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