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Motivation Fusion genes are both useful cancer biomarkers and important drug targets. Finding relevant fusion genes is challenging due to genomic instability resulting in a high number of passenger events. To reveal and prioritize relevant gene fusion events we have developed FUsionN Gene Identification toolset (FUNGI) that uses an ensemble of fusion detection algorithms with prioritization and visualization modules. Results We applied FUNGI to an ovarian cancer dataset of 107 tumor samples from 36 patients. Ten out of 11 detected and prioritized fusion genes were validated. Many of detected fusion genes affect the PI3K-AKT pathway with potential role in treatment resistance. Availabilityand implementation FUNGI and its documentation are available at https://bitbucket.org/alejandra_cervera/fungi as standalone or from Anduril at https://www.anduril.org. Supplementary information Supplementary data are available at Bioinformatics online.

Purpose: Gastrointestinal stromal tumor (GIST) is a common type of soft-tissue sarcoma. Imatinib, an inhibitor of KIT, platelet-derived growth factor receptor alpha (PDGFRA), and a few other tyrosine kinases, is highly effective for GIST, but advanced GISTs frequently progress on imatinib and other approved tyrosine kinase inhibitors. We investigated phosphodiesterase 3 (PDE3) as a potential therapeutic target in GIST cell lines and xenograft models. Experimental Design: The GIST gene expression profile was interrogated in the MediSapiens IST Online transcriptome database comprising human tissue and cancer samples, and PDE3A and PDE3B expression was studied using IHC on tissue microarrays (TMA) consisting of 630 formalin-fixed human tissue samples. GIST cell lines were screened for sensitivity to 217 anticancer compounds, and the efficacy of PDE inhibitors on GIST was further studied in GIST cell lines and patient-derived mouse xenograft models. Results: GISTs expressed PDE3A and PDE3B frequently compared with other human normal or cancerous tissues both in the in silico database and the TMAs. Anagrelide was identified as the most potent of the PDE3 modulators evaluated. It reduced cell viability, promoted cell death, and influenced cell signaling in GIST cell lines. Anagrelide inhibited tumor growth in GIST xenograft mouse models. Anagrelide was also effective in a GIST xenograft mouse model with KIT exon 9 mutation that may pose a therapeutic challenge, as these GISTs require a high daily dose of imatinib. Conclusions: PDE3A and PDE3B are frequently expressed in GIST. Anagrelide had anticancer efficacy in GIST xenograft models and warrants further testing in clinical trials.

Cancer is the most complex genetic disease known, with mutations implicated in more than 250 genes. However, it is still elusive which specific mutations found in human patients lead to tumorigenesis. Here we show that a combination of oncogenes that is characteristic of liver cancer (CTNNB1, TERT, MYC) induces senescence in human fibroblasts and primary hepatocytes. However, reprogramming fibroblasts to a liver progenitor fate, induced hepatocytes (iHeps), makes them sensitive to transformation by the same oncogenes. The transformed iHeps are highly proliferative, tumorigenic in nude mice, and bear gene expression signatures of liver cancer. These results show that tumorigenesis is triggered by a combination of three elements: the set of driver mutations, the cellular lineage, and the state of differentiation of the cells along the lineage. Our results provide direct support for the role of cell identity as a key determinant in transformation and establish a paradigm for studying the dynamic role of oncogenic drivers in human tumorigenesis.

