We performed the first proteogenomic study on a prospectively collected colon cancer cohort. Comparative proteomic and phosphoproteomic analysis of paired tumor and normal adjacent tissues produced a catalog of colon cancer-associated proteins and phosphosites, including known and putative new biomarkers, drug targets, and cancer/testis antigens. Proteogenomic integration not only prioritized genomically inferred targets, such as copy-number drivers and mutation-derived neoantigens, but also yielded novel findings. Phosphoproteomics data associated Rb phosphorylation with increased proliferation and decreased apoptosis in colon cancer, which explains why this classical tumor suppressor is amplified in colon tumors and suggests a rationale for targeting Rb phosphorylation in colon cancer. Proteomics identified an association between decreased CD8 T cell infiltration and increased glycolysis in microsatellite instability-high (MSI-H) tumors, suggesting glycolysis as a potential target to overcome the resistance of MSI-H tumors to immune checkpoint blockade. Proteogenomics presents new avenues for biological discoveries and therapeutic development.

1. Cell May 2, 2019
DOI: 10.1016/j.cell.2019.03.030
Gul Muneer
INSTITUTE OF CHEMISTRY
30102019Academia Sinica
Taiwan
Coach Professor: Dr. Yu-Ju Chen
Sit-in Professor: Dr. Hsiung-Lin Tu
Class Coordinator: Dr. Takashi Angata
1
Proteogenomic Analysis of Human Colon Cancer
Reveals New Therapeutic Opportunities
Suhas Vasaikar, Chen Huang, Xiaojing Wang, Vladislav A. Petyuk, Sara R. Savage, Bo Wen, Yongchao Dou, Yun Zhang,
Zhiao Shi, Osama A. Arshad, Marina A. Gritsenko, Lisa J. Zimmerman, Jason E. McDermott, Therese R. Clauss, Ronald J.
Moore, Rui Zhao, Matthew E. Monroe, Yi-Ting Wang, Matthew C. Chambers, Robbert J.C. Slebos, Ken S. Lau, Qianxing
Mo, Li Ding, Matthew Ellis, Mathangi Thiagarajan, Christopher R. Kinsinger, Henry Rodriguez, Richard D. Smith, Karin D.
Rodland, Daniel C. Liebler, Tao Liu, Bing Zhang, and Clinical Proteomic Tumor Analysis Consortium

2. Tao Liu
Biomedical Scientist
PNNL Laboratory
Bing Zhang
Professor
Baylor College of Medicine
Karin Rodland
Lab Fellow
PNNL Laboratory
Daniel Liebler
Professor
Vanderbilt University
2
About Authors
Suhas Vasaikar
Research Scientist
MD Anderson Cancer Center
Correspondence:

3. 3
What is Proteogenomics?
Cells/Tissues
Extraction
Digestion
LC-MS/MS
Peptides
Proteins
m/z
Intensity
Real
m/z
Intensity
Theoretical
Genome
Sequence
Protein Database
Protein Identification
(Known)
Matching
In-silico Digestion
Prediction
Cells/Tissues
RNA-Seq Exome-Seq
Genome Variants | Genome Variants
SNP, INDELS, gene fusions, SNV, RNA
edits, translocations, splice junctions,
Alternative events, Poly-adenylation
CUSTOMIZED
Protein Database
LC-MS/MS
RNA Exosome Protein
m/z
Intensity
Personalized
m/z
Intensity
Protein Identification
(Known + Novel)
Personalized

4. 4
Molecular Make-up of Colon Cancer with ‘Omics’
Colon cancer
2 types
Microsatellite Instable (MSI)
Chromosomal stable (CS)
High DNA mutations
Highly Immunogenic
Microsatellite stable (MSS)
Chromosomal instable (CIN)
Low DNA mutations
Low immunogenic
Deficient DNA mismatch Repair
For example, MLH1, MLH6
Mutations in APC, BRAF,
TP53, KRAS etc.
Nat Rev Cancer. 2017 Feb;17(2):79-92
What is microsatellite?
─di, tri, or tetra nucleotide repeats.
i.e., dinucleotide (CG CG CG) repeats
─ used for genetic fingerprinting
i.e., crime stains (forensic)
What is chromosomal instability (CIN)?
─ chromosomes are unstable
chromosomes are duplicated/deleted
~15% ~85%

