Identification of networks of co-occurring, tumor-related DNA copy number changes using a genome-wide scoring approach

Chromosomal Instability, Selection and Competition: Factors That Shape the Level of Karyotype Intra-Tumor Heterogeneity

Simple Summary

Each cancer consists of billions of cells. These cells are far from identical; hence, the population of cells that constitute a tumor is heterogeneous. A salient property that varies between cells in a tumor is their karyotype, the number and configuration of the chromosomes. The level of karyotype heterogeneity can be used to predict the survival of a patient. In this review, we describe the processes that shape the level of karyotype heterogeneity in a cancer.

Abstract

Intra-tumor heterogeneity (ITH) is a pan-cancer predictor of survival, with high ITH being correlated to a dismal prognosis. The level of ITH is, hence, a clinically relevant characteristic of a malignancy. ITH of karyotypes is driven by chromosomal instability (CIN). However, not all new karyotypes generated by CIN are viable or competitive, which limits the amount of ITH. Here, we review the cellular processes and ecological properties that determine karyotype ITH. We propose a framework to understand karyotype ITH, in which cells with new karyotypes emerge through CIN, are selected by cell intrinsic and cell extrinsic selective pressures, and propagate through a cancer in competition with other malignant cells. We further discuss how CIN modulates the cell phenotype and immune microenvironment, and the implications this has for the subsequent selection of karyotypes. Together, we aim to provide a comprehensive overview of the biological processes that shape the level of karyotype heterogeneity.

Identification of the KCNQ1OT1/ miR-378a-3p/ RBMS1 Axis as a Novel Prognostic Biomarker Associated With Immune Cell Infiltration in Gastric Cancer

Background: Gastric cancer (GC) is the second leading cause of cancer-related mortality and the fifth most common cancer worldwide. However, the underlying mechanisms of competitive endogenous RNAs (ceRNAs) in GC are unclear. This study aimed to construct a ceRNA regulation network in correlation with prognosis and explore a prognostic model associated with GC.

Methods: In this study, 1,040 cases of GC were obtained from TCGA and GEO datasets. To identify potential prognostic signature associated with GC, Cox regression analysis and the least absolute shrinkage and selection operator (LASSO) regression were employed. The prognostic value of the signature was validated in the GEO84437 training set, GEO84437 test set, GEO15459 set, and TCGA-STAD. Based on the public databases, TargetScan and starBase, an mRNA-miRNA-lncRNA regulatory network was constructed, and hub genes were identified using the CytoHubba plugin. Furthermore, the clinical outcomes, immune cell infiltration, genetic variants, methylation, and somatic copy number alteration (sCNA) associated with the ceRNA network were derived using bioinformatics methods.

Results: A total of 234 prognostic genes were identified. GO and GSEA revealed that the biological pathways and modules related to immune response and fibroblasts were considerably enriched in GC. A nomogram was generated to provide accurate prognostic outcomes and individualized risk estimates, which were validated in the training, test dataset, and two independent validation datasets. Thereafter, an mRNA-miRNA-lncRNA regulatory network containing 4 mRNAs, 22 miRNAs, 201 lncRNAs was constructed. The KCNQ1OT1/hsa-miR-378a-3p/RBMS1 ceRNA network associated with the prognosis was obtained by hub gene analysis and correlation analysis. Importantly, we found that the KCNQ1OT1/miR-378a-3p/RBMS1 axis may play a vital role in the diagnosis and prognosis of GC patients based on Cox regression analyses. Furthermore, our findings demonstrated that mutations and sCNA of the KCNQ1OT1/miR-378a-3p/RBMS1 axis were associated with increased immune infiltration, while the abnormal upregulation of the axis was primarily a result of hypomethylation.

Conclusion: Our findings suggest that the KCNQ1OT1/miR-378a-3p/RBMS1 axis may be a potential prognostic biomarker and therapeutic target for GC. Moreover, such findings provide insights into the molecular mechanisms of GC pathogenesis.

