic value of the41-gene signature, we utilized the Kaplan-Meier estimator to plot survival curves and the log-rank test to compare differences be-tween two groups[15].Fisher's exact test was employed to investigate the relevance between the41-gene signa-ture and clinical factors.Standard Cox proportional haz-ards regression were implemented to predict OS and DMFS.The performance of the41-gene signature and other standard criteria,including AOL,NPI,St.Gallen,Veridex,Oncotype DX,and MammaPrint were evalu-ated in terms of LHR and Akaike information criterion (AIC)in a full model(all systems included)and in a series of reduced models where each interested factor was removed once each time.When removed from the full model,the best option results in the largest drop in LHRχ2and an increase in AIC.All statistical analyses were performed by the R programming package with rms. End points considered in this study were time from diagnosis to distant metastases(DMFS)and OS,which was defined as time from diagnosis to death by any cause.The linearity of the relation between the relative hazard ratio and the diameter of the tumor,age,and ER expression level were tested using the Wald test for non-linear components of restricted cubic splines.No evi-dence for nonlinearity was found(P=.83for age,P=.75 for tumor diameter,P=.65for the number of positive nodes,and P=.27for ER expression).We evaluated whether the hazard ratio was proportional using the method of Grambsch and Therneau.

Results

Characteristics of patients

The study was carried out with frozen archived tumor material from early stage breast cancer patients using the Affymetrix HG-U133A chip as previously described by the TRANSBIG consortium[16].

Pattern of the41-gene expression profile in breast cancer patients

Functional annotation of these41genes(Table1)pro-vides insight into the underlying biological mechanism leading to breast cancer tumorigenesis and the cellular signaling pathways regulating BCSCs.

The gene-expression values of the41markers for all 198tumors in this study are shown in Figure 1.As shown in Figure1A,red indicates increased mRNA ex-pression in the tumor compared to the reference;green indicates low level expression.The dotted line represents the previously determined threshold between a good-prognosis signature and a poor-prognosis signature.Tu-mors are rank-ordered according to the expression level of the41prognostic genes in tumors from198patients. Figure1B shows the time in years to distant metastasis as a first event of this occurrence,as well as the total duration of follow-up for all patients.Figure1C shows the living status of these breast cancer patients. Association between the41-gene prognostic signature and clinical variables

The198patients were divided into two groups based on high expression level(high-EL,n=99)and low expres-sion level(low-EL,n=99),similar to earlier reports[17]. These levels correspond to a poor prognostic signature

Table1List and functional annotation of the41genes in the study

ID Gene name Function

23586DDX58DEAD box protein.

1041CDSN Corneocdesmosin,is a secreted protein found in corneodesmosomes.

259230SGMS1Sphingomyelin synthase1.

81669LOC643556Similar to Aurora kinase A-interacting protein(AURKA-interacting protein).

54809SAMD9A sterile alpha motif domain-containing protein,regulating cell proliferation/apoptosis.

6352CCL5Chemokine(C-C motif)ligand5.

90362FAM110B Family with sequence similarity110,member B.

4176MCM7DNA replication licensing factor,Minichromosome maintenance complex component7.

4938OAS1Encodes a member of the2-5A synthetase family,essential proteins involved in the innate

immune response to viral infection.

4939OAS2A member of the2-5A synthetase family.

27289RND1A small(~21kDa)signaling G protein,and is a member of the Rho family of GTPases.

3909LAMA3Laminin,alpha3.

10268RAMP3Receptor(G protein-coupled)activity modifying protein3.

5514PPP1R10A protein with similarity to a rat protein that has an inhibitory effect on protein phosphatase-1. 6324SCN1B Sodium channel,voltage-gated,type I,beta.

9687GREB1An estrogen-responsive gene.

11151CHRO1A Coronin,actin binding protein,1A.

3434IFIT1Interferon-induced protein with tetratricopeptide repeats1.

3433IFIT2Interferon-induced protein with tetratricopeptide repeats2.

3437IFIT3Interferon-induced protein with tetratricopeptide repeats3.

