Periodic Reporting for period 1 - CELLULO-EPI-BASE (Modeling how pre-existing TCR clones affect vaccine-induced T-cell responses)
Période du rapport: 2023-12-01 au 2025-05-31
T-cells are key players in adaptive immunity. Naïve T-cells become activated when their T-cell receptors (TCRs) recognize epitopes presented by major histocompatibility complexes (MHC) on antigen-presenting cells (APCs). Once activated, T-cells differentiate into effector types and proliferate, initiating the primary immune response. APCs process microbes into epitopes recognized by TCRs. Some activated T-cells become memory cells, enabling a faster, stronger secondary response upon re-exposure. However, the SARS-CoV-2 pandemic has challenged this model, sparking debate over whether pre-existing or cross-reactive TCRs help or hinder immunity. Studies show conflicting outcomes, suggesting that prior exposure to other coronaviruses may variably influence SARS-CoV-2-specific T-cell responses. A key concern is "original antigenic sin," where initial antigen exposure shapes future TCR responses, potentially limiting adaptability to new but related antigens.
From a biological perspective, the key and insufficiently addressed questions are:
- How do pre-existing and previously activated T-cells with high affinity interaction between their TCRs and the vaccine epitope(s) affect the development of “cognate” T-cells against these vaccine epitopes?
- How do pre-existing and previously activated T-cells with moderate affinity interaction between their TCRs and the vaccine epitope(s) affect the development of “cognate” T-cells against these vaccine epitopes? Thus does the existence of these “cross-reactive” T-cells negatively or positively affect the vaccine-specific T-cell response.
From a biological perspective, the key and insufficiently addressed questions are:
- How do pre-existing and previously activated T-cells with high affinity interaction between their TCRs and the vaccine epitope(s) affect the development of “cognate” T-cells against these vaccine epitopes?
- How do pre-existing and previously activated T-cells with moderate affinity interaction between their TCRs and the vaccine epitope(s) affect the development of “cognate” T-cells against these vaccine epitopes? Thus does the existence of these “cross-reactive” T-cells negatively or positively affect the vaccine-specific T-cell response.
Experiments
Yellow fever (YF) cohort: we recruited 49 individuals that weren’t previously exposed to yellow fever and gave them a de novo live-attenuated yellow fever vaccine. Peripheral blood mononuclear cells (PBMC) were obtained and frozen in liquid nitrogen at baseline (prior to vaccination), 3 weeks after vaccination, 6 weeks after vaccinations and in some individuals 1 year after vaccination.
We obtained CD4+ YF specific data (through CD154 and OX40 activation-induced marker measurement on novocyte quanteon, after overnight stimulation with YF peptide pools purchased from La Jolla Institute) on all four time points. Our data showed that participants had no YF-specific T cells prior to vaccination and that 3 weeks after vaccination on average more than 1% of circulating CD4+ T cells were YF-specific. This decreased to approximately 0.8% 6 weeks after vaccination and approximately 0.2% one year after vaccination.
We not only enumerated YF-specific CD4+ T cells, but using Aurora spectral sorter we also sorted the AIM+ cells and performed TCR sequencing (using TIRTL seq). In addition, we obtained bulk CD4+ TCR data prior to vaccination from approximately 250000 T cells per individual.
Poliovirus cohort: we obtained leftover PBMC from a previously completed polio vaccination trial (PMID: 36746739) where 70 individiuals were randomized into 3 different arms. Three study groups (IPV, IPV+dmLT, bOPV) received polio vaccine at d1 and bOPV challenge at d29. PBMC were available from baseline, d29, d57 and d169. Increasing the robustness of our project, we used an alternative read-out technology, being ELISPOT IFN-gamma to measure the number of poliovirus serotype 3 (PV3) specific spot forming units per ELISPOT-well and per time point. We pooled the data from the three cohorts. Our data showed that the ELISPOT counts increased from d1 to d29 and increased further to d57, after which a decrease to d169 was noted.
