Micro RNA Expression Profiles as Adjunctive Data to Assess the Risk of Hepatocellular Carcinoma Recurrence After Liver Transplantation

Article (PDF Available)inAmerican Journal of Transplantation 12(2):428-37 · February 2012with47 Reads
DOI: 10.1111/j.1600-6143.2011.03788.x · Source: PubMed
Abstract
Donor livers are precious resources and it is, therefore, ethically imperative that we employ optimally sensitive and specific transplant selection criteria. Current selection criteria, the Milan criteria, for liver transplant candidates with hepatocellular carcinoma (HCC) are primarily based on radiographic characteristics of the tumor. Although the Milan criteria result in reasonably high survival and low-recurrence rates, they do not assess an individual patient's tumor biology and recurrence risk. Consequently, it is difficult to predict on an individual basis the risk for recurrent disease. To address this, we employed microarray profiling of microRNA (miRNA) expression from formalin fixed paraffin embedded tissues to define a biomarker that distinguishes between patients with and without HCC recurrence after liver transplant. In our cohort of 64 patients, this biomarker outperforms the Milan criteria in that it identifies patients outside of Milan who did not have recurrent disease and patients within Milan who had recurrence. We also describe a method to account for multifocal tumors in biomarker signature discovery.
American Journal of Transplantation 2012; 12: 428–437
Wiley Periodicals Inc.
C
Copyright 2011 The American Society of Transplantation
and the American Society of Transplant Surgeons
doi: 10.1111/j.1600-6143.2011.03788.x
Micro RNA Expression Profiles as Adjunctive Data to
Assess the Risk of Hepatocellular Carcinoma
Recurrence After Liver Transplantation
C. T. Barry
a,b,
*
, M. D’Souza
a,c
, M. McCall
d
,
S. Safadjou
a,b
,C.Ryan
e
,R.Kashyap
a,b
,
C. Marroquin
a,b
,M.Orloff
a,b
, A. Almudevar
d
andT.E.Godfrey
a,c
a
Department of Surgery,
b
Division of Solid Organ
Transplant and Hepatobiliary Surgery,
c
Wilmot Cancer
Center,
d
Department of Biostatistics and Biocomputing,
e
Department of Pathology, University of Rochester
Medical Center, Rochester, NY
*
Corresponding author: Christopher Taylor Barry,
chris_barry@urmc.rochester.edu
Donor livers are precious resources and it is, there-
fore, ethically imperative that we employ optimally
sensitive and specific transplant selection criteria. Cur-
rent selection criteria, the Milan criteria, for liver trans-
plant candidates with hepatocellular carcinoma (HCC)
are primarily based on radiographic characteristics of
the tumor. Although the Milan criteria result in reason-
ably high survival and low-recurrence rates, they do
not assess an individual patient’s tumor biology and
recurrence risk. Consequently, it is difficult to predict
on an individual basis the risk for recurrent disease.
To address this, we employed microarray profiling of
microRNA (miRNA) expression from formalin fixed
paraffin embedded tissues to define a biomarker that
distinguishes between patients with and without HCC
recurrence after liver transplant. In our cohort of 64 pa-
tients, this biomarker outperforms the Milan criteria in
that it identifies patients outside of Milan who did not
have recurrent disease and patients within Milan who
had recurrence. We also describe a method to account
for multifocal tumors in biomarker signature discovery.
Key words: Biomarker, HCC, liver transplant, Milan
criteria, miRNA
Abbreviations: AUC, area under curve; CV, cross val-
idation; FDR, false discovery rate; HCC, hepatocellular
carcinoma; miRNA, micro RNA; ROC, receiver operator
characteristics.
Received 08 July 2011, revised 12 August 2011 and
accepted for publication 30 August 2011
Introduction
Hepatocellular carcinoma (HCC) is one of the most com-
mon cancers worldwide (1,2) and is a major cause of can-
cer mortalities particularly in Africa and Asia (3). The inci-
dence of HCC is increasing in western countries due to the
hepatitis C virus epidemic (4) and, more recently, the obe-
sity epidemic leading to nonalcoholic steatohepatitis (5).
Curative treatment is currently limited to surgical resec-
tion and liver transplantation, but resection results in re-
currence rates of greater than 70% within 5 years (6) and
most patients (80%) present with extensive disease that
is not amenable to surgery (7).
Transplanting within the Milan criteria (1 tumor 5cm,2
3 tumors 3 cm, and no evidence of intrahepatic vascu-
lar invasion or extrahepatic spread) is the accepted stan-
dard of care as 5-year survival rates of 80% or greater and
5-year recurrence rates of less than 15% can be achieved
(8). However , these criteria are based primarily on radio-
graphic characteristics and do not assess individual tumor
biology and risk of recurrence or overall survival. Clearly,
there are some patients outside of Milan criteria who have
a low incidence of recurrence and there are those within
Milan who do suffer from recurrent HCC after transplant.
A biomarker indicative of a tumor’s propensity for recur-
rence would be of great value in optimizing overall patient
outcomes.
