Dissecting super-enhancer driven transcriptional dependencies reveals novel therapeutic strategies and targets for group 3 subtype medulloblastoma.

BACKGROUND: Medulloblastoma is the most common malignant pediatric brain tumor and group 3 subtype medulloblastoma (G3-MB) exhibits the worst prognosis. Super enhancers (SEs) are large clusters of enhancers that play important roles in cancer through transcriptional control of cell identity genes, oncogenes and tumor-dependent genes. Dissecting SE-driven transcriptional dependencies of cancer leads to identification of novel oncogenic mechanisms, therapeutic strategies and targets. METHODS: Integrative SE analyses of primary tissues and patient-derived tumor cell lines of G3-MB were performed to extract the conserved SE-associated gene signatures and their oncogenic potentials were evaluated by gene expression, tumor-dependency and patient prognosis analyses. SE-associated subtype-specific upregulated tumor-dependent genes, which were revealed as members of SE-driven core transcriptional regulatory network of G3-MB, were then subjected to functional validation and mechanistic investigation. SE-associated therapeutic potential was further explored by genetic or pharmaceutical targeting of SE complex components or SE-associated subtype-specific upregulated tumor-dependent genes individually or in combination, and the underlying therapeutic mechanisms were also examined. RESULTS: The identified conserved SE-associated transcripts of G3-MB tissues and cell lines were enriched of subtype-specifically upregulated tumor-dependent genes and MB patients harboring enrichment of those transcripts exhibited worse prognosis. Fourteen such conserved SE-associated G3-MB-specific upregulated tumor-dependent genes were identified to be members of SE-driven core transcriptional regulatory network of G3-MB, including three well-recognized TFs (MYC, OTX2 and CRX) and eleven newly identified downstream effector genes (ARL4D, AUTS2, BMF, IGF2BP3, KIF21B, KLHL29, LRP8, MARS1, PSMB5, SDK2 and SSBP3). An OTX2-SE-ARL4D regulatory axis was further revealed to represent a subtype-specific tumor dependency and therapeutic target of G3-MB via contributing to maintaining cell cycle progression and inhibiting neural differentiation of tumor cells. Moreover, BET inhibition with CDK7 inhibition or proteasome inhibition, two combinatory strategies of targeting SE complex components (BRD4, CDK7) or SE-associated effector gene (PSMB5), were shown to exhibit synergistic therapeutic effects against G3-MB via stronger suppression of SE-associated transcription or higher induction of ER stress, respectively. CONCLUSIONS: Our study verifies the oncogenic role and therapeutic potential of SE-driven transcriptional dependencies of G3-MB, resulting in better understanding of its tumor biology and identification of novel SE-associated therapeutic strategies and targets.

Survival-based bioinformatics analysis to identify hub genes and key pathways in non-small cell lung cancer.

BACKGROUND: Lung cancer is one of the leading causes of cancer mortality worldwide. Here, we performed an integrative bioinformatics analysis to screen hub genes and critical pathways in non-small cell lung cancer (NSCLC) based on the overall survival rate of differentially expressed genes (DEGs). METHODS: Four datasets from the gene expression omnibus (GEO) were used to identify the DEGs. To obtain robust DEGs in NSCLC, only the DEGs that co-existed in the four datasets were selected for subsequent analysis. To identify the genes correlated with overall survival, the overall survival of these genes was then analyzed using the Kaplan-Meier plotter database. The genes significantly correlated with survival were used to perform gene ontology (GO) and Kyoto encyclopedia of genes and genomes (KEGG) analysis; next, these genes were used to construct a protein-protein interaction network. MCODE and CytoHubba were used to identify the clusters and hub genes. Finally, the hub genes were validated in the Cancer Genome Atlas (TCGA) and the Human Protein Atlas (HPA). RESULTS: We found 522 up-regulated DEGs, and 989 down-regulated DEGs between the NSCLC and normal lung tissue, and 895 of them were correlated with a higher overall survival. GO analysis showed that the DEGs that were associated with a higher overall survival were enriched in cell division, cell cycle, DNA replication, angiogenesis, and cell migration. KEGG analysis was consistent with GO analysis and showed that p53 signaling pathway, pyrimidine metabolism, cGMP-PKG signaling pathway and renin secretion pathway were associated with overall survival in NSCLC. In the protein-protein analysis, we identified seven clusters and six hub genes which were BUB1B, CCNB1, CENPE, KIF18A, NDC10, and MAD2L1. Of these genes, CENPE and KIF18A had not been reported until now. Finally, the dysregulated expression of the six hub genes was validated by the data from the TCGA and HPA. CONCLUSIONS: We identified the hub genes and potential mechanisms of NSCLC based on multiple-microarray analysis and overall survival; then, validated the hub genes in the TCGA and HPA database. These hub genes may serve as potential therapeutic targets.

