Cancer classification: Mutual information, target network and strategies of therapy

MISSING IMAGE

Material Information

Title:
Cancer classification: Mutual information, target network and strategies of therapy
Physical Description:
Mixed Material
Language:
English
Creator:
Hsu, Wen-Chin
Liu, Chan-Cheng
Chang, Fu
Chen, Su-Shing
Publisher:
Bio-Med Central (JCB, Journal of Clinical Bioinformatics)
Publication Date:

Notes

Abstract:
Background: Cancer therapy is a challenging research area because side effects often occur in chemo and radiation therapy. We intend to study a multi-targets and multi-components design that will provide synergistic results to improve efficiency of cancer therapy. Methods: We have developed a general methodology, AMFES (Adaptive Multiple FEature Selection), for ranking and selecting important cancer biomarkers based on SVM (Support Vector Machine) classification. In particular, we exemplify this method by three datasets: a prostate cancer (three stages), a breast cancer (four subtypes), and another prostate cancer (normal vs. cancerous). Moreover, we have computed the target networks of these biomarkers as the signatures of the cancers with additional information (mutual information between biomarkers of the network). Then, we proposed a robust framework for synergistic therapy design approach which includes varies existing mechanisms. Results: These methodologies were applied to three GEO datasets: GSE18655 (three prostate stages), GSE19536 (4 subtypes breast cancers) and GSE21036 (prostate cancer cells and normal cells) shown in. We selected 96 biomarkers for first prostate cancer dataset (three prostate stages), 72 for breast cancer (luminal A vs. luminal B), 68 for breast cancer (basal-like vs. normal-like), and 22 for another prostate cancer (cancerous vs. normal. In addition, we obtained statistically significant results of mutual information, which demonstrate that the dependencies among these biomarkers can be positive or negative. Conclusions: We proposed an efficient feature ranking and selection scheme, AMFES, to select an important subset from a large number of features for any cancer dataset. Thus, we obtained the signatures of these cancers by building their target networks. Finally, we proposed a robust framework of synergistic therapy for cancer patients. Our framework is not only supported by real GEO datasets but also aim to a multi-targets/multi-components drug design tool, which improves the traditional single target/single component analysis methods. This framework builds a computational foundation which can provide a clear classification of cancers and lead to an efficient cancer therapy. Keywords: Feature selection, Biomarkers, Microarray, Therapy design, Target network
General Note:
Publication of this article was funded in part by the University of Florida Open-Access publishing Fund. In addition, requestors receiving funding through the UFOAP project are expected to submit a post-review, final draft of the article to UF's institutional repository, IR@UF, (www.uflib.ufl.edu/UFir) at the time of funding. The institutional Repository at the University of Florida community, with research, news, outreach, and educational materials.
General Note:
Hsu et al. Journal of Clinical Bioinformatics 2012, 2:16 http://www.jclinbioinformatics.com/content/2/1/16; Pages 1-11
General Note:
doi:10.1186/2043-9113-2-16 Cite this article as: Hsu et al.: Cancer classification: Mutual information, target network and strategies of therapy. Journal of Clinical Bioinformatics 2012 2:16.

Record Information

Source Institution:
University of Florida
Holding Location:
University of Florida
Rights Management:
All rights reserved by the source institution.
System ID:
AA00014155:00001

Full Text
!DOCTYPE art SYSTEM 'http:www.biomedcentral.comxmlarticle.dtd'
ui 2043-9113-2-16
ji 2043-9113
fm
dochead Research
bibl
title
p Cancer classification: Mutual information, target network and strategies of therapy
aug
au id A1 snm Hsufnm Wen-Chininsr iid I1 I2 email wenchin@ufl.edu
A2 LiuChan-ChengI4 cheng@iis.sinica.edu.tw
A3 ChangFufchang@iis.sinica.edu.tw
A4 ca yes ChenSu-ShingI3 suchen@cise.ufl.edu
insg
ins System Biology Lab, University of Florida, Florida, USA
Department of Electrical and Computer Engineering, University of Florida, Florida, USA
Department of Computer and Information Science and Engineering, University of Florida, Florida, USA
Institute of Information Science, Academia Sinica, Taipei, Taiwan
source Journal of Clinical Bioinformatics
issn 2043-9113
pubdate 2012
volume 2
issue 1
fpage 16
url http://www.jclinbioinformatics.com/content/2/1/16
xrefbib pubidlist pubid idtype doi 10.1186/2043-9113-2-16pmpid 23031749
history rec date day 10month 7year 2012acc 2092012pub 2102012
cpyrt 2012collab Hsu et al.; licensee BioMed Central Ltd.note This is an Open Access article distributed under the terms of the Creative Commons Attribution License (http://creativecommons.org/licenses/by/2.0), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
kwdg
kwd Feature selection
Biomarkers
Microarray
Therapy design
Target network
abs
sec
st
Abstract
Background
Cancer therapy is a challenging research area because side effects often occur in chemo and radiation therapy. We intend to study a multi-targets and multi-components design that will provide synergistic results to improve efficiency of cancer therapy.
Methods
We have developed a general methodology, AMFES (Adaptive Multiple FEature Selection), for ranking and selecting important cancer biomarkers based on SVM (Support Vector Machine) classification. In particular, we exemplify this method by three datasets: a prostate cancer (three stages), a breast cancer (four subtypes), and another prostate cancer (normal vs. cancerous). Moreover, we have computed the target networks of these biomarkers as the signatures of the cancers with additional information (mutual information between biomarkers of the network). Then, we proposed a robust framework for synergistic therapy design approach which includes varies existing mechanisms.
Results
These methodologies were applied to three GEO datasets: GSE18655 (three prostate stages), GSE19536 (4 subtypes breast cancers) and GSE21036 (prostate cancer cells and normal cells) shown in. We selected 96 biomarkers for first prostate cancer dataset (three prostate stages), 72 for breast cancer (luminal A vs. luminal B), 68 for breast cancer (basal-like vs. normal-like), and 22 for another prostate cancer (cancerous vs. normal. In addition, we obtained statistically significant results of mutual information, which demonstrate that the dependencies among these biomarkers can be positive or negative.
Conclusions
We proposed an efficient feature ranking and selection scheme, AMFES, to select an important subset from a large number of features for any cancer dataset. Thus, we obtained the signatures of these cancers by building their target networks. Finally, we proposed a robust framework of synergistic therapy for cancer patients. Our framework is not only supported by real GEO datasets but also aim to a multi-targets/multi-components drug design tool, which improves the traditional single target/single component analysis methods. This framework builds a computational foundation which can provide a clear classification of cancers and lead to an efficient cancer therapy.
bdy
Background
Cancer therapy is a difficult research area due to its level of complexity. Lately, the mere superposition of single drugs is found to generate side-effects and crosstalk with another drug which may cancel out the final success of treatments. Thus, current research focuses on measuring the drug treatments as a whole rather than considering them individually abbrgrp
abbr bid B1 1
B2 2
. Later, a synergistic concept is proposed to evaluate the drug treatments
B3 3
. However, evaluations are still based on cases and do not have a systematic approach. In
B4 4
, a network methodology is first used to evaluate efficiency of drug treatments. Thus, Li et al. use a parameter, namely a SS (Synergy Score) to introduce the topology factor of the network based on the disease and the drug agent combination
B5 5
.Our approach is first to build a more precise target network from the selected biomarkers (by AMFES)
B6 6
. Then, we identify the intrinsic properties by computing mutual information of the interactions among these biomarkers. Our approach is to improve Li’s results by considering the mutual information in the target network. And we provide a general framework of synergistic therapy, which may include several different approaches.
Methods
AMFES
The COD (Curse of Dimensionality) has been a major challenge of microarray data analysis due to the large number of genes (features) and relatively small number of samples (patterns). To tackle this problem, many gene selection methodologies were developed to select only significant subsets of genes in a microarray dataset. AMFES selects an optimal subset of genes by training a SVM with subsets of genes generated adaptively
6
.When AMFES runs a dataset, all samples are randomly divided into a training subset it S of samples and a testing subset T of samples at a heuristic ratio of 5:1. S is used for ranking and selecting of genes and for constructing a classifier out of the selected genes. T is used for computing test accuracy. When a training subset S is given, we extract r training-validation pairs from S according to the heuristic rule r = max (5, (int) (500/n+0.5)) and n is the number of samples in S. Each pair randomly divides S into a training component of samples and a validation component of samples at a ratio of 4:1. The heuristic ratio and rule are chosen based on the experimental experiences at the balance of time consumption and performance. Basically, AMFES has two fundamental processes, ranking and selection. We first explain each process in details and then the integrated version at the end.
Ranking
The gene ranking process contains a few ranking stages. At first stage, all genes are ranked by their ranking scores in a descending order. Then, in the next stage, only the top half ranked genes are ranked again while the bottom half holds the current order in the subsequent stage. The same iteration repeats recursively until only three genes are remained to be ranked again to complete one ranking process. Assume at a given ranking stage, there are k genes indexed from 1 to k. To rank these k genes, we follow 4 steps below. (I) We first generate m independent subsets S
sub
1
… S
m. Each subset S
i
, i = 1, 2… m, has j genes which are selected randomly and independently from the k genes, where j = (int) (k/2). (II) Let C
i
be the SVM classifier that is trained on each subset of genes
,
i = 1, 2… m. For each gene of k genes, we compute the ranking score inline-formula
m:math name 2043-9113-2-16-i1 xmlns:m http:www.w3.org1998MathMathML m:msub
m:mi θ
m
(ul g) of the gene g, as equation (1). (III) We use the average weight of the gene g, the summation of weights of g in m subsets divided by the number of subsets for which g is randomly selected. This increases the robustness to represent the true classifying ability of the gene g. (IV) Rank k genes in the descending order by their ranking scores.
display-formula M1
2043-9113-2-16-i2 m:mrow
θ
m
m:mfenced open ( close )
g
m:mo =
m:mfrac
m:mstyle displaystyle true
m:munderover

i
=
m:mn 1
m
I
{ }
g

S
i
w
e
i
g
h
t
mathvariant italic i
g

i
=
1
m
I
g

S
i
where I is an indicator function such that Iproposition = 1 if the proposition is true; otherwise, Iproposition = 0. In other word, if gene g is randomly selected for the subset S
i
, it is denoted as
2043-9113-2-16-i3
g

S
i
and Iproposition = 1.We denote the objective function of C
i
as
2043-9113-2-16-i4
o
b
j
i
v
1
,
v
2
,

