omics technologies in cancer immunotherapymsto.dmsc.moph.go.th/data/63/3_2563ppt_3.pdf · 2020. 1....
TRANSCRIPT
OMICS Technologies in Cancer Immunotherapy
Trairak Pisitkun, MD
Center of Excellence in Systems Biology
CANCER: ONE CELL AT A TIME
Edward J. Fox & Lawrence A. Loeb, Nature 512, 143–144
CANCER GENOMICS
https://www.cancer.gov/about-cancer/causes-prevention/genetics
Acquired or somatic genomic
alterations account for 90 to 95
percent of all cases of cancer
PRECISION MEDICINE
Using the genetic changes in a patient’s tumor to determine their treatment is known as precision medicine
Credit: National Cancer Institute
1800 1900 1940 2000 2018
ผ่าตดั ฉายรงัสี ยาเคมบี าบดั ยามุ่งเป้า
ภมูคิุม้กนั
บ ำบดั
Immuno
therapy
ภมูคิุม้กนับ ำบดั
Immunotherapyคอือนำคตของกำรรกัษำมะเรง็
ข้อจ ำกัด• ไมไ่ดผ้ลกบัมะเรง็ในระยะแพรก่าระจาย• ท าลายเซลลป์กตทิ าใหเ้กิดผลขา้งเคียงสงู• ไดผ้ลไมย่ั่งยืน โอกาสมะเรง็กลบัเป็นซ า้สงู
http://www.parkerici.org/cancer-immunotherapy
CANCER-IMMUNITY CYCLE
EFFECTOR CYTOTOXIC T CELLS KILLING TARGET CELLS IN CULTURE
Structure-Based, Rational Design of T Cell Receptors.Zoete V, Irving M, Ferber M, Cuendet MA, Michielin O.Front Immunol. 2013 Sep 12;4:268.
Cancer-Immunity Cycle
Identification of personal tumor-specific neoantigens
RNA vaccineRNA-transfected DCPeptide vaccine
Highlighted areas indicate projects that are being developed at the Faculty
of Medicine, Chulalongkorn University
Advanced cancer models
Engineered NK cell
Personalized NeoAntigen Vaccine
Of 6 vaccinated patients - 4 had no recurrence at 25 months after vaccination- 2 had recurrent disease but experienced complete tumor regression with
subsequent anti-PD-1 therapy
The cumulative rate of metastatic events was highly significantly reduced after the start of vaccination, resulting in a sustained progression-free survival.
Personalized NeoAntigen Peptides and Poly-ICLC Trials in clinicaltrials.gov
Rank Conditions Other treatment Sponsor/Collaborators Phases Enrollment
1 Melanoma Dana-Farber Cancer Institute Phase 1 20
2 Renal Cell Carcinoma IpilimumabDana-Farber Cancer InstituteBristol-Myers SquibbOncovir, Inc.
Phase 1 20
3 Glioblastoma Radiation TherapyDana-Farber Cancer InstituteThe Ben & Catherine Ivy FoundationAccelerate Brain Cancer Cure
Phase 1 16
4 Follicular Lymphoma Rituximab Dana-Farber Cancer Institute Phase 1 20
5 Lymphocytic Leukemia CyclophosphamideDana-Farber Cancer InstituteOncovir, Inc.Neon Therapeutics, Inc.
Phase 1 10
6 Urothelial Cancer AtezolizumabGenentech, Inc.Icahn School of Medicine at Mount Sinai
Phase 1 15
7 Pediatric Brain Tumor Washington University School of Medicine Phase 1 10
8 Non Small Cell Lung Cancer Pembrolizumab Washington University School of Medicine Phase 1 20
9 Glioblastoma Temozolomide Washington University School of Medicine Phase 1 1
10Melanoma, Lung Cancer or Bladder Cancer
NivolumabNeon Therapeutics, Inc.Bristol-Myers Squibb
Phase 1 90
11 Lung CancerPembrolizumab + Carboplatin + Pemetrexed
Neon Therapeutics, Inc.Merck Sharp & Dohme Corp.
