Analysis of individual protein regions provides novel insights on cancer pharmacogenomics

RMut: R package for a Boolean sensitivity analysis against various types of mutations

There have been many in silico studies based on a Boolean network model to investigate network sensitivity against gene or interaction mutations. However, there are no proper tools to examine the network sensitivity against many different types of mutations, including user-defined ones. To address this issue, we developed RMut, which is an R package to analyze the Boolean network-based sensitivity by efficiently employing not only many well-known node-based and edgetic mutations but also novel user-defined mutations. In addition, RMut can specify the mutation area and the duration time for more precise analysis. RMut can be used to analyze large-scale networks because it is implemented in a parallel algorithm using the OpenCL library. In the first case study, we observed that the real biological networks were most sensitive to overexpression/state-flip and edge-addition/-reverse mutations among node-based and edgetic mutations, respectively. In the second case study, we showed that edgetic mutations can predict drug-targets better than node-based mutations. Finally, we examined the network sensitivity to double edge-removal mutations and found an interesting synergistic effect. Taken together, these findings indicate that RMut is a flexible R package to efficiently analyze network sensitivity to various types of mutations. RMut is available at https://github.com/csclab/RMut.

Identification of novel diagnostic biomarkers for thyroid carcinoma

Thyroid carcinoma (THCA) is the most universal endocrine malignancy worldwide. Unfortunately, a limited number of large-scale analyses have been performed to identify biomarkers for THCA. Here, we conducted a meta-analysis using 505 THCA patients and 59 normal controls from The Cancer Genome Atlas. After identifying differentially expressed long non-coding RNA (lncRNA) and protein coding genes (PCG), we found vast difference in various lncRNA-PCG co-expressed pairs in THCA. A dysregulation network with scale-free topology was constructed. Four molecules (LA16c-380H5.2, RP11-203J24.8, MLF1 and SDC4) could potentially serve as diagnostic biomarkers of THCA with high sensitivity and specificity. We further represent a diagnostic panel with expression cutoff values. Our results demonstrate the potential application of those four molecules as novel independent biomarkers for THCA diagnosis.

Structural and Functional View of Polypharmacology

Protein domains mediate drug-protein interactions and this principle can guide the design of multi-target drugs i.e. polypharmacology. In this study, we associate multi-target drugs with CATH functional families through the overrepresentation of targets of those drugs in CATH functional families. Thus, we identify CATH functional families that are currently enriched in drugs (druggable CATH functional families) and we use the network properties of these druggable protein families to analyse their association with drug side effects. Analysis of selected druggable CATH functional families, enriched in drug targets, show that relatives exhibit highly conserved drug binding sites. Furthermore, relatives within druggable CATH functional families occupy central positions in a human protein functional network, cluster together forming network neighbourhoods and are less likely to be within proteins associated with drug side effects. Our results demonstrate that CATH functional families can be used to identify drug-target interactions, opening a new research direction in target identification.

Edge-based sensitivity analysis of signaling networks by using Boolean dynamics

MOTIVATION

Biological networks are composed of molecular components and their interactions represented by nodes and edges, respectively, in a graph model. Based on this model, there were many studies with respect to effects of node-based mutations on the network dynamics, whereas little attention was paid to edgetic mutations so far.

RESULTS

In this paper, we defined an edgetic sensitivity measure that quantifies how likely a converging attractor is changed by edge-removal mutations in a Boolean network model. Through extensive simulations based on that measure, we found interesting properties of highly sensitive edges in both random and real signaling networks. First, the sensitive edges in random networks tend to link two end nodes both of which are susceptible to node-knockout mutations. Interestingly, it was analogous to an observation that the sensitive edges in human signaling networks are likely to connect drug-target genes. We further observed that the edgetic sensitivity predicted drug-targets better than the node-based sensitivity. In addition, the sensitive edges showed distinguished structural characteristics such as a lower connectivity, more involving feedback loops and a higher betweenness. Moreover, their gene-ontology enrichments were clearly different from the other edges. We also observed that genes incident to the highly sensitive interactions are more central by forming a considerably large connected component in human signaling networks. Finally, we validated our approach by showing that most sensitive interactions are promising edgetic drug-targets in p53 cancer and T-cell apoptosis networks. Taken together, the edgetic sensitivity is valuable to understand the complex dynamics of signaling networks.

