Veronique Frattini

The integrated landscape of driver genomic alterations in glioblastoma

Glioblastoma is one of the most challenging forms of cancer to treat. Here we describe a computational platform that integrates the analysis of copy number variations and somatic mutations and unravels the landscape of in-frame gene fusions in glioblastoma. We found mutations with loss of heterozygosity in LZTR1, encoding an adaptor of CUL3-containing E3 ligase complexes. Mutations and deletions disrupt LZTR1 function, which restrains the self renewal and growth of glioma spheres that retain stem cell features. Loss-of-function mutations in CTNND2 target a neural-specific gene and are associated with the transformation of glioma cells along the very aggressive mesenchymal phenotype. We also report recurrent translocations that fuse the coding sequence of EGFR to several partners, with EGFR-SEPT14 being the most frequent functional gene fusion in human glioblastoma. EGFR-SEPT14 fusions activate STAT3 signaling and confer mitogen independence and sensitivity to EGFR inhibition. These results provide insights into the pathogenesis of glioblastoma and highlight new targets for therapeutic intervention.

Clonal evolution of glioblastoma under therapy

Glioblastoma (GBM) is the most common and aggressive primary brain tumor. To better understand how GBM evolves, we analyzed longitudinal genomic and transcriptomic data from 114 patients. The analysis shows a highly branched evolutionary pattern in which 63% of patients experience expression-based subtype changes. The branching pattern, together with estimates of evolutionary rate, suggests that relapse-associated clones typically existed years before diagnosis. Fifteen percent of tumors present hypermutation at relapse in highly expressed genes, with a clear mutational signature. We find that 11% of recurrence tumors harbor mutations in LTBP4, which encodes a protein binding to TGF-β. Silencing LTBP4 in GBM cells leads to suppression of TGF-β activity and decreased cell proliferation. In recurrent GBM with wild-type IDH1, high LTBP4 expression is associated with worse prognosis, highlighting the TGF-β pathway as a potential therapeutic target in GBM.

Detection, Characterization, and Inhibition of FGFR–TACC Fusions in IDH Wild-type Glioma

Purpose: Oncogenic fusions consisting of fibroblast growth factor receptor (FGFR) and TACC are present in a subgroup of glioblastoma (GBM) and other human cancers and have been proposed as new therapeutic targets. We analyzed frequency and molecular features of FGFR–TACC fusions and explored the therapeutic efficacy of inhibiting FGFR kinase in GBM and grade II and III glioma. Experimental Design: Overall, 795 gliomas (584 GBM, 85 grades II and III with wild-type and 126 with IDH1/2 mutation) were screened for FGFR–TACC breakpoints and associated molecular profile. We also analyzed expression of the FGFR3 and TACC3 components of the fusions. The effects of the specific FGFR inhibitor JNJ-42756493 for FGFR3–TACC3–positive glioma were determined in preclinical experiments. Two patients with advanced FGFR3–TACC3–positive GBM received JNJ-42756493 and were assessed for therapeutic response. Results: Three of 85 IDH1/2 wild-type (3.5%) but none of 126 IDH1/2-mutant grade II and III gliomas harbored FGFR3–TACC3 fusions. FGFR–TACC rearrangements were present in 17 of 584 GBM (2.9%). FGFR3–TACC3 fusions were associated with strong and homogeneous FGFR3 immunostaining. They are mutually exclusive with IDH1/2 mutations and EGFR amplification, whereas they co-occur with CDK4 amplification. JNJ-42756493 inhibited growth of glioma cells harboring FGFR3–TACC3 in vitro and in vivo. The two patients with FGFR3–TACC3 rearrangements who received JNJ-42756493 manifested clinical improvement with stable disease and minor response, respectively. Conclusions: RT-PCR sequencing is a sensitive and specific method to identify FGFR–TACC–positive patients. FGFR3–TACC3 fusions are associated with uniform intratumor expression of the fusion protein. The clinical response observed in the FGFR3–TACC3–positive patients treated with an FGFR inhibitor supports clinical studies of FGFR inhibition in FGFR–TACC–positive patients. Clin Cancer Res; 21(14); 3307–17. ©2015 AACR. See related commentary by Ahluwalia and Rich, p. 3105

Pegasus: a comprehensive annotation and prediction tool for detection of driver gene fusions in cancer

