Epigenetic Activation of WNT5A Drives Glioblastoma Stem Cell Differentiation and Invasive Growth.

Glioblastoma stem cells (GSCs) are implicated in tumor neovascularization, invasiveness, and therapeutic resistance. To illuminate mechanisms governing these hallmark features, we developed a de novo glioblastoma multiforme (GBM) model derived from immortalized human neural stem/progenitor cells (hNSCs) to enable precise system-level comparisons of pre-malignant and oncogene-induced malignant states of NSCs. Integrated transcriptomic and epigenomic analyses uncovered a PAX6/DLX5 transcriptional program driving WNT5A-mediated GSC differentiation into endothelial-like cells (GdECs). GdECs recruit existing endothelial cells to promote peritumoral satellite lesions, which serve as a niche supporting the growth of invasive glioma cells away from the primary tumor. Clinical data reveal higher WNT5A and GdECs expression in peritumoral and recurrent GBMs relative to matched intratumoral and primary GBMs, respectively, supporting WNT5A-mediated GSC differentiation and invasive growth in disease recurrence. Thus, the PAX6/DLX5-WNT5A axis governs the diffuse spread of glioma cells throughout the brain parenchyma, contributing to the lethality of GBM.

Profiles of brain metastases: Prioritization of therapeutic targets.

We sought to compare the tumor profiles of brain metastases from common cancers with those of primary tumors and extracranial metastases in order to identify potential targets and prioritize rational treatment strategies. Tumor samples were collected from both the primary and metastatic sites of nonsmall cell lung cancer, breast cancer and melanoma from patients in locations worldwide, and these were submitted to Caris Life Sciences for tumor multiplatform analysis, including gene sequencing (Sanger and next-generation sequencing with a targeted 47-gene panel), protein expression (assayed by immunohistochemistry) and gene amplification (assayed by in situ hybridization). The data analysis considered differential protein expression, gene amplification and mutations among brain metastases, extracranial metastases and primary tumors. The analyzed population included: 16,999 unmatched primary tumor and/or metastasis samples: 8,178 nonsmall cell lung cancers (5,098 primaries; 2,787 systemic metastases; 293 brain metastases), 7,064 breast cancers (3,496 primaries; 3,469 systemic metastases; 99 brain metastases) and 1,757 melanomas (660 primaries; 996 systemic metastases; 101 brain metastases). TOP2A expression was increased in brain metastases from all 3 cancers, and brain metastases overexpressed multiple proteins clustering around functions critical to DNA synthesis and repair and implicated in chemotherapy resistance, including RRM1, TS, ERCC1 and TOPO1. cMET was overexpressed in melanoma brain metastases relative to primary skin specimens. Brain metastasis patients may particularly benefit from therapeutic targeting of enzymes associated with DNA synthesis, replication and/or repair.

Whole-exome sequencing combined with functional genomics reveals novel candidate driver cancer genes in endometrial cancer.

Endometrial cancer is the most common gynecological malignancy, with more than 280,000 cases occurring annually worldwide. Although previous studies have identified important common somatic mutations in endometrial cancer, they have primarily focused on a small set of known cancer genes and have thus provided a limited view of the molecular basis underlying this disease. Here we have developed an integrated systems-biology approach to identifying novel cancer genes contributing to endometrial tumorigenesis. We first performed whole-exome sequencing on 13 endometrial cancers and matched normal samples, systematically identifying somatic alterations with high precision and sensitivity. We then combined bioinformatics prioritization with high-throughput screening (including both shRNA-mediated knockdown and expression of wild-type and mutant constructs) in a highly sensitive cell viability assay. Our results revealed 12 potential driver cancer genes including 10 tumor-suppressor candidates (ARID1A, INHBA, KMO, TTLL5, GRM8, IGFBP3, AKTIP, PHKA2, TRPS1, and WNT11) and two oncogene candidates (ERBB3 and RPS6KC1). The results in the "sensor" cell line were recapitulated by siRNA-mediated knockdown in endometrial cancer cell lines. Focusing on ARID1A, we integrated mutation profiles with functional proteomics in 222 endometrial cancer samples, demonstrating that ARID1A mutations frequently co-occur with mutations in the phosphatidylinositol 3-kinase (PI3K) pathway and are associated with PI3K pathway activation. siRNA knockdown in endometrial cancer cell lines increased AKT phosphorylation supporting ARID1A as a novel regulator of PI3K pathway activity. Our study presents the first unbiased view of somatic coding mutations in endometrial cancer and provides functional evidence for diverse driver genes and mutations in this disease.

