Biomarkers for Presymptomatic Doxorubicin-Induced Cardiotoxicity in Breast Cancer Patients.

Cardiotoxicity of doxorubicin (DOX) remains an important health concern. DOX cardiotoxicity is cumulative-dose-dependent and begins with the first dose of chemotherapy. No biomarker for presymptomatic detection of DOX cardiotoxicity has been validated. Our hypothesis is that peripheral blood cells (PBC) gene expression induced by the early doses of DOX-based chemotherapy could identify potential biomarkers for presymptomatic cardiotoxicity in cancer patients. PBC gene expression of 33 breast cancer patients was conducted before and after the first cycle of DOX-based chemotherapy. Cardiac function was evaluated before the start of chemotherapy and at its completion. Differentially expressed genes (DEG) of patients who developed DOX-associated cardiotoxicity after the completion of chemotherapy were compared with DEG of patients who did not. Ingenuity database was used for functional analysis of DEG. Sixty-sevens DEG (P<0.05) were identified in PBC of patients with DOX-cardiotoxicity. Most of DEG encode proteins secreted by activated neutrophils. The functional analysis of the DEG showed enrichment for immune- and inflammatory response. This is the first study to identify the PBC transcriptome signature associated with a single dose of DOX-based chemotherapy in cancer patients. We have shown that PBC transcriptome signature associated with one dose of DOX chemotherapy in breast cancer can predict later impairment of cardiac function. This finding may be of value in identifying patients at high or low risk for the development of DOX cardiotoxicity during the initial doses of chemotherapy and thus to avoid the accumulating toxic effects from the subsequent doses during treatment.

Introduction

Doxorubicin (DOX), a commonly used anthracycline antibiotic for treatment of various malignancies may cause unpredictable cardiotoxicity [1]. DOX cardiotoxicity is cumulative-dose-dependent and begins with the first dose, suggesting that assessment of the cardiac function in patients at early doses of chemotherapy can avoid permanent cardiac damage [2]. According to the American College of Cardiology guidelines, patients receiving chemotherapy are at increased risk of developing cardiac dysfunction [3].

Evidence indicates that susceptibility to DOX cardiotoxicity is largely individual with some patients developing cardiomyopathy at doses of 200–400 mg/m2 [3], and others tolerating >1000 mg/m2 [4], suggesting the presence of a genetic predisposition.

Serial measurements of heart left ventricle ejection fraction (LVEF) is commonly used for cardiac monitoring during anthracycline treatment [5], although the prognostic value of LVEF appears to be controversial [6]. In some studies cardiotoxicity was defined as LVEF decrease by absolute 10% and/or to below 55% [7], in others cardiotoxicity was defined as a decrease below 45% [8]. A serious disadvantage of this test is the exposure to radioactivity along with the low predictability of pre-symptomatic cardiac damage [9]. Blood cardiac biomarkers, such as cardiac troponins and B-type natriuretic peptide (BNP) have been used in the diagnostics of heart failure [10], but other diseases have also been associated with increased troponin release [e.g. acute pulmonary embolism [11], and end-stage renal disease [12] and/or BNPs [e.g. end-stage renal disease [13]. Several studies failed to detect any correlation between concentrations of troponin and/or BNP and DOX-induced cardiotoxicity [14, 15].

Our previous study showed a high similarity between the gene expression of heart and peripheral blood cells (PBCs) in a rat model of DOX-cardiotoxicity [16], suggesting that PBC can be used as a surrogate tissue for assessing biomarkers of DOX cardiotoxicity. We hypothesize that PBC gene expression induced by the early doses of DOX-based chemotherapy could identify potential biomarkers for presymptomatic cardiotoxicity in cancer patients.

Materials and Methods

Study subjects and blood samples

Fifty-five women treated for breast cancer with DOX-based chemotherapy at the University of Arkansas for Medical Sciences were enrolled initially in an Institutional Review Board-approved protocol with written informed consent for each patient. All patients were treated with a predefined protocol which included a combination of DOX (Adriamycin, 60 mg/m2) with cyclophosphamide (600 mg/m2). However, 22 of these patients decided to dropout from the study for various reasons. We were able to obtain RNAs and data about cardiac function of 33 subjects.

Blood samples were collected prior to chemotherapy and after the first cycle of chemotherapy. PBCs were isolated from EDTA anti-coagulated blood using standard Ficoll-Paque Plus gradient centrifugation (density 1.073 g/mL) according to the instructions of the manufacturer (GE Healthcare, USA). Briefly, EDTA anti-coagulated blood, diluted with an equal volume of phosphate-buffered saline (PBS) was layered over the Ficoll-Paque Plus and was centrifuged at 400 g for 30 min at 18°C-20° with the brake off. After removing the upper layer containing plasma and platelets, the layer of peripheral blood mononuclear cells (PBMCs) was isolated and stored at -80° until further use. Total RNA was extracted from PBC using RNeasy columns (Qiagen; Valencia, CA) and samples with RNA integrity number (RIN) score>7 were used for expression analysis.

Ethics Statement

This study was carried out in accordance with the ethical guidelines of the 1975 Declaration of Helsinki and was approved by the ethics committee of the University of Arkansas for Medical Sciences. All subjects signed an IRB approved informed consent where they were informed for the use of their blood samples and medical records for research purposes.

Assessment of LVEF as a measure of cardiac function

Cardiac function of the patients was assessed by a multigated acquisition (MUGA) scan before the start of DOX- treatment and at its completion. A decline of LVEF by >10% or below 50% was considered abnormal [17].

Microarray gene expression and data analysis

The gene expression screen was performed using HumanHT-12 v4 Expression BeadChip array (Illumina, San Diego, California). Raw data were further analyzed using Illumina GenomeStudio software.

