A multiplexed, confinable CRISPR/Cas9 gene drive can propagate in caged Aedes aegypti populations.

Aedes aegypti is the main vector of several major pathogens including dengue, Zika and chikungunya viruses. Classical mosquito control strategies utilizing insecticides are threatened by rising resistance. This has stimulated interest in new genetic systems such as gene drivesHere, we test the regulatory sequences from the Ae. aegypti benign gonial cell neoplasm (bgcn) homolog to express Cas9 and a separate multiplexing sgRNA-expressing cassette inserted into the Ae. aegypti kynurenine 3-monooxygenase (kmo) gene. When combined, these two elements provide highly effective germline cutting at the kmo locus and act as a gene drive. Our target genetic element drives through a cage trial population such that carrier frequency of the element increases from 50% to up to 89% of the population despite significant fitness costs to kmo insertions. Deep sequencing suggests that the multiplexing design could mitigate resistance allele formation in our gene drive system.

A Natural Fungal Gene Drive Enacts Killing via DNA Disruption.

Fungal spore killers are a class of selfish genetic elements that positively bias their own inheritance by killing non-inheriting gametes following meiosis. As killing takes place specifically within the developing fungal ascus, a tissue which is experimentally difficult to isolate, our understanding of the mechanisms underlying spore killers are limited. In particular, how these loci kill other spores within the fungal ascus is largely unknown. Here, we overcome these experimental barriers by developing model systems in 2 evolutionary distant organisms, Escherichia coli (bacterium) and Saccharomyces cerevisiae (yeast), similar to previous approaches taken to examine the wtf spore killers. Using these systems, we show that the Podospora anserina spore killer protein SPOK1 enacts killing through targeting DNA. IMPORTANCE Natural gene drives have shaped the genomes of many eukaryotes and recently have been considered for applications to control undesirable species. In fungi, these loci are called spore killers. Despite their importance in evolutionary processes and possible applications, our understanding of how they enact killing is limited. We show that the spore killer protein Spok1, which has homologues throughout the fungal tree of life, acts via DNA disruption. Spok1 is only the second spore killer locus in which the cellular target of killing has been identified and is the first known to target DNA. We also show that the DNA disrupting activity of Spok1 is functional in both bacteria and yeast suggesting a highly conserved mode of action.

Association of mutation signature effectuating processes with mutation hotspots in driver genes and non-coding regions.

Cancer driving mutations are difficult to identify especially in the non-coding part of the genome. Here, we present sigDriver, an algorithm dedicated to call driver mutations. Using 3813 whole-genome sequenced tumors from International Cancer Genome Consortium, The Cancer Genome Atlas Program, and a childhood pan-cancer cohort, we employ mutational signatures based on single-base substitution in the context of tri- and penta-nucleotide motifs for hotspot discovery. Knowledge-based annotations on mutational hotspots reveal enrichment in coding regions and regulatory elements for 6 mutational signatures, including APOBEC and somatic hypermutation signatures. APOBEC activity is associated with 32 hotspots of which 11 are known and 11 are putative regulatory drivers. Somatic single nucleotide variants clusters detected at hypermutation-associated hotspots are distinct from translocation or gene amplifications. Patients carrying APOBEC induced PIK3CA driver mutations show lower occurrence of signature SBS39. In summary, sigDriver uncovers mutational processes associated with known and putative tumor drivers and hotspots particularly in the non-coding regions of the genome.

Accumulation of Molecular Aberrations Distinctive to Hepatocellular Carcinoma Progression.

