Systematic identification of novel cancer genes through analysis of deep shRNA perturbation screens

Systematic perturbation screens provide comprehensive resources for the elucidation of cancer driver genes. The perturbation of many genes in relatively few cell lines in such functional screens necessitates the development of specialized computational tools with sufficient statistical power. Here we developed APSiC (Analysis of Perturbation Screens for identifying novel Cancer genes) to identify genetic drivers and effectors in perturbation screens even with few samples. Applying APSiC to the shRNA screen Project DRIVE, APSiC identified well-known and novel putative mutational and amplified cancer genes across all cancer types and in specific cancer types. Additionally, APSiC discovered tumor-promoting and tumor-suppressive effectors, respectively, for individual cancer types, including genes involved in cell cycle control, Wnt/β-catenin and hippo signalling pathways. We functionally demonstrated that LRRC4B, a putative novel tumor-suppressive effector, suppresses proliferation by delaying cell cycle and modulates apoptosis in breast cancer. We demonstrate APSiC is a robust statistical framework for discovery of novel cancer genes through analysis of large-scale perturbation screens. The analysis of DRIVE using APSiC is provided as a web portal and represents a valuable resource for the discovery of novel cancer genes.     

Automated Network Analysis Identifies Core Pathways in Glioblastoma

Background

Glioblastoma multiforme (GBM) is the most common and aggressive type of brain tumor in humans and the first cancer with comprehensive genomic profiles mapped by The Cancer Genome Atlas (TCGA) project. A central challenge in large-scale genome projects, such as the TCGA GBM project, is the ability to distinguish cancer-causing “driver” mutations from passively selected “passenger” mutations.

Principal Findings

In contrast to a purely frequency based approach to identifying driver mutations in cancer, we propose an automated network-based approach for identifying candidate oncogenic processes and driver genes. The approach is based on the hypothesis that cellular networks contain functional modules, and that tumors target specific modules critical to their growth. Key elements in the approach include combined analysis of sequence mutations and DNA copy number alterations; use of a unified molecular interaction network consisting of both protein-protein interactions and signaling pathways; and identification and statistical assessment of network modules, i.e. cohesive groups of genes of interest with a higher density of interactions within groups than between groups.

Conclusions

We confirm and extend the observation that GBM alterations tend to occur within specific functional modules, in spite of considerable patient-to-patient variation, and that two of the largest modules involve signaling via p53, Rb, PI3K and receptor protein kinases. We also identify new candidate drivers in GBM, including AGAP2/CENTG1, a putative oncogene and an activator of the PI3K pathway; and, three additional significantly altered modules, including one involved in microtubule organization. To facilitate the application of our network-based approach to additional cancer types, we make the method freely available as part of a software tool called NetBox.     

A pan-cancer analysis of the oncogenic role of staphylococcal nuclease domain-containing protein 1 (SND1) in human tumors

Although emerging cell- or animal-based evidence supports the relationship between SND1 and cancers, no pan-cancer analysis is available. We thus first explored the potential oncogenic roles of SND1 across thirty-three tumors based on the datasets of TCGA (The cancer genome atlas) and GEO (Gene expression omnibus). SND1 is highly expressed in most cancers, and distinct associations exist between SND1 expression and prognosis of tumor patients. We observed an enhanced phosphorylation level of S426 in several tumors, such as breast cancer or lung adenocarcinoma. SND1 expression was associated with the CD8⁺ T-cell infiltration level in colon adenocarcinoma and melanoma, and cancer-associated fibroblast infiltration was observed in other tumors, such as bladder urothelial carcinoma or testicular germ cell tumors. Moreover, protein processing- and RNA metabolism-associated functions were involved in the functional mechanisms of SND1. Our first pan-cancer study offers a relatively comprehensive understanding of the oncogenic roles of SND1 across different tumors.     