PBC gene expression data were log2 –transformed and gene transcripts with average log2-intensities >7 were considered to be expressed. We first compared baseline levels of gene expression between the two groups of patients, and then examined expression changes from baseline to after the first cycle of chemotherapy in each group of patients. The group-specific means for each group (“abnormal MUGA scan” and “normal MUGA scan”) were analyzed via repeated-measures ANOVA for expression changes after DOX. Genes with p-value <0.05 were considered differentially expressed. To adjust for multiple testing, the Benjamini-Hochberg false discovery rate (FDR) was used [18]. Given our relatively small sample size, we chose to use a FDR corrected significance threshold P≤ 0.1 which is commonly used in hypothesis-generating, discovery-driven gene expression studies [19].

Functional annotation

Functional network analysis was performed using Ingenuity Pathways Analysis System; http://www.ingenuity.com) which identifies the most significant biological functions to a dataset based on the causal relationships previously reported in the literature.

QRT-PCR Validation

Quantitative real-time RT-PCR was used for evaluation and confirmation of the gene expression data. Total RNAs were reverse transcribed and cDNAs were amplified using Nugen Ovation RNA Amplification System V2 (NuGEN™ Technologies, San Carlos, CA). QPCR was performed using Taqman Universal Fast PCR master mix and specific primers for DEFA3, DEFA4, ELANE, ARG1, HP, CEACAM8, and 18S tRNA (Applied Biosystems, Foster City, CA). Data were analyzed using the 2-deltadelta Ct method. The Ct values of both the control and the samples of interest were normalized to 18S. DeltaCt was calculated as “deltaCt(gene) equals Ct(18S) minus Ct(gene)”. DOX response was calculated as deltaCt “After DOX” minus deltaCt “Before DOX”, and is in ddCt (delta-delta-Ct) units.

Results

Characteristics of study subjects

We have analyzed and compared PBC gene expression associated with one dose of DOX-based chemotherapy of 8 breast cancer patients who developed abnormal LVEF after the completion of chemotherapy and 25 breast cancer patients who did not. Patients’ characteristics are provided in Table 1.

Table 1

Cardiac function of women with breast cancer, treated with DOX-based chemotherapy.

MUGA scan was performed before the start of DOX chemotherapy and at its completion.

	Number of patients	Average LVEF (%) at baseline*	Average LVEF (%) at completion of chemotherapy*
Patients with abnormal LVEF (%)	8	69.875 ± 5.436	57.875 ± 3.833
18–47 years	2	68.5 ± 2.121	57.5 ± 0.707
48–55 years	2	64.0 ± 0	54.0 ± 0
>56 years	4	73.5 ± 5.066	60.0 ± 4.242
Patients with normal LVEF (%)	25	61.583 ± 5.523	61.833 ± 6.919
18–47 years	9	60.888 ± 6.050	62.222 ± 9.162
48–55 years	9	60.888 ± 5.065	59.777 ± 5.118
>56 years	7	63.142 ± 5.080	57.714 ± 6.499

LVEF, left ventricle ejection fraction

*Mean ± SD

Gene expression profile associated with a single dose of DOX-based chemotherapy

The volcano plots on Fig 1 show the probability that the gene is differentially expressed in a data of average ‘before vs. after’ one dose of DOX-based chemotherapy in all women (1A) and in the groups with normal (1B), and abnormal LVEF (1C). The analysis of the gene expression of PBC indicated that a single dose of DOX induced significant changes in the expression of 235 genes (fold change >1.5, FDR<0.1) (Table 2). There were no significant differences between the gene expression of the two groups of patients (abnormal and normal LVEF) at baselines (P>0.05, FDR>0.1).

Fig 1

Volcano plots of log2 fold change (x-axis) versus –log10 of unadjusted P-values (y-axis), representing the probability that the gene is differentially expressed in PBC of breast cancer patients treated with DOX-based chemotherapy in data of average before versus after one dose of DOX-based chemotherapy.

(A) comparison of average before vs after of all patients; (B) average before vs after in patients with normal LVEF after the completion of chemotherapy; (C) average before vs after in patients with abnormal LVEF after the completion of chemotherapy. P<0.05; FDR<0.1.

Table 2

Differentially expressed genes in PBC of breast cancer patients after the 1st cycle of DOX-based chemotherapy versus baseline (before the start of chemotherapy).

