Try the modernized ClinicalTrials.gov beta website. Learn more about the modernization effort.
Working…
ClinicalTrials.gov
ClinicalTrials.gov Menu

(COVID-19) Longitudinal Neutralizing Antibody Titers in Cancer Patients Receiving Different Anti-caner Therapies

The safety and scientific validity of this study is the responsibility of the study sponsor and investigators. Listing a study does not mean it has been evaluated by the U.S. Federal Government. Know the risks and potential benefits of clinical studies and talk to your health care provider before participating. Read our disclaimer for details.
 
ClinicalTrials.gov Identifier: NCT05384509
Recruitment Status : Recruiting
First Posted : May 20, 2022
Last Update Posted : May 20, 2022
Sponsor:
Information provided by (Responsible Party):
Chang Gung Memorial Hospital

Brief Summary:
Patients with cancer are considered vulnerable to SARS-CoV-2 infection and have been prioritized in the vaccination process in several countries, including Taiwan. In addition, international oncological societies favored COVID-19 vaccination for cancer patients on the basis of risk and benefits evaluation of all available data. However, patients with cancer were excluded from SARS- CoV-2 vaccines registrational trials and the investigators lack data regarding the safety and efficacy of vaccination in this population. Under this perspective, the investigators undertook a large prospective study enrolling patients with solid cancers, hematologic malignancies as well as healthy volunteers for the kinetics of anti- SARS-CoV-2 antibodies after COVID-19 vaccination on different anticancer therapy. Major inclusion criteria for this cohort of the study included: (1) age above 20 years; (2) presence of solid organ malignancies treated with immunotherapy, chemotherapy, Targeted therapy irrespective of the treatment phase; and (3) eligibility for vaccination.

Condition or disease Intervention/treatment
Cancer Diagnostic Test: solid organ malignancies treatment

Detailed Description:

Patients with cancer receiving systemic anti-cancer treatments have been generally assumed by many to be at a higher risk from the disease than their counterparts are who are not receiving anticancer treatment. However, their risk of morbidity and mortality from COVID-19 as a consequence of severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) infection is not uniform across the world. The evidence to support this claim is scarce and limited to retrospective series arising from China, the epicenter of the COVID-19 pandemic, and involving small numbers of patients. However, despite these severe limitations, the promulgation of this hypothesis has led to widespread global changes to patterns of prescribing chemotherapy and anticancer treatment. In a global health emergency, oncologists, must secure evidence from a large datasets, which can then inform their risk-benefit analyses for individual patients in terms of the use of anticancer treatments. On March 18, 2020, the investigators launched the UK Coronavirus Cancer Monitoring Project (UKCCMP), with widespread support across our national cancer network. 8 Within 5 weeks, the UKCCMP had generated the largest prospective database of COVID-19 in patients with cancer that had been generated to date. the investigators aimed to describe the clinical and demographic characteristics and COVID-19 outcomes in this cohort of patients with cancer and symptomatic COVID-19, and attempted to assess how the presence of cancer and the receipt of cytotoxic chemotherapy and other anticancer treatments affects the COVID-19 disease phenotype.

New Taipei City Municipal TuCheng Hospital has established a special infectious pneumonia ward in May 2021 to treat patients infected with symptomatic SARS-CoV-2 patients. During the period, 97 patients were admitted and treated with 10 infection-related deaths. In light of the current timing of the pandemic, most published serological studies are predominantly cross-sectional, or at most, include a longitudinal follow-up of few months. Severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) has spread globally over the past year, infecting an immunologically naive population and causing significant morbidity and mortality. Immunity to SARS-CoV-2 induced either through natural infection or vaccination has been shown to afford a degree of protection against reinfection and/or reduce the risk of clinically significant outcomes. Seropositive recovered subjects have been estimated to have 89% protection from reinfection, and vaccine efficacies from 50 to 95% have been reported. However, the duration of protective immunity is presently unclear, primary immune responses are inevitably waning, and there is ongoing transmission of increasingly concerning viral variants that may escape control by both vaccine-induced and convalescent immune responses.

Age is considered one of the most crucial covariates that affect phenotypes. However, aging rate may vary among different populations due to genetic variation or miscellaneous environmental exposures. Chronological age is not a perfect proxy for the true biological aging status of the body. A new biological aging measure, phenotypic age (PhenoAge), has been shown to capture morbidity and mortality risk in the general US population and diverse subpopulations. However, how the phenotypic age affect host immunity is not well investigated.

