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.

1. Identifying cancer selective
proteins
Martin McIntosh
Computational Biology Program
Fred Hutchinson Cancer Research Center

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. How we are looking for neoantigen
candidates: start with RNA-seq.

4. Central dogma

5. Central dogma

6. Central dogma

7. Central dogma

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. Example of putative “Novel” protein
Left: A four nucleotide extension and alternate exon for SF1 which together cause
frame shift that maintains the stop codon in the terminal exon. Right: Confirmation
of spectra by comparing tumor (red) to synthetic spectra (blue). Confirmed by
sequencing.

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. Cancer selective splicing events across
disease sites

12. Figure 2: (Left): Clustering of prevalent and abundant cancer selective transcripts to known CT
antigens observed in ovarian cancer tissues, a subset of 112 known tumor selective transcripts
identified. (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 or
normal ESTs. Figure shows splice viewer our group developed.
Right panel shows splicing viewer developed into IGV (broad) by my group (Damon May).

13. Lots of changes do not result in code

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. 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 ribosome's bound: as measured by op cal readout.

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. 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. 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. 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.