,
v
s
where b v
1, v
2… v
s are support vectors of C
i
. The weight
i
(g) is then defined as the change in the objective function due to g, i.e.,
M2
2043-9113-2-16-i5
w
e
i
g
h
t
i
g
=
|
o
b
j
i
v
1
,
v
2
,

v
s

o
b
j
i
m:msubsup
v
1
g
,
v
2
g
,

,
v
3
g
6
B7 7
B8 8
. Note that if v is a vector, v
sup (g) is the vector obtained by dropping gene g from v. Let θm be a vector comprising the ranking scores derived from the m gene subsets generated thus far and θm-1 is the vector at the previous stage. The m value is determined when θm satisfies the equation (3) by adding a gene to an empty subset once a time.
M3
2043-9113-2-16-i6
m:msup

bold θ
m

1
m:mspace width 0.5em

θ
m
2
θ
m

1
2
<
0.01
where ||θ|| is understood as the Euclidean norm of vector θ. The pseudo codes of ranking process are shown in below.
Pseudo codes for ranking process of AMFES
indent 1
RANK-SUBROUTINE
INPUT: a subset of k genes to be ranked
2
Generate k artificial genes and put them next to the original genes
Pick an initial tentative value of m
DO WHILE m does not satisfies equation (3)
3
FOR each subset Si of m subsets
Randomly select j elements from k genes to form the subset Si.
Train an SVM to get weight
i
(g) for each gene in the subset
ENDFOR
FOR each gene of k genes
Compute the average score of the gene from m subsets
ENDFOR
List k genes in descending order by their ranking scores
ENDDO
OUPUT: a ranked k genes
Selection
Ranking artificial features together with original features has been demonstrated as a useful tool to distinguish relevant features from irrelevant ones as in
B9 9
B10 10
B11 11
. In our selection process, we also use this technique to find the optimal subset of genes.Assume a set of genes is given. We generate artificial genes and rank them together with original ones. After finishing ranking the set, we assign a gene-index to each original gene by the proportion of artificial ones that are ranked above it where the gene-index is the real numerical value between 0 and 1. Then, we generate a few subset candidates from which the optimal subset is chosen. Let p
1
, p
2
, be the sequence of subset-indices of the candidates with p
1
< p
2
< ….where p
i
= i×0.005 and i= 1,2,…200. Let B(p
i
) denote the corresponding subset of subset-index p
i
, and it contains original genes whose indices are smaller than or equal to p
i
. Then, we train a SVM on every B(p
i
), and compute its validation accuracy v(p
i
).We stop at the first p
k
at which v(p
k
) ≥ v
baseline
and v(p
k
) ≥ v(p
l
) for k ≤ l ≤ k+10, where v
baseline
is the validation accuracy rate of the SVM trained on the baseline, i.e., the case in which all features are involved in training. The final result, B(p
k
), is then the optimal subset for the given set of genes. The pseudo codes for selection process of AMFES are listed below.
Pseudo codes for selection process of AMFES
SELECTION-SUBROUTINE
INPUT: a few subsets with their validation accuracies, av(p
i
)
Compute the validation accuracy of all genes, vbaseline.
FOR each subset given
IF v(p
k
) ≥ v
baseline
and v(p
k
) ≥ v(p
l
) for k ≤ l ≤ k+10 THEN
Resulted subset is B(p
k
)
ENDIF
ENDFOR
OUPUT: B(p
k
)
Integrated version
The ranking and selection processes from previous sections are for one training- validation pair. To increase the reliability of validation, we generate r pairs to find the optimal subset. We calculate the validation accuracy of the q
th pair for all p
q-i
subsets where q denotes pair-index and i denotes the subset-index. Then, we compute av(p
i
), the average of v(p
q-i
) over r training-validation pairs and perform the subset search as explained in selection section on av(p
i
) to find the optimal p
i
, denoted as p*.However, p* does not correspond to a unique subset, since each pair has its own B(p*) and they can be all different. Thus, we adopt all samples of S as training samples in order to find a unique subset. We generate artificial genes and rank them together with original genes. Finally, we select the original genes whose indices are smaller than or equal to the p* as the genes we select for S. The integrated version of process is shown below. In the pseudo codes below, the AMFES-ALGORITHM represents the integrated version of the whole process while RANK-SUBROUTINE represents the ranking process and SELECTION-SUBROUTINE represents the selection process.
Pseudo codes for integrated version of AMFES
AMFES ALGORITHM-Integrated Version
INPUT: a dataset
Divide a dataset into train samples and test
samples.
Divide the train samples into r training-validation components pairs
FOR each pair of r train-validation components pairs
Generate 200 candidate subsets p
q-
i
FOR each subset of 200 subsets
CALL RANK subroutine to rank each subset.
Assign each original gene a gene-index
Train each subset on an SVM and compute corresponding validation accuracy, v(p
q-i
), for the subset
END FOR
END FOR
FOR each subset of 200 subsets
Compute average validation rate, av(p
i
), of the subsetfrom r pairs.
END FOR
CALL SELECTION subroutine to search for the optimal subset by its average validation rate and denotes it as p*
CALL RANK subroutine to rank original genes again and select original genes which belong to the subset B(p*).
OUPUT: an optimal subset of genes B(p*)
Mutual information
Mutual information has been used to measure the dependency between two random variables based on the probability of them. If two random variables X and Y, the mutual information of X and Y, I(X; Y), can be expressed as these equivalent equations
B12 12
:
M4
2043-9113-2-16-i7
I
X
;
Y
=
H
X

H
X
stretchy |
Y
M5
2043-9113-2-16-i8
=
H
Y

H
Y
|
X
M6
2043-9113-2-16-i9
=
H
X
+
H
Y

H
X
,
Y
where H(X), H(Y) denote marginal entropies, H(X|Y) and H(Y|X) denote conditional entropies and H(X,Y) denotes joint entropy of the X and Y. To compute entropy, the probability distribution functions of the random variables are required to be calculated first. Because gene expressions are usually continuous numbers, we used the kernel estimation to calculate the probability distribution
B13 13
.Assume the two random variables X and Y are continuous numbers. The mutual information is defined as
12
:
M7
2043-9113-2-16-i10
I
X
,
Y
=


f
x
,
y
log
f
x
,
y
f
x
f
y
d
x
d
y
where f(x,y) denotes the joint probability distribution, and f(x) and f(y) denote marginal probability distribution of X and Y. By using the Gaussian kernel estimation, the f(x, y),f(x) and f(y) can be further represented as equations below
B14 14
:
M8
2043-9113-2-16-i11
f
x
,
y
=
1
M
m:munder

2
π
h
2
e

1
2
h
2
x

x
u
2
+
y

y
u
2
M9
2043-9113-2-16-i12
f
x
=
1
M
Σ
1
m:msqrt
2
π
h
2
e

1
2
h
2
x

y
u
2
m:mtext ,
where M represents the number of samples for both X and Y, u is index of samples
2043-9113-2-16-i13
u
=
1
,
2
,

M
,
and h is a parameter controlling the width of the kernels. Thus, the mutual information
2043-9113-2-16-i14
I
X
,
Y
can then be represented as:
M10
2043-9113-2-16-i15
I
X
,
Y
=
1
M

i
log
M

i
e

1
2
h
2
x
w

x
u
2
+
y
wi

y
u
2

j
e

1
2
h
2
x
w

x
u
2

j
e

1
2
h
2
y
wi

y
u
2
where both w, u are indices of samples
2043-9113-2-16-i16
w
,
u
=
1
,
2
,

M
.Computation of pairwise genes of a microarray dataset usually involves nested loops calculation which takes a dramatic amount of time. Assume a dataset has N genes and each gene has M samples. To calculate the pairwise mutual information values, the computation usually first finds the kernel distance between any two samples for a given gene. Then, the same process goes through every pair of genes in the dataset. In order to be computation efficient, two improvements are applied
13
. The first one is to calculate the marginal probability of each gene in advance and use it repeatedly during the process
13
B15 15
.The second improvement is to move the summation of each sample pair for a given gene to the most outer for-loop rather than inside a nested for-loop for every pairwise gene. As a result, the kernel distance between two samples is only calculated twice instead N times which saves a lot of computation time. LNO (Loops Nest Optimization) which changes the order of nested loops is a common time-saving technique in computer science field
B16 16
.
Target network
The effect of drugs with multiple components should be viewed as a whole rather than a superposition of individual components
1
2
. Thus, a synergic concept is formed and considered as an efficient manner to design a drug
3
. In
B17 17
, mathematical models are used to measure the effect generated by the multiple components. However, it does not consider practical situation such as crosstalk between pathways. A network approach starts to be used to analyze the interactions among multiple components
4
. Initiated by work in
4
, another system biological methodology, NIMS (Network-target-based Identification of Multicomponent Synergy) is proposed to measure the effect of drug agent pairs depending on their gene expression data
5
. NIMS focuses on ranking the drug agent pairs of Chinese Medicine components by their SS.In
5
, it assumes that a drug component is denoted as a drug agent and with which a set of genes associated are denoted as agent genes of the drug agent. For a given disease, assume there are N drug agents where N =1, 2…n. Initially, NIMS randomly chooses two drug agents from N, A1, and A2, and builds a background target network by their agent genes in a graph. From the graph, NIMS calculates TS (Topology Score) of the graph by applying the PCA (Principle Component Analysis) to form a IP value which is integrated by betweenness, closeness and a variant of Eigenvalues PageRank
B18 18
. The TS is used to evaluate the topology significance of the target network for the drug agent pair, A1 and A2, and is defined as
M11
2043-9113-2-16-i17
T
S
1
,
2
=
1
2
×
[ ]

i
I
P
1
i
×
exp

min
d
i
,
j

i
I
P
1
i
+

j
I
P
2
j
×
exp

min
d
j
,
i

j
I
P
2
j
,
where IP
1 and IP
2 denote IP values for drug agent A1 agent and A2. Min(d
i,j) denotes minimum shortest path from gene i of A1 to all genes of A2 and min(d
j,i) denotes the one from gene j of A1 to all genes of A2.NIMS define another term, AS (Agent Score), to evaluate the similarity of a disease phenotype for a drug agent. For a given drug agent, if one of its agent genes has a phenotype record in the OMIM (Online Mendelian Inheritance in Man) database, the drug agent has that phenotype as one of its phenotype. The similarity score of a drug agent pair is defined as the cosine value of the pair’s feature vector angle
B19 19
. The AS is defined as:
M12
2043-9113-2-16-i18
A
S
1
,
2
=