Computational analysis of data from a genome-wide screening identifies new PARP1 functional interactors as potential therapeutic targets

Knowledge of interaction network between different proteins can be a useful tool in cancer therapy. To develop new therapeutic treatments, understanding how these proteins contribute to dysregulated cellular pathways is an important task. PARP1 inhibitors are drugs used in cancer therapy, in particular where DNA repair is defective. It is crucial to find new candidate interactors of PARP1 as new therapeutic targets in order to increase efficacy of PARP1 inhibitors and expand their clinical utility. By a yeast-based genome wide screening, we previously discovered 90 candidate deletion genes that suppress growth-inhibition phenotype conferred by PARP1 in yeast. Here, we performed an integrated and computational analysis to deeply study these genes. First, we identified which pathways these genes are involved in and putative relations with PARP1 through g:Profiler. Then, we studied mutation pattern and their relation to cancer by interrogating COSMIC and DisGeNET database; finally, we evaluated expression and alteration in several cancers with cBioPortal, and the interaction network with GeneMANIA. We identified 12 genes belonging to PARP1-related pathways. We decided to further validate RIT1, INCENP and PSTA1 in MCF7 breast cancer cells. We found that RIT1 and INCENP affected PARylation and PARP1 protein level more significantly in PARP1 inhibited cells. Furthermore, downregulation of RIT1, INCENP and PSAT1 affected olaparib sensitivity of MCF7 cells. Our study identified candidate genes that could have an effect on PARP inhibition therapy. Moreover, we also confirm that yeast-based screenings could be very helpful to identify novel potential therapy factors.

The Discovery of Mutated Driver Pathways in Cancer: Models and Algorithms

The pathogenesis of cancer in human is still poorly understood. With the rapid development of high-throughput sequencing technologies, huge volumes of cancer genomics data have been generated. Deciphering that data poses great opportunities and challenges to computational biologists. One of such key challenges is to distinguish driver mutations, genes as well as pathways from passenger ones. Mutual exclusivity of gene mutations (each patient has no more than one mutation in the gene set) has been observed in various cancer types and thus has been used as an important property of a driver gene set or pathway. In this article, we aim to review the recent development of computational models and algorithms for discovering driver pathways or modules in cancer with the focus on mutual exclusivity-based ones.

Discovery of cancer common and specific driver gene sets

Cancer is known as a disease mainly caused by gene alterations. Discovery of mutated driver pathways or gene sets is becoming an important step to understand molecular mechanisms of carcinogenesis. However, systematically investigating commonalities and specificities of driver gene sets among multiple cancer types is still a great challenge, but this investigation will undoubtedly benefit deciphering cancers and will be helpful for personalized therapy and precision medicine in cancer treatment. In this study, we propose two optimization models to de novo discover common driver gene sets among multiple cancer types (ComMDP) and specific driver gene sets of one certain or multiple cancer types to other cancers (SpeMDP), respectively. We first apply ComMDP and SpeMDP to simulated data to validate their efficiency. Then, we further apply these methods to 12 cancer types from The Cancer Genome Atlas (TCGA) and obtain several biologically meaningful driver pathways. As examples, we construct a common cancer pathway model for BRCA and OV, infer a complex driver pathway model for BRCA carcinogenesis based on common driver gene sets of BRCA with eight cancer types, and investigate specific driver pathways of the liquid cancer lymphoblastic acute myeloid leukemia (LAML) versus other solid cancer types. In these processes more candidate cancer genes are also found.

A novel independence test for somatic alterations in cancer shows that biology drives mutual exclusivity but chance explains most co-occurrence

In cancer, mutually exclusive or co-occurring somatic alterations across genes can suggest functional interactions. Existing tests for such patterns make the unrealistic assumption of identical gene alteration probabilities across tumors. We present Discrete Independence Statistic Controlling for Observations with Varying Event Rates (DISCOVER), a novel test that is more sensitive than other methods and controls its false positive rate. A pan-cancer analysis using DISCOVER finds no evidence for widespread co-occurrence, and most co-occurrences previously detected do not exceed expectation by chance. Many mutual exclusivities are identified involving well-known genes related to cell cycle and growth factor signaling, as well as lesser known regulators of Hedgehog signaling.

