PIACAN: Pathway Integration and Analysis of Cancer Networks

Adrian Quintana

, Vinh Nguyen

, Tommy Dang

and Chiquito Crasto

Center for Biotechnology and Genomics, Texas Tech University, Lubbock, Texas, U.S.A.

Department of Computer Science, Texas Tech University, Lubbock, Texas, U.S.A.

Keywords: Cancer Biological Pathways, Merged Networks, Cytoscape, Javascript, Web Resource, PIACAN.

Abstract: We developed a web-based software tool, Pathway Integration and Analysis of Cancer Networks (PIACAN),

to identify key cancer genes, pathways and sub-pathways that are implicated in more than one type of cancer.

PIACAN is the result of merging biological pathways associated with 15 different human cancer types mined

from the Kyoto Encyclopaedia of Genes and Genomes (KEGG). The Cytoscape software was used to port the

mined information for pathway merging and subsequent analysis. Web-determined visualization of the

merged networks was achieved by programming using the JavaScript library Data-Drive-Documents (D3).

The results of PIACAN allow us a mechanistic glimpse into the potential development of secondary cancers

spreading to distant tissues, following the primary tumour-localization in a specific tissue, via traversal of the

blood-brain barrier. Given the similarities in biological networks between different cancers, PIACAN allows

us a glimpse into the similarities in cancer development in remote tissues. PIACAN is a free, public, web-

accessible resource (https://adrquint.github.io/integrated-cancer-networks/), where users can identify how and

where biological pathways and/or sub-pathways, depending on the cancer type. A video-demonstration of the

preliminary work can be found at: https://www.youtube.com/watch?v=tOJ-EOY33fU. PIACAN is also

developed as a knowledge- dissemination tool. In its current iteration, for each gene in the pathway, the system

links to cancer gene information in KEGG, GeneCards, Gene Ontology, NCBI AceView, and Ensembl.

1 INTRODUCTION

Cancer is the second leading cause of death in the

United States (US) accounting for approximately, a

million and a half diagnoses and six hundred thousand

deaths per year (Siegel, Miller, Fedewa, et al., 2017).

Targeting the local cancer tissue for one or more of

several specialized treatment modalities is crucial for

the remission of the cancer and an increase in patient

survival rate. If diagnosed early, survival-rates are

highest because the cancer cells are localized to a

specific tissue or organ (ACS, 2016). Breast cancer,

which is a leading cause of death in women, has a 99-

percent five-year survival rate when treatment begins

during the (tumour) localized stage. If left untreated,

and if distant tumour-formations occur, survival-rates

decrease to 26-percent (Wingo, Cardinez, Landis, et

al., 2003). Efforts to cure cancer have been underway

and have evolved over several decades. Though

https://orcid.org/0000-0001-8257-7038

https://orcid.org/0000-0002-1300-3943

https://orcid.org/0000-0001-8322-0014

https://orcid.org/0000-0003-2083-5366

survival rates have increased as treatment modalities

have improved, the overall morbidities associated with

cancer have not significantly decreased (Murphy,

Kocanel, Xu and Heron, 2015).

In the primary stages of cancer (stages I-II),

granular tumours are often small. It is recommended

that tumours discovered at initial stages be surgically

removed to deter the progression of the cancerous

tissue onto adjacent tissue. A serious health concern

is the metastasis of the cancer tissue, otherwise

characterized by Stage IV cancer. At this stage, the

cancer begins its progression to tissue that surrounds

the primary tumour (ACS, 2015). In an ideal world,

treatment would begin as soon as the patient began to

exhibit symptoms. Diagnosis and disease progression

is difficult to pinpoint however, due to the unknown

progression patterns exhibited by certain cancers.

(Nichols, Richmond & Daniels, 2017) Further

treatment complications occur when the cancer

246

Quintana, A., Nguyen, V., Dang, T. and Crasto, C.

PIACAN: Pathway Integration and Analysis of Cancer Networks.

DOI: 10.5220/0009185902460252

In Proceedings of the 13th International Joint Conference on Biomedical Engineering Systems and Technologies (BIOSTEC 2020) - Volume 3: BIOINFORMATICS, pages 246-252

ISBN: 978-989-758-398-8; ISSN: 2184-4305

progresses into the meninges of the brain in the form

of brain tumours. At this phase, the cancer has free

access to cross the blood-brain barrier (BBB) (Fidler

& Ellism 1994). Consequently, the survival rate at

this stage decreases dramatically due to the inability

for modern drug treatments to effectively penetrate

the BBB (Nieder, Spanne, Mehta, et al., 2011;

Marchesi, 2013).