There are currently no effective therapies for SARS-CoV-2, which causes severe respiratory illness or death. Serum neutralizing antibodies rapidly appear after SARS-CoV-2 infection and vaccination. However, little was known about the change of protective antibody titers both to nature infection and post vaccination. And there is ongoing transmission of increasingly concerning viral variants that may escape control by both vaccine-induced and convalescent immune responses. Defining the antibody response to SARS-CoV-2 in patients with cancer receiving anti-cancer therapy, (including chemotherapy, targeted therapy and immunotherapy) will be essential for understanding infection progression, long-term immunity, vaccine efficacy and how phenotypic age affect associated antibodies.

Layout table for study information
Study Type : Observational [Patient Registry]
Estimated Enrollment : 320 participants
Observational Model: Case-Control
Time Perspective: Prospective
Target Follow-Up Duration: 3 Months
Official Title: (COVID-19) Longitudinal Neutralizing Antibody Titers in Cancer Patients Receiving Different Anti-caner Therapies: A Retrospective Cost Research and Prospective Longitudinal Monitoring Study
Actual Study Start Date : December 1, 2021
Estimated Primary Completion Date : November 30, 2022
Estimated Study Completion Date : November 30, 2024

Resource links provided by the National Library of Medicine


Group/Cohort Intervention/treatment
Chemotherapy
Cancer Patients underwent chemotherapy
Diagnostic Test: solid organ malignancies treatment
Major inclusion criteria for this cohort of the study included: (1) age above 20 years; (2) presence of solid organ malignancies treated with immunotherapy, chemotherapy, Target therapy irrespective of the treatment phase; and (3) eligibility for vaccination.

Targeted therapy
Patients underwent targeted therapy
Diagnostic Test: solid organ malignancies treatment
Major inclusion criteria for this cohort of the study included: (1) age above 20 years; (2) presence of solid organ malignancies treated with immunotherapy, chemotherapy, Target therapy irrespective of the treatment phase; and (3) eligibility for vaccination.

Immunotherapy
Patients underwent immunotherapy
Diagnostic Test: solid organ malignancies treatment
Major inclusion criteria for this cohort of the study included: (1) age above 20 years; (2) presence of solid organ malignancies treated with immunotherapy, chemotherapy, Target therapy irrespective of the treatment phase; and (3) eligibility for vaccination.

Disease-free
Cancer patients have been disease-free for ≥ 6 months group
Diagnostic Test: solid organ malignancies treatment
Major inclusion criteria for this cohort of the study included: (1) age above 20 years; (2) presence of solid organ malignancies treated with immunotherapy, chemotherapy, Target therapy irrespective of the treatment phase; and (3) eligibility for vaccination.




Primary Outcome Measures :
  1. SARS COVID N Ab trends among different subgroups (anti-cancer therapies) [ Time Frame: 3 month ]
    anti- SARS-CoV-2 antibodies measure after COVID-19 vaccination

  2. SARS COVID N Ab trends among different subgroups (anti-cancer therapies) [ Time Frame: 6 month ]
    anti- SARS-CoV-2 antibodies measure after COVID-19 vaccination

  3. SARS COVID N Ab trends among different subgroups (anti-cancer therapies) [ Time Frame: 9 month ]
    anti- SARS-CoV-2 antibodies measure after COVID-19 vaccination

  4. SARS COVID N Ab trends among different subgroups (anti-cancer therapies) [ Time Frame: 12 month ]
    anti- SARS-CoV-2 antibodies measure after COVID-19 vaccination

  5. SARS COVID S Ab trends among different subgroups (anti-cancer therapies) [ Time Frame: 3 month ]
    anti- SARS-CoV-2 antibodies measure after COVID-19 vaccination

  6. SARS COVID S Ab trends among different subgroups (anti-cancer therapies) [ Time Frame: 6 month ]
    anti- SARS-CoV-2 antibodies measure after COVID-19 vaccination

  7. SARS COVID S Ab trends among different subgroups (anti-cancer therapies) [ Time Frame: 9 month ]
    anti- SARS-CoV-2 antibodies measure after COVID-19 vaccination

  8. SARS COVID S Ab trends among different subgroups (anti-cancer therapies) [ Time Frame: 12 month ]
    anti- SARS-CoV-2 antibodies measure after COVID-19 vaccination


Secondary Outcome Measures :
  1. SARS COVID N Ab trends among different baseline PhenoAge scores [ Time Frame: 3 month ]
    anti- SARS-CoV-2 antibodies measure after COVID-19 vaccination

  2. SARS COVID N Ab trends among different baseline PhenoAge scores [ Time Frame: 6 month ]
    anti- SARS-CoV-2 antibodies measure after COVID-19 vaccination