Cooperative genomic alteration network reveals molecular classification across 12 major cancer types

The accumulation of somatic genomic alterations that enables cells to gradually acquire growth advantage contributes to tumor development. This has the important implication of the widespread existence of cooperative genomic alterations in the accumulation process. Here, we proposed a computational method HCOC that simultaneously consider genetic context and downstream functional effects on cancer hallmarks to uncover somatic cooperative events in human cancers. Applying our method to 12 TCGA cancer types, we totally identified 1199 cooperative events with high heterogeneity across human cancers, and then constructed a pan-cancer cooperative alteration network. These cooperative events are associated with genomic alterations of some high-confident cancer drivers, and can trigger the dysfunction of hallmark associated pathways in a co-defect way rather than single alterations. We found that these cooperative events can be used to produce a prognostic classification that can provide complementary information with tissue-of-origin. In a further case study of glioblastoma, using 23 cooperative events identified, we stratified patients into molecularly relevant subtypes with a prognostic significance independent of the Glioma-CpG Island Methylator Phenotype (GCIMP). In summary, our method can be effectively used to discover cancer-driving cooperative events that can be valuable clinical markers for patient stratification.

Inferring Sequential Order of Somatic Mutations during Tumorgenesis based on Markov Chain Model

Tumors are developed and worsen with the accumulated mutations on DNA sequences during tumorigenesis. Identifying the temporal order of gene mutations in cancer initiation and development is a challenging topic. It not only provides a new insight into the study of tumorigenesis at the level of genome sequences but also is an effective tool for early diagnosis of tumors and preventive medicine. In this paper, we develop a novel method to accurately estimate the sequential order of gene mutations during tumorigenesis from genome sequencing data based on Markov chain model as TOMC (Temporal Order based on Markov Chain), and also provide a new criterion to further infer the order of samples or patients, which can characterize the severity or stage of the disease. We applied our method to the analysis of tumors based on several high-throughput datasets. Specifically, first, we revealed that tumor suppressor genes (TSG) tend to be mutated ahead of oncogenes, which are considered as important events for key functional loss and gain during tumorigenesis. Second, the comparisons of various methods demonstrated that our approach has clear advantages over the existing methods due to the consideration on the effect of mutation dependence among genes, such as co-mutation. Third and most important, our method is able to deduce the ordinal sequence of patients or samples to quantitatively characterize their severity of tumors. Therefore, our work provides a new way to quantitatively understand the development and progression of tumorigenesis based on high throughput sequencing data.

Concurrent DNA Copy-Number Alterations and Mutations in Genes Related to Maintenance of Genome Stability in Uninvolved Mammary Glandular Tissue from Breast Cancer Patients

Somatic mosaicism for DNA copy-number alterations (SMC-CNAs) is defined as gain or loss of chromosomal segments in somatic cells within a single organism. As cells harboring SMC-CNAs can undergo clonal expansion, it has been proposed that SMC-CNAs may contribute to the predisposition of these cells to genetic disease including cancer. Herein, the gross genomic alterations (>500 kbp) were characterized in uninvolved mammary glandular tissue from 59 breast cancer patients and matched samples of primary tumors and lymph node metastases. Array-based comparative genomic hybridization showed 10% (6/59) of patients harbored one to 359 large SMC-CNAs (mean: 1,328 kbp; median: 961 kbp) in a substantial portion of glandular tissue cells, distal from the primary tumor site. SMC-CNAs were partially recurrent in tumors, albeit with considerable contribution of stochastic SMC-CNAs indicating genomic destabilization. Targeted resequencing of 301 known predisposition and somatic driver loci revealed mutations and rare variants in genes related to maintenance of genomic integrity: BRCA1 (p.Gln1756Profs*74, p.Arg504Cys), BRCA2 (p.Asn3124Ile), NCOR1 (p.Pro1570Glnfs*45), PALB2 (p.Ser500Pro), and TP53 (p.Arg306*). Co-occurrence of gross SMC-CNAs along with point mutations or rare variants in genes responsible for safeguarding genomic integrity highlights the temporal and spatial neoplastic potential of uninvolved glandular tissue in breast cancer patients.

