Cancer A Novel Quantitative Multiplex Tissue & Prevention ...cebp.aacrjournals.org › content ›...

Research Article

A Novel Quantitative Multiplex TissueImmunoblotting for Biomarkers Predicts aProstate Cancer Aggressive PhenotypeGuangjing Zhu1, Zhi Liu1, Jonathan I. Epstein2, Christine Davis1,Christhunesa S. Christudass1, H. Ballentine Carter1, Patricia Landis1, Hui Zhang2,Joon-Yong Chung3, Stephen M. Hewitt3, M. Craig Miller4, and Robert W.Veltri1

Abstract

Background: Early prediction of disease progression in menwith very low-risk (VLR) prostate cancer who selected activesurveillance (AS) rather than immediate treatment could reducemorbidity associated with overtreatment.

Methods: We evaluated the association of six biomarkers[Periostin, (�5, �7) proPSA, CACNA1D, HER2/neu, EZH2, andKi-67] with different Gleason scores and biochemical recurrence(BCR) on prostate cancer TMAs of 80 radical prostatectomy (RP)cases. Multiplex tissue immunoblotting (MTI) was used to assessthese biomarkers in cancer and adjacent benign areas of 5 mmsections. Multivariate logistic regression (MLR) was applied tomodel our results.

Results: In the RP cases, CACNA1D, HER2/neu, and Peri-ostin expression were significantly correlated with aggressivephenotype in cancer areas. An MLR model in the cancerarea yielded a ROC-AUC ¼ 0.98, whereas in cancer-adjacent

benign areas, yielded a ROC-AUC ¼ 0.94. CACNA1D andHER2/neu expression combined with Gleason score in a MLRmodel yielded a ROC-AUC ¼ 0.79 for BCR prediction. In thesmall biopsies from an AS cohort of 61 VLR cases, anMLR model for prediction of progressors at diagnosis retained(�5, �7) proPSA and CACNA1D, yielding a ROC-AUC of0.78, which was improved to 0.82 after adding tPSA into themodel.

Conclusions: Themolecular profile of biomarkers is capable ofaccurately predicting aggressive prostate cancer on retrospectiveRP cases and identifying potential aggressive prostate cancerrequiring immediate treatment on the AS diagnostic biopsy butlimited in BCR prediction.

Impact: Comprehensive profiling of biomarkers using MTIpredicts prostate cancer aggressive phenotype in RP and AS bio-psies. Cancer Epidemiol Biomarkers Prev; 24(12); 1864–72. �2015 AACR.

IntroductionProstate cancer is the most common cancer in men in the

United States and is the second most common cause of cancerdeath in men. Prostate cancer usually occurs after age 50 and theincidence increases with age. There are 233,000 estimated newcases and 29,480 estimated prostate cancer deaths in 2014 in theUnited States (1). Radical prostatectomy (RP) is an effectivetreatment for patients with organ-confined disease and has beendemonstrated to reduce the risk of death fromprostate cancer (2).

Nearly 40% of prostate cancer patients who choose definitivetherapy will undergo RP. In 38% to 52% of cases, advanceddisease with potentially bad prognosis are found in surgicalspecimens (3). Each category of extraprostatic disease is associatedwith significantly increased risk of cancer recurrence and progres-sion, measured at the earliest time with a detectable prostate-specific antigen (PSA; >0.20 ng/mL), or biochemical recurrence(BCR; ref. 4). The natural history of prostate cancer progressionafter BCR following surgery can be highly variable (i.e., 3–13years); however, at least two thirds of BCR patients developdisease spread if left untreated and many will die because ofdistant progression (5).

In most cases, BCR is used as a surrogate measure for moreclinically meaningful critical endpoints such as distant progres-sion or cancer-specific mortality. Inaccurate risk classificationscould result in inappropriate or unnecessary treatments. Unfor-tunately, existing tools (e.g., nomograms like Shariat, ref. 6;Swanson, ref. 7; and Capra, ref. 8) that rely solely on clinicalvariables such as PSA velocity, grade, and stage are unable topredict which men will go on to metastasis and ultimatelyprostate cancer-specific death. These tools may be unable toidentify those men that have already demonstrated BCR. There-fore, a better method to predict whether a prostate cancer patienthas a more aggressive phenotype at surgery will help to standard-ize treatments of the more aggressive cancer patients early andefficiently and spare patients of less aggressive cancer from themorbidity associated with adjuvant therapy.

1The James Buchanan Brady Urological Institute, The Johns HopkinsUniversity School of Medicine, Baltimore, Maryland. 2Department ofPathology, The Johns Hopkins Hospital, Baltimore, Maryland. 3Exper-imental Pathology Laboratory, Laboratory of Pathology, Center forCancer Research, National Cancer Institute, NIH, Bethesda, Maryland.4Statistical Consultant, Lindale, Texas.

Note: Supplementary data for this article are available at Cancer Epidemiology,Biomarkers & Prevention Online (http://cebp.aacrjournals.org/).

Current address for C.S. Christudass: Department of Neurological Sciences,Christian Medical College, Vellore, Tamil Nadu, India.

Corresponding Author: Robert W. Veltri, The Brady Urological Institute, TheJohns Hopkins University School of Medicine, 600 North Wolfe Street, Balti-more, MD 21287. Phone: 410-955-6380; Fax: 410-502-7711; E-mail:[email protected]

doi: 10.1158/1055-9965.EPI-15-0496

�2015 American Association for Cancer Research.

