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CK Life Sciences to present Cancer Vaccine data at the 2024 American Association for Cancer Research (AACR) Annual Meeting

HONG KONG SAR - Media OutReach Newswire - 5 April 2024 - CK Life Sciences Int’l., (Holdings) Inc. will be presenting new data from its cancer vaccine research pipeline at the 2024 American Association for Cancer Research (AACR) Annual Meeting in San Diego, California, USA.

Two posters will highlight data from preclinical studies of 2 investigational cancer vaccines, targeting the KRAS (Kirsten rat sarcoma virus) and PD-L1 (programmed cell death ligand 1) proteins, respectively. The KRAS protein, frequently mutated in various cancers, is a key regulator of cell growth and survival, driving tumor development by promoting uncontrolled cell proliferation and resistance to treatment. The PD-L1 protein represents one of the most important immune checkpoint proteins highly expressed on cancer cells to limit T-cell activation in the tumor microenvironment.

In addition, CK Life Sciences and its research collaborator, XtalPi, will be presenting a poster showcasing their Artificial Intelligence (AI)-empowered platform for designing cancer vaccines.

“The preclinical efficacy results of our investigational cancer vaccines targeting KRAS and PD-L1 proteins are promising, and we hope to advance these and other vaccines into clinical development in the future. We are also thrilled about the progress made in our research collaboration with XtalPi to develop an AI platform aimed at better predicting immunogenicity and designing cancer vaccines that are more likely to be effective,” said Melvin Toh, Vice President & Chief Scientific Officer at CK Life Sciences.


Abstract 4111: Multi-peptide cancer vaccines targeting KRAS induce significant anti-tumor efficacy
Authors: Chi Han Samson Li, Melvin Toh
Session Date and Time: Tuesday, April 9th, 9:00 AM - 12:30 PM Pacific Standard Time

KRAS epitopes harbouring mutations are strong neoantigens to which the immune system generates anti-tumor effects. Evidence suggests that CD4 T cell activity plays a critical role in augmenting the cytotoxic effect of CD8 T cells. By selecting MHC II hotspots via multiple epitope prediction algorithms, we designed and synthesized 8 long peptides, covering mutant and/or wild-type (WT) regions of human KRAS protein, that activate both CD4 and CD8 T cells to kill KRAS-driven cancer cells. In addition, the corresponding keyhole limpet hemocyanin (KLH) conjugated peptides were also generated.

Different combinations of peptides were immunised into Balb/c mice before determining the immune response by mouse interferon γ (mIFNγ) ELISPOT assay. T cell responses were observed in two peptide mixes with or without KRAS G12D mutation, suggesting that both mutant-harbouring peptide and WT peptides were immunogenic. We found that KLH-conjugated and CpG+alum adjuvanted peptides elicited stronger immune responses than naked and CFA/IFA adjuvanted peptides.

Immunisation of KRAS naked peptide and KLH-conjugated peptide vaccines in a CT26 syngeneic mouse preventive colorectal cancer model significantly inhibited tumor growth by 51.2% (****p < 0.0001) and 44.7% (***p < 0.001) respectively, compared to control. mIFNγ ELISPOT assay showed that the tumor inhibitory effect was attributed to the activation of antigen-specific T cell response of the vaccines.

• Abstract 4106: A novel synthetic long peptide vaccine composition targeting multiple PD-L1 T-cell epitopes exhibits anti-tumor efficacy in a syngeneic mouse colorectal cancer model
Authors: Kenneth Nansheng Lin, Melvin Toh
Session Date and Time: Tuesday, April 9th, 9:00 AM - 12:30 PM Pacific Standard Time

We developed a blend of four novel synthetic peptide vaccines each containing one PD-L1 T-cell epitope with individual covalent conjugation of the carrier protein, KLH, to break immune tolerance towards self-molecules. The four PD-L1 epitopes ranged from 30 to 40 amino acids in length, targeting different domains of mouse PD-L1 protein. The KLH-conjugated peptide vaccines were formulated with CpG+alum for animal immunisation.