Ongoing research suggests that signalling

pathways associated with cancer progression are

interconnected (Andrew, 2008). Autophagy, which is

the programmed degradation of a cell and its proteins,

is initiated at the preliminary stages of cancer. It is

believed that certain chemical triggers resulting from

adjacent chemical signalling reactions activate this

process of degradation. These adjacent pathways

have been theorized to be part of p53 signalling and

the mTOR sub-network. More importantly, one

observes connections in certain cancer networks,

which contain in them sub-networks or through

specific nodes in the networks, progress to adjacent

networks. Studies suggest that the p53 signalling

pathway transcends through the mTOR sub-network

via the gene AMPK; it then exits the mTOR sub-

network via the gene FIP200, thereby resulting in the

activation of autophagy-related processes of (Ganley,

Lam, Wang, et al., 2009). The importance of targeting

certain signalling pathways for inhibition is further

complicated by the realization that if a part of a

pathway is altered this could lead to unwanted effects

downstream (Liu, Mou, Yu, et al., 2011).

In recent years, the development of bioinformatics

tools has allowed for the visualization of signalling

pathways via web resources. One such comprehensive

resource is the Kyoto Encyclopaedia of Genes and

Genomes (KEGG), created and constantly updated

since 2000 (Kanehisa & Goto, 2000). This resource

and others like it have propelled the study of genomic

pathways and their overall transcriptional effects

within different organisms (Arlt, Casper, Glover, et al.,

2003). In this study, we focused on libraries that

represent research related to cancer pathways and their

genomic interactions within humans.

In studying the pathways and genomic products

that are associated each independent cancer, different

notions of treatment can be considered (Krogan,

Lippman, Agard, et al., 2015). The segregation and

independent study of the most common genes found

in distinct cancers has led to the development of

diagnostic testing that is specialized in detecting the

abnormal transcription of one gene in a series of

pathways involved in one type of signalling.

The merging of different signalling pathways to

assess functional relatedness has allowed for the

analysis of once thought to be independent signalling

events. In merging pathway networks, one can begin

to track the differential centres found in the merged

networks. Key results of this process are the advances

in the research of personalized (now called) precision

medicine (Iyengar, Zhao, Chung, et al., 2012). Thus,

pharmacological applications can be specialized to

target the multiple genomic and epigenomic

signatures for a patient by targeting common centres

for pathways that are activated in a downstream or

upstream process. In this form of treatment plan, a

patient is treated not by their overall symptoms for a

disease but by their own distinct genomic markers

exhibited during disease progression.

The study of cancer pathway networks has

revealed that many of the genes and gene products

involved in each cancer are not unique to just one

cancer in general but are in fact in multiple cancer

pathways (Edelman, Guinney, Chi, et al., 2008)

Although previous research suggests that the

correlation between one specific cancer and the

development of a subsequent different cancers are

strong (Khatri, Sirota, Butte, et al., 2012), research

conducted to substantiate thise has been insufficient,

especially, in a way that allows one to visualize these

interactions.

Our systematic approach could lead to an

innovative targeting of cancers at key locations before

they metastasize and form secondary cancers. Our

research focuses on better understanding these

cancer-related gene interconnections by utilizing

available bioinformatics tools and online genomic

libraries to visually link networks at common gene

points—referred to as nodes—and document the

overlaps in the pathway-networks for 15 typically

identified cancers in humans.

2 MATERIALS AND METHODS

PIACAN—a meta-network system that allows users

to visualize commonalities in cancer-related

biological pathways is the first of its kind. We

anticipate that users: clinicians, biomedical

researchers and students will be able to easily access

through this resources, knowledge related to the

literature, clinical trials, drug-gene interactions, Big

Data and genomic data-driven mapping onto cancer

pathways. Novel discoveries and testable hypothesis

will be possible from the identification commonality

in genes and sub pathways among different cancer-

types.

PIACAN: Pathway Integration and Analysis of Cancer Networks

247

2.1 Data Processing

All the preliminary computational work and data

analysis conducted in this study was performed on an

Apple MacBook Pro (late 2013 model) running

MacOS version 10.12.3. The information used to

populate the studied cancer networks was obtained

from the KEGG online library via customized Python

script (section 2.2). The script was created using

Python version 2.7.11 and was executed in Python’s

Integrated Development and Learning Environment

(IDLE) version 2.7.11. Any additional code utilized

in the creation or updating of the networks can be

found in the attached appendix.

