We present a computational pipeline implemented in R programming language to perform to detect in the protein-protein human interactome the protein-protein interactions that are most likely affected by the state of the gene coding for Protein Tyrosine Phosphatase, Receptor Type, G (PTPRG) and by different treatments in a well-established cell model of chronic myeloid leukaemia (CML). The final concrete result of this research is a free software that allows oncologists to identify the protein-protein interaction pathways not properly working in patients suffering from CML, as well as he pathways that are altered by the pharmacological treatments currently being tested. (...)

1. A network analysis
computational
pipeline to detect
altered gene
pathways in chronic
myeloid leukaemia
Paola Lecca
Faculty of Computer Science
Free University of Bozen-Bolzano

2. The problem
This project concerns the analysis of the genome of patients affected by
Chronic Myeloid Leukemia (CML) with the two-fold aim of
o identifying the gene features and the gene interactions associated with the
onset of the disease
o detecting genomic alterations due to the effect of experimental therapeutic
treatments.

3. Chronic Myeloid Leukemia
A distinctive feature of CML is the reciprocal
translocation, originating in hematopoietic stem cells,
between the long arms of chromosomes 9 and 22, i.e.
t(9,22).
This genomic aberration generates a new fusion gene,
BCR-ABL1, which encodes for a tyrosine kinase
responsible of neoplastic transformation of these cells.
The abberration consequently affects normal cellular
pathways essential for tissue homeostasis, and thus
causing the alteration of crucial cellular processes, such
as apoptosis, cell cycle and autophagy

4. Protein Tyrosine Phosphatase receptor type γ
In this context, one primary goal of the research is to identify the
regulatory mechanisms antagonizing the kinase activity of BCR-ABL1.
Protein Tyrosine Phosphatase receptor type γ (PTPRG) is a member of
the protein tyrosine phosphatase (PTP) involved in the regulation of
cell growth, differentiation, mitotic cycle, and oncogenic
transformation
PTPRG expression inversely correlates with BCR/ABL1 expression and
activation: once activated, PTPRG can reduce the phosphorylation
level of BCR-ABL1.
BCR-ABL1
PTPRG

5. The wet experiments and the data
The data used in this study derive from the analysis carried out by microarray hybridization of the CML
Cell transcriptome (K562) in different conditions. The cells were transfected with full length PTPRG and
compared to several controls:
o cells transfected with the empty vector
o cells transfected with the same protein that presents a mutation on the catalytic domain (D1028A)
o cells treated with a tyrosine kinase inhibitor (TKI) utilized in clinic (Imatinib) targeting this oncogene
BCR/ABL1.
Using data of gene expression level, gene ontology and protein-protein network (commonly considered a
proxy of gene network), we preliminary studied
o the effect of the expression of gene Protein Tyrosine Phosphatase Receptor Type G (PTPRG) and its
status (active/inactive),
o the effect of the expression of PTPRG (active/inactive) in presence of Tyrosine-Kinase Inhibitor (TKI),
o the effect of Imatinib when PTPRG is active and PTPRG is inactive.

6. The size of the data
• 42,406 gene probes from micro-array experiments
• 12 pharmacological treatments
https://link.springer.com/article/10.1007/BF02931546 https://www.nature.com/articles/ng1201-365

7. PTPRG
active inactive
PTPRG
active inactive
TKI no TKI TKI no TKI
TKI
PTPRG No PTPRG
Network 1 Network 2 Network 3 Network 4 Network 5 Network 6 Network 7 Network 8
LIST OF PATHWAYS THAT DIFFER ACROSS THE NETWORKS
COMPARATIVE NETWORK ANALYSIS
by
Topological Centrality Measures Dynamical Centrality Measures
Modules detection
Experimental configuration, inputs, and outputs
Network reconstruction
Multiple comparisons of networks
Output
Selection of differentially
expressed genes and
transcription factors

8. The software tool
Differential expression analysis
• multiple Wilcoxon tests
Centrality measures
• node degree
• closeness
• betweenness
• clustering coefficient
• eigenvector centrality
• vibrational centrally
• subgraph centrality
• information centrality
• expected force
Analysis of gene sets subjected to different treatments
• Venn diagrams
• Heatmaps
• Gene functional analysis
node importance
node response to perturbation
node participation in network sub-graphs & in
pathways of information spreading across the
network
MODULE 1
MODULE 2
MODULE 3

9. The input
Network Node1 Node2
1 A1 B2
1 B2 B3
1 B3 A2
1 A2 A4
1 A1 A6
1 A11 B20
1 B2 B1
1 B12 A21
1 A9 A1
1 A12 A6
1 … ..
1 … ..
2 A1 B2
2 BB2 B3
2 B3 AA2
2 A2 A4
2 AA1 A6
2 AA11 BB20
2 B2 B1
2 B12 A21
2 A9 A1
2 A12 A6
2 .. …
2 … …
Network 1
Network 2
CSV file containing the N
networks to be compared

10. CNA is implemented in R
https://www.r-project.org/
The GUI is implemented with
the functions of Shiny library:
https://shiny.rstudio.com/

11. Control vs PTPRG Control+Imatinib vs PTPRG
Control vs PTPRG+Imatinib
Control vs PTPRG Control+Imatinib vs PTPRG
Control vs PTPRG+Imatinib
Differentially expressed genes Differentially expressed transcription factors
Control+Imatinib
vs
PTPRG+Imatinib
Control+Imatinib
vs
PTPRG+Imatinib
Results: gene sets analysis

14. CASE 2

15. CASE 3

16. Results: functional
analysis
Funtional analysis
implemented with the
functions of the librarues
• DOSE
• clusterProfiler
• org.Hs.eg.db
• RDAVIDWebService

18. Conclusions
o CNA is a software tool entirely implemenetd in R, a free programming language
available for the principal operating systems.
o CNA has a minimal and very simple user interface (also implemented in R)
designed for use by biologists and doctors who quickly want to compare
biological networks.
o CNA returns the results of the comparative analysis both in the form of graphs
commonly used in bioinformatics to represent differences between data and in
the form of tables of data that can be used for further analysis.
o CNA has been successfully applied to analyse gene networks in patients
affected by CML.

19. Acknowledgements
Claudio Sorio, Department of Medicine, Division of
General Pathology, University of Verona, Italy
Flavio Vella, Bruno Carpetieri, and Paolo Sylos Labini,
Laboratory of Advanced Computing and Systemts,
Faculty of Computer Science of the Free University of
Bozen-Bolzano, Italy
Angela Re, Centre for Sustainable Future Technologies,
Fondazione Istituto Italiano di Tecnologia, Environment
Park - Parco Scientifico Tecnologico per l'Ambiente,
Torino, Italy