Lactate Dehydrogenase B: A Metabolic Marker of Response ......Published OnlineFirst May 22, 2013; DOI: 10.1158/1078-0432.CCR-13-0623 types ofbiomarkers could bediscoveredby exploring

Predictive Biomarkers and Personalized Medicine

Lactate Dehydrogenase B: A Metabolic Marker of Responseto Neoadjuvant Chemotherapy in Breast Cancer

Jennifer B. Dennison1, Jennifer R. Molina1, Shreya Mitra1, Ana M. Gonz�alez-Angulo1,2, Justin M. Balko4,María G. Kuba5, Melinda E. Sanders5,7, Joseph A. Pinto8, Henry L. G�omez9, Carlos L. Arteaga4,6,7,Robert E. Brown3, and Gordon B. Mills1,2

AbstractPurpose: Although breast cancers are known to be molecularly heterogeneous, their metabolic pheno-

type is less well-understood and may predict response to chemotherapy. This study aimed to evaluate

metabolic genes as individual predictive biomarkers in breast cancer.

ExperimentalDesign:mRNAmicroarray data frombreast cancer cell lineswere used to identify bimodal

genes—those with highest potential for robust high/low classification in clinical assays. Metabolic function

was evaluated in vitro for the highest scoringmetabolic gene, lactate dehydrogenase B (LDHB). Its expression

was associated with neoadjuvant chemotherapy response and relapse within clinical and PAM50-derived

subtypes.

Results: LDHB was highly expressed in cell lines with glycolytic, basal-like phenotypes. Stable knock-

down of LDHB in cell lines reduced glycolytic dependence, linking LDHB expression directly to metabolic

function. Using patient datasets, LDHB was highly expressed in basal-like cancers and could predict basal-

like subtype within clinical groups [OR¼ 21 for hormone receptor (HR)-positive/HER2-negative; OR¼ 10for triple-negative]. Furthermore, high LDHB predicted pathologic complete response (pCR) to neoadju-

vant chemotherapy for both HR-positive/HER2-negative (OR ¼ 4.1, P < 0.001) and triple-negative (OR ¼3.0, P ¼ 0.003) cancers. For triple-negative tumors without pCR, high LDHB posttreatment also identifiedproliferative tumors with increased risk of recurrence (HR ¼ 2.2, P ¼ 0.006).Conclusions: Expression of LDHB predicted response to neoadjuvant chemotherapy within clinical

subtypes independently of standard prognostic markers and PAM50 subtyping. These observations support

prospective clinical evaluation of LDHB as a predictive marker of response for patients with breast cancer

receiving neoadjuvant chemotherapy. Clin Cancer Res; 19(13); 3703–13. �2013 AACR.

IntroductionMolecular subtyping of breast cancers has identified

multiple gene clusters that can predict outcomes indepen-dently of clinical characteristics and the standard biomar-kers: estrogen receptor (ER), progesterone receptor (PR),and human EGF receptor 2 (HER2; refs. 1,2). Subtype gene

signatures are often composed of different mRNAs but arehighly correlated and can equally predict outcomes, reflect-ing fundamental biologic differences between breast cancerlineages (3,4).

While able to provide clinically useful information for asubset of ER-positive, node-negative tumors (5,6), mRNAprofiling approaches such as Oncotype DX are limited bytype and purity of the specimen. For instance, OncotypeDXand immunohistochemistry (IHC)/FISH scoring of HER2may not agree if the stromal component of the tumorcontaminates the mRNA pool (7,8). Even with a relativelypure specimen, clinically useful results with Oncotype DXtesting are not guaranteed; 37% of scored tumors are"intermediate" without a treatment recommendation (9).In clinical research, mRNA profiling approaches can beproblematic if tumors are necrotic or have a low concen-tration of malignant cells as often found in cancers pre-treated with chemotherapy (10).

Additional predictive biomarkers, particularly for inter-mediate-grade or chemotherapy-resistant tumors, maycomplement existing mRNA profiling efforts for ER, HER2,and proliferation-related genes. We hypothesized that new

Authors' Affiliations: Departments of 1Systems Biology and 2BreastMedical Oncology; The University of Texas MD Anderson Cancer Center;3Department of Pathology and Laboratory Medicine, The University ofTexas Health Science Center, Houston, Texas; Departments of 4Medicine,5Pathology, and 6Cancer Biology, 7Breast Cancer Research Program,Vanderbilt-Ingram Comprehensive Cancer Center, Vanderbilt University,Nashville, Tennessee; 8Divisi€un de Investigaci€un, Oncosalud; and 9Depart-mento de Oncología M�edica, Instituto Nacional de EnfermedadesNeopl�asicas, Lima, Per�u

Note: Supplementary data for this article are available at Clinical CancerResearch Online (http://clincancerres.aacrjournals.org/).

Corresponding Author:Gordon B. Mills, University of Texas, M.D. Ander-son Cancer Center, 7435 Fannin Street, Houston, TX 77054. Phone: 713-563-4200; Fax: 713-563-4235; E-mail: [email protected]

doi: 10.1158/1078-0432.CCR-13-0623

�2013 American Association for Cancer Research.

ClinicalCancer

Research

www.aacrjournals.org 3703

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types of biomarkers could be discovered by exploring func-tional differences in metabolic pathways as these pathwaysmay predict response to therapy.

Whilemany cancers preferentially generateATP via anaer-obic glycolysis in the presence of oxygen, a process termedthe "Warburg effect" (11), the metabolic phenotypes ofbreast cancers are heterogeneous;more than a 20-fold rangein glucose uptake has been reported as quantified by 18FDG-PET maximum standard uptake values (12). To study met-abolic differences between breast cancers and to avoidinterference from the microenvironment, mRNA microar-ray data from breast cancer cell lines were used to identifybimodal metabolism-related genes, those with high/lowexpression. This type of analysis was chosen because clin-ically useful biomarkers, including ER and HER2, are oftenbimodal enabling a more robust threshold determinationduring assay development (13). The biomarker perfor-mance of the highest scoring metabolic bimodal gene,lactate dehydrogenase B (LDHB), was evaluated and com-pared with standard clinical characteristics and intrinsicsubtyping by PAM50 (14). Lactate dehydrogenase tetramer-ic enzymes, composed of lactate dehydrogenase A (LDHA)and/or LDHB subunits, are used by cancer cells to bypassoxidative phosphorylation and produce lactate from pyru-vate (15). We propose that LDHB expression marks funda-mental metabolic differences between breast cancers andmay predict response to neoadjuvant chemotherapy.

