Identifying cancer selective            proteins           Martin McIntosh    Computational Biology ProgramFred Hutchinson...
Background• A variety of alterations in cancer may result in cells encoding proteins or  polypeptides not observed in norm...
How we are looking for neoantigen candidates: start with RNA-seq.
Central dogma
Central dogma
Central dogma
Central dogma
What do we know about the human            transcript repertoire                                                          ...
Example of putative “Novel” proteinLeft: A four nucleotide extension and alternate exon for SF1 which together causeframe ...
Why not use MS proteomics?              MS/MS=Matching technology              Low sensitivity compared to RNAseq.        ...
Cancer selective splicing events across            disease sites
Figure 2: (Left): Clustering of prevalent and abundant cancer selective transcripts to known CTantigens observed in ovaria...
Lots of changes do not result in code
How we are trying to improve the           pipeline.                Specificity to tumor cells:                • Many puta...
How we are trying to improve pipeline                                                                                     ...
What exactly do we mean by a protein coding gene?           A           B           C     Result from one mouse pool (mous...
Is it really sufficient that we see ribosomes?       Non-coding RNA (Malat1) found in mouse heart.       Pronounced with 2...
Summary• Who cares about a millions of genomes.• Genomes looks to me like an engineering  problem and not really a researc...
Credit•   People who did the work:     – Matt Fitzgibbon (Computational lead).     – Nigel Clegg (visual curation and EST ...
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Dr. Martin McIntosh: Identifying Cancer Selective Proteins Using RNA-Sequencing and Bioinformatics Strategies

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On April 11, Dr. Martin McIntosh delivered a virtual presentation via Adobe Connect titled "Identifying Cancer Selective Proteins Using RNA-Sequencing and Bioinformatics Strategies." Dr. McIntosh is a Full Member at the Fred Hutchinson Cancer Research Center in Seattle, WA, and Principal Investigator of the Computational Proteomics Laboratory. His research is split between computational and laboratory activities involving a range of technologies for large-scale molecular profiling.

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Dr. Martin McIntosh: Identifying Cancer Selective Proteins Using RNA-Sequencing and Bioinformatics Strategies

  1. 1. Identifying cancer selective proteins Martin McIntosh Computational Biology ProgramFred Hutchinson Cancer Research Center
  2. 2. Background• A variety of alterations in cancer may result in cells encoding proteins or polypeptides not observed in normal somatic tissues.• They may be derived from cancer-related changes in genomes, splicing, post-translational modifications, etc.• These unique disease-related products may be useful for a variety of translational goals, including. – Therapy: specific targeting of disease tissues. – Diagnosis: circulating markers or targets for nanotechnology-based imaging.• I am going to talk about how we are trying to find these products, and implore people (NCI? Others?) to help out.
  3. 3. How we are looking for neoantigen candidates: start with RNA-seq.
  4. 4. Central dogma
  5. 5. Central dogma
  6. 6. Central dogma
  7. 7. Central dogma
  8. 8. What do we know about the human transcript repertoire tissue normal cancer• Un-annotated does not mean it is interesting: 15% of splicing events we brain 666467 37798 see in somatic tissues are un- testis 165655 1059 annotated. placenta 153235 4 eye 82100 0 spleen 75504 0• Annotated!= unimportant: Large bias of uterus 70546 35040 cancer tissues populate the EST blood 69245 24036 normal databases. cancer kidney 63980 30706 lung 63495 32601 Few Samples: thymus 62142 0 pancreas 59037 25447 muscle 55891 9730 heart 53531 0 liver 52532 36124 prostate 43049 11959 More Samples: ovary 8413 26755 UCSC EST Libraries (those that map to human tissues): Characterized by organ/tissue and development stage.
  9. 9. Example of putative “Novel” proteinLeft: A four nucleotide extension and alternate exon for SF1 which together causeframe shift that maintains the stop codon in the terminal exon. Right: Confirmationof spectra by comparing tumor (red) to synthetic spectra (blue). Confirmed bysequencing.
  10. 10. Why not use MS proteomics? MS/MS=Matching technology Low sensitivity compared to RNAseq. Low coverage per protein identified. Biology gets in the way. Exon-exon boundaries frequently cut by trypsin.
  11. 11. Cancer selective splicing events across disease sites
  12. 12. Figure 2: (Left): Clustering of prevalent and abundant cancer selective transcripts to known CTantigens observed in ovarian cancer tissues, a subset of 112 known tumor selective transcriptsidentified. (Right): A tandem 3’ splice site, with a NAGNAG motif, in BRCA1, is observed in ovarian(top) and prostate (bottom) cancer, in normal testis, but no other normal or control RNA-Seq data ornormal ESTs. Figure shows splice viewer our group developed. Right panel shows splicing viewer developed into IGV (broad) by my group (Damon May).
  13. 13. Lots of changes do not result in code
  14. 14. How we are trying to improve the pipeline. Specificity to tumor cells: • Many putative coding sequences may be un- annotated species belonging to infiltrating cells. • We are creating single-cell suspensions and separating tumor cells from other cells, and sequencing each component.
  15. 15. How we are trying to improve pipeline Specificity for coding sequences• Separa on following sucrose ultracentrifuge. • Enrich for mRNA’s undergoing active Derived from Ovcar 3 Cell Line A ?" B C translation. Ribosomes+ Transcript/ribosome M$ • Capture polysome-bound 2$ 3$ 4$ 5$ 6$ 7$ 8$ 9$ transcripts. 40S$ 60S$ 80S$ 120S$ Number of ribosomes bound: as measured by op cal readout.
  16. 16. What exactly do we mean by a protein coding gene? A B C Result from one mouse pool (mouse heart). Actin beta, including annotated exon known to be selected for NSMD. Brings up an epistemological issue for proteomics people
  17. 17. Is it really sufficient that we see ribosomes? Non-coding RNA (Malat1) found in mouse heart. Pronounced with 2 or 3 ribosomes . Interested in looking at ribosome foot printing
  18. 18. Summary• Who cares about a millions of genomes.• Genomes looks to me like an engineering problem and not really a research problem.• Relying on changes in proteins derived solely from changes in cancer genomes (e.g., mutations) may not provide a large number of putative candidates.• MS proteomics does not work well enough, RNA- seq works too well.• We need someone to begin to better characterize the nucleotides contained in somatic tissues.
  19. 19. Credit• People who did the work: – Matt Fitzgibbon (Computational lead). – Nigel Clegg (visual curation and EST database). – Damon May (IGV Visual curation). – Lindsay Bergen (all Laboratory work).• Funding: – No. HHSN261200800001E: NCI in-Silico Center of Excellence – Canary Foundation. – Illumina• Thanks: – Vivian MacKay (UW Biochem), polysome fractionation. – Nicole Urban, Chuck Drescher, FHCRC Ovarian SPORE.

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