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The Effect of the Microbiome on Immune Checkpoint Inhibitor Response in Melanoma Patients

A Pilot Study of the Effect of the Microbiome on Immune Checkpoint Inhibitor Response in Melanoma

Status
Completed
Phases
Unknown
Study type
Observational
Source
ClinicalTrials.gov
Registry ID
NCT05102773
Enrollment
88
Registered
2021-11-02
Start date
2020-02-10
Completion date
2022-05-05
Last updated
2023-04-24

For informational purposes only — not medical advice. Sourced from public registries and may not reflect the latest updates. Terms

Conditions

Clinical Stage IV Cutaneous Melanoma AJCC v8, Pathologic Stage IV Cutaneous Melanoma AJCC v8

Brief summary

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.

Detailed description

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.

Interventions

PROCEDUREBiospecimen Collection

Undergo collection of blood and stool

PROCEDUREComputed Tomography

Undergo CT

OTHERLaboratory Biomarker Analysis

Correlative studies

OTHERQuestionnaire Administration

Complete questionnaire

Sponsors

Ohio State University Comprehensive Cancer Center
Lead SponsorOTHER

Study design

Observational model
COHORT
Time perspective
PROSPECTIVE

Eligibility

Sex/Gender
ALL
Age
18 Years to No maximum
Healthy volunteers
No

Inclusion criteria

* Eligible patients include adults with stage III, IV melanoma, to be treated with pembrolizumab or nivolumab, regardless of other concurrent therapy or line of treatment

Exclusion criteria

* Patients will be excluded if they are undergoing active systemic or oral corticosteroid use at start of immune checkpoint inhibitors (ICI) cycle 1, with the exception of adrenal replacement dosing.

Design outcomes

Primary

MeasureTime frameDescription
Baseline microbiome alpha diversity in responders versus (vs) non-respondersAt 12 weeksThis 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.
Baseline microbiome alpha diversity in patients prescribed corticosteroids vs those who were not prescribed corticosteroidsWithin the 12-week treatment windowThis 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.

Secondary

MeasureTime frameDescription
Recruitment rates for longitudinal biospecimen collection, including stool, in melanoma patients12 weeksRecruitment 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.
Compliance rates for longitudinal biospecimen collection, including stool, in melanoma patients12 weeksCompliance 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.

Other

MeasureTime frameDescription
Microbes as significant predictors in logistic regressions where the outcomes are binary (clinical response or treatment toxicity requiring corticosteroids), with the inputs as relative abundances of individual microbesAt baseline, 12 weeks, or at corticosteroid prescriptionIndividual 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.

Countries

United States

Outcome results

None listed

Source: ClinicalTrials.gov · Data processed: Feb 12, 2026