Not provided
Not provided
| ID | Type | Description | Link |
|---|---|---|---|
| BASEC-Nr: 2024-01077 | Other Identifier | BASEC (Business Administration System for Ethics Committees) - Switzerland |
Not provided
Not provided
Not provided
Not provided
Not provided
Not provided
Not provided
Not provided
Not provided
Not provided
Not provided
Not provided
Not provided
Not provided
Not provided
This study aims to understand how well influenza vaccines work in some individuals with weakened immune systems compared to healthy individuals. Some people, such as those with HIV, multiple sclerosis, certain cancers, or autoimmune conditions, have more severe influenza disease courses due to their medical treatments. These individuals may also respond less effectively to vaccines. By comparing immune responses to the influenza vaccine in both immunocompromised patients and healthy participants, this study aims to identify patterns in vaccine effectiveness and side effects. The goal is to find better ways to predict vaccine response in vulnerable patients and improve protection against influenza.
The study is a single-center, prospective cohort study evaluating influenza vaccine responses in adults with weakened immune systems compared to healthy adults. Immunocompromised participants include individuals with HIV, multiple sclerosis, rheumatological diseases, and B-cell malignancies after CAR-T cell therapy. All participants will receive a standard influenza vaccine, as recommended in Switzerland, with immune response measured through blood tests at specific time points before and after vaccination.
The primary objective is to compare influenza vaccine antibody levels in immunocompromised and healthy participants to determine if immune responses are different in the former group. Secondary objectives include examining vaccine-induced immune cell activity, side effects, and the immune profile before vaccination in each patient subgroup. The study will also analyze gut microbiome differences between responders and non-responders and develop prediction models for vaccine effectiveness based on immune and demographic data. By doing so, researchers hope to enhance the understanding of how best to protect immunocompromised patients against influenza.
Not provided
Not provided
Not provided
Not provided
Not provided
| Label | Type | Description | Intervention Names |
|---|---|---|---|
| Control Group | Non-immunocompromised controls |
| |
| CAR-T Cell Recipients | Patients with B-cell malignancies receiving anti-CD19 CAR T-cell therapies |
| |
| Rheumatological Disorders | Patients with rheumatological disorders on methothrexate treatment |
| |
| People living with HIV | People living with HIV on successful antiretroviral treatment |
| |
| Multiple Sclerosis | Patients with multiple sclerosis on treatment with sphingosine-1-phosphate-receptor-agonists |
|
| Name | Type | Description | Arm Group Labels | Other Names |
|---|---|---|---|---|
| Influenza vaccine | Biological | Standard, commercially available, quadrivalent split-vaccine against influenza is given to all study participants. |
|
| Measure | Description | Time Frame |
|---|---|---|
| Influenza vaccine elicited humoral immune response | The primary endpoint is the baseline variable adjusted fold-change of influenza HAI titers in immunosuppressed patients versus non-immunocompromised controls. The sum of foldchanges of hemagglutinin inhibition assay (HAI) titers 4-6 weeks after influenza vaccination will be adjusted for age, sex and baseline HAI titers by regression analysis as these three baseline variables are reported to affect influenza vaccine responses. | Directly before and 4-6 weeks after Influenza vaccination |
| Measure | Description | Time Frame |
|---|---|---|
| Influenza vaccine elicited microneutralisation antibody titers | Influenza vaccine elicited microneutralisation titers will be compared between immunocompromised and non-immunocompromised participants. | Directly before and 4-6 weeks after Influenza vaccination |
| Seroprotection rate after influenza vaccination |
Not provided
Inclusion Criteria:
Exclusion Criteria:
Not provided
Not provided
Not provided
Adult (≥ 18 years old) patients with immunocuppression (Patients with B-cell malignancies after CAR-T cell therapy, multiple sclerosis on sphingosin-1 modulator therapy, rheumatological diseases on methotrexate therapy or people living with HIV on antiretroviral therapy) and non-immunocompromised participants as control group.