Adult-type granulosa cell tumors (AGCTs) are sex-cord derived neoplasms with a propensity for late relapse. Hormonal modulators have been used empirically in the treatment of recurrent AGCT, albeit with limited success. To provide a more rigorous foundation for hormonal therapy in AGCT, we used a multimodal approach to characterize the expressions of key hormone biomarkers in 175 tumor specimens and 51 serum samples using RNA sequencing, immunohistochemistry, RNA in situ hybridization, quantitative PCR, and circulating biomarker analysis, and correlated these results with clinical data. We show that FSH receptor and estrogen receptor beta (ERβ) are highly expressed in the majority of AGCTs, whereas the expressions of estrogen receptor alpha (ERα) and G-protein coupled estrogen receptor 1 are less prominent. ERβ protein expression is further increased in recurrent tumors. Aromatase expression levels show high variability between tumors. None of the markers examined served as prognostic biomarkers for progression-free or overall survival. In functional experiments, we assessed the effects of FSH, estradiol (E2), and the aromatase inhibitor letrozole on AGCT cell viability using 2 in vitro models: KGN cells and primary cultures of AGCT cells. FSH increased cell viability in a subset of primary AGCT cells, whereas E2 had no effect on cell viability at physiological concentrations. Letrozole suppressed E2 production in AGCTs; however, it did not impact cell viability. We did not find preclinical evidence to support the clinical use of aromatase inhibitors in AGCT treatment, and thus randomized, prospective clinical studies are needed to clarify the role of hormonal treatments in AGCTs.

Diffuse large B-cell lymphoma (DLBCL) is a biologically and clinically heterogeneous disease whose personalized clinical management requires robust molecular stratification. Here, we show that somatic hypermutation (SHM) patterns constitute a marker for DLBCL molecular classification. The activity of SHM mutational processes delineated the cell of origin (COO) in DLBCL. Expression of the herein identified 36 SHM target genes stratified DLBCL into four novel SHM subtypes. In a meta-analysis of patients with DLBCL treated with immunochemotherapy, the SHM subtypes were significantly associated with overall survival (1642 patients) and progression-free survival (795 patients). Multivariate analysis of survival indicated that the prognostic impact of the SHM subtypes is independent from the COO classification and the International Prognostic Index. Furthermore, the SHM subtypes had a distinct clinical outcome within each of the COO subtypes, and strikingly, even within unclassified DLBCL. The genetic landscape of the four SHM subtypes indicated unique associations with driver alterations and oncogenic signaling in DLBCL, which suggests a possibility for therapeutic exploitation. These findings provide a biologically driven classification system in DLBCL with potential clinical applications.

Cancer is the most complex genetic disease known, with mutations implicated in more than 250 genes. However, it is still elusive which specific mutations found in human patients lead to tumorigenesis. Here we show that a combination of oncogenes that is characteristic of liver cancer (CTNNB1, TERT, MYC) induces senescence in human fibroblasts and primary hepatocytes. However, reprogramming fibroblasts to a liver progenitor fate, induced hepatocytes (iHeps), makes them sensitive to transformation by the same oncogenes. The transformed iHeps are highly proliferative, tumorigenic in nude mice, and bear gene expression signatures of liver cancer. These results show that tumorigenesis is triggered by a combination of three elements: the set of driver mutations, the cellular lineage, and the state of differentiation of the cells along the lineage. Our results provide direct support for the role of cell identity as a key determinant in transformation, and establish a paradigm for studying the dynamic role of oncogenic drivers in human tumorigenesis.

Despite better therapeutic options and improved survival of diffuse large B-cell lymphoma (DLBCL), 30–40% of the patients experience relapse or have primary refractory disease with a dismal prognosis. To identify biological correlates for treatment resistance, we profiled microRNAs (miRNAs) of matched primary and relapsed DLBCL by next-generation sequencing. Altogether 492 miRNAs were expressed in the DLBCL samples. Thirteen miRNAs showed significant differential expression between primary and relapse specimen pairs. Integration of the differentially expressed miRNAs with matched mRNA expression profiles identified highly anti-correlated, putative targets, which were significantly enriched in cancer-associated pathways, including phosphatidylinositol (PI)), mitogen-activated protein kinase (MAPK), and B-cell receptor (BCR) signaling. Expression data suggested activation of these pathways during disease progression, and functional analyses validated that miR-370-3p, miR-381-3p, and miR-409-3p downregulate genes on the PI, MAPK, and BCR signaling pathways, and enhance chemosensitivity of DLBCL cells in vitro. High expression of selected target genes, that is, PIP5K1 and IMPA1, was found to be associated with poor survival in two independent cohorts of chemoimmunotherapy-treated patients (n = 92 and n = 233). Taken together, our results demonstrate that differentially expressed miRNAs contribute to disease progression by regulating key cell survival pathways and by mediating chemosensitivity, thus representing potential novel therapeutic targets.

