Innovative Cancer Vaccine: Fruit of Integration of AI and Immunology

BIO Asia – Taiwan Session 2 “AI for Biotech & Healthcare” July 24, 2019

Shun Doi, PhD, President and CEO CYTLIMIC Inc.

https://www.cytlimic.com/ Table of Contents Development of Innovative Cancer Vaccine

➢ CTL is main player in Cancer Immunotherapy ➢ Why and How AI in Cancer Vaccine? ➢ Cancer Vaccine: History of Failure, Challenges ➢ Cytlimic approaches to overcome Challenges ✓ Optimized Shared-antigen Peptides – advent of AI ✓ Optimized Combination Adjuvants – to boost CIC ➢ CYT001: Cytlimic’s Lead vaccine for HCC ➢ Future of AI in cancer vaccine

CTL: Cytotoxic T Lymphocyte, CIC: Cancer-Immunity Cycle, HCC: Hepatocellular Carcinoma 2 The Novel Prize in Physiology or Medicine 2018

Discovery of ICI (Immune-Checkpoint Inhibitors) to Release Negative Regulations to CTLs (Cytotoxic T Lymphocytes), to let those CTLs kill cancer cells

CTLA-4 PD-1

The improvements in clinical responses and overall survivals by ICI alone are still limited e.g., ORR are in 20% level, except for a certain lymphoma

Combination drugs/therapies for further improvements are highly desired

Drugs/therapies that tern “Cold Tumor* to Hot” are promising as such combination * Tumor whereCTLA CTLs-4 are not or rarelyPD -infiltrated1

Injection of antigen peptides induces Proliferation, Maturation, Migration to tumor sites of CTLs through activation via DCs, to kill cancer cells

Injection of Tumor cells antigen peptides DC CTL Dendritic Cell Cytotoxic T Lymphocytes CTL 9mer Antigen amino acids presentation Proliferation, Maturation, Killing by CTLs Migration to Tumor site

HLA-Class I molecule CTL

IL-12 Th0 etc. CTL IL: Interleukin Lymph nodes IFN: Interferon Th1 IFN-γ, IL-12, etc. Th0: Naïve T Helper Cell Helper T cell Th1: T Helper 1 Cell CTL: Cytotoxic T Lymphocyte HLA: Human Leukocyte Antigen

Launched : anti-CTLA-4 (Ipilimumab) 2011, anti-PD-1 (Nivolumab) 2014, … Cancer Releasing Immunotherapy peptide Accelerator Brake ICI Peptide Immune-Checkpoint １ vaccine Inhibitor ２

Tumor cells

Chemo Radio Surgery therapy therapy

Year Events

1990 Fundamental research in AI (Central Research Laboratories, NEC)

1998～ MHC-binding peptide prediction with AI Collaboration with Prof. Keiko Udaka, Kochi University

2001～ Development of WT1 peptide vaccine

2008～ First-in-Human study of WT1 vaccine, at Kochi University

2012～ Development of CYT001 vaccine Collaboration with Yamaguchi University NEC Headquarter Building and Kochi University (Tokyo) 2016/12 Establishment of CYTLIMIC Inc.

9mer amino acids Cancer Cell HLA-Class I molecule

Tumor Antigen How to find peptides, binding to HLA cell peptides molecule, and induce anti-tumor immunity

Hypothesis There must be a “rule” between the binding affinity and binding-peptide sequence. Question How to find the rule, within minimum experiments?

9mer Update of amino acids Rules HLA-Class I Binding Tests AI molecule algorithm (at Kochi Univ.)

