A map of tumor-host interactions in glioma at single-cell resolution.

BACKGROUND: Single-cell RNA sequencing is the reference technique for characterizing the heterogeneity of the tumor microenvironment. The composition of the various cell types making up the microenvironment can significantly affect the way in which the immune system activates cancer rejection mechanisms. Understanding the cross-talk signals between immune cells and cancer cells is of fundamental importance for the identification of immuno-oncology therapeutic targets. RESULTS: We present a novel method, single-cell Tumor-Host Interaction tool (scTHI), to identify significantly activated ligand-receptor interactions across clusters of cells from single-cell RNA sequencing data. We apply our approach to uncover the ligand-receptor interactions in glioma using 6 publicly available human glioma datasets encompassing 57,060 gene expression profiles from 71 patients. By leveraging this large-scale collection we show that unexpected cross-talk partners are highly conserved across different datasets in the majority of the tumor samples. This suggests that shared cross-talk mechanisms exist in glioma. CONCLUSIONS: Our results provide a complete map of the active tumor-host interaction pairs in glioma that can be therapeutically exploited to reduce the immunosuppressive action of the microenvironment in brain tumor.

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.

The genomic landscape of schwannoma.

Schwannomas are common peripheral nerve sheath tumors that can cause debilitating morbidities. We performed an integrative analysis to determine genomic aberrations common to sporadic schwannomas. Exome sequence analysis with validation by targeted DNA sequencing of 125 samples uncovered, in addition to expected NF2 disruption, recurrent mutations in ARID1A, ARID1B and DDR1. RNA sequencing identified a recurrent in-frame SH3PXD2A-HTRA1 fusion in 12/125 (10%) cases, and genomic analysis demonstrated the mechanism as resulting from a balanced 19-Mb chromosomal inversion on chromosome 10q. The fusion was associated with male gender predominance, occurring in one out of every six men with schwannoma. Methylation profiling identified distinct molecular subgroups of schwannomas that were associated with anatomical location. Expression of the SH3PXD2A-HTRA1 fusion resulted in elevated phosphorylated ERK, increased proliferation, increased invasion and in vivo tumorigenesis. Targeting of the MEK-ERK pathway was effective in fusion-positive Schwann cells, suggesting a possible therapeutic approach for this subset of tumors.

Molecular Profiling Reveals Biologically Discrete Subsets and Pathways of Progression in Diffuse Glioma.

Therapy development for adult diffuse glioma is hindered by incomplete knowledge of somatic glioma driving alterations and suboptimal disease classification. We defined the complete set of genes associated with 1,122 diffuse grade II-III-IV gliomas from The Cancer Genome Atlas and used molecular profiles to improve disease classification, identify molecular correlations, and provide insights into the progression from low- to high-grade disease. Whole-genome sequencing data analysis determined that ATRX but not TERT promoter mutations are associated with increased telomere length. Recent advances in glioma classification based on IDH mutation and 1p/19q co-deletion status were recapitulated through analysis of DNA methylation profiles, which identified clinically relevant molecular subsets. A subtype of IDH mutant glioma was associated with DNA demethylation and poor outcome; a group of IDH-wild-type diffuse glioma showed molecular similarity to pilocytic astrocytoma and relatively favorable survival. Understanding of cohesive disease groups may aid improved clinical outcomes.

TCGAbiolinks: an R/Bioconductor package for integrative analysis of TCGA data.

The Cancer Genome Atlas (TCGA) research network has made public a large collection of clinical and molecular phenotypes of more than 10 000 tumor patients across 33 different tumor types. Using this cohort, TCGA has published over 20 marker papers detailing the genomic and epigenomic alterations associated with these tumor types. Although many important discoveries have been made by TCGA's research network, opportunities still exist to implement novel methods, thereby elucidating new biological pathways and diagnostic markers. However, mining the TCGA data presents several bioinformatics challenges, such as data retrieval and integration with clinical data and other molecular data types (e.g. RNA and DNA methylation). We developed an R/Bioconductor package called TCGAbiolinks to address these challenges and offer bioinformatics solutions by using a guided workflow to allow users to query, download and perform integrative analyses of TCGA data. We combined methods from computer science and statistics into the pipeline and incorporated methodologies developed in previous TCGA marker studies and in our own group. Using four different TCGA tumor types (Kidney, Brain, Breast and Colon) as examples, we provide case studies to illustrate examples of reproducibility, integrative analysis and utilization of different Bioconductor packages to advance and accelerate novel discoveries.