CancerEpidemiology,Biomarkers& Prevention

Cancer Epidemiol Biomarkers Prev; 24(12) December 20151864

on July 7, 2021. © 2015 American Association for Cancer Research. cebp.aacrjournals.org Downloaded from

Published OnlineFirst September 24, 2015; DOI: 10.1158/1055-9965.EPI-15-0496

http://cebp.aacrjournals.org/

PSA screening and prostate biopsies have resulted in overdi-agnosis and overtreatment of prostate cancer (9–11). In fact, low-grade, low-stage prostate cancer of up to approximately 56%menremained undetected during their lifetime (9–12). In the presentera, 30 men require treatment for prostate cancer in order to saveoneman (13). Such overtreatment can always create a chance of adecreased quality of life once sexual function andurinary functionare compromised (14). The accurate identification of men withprostate cancer that are destined to progress to life threateningdisease and who will benefit from curative interventionmust be ahigh research priority, with the goal of reducing unnecessarytreatments.

In 1995, Dr. Ballentine Carter started an active surveillance (AS)program with the delayed surgical intervention as a treatmentoption in Johns Hopkins Medical Institutions (JHMI, Baltimore,MD). Patients are enrolled if theymeet Epstein inclusion very low-risk (VLR) criteria (15–17). These criteria include PSA density(PSAD) 6 on follow-upbiopsy/biopsies or retropubic radical prostatectomy criteriawere met during AS. For each patient, continuous 5 mm tissuesections were cut and used either for H&E staining and path-ologic assessment or for profiling biomarkers using MTI. TheH&E slides were first reviewed by a pathologist and the cancerarea were marked for reference of data analysis later on. Onlyslides with cancer and adjacent to the corresponding read H&Eslides were used for MTI study.

All cases used for TMAs and AS biopsies were consented underan Institutional Review Board-approved protocol at Johns Hop-kins University School of Medicine.

Biomarkers Predict a Prostate Cancer Aggressive Phenotype

www.aacrjournals.org Cancer Epidemiol Biomarkers Prev; 24(12) December 2015 1865




Multiplex tissue immunoblottingMTI is a method that can be used to detect multiple molecular

targets in FFPE tissues, while retaining both quantitative andhistomorphologic diagnostic and prognostic features (24–26).Starting with a 5-mm FFPE tissue section on a standard glass slide,proteinswere transferred from the tissue sections (TMAor biopsy)onto a series of overlapped thin membranes (P-Film, 20/20GeneSystems, Inc.). Each membrane was probed with one of thesix biomarkers [CACNA1D, Periostin,HER2/neu, EZH2,Ki67 and(�5, �7 proPSA)] followed by incubation with FITC-conjugatedsecondary antibodies. Total proteins collected on the blottedmembranes were biotinylated, and followed by incubation withstreptavidin-linked Cy5. The fluorescence signals of biomarkersand total proteins were acquired using a Typhoon 9410 imager(GE Healthcare) and quantified with ImageQuant5.2 software(GE Healthcare). The quantified signals of biomarkers weredivided by that of total protein for normalization. The log-transformed ratio was used for downstream data analysis. Sup-plementary Table S1 provides the detailed information about theprimary antibodies and dilutions used in the study. The quality ofthe antibodieswas checkedusingWestern blot analysis of prostatecancer cell lines (data not shown). This MTI method helps topreserve valuable tissue and while multiplexing prognostic bio-markers for prostate cancer.

Statistical analysisGraphPad Prism 6 software was used to generate scatterplots

of the biomarker expressions within the different Gleason scoregroups (3þ3, 3þ4, 4þ3, and �8) as well as to generate thepredictive probability plots. One-way ANOVA analysis fol-lowed by Dunnet multiple comparisons test was used to

evaluate the biomarker expressions in the four Gleason scoregroups. STATA 13.0 (STATA, StataCorp LP) was used for allstatistical modeling. Statistical significance was defined as a P <0.05. Statistical modeling included standard and/or backwardsstepwise multivariate logistic regression (MLR) to discriminateless aggressive from more aggressive prostate cancer. Decisioncurve analysis (DCA; refs. 27–29) was used to evaluate differentMLR models for the prediction of more aggressive prostatecancer, patient that experienced BCR, and progressors. DCA is amethod for evaluating and comparing different predictionmodels (27–29), which gives an expected net benefit perpatient relative to the assumption that all patients are treatedor not treated. For these evaluations, treatment was defined asthe significant/interested changes in the patients (aggres-siveness, BCR, progressors, etc.). The interpretation of netbenefit is made by comparing a model curve to a baselinecurve where all patients are considered as interested changes,and if the model curve is above the standard curve at a certainprobability, then the model would be considered a betterpredictor of the outcome.

ResultsDifferential expression of CACNA1D, HER2/neu, and Periostinin prostate cancer with different Gleason scores

We optimized all MTI primary antibodies for six of our inter-ested biomarkers on a test TMA (TMA 475) containing normalcontrol and prostate cancer tissue samples. Our results showedthat proteins on a single 5 mm FFPE tissue slide could be success-fully transferred onto a series of six membranes and probed withsix biomarkers simultaneously (Fig. 1).

Table 1. Prostate cancer patients demographics in TMA681 and 682 (N ¼ 80)Recurrence

Variable Subgroups No Yes Total

Age 59.04 � 6.57 58.82 � 5.80Gleason score 6 7 1 8

7 (3þ4) 14 3 177 (4þ3) 15 3 18� 8 11 15 26

TNM stage T2 26 2 28T3A 16 15 31T3B 3 5 8

Gland weight (g) 51.94 � 14.90 56.19 � 21.38PSA (ng/mL) 8.29 � 5.29 13.17 � 10.15PSAD (ng/mL/g) 0.17 � 0.11 0.25 � 0.20Exposure (y) 5.06 � 4.50 3.82 � 2.36Race A 6 3 9

H 1 0 1O 3 1 4C 37 18 55

Surgical margin Negative 41 16 57Positive 6 6 12

Seminal vesicle status Negative 46 16 62Positive 6 6 12

Lymph node status Negative 46 22 68Positive 1 0 1

Capsular penetration (fcp) Negative 38 16 54Positive 8 6 14

Capsular penetration (ecp) Negative 34 8 42Positive 13 14 27

Organ confinement status Nonconfined 21 20 41Confined 26 2 28

NOTE: Age, gland weight, PSA, PSAD, exposure data were shown as mean� SD. For race data, A, African American; H, Hispanic; O, Others; C, Caucasian. PSAD (ng/mL/g) ¼ PSA (ng/mL)/gland weight (g).