There is increasing evidence, primarily from global genomic
studies (9,10), that metastatic potential is inherent in the
primary tumor from an early stage and that this information
can be used to predict long-term outcomes. Several groups
have begun to study this in HCC using microarray tech-
nology to define messenger RNA (mRNA) and microRNA
(miRNA) expression profiles that correlate with survival,
recurrence and metastatic disease in the hopes of de-
scribing clinically useful biologic metrics to guide patient
selection and appropriate therapeutic interventions (11–
17). However , HCC is frequently associated with multifocal
intrahepatic disease and this causes problems for defining
such gene expression signatures. Intrahepatic metastasis
arises from local dissemination of the primary tumor and
can account for up to 75% of multifocal lesions whereas
de novo lesions (multiple primary tumors) arise as a result
of the diseased liver milieu that is predisposed to oncoge-
nesis (18–23). Thus, in a patient with multifocal HCC, any
428
miRNA Profiles Predicting HCC Recurrence
biomarker must take into account the possibility that the
tumors are not clonally related and, thus, will have differ-
ent genetic profiles and associated metastatic potential.
The question then arises as to which tumor (and which
gene expression profile) is associated with the phenotype
being studied. To address this we have devised a simple
approach, the MIN–MAX method, to account for multiple
expression patterns of the same miRNA in patients with
multifocal disease.
In this study, we sought to define a miRNA biomarker
that reliably d istinguishes patients with and without HCC
recurrence after liver transplant. We believe that such a
biomarker can be used in conjunction with the current
Milan criteria to improve selection decisions in liver trans-
plant candidates with HCC. Furthermore, such a metric of
HCC tumor biology could also be used to direct other ther-
apeutic interventions such as surgical resection, ablative
therapy, chemotherapy and radiation.
Materials and Methods
Patient cohort description
Our study was performed with approval of the University of Rochester
Research Subjects Review Board (RSRB00029467). A total of 95 tumor
nodules were studied from 69 patients who underwent liver transplanta-
tion for HCC at the University of Rochester Medical Center between 1996
and 2008. Forty patients had recurrent HCC within 3 years of transplant and
29 had no recurrent disease within 3 years. Patients were well-matched
with regards to etiology, gender and age (Table 1). Patients in the nonre-
current group tended to have lower grade tumors, earlier tumor stage and
less vascular invasion compared to the recurrent group, but there was sig-
nificant overlap in these characteristics between groups (Table 1). In the
nonrecurrent group, 17 of 29 patients were within Milan criteria and in the
recurrent group, 11 of 40 patients were within Milan. Milan criteria for this
cohort were determined by pathologic evaluation of explanted livers at the
time of transplant.
Histologic confirmation of tumor specimens
Representative H&E sections of the formalin-fixed paraffin embedded
(FFPE) blocks from the explanted tumors were reviewed by our pathologist
(C.R.) to ensure presence of >70% viable tumor. Tissue cores (7 mm diam-
eter) were then obtained from the corresponding blocks and re-embedded
for further processing and miRNA isolation.
miRNA purification and array hybridization
MiRNA was isolated from FFPE liver tumor tissues using the Roche High
Pure miRNA isolation kit (Roche Diagnostics, Mannheim, Germany). MiRNA
extraction was performed from individual tissue blocks using seven sections
of 10 microns each. One to three extractions were performed for each
tumor to generate sufficient miRNA for microarray analysis. All sam-
ples were assessed for presence of enriched miRNA using an Experion
Bioanalyzer (Bio-Rad, Hercules, CA, USA). MiRNAs were labeled using the
FlashTag Biotin RNA labeling kit (Genisphere, Hatfield, PA, USA) according
to the provided protocol and then hybridized to Affymetrix GeneChip miRNA
1.0 microarrays (Affymetrix, Santa Clara, CA, USA). These arrays are com-
prised of 46 228 probe sets representing over 6703 miRNA sequences (71
organisms) from the Sanger miRNA database (V.11) and an additional 922
sequences of human snoRNA and scaRNA from the Ensemble database
and snoRNABase. Array hybridization, washing and staining was performed
at the Upstate Medical University microarray core facility in Syracuse, New
York, per the manufacturer’s instructions and arrays were scanned with a
GeneChip Scanner 7G Plus. Data files (.cel files) were generated using the
miRNA-1_0_2X gain library file. Hybridization quality metrics were assessed
using the AffyMir miRNA QCTool program (version 1.0.33.0, Affymetrix,
Santa Clara, CA, USA). Only human miRNAs were considered in our anal-
ysis. All arrays were preprocessed using Robust Multiarray Average (RMA;
Ref. 24). RMA was performed on all 46 228 probe sets after which non-
human probe sets were removed leaving 847 human miRNA probe sets.
All data have been deposited onto the Gene Expression Omnibus (GEO
accession number: GSE30297, http://www.ncbi.nlm.nih.gov/geo/).
Description of the Min–Max procedure for biomarker
construction with multifocal tissue samples
The preprocessed data consisted of miRNA expression estimates for each
feature, miR-X, in each sample. In the case of multifocal tissue samples,
more than one sample were obtained from a single patient. To create a
biomarker for patient prognosis, we combined the miRNA expression es-
timates from each collection of samples belonging to the same patient.
This was done by constructing two new probe features, miR-X_MIN and
miR-X_MAX, defined as the m inimum and maximum expression for each
patient. As it cannot be generally anticipated whether high or low expression
is associated with recurrence, miR-X_MIN and miR-X_MAX were treated
as separate features in biomarker selection. Clearly, the MIN and MAX
features were identical for unifocal patients. Although both the MIN and
MAX features for a given miRNA can be statistically significant in a uni-
variate analysis, we expect to use at most one from each pair in any final
biomarker.