Not provided
| Name | Affiliation | Role |
|---|---|---|
| Christine Thurnheer, PD, MD | University Hospital Bern, Switzerland | Principal Investigator |
| Cédric Hirzel, PD, MD | University Hospital Bern, Switzerland | Principal Investigator |
| Facility | Status | City | State | ZIP | Country | Contacts |
|---|---|---|---|---|---|---|
| Universitsy Hospital Bern | Bern | 3010 | Switzerland |
| PubMed Identifier | Type | Citation | Retractions |
|---|---|---|---|
| 21377898 | Background | Manuel O, Humar A, Berutto C, Ely L, Giulieri S, Lien D, Meylan PR, Weinkauf J, Pascual M, Nador R, Aubert JD, Kumar D. Low-dose intradermal versus intramuscular trivalent inactivated seasonal influenza vaccine in lung transplant recipients. J Heart Lung Transplant. 2011 Jun;30(6):679-84. doi: 10.1016/j.healun.2011.01.705. Epub 2011 Mar 5. | |
| 29709447 |
| Label | URL |
|---|---|
| Word Health Organization. Correlates of vaccine-induced protection: methods and implications. World Health Organization, 2013: | View source |
Not provided
Not provided
Not provided
Not provided
Not provided
Not provided
Not provided
| ID | Term |
|---|---|
| D007251 | Influenza, Human |
| ID | Term |
|---|---|
| D012141 | Respiratory Tract Infections |
| D007239 | Infections |
| D009976 | Orthomyxoviridae Infections |
| D012327 | RNA Virus Infections |
Not provided
Not provided
| ID | Term |
|---|---|
| D007252 | Influenza Vaccines |
| ID | Term |
|---|---|
| D014765 | Viral Vaccines |
| D014612 | Vaccines |
| D001688 | Biological Products |
| D045424 | Complex Mixtures |
Not provided
Not provided
Not provided
Not provided
Not provided
Serum, EDTA Blood, PBMCs
The investigators will compare the proportion of patients with seroprotective antibody levels (defined as HAI titer ≥1:40) 4-6 weeks after influenza vaccination in healthy controls and immunosuppressed patients. A HAI titer of ≥1:40 is the accepted threshold for seroprotection by the FDA. |
| Directly before and 4-6 weeks after Influenza vaccination |
| Vaccine specific T-cell response | The investigators will measure influenza specific CD4+ and CD8+ T-cells before- and 4-6 weeks after influenza vaccination. Influenza specific T-cells will be measured by ELISPOT assays. The investigators will report differences in mean increase of influenza specific CD4+ and CD8+ Tcells between immunosuppressed and healthy controls 4-6 weeks after vaccination. | Directly before and 4-6 weeks after Influenza vaccination |
| Vaccine Reactogenicity | The investigators will collect participant information on vaccine reactogenicity by questionnaire (attached to the proposal) one week after vaccination. The investigators will report the frequency of moderate and severe reactogenicity-events per patient group and assess correlation with vaccine response by regression analysis. | Directly before and 1 week after Influenza vaccination |
| Baseline Immune Profile | The investigators will assess the impact of following baseline immunological parameters on the vaccine elicited immune response:
| Directly before Influenza vaccination (same day) |
| Change in PBMC gene-expression profiles | Changes in individual gene expression one week after influenza vaccination measured by bulk mRNA sequencing (Transcriptomics). Reporting of differential gene expression levels and visualisation in heat-maps. | Directly before and 1 week after Influenza vaccination |