Zhu et al.

Cancer Epidemiol Biomarkers Prev; 24(12) December 2015 Cancer Epidemiology, Biomarkers & Prevention1866




After optimization of the six biomarkers, we performed MTIwith these six biomarkers on our study, TMAs sections containingcancer and caner-adjacent benign tissue from80different prostatecancer patients and determined the relative expression of thebiomarkers. These sections include four groups with differentGleason scores (3þ3, 3þ4, 4þ3, and � 8). Among the six

biomarkers, CACNA1D, HER2/neu, and Periostin were differen-tially regulated among the four Gleason score groups in cancerarea and cancer-adjacent benign areas. In the cancer areas, CAC-NA1D and HER2/neu relative expression were significantly lowerin the Gleason score groups of 4þ3 and � 8 compared with theGleason score group of 3þ3 (P < 0.05). There was a trend of

Figure 1.Test of antibodies on MTI using TMA475. Representative results of MTIusing the six biomarkers. Signalintensity: white–red–yellow–green–blue–black from maximum tominimum.

0.6

0.4

0.2

0.0

-0.2

-0.4

0.6

0.4

0.2

0.0

-0.2

-0.4

0.4

0.2

0.0

-0.2

-0.4

0.4

0.2

0.0

-0.2

-0.4

-0.6

3+3 3+4 4+3 8 and above

3+3 3+4 4+3 8 and above

3+3 3+4 4+3 8 and above 3+3 3+4 4+3 8 and above

3+3 3+4 4+3 8 and above

3+3 3+4 4+3 8 and above

Gleason score

Gleason score

Gleason score Gleason score

Gleason score

Gleason score

2.0

1.5

1.0

0.5

0.0

-0.5

-1.0

2

1

0

-1

Exp

ress

ion

Exp

ress

ion

Exp

ress

ion

Exp

ress

ion

Exp

ress

ion

Exp

ress

ion

Cancer area Cancer adjacent benign area

CACNA1D CACNA1D

HER2/neu HER2/neu

Periostin Periostin

3+33+44+38 and above






A

B

C F

E

D

Figure 2.Expression of biomarkers in cancer areas and cancer-adjacent benign areas. A–C, prostate cancer areas. D–F, cancer-adjacent benign areas. Data are shown asmean� SD. � and �� , statistical significance (P < 0.05) between Gleason score groups of 4þ3 and �8 versus the Gleason score group of 3þ3, respectively.






gradually decreased relative expression of both markers withincreased Gleason scores (Fig. 2A and 2B). Pearson correlationanalysis showed that CACNA1Dhad a strong negative correlationwith Gleason score (correlation coefficient ¼ �0.73, P < 0.001),whereas HER2/neu showed amoderate negative correlation (cor-relation coefficient¼�0.39, P¼ 0.001). Interestingly, CACNA1Dand HER2/neu expression behaved similarly in the cancer-adja-cent benign areas (Fig. 2A and 2B vs. 2D and 2E), suggesting apossible field effect for these markers. Periostin expression wassignificantly higher in prostate cancer caseswithGleason score�8compared with that of Gleason score 3þ3 in cancer-adjacentbenign areas (P < 0.05, Fig. 2F). Although Periostin expressionwas not statistically significantly different in cancer areas, therewas a trend for the expression to increase with increasing Gleasonscore (Fig. 2C).

Biomarker expression in TMAs distinguishes less aggressivefrom more aggressive prostate cancer

Although the intermediate risk group of clinically localizedprostate has a Gleason score of 7, it includes both Gleason 3þ4and Gleason 4þ3, patients with the two pathologic statuses havesignificantly different treatment: most Gleason 3þ4, if it is organ-confined, could bemanaged conservativelywhile all Gleason4þ3necessitates immediate interventionwith surgery and/or adjuvanttherapy. Therefore, we grouped Gleason score 3þ3 and 3þ4 casesas a less aggressive phenotype and Gleason score 4þ3 and �8cases as a more aggressive phenotype. Using MLR, our resultsshow that a model retaining CACNA1D, HER2/neu, and Ki67

expression distinguishes less aggressive and more aggressiveprostate cancer in cancer areas (Fig. 3A and Supplementary TablesS2 and S3). Using this model, the predictive probability was closeto 100% formost cases in the aggressive groups (Gleason 4þ3 and� 8; Fig. 3B). The ORs of Ki-67, CACNA1D, HER2/neu were4.41e7,

respectively. DCA curve showed only a limited power for predic-tion of BCRwithGleason score alonewithin the probability rangeof 0.10 to 0.50, while combination of Gleason score withmolecular biomarkers showed improved prediction capabilitywithin a wider probability range (0.10–0.80; Fig. 4B). The pre-dictive probability of BCR using Gleason score alone or combi-nation of Gleason score with MBs is shown in Fig. 4C and 4D,respectively.