A Need for More Molecular Profiling in Brain Metastases.

As local disease control improves, the public health impact of brain metastases (BrM) continues to grow. Molecular features are frequently different between primary and metastatic tumors as a result of clonal evolution during neoplasm migration, selective pressures imposed by systemic treatments, and differences in the local microenvironment. However, biomarker information in BrM is not routinely obtained despite emerging evidence of its clinical value. We review evidence of discordance in clinically actionable biomarkers between primary tumors, extracranial metastases, and BrM. Although BrM biopsy/resection imposes clinical risks, these risks must be weighed against the potential benefits of assessing biomarkers in BrM. First, new treatment targets unique to a patient's BrM may be identified. Second, as BrM may occur late in a patient's disease course, resistance to initial targeted therapies and/or loss of previously identified biomarkers can occur by the time of occult BrM, rendering initial and other targeted therapies ineffective. Thus, current biomarker data can inform real-time treatment options. Third, biomarker information in BrM may provide useful prognostic information for patients. Appreciating the importance of biomarker analyses in BrM tissue, including how it may identify specific drivers of BrM, is critical for the development of more effective treatment strategies to improve outcomes for this growing patient population.

Qki deficiency maintains stemness of glioma stem cells in suboptimal environment by downregulating endolysosomal degradation.

Stem cells, including cancer stem cells (CSCs), require niches to maintain stemness, yet it is unclear how CSCs maintain stemness in the suboptimal environment outside their niches during invasion. Postnatal co-deletion of Pten and Trp53 in mouse neural stem cells (NSCs) leads to the expansion of these cells in their subventricular zone (SVZ) niches but fails to maintain stemness outside the SVZ. We discovered that Qki is a major regulator of NSC stemness. Qk deletion on a Pten-/-; Trp53-/- background helps NSCs maintain their stemness outside the SVZ in Nes-CreERT2; QkL/L; PtenL/L; Trp53L/L mice, which develop glioblastoma with a penetrance of 92% and a median survival time of 105 d. Mechanistically, Qk deletion decreases endolysosome-mediated degradation and enriches receptors essential for maintaining self-renewal on the cytoplasmic membrane to cope with low ligand levels outside niches. Thus, downregulation of endolysosome levels by Qki loss helps glioma stem cells (GSCs) maintain their stemness in suboptimal environments outside their niches.

Identification of a multi-cancer gene expression biomarker for cancer clinical outcomes using a network-based algorithm.

Cancer types are commonly classified by histopathology and more recently through molecular characteristics such as gene expression, mutations, copy number variations, and epigenetic alterations. These molecular characterizations have led to the proposal of prognostic biomarkers for many cancer types. Nevertheless, most of these biomarkers have been proposed for a specific cancer type or even specific subtypes. Although more challenging, it is useful to identify biomarkers that can be applied for multiple types of cancer. Here, we have used a network-based exploration approach to identify a multi-cancer gene expression biomarker highly connected by ESR1, PRKACA, LRP1, JUN and SMAD2 that can be predictive of clinical outcome in 12 types of cancer from The Cancer Genome Atlas (TCGA) repository. The gene signature of this biomarker is highly supported by cancer literature, biological terms, and prognostic power in other cancer types. Additionally, the signature does not seem to be highly associated with specific mutations or copy number alterations. Comparisons with cancer-type specific and other multi-cancer biomarkers in TCGA and other datasets showed that the performance of the proposed multi-cancer biomarker is superior, making the proposed approach and multi-cancer biomarker potentially useful in research and clinical settings.