Cancer develops through the accumulation of genetic and epigenetic aberrations. To identify sequential molecular alterations that occur during the development of hepatocellular carcinoma (HCC), we compared 52 early and 108 overt HCC samples by genome sequencing. Gene mutations in the p53/RB1 pathway, WNT pathway, MLL protein family, SWI/SNF complexes, and AKT/PI3K pathway were common in HCC. In the early phase of all entities, TERT was the most frequently upregulated gene owing to diverse mechanisms. Despite frequent somatic mutations in driver genes, including CTNNB1 and TP53, early HCC was a separate molecular entity from overt HCC, as each had a distinct expression profile. Notably, WNT target genes were not activated in early HCC regardless of CTNNB1 mutation status because ß-catenin did not translocate into the nucleus due to the E-cadherin/ß-catenin complex at the membrane. Conversely, WNT targets were definitively upregulated in overt HCC, with CTNNB1 mutation associated with downregulation of CDH1 and hypomethylation of CpG islands in target genes. Similarly, cell-cycle genes downstream of the p53/RB pathway were upregulated only in overt HCC, with TP53 or RB1 gene mutations associated with chromosomal deletion of 4q or 16q. HCC was epigenetically distinguished into four subclasses: normal-like methylation, global-hypomethylation (favorable prognosis), stem-like methylation (poor prognosis), and CpG island methylation. These methylation statuses were globally maintained through HCC progression. Collectively, these data show that as HCC progresses, additional molecular events exclusive of driver gene mutations cooperatively contribute to transcriptional activation of downstream targets according to methylation status. SIGNIFICANCE: In addition to driver gene mutations in the WNT and p53 pathways, further molecular events are required for aberrant transcriptional activation of these pathways as HCC progresses.

Difference in driver gene expression patterns between perihilar and peripheral intrahepatic cholangiocarcinoma in an experimental mouse model.

BACKGROUND: The prognosis of intrahepatic cholangiocarcinoma (ICC) is based on tumor localization; however, the mechanism remains unknown. Therefore, we investigated the biological characteristics of perihilar and peripheral ICC in a mouse model. METHODS: The model was established by the administration of three oncogenic plasmids harboring myristoylated AKT, mutated human YAP, and pCMV-Sleeping Beauty into the mice. The perihilar and peripheral ICC tumors that developed in the same mouse were assessed for the expression of cell adhesion factors and driver genes with immunohistochemistry and reverse transcription polymerase chain reaction (RT-PCR). RESULTS: The perihilar ICC tumors were irregularly shaped, whereas the peripheral tumors were mostly circular, similar to the differences found in patients. Alpha-smooth muscle actin was strongly expressed in the perihilar tumors at 10 weeks, and vimentin expression was significantly up-regulated in the perihilar ICC at 14 weeks. Fgfr2 level significantly increased in peripheral ICC at 10 weeks, whereas Idh2 expression was up-regulated in perihilar ICC. CONCLUSIONS: Despite diffuse injection of oncogenic plasmid, expression of driver genes and oncogenes in ICC tumor cells differs depending on the tumor localization, resulting in changes in epithelial-mesenchymal transition, which may explain the different outcomes of patients with peripheral and perihilar ICC.

Identifying cancer driver genes from functional genomics screens.

With the emerging advances made in genomics and functional genomics approaches, there is a critical and growing unmet need to integrate plural datasets in order to identify driver genes in cancer. An integrative approach, with the convergence of multiple types of genetic evidence, can limit false positives through a posterior filtering strategy and reduce the need for multiple hypothesis testing to identify true cancer vulnerabilities. We performed a pooled shRNA screen against 906 human genes in the oral cancer cell line AW13516 in triplicate. The genes that were depleted in the screen were integrated with copy number alteration and gene expression data and ranked based on ROAST analysis, using an integrative scoring system, DepRanker, to compute a Rank Impact Score (RIS) for each gene. The RIS-based ranking of candidate driver genes was used to identify the putative oncogenes AURKB and TK1 as essential for oral cancer cell proliferation. We validated the findings, showing that shRNA mediated genetic knockdown of TK1 or pharmacological inhibition of AURKB by AZD-1152 HQPA in AW13516 cells could significantly impede their proliferation. Next we analysed alterations in AURKB and TK1 genes in head and neck cancer and their association with prognosis using data on 528 patients obtained from TCGA. Patients harbouring alterations in AURKB and TK1 genes were associated with poor survival. To summarise, we present DepRanker as a simple yet robust package with no third-party dependencies for the identification of potential driver genes from a pooled shRNA functional genomic screen by integrating results from RNAi screens with gene expression and copy number data. Using DepRanker, we identify AURKB and TK1 as potential therapeutic targets in oral cancer. DepRanker is in the public domain and available for download at http://www.actrec.gov.in/pi-webpages/AmitDutt/DepRanker/DepRanker.html.

