Ohio State University Comprehensive Cancer Center
Columbus, Ohio, 43210, United States
NCT Number: NCT05102773
This pilot trial studies the effect of the microbiome on immune checkpoint inhibitors response in patients with melanoma by collecting stool and blood samples. Gut microbiome plays a critical role in response to immune checkpoint inhibitors. Studying the change in an individual's microbiome due to corticosteroid use may help researchers to determine whether an individual's microbiome can predict their response and toxicity to immune checkpoint inhibitors.
Looking for future studies?
Notify Me18 year and older
All sexes
Observational
Columbus, Ohio, 43210, United States
PRIMARY OBJECTIVE:
I. To determine if the microbiome alpha-diversity is predictive of response (Response Evaluation Criteria in Solid Tumors [RECIST] version [v] 1.1) at a 12-week computed tomography (CT) scan or toxicity.
SECONDARY OBJECTIVE:
I. To determine the recruitment and compliance rates for longitudinal biospecimen collection, including stool, in melanoma patients.
EXPLORATORY OBJECTIVE:
I. To determine if individual microbes or their changes in relative abundance are predictive of response or toxicity.
OUTLINE:
Patients complete a Food Frequency Questionnaire (FFQ) at baseline, undergo collection of stool samples at baseline, within 2 days of starting corticosteroid treatment (if applicable), when asked for a control sample, and at 12 weeks, and undergo collection of blood samples and computed tomography (CT) at baseline and 12 weeks.
Healthy volunteers accepted: No
Only the study team can determine whether someone qualifies for participation.
Inclusion criteria
Exclusion criteria
Undergo collection of blood and stool
Other names: Biological Sample Collection
Undergo CT
Other names: CAT, CAT Scan, Computerized Axial Tomography, Computerized Tomography, CT, CT Scan, tomography
Correlative studies
Complete questionnaire
Time frame: At 12 weeks
This analysis will follow a logistic regression structure. The dependent variable, response to treatment, will be evaluated using standardized criteria (Response Evaluation Criteria in Solid Tumors [RECIST] version [v] 1.1). Each patient will be classified as "respond", "stable" or "progression'' as a categorical variable and then binarized, with "respond" or "stable" in the category "responders", and "progression" in the category "non-responders". Independent variables will be alpha-diversity. Additional covariates will be included in the model to control for differences in age, sex, body mass index (BMI), Food Frequency Questionnaire (FFQ) dietary index, and medication history.
Time frame: Within the 12-week treatment window
This analysis will follow a logistic regression structure. The dependent variable, toxicity, will be evaluated by corticosteroid prescription. Dependent variables including alpha-diversity or individual microbes will be independent variables.
Time frame: 12 weeks
Recruitment rates will be defined as the fraction of screened adults who are eligible and agree to participate within the Cutaneous Oncology Clinic, with an estimated recruitment of 30%. Will track the monthly collection of data and documented reasons for missing any scheduled collection dates. The recruitment rate will be used in combination with the variance of the biospecimen data in power calculations to estimate the sample size needed for future trials.
Time frame: 12 weeks
Compliance will be defined as 90% of baseline, endpoint and corticosteroid collection. Will track the monthly collection of data and documented reasons for missing any scheduled collection dates. The compliance rate will be used in combination with the variance of the biospecimen data in power calculations to estimate the sample size needed for future trials.
Time frame: At baseline, 12 weeks, or at corticosteroid prescription
Individual microbe relative abundances will be compared between responders and non-responders with additional filtering to accommodate the sparseness of the microbiome data matrix. Specifically, microbes will be compared that are the most abundant, as well as being present in greater than 50% of the samples. An arcsine root transformation will be applied to the microbe relative abundances to approximate a Gaussian distribution, and then a generalized linear model applied where ''response'' is the response variable and individual microbes are the predictor variables.
P-values will be corrected by the Bonferroni method and then visualized by volcano plot. Microbes and covariates found to be most significant in the model will be combined into a single model to estimate the percent variance explainable by these predictors. Analyses will be performed in R using the stats package.
Ohio State University Comprehensive Cancer Center
Other
A Pilot Study of the Effect of the Microbiome on Immune Checkpoint Inhibitor Response in Melanoma
OpenTrials presents study information sourced from ClinicalTrials.gov. The official registry record should be consulted for the latest information.
View the official ClinicalTrials.gov record (opens in a new tab)This listing is for discovery and informational purposes only. It is not medical advice, does not guarantee that a study is recruiting, and does not determine eligibility. Contact the study team and a qualified healthcare professional when considering participation.
Published trials that share one or more normalized conditions with this study.
NCT03873818
Brain Diseases, Brain Neoplasms
Houston, Texas, United States
View Trial DetailsNCT04067960
Adnexal Diseases, Anatomic Stage III Breast Cancer AJCC v8
Scottsdale, Arizona, United States
View Trial DetailsNCT04752267
Adenocarcinoma, Anatomic Stage IV Breast Cancer AJCC v8
Los Angeles, California, United States
View Trial DetailsNCT01738139
Advanced Malignant Solid Neoplasm, C-KIT Tyrosine Kinase Protein Overexpression
Houston, Texas, United States
View Trial Details