Biomarkers and tPSA separate progressors and nonprogressorsin the active surveillance cohort

Using the biomarkers evaluated in the TMAs, we also evaluatedthe predictive ability of the biomarkermolecular profiles in a totalof 61 AS biopsy cases (32 nonprogressors and 29 progressors). Inthe AS program, progressors are defined as men that met all theabove VLR criteria at entry (tumor increased in volume and/orGleason score > 6); however, duringmonitoring, they were foundto have a more aggressive prostate cancer than entry criteria onbiopsy follow-up (i.e., AS event; or clinically aggressive prostatecancer). An MLR model that retained (�5, �7) proPSA andCACNA1D was able to distinguish progressors from nonprogres-sors in the AS cohort (Fig. 5A). This model had an accuracy of72.13% in the prediction of progressors. The ORs for (�5, �7)proPSA and CACNA1D are 0.20 and 7.93, respectively. Thepredictive probability of this model is shown in Fig. 5D.

We further tried to separate progressors and nonprogressors inthe AS cohort by using prebiopsy tPSA alone or adding the tPSAresult to the biomarker model. We found that tPSA alone haslimited prediction capability with an ROC-AUC of 0.73, whereascombining molecular biomarkers and tPSA improved the ROC-AUC of the model to 0.83, but without significant differencecompared with the biomarker-only model (P ¼ 0.26). The com-binationmodel (tPSA-MB) had a sensitivity of 79.31%, specificityof 78.13%, with the tPSA had an OR of 1.24 in this model. The

combined model could correctly classify the two groups with anaccuracy of 78.69% (Fig. 5A and Supplementary Tables S4 andS5). A plot of the predictive probabilities is shown in Fig. 5C and5E. DCA curve showed that the tPSA only and the molecularbiomarker-only model improved the net benefit within the prob-ability range of 0.35 to 0.80 and 0 to 0.80, respectively, while thecombined tPSA-MB model improved the net benefit within theprobability range of 0 to 0.75. DCA curve comparing the threemodels to each other showed that the combined model wassuperior to the biomarker-only model within the probabilityrange of 0.30 to 0.65 (Fig. 5B). This suggests that a subset of thebiomarkers evaluated have the potential to differentiate progres-sors from nonprogressor patients that can remain annual mon-itoring in our preliminary data of 61 biopsy cases with highspecificity. Adding tPSA improves the model, especially withinthe probability range of 0.30 to 0.65.

DiscussionThe development and validation of sensitive andmore accurate

methods for early detection are pivotal in the management ofprostate cancer, the overdiagnosis and overtreatment of which is asignificant healthcare problem (13). AS has become the preferredmethod for the management of VLR and many LR patients.Currently, the new NCCN guidelines have included VLR and LRgroup of prostate cancer for AS (23). For VLR or LR cases,abnormal morphologic changes are minimal, and the futuretumor progress could not be effectively predicted by pathologicgrading. Meanwhile, molecular level changes do occur and maysignal the potential disease progression. Characterization of pros-tate cancer necessitates a comprehensive panel of biomarkersconsidering the heterogeneity of prostate cancer biology (30).Therefore, we reason that a comprehensive panel of biomarkersinvolving the glandular acini and the stromal areas couldbeuseful

0.00 -1

00.

10.

20.

3

0.25

0.50

1.00

0.75

0.00 0.25 0.50 0.75 1.00

Sen

siti

vity

Net

ben

efit

1 – Specificity Threshold probability

0 0.2 0.4 0.6 0.8 1

AllGS

GS

NoneGS & MB

GS & MBReference

1.2

0.8

0.4

0.0

1.2

0.8

0.4

0.0

No recurrence Recurrence No recurrence Recurrence

GS GS & MB

Pre

dic

tive

pro

bab

ility

A

C D

B

Figure 4.Prediction of BCR using Gleason scorealone and Gleason score combinedwith biomarkers. Prediction BCR withGleason score only (A) versus Gleasonscore and biomarkers (B). DCA of BCRprediction using the GS or GS and MBmodel in cancer areas (C and D).Predictive probability of BCR usingthe GS or GS and MB model in cancerareas.






in the prediction of VLR and LR prostate cancer progression in anAS monitoring program.

To make full use of the valuable biopsy samples with limitedsize of cancer area, we adopted MTI as a method to profile theexpression of six biomarkers on a single 5-mm section in TMAs of80 RP cases. Using a backward stepwise MLR model, we foundCACNA1D, HER2/neu, and Periostin expression could signifi-cantly distinguish less aggressive from more aggressive prostatecancer with high sensitivity and specificity in the cancer area.Interestingly, in the cancer-adjacent benign area, a different MLRmodel retains CACNA1DandPeriostin could also distinguish lessaggressive from more aggressive prostate cancer, implying theimportance of these biomarkers in the prediction of aggres-siveness in RP cases. Moreover, our data of the cancer-adjacentbenign area support the concept of a possible field effect thatinvolves changes of tumor progression at the molecular levelbefore changes in glandularmorphology.Ourmodel also showedan improved prediction of BCR compared with the predictionusing Gleason score alone. Furthermore, in a total of 61 AS cases,we successfully separated progressors and nonprogressors in theAS cohort with (�5, �7) proPSA and CACNA1D among theseverified biomarkers, which are retained in a MLR model formolecular biomarkers to predict the AS cases that require defin-itive treatment.

It is worthwhile to note that our RP long-term follow-up MLRbiomarker model differs from the cancer area and the cancer-adjacent benign area suggests that biology of molecular altera-tions in progression and changes in the field may differ in thecancer areas. This is supported by a systemic genomic analysis ofprostate cancer andmorphologically normal tissue (30). Notably,the changes of biomarkers in the cancer-adjacent benign areashow a slightly different pattern compared with the cancer area,with changes of stromal protein Periostin expression being moresignificant. Further characterization of the molecular features ofprostate cancer with VLR or LR biomarker expression patterns inthese two histologic areas could provide an additional means tostratify the AS and RP cases to more accurately assess the progres-sion of prostate cancer and effectively personalize the manage-ment of treatment.