MicroRNA-218 Is Deleted and Downregulated in Lung Squamous Cell Carcinoma

MicroRNAs (miRNAs) are a family of small, non-coding RNA species functioning as negative regulators of multiple target genes including tumour suppressor genes and oncogenes. Many miRNA gene loci are located within cancer-associated genomic regions. To identify potential new amplified oncogenic and/or deleted tumour suppressing miRNAs in lung cancer, we inferred miRNA gene dosage from high dimensional arrayCGH data. From miRBase v9.0 (http://microrna.sanger.ac.uk), 474 human miRNA genes were physically mapped to regions of chromosomal loss or gain identified from a high-resolution genome-wide arrayCGH study of 132 primary non-small cell lung cancers (NSCLCs) (a training set of 60 squamous cell carcinomas and 72 adenocarcinomas). MiRNAs were selected as candidates if their immediately flanking probes or host gene were deleted or amplified in at least 25% of primary tumours using both Analysis of Copy Errors algorithm and fold change (≥±1.2) analyses. Using these criteria, 97 miRNAs mapped to regions of aberrant copy number. Analysis of three independent published lung cancer arrayCGH datasets confirmed that 22 of these miRNA loci showed directionally concordant copy number variation. MiR-218, encoded on 4p15.31 and 5q35.1 within two host genes (SLIT2 and SLIT3), in a region of copy number loss, was selected as a priority candidate for follow-up as it is reported as underexpressed in lung cancer. We confirmed decreased expression of mature miR-218 and its host genes by qRT-PCR in 39 NSCLCs relative to normal lung tissue. This downregulation of miR-218 was found to be associated with a history of cigarette smoking, but not human papilloma virus. Thus, we show for the first time that putative lung cancer-associated miRNAs can be identified from genome-wide arrayCGH datasets using a bioinformatics mapping approach, and report that miR-218 is a strong candidate tumour suppressing miRNA potentially involved in lung cancer.     

An Integrated Approach to Uncover Driver Genes in Breast Cancer Methylation Genomes

Background

Cancer cells typically exhibit large-scale aberrant methylation of gene promoters. Some of the genes with promoter methylation alterations play “driver” roles in tumorigenesis, whereas others are only “passengers”.

Results

Based on the assumption that promoter methylation alteration of a driver gene may lead to expression alternation of a set of genes associated with cancer pathways, we developed a computational framework for integrating promoter methylation and gene expression data to identify driver methylation aberrations of cancer. Applying this approach to breast cancer data, we identified many novel cancer driver genes and found that some of the identified driver genes were subtype-specific for basal-like, luminal-A and HER2+ subtypes of breast cancer.

Conclusion

The proposed framework proved effective in identifying cancer driver genes from genome-wide gene methylation and expression data of cancer. These results may provide new molecular targets for potential targeted and selective epigenetic therapy.     

Identification of candidate genes for lung cancer somatic mutation test kits

Over the past three decades, mortality from lung cancer has sharply and continuously increased in China, ascending to the first cause of death among all types of cancer. The ability to identify the actual sequence of gene mutations may help doctors determine which mutations lead to precancerous lesions and which produce invasive carcinomas, especially using next-generation sequencing (NGS) technology. In this study, we analyzed the latest lung cancer data in the COSMIC database, in order to find genomic “hotspots” that are frequently mutated in human lung cancer genomes. The results revealed that the most frequently mutated lung cancer genes are EGFR, KRAS and TP53. In recent years, EGFR and KRAS lung cancer test kits have been utilized for detecting lung cancer patients, but they presented many disadvantages, as they proved to be of low sensitivity, labor-intensive and time-consuming. In this study, we constructed a more complete catalogue of lung cancer mutation events including 145 mutated genes. With the genes of this list it may be feasible to develop a NGS kit for lung cancer mutation detection.     