The Pan-Cancer analysis of pseudogene expression reveals biologically and clinically relevant tumour subtypes.

Although individual pseudogenes have been implicated in tumour biology, the biomedical significance and clinical relevance of pseudogene expression have not been assessed in a systematic way. Here we generate pseudogene expression profiles in 2,808 patient samples of seven cancer types from The Cancer Genome Atlas RNA-seq data using a newly developed computational pipeline. Supervised analysis reveals a significant number of pseudogenes differentially expressed among established tumour subtypes and pseudogene expression alone can accurately classify the major histological subtypes of endometrial cancer. Across cancer types, the tumour subtypes revealed by pseudogene expression show extensive and strong concordance with the subtypes defined by other molecular data. Strikingly, in kidney cancer, the pseudogene expression subtypes not only significantly correlate with patient survival, but also help stratify patients in combination with clinical variables. Our study highlights the potential of pseudogene expression analysis as a new paradigm for investigating cancer mechanisms and discovering prognostic biomarkers.

Molecular subtypes of glioblastoma are relevant to lower grade glioma.

BACKGROUND: Gliomas are the most common primary malignant brain tumors in adults with great heterogeneity in histopathology and clinical course. The intent was to evaluate the relevance of known glioblastoma (GBM) expression and methylation based subtypes to grade II and III gliomas (ie. lower grade gliomas). METHODS: Gene expression array, single nucleotide polymorphism (SNP) array and clinical data were obtained for 228 GBMs and 176 grade II/II gliomas (GII/III) from the publically available Rembrandt dataset. Two additional datasets with IDH1 mutation status were utilized as validation datasets (one publicly available dataset and one newly generated dataset from MD Anderson). Unsupervised clustering was performed and compared to gene expression subtypes assigned using the Verhaak et al 840-gene classifier. The glioma-CpG Island Methylator Phenotype (G-CIMP) was assigned using prediction models by Fine et al. RESULTS: Unsupervised clustering by gene expression aligned with the Verhaak 840-gene subtype group assignments. GII/IIIs were preferentially assigned to the proneural subtype with IDH1 mutation and G-CIMP. GBMs were evenly distributed among the four subtypes. Proneural, IDH1 mutant, G-CIMP GII/III s had significantly better survival than other molecular subtypes. Only 6% of GBMs were proneural and had either IDH1 mutation or G-CIMP but these tumors had significantly better survival than other GBMs. Copy number changes in chromosomes 1p and 19q were associated with GII/IIIs, while these changes in CDKN2A, PTEN and EGFR were more commonly associated with GBMs. CONCLUSIONS: GBM gene-expression and methylation based subtypes are relevant for GII/III s and associate with overall survival differences. A better understanding of the association between these subtypes and GII/IIIs could further knowledge regarding prognosis and mechanisms of glioma progression.

A pan-cancer proteomic perspective on The Cancer Genome Atlas.

Protein levels and function are poorly predicted by genomic and transcriptomic analysis of patient tumours. Therefore, direct study of the functional proteome has the potential to provide a wealth of information that complements and extends genomic, epigenomic and transcriptomic analysis in The Cancer Genome Atlas (TCGA) projects. Here we use reverse-phase protein arrays to analyse 3,467 patient samples from 11 TCGA 'Pan-Cancer' diseases, using 181 high-quality antibodies that target 128 total proteins and 53 post-translationally modified proteins. The resultant proteomic data are integrated with genomic and transcriptomic analyses of the same samples to identify commonalities, differences, emergent pathways and network biology within and across tumour lineages. In addition, tissue-specific signals are reduced computationally to enhance biomarker and target discovery spanning multiple tumour lineages. This integrative analysis, with an emphasis on pathways and potentially actionable proteins, provides a framework for determining the prognostic, predictive and therapeutic relevance of the functional proteome.