Statistical analysis
Array quality was assessed using a suite of widely used quality measures
(25). The miR-X_MIN and miR-X_MAX features were constructed as previ-
ously described. Hierarchical clustering (Euclidean distance with complete
agglomeration; see Ref. 26) was used to assess both similarity of expres-
sion within subjects and within recurrence status.
The primary outcome was defined as recurrence free survival time. The
observation time of recurrence free subjects was, therefore, considered a
right-censored survival time. The ability of each feature to predict survival
time was assessed using a univariate Cox proportional hazards model. The
resulting p value was interpreted as a measure of the feature’s association
with recurrence. The false discovery rate (FDR) adjusted p values were
estimated using the Benjamini–Hochberg procedure (27).
Biomarker Model
A problem inherent in the development of biomarkers is the tendency for
multivariate models to overfit data. This results in biomarkers that are ini-
tially reported to perform extremely well but whose performance cannot be
reproduced. This tendency can be controlled to some degree using cross
validation (CV). However, the unpredictability of multivariate models re-
mains a problem, particularly when individual features exhibit a high degree
of correlation. To address this concern, we propose the following procedure
to generate a biomarker:
(1) For each feature, fit a univariate Cox proportional hazards model and
record the p value and direction of association. Positive association
means that greater feature expression yields longer recurrence free
survival and negative association, the opposite.
(2) Using the p values from step (1), rank the features from most to least
significant. Retain only a fixed number of the most significant features.
We investigate the optimal number of features to retain during CV.
American Journal of Transplantation 2012; 12: 428–437 429
Barry et al.
Ta b l e 1 : Patient cohort demographics. Patients received liver transplants for HCC (n = 69), 29 of whom did not have recurrent HCC within
3 years and 40 of whom did. Patients were equally matched for eitiology, age, sex, race and HCV antibody status. Recurrent patients
were more likely to have multifocal disease (42.5% vs. 17.2%), to be outside Milan criteria (72.5% vs. 41.4%), to have more advanced
HCC stage and less differentiated tumor grade, to have vascular invasion (70% vs. 24.1%) and larger mean tumor size
HCC Recurrence s/p OLT
No recurrence (n = 29) Recurrence (n = 40)
Etiology HCV 13 21
HBV 3 3
Laennec’s 3 5
NASH 3 5
Other
76
Age at time of transplant (Mean) 58.8 57.5
Sex (Recipient) F 7 5
M2235
Recipient race African American 1 2
Caucasian 25 37
Hispanic 0 1
Native American 2 0
HCV (Ab) Neg 13(44.8%) 19(47.5%)
Pos 16 (44.8%) 21 (52.5%)
Number of tumors n = 1 12 (41.4%) 12 (30%)
n = 2 6 (20.7%) 3 (7.5%)
n = 3 6 (20.7%) 8 (20.0%)
N>3 5(17.2%) 17(42.5%)
Milan criteria Within 17 (58.6%) 11 (27.5%)
outside 12 (41.4%) 29 (72.5%)
Explant cancer stage AJCC/UICC 2002 I 9 6
6th Edition (Native LiverBx) II 15 11
IIIA 4 22
IIIB 1 1
Tumor grade Well differentiated 17 8
Well-moderately differentiated 4 2
Moderately differentiated 6 20
Moderately-poorly differentiated 0 4
Poorly differentiated 1 5
Vascular invasion 7 (24.1%) 28 (70%)
Largest (Mean) tumor size (cm) 3.6 5.3
Hemachromatosis, autoimmune hepatitis, biliary atresia, cryptogenic cirrhosis.
(3) Create a survival score by robustly standardizing the expression esti-
mate of each retained feature by subtracting the median and dividing
by the interquartile range (IQR), where the median and IQR are calcu-
lated across patients for each feature. For features whose direction of
association from step (1) was negative, reverse the sign of the survival
score so that a higher score is always associated with greater survival.
(4) Define an initial biomarker as the survival score of the most significant
feature from step (2). Then proceed down the list of features from step
(2), moving from more to less significant. For each feature, define a
new (potentially improved) biomarker as the current biomarker plus the
survival score of that feature from step (3). Compare the performance of
the new biomarker to the current biomarker (assessed by the coefficient
of variation (R
2
) from a Cox regression) and keep the better one.
(5) The final biomarker in step (4) will be the sum of the survival scores of
those features that improved performance.
Additional predictor(s), such as the Milan criteria, can be easily incorpo-
rated into this procedure by adding the additional predictor(s) to the Cox
regression models in steps (1) and (4).
Cross Validation
To assess the predicted performance of our biomarker in an independent
data set, we performed a CV procedure that incorporated all steps used
to create our biomarker. Specifically, we performed K-fold CV training on
56 subjects and testing on the remaining 8 subjects (the total number of
subjects available was 64). Within each CV sample, a new biomarker was
created (following the steps described above) and biomarker scores were
obtained for all subjects. The quantiles of the biomarker scores for the test
subjects constituted the CV prediction. We repeated this procedure 500
times yielding 8 × 500 = 4000 total CV predictions.
Four types of biomarkers were evaluated, defined by how the samples
were combined for multifocal patients (MIN–MAX or mean) and whether
the Milan criteria was included as an additional predictor. For each model, a
receiver operator characteristics (ROC) curve was plotted. The area under
curve (AUC) is a straightforward way to assess the performance of a given
biomarker.