Similarly as for the YF cohort, we also obtained CD4+ (AIM+) PV3 specific TCR data on each time point and also bulk CD4+ TCR data prior to vaccination.
Hepatitis B surface antigen (HbsAg) cohort: data were already obtained in a previous study (PMID 35074048) for 34 individuals that received de novo Engerix vaccine (baseline, d30) and samples were obtained at d0, d60, d180 and d365. HbsAg-specific CD4+ T cells were measured using an AIM-assay on all time points and baseline CD4+ TCR data were obtained too (Adaptive Biotechnologies assay). Again, the T cell data showed a clear boosting dynamics with a peak on d60 and next a decrease to d180, with further stabilization on d365.
Computational analyses
We modified our novel in-house TRIASSIC tool (TCR Repertoire Integration And Statistical Selection of Informative Clusters) that allows a semi-unbiased identification of group-specific TCR-clusters thereby allowing the identification of baseline CD4+ TCR-clusters that are correlated with high or low responders.
For each cohort and on each time point, we identified high and low responders. Next, we ran TRIASSIC on the baseline CD4+ TCR repertoire and identified the TCR clusters that differed between the high or low responders.
Live-attenuated de novo YF vaccine:
93 TCRB clonotypes were enriched in high responders and 50 TCRb clonotypes were enriched in low responders. Week 3 was most predictive.
No baseline clusters showed statistically significant enrichment in high responders based on both convergence and p-value criteria.
Even prior to filtering, no consistent set of clusters was detected across all high responders, precluding the definition of robust, group-specific cluster signatures.
As a result, persistence analysis at the cluster level was not feasible.
Instead, clonotype-level predictive features were used to assess longitudinal patterns, revealing limited persistence in low responders and none in high responders.
Booster polio vaccine:
The analysis showed that baseline TCR clusters were found for both high responders and low responders (with d57 generating best predictive power), thereby indicating discriminative power in individuals that receive booster vaccination. However, the majority of post-vaccination TCRs clonotypes are newly generated, and do not originate from pre-existing baseline TCRs.
HbsAg vaccine:
The analysis could not detect significant baseline TCR clusters when comparing high and low responders with each other (irrespective of time point analyzed).
Conclusion
Collectively, CELLULO-EPI-BASE has shown that baseline TCR data prior to vaccination are only predictive for booster vaccinations and not for de novo vaccinations. Moreover, only limited persistence was shown for TCRs from baseline to the post-vaccination time points.
Yellow fever (YF) cohort: we recruited 49 individuals that weren’t previously exposed to yellow fever and gave them a de novo live-attenuated yellow fever vaccine. Peripheral blood mononuclear cells (PBMC) were obtained and frozen in liquid nitrogen at baseline (prior to vaccination), 3 weeks after vaccination, 6 weeks after vaccinations and in some individuals 1 year after vaccination.
We obtained CD4+ YF specific data (through CD154 and OX40 activation-induced marker measurement on novocyte quanteon, after overnight stimulation with YF peptide pools purchased from La Jolla Institute) on all four time points. Our data showed that participants had no YF-specific T cells prior to vaccination and that 3 weeks after vaccination on average more than 1% of circulating CD4+ T cells were YF-specific. This decreased to approximately 0.8% 6 weeks after vaccination and approximately 0.2% one year after vaccination.
We not only enumerated YF-specific CD4+ T cells, but using Aurora spectral sorter we also sorted the AIM+ cells and performed TCR sequencing (using TIRTL seq). In addition, we obtained bulk CD4+ TCR data prior to vaccination from approximately 250000 T cells per individual.
Poliovirus cohort: we obtained leftover PBMC from a previously completed polio vaccination trial (PMID: 36746739) where 70 individiuals were randomized into 3 different arms. Three study groups (IPV, IPV+dmLT, bOPV) received polio vaccine at d1 and bOPV challenge at d29. PBMC were available from baseline, d29, d57 and d169. Increasing the robustness of our project, we used an alternative read-out technology, being ELISPOT IFN-gamma to measure the number of poliovirus serotype 3 (PV3) specific spot forming units per ELISPOT-well and per time point. We pooled the data from the three cohorts. Our data showed that the ELISPOT counts increased from d1 to d29 and increased further to d57, after which a decrease to d169 was noted.