i
,
j
P
i
,
j
M
,
where P
i,j denotes similarity score of ith phenotype of A1 and jth phenotype of A2 and M denotes the total number of phenotypes.The SS of the pair is then defined as the product of TS and AS. NIMS calculates SS for all possible drug agent pairs for a disease and then can find potential drug agent pairs after ranking them by SS.
Results
MIROARRAY data description
We made a brief description of these three datasets in Table tblr tid T1 1. It listed the number of biomarkers, types of biomarkers, number of samples and variation of samples used.
table
Table 1
caption
Descriptions of 3 datasets: GSE18655 (prostate cancer), GSE19536 (breast cancer) and GSE21036 (prostate cancer)
tgroup align left cols 4
colspec colname c1 colnum colwidth 1*
c2
c3
c4
thead valign top
row rowsep
entry
Prostate Cancer (GSE18655)
Breast Cancer (GSE19536)
Prostate Cancer (GSE21036)
tbody
Number of Biomarkers
502
489
373
Type of Biomarkers
RNAs
miRNAs
miRNAs
Number of Samples
139
78
142
Variation of Samples
Grade1(38), Grade2(90), Grade3(11)
Luminal A ( 41), Luminal B (12), Basal-like (15), Normal-like(10)
Cancerous (114), Normal(28)
The prostate cancer dataset with RNA biomarkers
In order to give a better prognosis, pathologists have used a cancer stage to measure cell tissues and tumors’ aggressions as an indicator for doctors to choose a suitable treatment. The most widely used cancer staging system is TNM (Tumor, Node, and Metastasis) system
B20 20
. Depending on levels of differentiation between normal and tumor cells, a different histologic grade is given. Tumors with grade 1 indicate almost normal tissues, with grade 2 indicating somewhat normal tissues and with grade 3 indicating tissues far away from normal conditions. Although most of cancers can be adapted to TNM grading system, some specific cancers require additional grading systems for pathologists to better interpret tumors.The Gleason Grading System is especially used for prostate cancers and a GS (Gleason Score) is given based on cellular contents and tissues of cancer biopsies from patients. The higher the GS are, the worse the prognoses are. The prostate cancer dataset, GSE18655, includes 139 patients with 502 molecular markers, RNAs
B21 21
. In
21
, it showed that prostate tumors with gene fusions, TMPRSS2: ERG T1/E,4 have higher risk of recurrences than tumors without the gene fusions. 139 samples were prostate fresh-frozen tumor tissues of patients after a radical prostatectomy surgery. All samples were taken from the patients’ prostates at the time of prostatectomy and liquid nitrogen was used to freeze middle sections of prostates at extreme low temperature. Among these patients, 38 patient samples have GS 5–6 corresponding to histologic grade 1, 90 samples have GS 7 corresponding to histologic grade 2 and 11 samples have GS 8–9 corresponding to histologic grade 3. The platform used for the datasets is GPL5858, DASL (cDNA-mediated, annealing, selection, extension and ligation) Human Cancer Panel by Gene manufactured by Illumina. The FDR (false discovery rate) of all RNAs expressions in the microarray is less than 5%.
Breast cancer dataset with Non-coding miRNA biomarkers
The miRNAs have strong correlation with some cellular processes, such as proliferation, which has been used as a breast cancer dataset
B22 22
. It has 799 miRNAs and 101 patients’ samples. Differential expressions of miRNAs indicated different level of proliferations corresponding to 6 intrinsic breast cancer subtypes: luminal A, luminal B, basal-like, normal-like, and ERBB2. The original dataset has 101 samples and among them, 41 samples are luminal A, 15 samples are basal-like, 10 samples are normal-like, 12 samples are luminal B, 17 samples are ERBB2, 2 samples have T35 mutation status, another sample has T35 wide type mutation and 3 samples are not classified. GSE19536 was represented in two platforms GPL8227, an Agilient-09118 Human miRNA microarray 2.0 G4470B (miRNA ID version) and the GPL6480, an Agilent-014850 whole Human Genome Microarray 4x44k G4112F (Probe Name). For this paper, we only used the expressions from GPL8227.
Prostate cancer dataset of cancerous and normal samples with miRNA biomarkers
The CNAs (Copy Number Alterations) of some genes may associate with growth of prostate cancers
B23 23
. In addition, some changes are discovered in mutations of fusion gene, mRNA expressions and pathways in a majority of primary prostate samples. The analysis was applied to four platforms and consists of 3 subseries, GSE21034, GSE21035 and GSE21036
23
. For this paper, we only use the GSE 21036 for analysis. The microarray dataset has 142 samples which include 114 primary prostate cancer samples and 28 normal cells samples. The platform is Agilent-019118 Human miRNA Microarray 2.0 G4470B (miRNA ID version).
Results of AMFES
We employ the AMFES on the prostate cancer (GSE18655), breast cancer (GSE19536) and another prostate cancer (GSE21036) datasets. Consequently, for GSE18655, AMFES selects 96 biomarkers. The classification is performed in two steps. The first step performs classification between grade1 and above samples and it selects 93 biomarkers. At the second step, AMFES classifies between grade2 and grade3 samples and it selects 3 biomarkers. Thus, we can assume that these 96 biomarkers can classify among grade1, grade2 and grade3 samples
6
. For GSE19536, AMFES also performs classification in two steps. At the first step, AMFES classify between luminal and non-luminal types samples and it selects 47 biomarkers
6
. At the second step, AMFES further classifies luminal samples into luminal A and luminal B and selects 27 biomarkers. For the non-luminal samples, AMFES also classifies them into basal-like and normal-like samples and selects 25 biomarkers
6
. After removing duplicate biomarkers, AMFES has 72 (47+27-2(duplicated)) for classifying luminal samples and 68 (47+25-4(duplicated)) for classifying non-luminal ones
6
. For GSE21036, AMFES simply selects 22 biomarkers for classifying cancerous and normal samples. Table T2 2. shows the number of selected genes. The complete lists of these biomarkers can be found in Additional file supplr sid S1 1 GSE18655_96_Biomarkers.xlsx, Additional file S2 2 GSE19536_72_Biomarkers.xlsx, Additional file S3 3 GSE19536_68_Biomarkers.xlsx, and Additional file S4 4 GSE21036_22_Biomakers.xlsx.
suppl
Additional file 1
text
GSE18655_96_Biomarkers. An MS Office Excel file which contains a list of gene symbols of 96 biomarkers of GSE18655 samples.
file 2043-9113-2-16-S1.xlsx
Click here for file
Additional file 2
GSE19536_72_Biomarkers. An MS Office Excel file which contains a list of gene symbols of 72 biomarkers of GSE19536 luminal A and luminal B samples.
2043-9113-2-16-S2.xlsx
Click here for file
Additional file 3
GSE19536_68_Biomarkers. An MS Office Excel file which contains a list of gene symbols of 68 biomarkers of GSE19536 basal-like and normal-like samples.
2043-9113-2-16-S3.xlsx
Click here for file
Additional file 4
GSE21036_22_Biomarkers. An MS Office Excel file which contains a list of gene symbols of 22 biomarkers of GSE21036 samples.
2043-9113-2-16-S4.xlsx
Click here for file
Table 2
Results of selected subsets of genes
5
c5
Prostate Cancer (GSE18655)
Breast Cancer (GSE19536)
Breast Cancer (GSE19536)
Prostate Cancer (GSE21036)
Number of Biomarkers Selected
96
72
68
22
Variation of Samples
Grade1, Grade2, Grade3
Luminal A, Luminal B
Basal-like Normal-like
Cancerous Normal
We then apply the MI calculation described in the Mutual Information section on 96 biomarkers for GSE18655 and represent the pairwise MI values of grade 1, grade 2 and grade 3 samples in three 96*96 matrixes which can be found in Additional file S5 5 GSE18655 Grade1 MI.xlsx, Additional file S6 6 GSE18655 Grade2 MI.xlsx and Additional file S7 7 GSE18655 Grade3 MI.xlsx. We also represent the four MI matrixes of 72 and 68 biomarkers for GSE19536 in Additional file S8 8 GSE19536 Luminal-A MI.xlsx, Additional file S9 9 GSE19536 Luminal-B MI.xlsx, Additional file S10 10 GSE19536 Basal-Like MI.xlsx, and Additional file S11 11 GSE19536 Normal-Like MI.xlsx. The two MI matrixes for GSE21036 are in Additional file S12 12 GSE21036 Cancer MI.xlsx, Additional file S13 13 GSE21036 Normal MI.xlsx.
Additional file 5
18655 Grade1 MI. An MS Office Excel file which contains a matrix of the pairwise MI values of 96 biomarkers of grade1 samples.
2043-9113-2-16-S5.xlsx
Click here for file
Additional file 6
18655 Grade2 MI. An MS Office Excel file which contains a matrix of the pairwise MI values of 96 biomarkers of grade2 samples.
2043-9113-2-16-S6.xlsx
Click here for file
Additional file 7
18655 Grade3 MI. An MS Office Excel file which contains a matrix of the pairwise MI values of 96 biomarkers of grade3 samples.
2043-9113-2-16-S7.xlsx
Click here for file
Additional file 8
19536 Luminal-A MI. An MS Office Excel file which contains the pairwise MI values of 72 biomarkers of luminal A samples.
2043-9113-2-16-S8.xlsx
Click here for file
Additional file 9
19536 Luminal-B MI. An MS Office Excel file which contains the pairwise MI values of 72 biomarkers of luminal B samples.
2043-9113-2-16-S9.xlsx
Click here for file
Additional file 10
19536 Basal-Like MI. An MS Office Excel file which contains the pairwise MI values of 68 biomarkers of Basal-like samples.
2043-9113-2-16-S10.xlsx
Click here for file
Additional file 11
19536 Normal-Like MI. An MS Office Excel file which contains the pairwise MI values of 68 biomarkers of Normal-like samples.
2043-9113-2-16-S11.xlsx
Click here for file
Additional file 12
21036 Cancer MI. An MS Office Excel file which contains the pairwise MI values of 22 biomarkers of cancerous samples.
2043-9113-2-16-S12.xlsx
Click here for file
Additional file 13
21036 Normal MI. An MS Office Excel file which contains the pairwise MI values of 22 biomarkers of normal samples.
2043-9113-2-16-S13.xlsx
Click here for file
We analyze these MI matrixes and list differences between them under different conditions in Table T3 3. For a given matrix, the first column in Table 3 denotes the mean value; the second column denotes the standard deviation; the third column shows the number of positive values in the matrix; the fourth column shows the number of negative values; the sixth column shows the minimum value and the seventh column displays the maximum. In the fifth column, we compare MI matrixes under two different conditions such as luminal A vs. luminal B. If the signs of two entries at the same position in these two matrixes are different, we count it as one sign difference. The fifth column denotes the number of sign differences of the samples compared. We employ the same process for comparing basal-like versus normal-like for GSE19536 and the cancerous versus normal for GSE21036. To visualize the differences, we display the histograms of MI values of grade1s, grade2s and grade3s in Figure figr fid F1 1. Figure F2 2 shows the histograms for luminal As versus luminal Bs. Figure F3 3 shows basal-likes versus normal-likes and Figure F4 4 shows the cancerous versus normals.
Table 3
Results of analysis of MI matrices
8
c6 6
c7 7
c8
Mean value of MI
Standard deviation of MI
Num of positive values
Num of negative values
Num of values of different sign
Min value
Max value
GSE18655_grade1
0.00024
0.0015
6298
2918
N/A
−0.0011
0.0858
GSE18655_grade2
0.00020
0.0017
6468
2748
−0.0018
0.0949
GSE18655_grade3
0.0004
0.0021
6650
2566
−0.0029
0.0582
GSE19536_A(72)
0.00036
0.0022
3912
1272
2052
−0.0010
0.1293
GSE19536_B(72)
0.00053
0.0040
3388
1796
−0.0022
0.2279
GSE19536_BasalLike(68)
0.0017
0.0056
3491
998
1217
−0.0033
0.1648
GSE19536_NormalLike(68)
0.0056
0.008
4200
420
−0.002
0.1279
GSE21036_cancer
0.0165
0.0212
10
474
56
−0.002
0.1446
GSE21036_norm
0.0086
0.0146
46
438
−0.0015
0.1565
fig Figure 1Comparison of 96 MI of grade1, grade2 and grade3 samples
Comparison of 96 MI of grade1, grade2 and grade3 samples.
graphic 2043-9113-2-16-1
Figure 2Comparison of 72 MI of luminal A and luminal B samples
Comparison of 72 MI of luminal A and luminal B samples.
2043-9113-2-16-2
Figure 3Comparison of 68 MI of basal-like and normal-like samples
Comparison of 68 MI of basal-like and normal-like samples.
2043-9113-2-16-3
Figure 4Comparison of 22 MI of prostate cancerous and normal samples
Comparison of 22 MI of prostate cancerous and normal samples.
2043-9113-2-16-4 For the fifth column of comparison of GSE18655, since there are three types prostate, they cannot be fairly compared, so we skipped the process for it. In addition, because there are many MI entries for all histograms, we only show the densest section of each histogram in figures.
Results of calculating mutual information
The statistic results of calculating mutual information are shown in Table 3 at the end of this paper.
Synergistic therapy
Based on the interpretation of the network
4
5
, we proposed a framework that can help to elucidate the underlying interactions between multi-target biomarkers and multi-component drug agents. The framework consists of three parts: selecting biomarkers of a complex disease such as cancer, building target networks of biomarkers, and forming interaction between biomarkers and drug agents to provide a personalized and synergistic therapy plan.From the GEO datasets of cancers, we have discovered the genetic model of each cancer, called signature of that particular cancer. Among different cancers, their signatures (target networks) may be quite different which corresponds to different biomarkers in Additional file 1 GSE18655_96_Biomarkers.xlsx, Additional file 2 GSE19536_72_Biomarkers.xlsx, Additional file 3 GSE19536_68_Biomarkers.xlsx, and Additional file 4 GSE21036_22_Biomakers.xlsx. For these different signatures, we would discover various synergistic mechanisms which have exemplified in
B24 24
.Assume we would like to provide a synergistic therapy plan of a patient A. By collecting his/her bodily data such as saliva, blood samples, we first obtain the corresponding microarray dataset of patient A and apply it to the genetic model as shown in Figure F5 5.
Figure 5Diagram of detailed process of building the genetic model
Diagram of detailed process of building the genetic model.
2043-9113-2-16-5 A complete synergistic therapy should be able to select small subset of biomarkers and correlate them with drug agents in a multi-target multi-components network approach as shown in Figure F6 6. In Figure 6, a disease associates with several biomarkers such as RNAs, miRNAs or proteins denoted by R1, R2, R3, R4 and R5 which are the regulators for operons O1, O2, and O3. An operon is a basic unit of DNAs and formed by a group of genes controlled by a gene regulator. These operons initiate molecular mechanisms as promoters. The gene regulators can enable organs to regulate other genes either by induction or repression. For each target biomarker, it may have a list of pharmacons used as enzyme inhibitors. Traditionally, pharmacons are referred to biological active substances which are not limited to drug agents only. For example, the herbal extractions whose ingredients have a promising anti-AD (Alzheimer’s Disease) effect can be used as pharmacons
24
. Meanwhile, pharmacons denoted by D1, D2, and D3, have effects for some target biomarkers. For example, D1 affects target biomarker R3, D2 affects target biomarker R5 and D3 affects biomarker R1. Compared with drug agent pair methodology
5
, the proposed framework in Figure 6 represents a more accurate interpretation of biomarkers with multi-component drug agents.
Figure 6Relationships between biomarkers, pharmacons and operons where R1, R2, R3, R4 and R5 denote 5 biomarkers
Relationships between biomarkers, pharmacons and operons where R1, R2, R3, R4 and R5 denote 5 biomarkers. Among all the biomarkers, R2, R3 and R5 are regulators.
2043-9113-2-16-6
Discussion
Among the MI values obtained, we see positive values and negative values. The positive value can represent the attractions among the biomarkers while the negative may represent the repulsion among the biomarkers, which matches the concept of Yin-Yang in TCM (Traditional Chinese Medicine). From these results, we observed that there is minimal difference of mutual information values between cancer stages. However, the difference of mean MI value of the prostate cancer versus normal cells is move obvious. The mean MI value of the last prostate cancer cell is approximately twice that of normal cells. This may be intriguing for medical people for further investigations.
Conclusions
We have presented a comprehensive approach to diagnosis and therapy of complex diseases, such as cancer. A complete procedure is proposed for clinical application to cancer patients. While the genetic model provides a standard framework to design synergistic therapy, the actual plan for individual patient is personalized and flexible. With careful monitoring, physicians may adaptively change or modify the therapy plan. Much further analysis of this framework in clinical settings should be experimented.
Competing interests
The authors declare that they have no competing interests.
Author’s contributions
WH, CL: Implementation of project. FC, SC: Design the project. All authors read and approved the final manuscript.
bm
ack
Acknowledgements
We are grateful to the reviewers for their valuable comments and suggestions. We are also grateful to Dr. John Harris for his encouragements for this research. We are also thankful for Dr. Lung-Ji Chang for his discussion and encouragements.
refgrp Multi-target therapeutics: when the whole is greater than the sum of the partsZimmermannGRLeharJKeithCTDrug discovery today2007121–234lpage 42link fulltext 17198971Multicomponent therapeutics for networked systemsKeithCTBorisyAAStockwellBRNat Rev Drug Discov200541717810.1038/nrd160915688074Strategies for optimizing combinations of molecularly targeted anticancer agentsDanceyJEChenHXNature reviewsDrug discovery20065864965910.1038/nrd2089The efficiency of multi-target drugs: the network approach might help drug designCsermelyPAgostonVPongorSTrends Pharmacol Sci200526417818210.1016/j.tips.2005.02.00715808341Network target for screening synergistic drug combinations with application to traditional Chinese medicineLiSZhangBZhangNBMC Syst Biol20115Suppl 1S10Journal Article10.1186/1752-0509-5-S1-S10pmcid 328756522784616HsuW-CLiuC-CChangFChenS-SFeature Selection for Microarray Data Analysis: GEO & AMFESpublisher Florida: Technical Report, Gainesville2012247868818558008Gene Selection for Cancer Classification using Support Vector MachinesGuyonIWestonJBarnhillSVapnikVMach Learn2002461–3389422Variable selection using svm based criteriaRakotomamonjyAJ Mach Learn Res2003313571370Dimensionality reduction via sparse support vector machinesBiJBennettKEmbrechtsMBrenemanCSongMJ Mach Learn Res2003312291243Ranking a random feature for variable and feature selectionStoppigliaHDreyfusGDuboisROussarYJMachLearnRes20083Journal Article13991414Feature Selection Using Ensemble Based Ranking Against Artificial ContrastsTuvEBorisovATorkkolaKNeural Networks, 2006 IJCNN '06 International Joint Conference on: 0–0 0200621812186A mathematical theory of communicationShannonCESIGMOBILE Mob Comput Commun Rev20015135510.1145/584091.584093Fast calculation of pairwise mutual information for gene regulatory network reconstructionQiuPGentlesAJPlevritisSKComp Methods and Programs in Biomed200994217718010.1016/j.cmpb.2008.11.003Nonparametric entropy estimation: An overviewBeirlantJDudewiczEJoumlLG,rMeulenECVDInt J Math Stat Sci1997611739ARACNE: An Algorithm for the Reconstruction of Gene Regulatory Networks in a Mammalian Cellular ContextMargolinANemenmanIBassoKWigginsCStolovitzkyGFaveraRCalifanoABMC Bioinforma20067Suppl 1S710.1186/1471-2105-7-S1-S7MichaelEWMonicaSLA data locality optimizing algorithm1991Systems biology and combination therapy in the quest for clinical efficacyFitzgeraldJBSchoeberlBNielsenUBSorgerPKNat Chem Biol20062945846610.1038/nchembio81716921358The PageRank Citation Ranking: Bringing Order to the WebPageLBrinSMotwaniRWinogradTStanford InfoLab1999A text-mining analysis of the human phenomevan DrielMABruggemanJVriendGBrunnerHGLeunissenJAEur J Human Genet : EJHG200614553554210.1038/sj.ejhg.5201585SobinLHWittekindCTNM: classification of malignant tumoursNew York: Wiley-Liss2002Prostate cancer genes associated with TMPRSS2-ERG gene fusion and prognostic of biochemical recurrence in multiple cohortsBarwickBGAbramovitzMKodaniMMorenoCSNamRTangWBouzykMSethALeyland-JonesBBr J Cancer2010102357057610.1038/sj.bjc.6605519282294820068566miRNA-mRNA Integrated Analysis Reveals Roles for miRNAs in Primary Breast TumorsEnerlyESteinfeldIKleiviKLeivonenSKAureMRRussnesHGRonnebergJAJohnsenHNavonRRodlandEetal PLoS One201162e1691510.1371/journal.pone.0016915304307021364938Integrative genomic profiling of human prostate cancerTaylorBSSchultzNHieronymusHGopalanAXiaoYCarverBSAroraVKKaushikPCeramiERevaBCancer cell2010181112210.1016/j.ccr.2010.05.026319878720579941Towards a bioinformatics analysis of anti-Alzheimer's herbal medicines from a target network perspectiveSunYZhuRYeHTangKZhaoJChenYLiuQCaoZBriefings in bioinformatics2012