  3. SARS COVID N Ab trends among different baseline PhenoAge scores [ Time Frame: 9 month ]
    anti- SARS-CoV-2 antibodies measure after COVID-19 vaccination

  4. SARS COVID N Ab trends among different baseline PhenoAge scores [ Time Frame: 12 month ]
    anti- SARS-CoV-2 antibodies measure after COVID-19 vaccination

  5. SARS COVID S Ab trends among different baseline PhenoAge scores [ Time Frame: 3 month ]
    anti- SARS-CoV-2 antibodies measure after COVID-19 vaccination

  6. SARS COVID S Ab trends among different baseline PhenoAge scores [ Time Frame: 6 month ]
    anti- SARS-CoV-2 antibodies measure after COVID-19 vaccination

  7. SARS COVID S Ab trends among different baseline PhenoAge scores [ Time Frame: 9 month ]
    anti- SARS-CoV-2 antibodies measure after COVID-19 vaccination

  8. SARS COVID S Ab trends among different baseline PhenoAge scores [ Time Frame: 12 month ]
    anti- SARS-CoV-2 antibodies measure after COVID-19 vaccination


Biospecimen Retention:   Samples With DNA
plasma


Information from the National Library of Medicine

Choosing to participate in a study is an important personal decision. Talk with your doctor and family members or friends about deciding to join a study. To learn more about this study, you or your doctor may contact the study research staff using the contacts provided below. For general information, Learn About Clinical Studies.


Layout table for eligibility information
Ages Eligible for Study:   20 Years and older   (Adult, Older Adult)
Sexes Eligible for Study:   All
Accepts Healthy Volunteers:   No
Sampling Method:   Probability Sample
Study Population
At TuCheng Hospital and Linkou Chang Gung Memorial Hospital, Cancer Patients underwent chemotherapy were enrolled as the first group (1). Patients underwent targeted therapy were enrolled as the second group (2). Cancer Patients underwent immunotherapy were enrolled as the third group (3). Cancer patients have been disease-free for ≥ 6 months group were enrolled as the fourth group (4).
Criteria

Inclusion Criteria:

  1. adults >20 years old;
  2. cancer patients under active anti-cancer therapy, including chemotherapy (n=80), targeted therapy (n=80) and immunotherapy (n=80); and cancer patients have been disease-free for ≥ 6 months (n=80)
  3. cancer patients who were full vaccinated with any brand of vaccines (AZ, BNT, Moderna or others, as stratification factors in the analysis) or cancer patients who were unvaccinated agree to complete full vaccination later.
  4. patients who agreed with the content of informed consent of the study protocol.

Exclusion Criteria:

  1. Patients who refused the protocol of N-antibody test and OPD follow-up.
  2. The investigators suggest to withdraw.
  3. Patient asked to withdraw from the trial at any timepoints.

Information from the National Library of Medicine

To learn more about this study, you or your doctor may contact the study research staff using the contact information provided by the sponsor.

Please refer to this study by its ClinicalTrials.gov identifier (NCT number): NCT05384509


Contacts
Layout table for location contacts
Contact: Chia-Hsun Hsieh, PhD 0975366137 wisdom5000@cgmh.org.tw
Contact: Jeng How Yang, PhD 0975366159 woody1005@cgmh.org.tw

Locations
Layout table for location information
Taiwan
TuCheng Hospital Recruiting
New Taipei City, Taiwan, 23652
Contact: Chia-Hsun Hsieh, PhD    0975366137    wisdom5000@cgmh.org.tw   
Sponsors and Collaborators
Chang Gung Memorial Hospital
Investigators
Layout table for investigator information
Study Director: Chia-Hsun Hsieh, PhD Professor Attending Physicians
Layout table for additonal information
Responsible Party: Chang Gung Memorial Hospital
ClinicalTrials.gov Identifier: NCT05384509    
Other Study ID Numbers: 202101960B0
First Posted: May 20, 2022    Key Record Dates
Last Update Posted: May 20, 2022
Last Verified: December 2021

Layout table for additional information
Studies a U.S. FDA-regulated Drug Product: No
Studies a U.S. FDA-regulated Device Product: No
Keywords provided by Chang Gung Memorial Hospital:
Immunotherapy
Chemotherapy
Targeted therapy
COVID19 vaccination
Neutralizing antibody titers
Spike protein antibody titers
Additional relevant MeSH terms:
Layout table for MeSH terms
COVID-19
Respiratory Tract Infections
Infections
Pneumonia, Viral
Pneumonia
Virus Diseases
Coronavirus Infections
Coronaviridae Infections
Nidovirales Infections
RNA Virus Infections
Lung Diseases
Respiratory Tract Diseases