SYMBOL	Estimate*	P-value	FDR
FAM129C	-0.7106	<.0001	0.00149
LOC283663	-1.0472	<.0001	0.00149
HLA-DOB	-1.1215	<.0001	0.00149
VPREB3	-1.3445	<.0001	0.00149
TCL1A	-1.4775	<.0001	0.00149
FCRLA	-1.8054	<.0001	0.00171
CD19	-1.5499	<.0001	0.00196
DEFA3	3.7012	<.0001	0.00203
LOC653600	3.6557	<.0001	0.00203
PGLYRP1	3.396	<.0001	0.00203
CEACAM8	3.2916	<.0001	0.00203
KIAA0367	0.7253	<.0001	0.00203
OSBPL10	-0.9924	<.0001	0.00203
LOC90925	-1.0736	<.0001	0.00203
CAMP	3.2632	<.0001	0.00231
DEFA1	3.499	<.0001	0.00276
DEFA1B	3.3555	<.0001	0.00276
MMP9	3.287	<.0001	0.00276
MMP8	2.8754	<.0001	0.00276
TFF3	1.9749	<.0001	0.00276
PROM1	0.7141	<.0001	0.00276
CRISP3	1.3036	<.0001	0.00277
HLA-DOA	-0.7107	<.0001	0.0028
TCN1	3.052	<.0001	0.00331
ELANE	2.8036	<.0001	0.00331
OLFM4	2.7531	<.0001	0.00331
ARG1	2.6483	<.0001	0.00331
HP	2.6308	<.0001	0.00331
CXCR5	-0.907	<.0001	0.00331
CEACAM6	2.6645	<.0001	0.00336
DEFA4	2.9771	<.0001	0.00337
ANXA3	2.5028	<.0001	0.00348
BPI	2.5084	<.0001	0.00389
OLR1	2.3894	<.0001	0.00389
S100P	2.3653	<.0001	0.00414
ORM1	2.3809	<.0001	0.00486
DLGAP5	0.6505	<.0001	0.00488
SPIB	-0.6947	<.0001	0.00561
MS4A3	1.8872	<.0001	0.00598
BANK1	-0.6946	<.0001	0.00598
CTSG	2.3894	<.0001	0.00617
FCER2	-0.7478	<.0001	0.00617
CEACAM1	1.993	<.0001	0.00618
SPTA1	0.8716	<.0001	0.00618
TNFAIP6	0.7898	<.0001	0.00618
LOC728014	-0.6389	<.0001	0.00622
CDC20	0.7971	<.0001	0.00632
IFIT1L	2.2327	<.0001	0.00643
HBM	2.1896	<.0001	0.00643
TRPM6	0.6271	<.0001	0.00653
GYPB	1.3679	<.0001	0.00692
DACH1	0.6661	<.0001	0.00765
LIN7A	0.5931	<.0001	0.00765
CD79A	-1.6211	<.0001	0.00765
ASPM	0.6487	<.0001	0.00782
LOC653061	1.2216	<.0001	0.00849
COBLL1	-0.6696	<.0001	0.00858
PTPN20	1.1766	<.0001	0.00861
FCRL3	-0.5891	<.0001	0.00861
CA1	2.3957	<.0001	0.00884
IFI27	0.9104	<.0001	0.00884
BEX1	1.2861	<.0001	0.00944
LTF	2.6717	<.0001	0.00949
MPO	2	<.0001	0.00953
EPB42	1.7482	<.0001	0.00953
CA4	1.7017	<.0001	0.00953
BPGM	1.0836	<.0001	0.00953
ORM2	0.9493	<.0001	0.00953
GYPE	0.9377	<.0001	0.00953
RAP1GAP	1.1026	<.0001	0.00986
CCNB2	0.6868	<.0001	0.00986
BIRC3	-0.6637	<.0001	0.01018
NCAPG	0.5988	0.0001	0.01092
SESN3	0.8006	0.0001	0.01111
RAB3IL1	0.6721	0.0001	0.01111
C5orf32	1.4237	0.0001	0.0117
SERPINB10	1.1818	0.0001	0.0117
COL17A1	1.433	0.0001	0.0118
BLK	-0.6487	0.0001	0.01186
RETN	1.9094	0.0001	0.01241
C6orf173	0.5965	0.0001	0.0128
C19orf59	1.0429	0.0002	0.01297
TSPAN2	0.6938	0.0002	0.01297
ANKRD22	1.0776	0.0002	0.01306
CDC45L	0.6344	0.0002	0.01353
RNASE3	1.9597	0.0002	0.01361
LOC643332	1.0172	0.0002	0.01361
NFIX	0.7093	0.0002	0.01445
SLC22A4	0.732	0.0002	0.01464
NUSAP1	0.7046	0.0002	0.01466
LOC651524	1.0154	0.0002	0.01471
FAR2	1.1622	0.0002	0.01494
RNF182	0.6275	0.0002	0.01494
CPA3	0.697	0.0002	0.01608
FECH	0.8687	0.0002	0.01611
ERCC5	-0.6263	0.0003	0.01695
CD79B	-1.2326	0.0003	0.01703
CEP55	0.5971	0.0003	0.01732
UIMC1	-0.763	0.0003	0.01732
CHI3L1	1.3608	0.0003	0.01741
CDA	1.0502	0.0003	0.01783
ATP8B4	0.8687	0.0003	0.01836
OSBP2	1.1908	0.0003	0.01845
LOC100134379	1.5894	0.0003	0.01851
ABCA13	1.3413	0.0004	0.0193
SLC22A16	0.6917	0.0004	0.01962
DYSF	1.0349	0.0004	0.01989
GPR84	1.0967	0.0004	0.02007
CYP4F3	1.0235	0.0004	0.02117
LOC283392	0.7064	0.0004	0.02117
VNN1	0.6997	0.0004	0.02163
LOC642103	0.9778	0.0005	0.02428
FCGR1A	0.7202	0.0005	0.02439
SELENBP1	1.2147	0.0005	0.02454
TACSTD2	1.3185	0.0006	0.02542
TYMS	0.919	0.0006	0.02547
HBE1	0.714	0.0006	0.02547
SLPI	1.3417	0.0006	0.02675