Phase 1 15
12Triple Negative Breast Cancer
Washington University School of Medicine Phase 1 15
13 Follicular Lymphoma Nivolumab + Rituximab Washington University School of Medicine Phase 1 20
14 Glioblastoma Tumor Treating FieldsAdilia HormigoNovoCure Ltd.Icahn School of Medicine at Mount Sinai
Phase 1 20
15Pancreatic CancerColorectal Cancer
Pembrolizumab M.D. Anderson Cancer Center Phase 1 60
KCMHCU
Personalized NeoAntigen Vaccine
1. Identification of tumor associated mutations
2. MHC processing and peptide presentation
3. T-cell recognition
Somatic mutations- SNV, indels- Gene fusion- Mutation creating splice site- etc. all possible ways to
generate non-self peptides- Allele frequency
• RNA expression• Protein expression• Protein degradation
- Ubiquitination- Proteasome cleavage (ex.NetChop)
• Peptide-HLA assembly- TAP selection (ex. TAPPred)- Peptide-HLA binding (ex. NetMHC)
• MHC presentation- Stability (ex. NetMHCstab)- Direction of mutated amino acids (structure analysis)
• TCR-peptide binding• immunogenicity
Neoantigen Presentation
Sample preparation and genomic sequencing
OMICS Technologies in Cancer Immunotherapy• Genomic Sequencing
• Mutational landscape • Clonality• Neoantigen Prediction
• Immunogenotyping
• High-Throughput Tumor Antigen Screening
• High-throughput T Cell Receptor Sequencing – TCRseq
• Gene Expression• Bulk RNA-Seq• Single cell RNA-Seq
• High-dimensional Functional Immune Profiling
Neoantigen prediction algorithm MuPeXI
Number of cases in 1 year
Cancer types Cancer tissue samples PBMCs
Breast 1 1
Colon 13 13
Liver 1 1
Ovary 4 4
Pancreas 2 2
Total 21 21
Neoantigen prediction of colon cancer
Samplename
Missense Mutation
Insertion/Deletion
Frameshift mutation
Total mutations
HLA-A HLA-B HLA-CCandidate
neoantigensRNA available
Colon 1 330 11 16 357HLA-A02:07 HLA-A02:07
HLA-B46:01 HLA-B40:02
HLA-C01:02 HLA-C03:04 36 No
Colon 2 12 1 4 17HLA-A33:03 HLA-A33:03
HLA-B46:01 HLA-B07:02
HLA-C01:02 HLA-C07:02 4 No
Colon 3 327 7 11 345HLA-A24:02 HLA-A33:03
HLA-B44:03 HLA-B15:25
HLA-C07:06 HLA-C04:03 56 No
Colon 4 210 1 16 227HLA-A11:01 HLA-A29:01
HLA-B46:01 HLA-B35:01
HLA-C01:02 HLA-C04:01 48 No
Colon 5 236 10 15 261HLA-A33:03 HLA-A24:02
HLA-B40:01 HLA-B40:01
HLA-C03:04 HLA-C07:02 41 No
Colon 6 304 12 7 323HLA-A33:03 HLA-A02:01
HLA-B58:01 HLA-B15:13
HLA-C03:02 HLA-C08:01 29 Yes
Colon 7 327 43 35 405HLA-A33:03 HLA-A24:02
HLA-B40:01 HLA-B58:01
HLA-C03:04 HLA-C03:02 48 No
Colon 8 38 2 0 40HLA-A33:03 HLA-A24:02
HLA-B40:01 HLA-B58:02
HLA-C03:02 HLA-C07:02 4 Yes
Colon 9 40 2 0 42HLA-A33:03 HLA-A02:07
HLA-B46:01 HLA-B44:03
HLA-C01:02 HLA-C07:06 8 No
Advanced pancreatic cancer
NoUniprot Gene Symbol Mutation type Mutation Long peptide sequence for synthesis
GRAVY score (hydrophobicity)
Length (long peptide)
