Duangdao Wichadakul

Purpose:To evaluate the safety and immunogenicity of intramuscularly delivered personalized neoantigen synthetic long peptide (SLP) vaccines in patients with advanced solid tumors. Patients and Methods:In this phase I trial, 12 patients with advanced melanoma (n = 9) or renal cell carcinoma (n = 3) who lacked access to further reimbursed standard therapies at enrollment received intramuscular neoantigen SLP vaccines with Polyinosinic-Polycytidylic acid stabilized with polylysine and carboxymethylcellulose (poly-ICLC). Each vaccine contained about 20 predicted neoantigen peptides. Adverse events were monitored throughout vaccination and follow-up. Immune profiling was performed at baseline and predefined postvaccination time points. Results:Intramuscular neoantigen vaccination was well tolerated, with only grade 1 to 2 local pain or fever and no immune-mediated toxicities. All participants developed de novo T-cell responses, which were detectable as early as 1 week after vaccination in most patients. On average, 53% of peptides per patient were immunogenic, inducing both CD8+ and CD4+ neoantigen-specific responses. Patients previously treated with immune checkpoint inhibitors (ICI) had higher baseline immunity but achieved comparable postvaccination responses to ICI-naïve patients. IFNγ-dominant CD8+ and TNFα-dominant CD4+ responses were observed, along with increased effector memory differentiation. Two patients with higher CD8+ terminal effector memory RA (TEMRA) proportions were the longest survivors. Tumor biopsies revealed enhanced CD8+ infiltration, and epitope spreading occurred in two evaluable cases. An analysis of 239 peptides showed greater immunogenicity for dual MHC I/II–binding, cysteine-containing, in-frame insertions or deletions–derived or low variant allele frequency–derived mutations, whereas proline substitutions reduced responses. Conclusions:Intramuscular neoantigen SLP vaccination with poly-ICLC is safe and induces rapid, mutation-specific T-cell immunity with robust CD8+ effector responses. These findings support intramuscular administration as a promising strategy for peptide-based cancer vaccines.

Supplementary Data 3 from The Phase Ib IMPACT Trial of Intramuscular Personalized Neoantigen Synthetic Long Peptide Vaccines in Patients with Advanced Melanoma and Renal Cell Carcinoma

Supplementary Data 5 from The Phase Ib IMPACT Trial of Intramuscular Personalized Neoantigen Synthetic Long Peptide Vaccines in Patients with Advanced Melanoma and Renal Cell Carcinoma

Supplementary Figures and Tables.

Supplementary Data 2 from The Phase Ib IMPACT Trial of Intramuscular Personalized Neoantigen Synthetic Long Peptide Vaccines in Patients with Advanced Melanoma and Renal Cell Carcinoma

Supplementary Data 4 from The Phase Ib IMPACT Trial of Intramuscular Personalized Neoantigen Synthetic Long Peptide Vaccines in Patients with Advanced Melanoma and Renal Cell Carcinoma

Supplementary Data 1 from The Phase Ib IMPACT Trial of Intramuscular Personalized Neoantigen Synthetic Long Peptide Vaccines in Patients with Advanced Melanoma and Renal Cell Carcinoma

Purpose: To evaluate the safety and immunogenicity of intramuscularly delivered personalized neoantigen synthetic long peptide (SLP) vaccines in patients with advanced solid tumors. Patients and Methods: In this phase I trial, 12 patients with advanced melanoma (n = 9) or renal cell carcinoma (n = 3) who lacked access to further reimbursed standard therapies at enrollment received intramuscular neoantigen SLP vaccines with Polyinosinic-Polycytidylic acid stabilized with polylysine and carboxymethylcellulose (poly-ICLC). Each vaccine contained about 20 predicted neoantigen peptides. Adverse events were monitored throughout vaccination and follow-up. Immune profiling was performed at baseline and predefined postvaccination time points. Results: Intramuscular neoantigen vaccination was well tolerated, with only grade 1 to 2 local pain or fever and no immune-mediated toxicities. All participants developed de novo T-cell responses, which were detectable as early as 1 week after vaccination in most patients. On average, 53% of peptides per patient were immunogenic, inducing both CD8+ and CD4+ neoantigen-specific responses. Patients previously treated with immune checkpoint inhibitors (ICI) had higher baseline immunity but achieved comparable postvaccination responses to ICI-naïve patients. IFNγ-dominant CD8+ and TNFα-dominant CD4+ responses were observed, along with increased effector memory differentiation. Two patients with higher CD8+ terminal effector memory RA (TEMRA) proportions were the longest survivors. Tumor biopsies revealed enhanced CD8+ infiltration, and epitope spreading occurred in two evaluable cases. An analysis of 239 peptides showed greater immunogenicity for dual MHC I/II–binding, cysteine-containing, in-frame insertions or deletions–derived or low variant allele frequency–derived mutations, whereas proline substitutions reduced responses. Conclusions: Intramuscular neoantigen SLP vaccination with poly-ICLC is safe and induces rapid, mutation-specific T-cell immunity with robust CD8+ effector responses. These findings support intramuscular administration as a promising strategy for peptide-based cancer vaccines.

In the artificial intelligence (AI) era, data has become increasingly essential for learning and analysis. AI enables automated decision-making that may lead to violation of the General Data Protection Regulation (GDPR). The GDPR is the data protection law within the European Union (EU) that allows individuals (‘data subjects’) to control their personal data. According to the law, automated decision-making can be permitted where data subjects give explicit consent. Therefore, consent management (CM) has become an essential software component for managing data subjects’ data lifecycle and their consent. Bringing machine learning (ML) into production needs machine learning operations (MLOps). MLOps is a set of processes for delivering ML artifacts reliably and efficiently. However, current MLOps frameworks neglect the integration of CM into their processes, leading to the risk of GDPR violations. This research proposes a formal model for integrating CM into MLOps that takes upfront privacy by design (PbD). Finally, we provided a mapping from the formal model to a class diagram as guidelines to integrate CM into MLOps and demonstrated how to apply the proposed class diagram to existing ML developments, such as machine unlearning, in conjunction with the Purchase dataset.