In Silico Model Estimates the Clinical Trial Outcome of Cancer Vaccines

Abstract

Over 30 years after the first cancer vaccine clinical trial (CT), scientists still search the missing link between immunogenicity and clinical responses. A predictor able to estimate the outcome of cancer vaccine CTs would greatly benefit vaccine development. Published results of 94 CTs with 64 therapeutic vaccines were collected. We found that preselection of CT subjects based on a single matching HLA allele does not increase immune response rates (IRR) compared with non-preselected CTs (median 60% vs. 57%, p = 0.4490). A representative in silico model population (MP) comprising HLA-genotyped subjects was used to retrospectively calculate in silico IRRs of CTs based on the percentage of MP-subjects having epitope(s) predicted to bind ≥ 1–4 autologous HLA allele(s). We found that in vitro measured IRRs correlated with the frequency of predicted multiple autologous allele-binding epitopes (AUC 0.63–0.79). Subgroup analysis of multi-antigen targeting vaccine CTs revealed correlation between clinical response rates (CRRs) and predicted multi-epitope IRRs when HLA threshold was ≥ 3 (r = 0.7463, p = 0.0004) but not for single HLA allele-binding epitopes (r = 0.2865, p = 0.2491). Our results suggest that CRR depends on the induction of broad T-cell responses and both IRR and CRR can be predicted when epitopes binding to multiple autologous HLAs are considered.

Safety and Activity of PolyPEPI1018 Combined with Maintenance Therapy in Metastatic Colorectal Cancer: an Open-Label, Multicenter, Phase Ib Study

“…For the design of PolyPEPI1018, dominant epitopes of the target TAAs were determined using an in silico model cohort as described earlier ( 36 ). Briefly, overlapping 9 mers of the 7 protein antigens were scanned to predict their binding to the 152 HLA class I alleles of 433 individuals with mixed ethnicity comprising the in silico cohort representative of the global population, as previously described ( 37 ). As a next step, the predicted epitopes restricted to multiple (≥3) autologous HLA alleles (PEPI) were determined for each of the 433 subjects and selected those that are most frequently shared among them.…”

“…When considering all six 30 mers of PolyPEPI1018, 98% of subjects in the in silico cohort were predicted to have at least one vaccine-specific HLA class I PEPI, 91% have 2 PEPIs, and 100% of them have HLA class II PEPIs, potentially covering large fraction of the study population, as well (Supplementary Table S1; ref. 37 ).…”

T cell immunity ameliorates COVID-19 disease severity and provides post-exposure prophylaxis after peptide-vaccination, in Syrian hamsters

“…We developed a novel approach for the selection of immunoprevalent SARS-CoV-2-derived T cell epitopes using an in silico cohort of HLA-genotyped individuals with different ethnicities ( 24 ). Based on this tool, nine 30-mer peptides, containing T cell epitopes were selected from the four major structural proteins of SARS-CoV-2 and included in a peptide vaccine candidate, PolyPEPI-SCoV-2.…”

Predicting epitopes for vaccine development using bioinformatics tools

“…When vaccines were first created, they were intended to prevent diseases caused by infectious agents. However, as vaccine technology has developed, vaccines have been expanded in an effort to combat noninfectious diseases such as autoimmune diseases, 4 , 5 cancer, 6 – 8 , and degenerative diseases. 9 – 11 …”

“…2,3 When vaccines were first created, they were intended to prevent diseases caused by infectious agents. However, as vaccine technology has developed, vaccines have been expanded in an effort to combat noninfectious diseases such as autoimmune diseases, 4,5 cancer, [6][7][8] , and degenerative diseases. [9][10][11] Conventional vaccines are produced by inactivating or attenuating some part of an infectious agent and exposing it to the body's immune system.…”