| Baseline Prediction Model for Vaccine Response | Semi-supervised machine learning methods will be applied to develop a model for prediction of influenza vaccine response defined defined by a HAI-Titer fold change ≥ 4. Variables will contain sociodemographic, clinical data and immunologic baseline profiles. Model performance will be evaluated by means of areas under the receiver operating characteristic curve and confusion matrices. The model will be trained, tested and validated by a 70%, 30% and 15% data split respectively. | Directly before and 4-6 week after Influenza vaccination |
| Gene-Expression Updated Prediction Model for Vaccine Response | A Gene-expression updated model will assess the predictive power of vaccine induced change in gene expression by adding early post-vaccination immunologic profile data to the baseline model data. | Directly before , 1 week and 4-6 week after Influenza vaccination |
| Intestinal microbiome composition of vaccine responders and non-responders | To assess differently abundant microbiome composition the investigators will collect stool samples for 16s-rDNA sequencing in a subset of patients. The investigators will compare relative abundance of microbial taxa between vaccine-responders and non-responders defined by a HAI-Titer fold change ≥ 4, 4-6 weeks after vaccination. | Directly before Influenza vaccination |
| van de Witte S, Nauta J, Montomoli E, Weckx J. A Phase III randomised trial of the immunogenicity and safety of quadrivalent versus trivalent inactivated subunit influenza vaccine in adult and elderly subjects, assessing both anti-haemagglutinin and virus neutralisation antibody responses. Vaccine. 2018 Sep 25;36(40):6030-6038. doi: 10.1016/j.vaccine.2018.04.043. Epub 2018 Apr 27. |
| 24016810 | Background | Pepin S, Donazzolo Y, Jambrecina A, Salamand C, Saville M. Safety and immunogenicity of a quadrivalent inactivated influenza vaccine in adults. Vaccine. 2013 Nov 12;31(47):5572-8. doi: 10.1016/j.vaccine.2013.08.069. Epub 2013 Sep 7. |
| 16631409 | Background | Lindemann M, Witzke O, Lutkes P, Fiedler M, Kreuzfelder E, Philipp T, Roggendorf M, Grosse-Wilde H. ELISpot assay as a sensitive tool to detect cellular immunity following influenza vaccination in kidney transplant recipients. Clin Immunol. 2006 Sep;120(3):342-8. doi: 10.1016/j.clim.2006.03.002. Epub 2006 Apr 21. |
| 36371426 | Background | Riese P, Trittel S, Akmatov MK, May M, Prokein J, Illig T, Schindler C, Sawitzki B, Elfaki Y, Floess S, Huehn J, Blazejewski AJ, Strowig T, Hernandez-Vargas EA, Geffers R, Zhang B, Li Y, Pessler F, Guzman CA. Distinct immunological and molecular signatures underpinning influenza vaccine responsiveness in the elderly. Nat Commun. 2022 Nov 12;13(1):6894. doi: 10.1038/s41467-022-34487-z. |
| 38182669 | Background | Ravichandran S, Erra-Diaz F, Karakaslar OE, Marches R, Kenyon-Pesce L, Rossi R, Chaussabel D, Nehar-Belaid D, LaFon DC, Pascual V, Palucka K, Paust S, Nahm MH, Kuchel GA, Banchereau J, Ucar D. Distinct baseline immune characteristics associated with responses to conjugated and unconjugated pneumococcal polysaccharide vaccines in older adults. Nat Immunol. 2024 Feb;25(2):316-329. doi: 10.1038/s41590-023-01717-5. Epub 2024 Jan 5. |
| 33484237 | Background | Hirzel C, Chruscinski A, Ferreira VH, L'Huillier AG, Natori Y, Han SH, Cordero E, Humar A, Kumar D; Influenza in Transplant Study Group. Natural influenza infection produces a greater diversity of humoral responses than vaccination in immunosuppressed transplant recipients. Am J Transplant. 2021 Aug;21(8):2709-2718. doi: 10.1111/ajt.16503. Epub 2021 Feb 18. |
| 37750613 | Background | Cheuvart B, Spiessens B, van Heesbeen R, Hung D, Andrade C, Korejwo-Peyramond J, Tavares-Da-Silva F. Harmonizing the collection of solicited adverse events in prophylactic vaccine clinical trials. Expert Rev Vaccines. 2023 Jan-Dec;22(1):849-859. doi: 10.1080/14760584.2023.2262571. Epub 2023 Oct 9. |