Results
Quality assessment of miRNA purified from FFPE
tissues
We consistently obtained high yields of miRNA from
FFPE blocks based on electrophoresis and spectroscopy
(Figure S1). Furthermore, when we hybridized miRNA
430 American Journal of Transplantation 2012; 12: 428–437
miRNA Profiles Predicting HCC Recurrence
Ta b l e 2 : Univariate analysis of miRNAs significant for recurrent HCC within 3 years of transplant. Top 60 probes with FDRs <0.2 are
shown. MIN and MAX represent the minimum and maximum expression probe features for a given miRNA. Bolded probes are those
selected between 70–97% in cross validation
Unadj Unadj
Rank Probe features p-value FDR Rank Probe Features p-value FDR
1 hsa-miR-194_st_MIN 1.6E-06 0.003 31 hsa-miR-99a_st_MIN 0.0034 0.168
2 hsa-miR-454-star_st_MAX 3.8E-05 0.024 32 hsa-miR-146b-3p_st_MIN 0.0035 0.168
3 hsa-miR-125b-2-star_st_MIN 4.9E-05 0.024 33 hsa-miR-125b-2-star_st_MAX 0.0035 0.168
4 hsa-miR-122_st_MIN 5.7E-05 0.024 34 hsa-miR-372_st_MAX 0.0035 0.168
5 hsa-miR-182_st_MIN 1.8E-04 0.047 35 hsa-miR-224_st_MIN 0.0036 0.168
6 hsa-miR-365_st_MAX 2.0E-04 0.047 36 hsa-miR-186_st_MAX 0.0036 0.168
7 hsa-mi R-99a-sta r_st_M 1N 2.1E-04 0.047 37 hsa-miR-148a_st_MIN 0.0037 0.168
8 hsa-miR-192_st_MIN 2.4E-04 0.047 38 hsa-miR-576-3p_st_MAX 0.0041 0.183
9 hsa-miR-885-5p_st_MIN 2.5E-04 0.047 39 hsa-miR-1293_st_MAX 0.0043 0.183
10 hsa-miR-888_st_MAX 2.8E-04 0.047 40 hsa-miR-505_st_MAX 0.0043 0.183
11 hsa-miR-22_st_MIN 5.4E-04 0.084 41 hsa-miR-485-3p_st_MAX 0.0048 0.197
12 hsa-miR-1274b_st_MIN 7.5E-04 0.099 42 hsa-miR-610_st_MAX 0.0049 0.197
13 hsa-miR-497_st_MIN 8.1E-04 0.099 43 hsa-miR-671-3p_st_MIN 0.0051 0.197
14 hsa-miR-501-5p_st_MAX 8.6E-04 0.099 44 hsa-miR-20a-star_st_MIN 0.0053 0.197
15 hsa-miR-542-5p_st_MIN 8.9E-04 0.099 45 hsa-miR-132_st_MIN 0.0053 0.197
16 hsa-miR-152_st_MIN 9.4E-04 0.099 46 hsa-miR-422a_st_MIN 0.0055 0.197
17 hsa-miR-505_st_MIN 0.0011 0.107 47 hsa-miR-518d-3p_st_MAX 0.0055 0.197
18 hsa-miR-130a_st_MIN 0.0012 0.114 48 hsa-miR-99a-star_st_MAX 0.0058 0.197
19 hsa-miR-885-5p_st_MAX 0.0015 0.124 49 hsa-let-7d-star_st_MAX 0.0059 0.197
20 hsa-miR-137_st_MAX 0.0015 0.124 50 hsa-miR-373_st_MAX 0.0061 0.197
21 hsa-miR-212_st_MIN 0.0015 0.124 51 hsa-miR-224_st_MAX 0.0063 0.197
22 hsa-miR-192-star_st_MIN 0.0016 0.125 52 hsa-miR-147b_st_MAX 0.0063 0.197
23 hsa-miR-100_st_MIN 0.0017 0.126 53 hsa-miR-129-3p_st_MAX 0.0064 0.197
24 hsa-miR-1273_st_MAX 0.0020 0.138 54 hsa-miR-505-star_st_MIN 0.0065 0.197
25 hsa-miR-122_st_MAX 0.0020 0.138 55 hsa-miR-501-5p_st_MIN 0.0066 0.197
26 hsa-miR-571_st_MAX 0.0021 0.138 56 hsa-mi R-143-star_st_M IN 0.0067 0.197
27 hsa-miR-935_st_MAX 0.0026 0.164 57 hsa-miR-30a-star_st_MIN 0.0068 0.197
28 hsa-miR-139-5p_st_MIN 0.0027 0.164 58 hsa-miR-1273_st_MIN 0.0069 0.197
29 hsa-miR-1260_st_MIN 0.0030 0.168 59 hsa-miR-20a-star_st_MAX 0.0069 0.197
30 hsa-miR-377-star_st_MAX 0.0031 0.168 60 hsa-miR-365_st_MIN 0.0070 0.197
obtained from freshly frozen cell lines to the arrays
and compared these results to array hybridization of
miRNA from the same cell lines that had first been
FFPE, we noted excellent correlation (R
2
= 0.88–0.90,
Figure S2).
Quality metrics and univariate analysis
Seven arrays were removed due to poor quality as as-
sessed by one of the six quality metrics considered
(Table S1). This resulted in 88 samples and 64 subjects for
further analysis. The MIN–MAX procedure yielded 1694
features based on 847 probes.