CONTACT

kwonyk@ulsan.ac.kr

SUPPLEMENTARY INFORMATION

Supplementary data are available at Bioinformatics online.

Mutation Drivers of Immunological Responses to Cancer

In cancer immunology, somatic missense mutations have been mostly studied with regard to their role in the generation of neoantigens. However, growing evidence suggests that mutations in certain genes, such as CASP8 or TP53, influence the immune response against a tumor by other mechanisms. Identifying these genes and mechanisms is important because, just as the identification of cancer driver genes led to the development of personalized cancer therapies, a comprehensive catalog of such cancer immunity drivers will aid in the development of therapies aimed at restoring antitumor immunity. Here, we present an algorithm, domainXplorer, that can be used to identify potential cancer immunity drivers. To demonstrate its potential, we used it to analyze a dataset of 5,164 tumor samples from The Cancer Genome Atlas (TCGA) and to identify protein domains in which mutation status correlates with the presence of immune cells in cancer tissue (immune infiltrate). We identified 122 such protein regions, including several that belong to proteins with known roles in immune response, such as C2, CD163L1, or FCγR2A. In several cases, we show that mutations within the same protein can be associated with more or less immune cell infiltration, depending on the specific domain mutated. These results expand the catalog of potential cancer immunity drivers and highlight the importance of taking into account the structural context of somatic mutations when analyzing their potential association with immune phenotypes. Cancer Immunol Res; 4(9); 789-98. ©2016 AACR.

A Pan-Cancer Catalogue of Cancer Driver Protein Interaction Interfaces

Despite their importance in maintaining the integrity of all cellular pathways, the role of mutations on protein-protein interaction (PPI) interfaces as cancer drivers has not been systematically studied. Here we analyzed the mutation patterns of the PPI interfaces from 10,028 proteins in a pan-cancer cohort of 5,989 tumors from 23 projects of The Cancer Genome Atlas (TCGA) to find interfaces enriched in somatic missense mutations. To that end we use e-Driver, an algorithm to analyze the mutation distribution of specific protein functional regions. We identified 103 PPI interfaces enriched in somatic cancer mutations. 32 of these interfaces are found in proteins coded by known cancer driver genes. The remaining 71 interfaces are found in proteins that have not been previously identified as cancer drivers even that, in most cases, there is an extensive literature suggesting they play an important role in cancer. Finally, we integrate these findings with clinical information to show how tumors apparently driven by the same gene have different behaviors, including patient outcomes, depending on which specific interfaces are mutated.

Until now, most efforts in cancer genomics have focused on identifying genes and pathways driving tumor development. Although this has been unquestionably a success, as evidenced by the fact that we now have an extensive catalogue of cancer driver genes and pathways, there is still a poor understanding of why patients with the same affected driver genes may have different disease outcomes or drug responses. This is precisely the aim of this work-to show how by considering proteins as multifunctional factories instead of monolithic black boxes, it is possible to identify novel cancer driver genes and propose molecular hypotheses to explain such heterogeneity. To that end we have mapped the mutation profiles of 5,989 cancer patients from TCGA to more than 10,000 protein structures, leading us to identify 103 protein interaction interfaces enriched in somatic mutations. Finally, we have integrated clinical annotations as well as proteomics data to show how tumors apparently driven by the same gene can display different behaviors, including patient outcomes, depending on which specific interfaces are mutated.

Cancer3D: understanding cancer mutations through protein structures

The new era of cancer genomics is providing us with extensive knowledge of mutations and other alterations in cancer. The Cancer3D database at http://www.cancer3d.org gives an open and user-friendly way to analyze cancer missense mutations in the context of structures of proteins in which they are found. The database also helps users analyze the distribution patterns of the mutations as well as their relationship to changes in drug activity through two algorithms: e-Driver and e-Drug. These algorithms use knowledge of modular structure of genes and proteins to separately study each region. This approach allows users to find novel candidate driver regions or drug biomarkers that cannot be found when similar analyses are done on the whole-gene level. The Cancer3D database provides access to the results of such analyses based on data from The Cancer Genome Atlas (TCGA) and the Cancer Cell Line Encyclopedia (CCLE). In addition, it displays mutations from over 14 700 proteins mapped to more than 24 300 structures from PDB. This helps users visualize the distribution of mutations and identify novel three-dimensional patterns in their distribution.