Results of the tests STEP2: Finding peptides Antigen protein Candidates peptides amino acids sequence Sequence Affinity ABCDEFGHI 4.5 MPRAPRCRAVRSLLRSHYREVLPLATFVRRLGPQGWRLVQK GDPAAJFRALVAQCLVCVPWDARPPPAAPSFRQVSCLKELV BCDEFGHIJ 6.2 ARVLQRLCERGAKNVLAFGFALLDGARGGPPEAFTTSVRSYJ LPNTVTDALRGSGAWGLLLLRRVGDDVLVHLLARCALFVLVI CDEFGHIJK 3.5 APSCAYQVCGPPLYQLGAATQARPPPHASGPRRRLGCERW NHSVREAGVPLGLPAPGARRRGGSASRSLPLPKRPRRGAAPJ ・・ EPERTPVGQGSWAHPGRTRGPSDRGFCVVSPARPAEJEATN ・・ LEGALSGTRHSHPSVGRQHHAGPPSTSRPPRPWDTPCPPVY AMTKHFLYSSGDKEQLRPSFLLSSLRPSLTGARRLVETIFLGSW ・・ MPGTPNRRLPRLPQRYWQMRPLFLELLGNHAQCPYGVLLKI ・ PQRSTUVWX 8.2 QRSTUVWXY 3.3 RSTUVWXYZ 6.4 (9 amino acids)

Binding peptide sequence depends on HLA type Challenges ・Costs ・Personalization Conventional Peptide A Peptide B Peptide C T U T U T U P Q S V P Q S V P Q S V Vaccine R W X R W X R W X

HLA type HLA A*24:02 HLA A*02:01 HLA A*02:06 Human Leukocyte Antigen

CYTLIMIC T U P Q S V Vaccine R W X Peptide X A single peptide can induce anti-tumor Japanese: 85% response in patients having different HLA types. Caucasian: 60%

Year/ Name Company Indication month

2012/2 OTS102 Oncotherapy (J) PDAC

Tecemotide Merck KGaA (G) 2012/12 NSCLC (Stimuvax) (Oncothyroen) Melanoma 2013/9 MEGE-A3 GSK (UK) NSCLC Rindopepimut 2016/3 Celldex (USA) Glioblastoma (Rintega) Renal Cell 2016/11 IMA901 IMMATICS (G) Carcinoma Prostate 2018/5 ITK-1 BrightPath (J) Cancer No one succeeded yet in Phase III. What was wrong?

Optimized Shared-antigen Optimized Combination Peptides, escaping self-tolerance, Adjuvants, best boosting the and immunogenic Cancer-Immunity Cycle

Chen DS, et al. Immunity. 2013;39(1):1-10.

How can we find What combination Optimized Shared- most effectively boosts antigen peptides? Cancer-Immunity Cycle? (neoantigen-like*)

CYTLIMIC Development Approaches

AI-based prediction & Combination of clinical- screening by patient‘s stage adjuvant samples to find cryptic* materials; Multi-HLA peptides Poly ICLC and LAG-3Ig

© CYTLIMIC Inc. 2019 Proprietary 13 Discovery of optimized shared-antigen peptides Use of AI-based discovery tool and Dendritic Cell therapy patient samples enables the discovery of rarely existing cryptic multi-HLA peptides (Yamaguchi Univ. Medical School) HCC Patients s Patients Phase I study: HSP70 mRNA transfected DC ･･･ Therapy for unresectable or recurrent HCC patients

Selection of ･･･ Target Antigens - Proteome ELISPOT assay - mRNA (patients samples) - Histopathology Target HLA Target HLA A*24:02, 02:01, 02:06 Antigens NEC Kochi Univ. AI-base Identification of