Materials and MethodsSamples and clinical data

Two publically available mRNA microarray datasets ofbreast cancers were used to evaluate pCR and the predictive

ability of LDHB for PAM50 subtyping: Microarray QualityControl (MAQC) II study (16) [GSE20194] andMDAnder-son Cancer Center Super Series (MDACCSS; ref. 17)[GSE25066]. Any overlapping patient samples with theMAQC were removed from the MDACCSS dataset. BecauseHER2-positive cases were not part of the MDACCSScohort, only HER2-negative breast cancers were includedin the evaluation of LDHB levels to predict pCR. Twoadditional publically available datasets were used to eval-uate the predictive ability of LDHBonbreast cancer intrinsicsubtyping: The Cancer Genome Atlas (TCGA, Supplemen-tary Table S1) and Xeloda in NeoAdjuvant Trial (XeNA;ref. 18) [GSE22358]. LDHB mRNA expression was quanti-fied by platform-dependent probe sets [201030_x_at],[A_23_P53476], or those as defined by TCGA. To comparethresholds, LDHB levels were median-centered to the HR-positive/HER2-negative group within each cohort.

For the tissue microarray (TMA), archival formalin-fixed,paraffin-embedded (FFPE) blocks were from patients withclinically diagnosed triple-negative breast cancer (2008-2009) who received at least 3 cycles of anthracycline-basedneoadjuvant chemotherapy (with or without taxanes) andhad residual disease in the breast or lymphnodes at surgery.All patients were treated at the Instituto Nacional de Enfer-medades Neopl�asicas in Lima, Per�u. Blocks were from post-neoadjuvant chemotherapy mastectomy specimens withresidual disease. Recurrence-free survival was defined as thetimebetween the date of surgery and the date of diagnosis ofrecurrence. Samples and associated clinical data were col-lected under an institutionally approved protocol (INEN#10-018, Supplementary Table S2). PAM50 intrinsic sub-typing and scoring of LDHB and standard markers includ-ing Ki67, androgen receptor (AR), and HER2 are describedin the SupplementaryMethods. The immunohistochemicalprotocol for LDHB was validated using FFPE blocks ofMDAMB231 cell lines with short hairpin RNA (shRNA)knockdown of LDHA or LDHB (Supplementary Fig. S1).

Cell culture and glycolytic phenotypingBreast cancer cell lines (SKBR3, BT474, MDAMB231,

HCC38, BT20, MDAMB468, DU4475, HCC70, HCC1937,HCC1187, HCC1806, CAMA1, T47D, HCC1428, ZR751,MDAMB175, MCF7, MCF10A,MCF12A, andMDAMB453)were cultured in Dulbeccos’ Modified Eagles’ Media(DMEM) supplemented with 5% FBS at 37�C in 5% CO2atmosphere. MCF10A and MCF12A were supplementedwith additional cholera toxin (100 ng/mL), hydrocortisone(0.5 mg/mL), insulin (10 mg/mL), and EGF (20 ng/mL).Cell lines were routinely tested for mycoplasma infectionusing a MycoTect kit (Invitrogen). Stable isogenic cell linesofMDAMB231 andHCC1937were generated using LDHA,LDHB, or the nonsilencing control Expression Arrest GIPZlentiviral shRNA particles fromOpen Biosystems (Thermo-Fisher Scientific Inc.). Infected cells were selected and rou-tinely cultured with 1 mg/mL puromycin (Sigma-Aldrich).The identities of all cell lines were verified using AmpF/STRIdentifier kit (Applied Biosystems). Protein quantificationof whole-cell lysates and Western blotting using primary

Translational RelevanceEven within the most proliferative, aggressive sub-

types of breast cancer, only a fraction of breast cancersrespond to neoadjuvant chemotherapy. While mRNAprofiling methods and standard clinical markers[estrogen receptor (ER)/progesterone receptor (PR)/HER2] help identify those tumorsmore likely to respondto chemotherapy, other robust markers could benefitpatient care. We present evidence that a metabolicenzyme, LDHB, is highly expressed in the microenvi-ronment but is essentially absent in a subset of breastcancers. Independent of existing biomarkers, LDHB is anovel metabolic marker for breast cancer, identifyingcancer cells with a more glycolytic phenotype. Impor-tantly, high LDHB expression predicted pCR indepen-dently of ER expression and PAM50 subtyping. In anindependent triple-negative cohort, high LDHB pre-dicted relapse. Thus, measurement of LDHB may helpidentify breast cancers most likely to respond to neoad-juvant chemotherapy as well as those with the highestrisk of relapse thatmay benefit from additional adjuvanttherapy.

Dennison et al.

Clin Cancer Res; 19(13) July 1, 2013 Clinical Cancer Research3704




antibodies for LDHA (Cell Signaling; 3582S; 1:500) andLDHB (Abcam; ab85319; 1:2,000) and secondary antibo-dies, antirabbit or antimouse immunoglobulin G (IgG)horseradish peroxidase–linked secondary antibody (CellSignaling Technology; 1:2,000), were as described previ-ously (19).The oxygen consumption rates (OCR) and the extracel-

lular acidification rates (ECAR) of cell lines were quantifiedusing the Seahorse Extracellular Flux Analyzer (XF96, Sea-horse Biosciences). For adherent lines, at least 5 wells foreach cell line were seeded on XF 96-well microplates (Sea-horse Biosciences), 0.6 � 104 to 1.6 � 104 cells/well in 5%FBS DMEM, and left overnight to attach. Approximately 1hour before the Seahorse readings, the medium wasreplaced with exchange medium: serum-free, bicarbon-ate-free DMEM with phenol red (5 mmol/L glucose, 0.5glutamine, 1 mmol/L sodium lactate). For suspension celllines, wells were pretreated with CellTak (BD Biosciences)per the manufacturer instructions, and 1.6� 104 cells wereadded to the wells in the exchange medium on the day ofthe readings. OCR and ECAR readings were determined for6 cycles (2-minute mixing, 5-minute measuring), and thebaseline measurements were the average of the last 3readings before oligomycin addition (1 mg/mL final con-centration). The absolute OCR reduction after oligomycinadditionwas defined at the ATP-dependentOCR (OCRATP).