5. 5
What is known previously?
Nat Med. 2015;21(11):1350-6
Nature. 2014;513(7518):382-7
Multi-omics data have yet to bring novel biomarkers and clinical targets.
Potential vulnerabilities are inaccessible from genomic or proteomic assessment alone.
Nature. 2012;487(7407):330-7

6. 6
Aims and Motivations of the study
What is missing?
 Global proteomic differences has not been systematically explored in large cohorts.
 Global phosphoproteomics analyses of human colon cancer are lacking.
Why this warrants exploration (Motivation)?
─ Cancer immunotherapy need biomarkers:
to predict response to immune checkpoint inhibition
to select neoantigens for personalized vaccine development
What are aims?
Proteogenomics can provide fresh approaches to these needs.
To systematically identify new therapeutic opportunities

7. 7
Therapeutic Opportunities Through Proteogenomics
Sequence Read Archive (SRA), Copy Number alteration (CNA), Whole-eXome Seq (WXS), Single Nucleotide Polymorphism (SNP), Clinical Proteomic Tumor Analysis Consortium

8. 8
Data Quality Analysis
Cell. 2019 May 2;177(4):1035-1049.e19
Prospective colon cancer cohort
Nature. 2014 Sep 18;513(7518):382-7
The Cancer Genome Atlas (TCGA) cohort
mRNA profile correlation
Protein profile correlation

9. 9
Mutation Rates and Microsatellite Status
Matched
blood
Genomic
DNA
Exome
Capture
Exon Intron
HiSeq4000
Sequencer
Somatic mutation
Normal Disease
TAGTAG
ATCATC
Microsatellite Instability
Microsatellite
(1-6 bp)
SCNA
Deletion Normal Amplif.
YOUCANRUNFAST
YOUCANFAST
YOUCANRUNRUNFAST
Single Nucleotide Variance: 64,010
Insertion/Deletion (INDEL): 7,691
Microsatellite INDELs: 6,186
MSI-H (n = 24) MSS (n = 85)
MSI-H = Microsatellite Instability-High
MSS = Microsatellite Stable
SCNA= Somatic Copy Number Alteration
Hypermutated Non-Hypermutated
50% samples

10. 10
Proteomic Result of Somatic Mutations
Stop gain –truncated protein
Frameshift INDEL – completely different translation from original (AUG ACG AUU) → (AUA CGA UU)
Non-frame shift INDEL – insert/remove amino acid
Non-synonymous SNV – different amino acid
APC – tumor suppressor gene TMT-Proteomics and Phosphoproteomics

11. 11
Proteomic Result of Somatic Mutations
Stop gain –truncated protein
Frameshift INDEL – completely different translation from original (AUG ACG AUU) → (AUA CGA UU)
Non-frame shift INDEL – insert/remove amino acid
Non-synonymous SNV – different amino acid

12. 12
Somatic Copy Number Alteration (SCNA) analysis
Wait, what is CNA?
Normal = OU CAN RUN FAST
Deleted = YOU CAN FAST
Amplified = YOU CAN RUN RUN RUN FAST

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Effects of CNA on mRNA and Protein Abundance
CNA – Copy Number Alteration
Positive correlation = Red
Negative correlation = Blue
Black bars = correlation to both
mRNA and protein

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Prioritizing Genes in Focal Alteration Peaks

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Retinoblastoma (Rb) ─ Repeatedly Amplified Gene
Rb
E2F
P
Rb
CDK
Phosphorylation
Phosphorylation
Rb releases E2F
E2F
Proliferation
Apoptosis

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Rb Phosphorylation Drives Cancer Proliferation
H3.1 Histone =
marker for cell proliferation
Rb-Phospho has an apoptotic role.

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Rb-Phos─ Driver & Therapeutic Target in Colon Cancer
Rb-Phospho drives colon cancer development.

