Oncogenic KRAS Regulates Tumor Cell Signaling via Stromal Reciprocation.

Oncogenic mutations regulate signaling within both tumor cells and adjacent stromal cells. Here, we show that oncogenic KRAS (KRAS(G12D)) also regulates tumor cell signaling via stromal cells. By combining cell-specific proteome labeling with multivariate phosphoproteomics, we analyzed heterocellular KRAS(G12D) signaling in pancreatic ductal adenocarcinoma (PDA) cells. Tumor cell KRAS(G12D) engages heterotypic fibroblasts, which subsequently instigate reciprocal signaling in the tumor cells. Reciprocal signaling employs additional kinases and doubles the number of regulated signaling nodes from cell-autonomous KRAS(G12D). Consequently, reciprocal KRAS(G12D) produces a tumor cell phosphoproteome and total proteome that is distinct from cell-autonomous KRAS(G12D) alone. Reciprocal signaling regulates tumor cell proliferation and apoptosis and increases mitochondrial capacity via an IGF1R/AXL-AKT axis. These results demonstrate that oncogene signaling should be viewed as a heterocellular process and that our existing cell-autonomous perspective underrepresents the extent of oncogene signaling in cancer. VIDEO ABSTRACT.

Cancer-associated protein kinase C mutations reveal kinase's role as tumor suppressor.

Protein kinase C (PKC) isozymes have remained elusive cancer targets despite the unambiguous tumor promoting function of their potent ligands, phorbol esters, and the prevalence of their mutations. We analyzed 8% of PKC mutations identified in human cancers and found that, surprisingly, most were loss of function and none were activating. Loss-of-function mutations occurred in all PKC subgroups and impeded second-messenger binding, phosphorylation, or catalysis. Correction of a loss-of-function PKCß mutation by CRISPR-mediated genome editing in a patient-derived colon cancer cell line suppressed anchorage-independent growth and reduced tumor growth in a xenograft model. Hemizygous deletion promoted anchorage-independent growth, revealing that PKCß is haploinsufficient for tumor suppression. Several mutations were dominant negative, suppressing global PKC signaling output, and bioinformatic analysis suggested that PKC mutations cooperate with co-occurring mutations in cancer drivers. These data establish that PKC isozymes generally function as tumor suppressors, indicating that therapies should focus on restoring, not inhibiting, PKC activity.

twoddpcr: an R/Bioconductor package and Shiny app for Droplet Digital PCR analysis.

SUMMARY: Droplet Digital PCR (ddPCR) is a sensitive platform used to quantify specific nucleic acid molecules amplified by polymerase chain reactions. Its sensitivity makes it particularly useful for the detection of rare mutant molecules, such as those present in a sample of circulating free tumour DNA obtained from cancer patients. ddPCR works by partitioning a sample into individual droplets for which the majority contain only zero or one target molecule. Each droplet then becomes a reaction chamber for PCR, which through the use of fluorochrome labelled probes allows the target molecules to be detected by measuring the fluorescence intensity of each droplet. The technology supports two channels, allowing, for example, mutant and wild type molecules to be detected simultaneously in the same sample. As yet, no open source software is available for the automatic gating of two channel ddPCR experiments in the case where the droplets can be grouped into four clusters. Here, we present an open source R package 'twoddpcr', which uses Poisson statistics to estimate the number of molecules in such two channel ddPCR data. Using the Shiny framework, an accompanying graphical user interface (GUI) is also included for the package, allowing users to adjust parameters and see the results in real-time. AVAILABILITY AND IMPLEMENTATION: twoddpcr is available from Bioconductor (3.5) at https://bioconductor.org/packages/twoddpcr/ . A Shiny-based GUI suitable for non-R users is available as a standalone application from within the package and also as a web application at http://shiny.cruk.manchester.ac.uk/twoddpcr/ . CONTACT: ged.brady@cruk.manchester.ac.uk or crispin.miller@cruk.manchester.ac.uk. PACKAGE MAINTAINER: anthony.chiu@cruk.manchester.ac.uk.

Targeted genetic dependency screen facilitates identification of actionable mutations in FGFR4, MAP3K9, and PAK5 in lung cancer.