Analytical and statistical methodsPublically available mRNAmicroarray data from a panel

of 54 breast cancer cell lines (20) were used to identifybimodal genes based on KEGG function from central

carbon metabolic pathways. For probe sets with at least a10-fold range, R code (web site http://bioinformatics.mdanderson.org/Software/OOMPA) was used to computethe bimodality index as previously described (13). A probeset was considered bimodal if the bimodality index was>1.1, and the proportion of samples in one group was>10%. Bimodal genes were selected on the basis of theirKEGG function including genes from central carbon met-abolic pathways (21).

mRNA and protein expression differences betweengroups of breast cancers and cell lines were assessed usingt tests and one-way ANOVA. Survival curves were estimatedusing the Kaplan–Meier method, and differences were eval-uated using the log-rank test or univariate Cox regression.Fisher exact test for single variables and binary logisticalregression formultiple variables were used to determine theimpact of potential markers on pCR. Statistical analyseswere conducted using Prism 5.0c (GraphPad Software) orSPSS Statistics (Version 19.0, SPSS). P� 0.05 (2-sided) wasconsidered significant.

ResultsIdentification of LDHB as a bimodal metabolism gene

Using mRNA microarray data from a panel of breastcancer cell lines, metabolism-related genes were ranked bytheir bimodality index values (Supplementary Table S3). Ascompared with clinical markers ERBB2 and ESR1, 20metabolism-related genes with similar bimodality wereidentified including the highest-ranked gene, LDHB (Fig.1A and B). LDHB expression was the highest in basal-like ortriple-negative cell lines (Fig. 1C andD). Because LDHAand

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Figure 1. LDHB is highly expressed in basal-like breast cancer cells and contributes to glycolytic phenotype. A, LDHB was the highest scoring of metabolicgenes in a bimodal analysis, higher than ERBB2 and ESR1; >1.1 bimodality index was considered bimodal. The top scoring 25 genes for bimodality index areshown; seeSupplementary Table S3 for the entire list. B,mRNAexpression bymicroarray ofLDHB, ERBB2, andESR1were bimodal but not that of LDHA. C, LDHBexpression was the highest in triple-negative lines. mRNA microarray expression of LDHB separated by HER2 and ER status of cell lines. Lines represent themedian values. D, LDHB protein levels were differentially expressed in cell lines as shown by Western blotting of LDHA and LDHB in a panel of cell lines includingHER2-amplified, luminal, and basal-like subtypes as defined previously (20). Twenty micrograms of protein was loaded per lane. �, P < 0.05; ��, P < 0.001.

LDHB as a Marker of Response in Breast Cancer

www.aacrjournals.org Clin Cancer Res; 19(13) July 1, 2013 3705




LDHB form active tetrameric enzymes with each other, therelative expression levels of mRNA and protein were eval-uated for both subunits (Fig. 1B andD). Consistent with themRNA data (Fig. 1B) and a recent study (22), differentialprotein expression of LDHB but not LDHA was confirmedby Western blotting (Fig. 1D).

While LDHA was generally high, LDHB when presentcontributed substantially to the total lactate dehydrogenaseactivity in the cell lines (>50% for HCC1187, HCC1937,and MDAMB175; Supplementary Fig. S2). Certain basal Acell lines like BT20 andMDAMB468 did not express LDHB,but LDHB was highly expressed in basal B lines whichinclude the stem cell–like, claudin-low subset of breastcancers (refs. 23,24; Supplementary Fig. S2). Within theER-positive/HER2-negative lines, LDHB was expressed in aminority of cell lines, typically those with lower ESR1mRNA levels (LDHB vs. ESR1: Pearson r ¼ 0.58, P ¼ 0.04).

Glycolytic phenotyping of breast cancer cell lines androle of LDHB

To determine how LDHB expression is related to meta-bolic state, the rates of oxygen consumption (OCR) and

extracellular acidification (ECAR) were quantified using apanel of cell lines (n¼ 19)with representatives from each ofthe known subtypes (20). The ratios of the ATP-synthasedependent OCR in the mitochondria (OCRATP) and theECAR (a measure of lactate production rates) were used torank order the cell lines (Fig. 2A and B). Consistent withprevious reports of high 18FDG-PET uptake in basal-liketumors (12), the metabolic phenotypes as determined byOCRATP/ECAR ratios of breast cancer cell lines were highlyvariable (20-fold range) andmost glycolytic in basal-like ascompared with luminal-like cells (Fig. 2B, P ¼ 0.01).Importantly, LDHB was equally predictive of metabolicphenotype with the highest expression in the most glyco-lytic lines (Fig. 2B, P ¼ 0.005).

To evaluate the functional metabolic role of LDHB, wecreated 2 sets of isogenic cell lines with stable knockdownsof LDHAor LDHB using breast cancer cells with high LDHBin the parental lines (Supplementary Fig. S3). Knockdownof LDHA or LDHB promoted a more oxidative metabolicstate as shown by increased OCRATP/ECAR ratios for bothcell lines (Fig. 2C). The changes were primarily attributableto increased OCRATP (Supplementary Fig. S3) and

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matrix

Less gycolytic

Moregycolytic

Glucose

Figure 2. A, schematic of oxygen consumption rate (OCR) and extracellular acidification rate (ECAR) as they relate to ATP production in the cell. OCRATP isdefined as the OCR by ATP synthase experimentally determined by inhibition with oligomycin. High OCRATP/ECAR ratios occur with higher mitochondrialdependence for ATP production. B, ratio of OCRATP normalized to ECAR was determined for a panel of breast cancer cell lines. Intrinsic subtype asdetermined previously (20) is included in the legend. Floating bars represent the average � 1 SD. C, stable shRNA knockdown of LDHA or LDHB increasedmitochondrial dependence for ATP production inMDAMB231 and HCC1937 cell lines. The box andwhiskers plots show theminimum andmaximum values.Knockdown was confirmed by nondenatured electrophoresis of LDH (Supplementary Fig. S3). �, P < 0.05; ��, P < 0.001.

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consistent with previous reports of mitochondrial compen-sation after stable knockdown of LDHA (25).