634CEACAM1Carcinoembryonic antigen-related cell adhesion molecule1(biliary glycoprotein).

4680CEACAM6Carcinoembryonic antigen-related cell adhesion molecule6.

79971WLS wntless homolog(Drosophila).

3456IFNB1Interferon,beta1,fibroblast.

9442MED27Mediator complex subunit27,the activation of gene transcription.

8638OASL2′-5′-oligoadenylate synthetase-like gene.

1316KLF6A member of the Kruppel-like family of transcription factors.

55422ZNF331A zinc finger protein containing a KRAB(Kruppel-associated box)domain.

3853KRT6A A member of the keratin gene family.

653BMP5A member of the bone morphogenetic protein family.

10916MAGED2Melanoma-associated antigen D2.

3627CXCL10A chemokine of the CXC subfamily and ligand for the receptor CXCR3.

3433IHIH2Interferon induced with helicase C domain2.

3569IL6Interleukin6.

3576IL8Interleukin8.

347733TUBB2B A beta isoform of tubulin,which binds GTP and is a major component of microtubules.

629CFB Complement factor B.

56999ADAMTS9A disintegrin and metalloproteinase with thrombospondin motifs protein family.

6482HS.374257ST3beta-galactoside alpha-2,3-sialyltransferase1.

90627STARD13StAR-related lipid transfer(START)domain containing13.

64135IFIH1Interferon induced with helicase C domain1.

(See legend on next page.)

and a good prognostic signature,respectively.To gain insight into the relationship between the 41-gene prog-nostic signature and clinical variables,we performed correlation analysis with histopathologic data of patients,such as,age,surgery type,grade,and ER expression as determined by immunohistochemical (IHC)staining.The results showed that the 41-gene prognostic signa-ture was significantly associated with age (P =.0351)and ER status (P =.0095).Patients in the high-EL group were younger in age and had ER-negative tumors.There was also a slightly significant association with tumor grade.However,the p value showed no statistical significance.

Analysis of DMFS and OS based on the prognostic signature

Our analysis indicated that the likelihood of patients de-veloping distant metastasis at 5years and 10years was higher in the low-EL group than in the high-EL group

(5year DMFS:88%versus 75%,respectively;10years DMFS:83%versus 64%,respectively).Prolonged OS was also observed in low-EL patients.

Additionally,multivariate analysis was conducted to adjust for confounding variables including age,tumor size,tumor grade,and ER status.Results confirmed that the 41-gene signature was an independent prognostic factor for these breast cancer patients (OS:HR,1.96,P =.02;DMFS:HR,2.09,P =.008).

Survival comparison between the new markers and other standard criteria

The Kaplan-Meier curve (Figure 2A)showed a signifi-cant difference (HR,2.236;95%confidence interval [CI],1.319to 3.79)in the probability that patients would re-main metastasis-free in the low-EL compared to the high-EL group (P =.002).The 41-gene prognostic

Figure 2Kaplan-Meier analysis of the probability that patients would remain free of distant metastasis among all patients.A .prediction value of DMFS by the 41-gene signature.Patients were divided into those with a good-prognostic signature and those with a poor prognostic

signature according to gene-expression profiling;B .prediction value of OS by AOL consensus criteria;C .prediction value of DMFS by NPI consensus criteria;D .prediction value of DMFS by St.Gallen criteria;E .prediction of Veridex signature.The p values were calculated by log-rank

test.

signature was also extremely useful in predicting the outcome of OS(HR,2.050;95%CI,1.186to3.545;P= 0.009)(Figure3A).

To obtain a more powerful estimate of the signature in predicting clinical outcome,we compared the41-gene prognostic signature with other commonly used criteria, such as AOL,NPI,St.Gallen,and Veridex.Based on this analysis,patients in the database can be divided into a high-risk group and a low-risk group according to vari-ous histologic and clinical characteristics.We calculated DMFS and OS according to these different prognostic profiles.The analysis indicated that the41-gene signa-ture had the best prognostic value in predicting DMFS (P=.058for AOL;P=0.017for NPI;P=.11for Veridex; and P=.212for St.Gallen)(Figure2B,2C,2D,2E)and OS(P=.074for AOL;P=.031for NPI;P=.053for Veridex;and P=.312for St.Gallen)for early breast can-cer patients(Figure3B,3C,3D,3E).