Approximately 70% of patients with non-small-cell lung cancer present with late-stage disease and have limited treatment options, so there is a pressing need to develop efficacious targeted therapies for these patients. This remains a major challenge as the underlying genetic causes of ~50% of non-small-cell lung cancers remain unknown. Here we demonstrate that a targeted genetic dependency screen is an efficient approach to identify somatic cancer alterations that are functionally important. By using this approach, we have identified three kinases with gain-of-function mutations in lung cancer, namely FGFR4, MAP3K9, and PAK5. Mutations in these kinases are activating toward the ERK pathway, and targeted depletion of the mutated kinases inhibits proliferation, suppresses constitutive activation of downstream signaling pathways, and results in specific killing of the lung cancer cells. Genomic profiling of patients with lung cancer is ushering in an era of personalized medicine; however, lack of actionable mutations presents a significant hurdle. Our study indicates that targeted genetic dependency screens will be an effective strategy to elucidate somatic variants that are essential for lung cancer cell viability.

Spatial regulation of the glycocalyx component podocalyxin is a switch for prometastatic function.

The glycocalyx component and sialomucin podocalyxin (PODXL) is required for normal tissue development by promoting apical membranes to form between cells, triggering lumen formation. Elevated PODXL expression is also associated with metastasis and poor clinical outcome in multiple tumor types. How PODXL presents this duality in effect remains unknown. We identify an unexpected function of PODXL as a decoy receptor for galectin-3 (GAL3), whereby the PODXL-GAL3 interaction releases GAL3 repression of integrin-based invasion. Differential cortical targeting of PODXL, regulated by ubiquitination, is the molecular mechanism controlling alternate fates. Both PODXL high and low surface levels occur in parallel subpopulations within cancer cells. Orthotopic intraprostatic xenograft of PODXL-manipulated cells or those with different surface levels of PODXL define that this axis controls metastasis in vivo. Clinically, interplay between PODXL-GAL3 stratifies prostate cancer patients with poor outcome. Our studies define the molecular mechanisms and context in which PODXL promotes invasion and metastasis.

Programmed fluctuations in sense/antisense transcript ratios drive sexual differentiation in S. pombe.

Strand-specific RNA sequencing of S. pombe revealed a highly structured programme of ncRNA expression at over 600 loci. Waves of antisense transcription accompanied sexual differentiation. A substantial proportion of ncRNA arose from mechanisms previously considered to be largely artefactual, including improper 3' termination and bidirectional transcription. Constitutive induction of the entire spk1+, spo4+, dis1+ and spo6+ antisense transcripts from an integrated, ectopic, locus disrupted their respective meiotic functions. This ability of antisense transcripts to disrupt gene function when expressed in trans suggests that cis production at native loci during sexual differentiation may also control gene function. Consistently, insertion of a marker gene adjacent to the dis1+ antisense start site mimicked ectopic antisense expression in reducing the levels of this microtubule regulator and abolishing the microtubule-dependent 'horsetail' stage of meiosis. Antisense production had no impact at any of these loci when the RNA interference (RNAi) machinery was removed. Thus, far from being simply 'genome chatter', this extensive ncRNA landscape constitutes a fundamental component in the controls that drive the complex programme of sexual differentiation in S. pombe.

Gastrointestinal differentiation marker Cytokeratin 20 is regulated by homeobox gene CDX1.

CDX1 is a transcription factor that plays a key role in intestinal development and differentiation. However, the downstream targets of CDX1 are less well defined than those of its close homologue, CDX2. We report here the identification of downstream targets of CDX1 using microarray gene-expression analysis and other approaches. Keratin 20 (KRT20), a member of the intermediate filament and a well-known marker of intestinal differentiation, was initially identified as one of the genes likely to be directly regulated by CDX1. CDX1 and KRT20 mRNA expression were significantly correlated in a panel of 38 colorectal cancer cell lines. Deletion and mutation analysis of the KRT20 promoter showed that the minimum regulatory region for the control of KRT20 expression by CDX1 is within 246 bp upstream of the KRT20 transcription start site. ChIP analysis confirmed that CDX1 binds to the predicted CDX elements in this region of the KRT20 promoter in vivo. In addition, immunohistochemistry showed expression of CDX1 parallels that of KRT20 in the normal crypt, which further supports their close relationship. In summary, our observations strongly imply that KRT20 is directly regulated by CDX1, and therefore suggest a role for CDX1 in maintaining differentiation in intestinal epithelial cells. Because a key feature of the development of a cancer is an unbalanced program of proliferation and differentiation, dysregulation of CDX1 may be an advantage for the development of a colorectal carcinoma. This could, therefore, explain the relatively frequent down regulation of CDX1 in colorectal carcinomas by hypermethylation.

Traject3d allows label-free identification of distinct co-occurring phenotypes within 3D culture by live imaging.