The univariate analysis yielded 60 significant features at
20% FDR (Table 2). A majority of the miRNAs distinguish-
ing recurrence from nonrecurrence have been shown by
others (15,16) to be relevant to hepatocellular carcinogen-
esis (Table S2). We may expect both the MIN and MAX
feature for some probes to be significant, particularly when
there tends to be smaller variation within a multifocal sam-
ple, and the two features would then be approximately
equal. This effect was relatively small in our cohort. The
60 significant features represented 50 distinct probe sets
(of which 10 were represented by both MIN and MAX
features).
Unsupervised hierarchical clustering results are
refined using MIN–MAX
The results of unsupervised hierarchical clustering of all 88
samples (using all 847 miRNA probe sets without MIN–
MAX) show that patients with recurrent disease tend to
cluster together (Figure 1A). Employing the MIN–MAX
method reduces the results to 64 patients with a very sim-
ilar clustering, suggesting that information is not grossly
distorted or lost as a result of the MIN–MAX procedure
(Figure 1B). When we apply the MIN–MAX method and
then perform a univariate Cox regression analysis, cluster-
ing of the patients using probes with FDR < 0.2 reveals
a clearer distinction between recurrent and nonrecurrent
patients (Figure 1C). To f urther investigate the clustering
between recurrence and nonrecurrence samples, we ex-
amined the first two principal components (Figure S3). As
we observed in the hierarchical clustering, there is a clus-
ter of primarily recurrence samples and a cluster of m ixed
recurrence and nonrecurrence.
American Journal of Transplantation 2012; 12: 428–437 431
Barry et al.
Figure 1: Unsupervised hierarchi-
cal clustering dendograms of all 88
samples assayed (A), 64 samples
after MIN/MAX reduction (B) and
MIN/MAX reduced probes with
FDR < 0.2 (C). Recurrent samples are
highlighted in red.
HCC recurrence miRNA biomarker discovery and its
comparison to the Milan criteria
We generated our proposed biomarker using all available
data. The biomarker consists of 67 miRNAs that signif-
icantly distinguished patients with HCC recurrence after
transplant from those without recurrence (Figure 2) with
R
2
= 0.848 and AUC = 0.989. Analysis of recurrence-
free survival shows that the biomarker clearly delineates
patients with and without recurrence within three years
of transplant (Figure 3A) with a p value of 1.6 × 10
11
.
Applying the biomarker to patients in our cohort outside
Milan (Figure 3B) and inside Milan (Figure 3C) also yields
statistically significant separation (p = 6.9 × 10
5
and
p = 2.8 × 10
8
, respectively), demonstrating that the
biomarker can identify patients outside of Milan who have
favorable biology and patients within Milan who have un-
favorable tumor biology (as measured by disease recur-
rence). In fact, in our cohort, the biomarker identified 9 of
12 patients within Milan who recurred and 8 of 11 patients
outside of Milan who did not recur (Table 3). Note that all
67 miRNAs in this biomarker are employed in the analyses
presented.
Table S3 lists all probe features appearing in at least 50%
of CV biomarkers for the MIN–MAX model with Milan in-
corporated. The median number of features used in the
432 American Journal of Transplantation 2012; 12: 428–437
miRNA Profiles Predicting HCC Recurrence
Figure 2: Biomarker containing
67 miRNA probes distinguishing
patients with and without HCC
recurrence after liver transplant.
Hierarchical clustering heatmap of
individual patients (x-axis) versus
miRNAs (y-axis). The miRNAs are or-
dered by difference in average ex-
pression between recurrence and
nonrecurrence (most upregulated in
recurrence at the top). The horizontal
black line divides those upregulated
in recurrence from those downreg-
ulated in recurrence. Color bar in
upper right corner denotes relative
expression levels (blue = downregu-
lated, red = upregulated). Individual
patient recurrence status is shown
(yellow = recurrent HCC within
3 years of liver transplant, black = no
recurrence). MIN and MAX refer to
minimum and maximum miRNA ex-
pression levels, respectively, as de-
scribed in the text.
Figure 3: Kaplan–Meier
curves of recurrence-free
survival as delineated by
the miRNA biomarker in
the entire cohort (A), pa-
tients outside Milan criteria
at time of transplant (B)
and patients within Milan
(C).
American Journal of Transplantation 2012; 12: 428–437 433
Barry et al.
Ta b l e 3 : Cohort characteristics of patients according to Milan
and recurrent disease and performance of miRNA biomarker. The
biomarker (BM) successfully identifies 9 of 12 patients who were
outside Milan without recurrent disease and 8 of 11 patients within
Milan who did have recurrence
29 Nonrecurrent 40 Recurrent
Within Milan (n = 28) 17 11 (8/11 BM)
Outside Milan (n = 41) 12 (9/12 BM) 29
CV fits was 75 with a minimum of 44 and a maximum of
144. We note that collinearity plays an important role, in
that the fitting procedure tends to exclude features that
are highly correlated with features already incorporated in
the biomarker. It is interesting to note that the highest rank-
ing feature in the univariate analysis (hsa-miR-194_st_MIN,
Table 2) has Pearsons correlation coefficients of 0.58, 0.81
and 0.57 with features hsa-miR-125b-2-star_st_MIN, hsa-
miR-122_st_MIN and hsa-miR-182_st_MIN, respectively.
These are all among the top five ranked features, but the
latter two appear in less than 50% of CV biomarkers.