The evolution of thymic lymphomas in p53 knockout mice.

Germline deletion of the p53 gene in mice gives rise to spontaneous thymic (T-cell) lymphomas. In this study, the p53 knockout mouse was employed as a model to study the mutational evolution of tumorigenesis. The clonality of the T-cell repertoire from p53 knockout and wild-type thymic cells was analyzed at various ages employing TCRß sequencing. These data demonstrate that p53 knockout thymic lymphomas arose in an oligoclonal fashion, with tumors evolving dominant clones over time. Exon sequencing of tumor DNA revealed that all of the independently derived oligoclonal mouse tumors had a deletion in the Pten gene prior to the formation of the TCRß rearrangement, produced early in development. This was followed in each independent clone of the thymic lymphoma by the amplification or overexpression of cyclin Ds and Cdk6. Alterations in the expression of Ikaros were common and blocked further development of CD-4/CD-8 T cells. While the frequency of point mutations in the genome of these lymphomas was one per megabase, there were a tremendous number of copy number variations producing the tumors' driver mutations. The initial inherited loss of p53 functions appeared to delineate an order of genetic alterations selected for during the evolution of these thymic lymphomas.

Whole-genome sequencing analysis of phenotypic heterogeneity and anticipation in Li-Fraumeni cancer predisposition syndrome.

The Li-Fraumeni syndrome (LFS) and its variant form (LFL) is a familial predisposition to multiple forms of childhood, adolescent, and adult cancers associated with germ-line mutation in the TP53 tumor suppressor gene. Individual disparities in tumor patterns are compounded by acceleration of cancer onset with successive generations. It has been suggested that this apparent anticipation pattern may result from germ-line genomic instability in TP53 mutation carriers, causing increased DNA copy-number variations (CNVs) with successive generations. To address the genetic basis of phenotypic disparities of LFS/LFL, we performed whole-genome sequencing (WGS) of 13 subjects from two generations of an LFS kindred. Neither de novo CNV nor significant difference in total CNV was detected in relation with successive generations or with age at cancer onset. These observations were consistent with an experimental mouse model system showing that trp53 deficiency in the germ line of father or mother did not increase CNV occurrence in the offspring. On the other hand, individual records on 1,771 TP53 mutation carriers from 294 pedigrees were compiled to assess genetic anticipation patterns (International Agency for Research on Cancer TP53 database). No strictly defined anticipation pattern was observed. Rather, in multigeneration families, cancer onset was delayed in older compared with recent generations. These observations support an alternative model for apparent anticipation in which rare variants from noncarrier parents may attenuate constitutive resistance to tumorigenesis in the offspring of TP53 mutation carriers with late cancer onset.

Inference of synergy/antagonism between anticancer drugs from the pooled analysis of clinical trials.

BACKGROUND: Drug interactions can have a significant impact on the response to combinatorial therapy for anticancer treatment. In some instances these interactions can be anticipated based on pre-clinical models. However, the anticipation of drug interactions in the clinical context is in general a challenging task. METHODS: Here we propose the pooled analysis of clinical trials as a mean to investigate drug interactions in anticancer therapy. To this end we collected 1,163 Phase II clinical trials with response data on over 53,745 subjects. RESULTS: We provide statistical definitions of drugs resulting in clinical synergy and antagonism and identify drug combinations in each group. We also quantify the possibility of inferring interactions between three or more drugs from parameters characterizing the action of single and two-drugs combinations. CONCLUSIONS: Our analysis provides a statistical methodology to track the performance of drug combinations in anticancer therapy and to quantify drug interactions in the clinical context.