Prognostic value in high-risk patients defined by other standard criteria

The41-gene prognostic signature was also highly pre-dictive of the risk of DMFS and OS among the subgroup of patients,which were thought to be high risk accord-ing to other existing criteria.As shown in the Kaplan-Meier curves,we found significant differences in the probability of remaining metastasis-free between the high-EL signature and the low-EL signature,even though all were assigned to the high-risk group based on other criteria(P=.001for AOL;P=.001for NPI;P=.049 for Veridex;P=.004for St.Gallen;P=.006for MammaPrint;and P=.018for Oncotype Dx)(Figure4A, 4B,4C,4D;Figure5C,5F).A similar trend was observed when assessing OS(Figure4E,4F,4G,4H;Figure5B,5E). Thus,the new prognostic signature more accurately pre-dicts breast cancer survival rate(or metastasis)than other histologic and clinical characteristics.These results high-light the value of the prognosis profile and the robustness of the profiling technique.

Comparison of the prognostic value of the41-gene signature with Oncotype Dx and MammaPrint

To assess the concordance of the41-gene signature with published prognostic gene signatures,we implemented the original algorithms of the Oncotype Dx(Genomic Health)and MammaPrint(Agendia)gene signatures and applied them to the41-gene signature in our compen-dium of microarray datasets.

Using data from the198patients with node-negative tumors,we analyzed the prognostic value of the41-gene signature,Oncotype Dx,MammaPrint,and other criteria (Table2).The results of multivariate analysis indicated that there was significant prognostic power for the41-gene signature(P=.03)and Oncotype Dx(P=.002). However,there was no statistically significant difference observed for the analysis using MammaPrint(P=.647), AOL criteria(P=.551),NPI criteria(P=.16),St.Gallen criteria(P=.383),or Veridex criteria(P=.335).

We further investigated the prognostic ability of the 41-gene signature under different definitions of“high risk”using forest plots.As shown in Figure5A and Figure 5D,

Kaplan-Meier analysis of the probability of OS.A.prediction value of OS by the41-gene signature.Patients were good-prognostic signature and those with a poor prognostic signature according to gene-expression profiling;B.prediction criteria;C.prediction value of OS by NPI consensus criteria;D.prediction value of OS by St.Gallen criteria;E.Prediction values were calculated by log-rank test.

Figure4The41-gene signature was a stronger predictor of clinical outcome.A.prediction value of DMFS in high-risk patients defined by AOL criteria;B.prediction value of DMFS in high-risk patients defined by NPI criteria;C.prediction value of DMFS in high-risk patients defined by Veridex criteria;D.prediction value of DMFS in high-risk patients defined by St.Gallen criteria;E.prediction value of OS in high-risk patients defined by AOL criteria;F.prediction value of OS in high-risk patients defined by NPI criteria;G.prediction value of OS in high-risk patients defined by Veridex criteria;H.prediction value of OS in high-risk patients defined by St.Gallen criteria.

Figure5Prognostic value represented by forest plot in patients defined by other standard criteria.A.prognostic value of OS represented by forest plot;B.prognostic value of OS in high-risk patients defined by Mammaprint;C.prognostic value of DMFS in high-risk patients defined by Mammaprint;D.prognostic value of DMFS represented by forest plot;E.prognostic value of OS in high-risk patients defined by Oncotype; F.prognostic value of DMFS in high-risk patients defined by Oncotype.

the new markers displayed good predictive ability in al-most all subgroups except for ER-positive patients. Subgroup analysis according to ER status