xml version 1.0 encoding utf-8 standalone no
mets ID sort-mets_mets OBJID sword-mets LABEL DSpace SWORD Item PROFILE METS SIP Profile xmlns http:www.loc.govMETS
xmlns:xlink http:www.w3.org1999xlink xmlns:xsi http:www.w3.org2001XMLSchema-instance
xsi:schemaLocation http:www.loc.govstandardsmetsmets.xsd
metsHdr CREATEDATE 2012-12-18T14:58:01
agent ROLE CUSTODIAN TYPE ORGANIZATION
name BioMed Central
dmdSec sword-mets-dmd-1 GROUPID sword-mets-dmd-1_group-1
mdWrap SWAP Metadata MDTYPE OTHER OTHERMDTYPE EPDCX MIMETYPE textxml
xmlData
epdcx:descriptionSet xmlns:epdcx http:purl.orgeprintepdcx2006-11-16 xmlns:MIOJAVI
http:purl.orgeprintepdcxxsd2006-11-16epdcx.xsd
epdcx:description epdcx:resourceId sword-mets-epdcx-1
epdcx:statement epdcx:propertyURI http:purl.orgdcelements1.1type epdcx:valueURI http:purl.orgeprintentityTypeScholarlyWork
http:purl.orgdcelements1.1title
epdcx:valueString Cancer classification: Mutual information, target network and strategies of therapy
http:purl.orgdctermsabstract
Abstract
Background
Cancer therapy is a challenging research area because side effects often occur in chemo and radiation therapy. We intend to study a multi-targets and multi-components design that will provide synergistic results to improve efficiency of cancer therapy.
Methods
We have developed a general methodology, AMFES (Adaptive Multiple FEature Selection), for ranking and selecting important cancer biomarkers based on SVM (Support Vector Machine) classification. In particular, we exemplify this method by three datasets: a prostate cancer (three stages), a breast cancer (four subtypes), and another prostate cancer (normal vs. cancerous). Moreover, we have computed the target networks of these biomarkers as the signatures of the cancers with additional information (mutual information between biomarkers of the network). Then, we proposed a robust framework for synergistic therapy design approach which includes varies existing mechanisms.
Results
These methodologies were applied to three GEO datasets: GSE18655 (three prostate stages), GSE19536 (4 subtypes breast cancers) and GSE21036 (prostate cancer cells and normal cells) shown in. We selected 96 biomarkers for first prostate cancer dataset (three prostate stages), 72 for breast cancer (luminal A vs. luminal B), 68 for breast cancer (basal-like vs. normal-like), and 22 for another prostate cancer (cancerous vs. normal. In addition, we obtained statistically significant results of mutual information, which demonstrate that the dependencies among these biomarkers can be positive or negative.
Conclusions
We proposed an efficient feature ranking and selection scheme, AMFES, to select an important subset from a large number of features for any cancer dataset. Thus, we obtained the signatures of these cancers by building their target networks. Finally, we proposed a robust framework of synergistic therapy for cancer patients. Our framework is not only supported by real GEO datasets but also aim to a multi-targets/multi-components drug design tool, which improves the traditional single target/single component analysis methods. This framework builds a computational foundation which can provide a clear classification of cancers and lead to an efficient cancer therapy.
http:purl.orgdcelements1.1creator
Hsu, Wen-Chin
Liu, Chan-Cheng
Chang, Fu
Chen, Su-Shing
http:purl.orgeprinttermsisExpressedAs epdcx:valueRef sword-mets-expr-1
http:purl.orgeprintentityTypeExpression
http:purl.orgdcelements1.1language epdcx:vesURI http:purl.orgdctermsRFC3066
en
http:purl.orgeprinttermsType
http:purl.orgeprinttypeJournalArticle
http:purl.orgdctermsavailable
epdcx:sesURI http:purl.orgdctermsW3CDTF 2012-10-02
http:purl.orgdcelements1.1publisher
BioMed Central Ltd
http:purl.orgeprinttermsstatus http:purl.orgeprinttermsStatus
http:purl.orgeprintstatusPeerReviewed
http:purl.orgeprinttermscopyrightHolder
Wen-Chin Hsu et al.; licensee BioMed Central Ltd.
http:purl.orgdctermslicense
http://creativecommons.org/licenses/by/2.0
http:purl.orgdctermsaccessRights http:purl.orgeprinttermsAccessRights
http:purl.orgeprintaccessRightsOpenAccess
http:purl.orgeprinttermsbibliographicCitation
Journal of Clinical Bioinformatics. 2012 Oct 02;2(1):16
http:purl.orgdcelements1.1identifier
http:purl.orgdctermsURI http://dx.doi.org/10.1186/2043-9113-2-16
fileSec
fileGrp sword-mets-fgrp-1 USE CONTENT
file sword-mets-fgid-0 sword-mets-file-1
FLocat LOCTYPE URL xlink:href 2043-9113-2-16.xml
sword-mets-fgid-1 sword-mets-file-2 applicationpdf
2043-9113-2-16.pdf
sword-mets-fgid-3 sword-mets-file-3 applicationvnd.openxmlformats-officedocument.spreadsheetml.sheet
2043-9113-2-16-S11.XLSX
sword-mets-fgid-4 sword-mets-file-4
2043-9113-2-16-S12.XLSX
sword-mets-fgid-5 sword-mets-file-5
2043-9113-2-16-S1.XLSX
sword-mets-fgid-6 sword-mets-file-6
2043-9113-2-16-S3.XLSX
sword-mets-fgid-7 sword-mets-file-7
2043-9113-2-16-S6.XLSX
sword-mets-fgid-8 sword-mets-file-8
2043-9113-2-16-S7.XLSX
sword-mets-fgid-9 sword-mets-file-9
2043-9113-2-16-S13.XLSX
sword-mets-fgid-10 sword-mets-file-10
2043-9113-2-16-S4.XLSX
sword-mets-fgid-11 sword-mets-file-11
2043-9113-2-16-S10.XLSX
sword-mets-fgid-12 sword-mets-file-12
2043-9113-2-16-S2.XLSX
sword-mets-fgid-13 sword-mets-file-13
2043-9113-2-16-S9.XLSX
sword-mets-fgid-14 sword-mets-file-14
2043-9113-2-16-S5.XLSX
sword-mets-fgid-15 sword-mets-file-15
2043-9113-2-16-S8.XLSX
structMap sword-mets-struct-1 structure LOGICAL
div sword-mets-div-1 DMDID Object
sword-mets-div-2 File
fptr FILEID
sword-mets-div-3
sword-mets-div-4
sword-mets-div-5
sword-mets-div-6
sword-mets-div-7
sword-mets-div-8
sword-mets-div-9
sword-mets-div-10
sword-mets-div-11
sword-mets-div-12
sword-mets-div-13
sword-mets-div-14
sword-mets-div-15
sword-mets-div-16