Expression of LDHB is sufficient to predict basalphenotypeTo determine whether the cell line data translated to

primary tumors, the mRNA expression of LDHB was eval-uated using multiple cohorts of patients’ cancers. Asexpected on the basis of the cell line data and a recentreport (22), basal-like breast cancers as defined by PAM50expressed higher levels of LDHB in multiple datasets

(Fig. 3A; Supplementary Fig. S4). Using standard clinicalmarkers, LDHB was also higher in triple-negative breastcancers and the lowest in HR-positive/HER2-negative can-cers (Fig. 3B; Supplementary Fig. S5). In contrast, LDHAwasnot differentially expressed in the breast cancer subtypes(data not shown) in agreement with a previous report (26).

Clinical classification of breast cancers by ER/PR/HER2status did not exclusively define PAM50 intrinsic subtype inour cohorts; 17% to 28%of triple-negative tumors were notbasal-like, and 31% to 38% of HR-positive/HER2-negativetumors were not luminal-like (Table 1). Thus, we evaluated

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Figure 3. LDHB mRNA was highlyexpressed in basal-like and triple-negative disease and predictedbasal-like subtyping of breastcancers independently of HR-status. LDHB mRNA expressionseparated by (A) PAM50 intrinsicsubtype and (B) clinical ER/HER2status (TCGA, SupplementaryTable S1). The box and whiskersplots (A and B) show the minimumand maximum values. Forest plotsfor LDHB prediction of basal-likephenotype by cohort for (C) HR-positive/HER2-negative and (D)triple-negative breast cancers.Marker size represents the size ofthe cohort, and the bars cover the95% CI. Results from a triple-negative TMA stained for LDHB(Fig. 4) are also plotted (D) but arenot part of the average. HR,hormone receptor (ER and/or PR);TN, triple-negative. ��, P < 0.001.






the ability of LDHB to predict intrinsic subtype within HR-positive/HER2-negative and triple-negative cancers. LDHBwas highly associated with basal-like phenotype indepen-dently of HR status (Supplementary Figs. S6 and S7). Thethreshold for high/low expression of LDHBwas determinedby optimizing the ORs of the MAQC cohort based on theseparation of basal-like subtype within the HR-positive/HER2-negative group (0.60, Supplementary Fig. S6). Usingthis threshold for all remaining cohorts, LDHB was able topredict basal-like subtype within the HR-positive/HER2-negative and triple-negative breast cancer groups with ahigh degree of power (Fig. 3C and D). The ORs for predic-tion of basal phenotypewas overall lower for triple-negativeas compared with the HR-positive/HER-negative cancers.However, this trendwas not observed for the TCGA cohorts;LDHB levels were as predictive of basal phenotype for thetriple-negative group as compared with the HR-positive/HER2-negative group (Fig. 3CandD), perhaps reflecting thehigher purity of the TCGA specimens and consequently a

reduced number of normal-like breast cancers (27). In fact,exclusion of the normal-like breast cancers in the othercohorts increased the ORs for basal prediction within thetriple-negative groups (data not shown).

LDHBpredicts response to neoadjuvant chemotherapyGiven that pCR after neoadjuvant chemotherapy predicts

reduced risk of relapse independently of clinical subtype inbreast cancer (28), pCR was used to evaluate LDHB as abiomarker of response. The ability of LDHB to predict pCRafter neoadjuvant chemotherapy was evaluated in 2 inde-pendent cohorts (MAQC and MDACCSS).

First, the HR-positive/HER2-negative groups were evalu-ated for differences in pCR using the LDHB thresholddetermined from the basal-like prediction (0.60, Fig. 3C).High nuclear grade, basal-like subtyping, and high LDHBpredicted pCR to neoadjuvant chemotherapy by univariateanalyses for 2 independent cohorts (Table 1) and in thecombined cohort [OR, 4.1; 95% confidence interval (CI),

Table 1. Association of clinical characteristics to neoadjuvant pCR by cohort

MAQC MDACCSS

HRþ/HER2-neg Triple-negative HRþ/HER2-neg Triple-negative

Characteristic No. % pCR P No. % pCR P No. % pCR P No. % pCR P

No. of patients 137 6.6 � 68 35.3 � 167 16.2 � 111 30.6 �Age, y 1.000 0.309 0.676 0.14850 75 6.7 37 29.7 79 17.7 48 22.9

Tumor size atdiagnosis (TNM)

0.139 0.608 1.000 0.064

T0/T1/T2 94 4.3 38 39.5 86 16.3 50 40.0T3/T4 43 11.6 29 31.0 81 16.0 61 23.0

Lymph node 1.000 0.304 0.828 0.154Negative 48 6.3 11 18.2 62 14.5 28 42.9Positive 89 6.7 56 39.3 105 17.1 83 26.5

Modified Black'snuclear grade

0.006 0.182 0.003 0.542

1–2 90 2.2 11 18.2 84 7.1 14 21.43 45 15.6 50 42.0 71 25.4 82 31.7Unknown or indeterminate 2 0.0 7 14.3 12 25.0 15 33.3

PAM50 intrinsic subtype 0.050 0.048 0.016 1.000Luminal A 52 5.8 0 � 73 4.1 2 0.0Luminal B 33 9.1 4 25.0 42 23.8 0 �HER2-enriched 1 0.0 0 � 11 18.2 8 25.0Normal 37 0.0 15 13.3 15 20.0 9 44.4Basal 14 21.4 49 42.9 26 34.6 92 30.4

LDHB (0.60 threshold) 0.027 0.083 0.001 0.359Low 94 3.2 18 16.7 126 10.3 31 22.6High 43 14.0 50 42.0 41 34.1 80 33.8

LDHB (0.94 threshold) 0.117 0.020 0.006 0.087Low 106 4.7 24 16.7 134 11.9 40 20.0High 31 12.9 44 45.5 33 33.3 71 36.6

NOTE: P was calculated by Fisher exact test. For PAM50 intrinsic subtyping, basal-like subtype was compared with other subtypespooled (luminal A, luminal B, HER2-enriched, and normal-like).