In order to discuss the impact of ER status on the41-gene signature,we separately analyzed the predictive value of these markers in ER-positive and ER-negative patients.The survival curves were statistically signifi-cantly different between the high-EL patients and low-EL patients for DMFS(P=.014)and OS(P=.028)in ER negative patients,indicating a good predictive ability in this subgroup(Figure6A,6B).However,the signature did not show strong predictive ability for ER positive pa-tients(Figure6C,6D).These curves confirmed earlier results from forest plot analysis(Figure5A,5D). Discussion

Previous studies linking gene expression profiles to clin-ical outcome in breast cancer have demonstrated that the potential for distant metastasis and OS probability may be attributable to biological characteristics of the primary tumor[18-21].In their seminal work,Paik et al.

[22]reported that a21-gene recurrence score(RS)assay quantifies the likelihood of distant recurrence in women with ER-positive,lymph node-negative breast cancer treated with adjuvant tamoxifen;it also predicts the magnitude of chemotherapy benefit.Perou et al.[23] identified tumors with distinct patterns of gene expres-sion termed“basal type”and“luminal type”,using com-plementary DNA(cDNA)microarray to analyze breast

Table2Comparison of the prognostic value of41-gene

signature with other risk assessment criteria

OS DMFS

ΔLHR P-valueΔAICΔLHR P-valueΔAIC

Signature?4.9290.03* 2.929?10.5130.008* 5.475

ONCOTYPE?13.2860.002*11.286?13.7340.004*8.696

MAMMAPRINT?0.2210.647?1.779?3.0380.986?2

AOL?0.3770.551?1.623?3.3250.601?1.713

NPI?3.6580.16 2.764?6.8230.131 2.756

St.Gallen?0.7240.383?1.276?3.330.582?1.708

Veridex?0.9910.335?1.009?3.9870.343?1.051

The independent contribution of each interested factor to patient outcome

was assessed by first removing the factors concerned and then calculating the

difference of LHR and AIC.A larger drop of LHR and an increase in AIC

indicate a higher significance of the removed system.

ΔLHR,change of likelihood ratio between full model fitting and one concerned

system removed;ΔAIC,change of Akaike information criterion between full

model fitting and one concerned system removed;*,statistical significant.

prognostic value of41-gene signature in ER positive and ER negative patients.A.prognostic value of OS of DMFS in ER?patients;C.prognostic value of OS in ER+patients;D.prognostic value of DMFS in ER+patients.

cancer tissues.These subgroups differ with respect to disease outcome in patients with locally advanced breast cancer.Generally,it is agreed that patients with poor prognostic features benefit most from adjuvant therapy. We previously identified a gene expression profile of 41-gene markers that is associated with BCSCs.Since BCSCs are considered to be the root of metastasis,pro-mote recurrence of the malignancy,and are resistant to traditional therapy[24-27],we tested this profile in a series of198consecutive patients who were diagnosed with early breast cancer.The results showed that the41-gene profile performed best as a predictor of DMFS by classifying patients into high-EL and low-EL groups.The prognostic signature is also a strong predictor of OS in patients with lymph node negative disease in this cohort. To our knowledge,this is the first attempt at using cancer stem cell related markers as a prognostic signa-ture predicting the survival and recurrence of breast cancer patients.This finding is important since the pres-ence of cancer stem cells is a strong predictor of poor survival and resistance to traditional therapy.This find-ing also sheds new light on the common biological pro-cesses relevant for predicting outcome in breast cancer. Comparing Figure2A and Figure3A,we see a strong correlation between the good-prognostic signature and DMFS(P=.002).Similar results were observed in the analysis of OS(P=.0086).To obtain a more useful esti-mate of clinical outcome,we calculated the probability of patients who remained free of distant metastasis and OS according to the prognosis profile.For this analysis, our results indicated that the prognostic signature was highly predictive of the risk of distant metastasis.Pro-longed OS was also observed in patients with low ex-pression of the41-gene signature compared to patients in the high-EL group.These results highlight the value of prognostic profiles and the robustness of the profiling technique.