Single cell profiling by genetic, proteomic and imaging methods has expanded the ability to identify programmes regulating distinct cell states. The 3-dimensional (3D) culture of cells or tissue fragments provides a system to study how such states contribute to multicellular morphogenesis. Whether cells plated into 3D cultures give rise to a singular phenotype or whether multiple biologically distinct phenotypes arise in parallel is largely unknown due to a lack of tools to detect such heterogeneity. Here we develop Traject3d (Trajectory identification in 3D), a method for identifying heterogeneous states in 3D culture and how these give rise to distinct phenotypes over time, from label-free multi-day time-lapse imaging. We use this to characterise the temporal landscape of morphological states of cancer cell lines, varying in metastatic potential and drug resistance, and use this information to identify drug combinations that inhibit such heterogeneity. Traject3d is therefore an important companion to other single-cell technologies by facilitating real-time identification via live imaging of how distinct states can lead to alternate phenotypes that occur in parallel in 3D culture.

A comparison of massively parallel nucleotide sequencing with oligonucleotide microarrays for global transcription profiling.

BACKGROUND: RNA-Seq exploits the rapid generation of gigabases of sequence data by Massively Parallel Nucleotide Sequencing, allowing for the mapping and digital quantification of whole transcriptomes. Whilst previous comparisons between RNA-Seq and microarrays have been performed at the level of gene expression, in this study we adopt a more fine-grained approach. Using RNA samples from a normal human breast epithelial cell line (MCF-10a) and a breast cancer cell line (MCF-7), we present a comprehensive comparison between RNA-Seq data generated on the Applied Biosystems SOLiD platform and data from Affymetrix Exon 1.0ST arrays. The use of Exon arrays makes it possible to assess the performance of RNA-Seq in two key areas: detection of expression at the granularity of individual exons, and discovery of transcription outside annotated loci. RESULTS: We found a high degree of correspondence between the two platforms in terms of exon-level fold changes and detection. For example, over 80% of exons detected as expressed in RNA-Seq were also detected on the Exon array, and 91% of exons flagged as changing from Absent to Present on at least one platform had fold-changes in the same direction. The greatest detection correspondence was seen when the read count threshold at which to flag exons Absent in the SOLiD data was set to t<1 suggesting that the background error rate is extremely low in RNA-Seq. We also found RNA-Seq more sensitive to detecting differentially expressed exons than the Exon array, reflecting the wider dynamic range achievable on the SOLiD platform. In addition, we find significant evidence of novel protein coding regions outside known exons, 93% of which map to Exon array probesets, and are able to infer the presence of thousands of novel transcripts through the detection of previously unreported exon-exon junctions. CONCLUSIONS: By focusing on exon-level expression, we present the most fine-grained comparison between RNA-Seq and microarrays to date. Overall, our study demonstrates that data from a SOLiD RNA-Seq experiment are sufficient to generate results comparable to those produced from Affymetrix Exon arrays, even using only a single replicate from each platform, and when presented with a large genome.

Exon array analysis of head and neck cancers identifies a hypoxia related splice variant of LAMA3 associated with a poor prognosis.

The identification of alternatively spliced transcript variants specific to particular biological processes in tumours should increase our understanding of cancer. Hypoxia is an important factor in cancer biology, and associated splice variants may present new markers to help with planning treatment. A method was developed to analyse alternative splicing in exon array data, using probeset multiplicity to identify genes with changes in expression across their loci, and a combination of the splicing index and a new metric based on the variation of reliability weighted fold changes to detect changes in the splicing patterns. The approach was validated on a cancer/normal sample dataset in which alternative splicing events had been confirmed using RT-PCR. We then analysed ten head and neck squamous cell carcinomas using exon arrays and identified differentially expressed splice variants in five samples with high versus five with low levels of hypoxia-associated genes. The analysis identified a splice variant of LAMA3 (Laminin alpha 3), LAMA3-A, known to be involved in tumour cell invasion and progression. The full-length transcript of the gene (LAMA3-B) did not appear to be hypoxia-associated. The results were confirmed using qualitative RT-PCR. In a series of 59 prospectively collected head and neck tumours, expression of LAMA3-A had prognostic significance whereas LAMA3-B did not. This work illustrates the potential for alternatively spliced transcripts to act as biomarkers of disease prognosis with improved specificity for particular tissues or conditions over assays which do not discriminate between splice variants.

Eight-channel iTRAQ enables comparison of the activity of six leukemogenic tyrosine kinases.