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2.0–8.3; P < 0.001 for high LDHB]. In a logistic regressionmodel for the combined cohorts with these characteristicsas predictors, only LDHB and nuclear grade remainedsignificant (Table 2).For triple-negative cancers, adjustment of LDHB thresh-

old from 0.60 to 0.94 optimized the ability of LDHB topredict pCR within the MDACCSS cohort (approachingstatistical significance, P ¼ 0.087, Table 1). Using thisthreshold, LDHB predicted pCR in the MAQC cohort (P¼ 0.020) and in the combined cohort (OR, 3.0; 95% CI,1.4–6.2; P ¼ 0.003). Although the basal-like subtype pre-dicted pCR in theMAQC cohort by univariate analysis, onlyLDHB and tumor size were significant in a multivariatelogistic regressionmodel of the combined cohort (Table 2).Interestingly, LDHB was highly expressed in the mostaggressive triple-negative cancers within the basal-like sub-type as shown by its association with the proliferationmarker CCNB1 (Supplementary Fig. S8). Therefore, anadjustment of the LDHB threshold may allow stratificationwithin basal-like cancers.

LDHB expression in triple-negative breast cancers withresidual diseaseTo further evaluate the potential predictive role of LDHB,

an independent sample set of relatively advanced-stage,triple-negative disease (primarily stage III, SupplementaryTable S2) biopsied after neoadjuvant chemotherapy was

quantified for LDHBprotein expressionby IHC.As expectedon the basis of the cell line data (Fig. 1), intertumoralexpression of LDHBwas heterogeneous in the breast cancercells (Fig. 4A). Interestingly, LDHB expressionwas generallyubiquitous in the tumor microenvironment, whichmay, inpart, explain why differential expression of LDH isoformswas not previously detected in breast cancer (29). Consis-tent with mRNA microarray data of patient tumors, highLDHB was also able to predict basal-like phenotype[threshold ¼ 180 for lowest P; sensitivity ¼ 96% (47 of49); specificity ¼ 60% (18 of 30); Figs. 3D and 4C]. Usingthe same LDHB intensity threshold as the basal-like pre-diction analysis, LDHB was the only predictive marker forrelapse in this cohort (Fig. 4B; Supplementary Table S2).High LDHB marked the most aggressive disease that wasmore common in youngwomen as shownby association ofLDHBwith Ki67 and age (Fig. 4D). Cancers with low LDHBwere also more likely to have high AR and HER2 (Fig. 4D).

DiscussionMolecular profiling methods in breast cancer used for

prognosis and to predict lack of benefit from chemotherapysuch asOncotypeDX and PAM50 primarily focus onHER2,ER, and proliferation-related genes (9,14). Here, we evalu-ate another functional hallmark of cancer, deregulation ofcellular energetics (30). By starting with mRNA microarraydata of cell lines, bimodal genes withinmetabolic pathways

Table 2. Binary logistical regression for odds of pCR after neoadjuvant chemotherapy by HR status,combined MAQC and MDACCSS cohorts

HR-positive/HER2-negative Triple-negative

Characteristic OR (95% CI) P OR (95% CI) P

Tumor size at diagnosis (TNM) 0.017T0/T1/T2 1T3/T4 0.44 (0.23–0.87)

Modified Black's nuclear grade 0.0011–2 13 4.30 (1.78–10.42)

PAM50 intrinsic subtype 0.580 0.898Luminal A 1 1Luminal B 1 1HER2-enriched 1 1Normal 1 1Basal 1.32 (0.49–3.58) 1.06 (0.42–2.61)

LDHB (0.60 threshold) 0.018Low 1High 2.86 (1.19–6.84)

LDHB (0.94 threshold) 0.004Low 1High 3.18 (1.45–6.99)

NOTE: Characteristics were included in the models if P � 0.05 by univariate analysis in at least one cohort (Table 1) or the combinedcohort. Caseswere excluded if all characteristicswere not reported. TheORswere not statistically significant if basal-like and luminal Bcases were considered as one group. Values in bold were statistically significant.






were detected in the cancer cells without influence ordilution by the tumor microenvironment. This approachled to the evaluation of LDHB as a putative biomarker,which proved to behighly expressed in aggressive, glycolyticbreast cancers primarily of the basal subtype.

Bimodal expression of LDHBmay have clinical relevancebecause cancers with high LDHB were most responsive toneoadjuvant chemotherapy independently of establishedprognostic factors (grade, tumor size) and molecular mar-kers (HR status and PAM50 subtyping). Although it mightbe expected that higher response rates to neoadjuvantchemotherapy would lead to reduced rates of relapse, highLDHB tumors without pCR post-neoadjuvant chemother-apy were also more likely to relapse in our triple-negativecohort. One possible explanation is that LDHB markscancers with higher metastatic potential leading to more

residual micrometastases. Also, LDHB is reported to facil-itate tumor growth inbasal-like breast cancers (22), so fasterrates of relapse are consistent with higher rates of prolifer-ation as shown by association with Ki67 and proliferationmarkers in high LDHB cancers. However, LDHB not Ki67predicted relapse in our triple-negative cohort supportingthe hypothesis that LDHB is more than a surrogate prolif-eration marker.

We showed that LDHB expression levels in breast cancerswere bimodal, an important characteristic for a robustbiomarker. Themost striking observation was the near totaldepletion of LDHB in many samples. For breast cancer celllineswith lowLDHB levels, the contribution of LDHA to thetotal LDH activity was almost 100%. Likewise, in mostluminal tumors, LDHB was highly expressed in the tumormicroenvironment but not detected in cancer cells (Fig. 4).

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Figure 4. High LDHB expression byIHC was associated with relapse,PAM50 basal-like phenotype, andother standard breast cancermarkers in triple-negative breastcancers with residual diseasepost-neoadjuvant chemotherapy.A, representative immunostainingof LDHB in low- and high-expressing triple-negative breastcancers. B, Kaplan–Meier curve ofrecurrence-free survival of patientsseparated by LDHB expression. C,LDHB intensity by IHC identifiedbyPAM50 subtype. The dotted linerepresents the high/low separationfor LDHB in all analyses. D,association of LDHB with variouspatient characteristics andmarkers including age, Ki67, AR,and HER2. Symbols and error barsrepresent the average � 1 SD.��, P < 0.01; ��, P < 0.001.

Dennison et al.