For the purpose of comparison,we also analyzed well-established criteria currently used in the clinic predicting clinical outcomes for breast cancer patients,such as AOL,NPI,St.Gallen,and Veridex.Figure2and Figure3 shows the Kaplan-Meier estimates of the probability that patients would remain free of distant metastasis and OS among the198patients with lymph-node-negative breast cancer.In these analysis,patients were classified either by the41-gene-expression profile or by another com-monly used criteria,such as AOL,NPI consensus cri-teria,St.Gallen criteria,or Veridex criteria.The results indicated that only the NPI consensus criteria(P=.0172) predicted a statistically significant survival outcome in this cohort.It is worth noting that no statistical signifi-cance was observed for AOL,NPI,or St.Gallen criteria in predicting clinical outcome for this cohort of breast cancer patients.

MammaPrint[28]and Oncotype Dx[29]are currently commercially available diagnostic tests that quantify the likelihood of disease recurrence in women with early-stage breast cancer.Within this cohort,the analysis re-vealed that the41-gene signature and Oncotype Dx both had strong prognostic value in predicting DMFS and OS in this198patient group.However,there was no statisti-cally significant difference observed for the analysis with MammaPrint.

High-risk patients identified by AOL,NPI,St.Gallen, or Veridex criteria tended to have a lower likelihood of DMFS and OS than those classified according to the41-gene expression profiling.This result indicates that both sets of the currently used criteria“misclassified”a clinic-ally significant number of patients.Indeed,the high-risk group,defined according to these criteria,might include a number of patients who actually had a good-prognostic signature with a possible good outcome.Since both these subgroups contain some“misclassified”patients(who can be better identified through the prognosis signature),these patients might be mistreated in current clinical practice. Based on our analysis,we predict that the41-gene sig-nature profile significantly associates with clinical out-come in the entire patient cohort.Thus,we further evaluated the prognostic utility of these41-genes in ER positive and ER negative patients,respectively.In the subgroup analysis,there was a significant association be-tween the41-gene signature and both OS and DMFS in ER-negative breast cancer patients.In contrast,the sig-nature did not show strong predictive ability for ER positive patients.

The molecular mechanisms regulating BCSCs are dis-tinct from the mechanisms governing differentiated tumor cells.Our data indicate that classification of pa-tients into high-risk and low-risk subgroups on the basis of the41-gene prognostic profile could prove to be a very useful means of guiding adjuvant therapy in patients with lymph-node-negative breast cancer.This approach should also improve the selection of patients who would benefit from adjuvant systemic treatment,reducing the rate of both over-treatment and under-treatment.Even though these results are encouraging,a larger scale prospective study is required to confirm these results.

Conclusion

The41-gene prognostic profile demonstrates prognostic significance with strong capability of predicting DMFS and OS in node-negative breast cancer patients.This41-gene signature of BCSCs was even more strongly associated with clinical outcomes compared with other existing criteria, such as AOL,NPI,Veridex,St.Gallen,and MammaPrint. Competing interests

There are no competing interests among the authors.

Authors ’contributions

YZQ,LJJ,XYC,FY,TL,XYW,LXL,MY collected clinical information.XYW,YJ,DGH and LBH performed the statistical analysis.YZQ,LJJ and WHX

participated in the design of the study.XYC and YJ drafted the manuscript.WHX revised the manuscript.All authors read and approved the final manuscript.

Acknowledgements

This study was supported by the National Natural Science Funds (Project Number:81102015),the National Program on Key Basic Research Project (973Program)(Project Number:2013CB967201),Shanghai Health Bureau Key Disciplines and Specialties Foundation and the Special Funds for

Technological Innovation of Shanghai Jiaotong University (Project Number:YG2012MS46).

Author details 1

Shanghai Renji Hospital,Shanghai Jiaotong University School of Medicine,Shanghai 200127,China.2Shanghai Center for Bioinformation Technology,Shanghai 201203,China.3Department of Oncology,Renji Hospital,School of Medicine,Shanghai Jiaotong University,Shanghai 200127,China.Received:7January 2014Accepted:15April 2014Published:6June 2014

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