18. 18
Colon Cancer-Associated Proteomic Events
Total Identified Protein: 8,067
Quantified Protein: 6,422 (50% of the samples)
TMT-Global Proteomics

19. 19
Distribution and Clinical fate of 31-colon cancer-
associated proteins
31-proteins Overlapping:
Plasma = 19
Secreted = 18
Transmembrane = 9
Enzymes = 8
Clinical Utility = 15

20. 20
Colon cancer-associated Phosphosites
Quantified Phosphorylation Sites: 7,295 (50% of the samples)

21. 21
Cancer-Associated (Phospho) Proteome and Kinases
CGC = Cancer Gene Consensus
Cancer associated kinase based on:
(1) Increased phosphorylation of kinase activating site
(2) Enrichment analysis of known target sites (inferred)
Gray box = data not available
Black box = FDA approved drugs or under clinical trials.

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Identification of candidate tumor antigens
 173 proteomics-supported mutations.
 9 – 11 amino acids in length.
 Neoantigens in 38% of the tumors.
 16 Cancer-Testis antigens
 3 antigens were increased by 2-fold in 5% of all tumors
Label-free Proteomics
TMT-proteomics

23. 23
Unified, multi-omics view of colon cancer subtypes
Nat Med. 2015;21(11):1350-6
Nature. 2014;513(7518):382-7
Proteomics
Transcriptomics
Genomics
Association Network

24. 24
UMS Classification in Context of CNA, tumor
microenvironment
UMS = Unified Multi-Omics sub-types
MSI = NK cells and CD8 T cells (cytotoxic immune cells)
Mesenchymal = MDSCs, macrophages, Treg cells (suppressor immune cells)
UMS classification Provided Unified view of colon cancer subtypes with
distinct genomic, transcriptomic, proteomic and microenvironment profiles.
In silico deconvolution to quantify tumor infiltrating
lymphocyte population based on RNA-seq

25. 25
↑ glycolysis and Immune Suppression in MSI subtype
Lactate is a potent inhibitor of CD8 T cells (Brand, 2016)
A subset of MSI-H tumors respond to immune checkpoint Inhibitors.

26. 26
Validation of Interplay b/w Metabolic Reprogramming
and Immune function
PKM2 drives aerobic glycolysis and lactate production (Christofk, 2008)

27. 27
Interplay b/w Glycolysis and CD8 T Cell Activation

28. 28
Conclusion
1. Conon-cancer associated Proteins &
Phosphosites.
2. Neoantigens and cancer/testis antigens
in 78% of the tumors.
3. Rb Phosphorylation is an oncogenic
driver and a potential target.
4. Glycolysis inhibition may render MSI
tumors more sensitive to checkpoint
inhibition.

29. 29
Discussion and Future Perspective
1. mRNA levels do not reliably predict protein levels.
2. Protein networks better predict gene function than RNA networks.
3. Ideas to target signaling proteins and metabolic enzymes or tumour antigens for
therapeutic benefits were not tested in this study.
4. If the findings of this study could be validated then they will likely lead to the testing of
new strategies for personalized cancer treatment.
5. Proteogenomics approach to precision therapy will lead to more effective treatments
is remained to be witnessed.

30. • Thanks
30

31. 31
Proteomic Result of Somatic Mutations
Stop gain –truncated protein
Frameshift INDEL – completely different translation from original (AUG ACG AUU) → (AUA CGA UU)
Non-frame shift INDEL – insert/remove amino acid
Non-synonymous SNV – different amino acid
TGFBR2 – tumor suppressor gene

32. Copy number alteration and Microsatellite
32

33. 33

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A p-value of 0.05 implies that we are willing to accept that 5% of all tests
will be false positives.
An FDR-adjusted p-value (aka a q-value) of 0.05 implies that we are willing
to accept that 5% of the tests found to be statistically significant (e.g. by p-
value) will be false positives.
G–score of goodness-of-fit (also known as the likelihood ratio test,
the log-likelihood ratio test,
The Wilcoxon signed-rank test is a non-parametric statistical hypothesis test used to
compare two related samples, matched samples, or repeated measurements on a
single sample to assess whether their population mean ranks differ (i.e. it is a paired
difference test).

36. 36