PAGE 1

RESEARCHOpenAccessCancerclassification:Mutualinformation, targetnetworkandstrategiesoftherapyWen-ChinHsu1,2,Chan-ChengLiu4,FuChang4andSu-ShingChen1,3*AbstractBackground: Cancertherapyisachallengingresearchareabecausesideeffectsoftenoccurinchemoand radiationtherapy.Weintendtostudyamulti-targetsandmulti-componentsdesignthatwillprovidesynergistic resultstoimproveefficiencyofcancertherapy. Methods: Wehavedevelopedageneralmethodology,AMFES(AdaptiveMultipleFEatureSelection),forranking andselectingimportantcancerbiomarkersbasedonSVM(SupportVectorMachine)classification.Inparticular,we exemplifythismethodbythreedatasets:aprostatecancer(threestages),abreastcancer(foursubtypes),and anotherprostatecancer(normalvs.cancerous).Moreover,wehavecomputedthetargetnetworksofthese biomarkersasthesignaturesofthecancerswithadditionalinformation(mutualinformationbetweenbiomarkersof thenetwork).Then,weproposedarobustframeworkforsynergistictherapydesignapproachwhichincludesvaries existingmechanisms. Results: ThesemethodologieswereappliedtothreeGEOdatasets:GSE18655(threeprostatestages),GSE19536 (4subtypesbreastcancers)andGSE21036(prostatecancercellsandnormalcells)shownin.Weselected96 biomarkersforfirstprostatecancerdataset(threeprostatestages),72forbreastcancer(luminalAvs.luminalB), 68forbreastcancer(basal-likevs.normal-like),and22foranotherprostatecancer(cancerousvs.normal.In addition,weobtainedstatisticallysignificantresultsofmutualinformation,whichdemonstratethatthe dependenciesamongthesebiomarkerscanbepositiveornegative. Conclusions: Weproposedanefficientfeaturerankingandselectionscheme,AMFES,toselectanimportantsubset fromalargenumberoffeaturesforanycancerdataset.Thus,weobtainedthesignaturesofthesecancersby buildingtheirtargetnetworks.Finally,weproposedarobustframeworkofsynergistictherapyforcancerpatients. OurframeworkisnotonlysupportedbyrealGEOdatasetsbutalsoaimtoamulti-targets/multi-componentsdrug designtool,whichimprovesthetraditionalsingletarget/singlecomponentanalysismethods.Thisframework buildsacomputationalfoundationwhichcanprovideaclearclassificationofcancersandleadtoanefficient cancertherapy. Keywords: Featureselection,Biomarkers,Microarray,Therapydesign,TargetnetworkBackgroundCancertherapyisadifficultresearchareaduetoitslevel ofcomplexity.Lately,themeresuperpositionofsingle drugsisfoundtogenerateside-effectsandcrosstalkwith anotherdrugwhichmaycanceloutthefinalsuccessof treatments.Thus,currentresearchfocusesonmeasuring thedrugtreatmentsasawholeratherthanconsidering themindividually[1,2].Later,asynergisticconceptis proposedtoevaluatethedrugtreatments[3].However, evaluationsarestillbasedoncasesanddonothavea systematicapproach.In[4],anetworkmethodologyis firstusedtoevaluateefficiencyofdrugtreatments.Thus, Lietal.useaparameter,namelyaSS(SynergyScore)to introducethetopologyfactorofthenetworkbasedon thediseaseandthedrugagentcombination[5]. Ourapproachisfirsttobuildamoreprecisetarget networkfromtheselectedbiomarkers(byAMFES)[6]. Then,weidentifytheintrinsicpropertiesbycomputing *Correspondence: suchen@cise.ufl.edu1SystemBiologyLab,UniversityofFlorida,Florida,USA3DepartmentofComputerandInformationScienceandEngineering, UniversityofFlorida,Florida,USA Fulllistofauthorinformationisavailableattheendofthearticle JOURNAL OF CLINICAL BIOINFORMATICS 2012Hsuetal.;licenseeBioMedCentralLtd.ThisisanOpenAccessarticledistributedunderthetermsoftheCreative CommonsAttributionLicense(http://creativecommons.org/licenses/by/2.0),whichpermitsunrestricteduse,distribution,and reproductioninanymedium,providedtheoriginalworkisproperlycited.Hsu etal.JournalofClinicalBioinformatics 2012, 2 :16 http://www.jclinbioinformatics.com/content/2/1/16

PAGE 2

mutualinformationoftheinteractionsamongthesebiomarkers.OurapproachistoimproveLi ’ sresultsbyconsideringthemutualinformationinthetargetnetwork. Andweprovideageneralframeworkofsynergistictherapy,whichmayincludeseveraldifferentapproaches.MethodsAMFESTheCOD(CurseofDimensionality)hasbeenamajor challengeofmicroarraydataanalysisduetothelarge numberofgenes(features)andrelativelysmallnumber ofsamples(patterns).Totacklethisproblem,manygene selectionmethodologiesweredevelopedtoselectonly significantsubsetsofgenesinamicroarraydataset. AMFESselectsanoptimalsubsetofgenesbytraininga SVMwithsubsetsofgenesgeneratedadaptively[6]. WhenAMFESrunsadataset,allsamplesarerandomlydividedintoatrainingsubset S ofsamplesanda testingsubset T ofsamplesataheuristicratioof5:1. S isusedforrankingandselectingofgenesandforconstructingaclassifieroutoftheselectedgenes. T isused forcomputingtestaccuracy.Whenatrainingsubset S is given,weextract r training-validationpairsfrom S accordingtotheheuristicrule r =max(5,(int)(500/ n +0.5))and n isthenumberofsamplesin S .Eachpair randomlydivides S intoatrainingcomponentofsamplesandavalidationcomponentofsamplesataratioof 4:1.Theheuristicratioandrulearechosenbasedonthe experimentalexperiencesatthebalanceoftimeconsumptionandperformance.Basically,AMFEShastwo fundamentalprocesses,rankingandselection.Wefirst explaineachprocessindetailsandthentheintegrated versionattheend.RankingThegenerankingprocesscontainsafewrankingstages.At firststage,allgenesarerankedbytheirrankingscoresina descendingorder.Then,inthenextstage,onlythetophalf rankedgenesarerankedagainwhilethebottomhalfholds thecurrentorderinthesubsequentstage.Thesameiterationrepeatsrecursivelyuntilonlythreegenesare remainedtoberankedagaintocompleteoneranking process.Assumeatagivenrankingstage,thereare k genes indexedfrom 1 to k .Torankthese k genes,wefollow4 stepsbelow.(I)Wefirstgenerate m independentsubsets S1... Sm .Eachsubset Si, i =1,2 ... m ,has j geneswhichare selectedrandomlyandindependentlyfromthe k genes, where j =(int)( k /2).(II)LetCibetheSVMclassifierthatis trainedoneachsubsetofgenes,i =1,2 ... m .Foreachgene of k genes,wecomputetherankingscore m( g)ofthegene g ,asequation(1).(III)Weusetheaverageweightofthe gene g ,thesummationofweightsof g in m subsetsdivided bythenumberofsubsetsforwhich g israndomlyselected. Thisincreasestherobustnesstorepresentthetrue classifyingabilityofthegene g .(IV)Rank k genesinthe descendingorderbytheirrankingscores. mg Xm i 1Ig 2 Sifgweightig Xm i 1Ig 2 Sifg 1 whereIisanindicatorfunctionsuchthatIproposition=1if thepropositionistrue;otherwise,Iproposition=0.Inother word,ifgene g israndomlyselectedforthesubset Si,itis denotedas g 2 SiandIproposition=1. WedenotetheobjectivefunctionofCias objiv1; v2; ... ; vs where v1, v2... vsaresupportvectors ofCi.The weighti(g)isthendefinedasthechangeinthe objectivefunctionduetog,i.e., weightig objiv1; v2; ... vs objivg 1; vg 2; ... ; vg 3 2 [6][7,8].Notethatif v isavector, v( g )isthevector obtainedbydroppinggene g from v .Let mbeavector comprisingtherankingscoresderivedfromthe m gene subsetsgeneratedthusfarand m-1isthevectoratthe previousstage.The m valueisdeterminedwhen msatisfiestheequation(3)byaddingagenetoanemptysubsetonceatime. jj m 1 mjj2jj m 1jj2< 0 : 01 3 where|| ||isunderstoodastheEuclideannormofvector .Thepseudocodesofrankingprocessareshownin below.PseudocodesforrankingprocessofAMFESRANK-SUBROUTINE INPUT:asubsetofkgenestoberanked Generatekartificialgenesandputthemnexttothe originalgenes Pickaninitialtentativevalueofm DOWHILEmdoesnotsatisfiesequation(3) FOReachsubsetSiofmsubsets Randomlyselectjelementsfromkgenestoformthe subsetSi. TrainanSVMtogetweighti(g)foreachgeneinthe subset ENDFORHsu etal.JournalofClinicalBioinformatics 2012, 2 :16 Page2of11 http://www.jclinbioinformatics.com/content/2/1/16