A novel network control model for identifying personalized driver genes in cancer.

Although existing computational models have identified many common driver genes, it remains challenging to identify the personalized driver genes by using samples of an individual patient. Recently, the methods of exploiting the structure-based control principles of complex networks provide new clues for identifying minimum number of driver nodes to drive the state transition of large-scale complex networks from an initial state to the desired state. However, the structure-based network control methods cannot be directly applied to identify the personalized driver genes due to the unknown network dynamics of the personalized system. Here we proposed the personalized network control model (PNC) to identify the personalized driver genes by employing the structure-based network control principle on genetic data of individual patients. In PNC model, we firstly presented a paired single sample network construction method to construct the personalized state transition network for capturing the phenotype transitions between healthy and disease states. Then, we designed a novel structure-based network control method from the Feedback Vertex Sets-based control perspective to identify the personalized driver genes. The wide experimental results on 13 cancer datasets from The Cancer Genome Atlas firstly showed that PNC model outperforms current state-of-the-art methods, in terms of F-measures for identifying cancer driver genes enriched in the gold-standard cancer driver gene lists. Furthermore, these results showed that personalized driver genes can be explored by their network characteristics even when they are hidden factors in transcription and mutation profiles. Our PNC gives novel insights and useful tools into understanding the tumor heterogeneity in cancer. The PNC package and data resources used in this work can be freely downloaded from https://github.com/NWPU-903PR/PNC.

Adaptively Weighted and Robust Mathematical Programming for the Discovery of Driver Gene Sets in Cancers.

High coverage and mutual exclusivity (HCME), which are considered two combinatorial properties of mutations in a collection of driver genes in cancers, have been used to develop mathematical programming models for distinguishing cancer driver gene sets. In this paper, we summarize a weak HCME pattern to justify the description of practical mutation datasets. We then present AWRMP, a method for identifying driver gene sets through the adaptive assignment of appropriate weights to gene candidates to tune the balance between coverage and mutual exclusivity. It embeds the genetic algorithm into the subsampling strategy to provide the optimization results robust against the uncertainty and noise in the data. Using biological datasets, we show that AWRMP can identify driver gene sets that satisfy the weak HCME pattern and outperform the state-of-arts methods in terms of robustness.

Integrative analysis identifies lincRNAs up- and downstream of neuroblastoma driver genes.

Long intergenic non-coding RNAs (lincRNAs) are emerging as integral components of signaling pathways in various cancer types. In neuroblastoma, only a handful of lincRNAs are known as upstream regulators or downstream effectors of oncogenes. Here, we exploit RNA sequencing data of primary neuroblastoma tumors, neuroblast precursor cells, neuroblastoma cell lines and various cellular perturbation model systems to define the neuroblastoma lincRNome and map lincRNAs up- and downstream of neuroblastoma driver genes MYCN, ALK and PHOX2B. Each of these driver genes controls the expression of a particular subset of lincRNAs, several of which are associated with poor survival and are differentially expressed in neuroblastoma tumors compared to neuroblasts. By integrating RNA sequencing data from both primary tumor tissue and cancer cell lines, we demonstrate that several of these lincRNAs are expressed in stromal cells. Deconvolution of primary tumor gene expression data revealed a strong association between stromal cell composition and driver gene status, resulting in differential expression of these lincRNAs. We also explored lincRNAs that putatively act upstream of neuroblastoma driver genes, either as presumed modulators of driver gene activity, or as modulators of effectors regulating driver gene expression. This analysis revealed strong associations between the neuroblastoma lincRNAs MIAT and MEG3 and MYCN and PHOX2B activity or expression. Together, our results provide a comprehensive catalogue of the neuroblastoma lincRNome, highlighting lincRNAs up- and downstream of key neuroblastoma driver genes. This catalogue forms a solid basis for further functional validation of candidate neuroblastoma lincRNAs.