Identifying core gene modules in glioblastoma based on multilayer factor-mediated dysfunctional regulatory networks through integrating multi-dimensional genomic data

The driver genetic aberrations collectively regulate core cellular processes underlying cancer development. However, identifying the modules of driver genetic alterations and characterizing their functional mechanisms are still major challenges for cancer studies. Here, we developed an integrative multi-omics method CMDD to identify the driver modules and their affecting dysregulated genes through characterizing genetic alteration-induced dysregulated networks. Applied to glioblastoma (GBM), the CMDD identified a core gene module of 17 genes, including seven known GBM drivers, and their dysregulated genes. The module showed significant association with shorter survival of GBM. When classifying driver genes in the module into two gene sets according to their genetic alteration patterns, we found that one gene set directly participated in the glioma pathway, while the other indirectly regulated the glioma pathway, mostly, via their dysregulated genes. Both of the two gene sets were significant contributors to survival and helpful for classifying GBM subtypes, suggesting their critical roles in GBM pathogenesis. Also, by applying the CMDD to other six cancers, we identified some novel core modules associated with overall survival of patients. Together, these results demonstrate integrative multi-omics data can identify driver modules and uncover their dysregulated genes, which is useful for interpreting cancer genome.

Discovery of co-occurring driver pathways in cancer

Background

It has been widely realized that pathways rather than individual genes govern the course of carcinogenesis. Therefore, discovering driver pathways is becoming an important step to understand the molecular mechanisms underlying cancer and design efficient treatments for cancer patients. Previous studies have focused mainly on observation of the alterations in cancer genomes at the individual gene or single pathway level. However, a great deal of evidence has indicated that multiple pathways often function cooperatively in carcinogenesis and other key biological processes.

Results

In this study, an exact mathematical programming method was proposed to de novo identify co-occurring mutated driver pathways (CoMDP) in carcinogenesis without any prior information beyond mutation profiles. Two possible properties of mutations that occurred in cooperative pathways were exploited to achieve this: (1) each individual pathway has high coverage and high exclusivity; and (2) the mutations between the pair of pathways showed statistically significant co-occurrence. The efficiency of CoMDP was validated first by testing on simulated data and comparing it with a previous method. Then CoMDP was applied to several real biological data including glioblastoma, lung adenocarcinoma, and ovarian carcinoma datasets. The discovered co-occurring driver pathways were here found to be involved in several key biological processes, such as cell survival and protein synthesis. Moreover, CoMDP was modified to (1) identify an extra pathway co-occurring with a known pathway and (2) detect multiple significant co-occurring driver pathways for carcinogenesis.

Conclusions

The present method can be used to identify gene sets with more biological relevance than the ones currently used for the discovery of single driver pathways.

Electronic supplementary material

The online version of this article (doi:10.1186/1471-2105-15-271) contains supplementary material, which is available to authorized users.

Voting-based cancer module identification by combining topological and data-driven properties

Recently, computational approaches integrating copy number aberrations (CNAs) and gene expression (GE) have been extensively studied to identify cancer-related genes and pathways. In this work, we integrate these two data sets with protein-protein interaction (PPI) information to find cancer-related functional modules. To integrate CNA and GE data, we first built a gene-gene relationship network from a set of seed genes by enumerating all types of pairwise correlations, e.g. GE-GE, CNA-GE, and CNA-CNA, over multiple patients. Next, we propose a voting-based cancer module identification algorithm by combining topological and data-driven properties (VToD algorithm) by using the gene-gene relationship network as a source of data-driven information, and the PPI data as topological information. We applied the VToD algorithm to 266 glioblastoma multiforme (GBM) and 96 ovarian carcinoma (OVC) samples that have both expression and copy number measurements, and identified 22 GBM modules and 23 OVC modules. Among 22 GBM modules, 15, 12, and 20 modules were significantly enriched with cancer-related KEGG, BioCarta pathways, and GO terms, respectively. Among 23 OVC modules, 19, 18, and 23 modules were significantly enriched with cancer-related KEGG, BioCarta pathways, and GO terms, respectively. Similarly, we also observed that 9 and 2 GBM modules and 15 and 18 OVC modules were enriched with cancer gene census (CGC) and specific cancer driver genes, respectively. Our proposed module-detection algorithm significantly outperformed other existing methods in terms of both functional and cancer gene set enrichments. Most of the cancer-related pathways from both cancer data sets found in our algorithm contained more than two types of gene-gene relationships, showing strong positive correlations between the number of different types of relationship and CGC enrichment -values (0.64 for GBM and 0.49 for OVC). This study suggests that identified modules containing both expression changes and CNAs can explain cancer-related activities with greater insights.