1Q96CN7 ISOC1 Frameshift_mutation A/AIYI TKFSMVLPEVEAALAIYIEIPGVRS 0.708 25
2Q14789 GOLGB1 Frameshift_mutation /YLX KDEALQEERIPWKLLITKLKN -0.871 21
3Q92598 HSPH1 Anchor_mutation E/K NGGVGIKVMYMGKEHLFSVEQITAM 0.252 25
4Q03518 TAP1 Anchor_mutation F/Y AVSSGNLVTYVLYQMQFTQAVEVLL 0.84 25
5Q96T58 SPEN Frameshift_mutation I/IYFYX KIGGNKIYFYIRWILQIGKVSWLFI 0.592 25
6P08238 HSP90AB1 Anchor_mutation T/P IWTRNPDDIPQEEYGEFYKSLTNDW -1.38 25
7A0A087WUL8 NBPF19 Anchor_mutation V/F LELPDLGQPYSSAFYSLEEQYLGLA -0.084 25
8Q13439 GOLGA4 Frameshift_mutation /QLX TEKEKLLQRVAINGRKKKRQFLLIL -0.592 25
9Q99496 RNF2 Frameshift_mutation /X KPNGTLLRTYKGAQMSL -0.594 17
10Q9UGM3 DMBT1 Frameshift_mutation /X VICSAAQSHSQRPGQILG 0.006 18
11Q86YZ3 HRNR Anchor_mutation Q/K RGERHGSSSRSSSRYGKHGSGSRQS -2.012 25
12Q9HCI6 KIAA1586 Anchor_mutation R/I NCLNTRYSATIIAEHIAKEMKMKIF -0.024 25
13O60506 SYNCRIP Frameshift_mutation N/IFX VMAKVKVLFVRIFTLPIL 1.772 18
14Q14114 LRP8 Anchor_mutation N/K VVIALLCMSGYLIWRKWKRKNTKS 0.046 24
15P42702 LIFR Anchor_mutation S/Y IISVVAKNSVGSSPPYKIASMEIPN 0.292 25
16O75165 DNAJC13 Anchor_mutation F/L PAWVLRKPRELLIALLEKLTELLEKN -0.012 26
17Q8N1B4 VPS52 Deletion L/- AQRGEQRYPFEAFRSQHYALLDNSC -1.052 25
18Q9H0G5 NSRP1 Missense E/V KERNQEKPSNSVSSLGAKHRLTEEG -1.644 25
Date of
Collection
5-Jul-2018 Negative - - 20 Control Neg
Positive - - 2,136 Control Pos
ISOC1 TKFSMVLPEVEAALAIYIEIPGVRS 2ug/ml 28 -
GOLGB1 KDEALQEERIPWKLLITKLKN 2ug/ml 36 -
HSPH1 NGGVGIKVMYMGKEHLFSVEQITAM 2ug/ml 160 Positive
TAP1 AVSSGNLVTYVLYQMQFTQAVEVLL 2ug/ml 132 Positive
SPEN KIGGNKIYFYIRWILQIGKVSWLFI 2ug/ml 12 -
HSP90AB1 IWTRNPDDIPQEEYGEFYKSLTNDW 2ug/ml 72 Positive
NBPF19 LELPDLGQPYSSAFYSLEEQYLGLA 2ug/ml 20 -
GOLGA4 TEKEKLLQRVAINGRKKKRQFLLIL 2ug/ml 40 -
RNF2 KPNGTLLRTYKGAQMSL 2ug/ml 24 -
DMBT1 VICSAAQSHSQRPGQILG 2ug/ml 20 -
HRNR RGERHGSSSRSSSRYGKHGSGSRQS 2ug/ml 16 -
KIAA1586 NCLNTRYSATIIAEHIAKEMKMKIF 2ug/ml 20 -
SYNCRIP VMAKVKVLFVRIFTLPIL 2ug/ml 12 -
LIFR IISVVAKNSVGSSPPYKIASMEIPN 2ug/ml 12 -
DNAJC13 PAWVLRKPRELLIALLEKLTELLEKN 2ug/ml 24 -
VPS52 AQRGEQRYPFEAFRSQHYALLDNSC 2ug/ml 28 -
NSRP1 KERNQEKPSNSVSSLGAKHRLTEEG 2ug/ml 16 -
LRP8 VVIALLCMSGYLIWRKWKRKNTKS 2ug/ml 60 Positive
Peptides pool 18 peptides 2ug/ml 132 Positive
Verorab - 0.1IU/ml 36 -
Verorab - 0.25IL/ml 152 -
HepatitisB - 1ug/ml 752 Positive
peptide sequenceAntigens Dose IFN-g ELISpot (SFU/106 PBMC) Results
Peptide1
Neg PHA
Peptide2 Peptide3 Peptide4 Peptide5 Peptide6 Peptide7 Peptide8
Peptide11 Peptide12 Peptide13 Peptide14 Peptide15 Peptide16 Peptide17 Peptide18 Peptide2 Pool
Verorab1 Verorab2 HepatitisB
Peptide9 Peptide10
MHCSeqNet: a deep neural network model for universal MHC bindingprediction.Phloyphisut P, Pornputtapong N, Sriswasdi S, Chuangsuwanich E.BMC Bioinformatics. 2019 May 28;20(1):270.