MPRAPRCRAVRSLLRSHYREVLPLATFVRRLGPQGWRLVQKGDPPrediction AAJFRALVAQCLVCVPWDARPPPAAPSFRQVSCLKELVARVLQR Immunogenic LCERGAKNVLAFGFALLDGARGGPPEAFTTSVRSYJLPNTVTDAL RGSGAWGLLLLRRVGDDVLVHLLARCALFVLVIAPSCAYQVCGPsystem PLYQLGAATQARPPPHASGPRRRLGCERWNHSVREAGVPLGLP Peptide Cocktail APGARRRGGSASRSLPLPKRPRRGAAPJEPERTPVGQGSWAHPG RTRGPSDRGFCVVSPARPAEJEATNLEGALSGTRHSHPSVGRQH HAGPPSTSRPPRPWDTPCPPVYAMTKHFLYSSGDKEQLRPSFLLS Binding assay SLRPSLTGARRLVETIFLGSWMPGTPNRRLPRLPQRYWQMRPLFL ELLGNHAQCPYGVLLKIMPRAPRCRAVRSLLRSHYREVLPLATFVR RLGPQGWRLVQKGDPAAJFRALVAQCLVCVPWDARPPPAAPSF HLA A*24:02, 02:01, 02:06 RQVSCLKELVARVLQRLCERGAKNVLAFGFALLDGARGGPPEAFT HSP70 Multi-HLA Class I peptide TSVRSYJLPNTVTDALRGSGAWGLLLLRRVGDDVLVHLLARCALF VLVIAPS GPC3 Multi-HLA Class I peptide Screening

HSP70 GPC3 Peptides HLA allele 122 138 297 335 371 448 166 170 222

24:02 -5.82 -4.37 -5.21 -6.17 -5.50 -5.49 -4.70 -7.43 -5.34 Binding 02:01 -4.59 -4.76 -4.33 -4.86 -4.90 -5.56 -5.31 -5.07 -6.21 Assay 02:06 -5.53 -5.59 -5.58 -5.07 -5.37 -4.28 -6.14 > -3 -5.21 2402/(except 4/5 3/6 1/2 2/2 6/7 6/7 3/5 2/2 3/5 0201,0206) 0201/(except 0/1 0/2 0/1 0/1 2/3 1/3 1/2 0/2 2/2 ELISPOT 2402,0206) Assay Other 1/1 0/2 0/1 0/1 1/3 0/3 0/2 1/2 1/2 combination Total 5/7 3/10 1/4 2/4 9/13 7/13 4/9 3/6 6/9 Note: ✓ PBMC of Yamaguchi HSP70 DC therapy patients (total 13 samples, Slide 14) were used to conduct ELISPOT and ELISA assays ✓ Positivity criteria: Significantly higher in either ELISPOT or ELISA (ELISPOT p<0.05, ELISA p<0.015)

Reference: Menez-Jamet J. et al, Optimized tumor cryptic peptides: the basis for universal neo-antigen-like tumor vaccine, Ann. Transl. Med., 2016, 4(14), 266. © CYTLIMIC Inc. 2019 Proprietary 15 Challenge #2 Novel combination adjuvant

Possible combinations of clinical-stage adjuvant materials (～10) were tested using tumor-bearing mouse model, to find novel synergistic pair

Discovered Poly IC (TLR3, MDA5 agonist) + LAG-3Ig (MHC Class II agonist) Tumor growth (P815 mastocytoma tumor) Peptide + IFA

Peptide + Poly IC + LAG-3Ig

Peptide + Poly IC Peptide + LAG-3Ig

* registered in Japan

Reference: Kano Y., Tamada K., Doi S. et al, Cancer Science, 2016, 107: 398-406 © CYTLIMIC Inc. 2019 Proprietary 16 CYT001 – CYTLIMIC Lead Vaccine CYT001 - CYTLIMIC Peptide Vaccine

Optimized Shared-antigen Optimized combination of Multi-HLA-reactive peptides adjuvants in clinical-stage

peptide Hiltonol IMP321 Vial A + Vial B Vial C HSP70 GPC3 Poly-ICLC LAG-3Ig Class I Class I (Hiltonol®) (IMP321) peptide peptide Oncovir Immutep

Shared antigens highly expressed in APC activation and control of various tumors immunosuppressive mechanism including HCC, EsC, GaC, etc. (2 patents filed) (2 patents filed) SC injections simultaneously at both underarms and both groins Cross-reactivity to HLA*A- 24:02, 02:01,02:06, covers 85% of Japanese and about 60% of Caucasian* Caucasian * German: 58%, etc.