2.2 Network Design and Integration

The cumulative network containing all 15 cancer

networks was created using Cytoscape (Shannon,

Markiel, Ozier, et al., 2003) version 3.4.0 in

conjunction with Java version 1.8.0_111. Each cancer

network was imported individually into Cytoscape

(www.cytoscape.org) via the Cytoscape application

KEGGParser (Nersisyan, Samsonyan, Arakelyan,

2014) version 1.7.11. Importing each sub-network

individually allowed for the verification of the data,

especially comment tagging, assuring its

completeness before the cumulative merge was

initiated. The specific composition of each sub-

network was used by Cytoscape to determine which

nodes and edges would be fused in the cumulative

network. During the merging process, each network

element was analysed by Cytoscape to determine

whether it was common in the adjoining network;

and, if the match was found, it would lead to the

accumulation of comment tags. Cytoscape network

merge tools were used to integrate the 15 sub-

networks into a cumulative network.

Customized Python scripts requested the pathway

information for each cancer network by leveraging

the KEGG API. The script searched for the organism

Homo sapiens (code in KEGG for human: hsa) and

output all available files matching the specified

protocols. The files were all saved separately in the

eXtensible Markup Language (.xml) format.

2.3 Network Information Formatting

Cytoscape export controls were used to export the

background information of the cumulative network in

the Cytoscape.js (.cyjs) format.

The data contained in the exported file were first

reformatted into the JavaScript Object Notation

(JSON, .json) format by using Regular Expression

(regex) patterns thereby creating the required JSON

file. The data contained in the JSON file were further

adjusted to include a community object and to

account for the additional genomic libraries appended

to each individual node in the form of image

hyperlink objects (Figure 1).

Figure 1: The preliminary merging of 15 cancer-type

biological pathways from the KEGG resources by the

Cytoscape network analysis software. Green rectangular

nodes represent genes. Smaller circular nodes represent

chemical compounds. Each edge represents an individual

interaction within a pathway. 302 edges and 256 nodes are

represented in the merged pathways system.

Each image hyperlink object serves the purpose of

affixing a path that when clicked would redirect the

web browser to supplementary genomic content

stored in KEGG, NCBI AceView, GeneCards, Gene

Ontology, and Ensemble (depending on which icon is

selected) as will be illustrated in Figure 5b. The

community object includes information regarding

background pathway information of each individual

gene and compound node. The community object

serves two primary purposes: first, it allows for

further characterization of the node information

detailing the specific biological pathway(s) that

contains that node; and, second, it allows for future

implementation of features which will allow for the

visualization of each independent sub-pathway

contained in each of the 15 cancer sub-networks.

The final cumulative network was migrated to a

freely accessible webpage located on GitHub

(https://adrquint.github.io/integrated-cancer-

networks/). The migration of the cumulative network

allowed for final visualization of the cumulative

network as well as the implementation of JavaScript

applications. Network visualization was accomplished

by using the “Force Directed” Layout found on the

Data-Drive Documents JavaScript library (D3.js)

(https://d3js.org/). A selection colour palette was added

on the left side of the page, Figure 2).

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Figure 2: The fifteen cancer networks represented as circles

with specific colour codes. The size of the circles are

illustrative of the number of genes involved in each cancer

network.

This allowed for a specific cancer sub-network to

be selected and the background to become semi-

transparent; this visual aid identifies the network

being considered in the context of the overall merge-

system. The selection for each individual sub-

network grants the user the ability to visualize one

network at a time. By selecting multiple networks,

this process can be additive to form merged networks

which are strictly common to only those cancer types.

The palette also serves the purpose of associating the

node (gene product) with specific cancer networks. A

drop-down menu was incorporated to allow the user

to directly view and select the genes present after the

selection of one or several sub-networks.

3 RESULTS AND DISCUSSION

Figure 1 represents the results of work that involved

auto-downloading, merging and developing a

cumulative network containing 15 human cancer-

sub-networks.

The green rectangles are nodes that represent gene

products. Chemical compounds contained in a

pathway are represented by smaller circular nodes

(possibly too small to visualize). The edges that

connect the genes represent an individual type of

interaction in the pathway. The networks for

specifically chosen types of cancers represent 265

nodes and 302 edges.

