A brain metastasis liquid biopsy: Where are we now?

Brain metastases remain a challenging and feared complication for patients with cancer and research in this area has lagged behind research into metastases to other organs. Due to their location and the risks associated with neurosurgical biopsies, the biology underpinning brain metastases response to treatment and evolution over time remains poorly understood. Liquid biopsies are proposed to overcome many of the limitations present with tissue biopsies, providing a better representation of tumor heterogeneity, facilitating repeated sampling, and providing a noninvasive assessment of tumor biology. Several different liquid biopsy approaches have been investigated including circulating tumor cells, circulating tumor DNA, extracellular vesicles, and tumor-educated platelets; however, these have generally been less effective in assessing brain metastases compared to metastases to other organs requiring improved techniques to investigate these approaches, studies combining different liquid biopsy approaches and/or novel liquid biopsy approaches. Through this review, we highlight the current state of the art and define key unanswered questions related to brain metastases liquid biopsies.

Kinome-Wide Synthetic Lethal Screen Identifies PANK4 as a Modulator of Temozolomide Resistance in Glioblastoma.

Temozolomide (TMZ) represents the cornerstone of therapy for glioblastoma (GBM). However, acquisition of resistance limits its therapeutic potential. The human kinome is an undisputable source of druggable targets, still, current knowledge remains confined to a limited fraction of it, with a multitude of under-investigated proteins yet to be characterized. Here, following a kinome-wide RNAi screen, pantothenate kinase 4 (PANK4) isuncovered as a modulator of TMZ resistance in GBM. Validation of PANK4 across various TMZ-resistant GBM cell models, patient-derived GBM cell lines, tissue samples, as well as in vivo studies, corroborates the potential translational significance of these findings. Moreover, PANK4 expression is induced during TMZ treatment, and its expression is associated with a worse clinical outcome. Furthermore, a Tandem Mass Tag (TMT)-based quantitative proteomic approach, reveals that PANK4 abrogation leads to a significant downregulation of a host of proteins with central roles in cellular detoxification and cellular response to oxidative stress. More specifically, as cells undergo genotoxic stress during TMZ exposure, PANK4 depletion represents a crucial event that can lead to accumulation of intracellular reactive oxygen species (ROS) and subsequent cell death. Collectively, a previously unreported role for PANK4 in mediating therapeutic resistance to TMZ in GBM is unveiled.

Aneuploidy tolerance caused by BRG1 loss allows chromosome gains and recovery of fitness.

Aneuploidy results in decreased cellular fitness in many species and model systems. However, aneuploidy is commonly found in cancer cells and often correlates with aggressive growth, suggesting that the impact of aneuploidy on cellular fitness is context dependent. The BRG1 (SMARCA4) subunit of the SWI/SNF chromatin remodelling complex is frequently lost in cancer. Here, we use a chromosomally stable cell line to test the effect of BRG1 loss on the evolution of aneuploidy. BRG1 deletion leads to an initial loss of fitness in this cell line that improves over time. Notably, we find increased tolerance to aneuploidy immediately upon loss of BRG1, and the fitness recovery over time correlates with chromosome gain. These data show that BRG1 loss creates an environment where karyotype changes can be explored without a fitness penalty. At least in some genetic backgrounds, therefore, BRG1 loss can affect the progression of tumourigenesis through tolerance of aneuploidy.

Biological network topology features predict gene dependencies in cancer cell-lines.

Motivation: Protein-protein interaction (PPI) networks have been shown to successfully predict essential proteins. However, such networks are derived generically from experiments on many thousands of different cells. Consequently, conventional PPI networks cannot capture the variation of genetic dependencies that exists across different cell types, let alone those that emerge as a result of the massive cell restructuring that occurs during carcinogenesis. Predicting cell-specific dependencies is of considerable therapeutic benefit, facilitating the use of drugs to inhibit those proteins on which the cancer cells have become specifically dependent. In order to go beyond the limitations of the generic PPI, we have attempted to personalise PPI networks to reflect cell-specific patterns of gene expression and mutation. By using 12 topological features of the resulting PPIs, together with matched gene dependency data from DepMap, we trained random-forest classifiers (DependANT) to predict novel gene dependencies. Results: We found that DependANT improves the power of the baseline generic PPI models in predicting common gene dependencies, by up to 10.8% and is more sensitive than the baseline generic model when predicting genes on which only a small number of cell types are dependent. Availability and implementation: Software available at https://bitbucket.org/bioinformatics_lab_sussex/dependant2. Supplementary information: Supplementary data are available at Bioinformatics Advances online.