PAGE 3

FOReachgeneofkgenes Computetheaveragescoreofthegenefrommsubsets ENDFOR Listkgenesindescendingorderbytheirrankingscores ENDDO OUPUT:arankedkgenesSelectionRankingartificialfeaturestogetherwithoriginalfeatures hasbeendemonstratedasausefultooltodistinguish relevantfeaturesfromirrelevantonesasin[9-11].In ourselectionprocess,wealsousethistechniquetofind theoptimalsubsetofgenes. Assumeasetofgenesisgiven.Wegenerateartificial genesandrankthemtogetherwithoriginalones.After finishingrankingtheset,weassignagene-indextoeach originalgenebytheproportionofartificialonesthatare rankedaboveitwherethegene-indexistherealnumerical valuebetween0and1.Then,wegenerateafewsubset candidatesfromwhichtheoptimalsubsetischosen.Let p1, p2,bethesequenceofsubset-indicesofthecandidates with p1< p2< ... .where pi= i 0.005and i =1,2, ... 200. LetB( pi)denotethecorrespondingsubsetofsubset-index pi, anditcontainsoriginalgeneswhoseindicesaresmaller thanorequalto pi. Then wetrainaSVMoneveryB( pi), andcomputeitsvalidationaccuracy v ( pi). Westopatthefirst pkatwhichv( pk) vbaselineand v ( pk) v ( pl)for k l k +10,where vbaselineisthevalidationaccuracyrateoftheSVMtrainedonthebaseline, i.e.,thecaseinwhichallfeaturesareinvolvedintraining.Thefinalresult,B( pk),isthentheoptimalsubsetfor thegivensetofgenes.Thepseudocodesforselection processofAMFESarelistedbelow.PseudocodesforselectionprocessofAMFESSELECTION-SUBROUTINE INPUT:afewsubsetswiththeirvalidationaccuracies, av(pi) Computethevalidationaccuracyofallgenes,vbaseline. FOReachsubsetgiven IFv(pk) vbaselineandv(pk) v(pl)fork l k+10 THEN ResultedsubsetisB(pk) ENDIF ENDFOR OUPUT:B(pk)IntegratedversionTherankingandselectionprocessesfromprevioussectionsareforonetraining-validationpair.Toincrease thereliabilityofvalidation,wegenerate r pairstofind theoptimalsubset.Wecalculatethevalidationaccuracy ofthe qthpairforall pq-isubsetswhere q denotespairindexand i denotesthesubset-index.Then,wecompute av ( pi),theaverageof v ( pq-i)over r training-validation pairsandperformthesubsetsearchasexplainedinselectionsectionon av ( pi)tofindtheoptimal pi,denoted as p *.However, p *doesnotcorrespondtoauniquesubset,sinceeachpairhasitsownB( p *)andtheycanbeall different.Thus,weadoptallsamplesof S astraining samplesinordertofindauniquesubset.Wegenerate artificialgenesandrankthemtogetherwithoriginal genes.Finally,weselecttheoriginalgeneswhoseindices aresmallerthanorequaltothe p *asthegenesweselect for S .Theintegratedversionofprocessisshownbelow. Inthepseudocodesbelow,theAMFES-ALGORITHM representstheintegratedversionofthewholeprocess whileRANK-SUBROUTINErepresentstheranking processandSELECTION-SUBROUTINErepresentsthe selectionprocess.PseudocodesforintegratedversionofAMFESAMFESALGORITHM-IntegratedVersion INPUT:adataset Divideadatasetintotrainsamplesandtestsamples. Dividethetrainsamplesintortraining-validation componentspairs FOReachpairofrtrain-validationcomponentspairs Generate200candidatesubsetspq-iFOReachsubsetof200subsets CALLRANKsubroutinetorankeachsubset. Assigneachoriginalgeneagene-index TraineachsubsetonanSVMandcompute correspondingvalidationaccuracy,v(pq-i), forthesubset ENDFOR ENDFOR FOReachsubsetof200subsetsHsu etal.JournalofClinicalBioinformatics 2012, 2 :16 Page3of11 http://www.jclinbioinformatics.com/content/2/1/16

PAGE 4

Computeaveragevalidationrate,av(pi),ofthesubset fromrpairs. ENDFOR CALLSELECTIONsubroutinetosearchforthe optimalsubsetbyitsaveragevalidationrateand denotesitasp* CALLRANKsubroutinetorankoriginalgenesagain andselectoriginalgeneswhichbelongtothesubsetB (p*). OUPUT:anoptimalsubsetofgenesB(p*)MutualinformationMutualinformationhasbeenusedtomeasurethedependencybetweentworandomvariablesbasedonthe probabilityofthem.IftworandomvariablesXandY, themutualinformationofXandY,I(X;Y),canbe expressedastheseequivalentequations[12]: IX ; Y HX HXY j 4 HY HYX j 5 HX HY HX ; Y 6 whereH(X),H(Y)denotemarginalentropies,H(X|Y)and H(Y|X)denoteconditionalentropiesandH(X,Y)denotes jointentropyoftheXandY.Tocomputeentropy,the probabilitydistributionfunctionsoftherandomvariables arerequiredtobecalculatedfirst.Becausegeneexpressionsareusuallycontinuousnumbers,weusedthekernel estimationtocalculatetheprobabilitydistribution[13]. AssumethetworandomvariablesXandYarecontinuousnumbers.Themutualinformationisdefinedas [12]: IX ; Y ZZ fx ; y log fx ; y fx fy dxdy 7 where f (x,y)denotesthejointprobabilitydistribution,and f (x)and f (y)denotemarginalprobabilitydistributionofX andY.ByusingtheGaussiankernelestimation,the f (x,y), f (x)and f (y)canbefurtherrepresentedasequationsbelow [14]: fx ; y 1 M X2 h2e 1 2 h 2x xu2 y y2 u 8 fx 1 M 1 2 h2p e 1 2 h 2x yu2; 9 where M representsthenumberofsamplesforbothX andY, u isindexofsamples u 1 ; 2 ; ... M ; and h isaparametercontrollingthewidthofthekernels.Thus,themutualinformation IX ; Y canthenberepresentedas: IX ; Y 1 M Xilog M Xie 1 2 h 2xw xu2 ywi yu2 Xje 1 2 h 2xw xu2Xje 1 2 h 2ywi yu2 10 whereboth w,u areindicesofsamples w ; u 1 ; 2 ; ... M Computationofpairwisegenesofamicroarraydataset usuallyinvolvesnestedloopscalculationwhichtakesa dramaticamountoftime.Assumeadatasethas N genes andeachgenehas M samples.Tocalculatethepairwise mutualinformationvalues,thecomputationusuallyfirst findsthekerneldistancebetweenanytwosamplesfora givengene.Then,thesameprocessgoesthroughevery pairofgenesinthedataset.Inordertobecomputation efficient,twoimprovementsareapplied[13].Thefirst oneistocalculatethemarginalprobabilityofeachgene inadvanceanduseitrepeatedlyduringtheprocess[13] [15].Thesecondimprovementistomovethesummation ofeachsamplepairforagivengenetothemostouter for-loopratherthaninsideanestedfor-loopforevery pairwisegene.Asaresult,thekerneldistancebetween twosamplesisonlycalculatedtwiceinstead N times whichsavesalotofcomputationtime.LNO(Loops NestOptimization)whichchangestheorderofnested loopsisacommontime-savingtechniqueincomputer sciencefield[16].TargetnetworkTheeffectofdrugswithmultiplecomponentsshouldbe viewedasawholeratherthanasuperpositionofindividualcomponents[1][2].Thus,asynergicconceptis formedandconsideredasanefficientmannertodesign adrug[3].In[17],mathematicalmodelsareusedto measuretheeffectgeneratedbythemultiplecomponents.However,itdoesnotconsiderpracticalsituation suchascrosstalkbetweenpathways.Anetworkapproachstartstobeusedtoanalyzetheinteractions amongmultiplecomponents[4].Initiatedbyworkin [4],anothersystembiologicalmethodology,NIMS(Network-target-basedIdentificationofMulticomponent Synergy)isproposedtomeasuretheeffectofdrugagent pairsdependingontheirgeneexpressiondata[5].NIMS focusesonrankingthedrugagentpairsofChinese MedicinecomponentsbytheirSS. In[5],itassumesthatadrugcomponentisdenotedas adrugagentandwithwhichasetofgenesassociatedare denotedasagentgenesofthedrugagent.Foragivendisease,assumethereare N drugagentswhere N =1,2 ... n Initially,NIMSrandomlychoosestwodrugagentsfrom N ,A1,andA2,andbuildsabackgroundtargetnetworkHsu etal.JournalofClinicalBioinformatics 2012, 2 :16 Page4of11 http://www.jclinbioinformatics.com/content/2/1/16

PAGE 5

bytheiragentgenesinagraph.Fromthegraph,NIMS calculatesTS(TopologyScore)ofthegraphbyapplying thePCA(PrincipleComponentAnalysis)toformaIP valuewhichisintegratedbybetweenness,closenessand avariantofEigenvaluesPageRank[18].TheTSisusedto evaluatethetopologysignificanceofthetargetnetwork forthedrugagentpair,A1andA2,andisdefinedas TS1 ; 2 1 2 XiIP1i exp min di ; j XiIP1i 2 6 6 4 XjIP2j exp min dj ; i XjIP2j 3 7 7 5 ; 11 where IP1and IP2denoteIPvaluesfordrugagentA1agentandA2.Min( di,j)denotesminimumshortestpath fromgene i ofA1toallgenesofA2andmin( dj,i)denotes theonefromgene j ofA1toallgenesofA2. NIMSdefineanotherterm,AS(AgentScore),toevaluatethesimilarityofadiseasephenotypeforadrugagent. Foragivendrugagent,ifoneofitsagentgeneshasa phenotyperecordintheOMIM(OnlineMendelianInheritanceinMan)database,thedrugagenthasthat phenotypeasoneofitsphenotype.Thesimilarityscore ofadrugagentpairisdefinedasthecosinevalueofthe pair ’ sfeaturevectorangle[19].TheASisdefinedas: AS1 ; 2 Xi ; jPi ; jM ; 12 where Pi,jdenotessimilarityscoreof i thphenotypeofA1and j thphenotypeofA2and M denotesthetotalnumber ofphenotypes. TheSSofthepairisthendefinedastheproductofTS andAS.NIMScalculatesSSforallpossibledrugagent pairsforadiseaseandthencanfindpotentialdrugagent pairsafterrankingthembySS.ResultsMIROARRAYdatadescriptionWemadeabriefdescriptionofthesethreedatasetsin Table1.Itlistedthenumberofbiomarkers,typesofbiomarkers,numberofsamplesandvariationofsamples used.TheprostatecancerdatasetwithRNAbiomarkersInordertogiveabetterprognosis,pathologistshave usedacancerstagetomeasurecelltissuesandtumors ’ aggressionsasanindicatorfordoctorstochooseasuitabletreatment.ThemostwidelyusedcancerstagingsystemisTNM(Tumor,Node,andMetastasis)system[20]. Dependingonlevelsofdifferentiationbetweennormal andtumorcells,adifferenthistologicgradeisgiven. Tumorswithgrade1indicatealmostnormaltissues, withgrade2indicatingsomewhatnormaltissuesand withgrade3indicatingtissuesfarawayfromnormal conditions.Althoughmostofcancerscanbeadaptedto TNMgradingsystem,somespecificcancersrequireadditionalgradingsystemsforpathologiststobetterinterprettumors. TheGleasonGradingSystemisespeciallyusedforprostatecancersandaGS(GleasonScore)isgivenbasedon cellularcontentsandtissuesofcancerbiopsiesfrom patients.ThehighertheGSare,theworsetheprognoses are.Theprostatecancerdataset,GSE18655,includes139 patientswith502molecularmarkers,RNAs[21].In[21],it showedthatprostatetumorswithgenefusions,TMPRSS2: ERGT1/E,4havehigherriskofrecurrencesthantumors withoutthegenefusions.139sampleswereprostatefreshfrozentumortissuesofpatientsafteraradicalprostatectomysurgery.Allsamplesweretakenfromthepatients ’ prostatesatthetimeofprostatectomyandliquidnitrogen wasusedtofreezemiddlesectionsofprostatesatextreme lowtemperature.Amongthesepatients,38patientsamples haveGS5 – 6correspondingtohistologicgrade1,90sampleshaveGS7correspondingtohistologicgrade2and11 sampleshaveGS8 – 9correspondingtohistologicgrade3. TheplatformusedforthedatasetsisGPL5858,DASL (cDNA-mediated,annealing,selection,extensionand ligation)HumanCancerPanelbyGenemanufacturedby Illumina.TheFDR(falsediscoveryrate)ofallRNAs expressionsinthemicroarrayislessthan5%. Table1Descriptionsof3datasets:GSE18655(prostatecancer),GSE19536(breastcancer)andGSE21036(prostate cancer)ProstateCancer(GSE18655)BreastCancer(GSE19536)ProstateCancer(GSE21036) NumberofBiomarkers502489373 TypeofBiomarkersRNAsmiRNAsmiRNAs NumberofSamples13978142 VariationofSamplesGrade1(38),Grade2(90), Grade3(11) LuminalA(41),LuminalB(12), Basal-like(15),Normal-like(10) Cancerous(114),Normal(28) Hsu etal.JournalofClinicalBioinformatics 2012, 2 :16 Page5of11 http://www.jclinbioinformatics.com/content/2/1/16