Mutation burden and other molecular markers of prognosis in colorectal cancer treated with curative intent: results from the QUASAR 2 clinical trial and an Australian community-based series.

BACKGROUND: Molecular indicators of colorectal cancer prognosis have been assessed in several studies, but most analyses have been restricted to a handful of markers. We aimed to identify prognostic biomarkers for colorectal cancer by sequencing panels of multiple driver genes. METHODS: In stage II or III colorectal cancers from the QUASAR 2 open-label randomised phase 3 clinical trial and an Australian community-based series, we used targeted next-generation sequencing of 82 and 113 genes, respectively, including the main colorectal cancer drivers. We investigated molecular pathways of tumorigenesis, and analysed individual driver gene mutations, combinations of mutations, or global measures such as microsatellite instability (MSI) and mutation burden (total number of non-synonymous mutations and coding indels) for associations with relapse-free survival in univariable and multivariable models, principally Cox proportional hazards models. FINDINGS: In QUASAR 2 (511 tumours), TP53, KRAS, BRAF, and GNAS mutations were independently associated with shorter relapse-free survival (p<0·035 in all cases), and total somatic mutation burden with longer survival (hazard ratio [HR] 0·81 [95% CI 0·68-0·96]; p=0·014). MSI was not independently associated with survival (HR 1·12 [95% CI 0·57-2·19]; p=0·75). We successfully validated these associations in the Australian sample set (296 tumours). In a combined analysis of both the QUASAR 2 and the Australian sample sets, mutation burden was also associated with longer survival (HR 0·84 [95% CI 0·74-0·94]; p=0·004) after exclusion of MSI-positive and POLE mutant tumours. In an extended analysis of 1732 QUASAR 2 and Australian colorectal cancers for which KRAS, BRAF, and MSI status were available, KRAS and BRAF mutations were specifically associated with poor prognosis in MSI-negative cancers. MSI-positive cancers with KRAS or BRAF mutations had better prognosis than MSI-negative cancers that were wild-type for KRAS or BRAF. Mutations in the genes NF1 and NRAS from the MAPK pathway co-occurred, and mutations in the DNA damage-response genes TP53 and ATM were mutually exclusive. We compared a prognostic model based on the gold standard of clinicopathological variables and MSI with our new model incorporating clinicopathological variables, mutation burden, and driver mutations in KRAS, BRAF, and TP53. In both QUASAR 2 and the Australian cohort, our new model was significantly better (p=0·00004 and p=0·0057, respectively, based on a likelihood ratio test). INTERPRETATION: Multigene panels identified two previously unreported prognostic associations in colorectal cancer involving TP53 mutation and total mutation burden, and confirmed associations with KRAS and BRAF. Even a modest-sized gene panel can provide important information for use in clinical practice and outperform MSI-based prognostic models. FUNDING: UK Technology Strategy Board, National Institute for Health Research Oxford Biomedical Research Centre, Cancer Australia Project, Cancer Council Victoria, Ludwig Institute for Cancer Research, Victorian Government.

Treatment of lung adenocarcinoma by molecular-targeted therapy and immunotherapy.