Concordance of Gene Expression and Functional Correlation Patterns across the NCI-60 Cell Lines and the Cancer Genome Atlas Glioblastoma Samples

Background

The NCI-60 is a panel of 60 diverse human cancer cell lines used by the U.S. National Cancer Institute to screen compounds for anticancer activity. We recently clustered genes based on correlation of expression profiles across the NCI-60. Many of the resulting clusters were characterized by cancer-associated biological functions. The set of curated glioblastoma (GBM) gene expression data from the Cancer Genome Atlas (TCGA) initiative has recently become available. Thus, we are now able to determine which of the processes are robustly shared by both the immortalized cell lines and clinical cancers.

Results

Our central observation is that some sets of highly correlated genes in the NCI-60 expression data are also highly correlated in the GBM expression data. Furthermore, a “double fishing” strategy identified many sets of genes that show Pearson correlation ≥0.60 in both the NCI-60 and the GBM data sets relative to a given “bait” gene. The number of such gene sets far exceeds the number expected by chance.

Conclusion

Many of the gene-gene correlations found in the NCI-60 do not reflect just the conditions of cell lines in culture; rather, they reflect processes and gene networks that also function in vivo. A number of gene network correlations co-occur in the NCI-60 and GBM data sets, but there are others that occur only in NCI-60 or only in GBM. In sum, this analysis provides an additional perspective on both the utility and the limitations of the NCI-60 in furthering our understanding of cancers in vivo.     

Recursive Random Lasso (RRLasso) for Identifying Anti-Cancer Drug Targets

Uncovering driver genes is crucial for understanding heterogeneity in cancer. L ₁-type regularization approaches have been widely used for uncovering cancer driver genes based on genome-scale data. Although the existing methods have been widely applied in the field of bioinformatics, they possess several drawbacks: subset size limitations, erroneous estimation results, multicollinearity, and heavy time consumption. We introduce a novel statistical strategy, called a Recursive Random Lasso (RRLasso), for high dimensional genomic data analysis and investigation of driver genes. For time-effective analysis, we consider a recursive bootstrap procedure in line with the random lasso. Furthermore, we introduce a parametric statistical test for driver gene selection based on bootstrap regression modeling results. The proposed RRLasso is not only rapid but performs well for high dimensional genomic data analysis. Monte Carlo simulations and analysis of the “Sanger Genomics of Drug Sensitivity in Cancer dataset from the Cancer Genome Project” show that the proposed RRLasso is an effective tool for high dimensional genomic data analysis. The proposed methods provide reliable and biologically relevant results for cancer driver gene selection.     

WDR5 Expression Is Prognostic of Breast Cancer Outcome

WDR5 is a core component of the human mixed lineage leukemia-2 complex, which plays central roles in ER positive tumour cells and is a major driver of androgen-dependent prostate cancer cell proliferation. Given the similarities between breast and prostate cancers, we explore the potential prognostic value of WDR5 gene expression on breast cancer survival. Our findings reveal that WDR5 over-expression is associated with poor breast cancer clinical outcome in three gene expression data sets and BreastMark. The eQTL analysis reveals 130 trans-eQTL SNPs whose genes mapped with statistical significance are significantly associated with patient survival. These genes together with WDR5 are enriched with “cellular development, gene expression, cell cycle” signallings. Knocking down WDR5 in MCF7 dramatically decreases cell viability, but does not alter tumour cell response to doxorubicin. Our study reveals the prognostic value of WDR5 expression in breast cancer which is under long-range regulation of genes involved in cell cycle, and anthracycline could be coupled with treatments targeting WDR5 once such a regimen is available.     

Low immunogenicity of common cancer hot spot mutations resulting in false immunogenic selection signals

Cancer is driven by somatic mutations that result in a cellular fitness advantage. This selective advantage is expected to be counterbalanced by the immune system when these driver mutations simultaneously lead to the generation of neoantigens, novel peptides that are presented at the cancer cell membrane via HLA molecules from the MHC complex. The presentability of these peptides is determined by a patient’s MHC genotype and it has been suggested that this results in MHC genotype-specific restrictions of the oncogenic mutational landscape. Here, we generated a set of virtual patients, each with an identical and prototypical MHC genotype, and show that the earlier reported HLA affinity differences between observed and unobserved mutations are unrelated to MHC genotype variation. We demonstrate how these differences are secondary to high frequencies of 13 hot spot driver mutations in 6 different genes. Several oncogenic mechanisms were identified that lower the peptides’ HLA affinity, including phospho-mimicking substitutions in BRAF, destabilizing tyrosine mutations in TP53 and glycine-rich mutational contexts in the GTP-binding KRAS domain. In line with our earlier findings, our results emphasize that HLA affinity predictions are easily misinterpreted when studying immunogenic selection processes.     