Poly ICLC Peptides HSP70 Antigen presentation reactive TLR3/MDA5 agonist CTL

HSP70 HSP70 CTL reactive peptide CTL Hiltonol MHC Class I receptor HSP70 reactive CTL TLR3 MDA5 Pre- CD4 + T cell Cancer DC existing Helper Proliferation, Maturation, Adjuvants, synergistically peptide T cell help Migration to Tumor site cell activates DCs GPC3 reactive CTL MHC Class II receptor GPC3 GPC3 reactive peptide CTL IP321 CTL

GPC3 MHC Class II agonist Peptides reactive LAG-3Ig Antigen presentation CTL Poly ICLC binds to TLR3 (Toll-Like Receptor 3) LAG-3Ig binds to MHC (Major Histocompatibility and MDA5 (Melanoma Differentiation Antigen 5) Complex) Class II molecules of APCs, and of Antigen Presenting Cells (APCs), and activates APCs through MHC Class II signaling. activates APCs through this “danger signal” mimicking viral infection. The combination of Poly ICLC (Hiltonol, Oncovir) and LAG-3Ig (IMP321, Immutep) synergistically activates antigen-specific CTL reactions as effective combination adjuvants. Reference: Kano Y. et al., Cancer Science, 2016, 107: 398-406 © CYTLIMIC Inc. 2019 Proprietary 19 Partnering for adjuvant supply

A pharmaceutical corporation A globally active biotechnology dedicated to the development of company developing LAG-3 nucleic-acid-based clinical related immunotherapeutic immunotherapies for cancer and product. other diseases. Immutep Eftilagimod Alpha (LAG- Oncovir Hiltonol (Poly ICLC) is 3Ig, IMP321) is supplied to supplied to Cytlimic and used in Cytlimic and used in Cytlimic Cytlimic CYT001 cancer vaccine CYT001 cancer vaccine under under collaboration agreement collaboration agreement between between Oncovir and Cytlimic, to Immutep and Cytlimic, to synergistically boost patient synergistically boost patient immune responses to vaccine immune responses to vaccine antigens together with Immutep antigens together with Oncovir Efitilagimod Alpha. Hiltonol.

https://www.oncovir.com/ https://www.immutep.com/

Study Target disease, Study Site Name Phase Period patients (PI) (Status) 1 Yamaguchi Advanced solid 2016/1~ IIT (Specified Univ. YNP01 tumor (HCC, GI), 2019/3 Clinical Trial)* (Prof. Hiroaki n=17 (completed) jRCTs061180058 Nagano)

1 Resectable HCC Yamaguchi 2018/1~ IIT (Specified (Neoadjuvant, Univ. YCP02 (active, Clinical Trial)* adjuvant vaccine) (Prof. Hiroaki recruiting) jRCTs061180033 n=20 (planed) Nagano) 1b Investigator- 2019/8~ Chiba Univ. CRESCE Unresectable HCC Initiated Clinical (not yet (Prof. Naoya NT1 n=6 Trial recruiting) Kato) jRCT2031190072

* Specified Clinical Trial: Clinical trial conducted under Clinical Trial Act (Act No.16 of April 17, 2017)

© CYTLIMIC Inc. 2019 Proprietary 21 YNP01 trial: Study Design Download: https://www.cytlimic.com/download/SITC_2018_poster.pdf ▌Study name ⚫ A phase I study of combination immunotherapy with HSP70 derived peptide, GPC3 derived peptide, Poly ICLC and Soluble LAG-3 for patients with advanced or metastatic solid cancer (jRCTs061180058) (mg/injection) ▌Dose Level Peptides Adjuvants HSP70 GPC3 Hiltonol IMP321 Level 1 1.0 1.0 1.4 0.25 Level 2 2.0 2.0 1.4 0.25 Level 3 2.0 2.0 1.4 1.0 ▌Vaccination Schedule