Interestingly, CACNA1D stands out as a new useful marker inprostate cancer area, cancer-adjacent benign area, and also biop-sies in the prediction of cancer progression (upgrading). CAC-NA1D is an ERG target gene upregulated in TMPRSS2-ERG–positive prostate cancer cells (31). Overexpression of CACNA1Din prostate cancer cells was reported to regulate the ligand inde-pendent activation of AR. ERG-induced expression of CACNA1Dwas reported to promote entry of calcium ions into cytosol (32).Here, we found that the expression of CACNA1D showed a trend

0.00

0.25

0.50

0.75

1.00

0.00 0.25 0.50 0.75 1.00

Sen

siti

vity

Net

ben

efit

1 – Specificity Threshold probability

0 0.2 0.4 0.6 0.8 1

00.

10.

20.

30.

40.

5

1.2

0.8

0.4

0.0

1.2

0.8

0.4

0.0

1.2

0.8

0.4

0.0Pre

dic

tive

pro

bab

ility

Nonprogressor Progressor Nonprogressor Progressor Nonprogressor Progressor

tPSA

tPSA

tPSA

MB

MB

MB

MB & tPSA

MB & tPSA

MB & tPSA

Reference

All None

A

C D E

B

Figure 5.Prediction of active surveillance progressor status with tPSA, biomarkers alone, biomarkers and tPSA. A, ROC comparison of models using tPSA, molecularbiomarkers alone, and biomarkers combined with tPSA. B, DCA of the three models. Progressor predictive probability of tPSA-only (C), biomarker-only (D),and biomarkers and tPSA combined model (E).

Zhu et al.





of decrease with increasing Gleason score in RP specimens.However, the progressor AS biopsies have relatively higher expres-sion of CACNA1D as an early event that consists with results inour RP model for 3þ3 and 3þ4 Gleason scores. It has beenreported that blocking L-type calcium channel-mediated Ca2þ

influx suppressed castration-induced apoptotic cell death in pros-tate epithelial cells (33, 34). We presume that the reduced expres-sion of CACNA1D in more aggressive prostate cancer and pro-gressorsmay contribute to decreasedCa2þ influx and results in theescape of apoptosis, thus suggesting that decreased CACNA1Dexpression could be a more aggressive phenotype in higher gradeprostate cancer or progressors in AS.

The role of HER2/neu in prostate cancer remains controver-sial (35, 36). HER2/neu can often be overexpressed in prostatecancer (37, 38), but may depend on the antibody reagents usedand two differentially expressed forms of HER2/neu transcriptswere reported (39), which may need to be considered duringinterpretation of results. Antibodies detecting variable epitopesof HER2/neu may be one reason for the controversy. In thecurrent study, we showed that in cancer area, along withCACNA1D and Gleason score, HER2/neu could be used toefficiently predict aggressiveness and recurrence of prostatecancer in RP or biopsy specimens.

Moreover, prostate cancer progression involves both epithe-lial changes and stromal changes. Periostin was identified as animportant biomarker well correlating with the aggressive phe-notype (40–42). Both stromal and epithelial Periostin expres-sion was reported to be significantly increased in tumor tissues:stromal expression was significantly higher than epithelialexpression as compared with normal tissue. Although highstromal expression was significantly associated with shortersurvival, a low epithelial score significantly correlated withshorter PSA-free survival, suggesting that Periostin apparentlyplays an opposing biologic role depending on its tissue local-ization (43). Here, we included Periostin in our biomarkerpanel quantification in both epithelia and stroma since our MTImethod could not separate the expression of these two areas.Our results suggest that along with CACNA1D, Periostin couldpredict the malignancy of prostate cancer based on the cancer-adjacent area (Fig. 3B).

Our MLR modeling system showed its power in the predic-tion of prostate cancer progression in biopsies, and RP caseswith high sensitivity and specificity. Our early data were val-idated on 80 RP cases of our TMAs and 61 AS biopsies atdiagnosis (i.e., entry to AS). In the latter case, the AS study wascompromised by the fact that the total cancer surface area in theAS biopsies is often limited. Hence, due to this minimal cancerarea in VLR prostate cancer, the expansion of the AS biopsysample size is needed for obtaining additional results for theverification of the statistical stability of our models. Alterna-tively, the AS program can be altered to expand criteria for entryinto the program, which is already being done at JHMI andother institutions (21, 44). In addition, MTI shows its advan-tage in multiplexing up to six biomarkers using the same 5-mmtissue section, it does have technical disadvantages: it relies onthe relatively high expression of protein and the use of goodquality antibodies. The detection of lowly expressed proteinlevels is limited, such as Ki-67 in AS biopsies. Finally, even ifsuper-thin P-film was used in MTI, the protein transferredonto the membranes shows a gradual decrease from the oneat the bottom closest to the tissue to the one on top. We

normalized the expression of each biomarker to the totalprotein to balance these differences in total protein contenton the same membrane.

Notably, we have developed a panel of six biomarkersprofiled on the same 5-mm biopsy tissue slide using MTI, anintegrated, high-throughput quantitative molecular biomarker-based method for predicting prostate cancer progression. Ourmodel involving these biomarkers shows robust strength in theprediction of aggressiveness in the RP cases and more impor-tantly, this method and biomarkers could be used to predict theprogress of VLR and LR prostate cancer at diagnosis, which willreduce the overdiagnosis and overtreatment and greatly facil-itate the effective management of prostate cancer patients.Future studies will significantly expand the number of biopsycases studied and also include nuclear morphometry of Feulgen(DNA)-stained or H&E-stained biopsies of AS patients as avariable to more accurately predict the prostate cancer aggres-sive phenotype of progressors. Given the new problems ofmanaging AS cohorts, new criteria for selection and new tech-nology for multiplexing tissue biomarkers are needed to sup-port the advancement of this concept to improve healthcare inthe future for prostate cancer.