| 23659300 | Background | Reber A, Katz J. Immunological assessment of influenza vaccines and immune correlates of protection. Expert Rev Vaccines. 2013 May;12(5):519-36. doi: 10.1586/erv.13.35. |
| 26682988 | Background | Nakaya HI, Hagan T, Duraisingham SS, Lee EK, Kwissa M, Rouphael N, Frasca D, Gersten M, Mehta AK, Gaujoux R, Li GM, Gupta S, Ahmed R, Mulligan MJ, Shen-Orr S, Blomberg BB, Subramaniam S, Pulendran B. Systems Analysis of Immunity to Influenza Vaccination across Multiple Years and in Diverse Populations Reveals Shared Molecular Signatures. Immunity. 2015 Dec 15;43(6):1186-98. doi: 10.1016/j.immuni.2015.11.012. |
| 33422377 | Background | Caldera F, Mercer M, Samson SI, Pitt JM, Hayney MS. Influenza vaccination in immunocompromised populations: Strategies to improve immunogenicity. Vaccine. 2021 Mar 15;39 Suppl 1:A15-A23. doi: 10.1016/j.vaccine.2020.11.037. Epub 2021 Jan 7. |
| 34202032 | Background | Huang D, Liu AYN, Leung KS, Tang NLS. Direct Measurement of B Lymphocyte Gene Expression Biomarkers in Peripheral Blood Transcriptomics Enables Early Prediction of Vaccine Seroconversion. Genes (Basel). 2021 Jun 25;12(7):971. doi: 10.3390/genes12070971. |
| 32340868 | Background | Tsang JS, Dobano C, VanDamme P, Moncunill G, Marchant A, Othman RB, Sadarangani M, Koff WC, Kollmann TR. Improving Vaccine-Induced Immunity: Can Baseline Predict Outcome? Trends Immunol. 2020 Jun;41(6):457-465. doi: 10.1016/j.it.2020.04.001. Epub 2020 Apr 8. |
| 36316475 | Background | Hagan T, Gerritsen B, Tomalin LE, Fourati S, Mule MP, Chawla DG, Rychkov D, Henrich E, Miller HER, Diray-Arce J, Dunn P, Lee A; Human Immunology Project Consortium (HIPC); Levy O, Gottardo R, Sarwal MM, Tsang JS, Suarez-Farinas M, Sekaly RP, Kleinstein SH, Pulendran B. Transcriptional atlas of the human immune response to 13 vaccines reveals a common predictor of vaccine-induced antibody responses. Nat Immunol. 2022 Dec;23(12):1788-1798. doi: 10.1038/s41590-022-01328-6. Epub 2022 Oct 31. |
| 31589278 | Background | Luna G, Alping P, Burman J, Fink K, Fogdell-Hahn A, Gunnarsson M, Hillert J, Langer-Gould A, Lycke J, Nilsson P, Salzer J, Svenningsson A, Vrethem M, Olsson T, Piehl F, Frisell T. Infection Risks Among Patients With Multiple Sclerosis Treated With Fingolimod, Natalizumab, Rituximab, and Injectable Therapies. JAMA Neurol. 2020 Feb 1;77(2):184-191. doi: 10.1001/jamaneurol.2019.3365. |
| 34457269 | Background | Stewart AG, Henden AS. Infectious complications of CAR T-cell therapy: a clinical update. Ther Adv Infect Dis. 2021 Aug 24;8:20499361211036773. doi: 10.1177/20499361211036773. eCollection 2021 Jan-Dec. |
| 31673420 | Background | Furer V, Rondaan C, Heijstek M, van Assen S, Bijl M, Agmon-Levin N, Breedveld FC, D'Amelio R, Dougados M, Kapetanovic MC, van Laar JM, Ladefoged de Thurah A, Landewe R, Molto A, Muller-Ladner U, Schreiber K, Smolar L, Walker J, Warnatz K, Wulffraat NM, Elkayam O. Incidence and prevalence of vaccine preventable infections in adult patients with autoimmune inflammatory rheumatic diseases (AIIRD): a systemic literature review informing the 2019 update of the EULAR recommendations for vaccination in adult patients with AIIRD. RMD Open. 2019 Sep 19;5(2):e001041. doi: 10.1136/rmdopen-2019-001041. eCollection 2019. |
| 31439534 | Background | GBD 2017 HIV collaborators. Global, regional, and national incidence, prevalence, and mortality of HIV, 1980-2017, and forecasts to 2030, for 195 countries and territories: a systematic analysis for the Global Burden of Diseases, Injuries, and Risk Factors Study 2017. Lancet HIV. 2019 Dec;6(12):e831-e859. doi: 10.1016/S2352-3018(19)30196-1. Epub 2019 Aug 19. |
| D014777 | Virus Diseases |
| D012140 | Respiratory Tract Diseases |