CLC	1.3377	0.0006	0.0268
CKAP4	0.8558	0.0006	0.0268
VAV1	-0.6377	0.0006	0.0268
RNASE2	1.463	0.0006	0.02709
CEBPE	1.1338	0.0006	0.0271
CEACAM3	0.6023	0.0007	0.0273
AHSP	2.0537	0.0007	0.02786
HMBS	0.602	0.0007	0.02788
RBM33	-0.6091	0.0007	0.02844
SLC27A2	0.6294	0.0007	0.02855
MANSC1	0.6188	0.0008	0.02941
TMCC2	0.808	0.0008	0.02952
ITPR3	-0.797	0.0008	0.02975
S100A12	1.208	0.0008	0.02986
MYB	0.9609	0.0008	0.03063
HBD	2.0496	0.0008	0.03076
GOLGA8B	-0.856	0.0008	0.0309
ALPL	1.3904	0.0008	0.03113
MGC42367	0.6187	0.0008	0.03113
LCN2	2.2325	0.0008	0.03136
STX3	0.6528	0.0009	0.03263
PLSCR1	0.7974	0.0009	0.03323
CLEC4D	0.6695	0.0009	0.03382
GADD45G	0.6235	0.001	0.03449
KRT1	0.9562	0.001	0.03461
CD24	1.4399	0.001	0.0351
BST1	0.7232	0.001	0.03512
PCOLCE2	0.9448	0.0011	0.03573
TOP2A	0.697	0.0011	0.03615
LOC440313	0.6087	0.0012	0.0375
B4GALT5	0.9484	0.0012	0.03821
NLRC4	0.6716	0.0012	0.03859
C1QB	0.6326	0.0013	0.03971
XK	0.7828	0.0013	0.03989
FSTL3	0.607	0.0014	0.04028
ABHD5	0.6037	0.0015	0.04212
PFKFB3	0.7688	0.0015	0.04228
CITED4	0.6897	0.0015	0.04269
PRC1	0.6001	0.0015	0.04273
QPCT	0.8802	0.0016	0.04286
CD177	0.8838	0.0017	0.04393
TFDP1	0.6815	0.0019	0.04653
TESC	0.6841	0.0021	0.04934
TMOD1	0.7627	0.0022	0.05019
FCGR1C	0.5994	0.0022	0.05032
HBG1	2.077	0.0023	0.05116
NPL	0.5947	0.0023	0.05121
FAIM3	-0.7711	0.0023	0.05195
LOC653778	1.0179	0.0024	0.05256
SCD	0.6756	0.0024	0.05256
LOC389599	0.9621	0.0025	0.05334
LRG1	0.7248	0.0025	0.05334
SLAMF6	-0.7282	0.0025	0.05334
EEF1D	-0.7883	0.0026	0.05387
C5AR1	0.6534	0.0026	0.05407
ZMAT3	-0.7805	0.0026	0.05487
TNS1	0.6804	0.0028	0.05614
BASP1	0.7966	0.003	0.05803
LRAP	-1.0002	0.003	0.05857
LOC100132391	-1.1762	0.0031	0.05908
HBG2	2.0155	0.0032	0.0599
LOC100129362	-1.0645	0.0032	0.06051
GPR160	0.6186	0.0033	0.0611
LRRFIP1	-0.712	0.0034	0.06199
ARL16	-0.8601	0.0035	0.06261
SAP30	0.6245	0.0035	0.0627
HK3	0.6149	0.0035	0.06275
RGL4	1.0261	0.0035	0.06297
SERPINA13	0.6036	0.0036	0.06345
E2F2	0.832	0.0039	0.06504
STMN3	-0.709	0.0039	0.06506
ALAS2	1.4889	0.004	0.06583
FPR2	0.7452	0.0041	0.06685
LOC441087	-0.9447	0.0041	0.06685
LOC728620	-0.8585	0.0043	0.06822
LOC100132499	-0.7917	0.0044	0.06922
MLLT6	-0.711	0.0045	0.06996
QRFPR	-0.8583	0.0047	0.07174
STRADB	0.8962	0.0047	0.07192
SLC2A5	0.99	0.005	0.07341
CREG1	0.6149	0.0052	0.07511
ZNF549	-0.7673	0.0053	0.07601
UGCG	0.8707	0.0054	0.07686
LOC728809	-1.1654	0.0058	0.07957
KRT72	-0.6038	0.0059	0.07973
DUSP19	-1.0172	0.0059	0.07973
TP53I13	-0.5914	0.0059	0.08
XPNPEP3	-0.9484	0.0061	0.08109
C14orf85	-1.0189	0.0065	0.08295
FLJ22662	0.7316	0.0066	0.08321
LOC654103	0.9414	0.0071	0.08649
GYG1	0.6746	0.0072	0.08668
CDKN2AIPNL	-0.9848	0.0073	0.08745
FCGR1B	0.6819	0.0073	0.08753
PLA2G4B	-0.6336	0.0073	0.08753
GCA	0.8787	0.0074	0.08772
BMS1P5	-0.9416	0.0075	0.08887
MOSC1	0.5856	0.0078	0.09043
IL7R	-0.6069	0.0078	0.09095
GBE1	0.5874	0.0079	0.09142
HSPC268	-0.7923	0.0081	0.0917
HMHA1	-0.6014	0.0081	0.0917
LOC728888	-0.6524	0.0083	0.093
SLC25A37	0.8626	0.0085	0.09382
LOC100133516	-0.6214	0.0085	0.09412
GPR175	0.6802	0.0086	0.09427
HCG2P7	-0.9518	0.0087	0.09435
CD27	-0.7256	0.009	0.09578
LOC100128288	-0.8262	0.009	0.09603
FAM175A	-0.9464	0.0093	0.09751
DUXAP3	-0.808	0.0094	0.09823
EID2B	-0.8188	0.0094	0.09824
DDX51	-0.8393	0.0094	0.09824
C3orf34	-0.6914	0.0096	0.09932
LOC100128084	-1.0162	0.0097	0.09932
CD6	-0.7416	0.0097	0.09945
LMOD3	-0.7242	0.0097	0.09947