Identifying the Impact of Inframe Insertions and Deletions on Protein Function in Cancer.

Inframe insertion and deletion mutations (indels) are commonly observed in cancer samples accounting for over 1% of all reported mutations. Few somatic inframe indels have been clinically documented as pathogenic and at present there are few tools to predict which indels drive cancer development. However, indels are a common feature of hereditary disease and several tools have been developed to predict the impact of inframe indels on protein function. In this study, we test whether six of the popular prediction tools can be adapted to test for cancer driver mutations and then develop a new algorithm (IndelRF) that discriminates between recurrent indels in known cancer genes and indels not associated with disease. IndelRF was developed to try and identify somatic, driver, and inframe indel mutations. Using a random forest classifier with 11 features, IndelRF achieved accuracies of 0.995 and 0.968 for insertion and deletion mutations, respectively. Finally, we use IndelRF to classify the inframe indel cancer mutations in the MOKCa database.

Defining Signatures of Arm-Wise Copy Number Change and Their Associated Drivers in Kidney Cancers.

Using pan-cancer data from The Cancer Genome Atlas (TCGA), we investigated how patterns in copy number alterations in cancer cells vary both by tissue type and as a function of genetic alteration. We find that patterns in both chromosomal ploidy and individual arm copy number are dependent on tumour type. We highlight for example, the significant losses in chromosome arm 3p and the gain of ploidy in 5q in kidney clear cell renal cell carcinoma tissue samples. We find that specific gene mutations are associated with genome-wide copy number changes. Using signatures derived from non-negative factorisation, we also find gene mutations that are associated with particular patterns of ploidy change. Finally, utilising a set of machine learning classifiers, we successfully predicted the presence of mutated genes in a sample using arm-wise copy number patterns as features. This demonstrates that mutations in specific genes are correlated and may lead to specific patterns of ploidy loss and gain across chromosome arms. Using these same classifiers, we highlight which arms are most predictive of commonly mutated genes in kidney renal clear cell carcinoma (KIRC).

Cell-derived extracellular vesicles can be used as a biomarker reservoir for glioblastoma tumor subtyping.

Glioblastoma (GBM) is one of the most aggressive solid tumors for which treatment options and biomarkers are limited. Small extracellular vesicles (sEVs) produced by both GBM and stromal cells are central in the inter-cellular communication that is taking place in the tumor bulk. As tumor sEVs are accessible in biofluids, recent reports have suggested that sEVs contain valuable biomarkers for GBM patient diagnosis and follow-up. The aim of the current study was to describe the protein content of sEVs produced by different GBM cell lines and patient-derived stem cells. Our results reveal that the content of the sEVs mirrors the phenotypic signature of the respective GBM cells, leading to the description of potential informative sEV-associated biomarkers for GBM subtyping, such as CD44. Overall, these data could assist future GBM in vitro studies and provide insights for the development of new diagnostic and therapeutic methods as well as personalized treatment strategies.

Predicting synthetic lethal interactions using conserved patterns in protein interaction networks.

In response to a need for improved treatments, a number of promising novel targeted cancer therapies are being developed that exploit human synthetic lethal interactions. This is facilitating personalised medicine strategies in cancers where specific tumour suppressors have become inactivated. Mainly due to the constraints of the experimental procedures, relatively few human synthetic lethal interactions have been identified. Here we describe SLant (Synthetic Lethal analysis via Network topology), a computational systems approach to predicting human synthetic lethal interactions that works by identifying and exploiting conserved patterns in protein interaction network topology both within and across species. SLant out-performs previous attempts to classify human SSL interactions and experimental validation of the models predictions suggests it may provide useful guidance for future SSL screenings and ultimately aid targeted cancer therapy development.

Repression of Transcription at DNA Breaks Requires Cohesin throughout Interphase and Prevents Genome Instability.