Table 1 represents the number of nodes and edges

identified for each of the 15 types of cancers studied.

Breast cancer is significantly overrepresented in the

table.

The primary aim of this paper is to illustrate the

merging of cancer networks. The methodologies

described in the previous section demonstrated how

the position of the node in the network is transferred

from the cancer biological pathway name to the gene.

Table 2 represents a truncated list of sub-pathways

and networks that are associated with common genes.

The full Excel spreadsheet is available upon request

from the corresponding author of the paper (CJC).

Table 1: The Table shows the total number of nodes (genes)

involved in the pathways and the total edges (interactions)

for each cancer-type.

Cancer Network Nodes Edges

Breast 119 104

Glioma 78 73

Renal Cell Carcinoma 64 34

Pancreatic 57 45

Prostate 57 47

Colorectal 55 32

Chronic Myeloid Leukaemia 54 42

on-small Lung 54 47

Small Lung 47 34

Acute Myeloid Leukaemia 44 40

Endometrial 43 25

Bladder 39 17

Melanoma 34 23

Basal Cell Carcinoma 27 11

Thyroid 26 14

Cytoscape is a powerful network design and

analysis software. It can be used for different

networks—not necessarily for biological pathways.

One of the drawbacks of using the Cytoscape network

analysis software however, is in the area of universal

knowledge dissemination. This standalone software

has to be downloaded to one’s computer (free). In

order to make the merged-network resource,

PIACAN, accessible over the Internet, the web

resources were developed as described in Section 2.3.

Table 2: The table shows which genes are common to the

four pathways that contain the most number of common

genes.

Gene Node Sub-Networks

ARAF

ENDO, nSCLC, PROS, BRCA, AMLE, BLAD, COLO, GLIO,

RENA, PANC, CMLE, MELA

MAP2K1

ENDO, nSCLC, COLO, PROS, BRCA, AMLE, BLAD, GLIO,

THYR, RENA, PANC, CMLE, MELA

MAPK1

ENDO, nSCLC, COLO, BRCA, PROS, AMLE, GLIO, BLAD,

THYR, PANC, RENA, CMLE. MELA

PIK3R5

BRCA, CMLE, MELA, PANC, nSCLC, ENDO, SCLC, PROS,

GLIO, AMLE, COLO, RENA

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249

When the user accesses the PIACAN web-

resource (https://adrquint.github.io/integrated-

cancer-networks), the web page dynamically opens

into a two-panel arrangement. The first panel (Figure

2) indicates the 15 cancers in colour-coded circles.

The size of the circles represent the number of

pathways associated with that specific cancer type.

Each gene in figure 3 is represented only once in

the merged pathway system. Each of these genes is

colour coded. Most have multiple colours. The

colours allow the user at a glance to see which cancer

pathways contain that gene. Figure 4 is a close-up of

a portion of Figure 2.

Figure 3: The figure shows all the merged cancer networks

for all cancer types. The gene-nodes in this merged pathway

are represented by circles. Each gene is represented by

colours depending on the number of pathways of cancer

types they represent.

Figure 4: Close-up of a region of the merged cancer

networks that show colours for each gene representing

cancer types with which they are associated.

In figure 4, (a zoomed-in area of the merged

network represented in Figure 3), one can see, for

example, that two colours represent the BRAF gene:

purple and dark gray. The colours can be matched by

the cancer type in figure 2a which shows that BRAF

gene can be found in Thyroid Cancer (dark gray) and

Melanoma (purple). One can also see that the ARAF

and PIK3R5 genes are present in many of the cancers

whose pathways are represented here.

The web resource also helps users dynamically

assess genes, pathways and cancer types. Users can

click multiple cancer types and only those nodal-

genes implicated in selected cancers and their

associated networks and sub-networks become

visible (Figure 5 and 6).

Figure 5: Figure shows a use-case where the user has

selected three cancer-types. The other cancer-types faded

for additional clarity.

Figure 6: When specific cancers are selected, only the

merged pathways related to those cancers are illustrated

from the complete pathway show in Figure 3. The red-

bordered inset shows how when a mouse is placed over a

gene, dynamic links are created (via icons) for more

information about that gene at KEGG, AceView, NCBI,

Gene Ontology, Gene Cards and Ensemble.