Similarly as for the YF cohort, we also obtained CD4+ (AIM+) PV3 specific TCR data on each time point and also bulk CD4+ TCR data prior to vaccination.
Hepatitis B surface antigen (HbsAg) cohort: data were already obtained in a previous study (PMID 35074048) for 34 individuals that received de novo Engerix vaccine (baseline, d30) and samples were obtained at d0, d60, d180 and d365. HbsAg-specific CD4+ T cells were measured using an AIM-assay on all time points and baseline CD4+ TCR data were obtained too (Adaptive Biotechnologies assay). Again, the T cell data showed a clear boosting dynamics with a peak on d60 and next a decrease to d180, with further stabilization on d365.
Computational analyses
We modified our novel in-house TRIASSIC tool (TCR Repertoire Integration And Statistical Selection of Informative Clusters) that allows a semi-unbiased identification of group-specific TCR-clusters thereby allowing the identification of baseline CD4+ TCR-clusters that are correlated with high or low responders.
For each cohort and on each time point, we identified high and low responders. Next, we ran TRIASSIC on the baseline CD4+ TCR repertoire and identified the TCR clusters that differed between the high or low responders.
Live-attenuated de novo YF vaccine:
93 TCRB clonotypes were enriched in high responders and 50 TCRb clonotypes were enriched in low responders. Week 3 was most predictive.
No baseline clusters showed statistically significant enrichment in high responders based on both convergence and p-value criteria.
Even prior to filtering, no consistent set of clusters was detected across all high responders, precluding the definition of robust, group-specific cluster signatures.
As a result, persistence analysis at the cluster level was not feasible.
Instead, clonotype-level predictive features were used to assess longitudinal patterns, revealing limited persistence in low responders and none in high responders.
Booster polio vaccine:
The analysis showed that baseline TCR clusters were found for both high responders and low responders (with d57 generating best predictive power), thereby indicating discriminative power in individuals that receive booster vaccination. However, the majority of post-vaccination TCRs clonotypes are newly generated, and do not originate from pre-existing baseline TCRs.
HbsAg vaccine:
The analysis could not detect significant baseline TCR clusters when comparing high and low responders with each other (irrespective of time point analyzed).
Conclusion
Collectively, CELLULO-EPI-BASE has shown that baseline TCR data prior to vaccination are only predictive for booster vaccinations and not for de novo vaccinations. Moreover, only limited persistence was shown for TCRs from baseline to the post-vaccination time points.
Valorization importance:
Preventive vaccination: baseline TCR data are irrelevant to predict immune responses for de novo vaccination, but are relevant for booster vaccines. Given that only limited TCR persistence was noted, it seems that the baseline TCRs aren’t cognate, although further research is needed.
Therapeutic vaccination: given that baseline TCRs are predictive with certain TCR clusters present at baseline in +/- 50% of high responders, there lays potential in investigating this further in the next valorization steps given that candidates for therapeutic vaccines could be screened before vaccination and only those with a to-be-defined TCR cluster score should be vaccinated.
Next steps: further research is needed, preferably in collaboration with industry to obtain needed data to present a use case. Next, licensing or spin-off shall be investigated.
Preventive vaccination: baseline TCR data are irrelevant to predict immune responses for de novo vaccination, but are relevant for booster vaccines. Given that only limited TCR persistence was noted, it seems that the baseline TCRs aren’t cognate, although further research is needed.
Therapeutic vaccination: given that baseline TCRs are predictive with certain TCR clusters present at baseline in +/- 50% of high responders, there lays potential in investigating this further in the next valorization steps given that candidates for therapeutic vaccines could be screened before vaccination and only those with a to-be-defined TCR cluster score should be vaccinated.
Next steps: further research is needed, preferably in collaboration with industry to obtain needed data to present a use case. Next, licensing or spin-off shall be investigated.