▌Patient: 17

© CYTLIMIC Inc. 2019 Proprietary 22 YNP01: Endpoints and summary of results ▌Endpoints ⚫Primarily endpoint: Safety ⚫Secondary endpoints: Peptide-specific immune responses, biomarker response, clinical response Summary Safety All AEs related to CYT001 were Grade 1/2

Vaccine-specific 88% (15/17) responded, Prompt (most responders in 1 mo.) immune response and Strong (IFNɤ ELISPOT≧3+, in grades -/1+/2+/3+/4+/5+)

Anti-tumor response DCR: 29% (5/17), SD – 5 pts., PD – 12 pts. (no CR, PR) (RECIST v1.1) Tumor marker decrease: 58% (10/17) Median OS: 10.3 mo. (95% CI 5.52–20.4 mo.) Median PFS: 1.9 mo. (95% CI 1.2–3.0 mo.) Long survivor (≧ 24 mo.): 3 pts. (HCC) (HCC: 75% (3/4))

Biomarker response TIM3+/CD4 decreased in Level 3 after 1 course (p=0.012)

Sub-class analysis Longer survival in pts. with lower PD-1+/CD4, TIM3+/CD4, TIGIT+/CD8 than higher ones, respectively

Level 1 Level 2 Level 3 (n=3) (n=3) (n=11) Total 2 1 7

Edema 1 0 1 irAE (Grade Fever 0 0 2 1/2) Injection site reaction/ 1 1 6 Skin reaction note: There is no irAE as Grade 3/4. (ir: immune related)

Grade 1+2 3 4 3 Other AE Grade 3 8 7 4

note: All of Other AEs are not related to CYT001. There is no AE as Grade 4.

Level # Patients HSP70 (%) GPC3 (%)

# Pts. Level 1 3 2 2 (Post CTL≧3+) (0) (0) Post CTL grade Level 2 3 1 2 > (Post CTL≧3+) (0) (0) Pre CTL grade Level 3 11 8 9 (Post CTL≧3+) (6)*** (5)***

11 (65%)* 13 (76%)* all 17 15 (88%)**

(Notes) ✓ CTL grade: -/+/2+/3+/4+/5+ ✓ Among 10 responses to HSP70, 8 (80%) were observed after 1 course (*) ✓ Among 12 responses to GPC3, 9 (75%) were observed after 1 course (*) ✓ Response to at least one of antigen-peptides (**) ✓ Among responders, strong responses (ELISPOT≧3+, after 1 course) were observed only in Level 3 (***)

© CYTLIMIC Inc. 2019 Proprietary 25 (3) Biomarker response in PBMC In patients recieving Level 3 dosage, the population of TIM3+ cells in CD4+ T cells was decreased significantly, and MDSC (CD11b+CD33+HLR-DR- /PBMC) showed trend to decrease, after 1 course of treatment.

TIM-3/CD4 MDSC CD11b+CD33+HLA-DR-/PBMC P=0.012 (Level3) 12 6 P=0. 132 (Level3)

9 4

2 3

0 0 Pre Post Pre Post Note: Solid brown line– Level 3 pts., Dashed line– Level 1 or 2 pts. Biomarkerts tested: Proportion of CD4+T cells/ CD8+T cells/ Monocyte cells/ Treg cells (FrⅠ,Ⅱ, Ⅲ)/ MDSC (CD11b+CD33+HLA-DR-)/ Memory T cells, Exhaustion marker (PD-1, LAG-3, TIM3, TIGIT) on CD4/8+ T cells © CYTLIMIC Inc. 2019 Proprietary 26 (3) Anti-tumor response (Swimmer plot)

✓ Best response: SD (long-SD for > 38 months) ✓ Disease control rate (DCR)：29% as of 2019/7

Surface marker expression on the PBMC (pre-treatment) and prognosis (Cut-off value = median value, Gehan-Wilcoxon test)

PD-1/CD4 TIM-3/CD4

p=0.0399 p=0.0245 p=0.0578 p=0.2495 p=0.2374

TIGIT/CD8

p=0.3327 p=0.2679 p=0.5661 p=0.0317 p=0.4732

Suggesting that the overall survival of CYT001 treatment could be improved by the combination with ICI treatment.