Biomarker discovery is facilitated by the MIN–MAX
method
Perhaps the most common way to handle multifocal data
is to compute the average expression across samples for
each patient. However , this ignores the possibility of het-
erogeneity in both the tumor phenotype and expression
profile. In this study, it may be reasonable to assume that
tumors in nonrecurrent patients are more homogeneous
as they all lack the recurrence biomarker. However, recur-
rence likely only requires the recurrence biomarker to be
present in one of a patient’s tumors. The result of our
CV comparison of the mean and the MIN–MAX proce-
dures support this hypothesis (Figure S4) as the methods
performed comparably with regard to specificity but the
MIN–MAX procedure had much better sensitivity.
Cross validation reveals synergy between biomarker
and Milan
The four ROC plots are shown in Figure 4. The sensi-
tivity and specificity attained by Milan is superimposed.
The MIN–MAX procedure is clearly able to exceed Milan
in prognostic accuracy. Furthermore, this accuracy is en-
hanced by incorporating Milan itself in the biomarker.
Figure S5 demonstrates the construction of the biomarker,
indicating the improvement in the R
2
value as additional
features are added. The rate of increase clearly increases
when Milan is included in the model, indicating greater
predictive ability of the probes when Milan information is
incorporated.
Finally, CV was used to assess the optimal number of
features to retain in step (2) of the biomarker genera-
tion procedure. The resulting AUC statistics are shown in
Figure S6. The prognostic ability of the various markers
is clearly sensitive to this parameter, but the superiority
of the MIN–MAX biomarker which incorporates Milan is
evident over the whole range.
Discussion
There is a growing consensus that evaluation of HCC tumor
biology via molecular characterization holds most promise
in achieving accurate clinical risk stratification of patients
(28,29). Intriguing preliminary results with various biologic
metrics such as fractional allelic imbalance (30) and gene
expression profiles (11–14) strongly suggest that addition
of tumor biology information to current liver transplant se-
lection criteria is possible and desirable. We are rapidly
approaching the point where it will be reasonable to pur-
sue biomarker testing in pretransplant tumor biopsy speci-
mens to more efficiently direct our resources and improve
transplant outcomes, as well as to direct other available
therapeutic modalities for HCC.
MiRNAs are attractive markers as they are known master
regulators of gene expression and are highly effective in
classifying tissue types and tumor tissues of origin (31,32).
One potential advantage to studying miRNA over mRNA in
biomarker signature building is that there are only just over
1400 miRNAs compared to the over 20 000 mRNAs. There-
fore, statistical analysis is inherently less noisy and tighter.
Also, the small size and stability of miRNAs make them far
more amenable to analysis from FFPE tissues compared
to much larger and less stable mRNAs. One logistical con-
cern is that HCC often presents as multifocal disease and
that subcentimeter lesions often demonstrate radiographic
characteristics that are highly suspicious for HCC (i.e. arte-
rial enhancement, venous washout and T2 brightness on
MRI). In these cases, obtaining appropriate amounts of tis-
sue for biomarker testing may often prove to be logistically
cumbersome, if not sometimes impossible. Protocols for
reliable miRNA amplification from needle biopsy samples
and statistical probability calculations of biomarker perfor-
mance with multifocal lesions will be necessary t o begin
to address these issues.
Evidence already exists that the many miRNAs compris-
ing the HCC recurrence biomarker may be important to
hepatocellular carcinogenesis. MiR-194 has been shown
to be expressed in hepatic epithelial cells and to suppress
HCC metastasis in a murine model (33). This miRNA has
also been shown to be downregulated in human HCCs that
metastasize (15). MiR-125b-2
is expressed in human fetal
liver cells (34) and its dysregulated expression is noted in
colorectal cancer with liver metast ases (35). MiR-182 ex-
pression has been shown in two independent studies com-
paring HCC to adjacent uninvolved liver to be significantly
upregulated (16,36). All HCC recurrence miRNA studies to
date have been performed in the context of hepatic re-
section rather than transplant, and it will be important to
perform a similar analysis of the biomarker on a cohort
434 American Journal of Transplantation 2012; 12: 428–437
miRNA Profiles Predicting HCC Recurrence
Figure 4. Receiver operator charac-
teristic curves of miRNA biomarker
performance relative to Milan (red
dots) in distinguishing patients with
and without recurrent disease. Area
under curve (AUC) values are shown
for Min–Max reduced miRNAs in ad-
dition to Milan criteria (top left), Min–
Max reduction alone (top right), mean
expression reduced miRNAs in addition
to Milan (bottom left) and mean reduc-
tion alone (bottom right).
of hepatic resection patients from our own institution to
assess its generalizability.
When we look at the variation in expression of particu-
lar miRNAs in the context of individual patients, there is
more probe expression variation in recurrent patients ver-
sus nonrecurrent (Figure S7). In fact, of the 847 miRNAs,
over 85% show greater average within-patient variance in
the recurrence group than in the nonrecurrence group. This
is strongly suggestive of the distributional mixture, which
would result from nonhomogeneity of genetic response
among multifocal samples. This argues strongly that we
should be using the MIN–MAX approach to select which
of the varied expression levels for a given miRNA is driving
the phenotype.
