Genome sequencing is emerging as a powerful tool to identify the molecular mechanism of cancer progression. However, the software tools to define genomic and post-genomic mutations are just in its infancy. We have used a novel software algorithm to analyse the cancer genome by DNAseq and expressed cancer genome arising from transcription by RNAseq to define dominant sources of potentially expressed tumour-specific mutations and oncogenic targets. We focus primarily on the rare human pleomorphic sarcoma as a disease of high unmet clinical need but use a range of cancer models to accelerate the development of the pipeline. First, we applied next generation sequencing of whole exomes of tumour tissues and two matched normal tissues (blood and “normal” tumour adjacent tissue) from a small set of patients to define parameters for use of the new software. The approaches identified significant mutations in tumour relative to germline DNA, but also in normal adjacent tissue, relative to normal germline, consistent with known field cancerization. Thus, in setting up the larger sequencing screen in the subsequent set of twenty cancer pleomorphic sarcoma cancer patients, whole exome sequencing was performed on tumour tissue and their matched normal adjacent tissues, rather than germline blood derived DNA, to define truly tumour-specific mutations. This approach provided sets of recurrent non-synonymous mutations in tumour tissue such as a transmembrane protease and suggests potential therapeutic targets for future focus that are highly tumour specific in pleomorphic sarcoma. A major problem with using DNA genomics only to define drugable landscapes in cancer is that the tumour genome is static and the mutations do not reflect the expressed cancer landscape at the time of surgery. Thus, in a smaller subset of patients we also applied shotgun RNAseq to determine the number of expressed mutated genes. We defined within the parameters chosen, from 8-17% of the mutated genome is expressed as defined at the RNA level. However, to our surprise, there were an order of magnitude more RNA mutations that were not DNA encoded suggestive of RNA editing events. Each patient showed elevated RNA edits that were independent of each other suggesting a highly-patient, cancer-specific perturbation in the specificity of the RNA editing machinery. We thus developed a cancer cell model to validate the RNA-editing software and we found we could recapitulate some of the RNA edits observed in clinical tumour tissue, in particular the signalling kinase in the MAP kinase-kinase-kinase-kinase super-family. It was interesting that RNA edits can often cluster in exon-intron boundaries suggesting a link to splicing and allows us to begin to produce “rules” for RNA editing. These data provide future direction to understand the role of RNA editing, as well as DNA encoded mutations, as mutagenic events and possible drugable targets in cancer signalling. Lastly, novel or orphan mutant proteins observed in human cancers, whether from DNA encoded mutant proteins or from RNA-edited driven mutant protein synthesis require new tools and technologies to discover new oncogenic signalling mechanisms. We developed an SBP-tagged affinity purification method in combination with label-free SWATH mass spectrometry to identify a novel binding protein for the gain-of-function mutant protein in a key metastatic gene, ELMO1. This identified an elevated interaction with another oncogenic protein encoded by AGR2 gene and validates this proteomics discovery platform to further advance function of new mutated proteins. In conclusion, we have applied and validated newly emerging software to begin to interrogate cancer tissue from patients of unmet clinical need in order to define new mechanisms of cancer progression and to define possibly new or better drug targets for new therapies. The data identified highly recurrent genome encoded mutations in human pleomorphic sarcoma and a potentially novel, targetable landscape represented by RNA editing driven mutant protein production. This will provide a foundation for future work on making better choices to advance our ability to improve patient management in human pleomorphic sarcoma.
| Identifer | oai:union.ndltd.org:bl.uk/oai:ethos.bl.uk:723890 |
| Date | January 2017 |
| Creators | Alsaadi, Ali |
| Contributors | Hupp, Ted ; Argyle, David |
| Publisher | University of Edinburgh |
| Source Sets | Ethos UK |
| Detected Language | English |
| Type | Electronic Thesis or Dissertation |
| Source | http://hdl.handle.net/1842/23595 |
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