In contrast, LDHB levels were high in many basal-likecancer cells, in some cell lines exceeding that of LDHA. Todeterminewhether overexpressionwas regulated at the genelevel, we evaluated copy number gain for LDHB at chro-mosome 12p12, the same amplicon as KRAS. While we didsee gain in approximately 40% of TCGA basal-like tumorsthat correlated with mRNA expression, the differences inmRNA expression of LDHB between luminal and basalcancers were independent of gene copy number (Supple-mentary Fig. S9). This observation for breast cancer is incontrast to that of lung cancer; LDHBwas recently shown tobe highly expressed in lung adenocarcinomas with KRASamplification or mutations (31). Recently reported forbreast cancer (32) and similar to regulation of LDHB inprostate cancer (33), DNA hypermethylation of LDHBmaycontribute to low expression of LDHB mRNA in luminalbreast cancer (Supplementary Fig. S9).While LDHBexpression levelswere associatedwith breast

cancer subtype as defined by PAM50, high LDHB identifiedaggressive cancers were predominantly but not exclusivelybasal-like (Fig. 4). Importantly, as comparedwith basal-likephenotype, LDHB was a more robust predictor of responseto neoadjuvant chemotherapy (Table 2). Our TMAof triple-negativebreast cancerswithmatchedPAM50 subtyping alsoshowed that LDHB was highly expressed not only in basalbut also in a subset ofHER2-enriched and luminal B cancerswith increased rates of relapse (Fig. 4). Consistentwith thesefindings, another study reported that only basal-like, HER2-enriched, and luminal B cancers with the worst clinicaloutcomes were able to form stable grafts in mice (34);importantly, we determined that 11 of the 12 stable grafts(of 49 tumors transplanted) expressed high levels of LDHB(GSE32532, data not shown). On the basis of these results,we anticipate that LDHB expressionmay provide additionalinformation beyond the standard proliferation and hor-mone markers used in molecular profiling tests like Onco-type DX and PAM50. However, additional studies will berequired to understand the prognostic or predictive value ofLDHB expression as compared to othermolecular classifiersand signatures for breast cancer.LDHB expression is likely to also provide information on

the metabolic phenotype of breast cancers that could con-tribute to selection of new treatment modalities. HighLDHB marked the most glycolytic breast cancer cells, oftenbasal-like, within the HR-positive/HER2-negative and tri-ple-negative groups. Our cell line studies showed a directeffect and strong positive association with LDHB and gly-colytic phenotype (Fig. 2). Consistent with 18FDG-PETreports (12, 35), cell lines with amore glycolytic phenotypein our study were primarily basal-like: those with geneticinstability or increased proliferation often caused by loss ofTP53, MYC amplification, or BRCA1 mutations. High gly-colytic flux may provide these types of cancers a survivaladvantage because the Warburg effect allows more rapidconsumption of glucose and consequently can supportincreased rates of proliferation, regardless of tumoroxygenation (15). This adaptation may be less importantfor luminal-like cancers whose survival may be more

dependent on antiapoptotic mechanisms such as Bcl-2expression (36).

Although the general enzymatic function of LDHB isknown, the functional role of LDHB is cell type and contextdependent. In tissues like the brain that use lactate as energysource, LDHB promotes lactate uptake during exercise;other cells like erythrocytes use LDHB for glycolysis tosynthesize lactate (37). While LDHB is thought to convertlactate to pyruvate in certain tissues, our breast cancer cellline results show that LDHB is functionally similar to LDHAand contributes to the conversion of pyruvate to lactate (Fig.2). We propose that LDHB is constitutively expressed inmost basal-like breast cancers and significantly contributesto the overall LDH activity. Consequently, inhibition ofLDHA, a proposed therapeutic target in breast cancer (25,38), would have less effect on the total lactate dehydroge-nase activity if LDHBwere coexpressed. Indeed, high LDHBexpression may identify tumors less likely to respond toLDHA inhibitors.

While the functionofLDHBis consistentwithan increasedglycolytic phenotype, LDHB expression was insufficient tocompletely explain the metabolic variability between breastcancer cell lines (Fig. 1B). LDHB is likely part of a network ofmetabolic proteins that together create a specific glycolyticphenotype. Low expression of fructose-1,6-bisphosphatase 1(FBP1) and high expression of monocarboxylic acid trans-porter 1 (SLC16A1) and glutaminase (GLS), previouslyreported for basal-like phenotype (39, 40), were associatedwith high LDHB (data not shown). We propose that LDHBexpression may mark a particular lineage or state of differ-entiation with altered metabolic demands. Consistent withthis hypothesis, LDH isoform distribution is well-known tobe a fundamental property of cell type and developmentalstage (41). Also, low LDHB in HER2-positive and luminal-like cancers is consistentwithamodelofbreast cancer lineage(42), from least tomostdifferentiated (LDHB levels: claudin-low/basal>HER2> luminal).Given thatLDHBmRNA levelswere unchanged by chemotherapy in breast cancer(GSE28844, n¼ 28pairs, datanot shown), LDHBexpressionappears to be a property of lineage and independent of thetumor microenvironment.

In conclusion, LDHB expression in breast cancer cells wasbimodal, a desirable property for a clinical biomarker. Asexpected on the basis of its enzymatic function, high LDHBwas associated with glycolytic, basal-like phenotype. Whileadditional prospective studies are required, LDHB in ourcohorts was an independent predictive marker of pCR forHER2-negative cancers (HR-positive or HR-negative) andrelapse in triple-negative disease.

Disclosure of Potential Conflicts of InterestG.B. Mills has a commercial research grant from AstraZeneca, Celgene,

CeMines, Exelixis/Sanofi, GSK, Roche, Wyeth Research, and Pfizer/Puma;has ownership interest (including patents) from Catena Pharmaceuticals,PTV Ventures, and Spindle Top Ventures; and is a consultant/advisory boardmember of AstraZeneca, Catena Pharmaceuticals, Tau Therapeutics, CriticalOutcome Technologies, Daiichi Pharm., Targeted Molecular DiagnosticsLLC, Foundation Medicine, HanAll Bio Pharm Korea, Komen Foundation,Novartis, and Symphogen. No potential conflicts of interest were disclosedby the other authors.






