Proto-Oncogenes

Clinical investigations have highlighted cell infiltrations in TME as pivotal contributors to the complex anti-tumor immunity in malignancies. TME-cell deconvolution is the major technological hurdle, and the deconvolution algorithms vary in their merits and pitfalls (10 (link), 11 (link)). IOBR integrates eight open-source deconvolution methodologies, namely, CIBERSORT (12 (link)), ESTIMATE (13 (link)), quanTIseq (14 (link)), TIMER (15 (link)), IPS (16 (link)), MCPCounter (17 (link)), xCell (18 (link)), and EPIC (19 (link)).
CIBERSORT is the most well-recognized method for detecting 22 immune cells in TME, allowing large-scale analysis of RNA mixtures for cellular biomarkers and therapeutic targets with promising accuracy (12 (link)). Notably, through the adoption of the linear vector regression principle of CIBERSORT, IOBR allows users to construct a self-defined signature. The availability of its input file was extended to cell-subsets derived from single-cell sequencing results. ESTEMATE dissects non-malignant contextures, including stromal and immune signatures, to determine tumor purity (13 (link)). The quanTIseq method enumerates 10 immune cell subsets from bulk RNAseq data (14 (link)). TIMER quantifies the abundance of six tumor-infiltrating immune compartments and provides six major analytic modules for analyzing the immune infiltration with other cancer molecular profiles (15 (link)). IPS estimates 28 TIL subpopulations, including effector and memory T cells and immunosuppressive cells (16 (link)). MCP-counter conducts robust quantification of the absolute abundance of eight immune and two stromal cell populations in heterogeneous tissues from transcriptomic data (17 (link)). xCell provides a comprehensive view of 64 immune cells from RNA-seq data and other cell subsets in bulk tumor tissue (18 (link)). EPIC decodes the proportion of immune and cancer cells from the expression of genes and compares it with the gene expression profiles from specific cells to predict the cell subpopulation landscape (19 (link)). In a nutshell, IOBR R package and web-based interface enable the convenient integration and visualization of the above-mentioned deconvolution results and a flexible selection of particular methodologies of interest.

The main usage of CentiScaPe is to rank the nodes of a network depending on their topological and experimental relevance. The numerical results are saved as node, edge or network attributes in the Cytoscape attributes browser, depending on the kind of parameters, so all the Cytoscape features for managing attributes are supported; after the computation the centralities are treated as normal Cytoscape attributes. CentiScaPe can be used in undirected networks
^{8 (link)}, in directed networks and in weighted networks. Centralities for directed networks (see
Supplementary materials: CentralitiesTutorial) are useful in the case of metabolic networks in which the direction describes the interaction between the substrates and reactants and the products of the chemical reactions and also in signal transduction networks, in which the direction depends on the flux of information. Considering the direction in the computation of centralities can lead to different results and interpretations than the undirected version.
As an example, in
Figure 1 we show the computation of the directed and undirected Stress applied to a network of Oncogenes (see
Supplementary materials: Oncogenes.txt and Oncogenes_edge_directions.txt). Results of both the computations are shown and discussed.
The image, obtained using Cytoscape’s graphical tool, represents the different Stress values by using the colour and the size of the nodes. The size describes the value obtained by using the directed Stress: the bigger the node the higher the value; the colour describes the value obtained by using the undirected Stress: red is used for the highest value, blue for the lowest value. For example a large blue node requires particular attention because it is showing a node with a high centrality value if the network is considered as directed but with a very low value if the network is considered as undirected.
By analyzing the Oncogenes network we saw that the large red node, i.e. AKT1, shows how its Stress values are high using both algorithms. It plays a central role in different cell processes like metabolism, proliferation, cell survival, growth and angiogenesis. This role may highlight its high Stress value but, on the other hand, the high values suggest us to deeply investigate its characteristics; it is also involved in two different kind of cancer: breast and colorectal (see
http://www.uniprot.org/uniprot/P31749). This evidence suggest that the node could be involved in cancer related processes but this assertion needs validation from several lab experiments.
Another interesting result is shown by the blue medium sized node RAF1. It shows how, using undirected Stress we obtained a low value, but by using the new algorithm we obtained an interesting Stress value. RAF1 was identified as a proto-oncogene with different and fundamental cellular functions (see
http://www.omim.org/entry/164760). The results could be interpreted by saying that the directed network gives us a better understanding about how the gene, and its product, are involved in the development of cancer and could highlight that the use of the direction enhance our ability in describing a complex biological process.
The opposite situation is found in the third highlighted node, the small green node, RB1 in the right bottom corner. In this case the value computed with undirected Stress is not very high, but the value computed with the directed Stress is very low. RB1 is a gene involved in coding a protein involved in the retinoblastoma and other type of cancer like bladder cancer and osteogenic sarcoma. It was the first tumor suppressor gene found (see
http://www.ncbi.nlm.nih.gov/gene/5925). As already said for RAF1, if the directed analysis is considered more reliable than the undirected one, then RB1 seems to be marginally involved in the Oncogenes network otherwise the undirected network is a better description. As already stated an experimental validation should be carried out in order to improve the results from the topological analysis and to better understand the role of each highlighted node.
All the results shown and described must be considered as a possible direction for further lab experiments. The main goal of this kind of analysis is to give us a comprehensive view that could be useful in describing the role of each node involved in a specific biological process and to drive future insights and investigations.
Second important features of the new version of CentiScaPe is the possibility of computing centralities for weighted networks, that are networks in which the edges are provided with an attribute that can be interpreted as a distance between the two connected nodes.
In the network in
Figure 2 we have a distance (
dist) attribute for each edge. The values are
dist(
A,
B) = 2,
dist(
B,
C) = 3 and
dist(
A,
C) = 7. Since A and C are connected by a single edge, in an unweighted computation, the distance from A to C is equal to one. But if the attributes of the edges are considered as distances, the shortest path between A and C is the one passing through B (= 2 + 3 = 5) since it is shorter, or
lighter, than the one connecting A directly to C (= 7). The computation of weighted shortest paths will result in completely different values from the case wherein the weight is not considered. The user should consider that the weight is used in the sense that close nodes are more important than distant nodes. Therefore depending on the meaning of the attributes, one can use the real value or its reciprocal. For example if the attribute represents the speed of a reaction instead of a distance, the reciprocal should be used. This is because the higher the value of the speed, the nearer the nodes are: an increasing speed determines a decreasing reciprocal and the distance decrease by consequence.
An example of usage of weighted networks centralities analysis, can be found in Holly
et al.^{9 (link)} where an euclidean distance is given to each edge depending on the difference between the phosphorylation level of the proteins connected by that edge.
All the graphical features of the previous version of CentiScaPe, as the plot-by-node, the plot-by-centrality and the boolean-based result panel have been maintained in the new version. A complete guide can be found in Scardoni
et al.^{8 (link)} or in the CentiScaPe userguide available from the website.