1. Identification of tumor associated mutations
2. MHC processing and peptide presentation
3. T-cell recognition
Somatic mutations- SNV, indels- Gene fusion- Mutation creating splice site- etc. all possible ways to
generate non-self peptides- Allele frequency
• RNA expression• Protein expression• Protein degradation
- Ubiquitination- Proteasome cleavage (ex.NetChop)
• Peptide-HLA assembly- TAP selection (ex. TAPPred)- Peptide-HLA binding (ex. NetMHC)
• MHC presentation- Stability (ex. NetMHCstab)- Direction of mutated amino acids (structure analysis)
• TCR-peptide binding• immunogenicity
Neoantigen Presentation
Optimizing HLA pulldowns from HCT116
HCT116Known mutation
Database
HCT116 HLA class IImmunoprecipitation
MS analysis NeoantigenSearch
W6/32 antibody immunopeptidome
Eluted with 1% TFACleaned up by StageTips
125 min gradient 1464 peptides identified108 cells
Current Workflow
Immunoprecipitation
150 uL Invitrogen
Sepharose 4B
375 ug W6/32 Ab
Washing
4x 20mM Tris, 150mM NaCl
4x 20mM Tris, 400mM NaCl
4x 20mM Tris, 150mM NaCl
2x 20mM Tris
Elution
1% TFA
C18 Stagetip
Equilibrated with 0.1%TFA
Elution
28% CAN 0.1%TFA
For MHC class I peptides
Elution
80% CAN 0.1%TFA
For MHC proteins
HCT116
10 plates
or 108 cells
Cell Lysis
1% OG
0.25% SDC
Speedvac
MS analysis
This protocol was adapted from:
Mann, MCP 2015
Mann, MCP 2018
2306 Peptides Were Identified
• 2325 peptides were identified at 1% FDR
• Corresponding to 1725 proteins
• 8 phosphopeptides
• 4 neoantigen peptides
675 1032 618
Include 1+ > 2+
Comparing with Published Data
HCT116 IP Peptides
1 2859
2 2830
3 2921
4 2945
Total 4207
MCP 2015
Total
CUSB
1302 1023 31841508 817 2104
CUSB
MCP 2015 #3
4 Neoantigen Identified
Gene Peptide Mutation
CHMP7 QTDQMVFNTY A324T
RBBP7 EERVIDEEY N17D
RNPEP ALFEVPDGFTA I195F
UQCRB EEEKFYLEP N88K
Mann MCP 2015
CUSB
Where We Are
Anticipated Results from Purcell 2018 Nature Protocol (accepted)
We identified 2325 peptides (1% peptide FDR)
Peptide Length and Sequence Patterns
8-AA
9-AA
10-AA
11-AA
0
200
400
600
800
1000
1200
8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24
# p
eptides
Length
Peptide Length Distribution
Comparing Motifs with IEDB
HLA-IP
9-AA
1140 entries
A*02:01 7861 entries
A*01:01 1991 entries
B*45:01 475 entries
C*05:01 2559 entries
C*07:01 323 entries
B*18:01 652 entries
Summary
• The pipeline of somatic mutation, HLA typing and neoantigen
prediction could be performed
• The burden of non-synonymous mutation is influent to number of
neoantigens
• Most mutations are from passenger genes rather than cancer driver
genes
• Common mutated genes are cancer driver genes
• HLA pull-down were adapted from Mann (MCP 2015) and optimized
with HCT116 cell line
• >2300 peptides were identified, containing 4 neoepitope peptides
• Peptide motifs agree with known HLA peptides from IEDB
• In-house peptide synthesis and in vitro immunology testing are
ongoing
Accurate de novo peptide sequencing with
SMSNet uncovers thousands of new HLA
ligands and phosphopeptides