*log2 fold change

We have also compared the gene expression of the 33 patients divided into 3 age groups, each comprising 11 patients, 18–47 years (youngest), 48–55 years (mid-age) and 56–99 years (oldest). A single dose of DOX-based chemotherapy resulted in 66 differentially expressed genes (DEG) with log2 fold change (FC)>1.0 (p<0.05, FDR<0.05) in the oldest group of patients in comparison with the other two age-groups (Table 3).

Table 3

Age-associated differences in the gene expression.

Symbol	56–99 years*#	48–55 years*	18–47 years*
MMP9	3.9123	1.2884	3.033
PGLYRP1	3.8371	1.4919	3.293
CEACAM8	3.5834	1.4616	2.977
MMP8	3.5284	1.5207	2.002
TCN1	3.4311	1.0817	2.783
LTF	3.4051	1.0024	2.083
DEFA4	3.3439	1.2729	2.607
HP	3.3082	1.1380	1.977
ELANE	3.2522	1.1366	2.541
ARG1	3.2388	1.2595	1.874
CEACAM6	3.1734	1.3527	2.143
OLFM4	3.1396	1.4073	2.447
ANXA3	3.1026	1.5605	1.846
BPI	3.0445	0.9567	2.189
OLR1	2.9252	1.1325	1.825
S100P	2.9251	1.1516	1.996
ORM1	2.8422	1.1065	1.895
CTSG	2.8168	0.9180	2.038
RNASE3	2.6924	0.8301	1.138
RETN	2.6141	0.6415	1.574
CEACAM1	2.5762	1.0244	1.238
MPO	2.5563	0.8955	1.358
TFF3	2.3798	0.9370	1.590
MS4A3	2.3296	0.6207	1.363
CA4	2.2531	0.7716	1.039
ALPL	2.1398	0.2850	1.074
ABCA13	1.9824	0.7552	0.577
COL17A1	1.9809	0.6801	0.833
C5orf32	1.9765	0.5603	0.833
CHI3L1	1.9577	0.6048	0.743
SLPI	1.9103	0.6521	0.746
CEBPE	1.7418	0.4987	0.524
SERPINB10	1.7352	0.4157	0.778
BEX1	1.7131	0.7104	0.891
FAR2	1.6847	0.4184	0.567
GPR84	1.6349	0.4278	0.469
CDA	1.5593	0.1996	0.724
CYP4F3	1.5434	0.4521	0.487
UGCG	1.5267	0.3211	0.364
DYSF	1.5088	0.2250	0.641
CRISP3	1.4774	0.8383	0.942
PCOLCE2	1.4609	0.4807	0.493
PTPN20	1.4439	0.5310	0.875
B4GALT5	1.4165	0.3049	0.641
TYMS	1.4060	0.3998	0.423
ORM2	1.3950	0.3985	0.524
CD177	1.3648	0.3258	0.366
CKAP4	1.2778	0.3066	0.594
ATP8B4	1.2184	0.4067	0.423
LRG1	1.2139	0.0939	0.398
VNN1	1.1638	0.2104	0.195
BST1	1.1578	0.2298	0.325
TNFAIP6	1.1326	0.2886	0.406
CITED4	1.1228	0.2629	0.353
GPR160	1.0819	0.1431	0.098
SCD	1.0804	0.2424	0.225
SLC22A16	1.0467	0.3010	0.398
STX3	1.0319	0.3519	0.231
MANSC1	1.0227	0.1727	0.238
CLEC4D	1.0037	0.1743	0.204
ADARB1	-0.5014	-0.2387	-0.351
HLA-DOB	-1.2817	-0.5855	-0.931
VPREB3	-1.4604	-0.9855	-1.053
TCL1A	-1.4833	-1.3079	-1.210
CD19	-1.7652	-1.0990	-1.258
FCRLA	-2.1067	-1.2568	-1.254

*Log2 fold change

# p<0.05, FDR<0.05

Gene expression profile in patients with abnormal decline of LVEF

The analysis of the expression profiling of PBC after one dose of DOX-based chemotherapy in women who developed abnormal LVEF after a full course of chemotherapy found 80 transcripts that differed >1.5-fold (p<0.05, FDR<0.1) from the baseline, 13 of which were shared with women with normal LVEF. Table 4 shows the unique DEG in patients with abnormal LVEF.

Table 4

Unique DOX-induced DEG in patients with abnormal decline of LVEF after one dose of DOX-based chemotherapy (p<0.05, FDR<0.1).