BackgroundThe extraordinary success of imatinib in the treatment of BCR-ABL1 associated cancers underscores the need to identify novel functional gene fusions in cancer. RNA sequencing offers a genome-wide view of expressed transcripts, uncovering biologically functional gene fusions. Although several bioinformatics tools are already available for the detection of putative fusion transcripts, candidate event lists are plagued with non-functional read-through events, reverse transcriptase template switching events, incorrect mapping, and other systematic errors. Such lists lack any indication of oncogenic relevance, and they are too large for exhaustive experimental validation.ResultsWe have designed and implemented a pipeline, Pegasus, for the annotation and prediction of biologically functional gene fusion candidates. Pegasus provides a common interface for various gene fusion detection tools, reconstruction of novel fusion proteins, reading-frame-aware annotation of preserved/lost functional domains, and data-driven classification of oncogenic potential. Pegasus dramatically streamlines the search for oncogenic gene fusions, bridging the gap between raw RNA-Seq data and a final, tractable list of candidates for experimental validation.ConclusionWe show the effectiveness of Pegasus in predicting new driver fusions in 176 RNA-Seq samples of glioblastoma multiforme (GBM) and 23 cases of anaplastic large cell lymphoma (ALCL). Contact: fa2306@columbia.edu.

A metabolic function of FGFR3-TACC3 gene fusions in cancer

Chromosomal translocations that generate in-frame oncogenic gene fusions are notable examples of the success of targeted cancer therapies. We have previously described gene fusions of FGFR3-TACC3 (F3–T3) in 3% of human glioblastoma cases. Subsequent studies have reported similar frequencies of F3–T3 in many other cancers, indicating that F3–T3 is a commonly occuring fusion across all tumour types. F3–T3 fusions are potent oncogenes that confer sensitivity to FGFR inhibitors, but the downstream oncogenic signalling pathways remain unknown. Here we show that human tumours with F3–T3 fusions cluster within transcriptional subgroups that are characterized by the activation of mitochondrial functions. F3–T3 activates oxidative phosphorylation and mitochondrial biogenesis and induces sensitivity to inhibitors of oxidative metabolism. Phosphorylation of the phosphopeptide PIN4 is an intermediate step in the signalling pathway of the activation of mitochondrial metabolism. The F3–T3–PIN4 axis triggers the biogenesis of peroxisomes and the synthesis of new proteins. The anabolic response converges on the PGC1α coactivator through the production of intracellular reactive oxygen species, which enables mitochondrial respiration and tumour growth. These data illustrate the oncogenic circuit engaged by F3–T3 and show that F3–T3-positive tumours rely on mitochondrial respiration, highlighting this pathway as a therapeutic opportunity for the treatment of tumours with F3–T3 fusions. We also provide insights into the genetic alterations that initiate the chain of metabolic responses that drive mitochondrial metabolism in cancer.

RGBM: regularized gradient boosting machines for identification of the transcriptional regulators of discrete glioma subtypes

Abstract We propose a generic framework for gene regulatory network (GRN) inference approached as a feature selection problem. GRNs obtained using Machine Learning techniques are often dense, whereas real GRNs are rather sparse. We use a Tikonov regularization inspired optimal L-curve criterion that utilizes the edge weight distribution for a given target gene to determine the optimal set of TFs associated with it. Our proposed framework allows to incorporate a mechanistic active biding network based on cis-regulatory motif analysis. We evaluate our regularization framework in conjunction with two non-linear ML techniques, namely gradient boosting machines (GBM) and random-forests (GENIE), resulting in a regularized feature selection based method specifically called RGBM and RGENIE respectively. RGBM has been used to identify the main transcription factors that are causally involved as master regulators of the gene expression signature activated in the FGFR3-TACC3-positive glioblastoma. Here, we illustrate that RGBM identifies the main regulators of the molecular subtypes of brain tumors. Our analysis reveals the identity and corresponding biological activities of the master regulators characterizing the difference between G-CIMP-high and G-CIMP-low subtypes and between PA-like and LGm6-GBM, thus providing a clue to the yet undetermined nature of the transcriptional events among these subtypes.

Single-Cell Transcriptome Analysis of Lineage Diversity and Microenvironment in High-Grade Glioma

Despite extensive molecular characterization, we lack a comprehensive picture of lineage identity, differentiation, and microenvironmental composition in high-grade gliomas (HGGs). We sampled the cellular milieu of HGGs with massively-parallel single-cell RNA-Seq. While HGG cells can resemble glia or even immature neurons and form branched lineage structures, mesenchymal transformation results in unstructured populations. Glioma cells in a subset of mesenchymal tumors lose their neural lineage identity, express inflammatory genes, and co-exist with marked myeloid infiltration, implying a molecular interaction between glioma and immune cells. Finally, we found that myeloid cells are highly diverse in HGG with high expression of pro-inflammatory cytokines and microglial markers on one extreme to a macrophage-like phenotype on the other. However, enrichment of either gene signature is predictive of poor survival. Statement of Significance We used large-scale single-cell RNA-Seq to establish the extent of neural and non-neural lineage diversity in high-grade gliomas, discovery a tight coupling between proliferation and cell type, and identify disparate myeloid phenotypes that are predictive of poor survival.