Previously published gene expression profiling studies of
HCC using microarrays to assay mRNA changes have not
rigorously addressed the multifocal issue (11–17). The ra-
tionale for this is based on the observation that metastatic
tumors from an individual patient tend to have gene ex-
pression profiles that are far more similar to that patient’s
primary tumor compared to expression profiles from other
patients’ tumors (33,34). Our analysis, however , demon-
strates that miRNA expression profiles can vary signifi-
cantly between multifocal tumors from the same patient
and this is therefore likely the case with mRNA. Application
of the MIN–MAX procedure to mRNA signature discovery
in HCC may significantly reduce false discovery of genes
thought to be associated with important clinical outcomes
such as survival, metastasis and recurrence. We further
believe that the MIN–MAX approach is generalizable and
can be applied to other multifocal disease processes such
as the analysis of metastatic lesions or premalignant le-
sions in HCC and other cancer types.
Our cohort is somewhat unique in that we experienced
a high degree of HCC recurrence. We attribute this to
our previously aggressive practice of transplanting patients
who were outside of Milan criteria, because our overall
recurrence rate of patients transplanted within Milan is
10.5% over this 12-year period. The only change in our im-
munosuppressive management over this period was the
introduction of mycophenolate mofetil in 2001 and we
were unable to detect an era effect on recurrence as a
result of this change (data not shown).
Our study is limited by a relatively small number of patients
studied and the lack of external validation. Expansion of
the test cohort to several hundred patients where exam-
ination of all tumor nodules in every patient is necessary
to both further assess the performance of the MIN–MAX
procedure and to refine the miRNA biomarker. We are cur-
rently using qPCR to verify the 67-miRNA biomarker and
American Journal of Transplantation 2012; 12: 428–437 435
Barry et al.
to complete signature building on an additional 150 tumors
from 50 patients. In addition, we have identified another
150 tumors from 50 more patients to perform external CV.
External validation has been predicted to require fewer pa-
tients (35), but complete surveillance of each individual’s
entire tumor burden is essential to clearly define which
miRNAs are truly driving the clinical phenotype of inter-
est. Although the 67-miRNA biomarker we report here per-
forms well in our cohort, we expect that expansion of our
study will result in a m ore comprehensive and clinically
robust biomarker. Therefore, we do not propose that the
particular biomarker presented in this study be used as a
clinical adjunct to Milan. Rather, we present compelling ev-
idence that a biologic metric can be developed that could,
with further study, enhance the efficiency and performance
of the Milan criteria.
Conclusions
We demonstrate that global miRNA analysis of FFPE sam-
ples from explanted HCCs can be used to develop molec-
ular signatures defining clinically important outcomes and
we present a preliminary miRNA biomarker that distin-
guishes patients with and without recurrent HCC within 3
years of transplant. We have further shown that the MIN–
MAX method is effective in directing appropriate probe
selection when analyzing multifocal specimens. Upon re-
finement of this biomarker with study of a much larger
cohort and external validation with additional patients, we
envision utilizing this biologic metric in concert with the
existing Milan criteria to more efficiently utilize resources
and improve outcomes in liver transplant for patients with
HCC. The biomarker may also be used to help rationally di-
rect other HCC treatments such as chemotherapy, ablation
and resection.
Disclosure
The authors of this manuscript have no conflicts of inter-
est to disclose as described by the American Journal of
Transplantation.
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Supporting Information
Additional Supporting Information may be found at
http://www.livercancergenomics.com/?page_id=989
Please note: Wiley-Blackwell is not responsible for the con-
tent or functionality of any supporting materials supplied
by the authors. Any queries should be directed to the cor-
responding author.
American Journal of Transplantation 2012; 12: 428–437 437
    • "For patients with multifocal disease this implies that not all foci are equally responsible for recurrence. Previous approaches either analyzed only one sample per patient [9][10][11][12]or used summarized sample-level information from multifocal patients [6], whereas our approach uses both sample-level and patientlevel information to predict recurrence. This has implications for patients with highly heterogeneous microRNA expression profiles. "
    [Show abstract] [Hide abstract] ABSTRACT: Background: Liver cancer, of which hepatocellular carcinoma (HCC) is by far the most common type, is the second most deadly cancer (746,000 deaths in 2012). Currently, the only curative treatment for HCC is surgery to remove the malignancy (resection) or to remove the entire diseased liver followed by transplantation of healthy liver tissue. Given the shortage of healthy livers, it is crucial to provide transplants to patients that have the best chance of long-term survival. Currently, transplantation is determined via the Milan criteria-patients within Milan (single tumor < 5 cm or 2-3 tumors < 3 cm with no extrahepatic spread nor intrahepatic vascular invasion) are typically eligible for transplantation. However, combining microRNA expression profiling with the Milan criteria can improve prediction of recurrence. HCC often presents with multiple distinct tumor foci arising from local spread of a primary tumor or from the oncogenic predisposition of the diseased liver. Substantial genomic heterogeneity between tumor foci within a single patient has been reported; therefore, biomarker development must account for the possibility of highly heterogeneous genomic profiles from the same individual. Methods: MicroRNA profiling was performed on 180 HCC tumor samples from 89 patients who underwent liver transplantation at the University of Rochester Medical Center. The primary outcome was recurrence-free survival time, and patients were observed for 3 years post-transplantation. Results: MicroRNA expression profiles were used to develop a biomarker that distinguishes HCC patients at greater risk of recurrence post-transplantation. Unsupervised clustering uncovered two distinct subgroups with vast differences in standard transplantation selection criteria and recurrence-free survival times. These subgroups were subsequently used to identify microRNAs strongly associated with HCC recurrence. Our results show that reduced expression of five specific microRNAs is significantly associated with HCC recurrence post-transplantation. Conclusions: MicroRNA profiling of distinct tumor foci, coupled with methods that address within-subject tumor heterogeneity, has the potential to significantly improve prediction of HCC recurrence post-transplantation. The development of a clinically applicable HCC biomarker would inform treatment options for patients and contribute to liver transplant selection criteria for practitioners.