SYMBOL	Log 2 FC*	GENE ID	DESCRIPTION
DEFA3	5.14	HGNC = 2762|UniProtKB = P59666	Neutrophil defensin 3;DEFA3
DEFA1	4.96	HGNC = 33596|UniProtKB = P59665	Neutrophil defensin 1;DEFA1
CEACAM8	4.49	HGNC = 1820|UniProtKB = P31997	Carcinoembryonic antigen-related cell adhesion molecule 8
CAMP	4.41	HGNC = 1472|UniProtKB = P49913	Cathelicidin antimicrobial peptide;CAMP;ortholog
TCN1	4.26	HGNC = 11652|UniProtKB = P20061	Transcobalamin-1;TCN1;ortholog
DEFA4	4.08	HGNC = 2763|UniProtKB = P12838	Neutrophil defensin 4;DEFA4;ortholog
MMP8	3.89	HGNC = 7175|UniProtKB = P22894	Neutrophil collagenase;MMP8;ortholog
ELANE	3.76	HGNC = 3309|UniProtKB = P08246	Neutrophil elastase;ELANE;ortholog
ARG1	3.67	HGNC = 663|UniProtKB = P05089	Arginase-1;ARG1;ortholog
HP	3.58	HGNC = 5141|UniProtKB = P00738	Haptoglobin;HP;ortholog
CEACAM6	3.52	HGNC = 1818|UniProtKB = P40199	Carcinoembryonic antigen-related cell adhesion molecule 6
BPI	3.37	HGNC = 1095|UniProtKB = P17213	Bactericidal permeability-increasing protein;BPI;ortholog
CTSG	3.29	HGNC = 2532|UniProtKB = P08311	Cathepsin G;CTSG;ortholog
OLR1	3.22	HGNC = 8133|UniProtKB = P78380	Oxidized low-density lipoprotein receptor 1;OLR1;ortholog
ORM1	3.22	HGNC = 8498|UniProtKB = P02763	Alpha-1-acid glycoprotein 1;ORM1;ortholog
S100P	3.03	HGNC = 10504|UniProtKB = P25815	Protein S100-P;S100P;ortholog
MS4A3	2.76	HGNC = 7317|UniProtKB = Q96HJ5	Membrane-spanning 4-domains subfamily A member 3
CEACAM1	2.73	HGNC = 1814|UniProtKB = P13688	Carcinoembryonic antigen-related cell adhesion molecule 1
TFF3	2.63	HGNC = 11757|UniProtKB = Q07654	Trefoil factor 3;TFF3;ortholog
CLC	2.45	HGNC = 2014|UniProtKB = Q05315	Galectin-10;CLC;ortholog
GYPB	1.99	HGNC = 4703|UniProtKB = P06028	Glycophorin-B;GYPB;ortholog
CRISP3	1.73	HGNC = 16904|UniProtKB = P54108	Cysteine-rich secretory protein 3;CRISP3;ortholog
RAP1GAP	1.69	HGNC = 9858|UniProtKB = P47736	Rap1 GTPase-activating protein 1;RAP1GAP;ortholog
ANKRD22	1.64	HGNC = 28321|UniProtKB = Q5VYY1	Ankyrin repeat domain-containing protein 22;ANKRD22
BPGM	1.55	HGNC = 1093|UniProtKB = P07738	Bisphosphoglycerate mutase;BPGM;ortholog
GYPE	1.44	HGNC = 4705|UniProtKB = P15421	Glycophorin-E;GYPE;ortholog
IFI27	1.42	HGNC = 5397|UniProtKB = P40305	Interferon alpha-inducible protein 27, mitochondrial;IFI27
FECH	1.35	HGNC = 3647|UniProtKB = P22830	Ferrochelatase, mitochondrial;FECH;ortholog
SPTA1	1.29	HGNC = 11272|UniProtKB = P02549	Spectrin alpha chain, erythrocytic 1;SPTA1;ortholog
CPA3	1.27	HGNC = 2298|UniProtKB = P15088	Mast cell carboxypeptidase A;CPA3;ortholog
SESN3	1.20	HGNC = 23060|UniProtKB = P58005	Sestrin-3;SESN3;ortholog
NFIX	1.18	HGNC = 7788|UniProtKB = Q14938	Nuclear factor 1 X-type;NFIX;ortholog
TNFAIP6	1.14	HGNC = 11898|UniProtKB = P98066	Tumor necrosis factor-inducible gene 6 protein;TNFAIP6
SLC22A4	1.12	HGNC = 10968|UniProtKB = Q9H015	Solute carrier family 22 member 4;SLC22A4;ortholog
CDC20	1.10	HGNC = 1723|UniProtKB = Q12834	Cell division cycle protein 20 homolog;CDC20;ortholog
KIAA0367	1.10	HGNC = 25209|UniProtKB = Q8WUY3	Protein prune homolog 2;PRUNE2;ortholog
RAB3IL1	0.98	HGNC = 9780|UniProtKB = Q8TBN0	Guanine nucleotide exchange factor for Rab-3A;RAB3IL1;ortholog
PROM1	0.98	HGNC = 9454|UniProtKB = O43490	Prominin-1;PROM1;ortholog
C6orf173	0.93	HGNC = 21488|UniProtKB = O5EE01	C6orf173 protein;C6orf173;ortholog
TRPM6	0.93	HGNC = 17995|UniProtKB = Q9BX84	Transient receptor potential cation channel subfamily M member 6
DACH1	0.92	HGNC = 2663|UniProtKB = Q9UI36	Dachshund homolog 1;DACH1;ortholog
ITLN1	0.87	HGNC = 18259|UniProtKB = Q8WWA0	Intelectin-1;ITLN1;ortholog
GYPA	0.85	HGNC = 4702|UniProtKB = P02724	Glycophorin-A;GYPA;ortholog
DLGAP5	0.85	HGNC = 16864|UniProtKB = Q15398	Disks large-associated protein 5;DLGAP5;ortholog
SERPINB2	0.85	HGNC = 8584|UniProtKB = P05120	Plasminogen activator inhibitor 2;SERPINB2;ortholog
RHAG	0.83	HGNC = 10006|UniProtKB = Q02094	Ammonium transporter Rh type A;RHAG;ortholog
LIN7A	0.82	HGNC = 17787|UniProtKB = O14910	Protein lin-7 homolog A;LIN7A;ortholog
TFDP2	0.81	HGNC = 11751|UniProtKB = Q14188	Transcription factor Dp-2;TFDP2;ortholog
HMMR	0.75	HGNC = 5012|UniProtKB = O75330	Hyaluronan mediated motility receptor;HMMR;ortholog
TGM2	0.71	HGNC = 11778|UniProtKB = P21980	Protein-glutamine gamma-glutamyltransferase 2;TGM2;ortholog
CKS2	0.70	HGNC = 2000|UniProtKB = P33552	Cyclin-dependent kinases regulatory subunit 2;CKS2
PRSSL1	0.68	HGNC = 31397|UniProtKB = Q6UWY2	Uncharacterized protein;PRSSL1;ortholog
PTTG1	0.64	HGNC = 9690|UniProtKB = O95997	Securin;PTTG1;ortholog
CD34	0.63	HGNC = 1662|UniProtKB = P28906	Hematopoietic progenitor cell antigen CD34;CD34;ortholog
PTRF	0.63	HGNC = 9688|UniProtKB = Q6NZI2	Polymerase I and transcript release factor;PTRF;ortholog
UBE2W	0.60	HGNC = 25616|UniProtKB = Q96B02	Ubiquitin-conjugating enzyme E2 W;UBE2W;ortholog
PRG2	0.59	HGNC = 9362|UniProtKB = P13727	Bone marrow proteoglycan;PRG2;ortholog
ZNF783	-0.59	HGNC = 27222|UniProtKB = Q6ZMS7	Protein ZNF783;ZNF783;ortholog
ZNF33B	-0.64	HGNC = 13097|UniProtKB = Q06732	Zinc finger protein 33B;ZNF33B;ortholog
C13orf18	-0.65	HGNC = 20420UniProtKB = Q9H714	Uncharacterized protein KIAA0226-like
CD72	-0.67	HGNC = 1696|UniProtKB = P21854	B-cell differentiation antigen CD72;CD72;ortholog
CD1C	-0.68	HGNC = 1636|UniProtKB = P29017	T-cell surface glycoprotein CD1c;CD1C;ortholog
GNG7	-0.69	HGNC = 4410|UniProtKB = O60262	Guanine nucleotide-binding protein G(I)/G(S)/G(O) subunit gamma-7
ZNF263	-0.70	HGNC = 13056|UniProtKB = O14978	Zinc finger protein 263;ZNF263;ortholog
SEL1L3	-0.75	HGNC = 29108|UniProtKB = Q68CR1	Protein sel-1 homolog 3;SEL1L3;ortholog
HLA-DOA	-0.91	HGNC = 4936|UniProtKB = P06340	HLA class II histocompatibility antigen, DO alpha chain
BANK1	-0.93	HGNC = 18233|UniProtKB = Q8NDB2	B-cell scaffold protein with ankyrin repeats