© CYTLIMIC Inc. 2019 Proprietary 28 YCP02 Study ▌A phase I study of combination adjuvant immunotherapy with HSP70 derived peptide, GPC3 derived peptide, Poly ICLC and Soluble LAG-3 for patients with resectable hepatocellular carcinoma (jRCTs061180033)

-2 -1 0 1 2 3 4 12 Evaluation of recurrence Neoadjuvant therapy Adjuvant therapy (6 injections (weekly)) Surgical (10 injections: 4 times weekly + 6 times biweekly resection Treatment period (6 months)

Pathological analysis to assess if the vaccine shifts “Cold tumor to Hot”

Pathological slides

Surgical Phenotypic analysis of TIL (Tumor specimen infiltrated Lymphocytes) using CyTOF TIL samples © CYTLIMIC Inc. 2019 Proprietary 29 On Interim results of YCP02 study (neoadjuvant part)

Trigger

By CYT001 neoadjuvant vaccination, ✓ CTLs infiltrate into tumor sites (HCC area) ✓ These CTLs recognize vaccine-target antigens ✓ Infiltrated T cells (including CTLs and Helper T cells) express PD-1 exhaustion marker (abstract only, no detail data presented yet)

Chen DS, et al. Immunity. 2013;39(1):1-10.

The interim analysis results suggest that CYT001 terns “Cold tumor to Hot”, thus the combination to anti-PD-1 drug improves the clinical response of anti-PD-1 drug. HOT COLD Hot, but suppressed Cold and suppressed

BrakeCancer Accelerator Cancer Brake CTL × cell ×cell Cancer Cancer cell CTL cell CTL Cancer Cancer cell cell Cancer Cancer cell cell CTL CTL

suppressed Cytlimic CI ICI Vial A Vial B Vial C Vaccine CI ICI

Hot and not-suppressed Cold, and not-suppressed No Cancer growth

Accelerator Cancer CTL cell

Cancer cell Cancer CTL cell CTL Cancer cell Cancer

suppressed cell

- CTL CTL Cytlimic

not Vial A Vial B Vial C Vaccine

© CYTLIMIC Inc. 2019 Proprietary 31 Future of AI in Cancer Vaccine （NEC） t ransgene Optimized Shared-antigen Individualized Multi-HLA peptide vaccine Neo-antigen vaccine (One-fit-all vaccine) (Personalized vaccine)

Advancing AI technologies for Cancer Vaccine

The author gratefully acknowledges the following contributors for their scientific works and supports: - Dr. Tomoya Miyakawa*, NEC Corporation - Prof.Dr. Keiko Udaka, Kochi Medical School - Prof.Drs. Masaaki Oka, Hiroaki Nagano, Shoichi Hazama, and doctors and laboratory members involved in YNP01 and YCP02 studies, Yamaguchi University School of Medicine - Prof.Dr. Koji Tamada, Dr. Daisuke Umezu, Yamaguchi University School of Medicine - Prof.Dr. Naoya Kato, Dr. Sadahisa Ogasawara, and doctors and laboratory members involved in CRESCENT1 study, Chiba University School of Medicine - Prof.Dr. Akira Saito, Tokyo Medical University - Drs. Frederic Triebel, Claudia Jacoby, Immutep Limited - Dr. Andres Salazar, Oncovir, Inc. - Dr. Shiro Akinaga, AccuRna, Inc. - Dr. Yoshihide Segawa, Mr. Genji Kawano, Mr. Toru Kano, CYTLILMIC Inc. and patients participating YNP01, YCP02 and CRESCENT1 studies

* Passed away from thyroid cancer in May 2018