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Circles representing the other cancers are

rendered faded. In the right panel only the nodes that

are common to breast and prostate cancer are

rendered—the rest of the meta-network is faded. The

colour codes on the genes that are common to cancer-

types to which they belong.

4 DISCUSSION

PIACAN allows the direct and dynamic comparison

each of the 15 cancer networks against each other in

terms of gene content. In this study, we have changed

the paradigm of the assessment of gene- networks

from the “network name” to the “gene”. The latter

now becomes the pivotal node around which the

merged networks are illustrated.

The comparative analyses resulted in the

conclusion that a common set of genes initiated

several cancer progression origin sites. Furthermore,

this information can be utilized to actively monitor

the organism’s evolutionary developments and how

this process affects cancer progression. To illustrate

this, we assessed the merged pathways and coinciding

sub-networks for three cancers: breast and

endometrial cancers, breast and prostate cancers, and

endometrial and prostate cancers. The rationale

behind the selection of these pair-wise comparisons

was due to these groups having the closest alignments

in regard to the number of genes they had in common.

Breast and Endometrial Cancer. The comparison of

the breast and endometrial cancer groups yielded 20

common genes between the two groups. This

comparison produced the highest number of common

genes of all the three groups compared. Out of the 20

common genes, eight of these are found in the 10

genes most commonly found in all 15 networks. The

only two that weren’t found in the 20, were RB1 and

E2F1. Although, drawing connections between

pathways in terms of a mechanism of the progression

of cancer from a primary to a secondary tissue is

premature, it is noteworthy that the overlap between

the breast cancer and endometrial cancer networks is

significant. One can make the case the genomic

relatedness of these two cancers can be attributed to

the fact that both tissues are anatomically present

primarily in females and thus the possibility that they

both are active is much higher than in a study

comparing differences in cancer that primarily affect

one sex over the other. The connection between breast

and endometrial tissues can be attributed to the stages

of embryonic development. In these processes, the

tissues differentiating the male and female sexes

develop resulting in distinctive developmental

processes uniquely found in one sex and not the other.

In females,

Breast and Prostate Cancer. In our second group, we

compared the levels of overlap in gene contents of

breast and prostate cancers. This group contained 19

common genes which were found to be active in both

cancers. Out of 19 common genes, nine of which were

found in the list of the top most commonly found

genes among the networks processed. The only one

that wasn’t was TP53. Generally, cancers affecting

primarily one sex have a much higher percentage of

cases reported within that sex. Cases of occurrences

in the opposite sex however, are also common. Breast

cancer is predominately present in females; however,

cases in males have been reported.

Endometrial and Prostate Cancer. In the third group,

we compared the gene contents of endometrial and

prostate cancers. This group contained 18 common

genes involved in both cancer networks. Out of the 18

common genes in this group, seven of these were

found in our top 10 common genes in all of our

networks.

5 CONCLUSIONS

The merging of the cancer networks demonstrated

that the gene products found within certain cancer

networks are not unique. They are found in many

other mapped networks. PIACAN leverages on-line

resources of cancer-pathways, already available

network merging pathways as well as web-

development for universal free access. Although this

is a valid step forward and provides many

opportunities for discovery, more work remains to be

done. Integrating more data from addition resource

into our dynamic networks would be highly beneficial

to visually expressing the similarities found between

different cancers.

The information contained within KEGG is vast

and diverse; it is not however, the only online

resource that can be incorporated into our research.

What was demonstrated in this report is a pilot

system. To make this a truly comprehensive system,

future work will involve the incorporation of

information of online libraries including PubMed,

PubChem, and the Protein Data Bank (PDB). The

resources for gene-product information which

PIACAN can currently access are those where the

gene product name can be directly incorporated into

a URL link. If we were to create additional links to

resources where gene information is mapped onto

alphanumeric IDs, the one would have to dedicate

effort to translating these IDs into gene names.

PIACAN: Pathway Integration and Analysis of Cancer Networks

251

With the array of other online bioinformatics

libraries, which are freely accessible, it possible to

begin to make conjectures and generate hypotheses as

to how diseases, in this case cancer, are related and

how they interact with each other. This systematic

approach could lead to an innovative targeting of

cancers at key locations before they metastasize and

form secondary cancers, which is a significant health

concern.

ACKNOWLEDGEMENTS

The authors wish to thank the Interactive Data

Visualization Lab (iDVL), from the Department of

Computer Science and the Center for Biotechnology

and Genomics at Texas Tech University where the

development of this resource was conducted.

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