Lung adenocarcinoma (LADC) is a cancer treatable using targeted therapies against driver gene aberrations. EGFR mutations and ALK fusions are frequent gene aberrations in LADC, and personalized therapies against those aberrations have become a standard therapy. These targeted therapies have shown significant positive efficacy and tolerable toxicity compared to conventional chemotherapy, so it is necessary to identify additional druggable genetic aberrations. Other than EGFR mutations and ALK fusions, mutations in KRAS, HER2, and BRAF, and driver fusions involving RET and ROS1, have also been identified in LADC. Interestingly, the frequency of driver gene aberrations differs according to ethnicity, sex, and smoking, which leads to differences in treatment efficacy. To date, several molecular-targeted drugs against driver genes have been developed, and several clinical trials have been conducted to evaluate the efficacy. However, targeted therapies against driver-gene-negative cases have not yet been well developed. Efforts to identify a new druggable target for such cases are currently underway. Furthermore, immune checkpoint blockade therapy might be effective for driver-negative cases, especially those with accumulated mutations.

Identification of Driver Genes and Key Pathways of Pediatric Brain Tumors and Comparison of Molecular Pathogenesis Based on Pathologic Types.

OBJECTIVE: This study is to identify pediatric brain tumors (PBT) driver genes and key pathways to detect the expression of the driver genes and also to clarify the relationship between patients' prognosis and expression of driver genes. METHODS: The gene expression profile of GSE50161 was analyzed to identify the differentially expressed genes (DEGs) between tumor tissue and the normal tissue. Gene ontology, Kyoto Encyclopedia of Genes and Genomes analysis, and protein-protein interaction network analysis were conducted to identify the enrichment functions, pathways, and hub genes. After hub genes were identified, quantitative reverse transcription polymerase chain reaction was used to confirm the differential expression of these hub genes. Survival data of 325 patients' were analyzed to clarify the relationship between prognosis and expression levels of the mutual hub genes. RESULTS: Gene ontology and Kyoto Encyclopedia of Genes and Genomes analysis showed that there were 13 common functions and 3 common pathways which were upregulated or downregulated among the 4 groups. Mutual hub genes were somatostatin (SST), glutamate decarboxylase 2 (GAD2), and single copy human parvalbumin gene (PVALB). The expression of SST, GAD2, and PVALB in glioma cells significantly decreased compared with normal glial cells (P < 0.05). In addition, survival analysis showed a favorable progression-free and overall survival in patients with glioma with SST, GAD2, and PVALB high expression (P < 0.05). CONCLUSIONS: SST, GAD2, and PVALB significantly decrease in glioma cells compared with normal glial cells. Survival analysis suggests that patients with high-expressed SST, GAD2, and PVALB have a longer overall and progression-free survival. The differential expressed genes identified in this study provide novel targets for diagnosis and treatment.

X chromosome drive in a widespread Palearctic woodland fly, Drosophila testacea.

Selfish genes that bias their own transmission during meiosis can spread rapidly in populations, even if they contribute negatively to the fitness of their host. Driving X chromosomes provide a clear example of this type of selfish propagation. These chromosomes have important evolutionary and ecological consequences, and can be found in a broad range of taxa including plants, mammals and insects. Here, we report a new case of X chromosome drive (X drive) in a widespread woodland fly, Drosophila testacea. We show that males carrying the driving X (SR males) sire 80-100% female offspring and possess a diagnostic X chromosome haplotype that is perfectly associated with the sex ratio distortion phenotype. We find that the majority of sons produced by SR males are sterile and appear to lack a Y chromosome, suggesting that meiotic defects involving the Y chromosome may underlie X drive in this species. Abnormalities in sperm cysts of SR males reflect that some spermatids are failing to develop properly, confirming that drive is acting during gametogenesis. By screening wild-caught flies using progeny sex ratios and a diagnostic marker, we demonstrate that the driving X is present in wild populations at a frequency of ~ 10% and that suppressors of drive are segregating in the same population. The testacea species group appears to be a hot spot for X drive, and D. testacea is a promising model to compare driving X chromosomes in closely related species, some of which may even be younger than the chromosomes themselves.