Activation of multiple cancer pathways and tumor maintenance function of the 3q amplified oncogene FNDC3B

FNDC3B was recently identified in an oncogenomic screen for amplified oncogenes in hepatocellular carcinoma. It is located at 3q26 and is amplified in over 20% of cancers, usually as part of a broad amplified region encompassing the entire 3q arm. Consistent with an oncogenic role in multiple cancer types, we show here that overexpression of FNDC3B is capable of malignantly transforming mammary and kidney epithelial cells in addition to hepatocytes. To explore how FNDC3B transforms cells, we determined the cellular localization of its gene product and the cancer pathways that it activates. We found that the FNDC3B oncoprotein localizes to the Golgi network, and that its correct localization is essential for its transforming function. We found that overexpression of FNDC3B induces the epithelial-to-mesenchymal transition (EMT) and activates several cancer pathways, including PI3-kinase/Akt, Rb1 and TGFβ signaling. For TGFβ signaling, we analyzed the point in the pathway at which FNDC3B operates and obtained evidence that it induces expression of all three TGFβ ligands and also promotes TGFBR1 cell-surface localization. We found that RNAi-mediated knockdown of FNDC3B in cancer cells with 3q amplification suppressed their clonogenicity and tumorigenicity, but that the same RNAi knockdown had no effect on single-copy 3q cancer cells. These results indicate that FNDC3B is an important oncogenic driver gene of the 3q amplicon, adding to the growing list of oncogenic drivers within this commonly amplified region.     

A special subtype: Revealing the potential intervention and great value of KRAS wildtype pancreatic cancer

Pancreatic ductal adenocarcinoma (PDAC) is the predominant form of pancreatic cancer and has devastating consequences on affected families and society. Its dismal prognosis is attributed to poor specificity of symptoms during early stages. It is widely believed that PDAC patients with the wildtype (WT) KRAS gene benefit more from currently available treatments than those with KRAS mutations. The oncogenic genetic changes alternations generally found in KRAS wildtype PDAC are related to either the KRAS pathway or microsatellite instability/mismatch repair deficiency (MSI/dMMR), which enable the application of tailored treatments based on each patient's genetic characteristics. This review focuses on targeted therapies against alternative tumour mechanisms in KRAS WT PDAC.     

Frequent Amplification of CENPF, GMNN and CDK13 Genes in Hepatocellular Carcinomas

Genomic changes frequently occur in cancer cells during tumorigenesis from normal cells. Using the Illumina Human NS-12 single-nucleotide polymorphism (SNP) chip to screen for gene copy number changes in primary hepatocellular carcinomas (HCCs), we initially detected amplification of 35 genes from four genomic regions (1q21–41, 6p21.2–24.1, 7p13 and 8q13–23). By integrated screening of these genes for both DNA copy number and gene expression in HCC and colorectal cancer, we selected CENPF (centromere protein F/mitosin), GMNN (geminin, DNA replication inhibitor), CDK13 (cyclin-dependent kinase 13), and FAM82B (family with sequence similarity 82, member B) as common cancer genes. Each gene exhibited an amplification frequency of ∼30% (range, 20–50%) in primary HCC (n = 57) and colorectal cancer (n = 12), as well as in a panel of human cancer cell lines (n = 70). Clonogenic and invasion assays of NIH3T3 cells transfected with each of the four amplified genes showed that CENPF, GMNN, and CDK13 were highly oncogenic whereas FAM82B was not. Interestingly, the oncogenic activity of these genes (excluding FAM82B) was highly correlated with gene-copy numbers in tumor samples (correlation coefficient, r>0.423), indicating that amplifications of CENPF, GMNN, and CDK13 genes are tightly linked and coincident in tumors. Furthermore, we confirmed that CDK13 gene copy number was significantly associated with clinical onset age in patients with HCC (P = 0.0037). Taken together, our results suggest that coincidently amplified CDK13, GMNN, and CENPF genes can play a role as common cancer-driver genes in human cancers.     