Cohesin subunits are frequently mutated in cancer, but how they function as tumor suppressors is unknown. Cohesin mediates sister chromatid cohesion, but this is not always perturbed in cancer cells. Here, we identify a previously unknown role for cohesin. We find that cohesin is required to repress transcription at DNA double-strand breaks (DSBs). Notably, cohesin represses transcription at DSBs throughout interphase, indicating that this is distinct from its known role in mediating DNA repair through sister chromatid cohesion. We identified a cancer-associated SA2 mutation that supports sister chromatid cohesion but is unable to repress transcription at DSBs. We further show that failure to repress transcription at DSBs leads to large-scale genome rearrangements. Cancer samples lacking SA2 display mutational patterns consistent with loss of this pathway. These findings uncover a new function for cohesin that provides insights into its frequent loss in cancer.

Computational Approaches to Identify Genetic Interactions for Cancer Therapeutics.

The development of improved cancer therapies is frequently cited as an urgent unmet medical need. Here we describe how genetic interactions are being therapeutically exploited to identify novel targeted treatments for cancer. We discuss the current methodologies that use 'omics data to identify genetic interactions, in particular focusing on synthetic sickness lethality (SSL) and synthetic dosage lethality (SDL). We describe the experimental and computational approaches undertaken both in humans and model organisms to identify these interactions. Finally we discuss some of the identified targets with licensed drugs, inhibitors in clinical trials or with compounds under development.

'Big data' approaches for novel anti-cancer drug discovery.

INTRODUCTION: The development of improved cancer therapies is frequently cited as an urgent unmet medical need. Recent advances in platform technologies and the increasing availability of biological 'big data' are providing an unparalleled opportunity to systematically identify the key genes and pathways involved in tumorigenesis. The discoveries made using these new technologies may lead to novel therapeutic interventions. Areas covered: The authors discuss the current approaches that use 'big data' to identify cancer drivers. These approaches include the analysis of genomic sequencing data, pathway data, multi-platform data, identifying genetic interactions such as synthetic lethality and using cell line data. They review how big data is being used to identify novel drug targets. The authors then provide an overview of the available data repositories and tools being used at the forefront of cancer drug discovery. Expert opinion: Targeted therapies based on the genomic events driving the tumour will eventually inform treatment protocols. However, using a tailored approach to treat all tumour patients may require developing a large repertoire of targeted drugs.

Identification and analysis of mutational hotspots in oncogenes and tumour suppressors.

BACKGROUND: The key to interpreting the contribution of a disease-associated mutation in the development and progression of cancer is an understanding of the consequences of that mutation both on the function of the affected protein and on the pathways in which that protein is involved. Protein domains encapsulate function and position-specific domain based analysis of mutations have been shown to help elucidate their phenotypes. RESULTS: In this paper we examine the domain biases in oncogenes and tumour suppressors, and find that their domain compositions substantially differ. Using data from over 30 different cancers from whole-exome sequencing cancer genomic projects we mapped over one million mutations to their respective Pfam domains to identify which domains are enriched in any of three different classes of mutation; missense, indels or truncations. Next, we identified the mutational hotspots within domain families by mapping small mutations to equivalent positions in multiple sequence alignments of protein domainsWe find that gain of function mutations from oncogenes and loss of function mutations from tumour suppressors are normally found in different domain families and when observed in the same domain families, hotspot mutations are located at different positions within the multiple sequence alignment of the domain. CONCLUSIONS: By considering hotspots in tumour suppressors and oncogenes independently, we find that there are different specific positions within domain families that are particularly suited to accommodate either a loss or a gain of function mutation. The position is also dependent on the class of mutation.We find rare mutations co-located with well-known functional mutation hotspots, in members of homologous domain superfamilies, and we detect novel mutation hotspots in domain families previously unconnected with cancer. The results of this analysis can be accessed through the MOKCa database (http://strubiol.icr.ac.uk/extra/MOKCa).

A first generation inhibitor of human Greatwall kinase, enabled by structural and functional characterisation of a minimal kinase domain construct.