Disclosure of Potential Conflicts of InterestC.S. Christudass has ownership interest in a patent filed in the United States

(PCT/US2015/015074). No potential conflicts of interest were disclosed by theother authors.

Authors' ContributionsConception and design: Z. Liu, C.S. Christudass, H. Zhang, S.M. Hewitt, R.W.VeltriDevelopment of methodology: G. Zhu, Z. Liu, C.S. Christudass, S.M. Hewitt,R.W. VeltriAcquisition of data (provided animals, acquired and managed patients,provided facilities, etc.): G. Zhu, Z. Liu, J.I. Epstein, C. Davis, C.S. Christudass,H.B. Carter, P. LandisAnalysis and interpretation of data (e.g., statistical analysis, biostatistics,computational analysis): G. Zhu, Z. Liu, C. Davis, M.C. Miller, R.W. VeltriWriting, review, and/or revision of themanuscript:G. Zhu, Z. Liu, J.I. Epstein,C.S. Christudass, H. Zhang, J.-Y. Chung, M.C. Miller, R.W. VeltriAdministrative, technical, or material support (i.e., reporting or organizingdata, constructing databases): G. Zhu, C. Davis, H.B. Carter, P. Landis, J.-Y.Chung, S.M. Hewitt, M.C. Miller, R.W. VeltriStudy supervision: G. Zhu, H.B. Carter, H. Zhang, R.W. Veltri

AcknowledgmentsThe authors appreciate the help of Department of Pathology in Johns

Hopkins Medical Institutions and the PCBN for the construction of TMA. TheP-Films used in MTI were kindly provided by Vladimir Knezevic in 20/20GeneSystems, Inc. The authors also appreciate the help of Nan Hu and ChaoyuWang in Dr. Philip Taylor's lab at NCI for assisting in the conduction ofexperiments.

Grant SupportThis work was supported by two grants: EDRN/NCI U01CA152813 grant (to

H. Zhang P.I.) with an administrative supplement to Dr. Robert W. Veltri,Prostate Cancer Foundation and Patana Fund of The Brady Urological Instituteat the Johns Hopkins University.

The costs of publication of this articlewere defrayed inpart by the payment ofpage charges. This article must therefore be hereby marked advertisement inaccordance with 18 U.S.C. Section 1734 solely to indicate this fact.

Received May 19, 2015; revised July 28, 2015; accepted August 28, 2015;published OnlineFirst September 24, 2015.






References1. Siegel R, Ma J, Zou Z, Jemal A. Cancer statistics, 2014. CA Cancer J Clin

2014;64:9–29.2. Loeb S, Schaeffer EM, Trock BJ, Epstein JI, Humphreys EB, Walsh PC. What

are the outcomes of radical prostatectomy for high-risk prostate cancer?Urology 2010;76:710–4.

3. Ward JF, ZinckeH, Bergstralh EJ, Slezak JM,Myers RP, BluteML. The impactof surgical approach (nerve bundle preservation versus wide local excision)on surgical margins and biochemical recurrence following radical prosta-tectomy. J Urol 2004;172:1328–32.

4. Stephenson AJ, Scardino PT, Eastham JA, Bianco FJ Jr, Dotan ZA, DiBlasioCJ, et al. Postoperative nomogram predicting the 10-year probability ofprostate cancer recurrence after radical prostatectomy. J Clin Oncol2005;23:7005–12.

5. Pound CR, Partin AW, Eisenberger MA, Chan DW, Pearson JD, Walsh PC.Natural history of progression after PSA elevation following radical pros-tatectomy. JAMA 1999;281:1591–7.

6. Shariat SF, Kattan MW, Vickers AJ, Karakiewicz PI, Scardino PT. Criticalreview of prostate cancer predictive tools. Future Oncol 2009;5:1555–84.

7. Swanson GP, Yu C, Kattan MW, Hermans MR. Validation of postoperativenomograms in prostate cancer patients with long-term follow-up. Urology2011;78:105–9.

8. Cooperberg MR, Hilton JF, Carroll PR. The CAPRA-S score: A straightfor-ward tool for improved prediction of outcomes after radical prostatectomy.Cancer 2011;117:5039–46.

9. DraismaG, Boer R,Otto SJ, van derCruijsen IW,Damhuis RA, Schroder FH,et al. Lead times and overdetection due to prostate-specific antigen screen-ing: estimates from the European Randomized Study of Screening forProstate Cancer. J Natl Cancer Inst 2003;95:868–78.

10. Etzioni R, Penson DF, Legler JM, di Tommaso D, Boer R, Gann PH, et al.Overdiagnosis due to prostate-specific antigen screening: lessons fromU.S.prostate cancer incidence trends. J Natl Cancer Inst 2002;94:981–90.

11. Walsh PC. Overdiagnosis due to prostate-specific antigen screening: les-sons from U.S. prostate cancer incidence trends. J Urol 2003;170:313–4.

12. Tornblom M, Eriksson H, Franzen S, Gustafsson O, Lilja H, Norming U,et al. Lead time associated with screening for prostate cancer. Int J Cancer2004;108:122–9.

13. Schroder FH, Hugosson J, Roobol MJ, Tammela TL, Ciatto S, Nelen V, et al.Prostate-cancer mortality at 11 years of follow-up. N Engl J Med 2012;366:981–90.