PAGE 6

BreastcancerdatasetwithNon-codingmiRNAbiomarkersThemiRNAshavestrongcorrelationwithsomecellular processes,suchasproliferation,whichhasbeenusedas abreastcancerdataset[22].Ithas799miRNAsand101 patients ’ samples.DifferentialexpressionsofmiRNAs indicateddifferentlevelofproliferationscorresponding to6intrinsicbreastcancersubtypes:luminalA,luminal B,basal-like,normal-like,andERBB2.Theoriginaldatasethas101samplesandamongthem,41samplesareluminalA,15samplesarebasal-like,10samplesare normal-like,12samplesareluminalB,17samplesare ERBB2,2sampleshaveT35mutationstatus,another samplehasT35widetypemutationand3samplesare notclassified.GSE19536wasrepresentedintwoplatformsGPL8227,anAgilient-09118HumanmiRNA microarray2.0G4470B(miRNAIDversion)andthe GPL6480,anAgilent-014850wholeHumanGenome Microarray4x44kG4112F(ProbeName).Forthispaper, weonlyusedtheexpressionsfromGPL8227.Prostatecancerdatasetofcancerousandnormalsamples withmiRNAbiomarkersTheCNAs(CopyNumberAlterations)ofsomegenes mayassociatewithgrowthofprostatecancers[23].In addition,somechangesarediscoveredinmutationsof fusiongene,mRNAexpressionsandpathwaysinamajorityofprimaryprostatesamples.Theanalysiswasappliedtofourplatformsandconsistsof3subseries, GSE21034,GSE21035andGSE21036[23].Forthis paper,weonlyusetheGSE21036foranalysis.The microarraydatasethas142sampleswhichinclude114 primaryprostatecancersamplesand28normalcells samples.TheplatformisAgilent-019118HumanmiRNA Microarray2.0G4470B(miRNAIDversion).ResultsofAMFESWeemploytheAMFESontheprostatecancer (GSE18655),breastcancer(GSE19536)andanotherprostatecancer(GSE21036)datasets.Consequently,for GSE18655,AMFESselects96biomarkers.Theclassificationisperformedintwosteps.Thefirststepperformsclassificationbetweengrade1andabovesamplesanditselects 93biomarkers.Atthesecondstep,AMFESclassifiesbetweengrade2andgrade3samplesanditselects3biomarkers.Thus,wecanassumethatthese96biomarkerscan classifyamonggrade1,grade2andgrade3samples[6].For GSE19536,AMFESalsoperformsclassificationintwo steps.Atthefirststep,AMFESclassifybetweenluminal andnon-luminaltypessamplesanditselects47biomarkers[6].Atthesecondstep,AMFESfurtherclassifiesluminalsamplesintoluminalAandluminalBandselects27 biomarkers.Forthenon-luminalsamples,AMFESalso classifiesthemintobasal-likeandnormal-likesamplesand selects25biomarkers[6].Afterremovingduplicate biomarkers,AMFEShas72(47+27-2(duplicated))for classifyingluminalsamplesand68(47+25-4(duplicated)) forclassifyingnon-luminalones[6].ForGSE21036, AMFESsimplyselects22biomarkersforclassifyingcancerousandnormalsamples.Table2.showsthenumber ofselectedgenes.Thecompletelistsofthesebiomarkers canbefoundinAdditionalfile1GSE18655_96_Biomarkers.xlsx,Additionalfile2GSE19536_72_Biomarkers.xlsx, Additionalfile3GSE19536_68_Biomarkers.xlsx,and Additionalfile4GSE21036_22_Biomakers.xlsx. WethenapplytheMIcalculationdescribedintheMutualInformationsectionon96biomarkersforGSE18655 andrepresentthepairwiseMIvaluesofgrade1,grade2 andgrade3samplesinthree96*96matrixeswhichcan befoundinAdditionalfile5GSE18655Grade1MI.xlsx, Additionalfile6GSE18655Grade2MI.xlsxandAdditionalfile7GSE18655Grade3MI.xlsx.WealsorepresentthefourMImatrixesof72and68biomarkersfor GSE19536inAdditionalfile8GSE19536Luminal-AMI. xlsx,Additionalfile9GSE19536Luminal-BMI.xlsx, Additionalfile10GSE19536Basal-LikeMI.xlsx,and Additionalfile11GSE19536Normal-LikeMI.xlsx.The twoMImatrixesforGSE21036areinAdditionalfile12 GSE21036CancerMI.xlsx,Additionalfile13GSE21036 NormalMI.xlsx. WeanalyzetheseMImatrixesandlistdifferencesbetweenthemunderdifferentconditionsinTable3.Fora givenmatrix,thefirstcolumninTable3denotesthe meanvalue;thesecondcolumndenotesthestandarddeviation;thethirdcolumnshowsthenumberofpositive valuesinthematrix;thefourthcolumnshowsthenumberofnegativevalues;thesixthcolumnshowstheminimumvalueandtheseventhcolumndisplaysthe maximum.Inthefifthcolumn,wecompareMImatrixes undertwodifferentconditionssuchasluminalAvs.luminalB.Ifthesignsoftwoentriesatthesameposition inthesetwomatrixesaredifferent,wecountitasone signdifference.Thefifthcolumndenotesthenumberof signdifferencesofthesamplescompared.Weemploy thesameprocessforcomparingbasal-likeversus Table2ResultsofselectedsubsetsofgenesProstateCancer (GSE18655) BreastCancer (GSE19536) BreastCancer (GSE19536) ProstateCancer (GSE21036) NumberofBiomarkers Selected 96726822 VariationofSamplesGrade1,Grade2,Grade3LuminalA,LuminalBBasal-likeNormal-likeCancerousNormal Hsu etal.JournalofClinicalBioinformatics 2012, 2 :16 Page6of11 http://www.jclinbioinformatics.com/content/2/1/16

PAGE 7

normal-likeforGSE19536andthecancerousversusnormalforGSE21036.Tovisualizethedifferences,wedisplaythehistogramsofMIvaluesofgrade1s,grade2sand grade3sinFigure1.Figure2showsthehistogramsfor luminalAsversusluminalBs.Figure3showsbasal-likes versusnormal-likesandFigure4showsthecancerous versusnormals. ForthefifthcolumnofcomparisonofGSE18655,since therearethreetypesprostate,theycannotbefairlycompared,soweskippedtheprocessforit.Inaddition,becausetherearemanyMIentriesforallhistograms,we onlyshowthedensestsectionofeachhistograminfigures.ResultsofcalculatingmutualinformationThestatisticresultsofcalculatingmutualinformation areshowninTable3attheendofthispaper.SynergistictherapyBasedontheinterpretationofthenetwork[4,5],weproposedaframeworkthatcanhelptoelucidatetheunderlyinginteractionsbetweenmulti-targetbiomarkersand multi-componentdrugagents.Theframeworkconsistsof threeparts:selectingbiomarkersofacomplexdiseasesuch ascancer,buildingtargetnetworksofbiomarkers,and Table3ResultsofanalysisofMImatricesMeanvalue ofMI Standard deviationofMI Numof positivevalues Numof negativevalues Numofvalues ofdifferentsign Min value Max value GSE18655_grade10.000240.001562982918N/A 0.00110.0858 GSE18655_grade20.000200.001764682748 0.00180.0949 GSE18655_grade30.00040.002166502566 0.00290.0582 GSE19536_A(72)0.000360.0022391212722052 0.00100.1293 GSE19536_B(72)0.000530.004033881796 0.00220.2279 GSE19536_BasalLike(68)0.00170.005634919981217 0.00330.1648 GSE19536_NormalLike(68)0.00560.0084200420 0.0020.1279 GSE21036_cancer0.01650.02121047456 0.0020.1446 GSE21036_norm0.00860.014646438 0.00150.1565 0 500 1000 1500 2000 2500 3000 3500 4000 4500 5000 FrequencyBin Grade3 Grade2 Grade1 Figure1 Comparisonof96MIofgrade1,grade2andgrade3samples. Hsu etal.JournalofClinicalBioinformatics 2012, 2 :16 Page7of11 http://www.jclinbioinformatics.com/content/2/1/16

PAGE 8

forminginteractionbetweenbiomarkersanddrugagents toprovideapersonalizedandsynergistictherapyplan. FromtheGEOdatasetsofcancers,wehavediscovered thegeneticmodelofeachcancer,calledsignatureofthat particularcancer.Amongdifferentcancers,theirsignatures (targetnetworks)maybequitedifferentwhichcorresponds todifferentbiomarkersinAdditionalfile1GSE18655_96_ Biomarkers.xlsx,Additionalfile2GSE19536_72_Biomarkers.xlsx,Additionalfile3GSE19536_68_Biomarkers.xlsx, andAdditionalfile4GSE21036_22_Biomakers.xlsx..For thesedifferentsignatures,wewoulddiscovervarioussynergisticmechanismswhichhaveexemplifiedin[24]. 0 100 200 300 400 500 600 700 FrequencyBin LuminalB LuminalA Figure2 Comparisonof72MIofluminalAandluminalBsamples. 0 100 200 300 400 500 600 700 800 900 1000-0.0003 0 0.0003 0.0006 0.0009 0.0012 0.0015 0.0018 0.0021 0.00240.0027 0.0030.0033 0.0036 0.0039 0.0042 0.0045 0.0048 0.0051 0.0054 0.0057 0.006 0.0063 0.0066 0.0069 0.0072 0.0075 0.0078 0.0081 0.0084 0.0087 0.009 0.0093 0.0096 FrequencyBin Normal-like Basal Figure3 Comparisonof68MIofbasal-likeandnormal-likesamples. Hsu etal.JournalofClinicalBioinformatics 2012, 2 :16 Page8of11 http://www.jclinbioinformatics.com/content/2/1/16

PAGE 9

Assumewewouldliketoprovideasynergistictherapy planofapatientA.Bycollectinghis/herbodilydatasuch assaliva,bloodsamples,wefirstobtainthecorrespondingmicroarraydatasetofpatientAandapplyittothe geneticmodelasshowninFigure5. Acompletesynergistictherapyshouldbeabletoselect smallsubsetofbiomarkersandcorrelatethemwithdrug agentsinamulti-targetmulti-componentsnetworkapproachasshowninFigure6.InFigure6,adiseaseassociateswithseveralbiomarkerssuchasRNAs,miRNAsor proteinsdenotedbyR1,R2,R3,R4andR5whicharethe regulatorsforoperonsO1,O2,andO3.Anoperonisa basicunitofDNAsandformedbyagroupofgenescontrolledbyageneregulator.Theseoperonsinitiate molecularmechanismsaspromoters.Thegeneregulators canenableorganstoregulateothergeneseitherbyinductionorrepression.Foreachtargetbiomarker,itmayhave alistofpharmaconsusedasenzymeinhibitors.Traditionally,pharmaconsarereferredtobiologicalactivesubstanceswhicharenotlimitedtodrugagentsonly.For example,theherbalextractionswhoseingredientshavea promisinganti-AD(Alzheimer ’ sDisease)effectcanbe usedaspharmacons[24].Meanwhile,pharmacons denotedbyD1,D2,andD3,haveeffectsforsometarget biomarkers.Forexample,D1affectstargetbiomarkerR3, D2affectstargetbiomarkerR5andD3affectsbiomarker R1.Comparedwithdrugagentpairmethodology[5], theproposedframeworkinFigure6representsa 0 272 135 42 6 5 9 4 3 1 2 1111 00000 1 2 142 166 70 26 202211 6 22 3 1 3 22 0 1 0 3 00 50 100 150 200 250 300 FrequencyBin Normal samples Cancer samples Figure4 Comparisonof22MIofprostatecancerousandnormalsamples. Figure5 Diagramofdetailedprocessofbuildingthegeneticmodel. Hsu etal.JournalofClinicalBioinformatics 2012, 2 :16 Page9of11 http://www.jclinbioinformatics.com/content/2/1/16