There are a number of leukemogenic protein-tyrosine kinases (PTKs) associated with leukemic transformation. Although each is linked with a specific disease their functional activity poses the question whether they have a degree of commonality in their effects upon target cells. Exon array analysis of the effects of six leukemogenic PTKs (BCR/ABL, TEL/PDGFRbeta, FIP1/PDGFRalpha, D816V KIT, NPM/ALK, and FLT3ITD) revealed few common effects on the transcriptome. It is apparent, however, that proteome changes are not directly governed by transcriptome changes. Therefore, we assessed and used a new generation of iTRAQ tagging, enabling eight-channel relative quantification discovery proteomics, to analyze the effects of these six leukemogenic PTKs. Again these were found to have disparate effects on the proteome with few common targets. BCR/ABL had the greatest effect on the proteome and had more effects in common with FIP1/PDGFRalpha. The proteomic effects of the four type III receptor kinases were relatively remotely related. The only protein commonly affected was eosinophil-associated ribonuclease 7. Five of six PTKs affected the motility-related proteins CAPG and vimentin, although this did not correspond to changes in motility. However, correlation of the proteomics data with that from the exon microarray not only showed poor levels of correlation between transcript and protein levels but also revealed alternative patterns of regulation of the CAPG protein by different oncogenes, illustrating the utility of such a combined approach.

X:Map: annotation and visualization of genome structure for Affymetrix exon array analysis.

Affymetrix exon arrays aim to target every known and predicted exon in the human, mouse or rat genomes, and have reporters that extend beyond protein coding regions to other areas of the transcribed genome. This combination of increased coverage and precision is important because a substantial proportion of protein coding genes are predicted to be alternatively spliced, and because many non-coding genes are known also to be of biological significance. In order to fully exploit these arrays, it is necessary to associate each reporter on the array with the features of the genome it is targeting, and to relate these to gene and genome structure. X:Map is a genome annotation database that provides this information. Data can be browsed using a novel Google-maps based interface, and analysed and further visualized through an associated BioConductor package. The database can be found at http://xmap.picr.man.ac.uk.

A model of k-mer surprisal to quantify local sequence information content surrounding splice regions.

Molecular sequences carry information. Analysis of sequence conservation between homologous loci is a proven approach with which to explore the information content of molecular sequences. This is often done using multiple sequence alignments to support comparisons between homologous loci. These methods therefore rely on sufficient underlying sequence similarity with which to construct a representative alignment. Here we describe a method using a formal metric of information, surprisal, to analyse biological sub-sequences without alignment constraints. We applied our model to the genomes of five different species to reveal similar patterns across a panel of eukaryotes. As the surprisal of a sub-sequence is inversely proportional to its occurrence within the genome, the optimal size of the sub-sequences was selected for each species under consideration. With the model optimized, we found a strong correlation between surprisal and CG dinucleotide usage. The utility of our model was tested by examining the sequences of genes known to undergo splicing. We demonstrate that our model can identify biological features of interest such as known donor and acceptor sites. Analysis across all annotated coding exon junctions in Homo sapiens reveals the information content of coding exons to be greater than the surrounding intron regions, a consequence of increased suppression of the CG dinucleotide in intronic space. Sequences within coding regions proximal to exon junctions exhibited novel patterns within DNA and coding mRNA that are not a function of the encoded amino acid sequence. Our findings are consistent with the presence of secondary information encoding features such as DNA and RNA binding sites, multiplexed through the coding sequence and independent of the information required to define the corresponding amino-acid sequence. We conclude that surprisal provides a complementary methodology with which to locate regions of interest in the genome, particularly in situations that lack an appropriate multiple sequence alignment.

In silico prediction of housekeeping long intergenic non-coding RNAs reveals HKlincR1 as an essential player in lung cancer cell survival.

Prioritising long intergenic noncoding RNAs (lincRNAs) for functional characterisation is a significant challenge. Here we applied computational approaches to discover lincRNAs expected to play a critical housekeeping (HK) role within the cell. Using the Illumina Human BodyMap RNA sequencing dataset as a starting point, we first identified lincRNAs ubiquitously expressed across a panel of human tissues. This list was then further refined by reference to conservation score, secondary structure and promoter DNA methylation status. Finally, we used tumour expression and copy number data to identify lincRNAs rarely downregulated or deleted in multiple tumour types. The resulting list of candidate essential lincRNAs was then subjected to co-expression analyses using independent data from ENCODE and The Cancer Genome Atlas (TCGA). This identified a substantial subset with a predicted role in DNA replication and cell cycle regulation. One of these, HKlincR1, was selected for further characterisation. Depletion of HKlincR1 affected cell growth in multiple lung cancer cell lines, and led to disruption of genes involved in cell growth and viability. In addition, HKlincR1 expression was correlated with overall survival in lung adenocarcinoma patients. Our in silico studies therefore reveal a set of housekeeping noncoding RNAs of interest both in terms of their role in normal homeostasis, and their relevance in tumour growth and maintenance.