*Log2 FC, Log2 fold change

Downregulation of TCL1A and FCRL was reported in breast cancer patients treated with several doses of DOX-based chemotherapy who developed cardiomyopathy [20]. Downregulation of CXCR5, which encodes chemokine expressed on B-cells could be explained with the depletion of B-cells in cancer patients treated with DOX-based chemotherapy [21, 22]. Lower expression of CD72 was detected in patients with systemic lupus erythematosus (SLE) and it correlated inversely with SLE disease activity [23].

The top upregulated DEG in the “abnormal” dataset were genes encoding the alpha-defensins DEFA1-4 (HNP-1 to -4), which are secreted by activated neutrophils and are involved in involved in innate immune response [24]. Significantly upregulated were several other genes encoding proteins secreted by activated neutrophils and associated with inflammation, such as BPI (bactericidal permeability increasing protein), ELANE (neutrophil elastase), CTSG (neutrophil cathepsin) [25,26]. ARG1 [27] which encodes arginase and HP which encodes haptoglobin [28] are involved in a variety of inflammatory diseases.The protein encoded by TNFAIP6 (tumor necrosis factor, alpha-induced protein 6) was found to be increased in the synovial fluid of patients with osteoarthritis and rheumatoid arthritis [29].

The 67 uniquely altered DEG in the group of patients with abnormal decline of LVEF was analyzed using Ingenuity software. The most enriched “biological functions” of this dataset identified using Ingenuity software were “cell-to-cell signaling” (p-value = 1.40E-07–3.2E-02), “cellular movement” (p value = 2.23E-04–2.02E-02) and “cellular development” (p value = 2.85E-04–2.89E-03) (Fig 2). In this category there were 8 molecules associated with “free radical scavenging” (p value = 8.26E-05–1.38E-03), which was expected, because one of the major mechanisms of DOX cardiotoxicity is oxidative damage. The most represented “diseases and disorders” were “connective tissue disorders” (p value 2.53E-12–2.57E-02), “inflammatory diseases” (p value = 2.53E-12–3.13E-02), “skeletal and muscular disorders” (2.53E-12–1.23E-02) and “immunological diseases” (p value = 5.59E-12–3.12E-02). Based on the curated literature data, an overlapping enrichment of molecules were identified in all of the top disease categories for rheumatic disease (RD), rheumatic arthritis (RA), SLE and infectious diseases. Multiple linked signaling pathways enriched for genes known to be involved primarily in inflammation and immunity were identified by Ingenuity in network 1 “Connective tissue disorder, Immunological disease, Inflammatory disease” which had 4 central nodes—NFKB, p38, ERK1/2 and AKT, all predicted to be upregulated (Fig 3A). The central nodes in network 2 “Cellular movement, Hematological system development and function, Immune cell trafficking” were IFN-gamma, TGFB, ARG1 and IL-4 (Fig 3B).

Fig 2

Ingenuity biological function analysis of DEG associated with 1 dose of DOX-based chemotherapy in patients with abnormal decline of LVEF at completion of chemotherapy.

The most enriched “biological functions” of this dataset identified using Ingenuity software were “cell-to-cell signaling” (p-value = 1.40E-07–3.2E-02), “cellular movement” (p value = 2.23E-04–2.02E-02) and “cellular development” (p value = 2.85E-04–2.89E-03). The most represented “diseases and disorders” were “connective tissue disorders” (p value 2.53E-12–2.57E-02), “inflammatory diseases” (p value = 2.53E-12–3.13E-02), “skeletal and muscular disorders” (2.53E-12–1.23E-02) and “immunological diseases” (p value = 5.59E-12–3.12E-02).

Fig 3

Top interaction networks for the 67 DEG in PBC of patients with abnormal decline of LVEF after DOX-chemotherapy identified by Ingenuity pathway analysis.

(A) Network 1 “Connective tissue disorder, Immunological disease, Inflammatory disease”; (B) Network 2 “Cellular movement, Hematological system development and function, Immune cell trafficking”. Red filled (up-regulation) and Green filled (down-regulation); Blue line (leads to inhibition); Orange line (leads to activation); Black line (effect not predicted).