Authors' ContributionsConception and design: J.B. Dennison, A. Gonz�alez-Angulo, R.E. BrownDevelopment of methodology: J.B. Dennison, J.R. Molina, S. Mitra, R.E.Brown, G.B. MillsAcquisitionofdata (provided animals, acquired andmanagedpatients,provided facilities, etc.): J.B. Dennison, J.R. Molina, S. Mitra, A. Gonz�alez-Angulo, J.M. Balko, M.G. Kuba, M.E. Sanders, J.A. Pinto, H.L. Gomez, C.L.Arteaga, R.E. Brown, G.B. MillsAnalysis and interpretation of data (e.g., statistical analysis, biosta-tistics, computational analysis): J.B. Dennison, A. Gonz�alez-Angulo, J.M.Balko, M.E. Sanders, R.E. Brown, G.B. MillsWriting, review, and/or revision of the manuscript: J.B. Dennison, J.R.Molina, S. Mitra, A. Gonz�alez-Angulo, M.E. Sanders, J.A. Pinto, H.L. Gomez,R.E. Brown, G.B. MillsAdministrative, technical, or material support (i.e., reporting or orga-nizing data, constructing databases): J.M. Balko, M.E. Sanders, J.A. Pinto,H.L. Gomez, C.L. Arteaga

AcknowledgmentsThe authors thank Pamela K. Johnston for help with the immunohisto-

chemical assay for LDHB, Ju-Seog Lee and Fan Zhang for assistance with

patient datasets, and Xiudong Lei for suggestions on the biostatisticalanalyses.

Grant SupportThis work was supported by the Susan G. Komen Foundation

(KG081694), GlaxoSmithKline, Vanderbilt Breast Cancer Specialized Pro-gram of Research Excellence (SPORE; P50CA98131), American CancerSociety Clinical Research Professorship Grant (CRP-07-234) EntertainmentIndustry Foundation, and Susan G. Komen for the Cure Scientific AdvisoryCouncil Grant (SAC100013). S. Mitra is supported by a Susan G. KomenPostdoctoral Fellowship. J.B. Dennison is supported by a GlaxoSmithKlineTRIUMPH post-doctoral fellowship and the American Cancer Society, Joeand Jessie Crump Medical Research Fund Postdoctoral Fellowship.

The costs of publication of this article were defrayed in part by thepayment of page charges. This article must therefore be hereby markedadvertisement in accordance with 18 U.S.C. Section 1734 solely to indicatethis fact.

Received March 5, 2013; revised May 13, 2013; accepted May 15, 2013;published OnlineFirst May 22, 2013.

References1. PerouCM,Sørlie T, EisenMB, vandeRijnM, JeffreySS, ReesCA, et al.

Molecular portraits of human breast tumours. Nature 2000;406:747–52.

2. Sørlie T, Perou CM, Tibshirani R, Aas T, Geisler S, Johnsen H, et al.Gene expression patterns of breast carcinomas distinguish tumorsubclasses with clinical implications. Proc Natl Acad Sci U S A2001;98:10869–74.

3. Prat A, Parker JS, Fan C, Cheang MCU, Miller LD, Bergh J, et al.Concordance among gene expression-based predictors for ER-pos-itive breast cancer treated with adjuvant tamoxifen. Ann Oncol2012;23:2866–73.

4. EssermanLJ, BerryDA,CheangMCU,YauC, PerouCM,Carey L, et al.Chemotherapy response and recurrence-free survival in neoadjuvantbreast cancer depends on biomarker profiles: results from the I-SPY 1TRIAL (CALGB 150007/150012; ACRIN 6657). Breast Cancer ResTreat 2012;132:1049–62.

5. Giordano SH, Lin YL, Kuo YF, Hortobagyi GN, Goodwin JS. Decline inthe use of anthracyclines for breast cancer. J Clin Oncol 2012;30:2232–9.

6. Carlson RW, Allred DC, Anderson BO, Burstein HJ, Carter WB, EdgeSB, et al. Breast cancer. Clinical practice guidelines in oncology. J NatlCompr Canc Netw 2009;7:122–92.

7. DabbsDJ, KleinME,Mohsin SK, TubbsRR, Shuai Y, BhargavaR. Highfalse-negative rate of HER2 quantitative reverse transcription poly-merase chain reaction of theOncotypeDX test: an independent qualityassurance study. J Clin Oncol 2011;29:4279–85.

8. BhargavaR.OncotypeDX test on unequivocally HER2-positive cases:potential for harm. J Clin Oncol 2012;30:570–1.

9. Sparano JA, Paik S. Development of the 21-gene assay and itsapplication in clinical practice and clinical trials. J Clin Oncol 2008;26:721–8.

10. Balko JM, Cook RS, Vaught DB, Kuba MG, Miller TW, Bhola NE, et al.Profiling of residual breast cancers after neoadjuvant chemotherapyidentifies DUSP4 deficiency as a mechanism of drug resistance. NatMed 2012;18:1052–9.

11. Warburg O,Wind F, Negelein E. Themetabolism of tumors in the body.J Gen Physiol 1927;8:519–30.

12. Palaskas N, Larson SM, Schultz N, Komisopoulou E,Wong J, Rohle D,et al. 18F-fluorodeoxy-glucose positron emission tomography marksMYC-overexpressing human basal-like breast cancers. Cancer Res2011;71:5164–74.

13. Wang J, Wen S, Symmans WF, Pusztai L, Coombes KR. The bimo-dality index: a criterion for discovering and ranking bimodal signaturesfrom cancer gene expression profiling data. Cancer Inform 2009;7:199–216.

14. Parker JS, Mullins M, Cheang MC, Leung S, Voduc D, Vickery T, et al.Supervised risk predictor of breast cancer based on intrinsic subtypes.J Clin Oncol 2009;27:1160–7.

15. Vander Heiden MG, Cantley LC, Thompson CB. Understanding theWarburg effect: the metabolic requirements of cell proliferation. Sci-ence 2009;324:1029–33.

16. Popovici V, ChenW, Gallas BG, Hatzis C, Shi W, Samuelson FW, et al.Effect of training-sample size and classification difficulty on the accu-racy of genomic predictors. Breast Cancer Res 2010;12:R5.

17. Hatzis C, Pusztai L, Valero V, Booser DJ, Esserman L, Lluch A, et al. Agenomic predictor of response and survival following taxane-anthra-cycline chemotherapy for invasive breast cancer. JAMA 2011;305:1873–81.

18. Gl€uck S, Ross JS, Royce M, McKenna EF, Perou CM, Avisar E, et al.TP53 genomics predict higher clinical and pathologic tumor responsein operable early-stage breast cancer treated with docetaxel-capeci-tabine � trastuzumab. Breast Cancer Res Treat 2012;132:781–91.