Three testing scenarios were defined in the model to illustrate the gradual introduction of biomarker testing followed by the administration of matching systemic therapies in the first-line treatment of aNSCLC; see Figure 1. The first scenario was “no biomarker testing,” which did not include any upfront biomarker testing and led to the administration of chemotherapy in all patients. This scenario modeled the situation in high-income countries in the mid-2000s.
The second scenario was “sequential testing,” which included upfront sequential and single biomarker testing for EGFR and anaplastic lymphoma kinase (ALK) followed by treatment with targeted therapy for mutation-positive patients and with chemotherapy for all other patients. This scenario modeled the situation in high-income countries just before the introduction of immunotherapy in 2015 and the current situation in many middle-income countries.
The third scenario was “multigene testing,” which included upfront next-generation sequencing (NGS) testing and treatment with targeted therapy if tested positive for the presence of EGFR, ALK, ROS proto-oncogene 1 (ROS1), B-Raf proto-oncogene (BRAF), neurotrophic tyrosine receptor kinase (NTRK), mesenchymal epithelial transition (MET), or ret proto-oncogene (RET), and testing for PD-L1 status and treatment with immuno(chemo)therapy for all other patients. This scenario modeled the situation in 2022 or the near future in most high-income countries.

To determine the copy number of the relevant gene segments of KITLG, molecular genetic analyses were performed on blood samples as follows: Genomic DNA was isolated from EDTA blood with the MagNA Pure 96 system using DNA Tissue Lysis Buffer and viral NA Small RNA kit (Roche, Basel, Switzerland) according to the manufacturer’s instructions. Copy number quantification of the KITLG was performed by digital droplet PCR (ddPCR) using TaqMan^® probes and primers specific for the KITLG sequence and proto-oncogene 1 (ETS1) as reference gene based on the paper of Bannasch et al. (2021) and as performed previously [36 (link),37 ]. Measurement was take in duplicate. The mean value was used for further analyses. The intra-assay correlation was 0.85. The copy number was determined using the DropletReader (Bio-Rad, Feldkirchen, Germany) and QuantaSoftware 1.7.4.0917 (Bio-Rad, Feldkirchen, Germany).