MASTL (microtubule-associated serine/threonine kinase-like), more commonly known as Greatwall (GWL), has been proposed as a novel cancer therapy target. GWL plays a crucial role in mitotic progression, via its known substrates ENSA/ARPP19, which when phosphorylated inactivate PP2A/B55 phosphatase. When over-expressed in breast cancer, GWL induces oncogenic properties such as transformation and invasiveness. Conversely, down-regulation of GWL selectively sensitises tumour cells to chemotherapy. Here we describe the first structure of the GWL minimal kinase domain and development of a small-molecule inhibitor GKI-1 (Greatwall Kinase Inhibitor-1). In vitro, GKI-1 inhibits full-length human GWL, and shows cellular efficacy. Treatment of HeLa cells with GKI-1 reduces ENSA/ARPP19 phosphorylation levels, such that they are comparable to those obtained by siRNA depletion of GWL; resulting in a decrease in mitotic events, mitotic arrest/cell death and cytokinesis failure. Furthermore, GKI-1 will be a useful starting point for the development of more potent and selective GWL inhibitors.

Mutational patterns in oncogenes and tumour suppressors.

All cancers depend upon mutations in critical genes, which confer a selective advantage to the tumour cell. Knowledge of these mutations is crucial to understanding the biology of cancer initiation and progression, and to the development of targeted therapeutic strategies. The key to understanding the contribution of a disease-associated mutation to the development and progression of cancer, comes from an understanding of the consequences of that mutation on the function of the affected protein, and the impact on the pathways in which that protein is involved. In this paper we examine the mutation patterns observed in oncogenes and tumour suppressors, and discuss different approaches that have been developed to identify driver mutations within cancers that contribute to the disease progress. We also discuss the MOKCa database where we have developed an automatic pipeline that structurally and functionally annotates all proteins from the human proteome that are mutated in cancer.

Therapeutic opportunities within the DNA damage response.

The DNA damage response (DDR) is essential for maintaining the genomic integrity of the cell, and its disruption is one of the hallmarks of cancer. Classically, defects in the DDR have been exploited therapeutically in the treatment of cancer with radiation therapies or genotoxic chemotherapies. More recently, protein components of the DDR systems have been identified as promising avenues for targeted cancer therapeutics. Here, we present an in-depth analysis of the function, role in cancer and therapeutic potential of 450 expert-curated human DDR genes. We discuss the DDR drugs that have been approved by the US Food and Drug Administration (FDA) or that are under clinical investigation. We examine large-scale genomic and expression data for 15 cancers to identify deregulated components of the DDR, and we apply systematic computational analysis to identify DDR proteins that are amenable to modulation by small molecules, highlighting potential novel therapeutic targets.

Conserved regulation of cardiac calcium uptake by peptides encoded in small open reading frames.

Small open reading frames (smORFs) are short DNA sequences that are able to encode small peptides of less than 100 amino acids. Study of these elements has been neglected despite thousands existing in our genomes. We and others previously showed that peptides as short as 11 amino acids are translated and provide essential functions during insect development. Here, we describe two peptides of less than 30 amino acids regulating calcium transport, and hence influencing regular muscle contraction, in the Drosophila heart. These peptides seem conserved for more than 550 million years in a range of species from flies to humans, in which they have been implicated in cardiac pathologies. Such conservation suggests that the mechanisms for heart regulation are ancient and that smORFs may be a fundamental genome component that should be studied systematically.

MoKCa database--mutations of kinases in cancer.

Members of the protein kinase family are amongst the most commonly mutated genes in human cancer, and both mutated and activated protein kinases have proved to be tractable targets for the development of new anticancer therapies The MoKCa database (Mutations of Kinases in Cancer, http://strubiol.icr.ac.uk/extra/mokca) has been developed to structurally and functionally annotate, and where possible predict, the phenotypic consequences of mutations in protein kinases implicated in cancer. Somatic mutation data from tumours and tumour cell lines have been mapped onto the crystal structures of the affected protein domains. Positions of the mutated amino-acids are highlighted on a sequence-based domain pictogram, as well as a 3D-image of the protein structure, and in a molecular graphics package, integrated for interactive viewing. The data associated with each mutation is presented in the Web interface, along with expert annotation of the detailed molecular functional implications of the mutation. Proteins are linked to functional annotation resources and are annotated with structural and functional features such as domains and phosphorylation sites. MoKCa aims to provide assessments available from multiple sources and algorithms for each potential cancer-associated mutation, and present these together in a consistent and coherent fashion to facilitate authoritative annotation by cancer biologists and structural biologists, directly involved in the generation and analysis of new mutational data.