Distinguishing between driver and passenger mutations in individual cancer genomes by network enrichment analysis

Background

In somatic cancer genomes, delineating genuine driver mutations against a background of multiple passenger events is a challenging task. The difficulty of determining function from sequence data and the low frequency of mutations are increasingly hindering the search for novel, less common cancer drivers. The accumulation of extensive amounts of data on somatic point and copy number alterations necessitates the development of systematic methods for driver mutation analysis.

Results

We introduce a framework for detecting driver mutations via functional network analysis, which is applied to individual genomes and does not require pooling multiple samples. It probabilistically evaluates 1) functional network links between different mutations in the same genome and 2) links between individual mutations and known cancer pathways. In addition, it can employ correlations of mutation patterns in pairs of genes. The method was used to analyze genomic alterations in two TCGA datasets, one for glioblastoma multiforme and another for ovarian carcinoma, which were generated using different approaches to mutation profiling. The proportions of drivers among the reported de novo point mutations in these cancers were estimated to be 57.8% and 16.8%, respectively. The both sets also included extended chromosomal regions with synchronous duplications or losses of multiple genes. We identified putative copy number driver events within many such segments. Finally, we summarized seemingly disparate mutations and discovered a functional network of collagen modifications in the glioblastoma. In order to select the most efficient network for use with this method, we used a novel, ROC curve-based procedure for benchmarking different network versions by their ability to recover pathway membership.

Conclusions

The results of our network-based procedure were in good agreement with published gold standard sets of cancer genes and were shown to complement and expand frequency-based driver analyses. On the other hand, three sequence-based methods applied to the same data yielded poor agreement with each other and with our results. We review the difference in driver proportions discovered by different sequencing approaches and discuss the functional roles of novel driver mutations. The software used in this work and the global network of functional couplings are publicly available at http://research.scilifelab.se/andrej_alexeyenko/downloads.html.

Electronic supplementary material

The online version of this article (doi:10.1186/1471-2105-15-308) contains supplementary material, which is available to authorized users.     

A stop-gain mutation in GXYLT1 promotes metastasis of colorectal cancer via the MAPK pathway

Genomic instability plays a key role in the initiation and progression of colorectal cancer (CRC). Although cancer driver genes in CRC have been well characterized, identifying novel genes associated with carcinogenesis and treatment remains challenging because of tumor heterogeneity. Here, we analyzed the genomic alterations of 45 samples from CRC patients in northern China by whole-exome sequencing. In addition to the identification of six well-known CRC driver genes (APC, TP53, KRAS, FBXW7, PIK3CA, and PABPC), two tumor-related genes (MTCH2 and HSPA6) were detected, along with RRP7A and GXYLT1, which have not been previously linked to cancer. GXYLT1 was mutated in 40% (18/45) of the samples in our cohort. Functionally, GXYLT1 promoted migration and invasion in vitro and metastasis in vivo, while the GXYLT1^S212* mutant induced significantly greater effect. Furthermore, both GXYLT1 and GXYLT1^S212* interacted with ERK2. GXYLT1 induced metastasis via a mechanism involving the Notch and MAPK pathways, whereas the GXYLT1^S212* mutant mainly promoted metastasis by activating the MAPK pathway. We propose that GXYLT1 acts as a novel metastasis-associated driver gene and GXYLT1^S212* might serve as a potential indicator for therapies targeting the MAPK pathway in CRC.Subject terms: Cancer genomics, Colorectal cancer, Metastasis, Oncogenes, Cell signalling