PAGE 10

moreaccurateinterpretationofbiomarkerswithmulticomponentdrugagents.DiscussionAmongtheMIvaluesobtained,weseepositivevalues andnegativevalues.Thepositivevaluecanrepresentthe attractionsamongthebiomarkerswhilethenegative mayrepresenttherepulsionamongthebiomarkers, whichmatchestheconceptofYin-YanginTCM(TraditionalChineseMedicine).Fromtheseresults,we observedthatthereisminimaldifferenceofmutualinformationvaluesbetweencancerstages.However,the differenceofmeanMIvalueoftheprostatecancerversusnormalcellsismoveobvious.ThemeanMIvalueof thelastprostatecancercellisapproximatelytwicethat ofnormalcells.Thismaybeintriguingformedical peopleforfurtherinvestigations.ConclusionsWehavepresentedacomprehensiveapproachtodiagnosisandtherapyofcomplexdiseases,suchascancer.A completeprocedureisproposedforclinicalapplication tocancerpatients.Whilethegeneticmodelprovidesa standardframeworktodesignsynergistictherapy,the actualplanforindividualpatientispersonalizedand flexible.Withcarefulmonitoring,physiciansmayadaptivelychangeormodifythetherapyplan.Muchfurther analysisofthisframeworkinclinicalsettingsshouldbe experimented.AdditionalfilesAdditionalfile1: GSE18655_96_Biomarkers. AnMSOfficeExcelfile whichcontainsalistofgenesymbolsof96biomarkersofGSE18655 samples. Additionalfile2: GSE19536_72_Biomarkers. AnMSOfficeExcelfile whichcontainsalistofgenesymbolsof72biomarkersofGSE19536 luminalAandluminalBsamples. Additionalfile3: GSE19536_68_Biomarkers. AnMSOfficeExcelfile whichcontainsalistofgenesymbolsof68biomarkersofGSE19536 basal-likeandnormal-likesamples. Additionalfile4: GSE21036_22_Biomarkers. AnMSOfficeExcelfile whichcontainsalistofgenesymbolsof22biomarkersofGSE21036 samples. Additionalfile5: 18655Grade1MI. AnMSOfficeExcelfilewhich containsamatrixofthepairwiseMIvaluesof96biomarkersofgrade1 samples. Additionalfile6: 18655Grade2MI. AnMSOfficeExcelfilewhich containsamatrixofthepairwiseMIvaluesof96biomarkersofgrade2 samples. Additionalfile7: 18655Grade3MI. AnMSOfficeExcelfilewhich containsamatrixofthepairwiseMIvaluesof96biomarkersofgrade3 samples. Additionalfile8: 19536Luminal-AMI. AnMSOfficeExcelfilewhich containsthepairwiseMIvaluesof72biomarkersofluminalAsamples. Additionalfile9: 19536Luminal-BMI. AnMSOfficeExcelfilewhich containsthepairwiseMIvaluesof72biomarkersofluminalBsamples. Additionalfile10: 19536Basal-LikeMI. AnMSOfficeExcelfilewhich containsthepairwiseMIvaluesof68biomarkersofBasal-likesamples. R1 R2R3R4R5 MI Target Network for a cancer O1O2O3 Regulate D1 D2D3D4 Good interaction Chosen by algorithm Figure6 Relationshipsbetweenbiomarkers,pharmaconsandoperonswhereR1,R2,R3,R4andR5denote5biomarkers.Amongallthe biomarkers,R2,R3andR5areregulators. Hsu etal.JournalofClinicalBioinformatics 2012, 2 :16 Page10of11 http://www.jclinbioinformatics.com/content/2/1/16

PAGE 11

Additionalfile11: 19536Normal-LikeMI. AnMSOfficeExcelfile whichcontainsthepairwiseMIvaluesof68biomarkersofNormal-like samples. Additionalfile12: 21036CancerMI. AnMSOfficeExcelfilewhich containsthepairwiseMIvaluesof22biomarkersofcanceroussamples. Additionalfile13: 21036NormalMI. AnMSOfficeExcelfilewhich containsthepairwiseMIvaluesof22biomarkersofnormalsamples. Competinginterests Theauthorsdeclarethattheyhavenocompetinginterests. Author ’ scontributions WH,CL:Implementationofproject.FC,SC:Designtheproject.Allauthors readandapprovedthefinalmanuscript. Acknowledgements Wearegratefultothereviewersfortheirvaluablecommentsand suggestions.WearealsogratefultoDr.JohnHarrisforhisencouragements forthisresearch.WearealsothankfulforDr.Lung-JiChangforhisdiscussion andencouragements. Authordetails1SystemBiologyLab,UniversityofFlorida,Florida,USA.2Departmentof ElectricalandComputerEngineering,UniversityofFlorida,Florida,USA.3DepartmentofComputerandInformationScienceandEngineering, UniversityofFlorida,Florida,USA.4InstituteofInformationScience,Academia Sinica,Taipei,Taiwan. Received:10July2012Accepted:20September2012 Published:2October2012 References1.ZimmermannGR,LeharJ,KeithCT: Multi-targettherapeutics:whenthe wholeisgreaterthanthesumoftheparts. Drugdiscoverytoday 2007, 12 (1 – 2):34 – 42. 2.KeithCT,BorisyAA,StockwellBR: Multicomponenttherapeuticsfor networkedsystems. NatRevDrugDiscov 2005, 4 (1):71 – 78. 3.DanceyJE,ChenHX: Strategiesforoptimizingcombinationsof molecularlytargetedanticanceragents. NaturereviewsDrugdiscovery 2006, 5 (8):649 – 659. 4.CsermelyP,AgostonV,PongorS: Theefficiencyofmulti-targetdrugs:the networkapproachmighthelpdrugdesign. TrendsPharmacolSci 2005, 26 (4):178 – 182. 5.LiS,ZhangB,ZhangN: Networktargetforscreeningsynergisticdrug combinationswithapplicationtotraditionalChinesemedicine. BMCSyst Biol 2011, 5 (Suppl1):S10.JournalArticle. 6.HsuW-C,LiuC-C,ChangF,ChenS-S: FeatureSelectionforMicroarrayData Analysis:GEO&AMFES .Gainesville,Florida:TechnicalReport;2012. 7.GuyonI,WestonJ,BarnhillS,VapnikV: GeneSelectionforCancer ClassificationusingSupportVectorMachines. MachLearn 2002, 46 (1 – 3):389 – 422. 8.RakotomamonjyA: Variableselectionusingsvmbasedcriteria. JMach LearnRes 2003, 3 :1357 – 1370. 9.BiJ,BennettK,EmbrechtsM,BrenemanC,SongM: Dimensionality reductionviasparsesupportvectormachines. JMachLearnRes 2003, 3 :1229 – 1243. 10.StoppigliaH,DreyfusG,DuboisR,OussarY: Rankingarandomfeaturefor variableandfeatureselection. JMachLearnRes 2008, 3 (Journal Article):1399 – 1414. 11.TuvE,BorisovA,TorkkolaK: FeatureSelectionUsingEnsembleBased RankingAgainstArtificialContrasts .In NeuralNetworks,2006IJCNN'06 InternationalJointConferenceon:0 – 00 .;2006:2181 – 2186. 12.ShannonCE:Amathematicaltheoryofcommunication. SIGMOBILEMob ComputCommunRev 2001, 5 (1):3 – 55. 13.QiuP,GentlesAJ,PlevritisSK: Fastcalculationofpairwisemutual informationforgeneregulatorynetworkreconstruction. CompMethods andProgramsinBiomed 2009, 94 (2):177 – 180. 14.BeirlantJ,DudewiczEJ,oumlLG,r,MeulenECVD: Nonparametricentropy estimation:Anoverview. IntJMathStatSci 1997, 6 (1):17 – 39. 15.MargolinA,NemenmanI,BassoK,WigginsC,StolovitzkyG,FaveraR, CalifanoA: ARACNE:AnAlgorithmfortheReconstructionofGene RegulatoryNetworksinaMammalianCellularContext. BMCBioinforma 2006, 7 (Suppl1):S7. 16.MichaelEW,MonicaSL: Adatalocalityoptimizingalgorithm .;1991. 17.FitzgeraldJB,SchoeberlB,NielsenUB,SorgerPK: Systemsbiologyand combinationtherapyinthequestforclinicalefficacy. NatChemBiol 2006, 2 (9):458 – 466. 18.PageL,BrinS,MotwaniR,WinogradT: ThePageRankCitationRanking: BringingOrdertotheWeb .In StanfordInfoLab .1999. 19.vanDrielMA,BruggemanJ,VriendG,BrunnerHG,LeunissenJA: Atextmininganalysisofthehumanphenome. EurJHumanGenet:EJHG 2006, 14 (5):535 – 542. 20.SobinLH,WittekindC: TNM:classificationofmalignanttumours .NewYork: Wiley-Liss;2002. 21.BarwickBG,AbramovitzM,KodaniM,MorenoCS,NamR,TangW,Bouzyk M,SethA,Leyland-JonesB: Prostatecancergenesassociatedwith TMPRSS2-ERGgenefusionandprognosticofbiochemicalrecurrencein multiplecohorts. BrJCancer 2010, 102 (3):570 – 576. 22.EnerlyE,SteinfeldI,KleiviK,LeivonenSK,AureMR,RussnesHG,Ronneberg JA,JohnsenH,NavonR,RodlandE, etal : miRNA-mRNAIntegrated AnalysisRevealsRolesformiRNAsinPrimaryBreastTumors. PLoSOne 2011, 6 (2):e16915. 23.TaylorBS,SchultzN,HieronymusH,GopalanA,XiaoY,CarverBS,AroraVK, KaushikP,CeramiE,RevaB, etal : Integrativegenomicprofilingofhuman prostatecancer. Cancercell 2010, 18 (1):11 – 22. 24.SunY,ZhuR,YeH,TangK,ZhaoJ,ChenY,LiuQ,CaoZ: Towardsabioinformaticsanalysisofanti-Alzheimer'sherbalmedicinesfroma targetnetworkperspective .In Briefingsinbioinformatics .2012.doi:10.1186/2043-9113-2-16 Citethisarticleas: Hsu etal. : Cancerclassification:Mutualinformation, targetnetworkandstrategiesoftherapy. JournalofClinicalBioinformatics 2012 2 :16. Submit your next manuscript to BioMed Central and take full advantage of: € Convenient online submission € Thorough peer review € No space constraints or color “gure charges € Immediate publication on acceptance € Inclusion in PubMed, CAS, Scopus and Google Scholar € Research which is freely available for redistribution Submit your manuscript at www.biomedcentral.com/submit Hsu etal.JournalofClinicalBioinformatics 2012, 2 :16 Page11of11 http://www.jclinbioinformatics.com/content/2/1/16