Publisher Correction: In silico prediction of housekeeping long intergenic non-coding RNAs reveals HKlincR1 as an essential player in lung cancer cell survival.

An amendment to this paper has been published and can be accessed via a link at the top of the paper.

Exon level integration of proteomics and microarray data.

BACKGROUND: Previous studies comparing quantitative proteomics and microarray data have generally found poor correspondence between the two. We hypothesised that this might in part be because the different assays were targeting different parts of the expressed genome and might therefore be subjected to confounding effects from processes such as alternative splicing. RESULTS: Using a genome database as a platform for integration, we combined quantitative protein mass spectrometry with Affymetrix Exon array data at the level of individual exons. We found significantly higher degrees of correlation than have been previously observed (r = 0.808). The study was performed using cell lines in equilibrium in order to reduce a major potential source of biological variation, thus allowing the analysis to focus on the data integration methods in order to establish their performance. CONCLUSION: We conclude that part of the variation observed when integrating microarray and proteomics data may occur as a consequence both of the data analysis and of the high granularity to which studies have until recently been limited. The approach opens up the possibility for the first time of considering combined microarray and proteomics datasets at the level of individual exons and isoforms, important given the high proportion of alternative splicing observed in the human genome.

Exploiting sample variability to enhance multivariate analysis of microarray data.

MOTIVATION: Biological and technical variability is intrinsic in any microarray experiment. While most approaches aim to account for this variability, they do not actively exploit it. Here, we consider a novel approach that uses the variability between arrays to provide an extra source of information that can enhance gene expression analyses. RESULTS: We develop a method that uses sample similarity to incorporate sample variability into the analysis of gene expression profiles. This allows each pairwise correlation calculation to borrow information from all the data in the experiment. Results on synthetic and human cancer microarray datasets show that the inclusion of this information leads to a significant increase in the ability to identify previously characterized relationships and a reduction in false discovery rate, when compared to a standard analysis using Pearson correlation. The information carried by the variability between arrays can be exploited to significantly improve the analysis of gene expression data. AVAILABILITY: Matlab script files are available from the author. SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.

Mutation pattern analysis reveals polygenic mini-drivers associated with relapse after surgery in lung adenocarcinoma.

The genomic lesions found in malignant tumours exhibit a striking degree of heterogeneity. Many tumours lack a known driver mutation, and their genetic basis is unclear. By mapping the somatic mutations identified in primary lung adenocarcinomas onto an independent coexpression network derived from normal tissue, we identify a critical gene network enriched for metastasis-associated genes. While individual genes within this module were rarely mutated, a significant accumulation of mutations within this geneset was predictive of relapse in lung cancer patients that have undergone surgery. Since it is the density of mutations within this module that is informative, rather than the status of any individual gene, these data are in keeping with a 'mini-driver' model of tumorigenesis in which multiple mutations, each with a weak effect, combine to form a polygenic driver with sufficient power to significantly alter cell behaviour and ultimately patient outcome. These polygenic mini-drivers therefore provide a means by which heterogeneous mutation patterns can generate the consistent hallmark changes in phenotype observed across tumours.

Truncation- and motif-based pan-cancer analysis reveals tumor-suppressing kinases.

A major challenge in cancer genomics is identifying "driver" mutations from the many neutral "passenger" mutations within a given tumor. To identify driver mutations that would otherwise be lost within mutational noise, we filtered genomic data by motifs that are critical for kinase activity. In the first step of our screen, we used data from the Cancer Cell Line Encyclopedia and The Cancer Genome Atlas to identify kinases with truncation mutations occurring within or before the kinase domain. The top 30 tumor-suppressing kinases were aligned, and hotspots for loss-of-function (LOF) mutations were identified on the basis of amino acid conservation and mutational frequency. The functional consequences of new LOF mutations were biochemically validated, and the top 15 hotspot LOF residues were used in a pan-cancer analysis to define the tumor-suppressing kinome. A ranked list revealed MAP2K7, an essential mediator of the c-Jun N-terminal kinase (JNK) pathway, as a candidate tumor suppressor in gastric cancer, despite its mutational frequency falling within the mutational noise for this cancer type. The majority of mutations in MAP2K7 abolished its catalytic activity, and reactivation of the JNK pathway in gastric cancer cells harboring LOF mutations in MAP2K7 or the downstream kinase JNK suppressed clonogenicity and growth in soft agar, demonstrating the functional relevance of inactivating the JNK pathway in gastric cancer. Together, our data highlight a broadly applicable strategy to identify functional cancer driver mutations and define the JNK pathway as tumor-suppressive in gastric cancer.