1 Department of Computer Engineering, Faculty of Engineering, Chulalongkorn University2 Computational Molecular Biology Group, Chulalongkorn University3 The School of Information Science & Technology, Vidyasirimedhi Institute of Science and Technology4 Research Affairs, Faculty of Medicine, Chulalongkorn University
Korrawe Karunratanakul1, Ekapol
Chuangsuwanich1,2,3, and Sira Sriswasdi2,4
Encoder-Decoder architecture
Motivated by machine translation problem
• MS/MS masses + abundances amino acid sequence
Integration of domain knowledge
• Adjacent MS/MS masses should differ by the total mass of
some amino acid composition36
Encoder-Decoder LSTM Series
Peptides identified by SMSNet are likely true
HLA ligands
37
(LEFT) SMSNet peptides are predicted to bind strongly to HLA
(RIGHT) Peptides predicted by SMSNet alone and reported peptides
exhibit the same HLA binding motif
HLA-A0101 binding motif
Peptides predicted by both tools
SMSNet’s unique predictions
Red bar indicates the 2%
threshold for characterizing
strong HLA ligands
Unique peptides identified by SMSNet
represent completely novel HLA ligands
38
Among 8,301 unique peptides predicted by SMSNet
• 3,837 peptides have never been characterized against any HLA
allele (based on IEDB’s ligand database)
• 4,464 peptides are present in IEDB but have not been tested
against HLA alleles used in this dataset
– These 4,464 peptides are positive ligands of other alleles
KCMHCU
Personalized NeoAntigen Vaccine
Customized peptide synthesis at KCMH
Project Gene Neoantigen Peptide Sequence
Patient #1 HSPH1-WT HSPH1-Mut HSPH1-A11 HSP90AB1-A11
MYMGEEHLF MYMGKEHLF GIKVMYMGK DIPQEEYGEFYK
Patient #2 C2CD5 ABCA2 MARF1 TET3 ITPR3 DOCK7 IRS2 KIAA1671
KKAQAEAKLQLSVISCHLWNMK PIMYPASFWFEAQLRLRVPH FEEFISVLPPRLPLKMPQCH TGKEGKSSRGCTIAKWVIRR EENEDIVMMETKLKILEILQ DLDEQEFVYKVPAITKLAEI TSAAGRTFPESGGGYKASSP RSPLEDETDNMWMFKDSTEEK
Patient #3 LYL1 UROS KMT2C ATN1 CCDC85C AGO4
RLKRRPSHWELDLAEGH EKLAVIFVPVSRKTSLQMKPFIFC DLWVHLNCALCPRRSMRLRLV AAASRKLWAPSSWSISPPTGGR AVVHAMKVLELHENLDRQLQD IQFYKSTRFKLTRIIYYRGGV
Mouse Model MC-38
Reps1 Adpgk
GRVLELFRAAQLANDVVLQIMELCGATR GIPVHLELASMTNMELMSSIVHQQVFPT
EGFR L858R EGFR_WT EGFR_L858R EGFR_WT_A11 EGFR_L858R_A11 EGFR_WT_C07 EGFR_L858R_C07
KTPQHVKITDFGLAKLLGAEEKE KTPQHVKITDFGRAKLLGAEEKE KITDFGLAK KITDFGRAK HVKITDFGL HVKITDFGR
KCMHCU
Personalized NeoAntigen Vaccine
Adjuvants for peptide vaccines
OMICS Technologies in Cancer Immunotherapy• Genomic Sequencing
• Mutational landscape • Clonality• Neoantigen Prediction
• Immunogenotyping
• High-Throughput Tumor Antigen Screening
• High-throughput T Cell Receptor Sequencing – TCRseq
• Gene Expression• Bulk RNA-Seq• Single cell RNA-Seq
• High-dimensional Functional Immune Profiling
Single Cell OMICS Analysis
• Integrated single-cell profiles
• CRIPSR imaging
• Understanding tumor genomic diversity using single cell analysis
• Single-cell transcriptomics applied to embryonic stem cells
https://commonfund.nih.gov/singlecell
Characterize single cell types from tissues/organs
https://www.humancellatlas.org/
https://www.humancellatlas.org/
Single-cell RNA sequencing (scRNA-seq)
Mass cytometry
Mass cytometry