Effect of alternative splicing (AS) on diffuse large B-cell lymphoma (DLBCL) pathogenesis and survival has not been systematically addressed. Here, we compared differentially expressed genes and exons in association with survival after chemoimmunotherapy, and between germinal center B-cell like (GCB) and activated B-cell like (ABC) DLBCLs. Genome-wide exon array-based screen was performed from samples of 38 clinically high-risk patients who were treated in a Nordic phase II study with dose-dense chemoimmunotherapy and central nervous system prophylaxis. The exon expression profile separated the patients according to molecular subgroups and survival better than the gene expression profile. Pathway analyses revealed enrichment of AS genes in inflammation and adhesion-related processes, and in signal transduction, such as phosphatidylinositol signaling system and adenosine triphosphate binding cassette transporters. Altogether, 49% of AS-related exons were protein coding, and domain prediction showed 28% of such exons to include a functional domain, such as transmembrane helix domain or phosphorylation sites. Validation in an independent cohort of 92 DLBCL samples subjected to RNA-sequencing confirmed differential exon usage of selected genes and association of AS with molecular subtypes and survival. The results indicate that AS events are able to discriminate GCB and ABC DLBCLs and have prognostic impact in DLBCL.

Background Despite better therapeutic options and improved survival of diffuse large B-cell lymphoma (DLBCL), 30-40% of the patients still experience relapse with a dismal prognosis. To identify biological correlates for poor survival, we compared differentially expressed genes between high-risk DLBCL patients, who had relapsed or remained in remission after dose-dense chemoimmunotherapy. Design and methods We performed genome-wide exon array analysis (Affymetrix Human Exon 1.0 ST) with 38 de novo high-risk (aaIPI>1) DLBCL patients less than 65 years old. The patients were treated in a Nordic phase II protocol with six courses of R-CHOEP-14 followed by systemic central nervous system prophylaxis with one course of high dose methotrexate and high dose cytarabine. We quantified the expression data by MEAP (Multiple Exon Array Preprocessing) algorithm and identified differentially expressed genes between the patients, who relapsed (n=9; poor prognosis group) or remained in long-term remission (n=29; good prognosis group). RNA sequencing and oligonucleotide-based microarray data from 92 (Cancer Genome Characterization Initiative, CGCI) and 233 (Lymphoma/Leukemia Molecular Profiling Project, LLMPP) DLBCL patients were utilized for validation. In both validation cohorts, the patients were treated with chemoimmunotherapy. Results Gene expression profiling (GEP) identified 215 differentially expressed genes (DEGs) between the good and poor prognosis groups. Of these, 41% were upregulated and 59% suppressed in the patients with poor prognosis. Pathway analyses indicated that the DEGs were enriched in many immune response related processes, such as the antigen processing and presenting pathway. Of note was the observation that major histocompatibility complex class II transactivator (CIITA) and selected human leucocyte antigen- (HLA) genes were significantly suppressed in the poor prognosis group. A correlation between CIITA and HLA gene expression levels was also observed. In Cox univariate analysis with continuous variables, low CIITA expression predicted poor overall survival (OS; p = 0.035). Five-year OS rates were 89% and 50% for the patients with high and low CIITA mRNA levels, respectively (p = 0.007). The prognostic impact of CIITA expression on survival was confirmed in independent CGCI (OS; p = 0.020, PFS; p = 0.002) and LLMPP validation cohorts (OS; p = 0.002, PFS data not available). In the CGCI cohort, the association of CIITA expression with survival was independent of IPI. Consistent with previous data, suppression of certain HLA-genes was also associated with poor outcome. Conclusions The results demonstrate that low CIITA expression is a novel independent predictor of poor survival in patients with DLBCL. The loss of CIITA offers, through the loss of MHC class II expression, a mechanism of immune escape and treatment resistance. Disclosures Leppä: Amgen: Research Funding; CTI Life Sciences: Honoraria; Janssen: Research Funding; Takeda: Honoraria, Other: Travel expenses; Roche: Honoraria, Other: Travel Expenses, Research Funding; Mundipharma: Research Funding; Bayer: Honoraria, Research Funding.