    Full-text · Article · Dec 2016
    • "Studies involving large clinical cohorts within a population-based setting are required. C19MC microRNA cluster Up Tissue Poor clinico-pathological features, recurrence, and shorter overall survival [112] miR-155, miR-15a, miR-432, miR-486-3p, miR-15b, miR-30b Up Tissue Recurrence-free survival [113] miR-19a, miR-886, miR-126, miR-223, miR-24, and miR-147 Signature Tissue Overall survival and recurrent free survival [65] 67 miRs signature Signature Tissue Differentiate recurrence after liver transplantation [114] miR signatures in tumor and non-tumor tissues Signature Tissue Differentiate early and late recurrence [115] miR-326, miR-3677, miR-511-1, miR- 511-2, miR-9-1, and miR-9-2 Signature Tissue Negatively associated with overall survival [116] Predictive Therapeutic Response Markers miR-122 Down Cells, tissue Decreased sensitivity to Doxorubicin [81] miR-122 Down Cells, tissue Decreased sensitivity to Adriamycin, Vincristin [80] miR-122 Down Cells, tissue Suppressed sensitivity to sorafenib [76] miR-146a Up Cells Suppresses sensitivity to interferon-α [71] miR-193a-3p Down Cells, tissue Resistance to 5-FU [84] miR-193b Up Cells, Tissue Sensitivity to cisplatin [117] miR-199a-3p Down Cells, tissue Increased sensitivity to Doxorubicin [82] miR-1247a Down Cells Resistance to sorafenib [118] miR-21 Up Cells, tissue Resistance to interferon-α/5FU in HCC cells [74] miR-34a Down Cells, tissue Resistance to sorafenib [94] 13 microRNA signature Signature Cells, tissue Multidrug resistance [90] "
    [Show abstract] [Hide abstract] ABSTRACT: The discovery of small non-coding RNAs known as microRNAs has refined our view of the complexity of gene expression regulation. In hepatocellular carcinoma (HCC), the fifth most frequent cancer and the third leading cause of cancer death worldwide, dysregulation of microRNAs has been implicated in all aspects of hepatocarcinogenesis. In addition, alterations of microRNA expression have also been reported in non-cancerous liver diseases including chronic hepatitis and liver cirrhosis. MicroRNAs have been proposed as clinically useful diagnostic biomarkers to differentiate HCC from different liver pathologies and healthy controls. Unique patterns of microRNA expression have also been implicated as biomarkers for prognosis as well as to predict and monitor therapeutic responses in HCC. Since dysregulation has been detected in various specimens including primary liver cancer tissues, serum, plasma, and urine, microRNAs represent novel non-invasive markers for HCC screening and predicting therapeutic responses. However, despite a significant number of studies, a consensus on which microRNA panels, sample types, and methodologies for microRNA expression analysis have to be used has not yet been established. This review focuses on potential values, benefits, and limitations of microRNAs as new clinical markers for diagnosis, prognosis, prediction, and therapeutic monitoring in HCC.
    Full-text · Article · Aug 2015
    • "In HCC, a number of miRNAs have been associated with survival or response to chemotherapy such as sorafenib or doxorubicin [5][6][7]. However, the sample size, the numbers of candidate miRNAs or miRNA detection method in previous studies were relatively limited [8][9][10]. TCGA project provides a collection of clinical data, RNA sequence, DNA methylation, DNA copy number variations, and miRNA sequence profiles for LIHC. "
    [Show abstract] [Hide abstract] ABSTRACT: Hepatocellular carcinoma (HCC) is the fifth common cancer. The differential expression of microRNAs (miRNAs) has been associated with the prognosis of various cancers. However, limited information is available regarding genome-wide miRNA expression profiles in HCC to generate a tumor-specific miRNA signature of prognostic values. In this study, the miRNA profiles in 327 HCC patients, including 327 tumor and 43 adjacent non-tumor tissues, from The Cancer Genome Atlas (TCGA) Liver hepatocellular carcinoma (LIHC) were analyzed. The associations of the differentially expressed miRNAs with patient survival and other clinical characteristics were examined with t-test and Cox proportional regression model. Finally, a tumor-specific miRNA signature was generated and examined with Kaplan–Meier survival, univariate\multivariate Cox regression analyses and KEGG pathway analysis. Results showed that a total of 207 miRNAs were found differentially expressed between tumor and adjacent non-tumor HCC tissues. 78 of them were also discriminatively expressed with gender, race, tumor grade and AJCC tumor stage. Seven miRNAs were significantly associated with survival (P value <0.001). Among the seven significant miRNAs, six (hsa-mir-326, hsa-mir-3677, hsa-mir-511-1, hsa-mir-511-2, hsa-mir-9-1, and hsa-mir-9-2) were negatively associated with overall survival (OS), while the remaining one (hsa-mir-30d) was positively correlated. A tumor-specific 7-miRNAs signature was generated and validated as an independent prognostic predictor. Collectively, we have identified and validated an independent prognostic model based on the expression of seven miRNAs, which can be used to assess patients’ survival. Additional work is needed to translate our model into clinical practice.
    Full-text · Article · Jun 2015
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