Functional genomic analysis of chromosomal aberrations in a compendium of 8000 cancer genomes

A large database of copy number profiles from cancer genomes can facilitate the identification of recurrent chromosomal alterations that often contain key cancer-related genes. It can also be used to explore low-prevalence genomic events such as chromothripsis. In this study, we report an analysis of 8227 human cancer copy number profiles obtained from 107 array comparative genomic hybridization (CGH) studies. Our analysis reveals similarity of chromosomal arm-level alterations among developmentally related tumor types as well as a number of co-occurring pairs of arm-level alterations. Recurrent (“pan-lineage”) focal alterations identified across diverse tumor types show an enrichment of known cancer-related genes and genes with relevant functions in cancer-associated phenotypes (e.g., kinase and cell cycle). Tumor type-specific (“lineage-restricted”) alterations and their enriched functional categories were also identified. Furthermore, we developed an algorithm for detecting regions in which the copy number oscillates rapidly between fixed levels, indicative of chromothripsis. We observed these massive genomic rearrangements in 1%–2% of the samples with variable tumor type-specific incidence rates. Taken together, our comprehensive view of copy number alterations provides a framework for understanding the functional significance of various genomic alterations in cancer genomes.

Identification of functional CNV region networks using a CNV-gene mapping algorithm in a genome-wide scale

MOTIVATION

Identifying functional relation of copy number variation regions (CNVRs) and gene is an essential process in understanding the impact of genotypic variations on phenotype. There have been many related works, but only a few attempts were made to normal populations.

RESULTS

To analyze the functions of genome-wide CNVRs, we applied a novel correlation measure called Correlation based on Sample Set (CSS) to paired Whole Genome TilePath array and messenger RNA (mRNA) microarray data from 210 HapMap individuals with normal phenotypes and calculated the confident CNVR-gene relationships. Two CNVR nodes form an edge if they regulate a common set of genes, allowing the construction of a global CNVR network. We performed functional enrichment on the common genes that were trans-regulated from CNVRs clustered together in our CNVR network. As a result, we observed that most of CNVR clusters in our CNVR network were reported to be involved in some biological processes or cellular functions, while most CNVR clusters from randomly constructed CNVR networks showed no evidence of functional enrichment. Those results imply that CSS is capable of finding related CNVR-gene pairs and CNVR networks that have functional significance.

AVAILABILITY

http://embio.yonsei.ac.kr/~ Park/cnv_net.php.

CONTACT

sanghyun@cs.yonsei.ac.kr

SUPPLEMENTARY INFORMATION

Supplementary data are available at Bioinformatics online.

Analysis of pathway mutation profiles highlights collaboration between cancer-associated superpathways

The biological interpretation of the complexity of cancer somatic mutation profiles is a major challenge in current cancer research. It has been suggested that mutations in multiple genes that participate in different pathways are collaborative in conferring growth advantage to tumor cells. Here, we propose a powerful pathway-based approach to study the functional collaboration of gene mutations in carcinogenesis. We successfully identify many pairs of significantly comutated pathways for a large-scale somatic mutation profile of lung adenocarcinoma. We find that the coordinated pathway pairs detected by comutations are also likely to be coaltered by other molecular changes, such as alterations in multifunctional genes in cancer. Then, we cluster comutated pathways into comutated superpathways and show that the derived superpathways also tend to be significantly coaltered by DNA copy number alterations. Our results support the hypothesis that comprehensive cooperation among a few basic functions is required for inducing cancer. The results also suggest biologically plausible models for understanding the heterogeneous mechanisms of cancers. Finally, we suggest an approach to identify candidate cancer genes from the derived comutated pathways. Together, our results provide guidelines to distill the pathway collaboration in carcinogenesis from the complexity of cancer somatic mutation profiles.