These adjuvanted synthetic long PD-L1 peptide vaccines were highly immunogenic in a syngeneic BALB/c mouse model for a cell-mediated immune response. The vaccine formulation was well tolerated, with no evidence of toxicity and autoimmunity. In vivo administration of the vaccine formulation elicited a positive PD-L1-specific T cell response in splenocytes with an IFN-γ ELISPOT assay. Moreover, in vivo vaccination of these PD-L1 peptide vaccines in a CT26 syngeneic mouse preventive colorectal cancer model exhibited anti-tumor activity, with a 54% tumor growth inhibition (p<0.001) in tumor volume compared to that of the adjuvant-control group at the end of the study.

• Abstract 3525: Towards the efficient design of shared neoantigen peptide cancer vaccines using artificial intelligence
Authors: Genwei Zhang, Jiewen Du, Xiangrui Gao, Tianyuan Wang, Zhenghui Wang, Qingxia Zhang, Tongren Liu, Dong Chen, Ruohan Zhu, Yalong Zhao, Samson Li, Melvin Toh, Lipeng Lai
Session Date and Time: Monday, April 8th, 1:30 PM - 5:00 PM Pacific Standard Time

The accurate prediction of immunogenicity of cancer vaccines remains elusive. We developed new models that predict the probability of a given peptide derived from the protein of interest to be presented by MHC-I or MHC-II.

For MHC-I antigen presentation model development, over 17 million entries in the dataset were collected from published literature and available databases, e.g., IEDB, with peptide lengths ranging from 8 to 11. The peptides were restricted to 150 unique MHC-I alleles. Similarly, ~4 million entries with peptide lengths ranging from 13 to 21 were collected for MHC-II antigen presentation model development, and the peptides were restricted to 19 unique MHC-II alleles. To develop advanced antigen presentation models, a language model was chosen as the backbone network and contrast learning was used to better discriminate the peptide-MHC match versus mismatch. Overall, both MHC-I and MHC-II presentation models were constructed with about 30 million parameters.

To validate algorithm prediction accuracy and peptide immunogenicity, 28 predicted patentable peptides derived from mutated TP53 protein were synthesized and their binding to respective common HLA alleles was validated using surface plasmon resonance. We found that >80% of the peptides displayed binding affinities stronger than the positive control, suggesting that AI significantly improves neoantigen peptide vaccine design. Our developed AI models surpassed the performance of state-of-the-art prediction algorithms, the latest versions of NetMHCpan and MixMHCpred, for both MHC-I and MHC-II antigen presentation.

Hashtag: #CKLifeSciences #CancerVaccines #R&D #AACR

The issuer is solely responsible for the content of this announcement.

CK Life Sciences Int'l., (Holdings) Inc.

CK Life Sciences Int’l., (Holdings) Inc. (stock code: 0775) is listed on the Stock Exchange of Hong Kong. With a mission of improving the quality of life, CK Life Sciences is engaged in the business of research and development, manufacturing, commercialisation, marketing, sale of, and investment in, products and assets which fall into three core categories: nutraceuticals, pharmaceuticals & diagnostics, and agriculture-related. Regarding pharmaceutical research and development, CK Life Sciences’ operations are focused on conducting research and development into cancer vaccines and pain management solutions. CK Life Sciences is a member of the CK Hutchison Group. For additional information, please visit

This press release may contain forward-looking statements regarding the Group's research and development projects which may involve risks and uncertainties. Actual results may differ materially from expectations discussed in such forward-looking statements.

All information in this press release is for general reference only and is not intended as investment advice or medical advice. CK Life Sciences Int'l., (Holdings) Inc. does not warrant or represent, express or implied, as to the accuracy, completeness or updated status of such information. No liability will be accepted for any loss or damage howsoever arising from or in reliance upon such information.

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