Identification of responsive genes to an extra-cellular cue enables characterization of pathophysiologically crucial biological processes. Deep sequencing technologies provide a powerful means to identify responsive genes, which creates a need for computational methods able to analyze dynamic and multi-level deep sequencing data. To answer this need we introduce here a data-driven algorithm, SPINLONG, which is designed to search for genes that match the user-defined hypotheses or models. SPINLONG is applicable to various experimental setups measuring several molecular markers in parallel. To demonstrate the SPINLONG approach, we analyzed ChIP-seq data reporting PolII, estrogen receptor (), H3K4me3 and H2A.Z occupancy at five time points in the MCF-7 breast cancer cell line after estradiol stimulus. We obtained 777 early responsive genes and compared the biological functions of the genes having binding within 20 kb of the transcription start site (TSS) to genes without such binding site. Our results show that the non-genomic action of via the MAPK pathway, instead of direct binding, may be responsible for early cell responses to activation. Our results also indicate that the responsive genes triggered by the genomic pathway are transcribed faster than those without binding sites. The survival analysis of the 777 responsive genes with 150 primary breast cancer tumors and in two independent validation cohorts indicated the ATAD3B gene, which does not have binding site within 20 kb of its TSS, to be significantly associated with poor patient survival., Author Summary Cellular processes in mammalian cells are tightly regulated to ensure that the cells function properly as a part of an organism. Dysregulation of some of these processes, such as apoptosis, cell proliferation and growth, can lead to cancer. One of the most important regulation mechanisms for cellular processes is via activation of membrane receptors by extra-cellular stimulus. Such cues trigger signal cascades that lead to altered expression of a number of genes in the cell nucleus; a key challenge in biomedicine is to identify which genes respond to a specific stimulus. These so called response genes can be investigated on a whole-genome scale with genomic sequencing, which is a technology that can quantify protein binding to DNA or gene activation. Analysis of such whole-genome data, however, is challenging due to billions of data points measured in the experiments. Here we introduce a novel computational method, SPINLONG, which is a widely applicable novel computational method that integrates multiple levels of deep sequencing data to produce experimentally testable hypotheses. We applied SPINLONG to breast cancer data and found early responsive genes for estrogen receptor and analyzed their regulation. These analyses resulted in a gene whose high activity is associated with decreased breast cancer patient survival.

Background Alternative splicing (AS) increases dramatically the diversity of the proteome by allowing a single gene to generate multiple transcripts and functionally diverse protein isoforms. Although previous studies have provided evidence that AS is often deregulated in cancers, the role of AS events in diffuse large B-cell lymphoma (DLBCL) remains largely unexplored. Here, we design and apply a novel method to analyze the global prognostically significant mRNA and AS variation by comparing differentially expressed exons and splicing variants between diffuse large B-cell lymphoma (DLBCL) patients, who have relapsed (poor prognosis group) or remained in remission (good prognosis group) after chemoimmunotherapy. Design and methods We performed genome-wide exon array analysis on RNAs isolated from 38 tumor tissues from young (1) disease. The patients were treated in a Nordic phase II protocol with six courses of R-CHOEP-14 followed by systemic central nervous system prophylaxis with one course of high dose methotrexate and high dose cytarabine. At the time of the analysis, median follow up was 50 months, predicted 5-year progression free survival (PFS) 74% and overall survival (OS) 75%. To identify genes with differential expression or with differential splicing between patients with poor or good prognosis, a novel algorithm based on a splicing index was developed. Results With a criteria of p Conclusions AS index is a more potential discriminator than conventional gene profiling between good and bad prognosis patients in DLBCL, and may provide a new class of cancer biomarker candidates. Disclosures: No relevant conflicts of interest to declare.