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 PKCβ 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 PKCβ 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.
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•Cancer-associated PKC mutations are LOF and can act in a dominant-negative manner•Correcting a heterozygous PKCβ LOF mutation reduces tumor volume•Hemizygous deletion shows PKC is haploinsufficient for tumor suppression•Therapeutic strategies should aim to restore PKC activity instead of inhibiting it
Cancer-associated kinase mutations have generally been characterized as oncogenic, but an analysis of PKC mutations reveals that the majority are loss of function, indicating a tumor-suppressive role for this kinase and a shift in therapeutic strategies targeting PKC.
Oncogenic mutations regulate signaling within both tumor cells and adjacent stromal cells. Here, we show that oncogenic KRAS (KRASG12D) also regulates tumor cell signaling via stromal cells. By ...combining cell-specific proteome labeling with multivariate phosphoproteomics, we analyzed heterocellular KRASG12D signaling in pancreatic ductal adenocarcinoma (PDA) cells. Tumor cell KRASG12D 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 KRASG12D. Consequently, reciprocal KRASG12D produces a tumor cell phosphoproteome and total proteome that is distinct from cell-autonomous KRASG12D 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.
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•KRASG12D establishes a reciprocal signaling axis via heterotypic stromal cells•Reciprocal signaling further regulates tumor cell signaling downstream of KRASG12D•Reciprocal signaling regulates tumor cell behavior via AXL/IGF1R-AKT•Heterocellularity expands tumor cell signaling beyond cell-autonomous pathways
Cell-specific proteome labeling reveals that oncogenic KRAS stimulates stromal cells to initiate reciprocal signaling back to pancreatic tumor cells, thereby enabling signaling capacity beyond the traditionally studied cell-autonomous pathways.
Small-cell lung cancer (SCLC), an aggressive neuroendocrine tumor with early dissemination and dismal prognosis, accounts for 15-20% of lung cancer cases and ∼200,000 deaths each year. Most cases are ...inoperable, and biopsies to investigate SCLC biology are rarely obtainable. Circulating tumor cells (CTCs), which are prevalent in SCLC, present a readily accessible 'liquid biopsy'. Here we show that CTCs from patients with either chemosensitive or chemorefractory SCLC are tumorigenic in immune-compromised mice, and the resultant CTC-derived explants (CDXs) mirror the donor patient's response to platinum and etoposide chemotherapy. Genomic analysis of isolated CTCs revealed considerable similarity to the corresponding CDX. Most marked differences were observed between CDXs from patients with different clinical outcomes. These data demonstrate that CTC molecular analysis via serial blood sampling could facilitate delivery of personalized medicine for SCLC. CDXs are readily passaged, and these unique mouse models provide tractable systems for therapy testing and understanding drug resistance mechanisms.
Using a mouse model of human MLL-AF9 leukemia, we identified the lysine-specific demethylase KDM1A (LSD1 or AOF2) as an essential regulator of leukemia stem cell (LSC) potential. KDM1A acts at ...genomic loci bound by MLL-AF9 to sustain expression of the associated oncogenic program, thus preventing differentiation and apoptosis. In vitro and in vivo pharmacologic targeting of KDM1A using tranylcypromine analogs active in the nanomolar range phenocopied Kdm1a knockdown in both murine and primary human AML cells exhibiting MLL translocations. By contrast, the clonogenic and repopulating potential of normal hematopoietic stem and progenitor cells was spared. Our data establish KDM1A as a key effector of the differentiation block in MLL leukemia, which may be selectively targeted to therapeutic effect.
► KDM1A is a key effector of the differentiation block in MLL leukemia ► KDM1A sustains expression of the MLL-AF9 oncogenic program ► Nanomolar KDM1A inhibitor concentrations induce differentiation of human AML cells ► KDM1A inhibition in vivo targets MLL-AF9 cells but spares normal repopulating cells
Quality Control is a fundamental aspect of successful microarray data analysis. Simpleaffy is a BioConductor package that provides access to a variety of QC metrics for assessing the quality of RNA ...samples and of the intermediate stages of sample preparation and hybridization. Simpleaffy also offers fast implementations of popular algorithms for generating expression summaries and detection calls. Availability: Simpleaffy can be downloaded from http://www.bioconductor.org Contact: cmiller@picr.man.ac.uk Supplementary information: Additional information can be found on the supplementary website located at http://bioinformatics.picr.man.ac.uk
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.
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.
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.
Microarrays measure the binding of nucleotide sequences to a set of sequence specific probes. This information is combined with annotation specifying the relationship between probes and targets and ...used to make inferences about transcript- and, ultimately, gene expression. In some situations, a probe is capable of hybridizing to more than one transcript, in others, multiple probes can target a single sequence. These 'multiply targeted' probes can result in non-independence between measured expression levels.
An analysis of these relationships for Affymetrix arrays considered both the extent and influence of exact matches between probe and transcript sequences. For the popular HGU133A array, approximately half of the probesets were found to interact in this way. Both real and simulated expression datasets were used to examine how these effects influenced the expression signal. It was found not only to lead to increased signal strength for the affected probesets, but the major effect is to significantly increase their correlation, even in situations when only a single probe from a probeset was involved. By building a network of probe-probeset-transcript relationships, it is possible to identify families of interacting probesets. More than 10% of the families contain members annotated to different genes or even different Unigene clusters. Within a family, a mixture of genuine biological and artefactual correlations can occur.
Multiple targeting is not only prevalent, but also significant. The ability of probesets to hybridize to more than one gene product can lead to false positives when analysing gene expression. Comprehensive annotation describing multiple targeting is required when interpreting array data.
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.
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/ .
ged.brady@cruk.manchester.ac.uk or crispin.miller@cruk.manchester.ac.uk.
anthony.chiu@cruk.manchester.ac.uk.
Used alone, the MAS5.0 algorithm for generating expression summaries has been criticized for high False Positive rates resulting from exaggerated variance at low intensities.
Here we show, with ...replicated cell line data, that, when used alongside detection calls, MAS5 can be both selective and sensitive. A set of differentially expressed transcripts were identified that were found to be changing by MAS5, but unchanging by RMA and GCRMA. Subsequent analysis by real time PCR confirmed these changes. In addition, with the Latin square datasets often used to assess expression summary algorithms, filtered MAS5.0 was found to have performance approaching that of its peers.
When used alongside detection calls, MAS5 is a sensitive and selective algorithm for identifying differentially expressed genes.
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