Network-guided analysis of genes with altered somatic copy number and gene expression reveals pathways commonly perturbed in metastatic melanoma

Cancer genomes frequently contain somatic copy number alterations (SCNA) that can significantly perturb the expression level of affected genes and thus disrupt pathways controlling normal growth. In melanoma, many studies have focussed on the copy number and gene expression levels of the BRAF, PTEN and MITF genes, but little has been done to identify new genes using these parameters at the genome-wide scale. Using karyotyping, SNP and CGH arrays, and RNA-seq, we have identified SCNA affecting gene expression ('SCNA-genes') in seven human metastatic melanoma cell lines. We showed that the combination of these techniques is useful to identify candidate genes potentially involved in tumorigenesis. Since few of these alterations were recurrent across our samples, we used a protein network-guided approach to determine whether any pathways were enriched in SCNA-genes in one or more samples. From this unbiased genome-wide analysis, we identified 28 significantly enriched pathway modules. Comparison with two large, independent melanoma SCNA datasets showed less than 10% overlap at the individual gene level, but network-guided analysis revealed 66% shared pathways, including all but three of the pathways identified in our data. Frequently altered pathways included WNT, cadherin signalling, angiogenesis and melanogenesis. Additionally, our results emphasize the potential of the EPHA3 and FRS2 gene products, involved in angiogenesis and migration, as possible therapeutic targets in melanoma. Our study demonstrates the utility of network-guided approaches, for both large and small datasets, to identify pathways recurrently perturbed in cancer.

CDCOCA: a statistical method to define complexity dependence of co-occuring chromosomal aberrations

Background

Copy number alterations (CNA) play a key role in cancer development and progression. Since more than one CNA can be detected in most tumors, frequently co-occurring genetic CNA may point to cooperating cancer related genes. Existing methods for co-occurrence evaluation so far have not considered the overall heterogeneity of CNA per tumor, resulting in a preferential detection of frequent changes with limited specificity for each association due to the high genetic instability of many samples.

Method

We hypothesize that in cancer some linkage-independent CNA may display a non-random co-occurrence, and that these CNA could be of pathogenetic relevance for the respective cancer. We also hypothesize that the statistical relevance of co-occurring CNA may depend on the sample specific CNA complexity. We verify our hypotheses with a simulation based algorithm CDCOCA (complexity dependence of co-occurring chromosomal aberrations).

Results

Application of CDCOCA to example data sets identified co-occurring CNA from low complex background which otherwise went unnoticed. Identification of cancer associated genes in these co-occurring changes can provide insights of cooperative genes involved in oncogenesis.

Conclusions

We have developed a method to detect associations of regional copy number abnormalities in cancer data. Along with finding statistically relevant CNA co-occurrences, our algorithm points towards a generally low specificity for co-occurrence of regional imbalances in CNA rich samples, which may have negative impact on pathway modeling approaches relying on frequent CNA events.

KC-SMARTR: An R package for detection of statistically significant aberrations in multi-experiment aCGH data

Background

Most approaches used to find recurrent or differential DNA Copy Number Alterations (CNA) in array Comparative Genomic Hybridization (aCGH) data from groups of tumour samples depend on the discretization of the aCGH data to gain, loss or no-change states. This causes loss of valuable biological information in tumour samples, which are frequently heterogeneous. We have previously developed an algorithm, KC-SMART, that bases its estimate of the magnitude of the CNA at a given genomic location on kernel convolution (Klijn et al., 2008). This accounts for the intensity of the probe signal, its local genomic environment and the signal distribution across multiple samples.

Results

Here we extend the approach to allow comparative analyses of two groups of samples and introduce the R implementation of these two approaches. The comparative module allows for a supervised analysis to be performed, to enable the identification of regions that are differentially aberrated between two user-defined classes.

We analyzed data from a series of B- and T-cell lymphomas and were able to retrieve all positive control regions (VDJ regions) in addition to a number of new regions. A t-test employing segmented data, that we implemented, was also able to locate all the positive control regions and a number of new regions but these regions were highly fragmented.

Conclusions

KC-SMARTR offers recurrent CNA and class specific CNA detection, at different genomic scales, in a single package without the need for additional segmentation. It is memory efficient and runs on a wide range of machines. Most importantly, it does not rely on data discretization and therefore maximally exploits the biological information in the aCGH data.

The program is freely available from the Bioconductor website http://www.bioconductor.org/ under the terms of the GNU General Public License.