The results (Table 5) from QRT-PCR confirmed the upregulation of the selected genes in patients with abnormal LVEF in comparison with the patients with normal LVEF.

Table 5

Results from QPCR of the target genes in breast cancer patients treated with DOX-based chempotherapy who developed abnromal decline in LVEF after the completion of chemotherapy.

Genes	Response differences ΔΔCt Units	95% Lower confidence limit	95% Upper confidence limit	P value	Fold change	Log2 fold change array data
ARG1	10.53	1.802	19.267	0.018	1483.1	3.6659
CEACAM8	20.92	5.154	36.687	0.0093	1984332	4.4908
DEFA4	16.312	6.135	26.489	0.0017	81350	4.0818
DEFA3	8.263	3.915	12.61	0.0002	307.126	5.137
ELANE	20.309	-3.113	43.73	0.0892	129871	3.762
HP	7.154	-0.257	14.564	0.0585	142.382	3.5814
MMP9	6.528	-2.515	15.57	0.1571	92.27	4.3398
OLFM4	37.053	-10.556	84.662	0.1272	1.43E+11	3.5721

Discussion

DOX-based chemotherapy has greatly increased the number of long-term cancer survivors but has also led to an increasing number of patients experiencing DOX-induced cardiotoxicity [30]. Because there are no clinical methods for early detection of subclinical DOX cardiotoxicity, attempts to minimize cardiotoxicity include empiric DOX dose limitation or modification by risk factors, which pose a risk of premature discontinuation of effective anthracycline therapy. In addition, because of a wide individual variability in toxic anthracycline doses [31,32] cardiotoxicity may occur at unexpectedly low doses.

This is the first study to identify the PBC transcriptome signature associated with a single dose of DOX-based chemotherapy in cancer patients. We have identified a transcriptome signature associated with one dose of DOX-based chemotherapy which distinguished patients developing abnormal cardiac function after a full course of chemotherapy from those who maintained normal cardiac function. In addition, we have identified a gene expression profile associated with a single dose of DOX-based chemotherapy which distinguished older than younger patients.

As PBC are a subset of white blood cells, it is not surprising that the immune system was identified as the first and the most affected responder to the systemic stress of DOX-based chemotherapy. It has been found that the hyper-activated innate immune responses, including cytokine production, augmentation of natural killer (NK) cell activity [33], stimulation of cytotoxic T-lymphocyte (CTL) responses and augmentation of macrophages differentiation [34], could contribute to the progression of congestive heart failure. The top most significantly upregulated genes in the group of patients with abnormal LVEF decline encode proteins secreted by activated neutrophils, such as alpha-defensins, cathelicidins, arginase, cathepsin G, elastase and haptoglobin. Neutrophils provide the first line of innate immunity and are the major effectors of inflammation associated with cardiovascular diseases [35,36]. Several reports suggested the prognostic value of alpha-defensins [37], cathepsin G [38] and arginase [39] in heart failure. Our results indicate that there is a correlation between the severity of DOX-associated cardiotoxicity and the levels of activated neutrophils in the PBMC fraction of peripheral blood of breast cancer patients. The presence of abnormal subset of low density neutrophils in PBMC preparations have been reported in a range of conditions such as systemic lupus erythematosus (SLE), rheumatoid arthritis (RA) and acute rheumatic fever [40,41], cancer [42-45], vasculitis [46] and asthma [47], and their presence was correlated with disease activity. Several distinct features were identified in the low density neutrophils, such as different nuclear morphology, enhanced capacity to synthesize IFN I upon stimulation, higher expression of TNF-alpha, IL-6 and IL-8, and enhanced capacity to form neutrophil extracellular traps (NETs) [48,49]. NETs are characterized as chromatin fibers associated to granular proteins that are released to the extracellular space in order to immobilize and kill invading microbes during a process of cell death termed “NETosis” [50,51]. In addition to their antimicrobial role, recent evidence suggests that NETs can induce endothelial damage [52,53]. Denny et al [54] characterized the phenotype of low density neutrophil subset in SLE patients and found that the low density neutrophils displayed an impairment in the phagocytic potential, had proinflammatory phenotype and induced vascular damage, indicating that they contribute to accelerated atherosclerosis and cardiovascular disorders observed in SLE patients.

Because age-associated changes in DOX-induced gene expression has not been reported, we have compared the gene expression of the 33 patients divided into 3 age groups, 18–47 years (youngest), 48–55 years (mid-age), and 56–99 years (oldest). The results showed that a single dose of DOX-based chemotherapy resulted in 66 DEG in the oldest group of patients in comparison with the other two age-groups

The functional analysis of DEG in PBC of the subjects with abnormal cardiac function identified several linked pathways enriched for genes involved in inflammation and immunity and interconnected with NFKB, p38, ERK1/2, AKT, IFN-gamma, TGFB, ARG1 and IL-4, which were associated with ischemia [55], cardiac hypertrophy [56] and chronic heart failure [57]. Consistent with these reports, we found an overlapping enrichment of molecules for chronic inflammatory disorder, rheumatic arthritis, systemic lupus erythematosus and systemic autoimmune syndrome.

In conclusion, the results from this study show for the first-time that: 1) PBC transcriptome signature associated with early doses of DOX-based chemotherapy has the potential to predict later impairment of cardiac function and can be used as a surrogate marker for DOX-induced cardiotoxicity, 2) individual sensitivity to a single low dose of DOX is associated with differential expression of several genes implicated in inflammatory response and immune trafficking; 3) there is an overlapping but distinctive age-related pattern of gene expression associated with a single dose of DOX-based chemotherapy.

This finding may be of value in identifying patients at high or low risk for the development of DOX cardiotoxicity during the initial doses of chemotherapy and thus to avoid the accumulating toxic effects from the subsequent doses during treatment.