14. CooperbergMR, LubeckDP,MengMV,Mehta SS, Carroll PR. The changingface of low-risk prostate cancer: trends in clinical presentation and primarymanagement. J Clin Oncol 2004;22:2141–9.

15. Epstein JI, Walsh PC, Carmichael M, Brendler CB. Pathologic and clinicalfindings topredict tumor extent of nonpalpable (stage T1c) prostate cancer.JAMA 1994;271:368–74.

16. Bastian PJ, Mangold LA, Epstein JI, Partin AW. Characteristics of insignif-icant clinical T1c prostate tumors. A contemporary analysis. Cancer2004;101:2001–5.

17. Tosoian JJ, Trock BJ, Landis P, Feng Z, Epstein JI, Partin AW, et al. Activesurveillance program for prostate cancer: an update of the Johns Hopkinsexperience. J Clin Oncol 2011;29:2185–90.

18. Johansson JE, Andren O, Andersson SO, Dickman PW, Holmberg L,Magnuson A, et al. Natural history of early, localized prostate cancer.JAMA 2004;291:2713–9.

19. Walsh PC. Radical prostatectomy versus watchful waiting in early prostatecancer. J Urol 2005;174:1291–2.

20. Bill-Axelson A, Holmberg L, Ruutu M, Haggman M, Andersson SO, BratellS, et al. Radical prostatectomy versus watchful waiting in early prostatecancer. N Engl J Med 2005;352:1977–84.

21. Klotz L. Active surveillance for prostate cancer: a review. Curr Urol Rep2010;11:165–71.

22. Veltri R, Isharwal S. Reply to indolent prostate cancer. Urology 2011;77:e2.23. Mohler J, Bahnson RR, Boston B, Busby JE, D'Amico A, Eastham JA, et al.

NCCN clinical practice guidelines in oncology: prostate cancer. J NatlCompr Canc Netw 2010;8:162–200.

24. Chung JY, Braunschweig T, Baibakov G, Galperin M, Ramesh A, Skacel M,et al. Transfer and multiplex immunoblotting of a paraffin embeddedtissue. Proteomics 2006;6:767–74.

25. Chung JY, Hong SM, Choi BY, Cho H, Yu E, Hewitt SM. The Expression ofPhospho-AKT, Phospho-mTOR, and PTEN in Extrahepatic Cholangiocar-cinoma. Clin Cancer Res 2009;15:660–7.

26. Chung JY, Braunschweig T, Hu N, Roth M, Traicoff JL, Wang QH, et al. Amultiplex tissue immunoblotting assay for proteomic profiling: a pilotstudy of the normal to tumor transition of esophageal squamous cellcarcinoma. Cancer Epidemiol Biomarkers Prev 2006;15:1403–8.

27. Vickers AJ, Cronin AM, Elkin EB, Gonen M. Extensions to decision curveanalysis, a novel method for evaluating diagnostic tests, predictionmodels and molecular markers. BMC Med Inform Decis Mak 2008;8:53.

28. Vickers AJ, Elkin EB. Decision curve analysis: a novelmethod for evaluatingprediction models. Med Decis Making 2006;26:565–74.

29. Steyerberg EW, Vickers AJ. Decision curve analysis: a discussion. MedDecisMaking 2008;28:146–9.

30. CooperCS, Eeles R,WedgeDC,Van LooP,GundemG,Alexandrov LB, et al.Analysis of the genetic phylogeny of multifocal prostate cancer identifiesmultiple independent clonal expansions in neoplastic and morphologi-cally normal prostate tissue. Nat Genet 2015;47:367–72.

31. Paulo P, Ribeiro FR, Santos J, Mesquita D, Almeida M, Barros-Silva JD,et al. Molecular subtyping of primary prostate cancer reveals specificand shared target genes of different ETS rearrangements. Neoplasia2012;14:600–11.

32. Chen R, Zeng X, Zhang R, Huang J, Kuang X, Yang J, et al. Ca1.3 channelalpha protein is overexpressed and modulates androgen receptor transac-tivation in prostate cancers. Urol Oncol 2014;32:524–36.

33. Martikainen P, Isaacs J. Role of calcium in the programmed death of ratprostatic glandular cells. Prostate 1990;17:175–87.

34. Connor J, Sawczuk IS, Benson MC, Tomashefsky P, O'Toole KM, OlssonCA, et al. Calcium channel antagonists delay regression of androgen-dependent tissues and suppress gene activity associated with cell death.Prostate 1988;13:119–30.

35. Carles J, Lloreta J, Salido M, Font A, Suarez M, Baena V, et al. Her-2/neuexpression in prostate cancer: a dynamic process? Clin Cancer Res2004;10:4742–5.

36. Salomon DS, Brandt R, Ciardiello F, Normanno N. Epidermal growthfactor-related peptides and their receptors in humanmalignancies. Crit RevOncol Hematol 1995;19:183–232.

37. Neto AS, Tobias-Machado M, Wroclawski ML, Fonseca FL, Teixeira GK,Amarante RD, et al. Her-2/neu expression in prostate adenocarcinoma: asystematic review and meta-analysis. J Urol 2010;184:842–50.

38. Isharwal S, Miller MC, Epstein JI, Mangold LA, Humphreys E, PartinAW, et al. Prognostic value of Her-2/neu and DNA index for progres-sion, metastasis and prostate cancer-specific death in men with long-term follow-up after radical prostatectomy. Int J Cancer 2008;123:2636–43.

39. Gang A. Differential expression of full-length and a truncated Her-2/neuoncogene receptor in prostate cancer assessed using relative quantitativeRT-PCR. Mol Urol 1998;2:305–10.

40. Chen J, Xi J, Tian Y, Bova GS, Zhang H. Identification, prioritization, andevaluation of glycoproteins for aggressive prostate cancer using quantita-tive glycoproteomics and antibody-based assays on tissue specimens.Proteomics 2013;13:2268–77.