19. DennisonJB, ShanmugamM,AyresML,Qian J, Krett NL,Medeiros LJ,et al. 8-Aminoadenosine inhibits Akt/mTORandErk signaling inmantlecell lymphoma. Blood 2010;116:5622–30.

20. Neve RM, Chin K, Fridlyand J, Yeh J, Baehner FL, Fevr T, et al. Acollection of breast cancer cell lines for the studyof functionally distinctcancer subtypes. Cancer Cell 2006;10:515–27.

21. Possemato R, Marks KM, Shaul YD, Pacold ME, Kim D, Birsoy K, et al.Functional genomics reveal that the serine synthesis pathway isessential in breast cancer. Nature 2011;476:346–50.

22. McCleland ML, Adler AS, Shang Y, Hunsaker T, Truong T, Peterson D,et al. An integrated genomic screen identifies LDHB as an essentialgene for triple-negative breast cancer. Cancer Res 2012;72:5812–23.

23. Hennessy BT, Gonzalez-Angulo AM, Stemke-Hale K, Gilcrease MZ,Krishnamurthy S, Lee J-S, et al. Characterization of a naturally occur-ring breast cancer subset enriched in epithelial-to-mesenchymal tran-sition and stem cell characteristics. Cancer Res 2009;69:4116–24.

24. Prat A, Parker JS, Karginova O, Fan C, Livasy C, Herschkowitz JI, et al.Phenotypic and molecular characterization of the claudin-low intrinsicsubtype of breast cancer. Breast Cancer Res 2010;12:R68.

25. Fantin VR, St-Pierre J, Leder P. Attenuation of LDH-A expressionuncovers a link between glycolysis, mitochondrial physiology, andtumor maintenance. Cancer Cell 2006;9:425–34.

26. Wang Z-Y, Loo TY, Shen J-G, Wang N, Wang D-M, Yang D-P, et al.LDH-A silencing suppresses breast cancer tumorigenicity throughinduction of oxidative stress mediated mitochondrial pathway apo-ptosis. Breast Cancer Res Treat 2012;131:791–800.

27. Elloumi F, Hu Z, Li Y, Parker JS, GulleyML, Amos KD, et al. Systematicbias in genomic classification due to contaminating non-neoplastictissue in breast tumor samples. BMC Med Genomics 2011;4:54.

28. Esserman LJ, Berry DA, DeMichele A, Carey L, Davis SE, Buxton M,et al. Pathologic complete response predicts recurrence-free sur-vival more effectively by cancer subset: results from the I-SPY 1TRIAL–CALGB 150007/150012, ACRIN 6657. J Clin Oncol 2012;30:3242–9.

Dennison et al.





29. Balinsky D, Platz CE, Lewis JW. Isozyme patterns of normal, benign,and malignant human breast tissues. Cancer Res 1983;43:5895–901.

30. Hanahan D, Weinberg RA. Hallmarks of cancer: the next generation.Cell 2011;144:646–74.

31. McCleland ML, Adler AS, Deming L, Cosino E, Lee LB, BlackwoodEM, et al. Lactate dehydrogenase B is required for the growth ofKRAS-dependent lung adenocarcinomas. Clin Cancer Res 2013;19:773–84.

32. Brown NJ, Higham SE, Perunovic B, Arafa M, Balasubramanian S,Rehman I. Lactate dehydrogenase-B is silenced by promoter meth-ylation in a high frequency of human breast cancers. PLoS ONE2013;8:e57697.

33. Maekawa M, Taniguchi T, Ishikawa J, Sugimura H, Sugano K,Kanno T. Promoter hypermethylation in cancer silences LDHB,eliminating lactate dehydrogenase isoenzymes 1–4. Clin Chem2003;49:1518–20.

34. DeRoseYS,WangG, LinY-C,BernardPS,BuysSS,EbbertMTW, et al.Tumor grafts derived from women with breast cancer authenticallyreflect tumor pathology, growth, metastasis and disease outcomes.Nat Med 2011;17:1514–20.

35. Basu S, Chen W, Tchou J, Mavi A, Cermik T, Czerniecki B, et al.Comparison of triple-negative and estrogen receptor-positive/proges-terone receptor-positive/HER2-negative breast carcinoma usingquantitative fluorine-18 fluorodeoxyglucose/positron emission

tomography imaging parameters: a potentially useful method fordisease characterization. Cancer 2008;112:995–1000.

36. Leek RD, Kaklamanis L, Pezzella F, Gatter KC, Harris AL. bcl-2 innormal human breast and carcinoma, association with oestrogenreceptor-positive, epidermal growth factor receptor-negative tumoursand in situ cancer. Br J Cancer 1994;69:135–9.

37. Quistorff B, Secher NH, Van Lieshout JJ. Lactate fuels the human brainduring exercise. FASEB J 2008;22:3443–9.

38. Le A, Cooper CR, Gouw AM, Dinavahi R, Maitra A, Deck LM, et al.Inhibition of lactate dehydrogenase A induces oxidative stress andinhibits tumor progression. Proc Natl Acad Sci U S A 2010;107:2037–42.

39. Kung H-N, Marks JR, Chi J-T. Glutamine synthetase is a geneticdeterminant of cell type-specific glutamine independence in breastepithelia. PLoS Genet 2011;7:e1002229.

40. Pinheiro C, Albergaria A, Paredes J, Sousa B, Dufloth R, Vieira D, et al.Monocarboxylate transporter 1 is up-regulated in basal-like breastcarcinoma. Histopathology 2010;56:860–7.

41. Cahn RD, Zwilling E, Kaplan NO, Levine L. Nature and development oflactic dehydrogenases: the two major types of this enzyme formmolecular hybrids which change in makeup during development.Science 1962;136:962–9.

42. Prat A, Perou CM. Mammary development meets cancer genomics.Nat Med 2009;15:842–4.






2013;19:3703-3713. Published OnlineFirst May 22, 2013.Clin Cancer Res Jennifer B. Dennison, Jennifer R. Molina, Shreya Mitra, et al. Neoadjuvant Chemotherapy in Breast CancerLactate Dehydrogenase B: A Metabolic Marker of Response to

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Lactate Dehydrogenase B: A Metabolic Marker of Response ......Published OnlineFirst May 22, 2013; DOI: 10.1158/1078-0432.CCR-13-0623 types ofbiomarkers could bediscoveredby exploring