Single-cell transcriptome analysis of lineage diversity in high-grade glioma

BackgroundDespite extensive molecular characterization, we lack a comprehensive understanding of lineage identity, differentiation, and proliferation in high-grade gliomas (HGGs).MethodsWe sampled the cellular milieu of HGGs by profiling dissociated human surgical specimens with a high-density microwell system for massively parallel single-cell RNA-Seq. We analyzed the resulting profiles to identify subpopulations of both HGG and microenvironmental cells and applied graph-based methods to infer structural features of the malignantly transformed populations.ResultsWhile HGG cells can resemble glia or even immature neurons and form branched lineage structures, mesenchymal transformation results in unstructured populations. Glioma cells in a subset of mesenchymal tumors lose their neural lineage identity, express inflammatory genes, and co-exist with marked myeloid infiltration, reminiscent of molecular interactions between glioma and immune cells established in animal models. Additionally, we discovered a tight coupling between lineage resemblance and proliferation among malignantly transformed cells. Glioma cells that resemble oligodendrocyte progenitors, which proliferate in the brain, are often found in the cell cycle. Conversely, glioma cells that resemble astrocytes, neuroblasts, and oligodendrocytes, which are non-proliferative in the brain, are generally non-cycling in tumors.ConclusionsThese studies reveal a relationship between cellular identity and proliferation in HGG and distinct population structures that reflects the extent of neural and non-neural lineage resemblance among malignantly transformed cells.

Abstract 3403: The neural stem cell marker GLAST, is involved in proliferation and invasion by glutamate release

The identification of antigens preferentially expressed in glioblastoma (GBM) and involved in its malignant phenotype is critical for for developing therapeutic strategies. We observed by DNA microarray analysis and confirmed by western blot and immunocytochemistry that the neural stem cells marker GLAST (Glutamate Aspartate Transporter), highly expressed in cells of the radial glia, is also expressed in the plasma membrane of murine and human GBM stem-like cells (GSC). GLAST is a membrane protein expressed by astrocytes with a relevant role in glutamate uptake. When we evaluated the uptake in different GSC lines we found that their glutamate uptake is up to 100 fold lower than in normal astrocytes. GSC are able to release glutamate in culture medium just as hypoxic astrocytes, showing that in pathological conditions such as ischemia, transporters can reverse uptake and release glutamate. We have used GLAST as a marker to isolate a GSC subpopulation by immunomagnetic sorting and test the capacity of GLAST-enriched cells for tumor formation in mice. Using the murine glioma model GL261, GLAST+ cells injected intracranially were significantly more tumorigenic than GLAST- or unsorted cells (p=0.00057 and p=0.00028, respectively). We confirmed these observations in nude mice using GSC from one human recurrent GBM. Histological analysis provided evidence of the invasive nature of GLAST+ GSC asin the contralateral hemisphere many tumor cellsexpress GLAST and the migration marker Double-Cortin. To evaluate the functional role of GLAST in GSC we decided to target GLAST expression using lentiviral particles expressing GLAST shRNA. GLAST inhibition impacts on cell proliferation and especially on Matrigel invasion in vitro and interferes with tumor progression, improving survival in injected nude mice (p = 0.0034 shGLAST vs Scrambled). As a consequence of GLAST interference we also found in vitro a significant reduction of glutamate release in silenced cells compared to control cells(non specific shRNA). We investigated the expression of GLAST in 60 human primary GBM and 15 low grade gliomas (LGG) by immunohistochemistry. We found marked expression of GLAST in the large majority of GBM, in contrast LGG were negative or showed nuclear expression of GLAST. When we correlated GLAST expression to overall survival of 51 GBM patients we found that high percentage of GLAST pos cells and moderate or strong reactivity correlate with decreased overall survival (p=0.02). Our data suggest that GLAST is expressed in GBM and may be involved in proliferation and invasion. In addition GLAST could be investigated as clinical marker in association with prognosis of GBM patients and for development of novel therapeutic strategies. Citation Format: {Authors}. {Abstract title} [abstract]. In: Proceedings of the 102nd Annual Meeting of the American Association for Cancer Research; 2011 Apr 2-6; Orlando, FL. Philadelphia (PA): AACR; Cancer Res 2011;71(8 Suppl):Abstract nr 3403. doi:10.1158/1538-7445.AM2011-3403