41. Tian Y, Bova GS, Zhang H. Quantitative glycoproteomic analysis ofoptimal cutting temperature-embedded frozen tissues identifying gly-coproteins associated with aggressive prostate cancer. Anal Chem2011;83:7013–9.

42. Tian Y, Choi CH, Li QK, Rahmatpanah FB, Chen X, Kim SR, et al. Over-expression of periostin in stroma positively associated with aggressiveprostate cancer. PLoS ONE 2015;10:e0121502.

43. Nuzzo PV, Rubagotti A, Zinoli L, Ricci F, Salvi S, Boccardo S, et al.Prognostic value of stromal and epithelial periostin expression in humanprostate cancer: correlation with clinical pathological features and the riskof biochemical relapse or death. BMC Cancer 2012;12:625.

44. Suardi N, Briganti A, Gallina A, Salonia A, Karakiewicz PI, Capitanio U,et al. Testing themost stringent criteria for selection of candidates for activesurveillance in patients with low-risk prostate cancer. BJU Int 2010;105:1548–52.


Zhu et al.




2015;24:1864-1872. Published OnlineFirst September 24, 2015.Cancer Epidemiol Biomarkers Prev Guangjing Zhu, Zhi Liu, Jonathan I. Epstein, et al. Biomarkers Predicts a Prostate Cancer Aggressive PhenotypeA Novel Quantitative Multiplex Tissue Immunoblotting for

Updated version

10.1158/1055-9965.EPI-15-0496doi:

Access the most recent version of this article at:

Material

Supplementary

http://cebp.aacrjournals.org/content/suppl/2015/09/24/1055-9965.EPI-15-0496.DC1

Access the most recent supplemental material at:

Cited articles

http://cebp.aacrjournals.org/content/24/12/1864.full#ref-list-1

This article cites 44 articles, 7 of which you can access for free at:

E-mail alerts related to this article or journal.Sign up to receive free email-alerts

Subscriptions

Reprints and

[email protected]

To order reprints of this article or to subscribe to the journal, contact the AACR Publications Department

Permissions

Rightslink site. Click on "Request Permissions" which will take you to the Copyright Clearance Center's (CCC)

.http://cebp.aacrjournals.org/content/24/12/1864To request permission to re-use all or part of this article, use this link



http://cebp.aacrjournals.org/lookup/doi/10.1158/1055-9965.EPI-15-0496http://cebp.aacrjournals.org/content/suppl/2015/09/24/1055-9965.EPI-15-0496.DC1http://cebp.aacrjournals.org/content/24/12/1864.full#ref-list-1http://cebp.aacrjournals.org/cgi/alertsmailto:[email protected]://cebp.aacrjournals.org/content/24/12/1864http://cebp.aacrjournals.org/

/ColorImageDict > /JPEG2000ColorACSImageDict > /JPEG2000ColorImageDict > /AntiAliasGrayImages false /CropGrayImages false /GrayImageMinResolution 200 /GrayImageMinResolutionPolicy /Warning /DownsampleGrayImages true /GrayImageDownsampleType /Bicubic /GrayImageResolution 300 /GrayImageDepth -1 /GrayImageMinDownsampleDepth 2 /GrayImageDownsampleThreshold 1.50000 /EncodeGrayImages true /GrayImageFilter /DCTEncode /AutoFilterGrayImages true /GrayImageAutoFilterStrategy /JPEG /GrayACSImageDict > /GrayImageDict > /JPEG2000GrayACSImageDict > /JPEG2000GrayImageDict > /AntiAliasMonoImages false /CropMonoImages false /MonoImageMinResolution 600 /MonoImageMinResolutionPolicy /Warning /DownsampleMonoImages true /MonoImageDownsampleType /Bicubic /MonoImageResolution 900 /MonoImageDepth -1 /MonoImageDownsampleThreshold 1.50000 /EncodeMonoImages true /MonoImageFilter /CCITTFaxEncode /MonoImageDict > /AllowPSXObjects false /CheckCompliance [ /None ] /PDFX1aCheck false /PDFX3Check false /PDFXCompliantPDFOnly false /PDFXNoTrimBoxError true /PDFXTrimBoxToMediaBoxOffset [ 0.00000 0.00000 0.00000 0.00000 ] /PDFXSetBleedBoxToMediaBox true /PDFXBleedBoxToTrimBoxOffset [ 0.00000 0.00000 0.00000 0.00000 ] /PDFXOutputIntentProfile (None) /PDFXOutputConditionIdentifier () /PDFXOutputCondition () /PDFXRegistryName () /PDFXTrapped /False

/CreateJDFFile false /Description > /Namespace [ (Adobe) (Common) (1.0) ] /OtherNamespaces [ > /FormElements false /GenerateStructure false /IncludeBookmarks false /IncludeHyperlinks false /IncludeInteractive false /IncludeLayers false /IncludeProfiles false /MarksOffset 18 /MarksWeight 0.250000 /MultimediaHandling /UseObjectSettings /Namespace [ (Adobe) (CreativeSuite) (2.0) ] /PDFXOutputIntentProfileSelector /NA /PageMarksFile /RomanDefault /PreserveEditing true /UntaggedCMYKHandling /LeaveUntagged /UntaggedRGBHandling /LeaveUntagged /UseDocumentBleed false >> > ]>> setdistillerparams> setpagedevice

Cancer A Novel Quantitative Multiplex Tissue & Prevention ...cebp.aacrjournals.org › content ›...

Documents

Transcript of Cancer A Novel Quantitative Multiplex Tissue & Prevention ...cebp.aacrjournals.org › content ›...