This pilot phase I clinical trial studies the side effects and best dose of toll-like receptor 4 (TLR4) agonist glucopyranosyl lipid A (GLA)-stable-emulsion (SE) when given together with radiation therapy in treating patients with soft tissue sarcoma that has spread to other parts of the body (metastatic) or cannot be removed by surgery (unresectable). TLR4 agonist GLA-SE may stimulate the immune system to kill sarcoma cells. Radiation therapy uses high energy x rays to kill tumor cells. Giving TLR4 agonist GLA-SE with radiation therapy may be a better treatment to treat sarcoma that cannot be removed by surgery.
PRIMARY OBJECTIVES: I. To evaluate the safety of weekly injections of GLA-SE (TLR4 agonist GLA-SE) in combination with palliative radiation in patients with metastatic sarcoma. SECONDARY OBJECTIVES: I. To look for preliminary evidence of efficacy at distant tumor sites following the combination of radiation and intra-tumor injection of GLA-SE. II. To analyze changes in tumor-immune infiltrates following radiation and intra-tumor injection of GLA-SE. OUTLINE: This is a dose-escalation study of TLR4 agonist GLA-SE. Patients receive TLR4 agonist GLA-SE intratumorally once weekly for 8 weeks. Within 2 weeks of starting treatment, patients also undergo radiation therapy over 2 weeks for a total of 5-6 fractions. After completion of study treatment, patients are followed up every 6 weeks for 6 months and then every 3 months for up to 1 year.
Study Type
INTERVENTIONAL
Allocation
NA
Purpose
TREATMENT
Masking
NONE
Enrollment
16
Fred Hutch/University of Washington Cancer Consortium
Seattle, Washington, United States
Incidence of severe adverse events, defined as any grade 3 or higher adverse event (AE) according to National Cancer Institute Common Terminology Criteria for Adverse Events version 4.03
The highest toxicity grades per patient will be tabulated for AEs and laboratory measurements as will the numbers and percentages of patients reporting AEs.
Time frame: Up to week 9
Change in biomarker outcomes from the peripheral blood
Summary statistics will be used to describe changes across time. In addition, the time course of biomarker outcomes from the peripheral blood will be investigated graphically, by summary plots or individual patient plots. If there is suggestion of meaningful trend, methods such as linear mixed models may be used to characterize the pattern of change over time.
Time frame: Baseline up to 1 year
Clinical benefit based on RECIST v1.1 and iRRC evaluations
Categorical data analysis and logistic regression will be used to evaluate the associations between correlative measures and clinical outcome (e.g., response, clinical benefit, time to progression, progression-free survival, and survival).
Time frame: Up to 1 year
Immune infiltrates, measured quantitatively as number of cells per unit area
Tumor infiltrating lymphocytes will be analyzed directly by flow and grown in vitro so that functional characteristics can be analyzed. Metrics based on flow cytometry (e.g. cell phenotype and inhibitory receptor expression) will be reported both with respect to the mean florescence intensity of the staining as well as the absolute and relative numbers of positive and negative cells compared with established controls.
Time frame: Up to 1 year
Progression free survival
Categorical data analysis and logistic regression will be used to evaluate the associations between correlative measures and clinical outcome (e.g., response, clinical benefit, time to progression, progression-free survival, and survival). Kaplan-Meier methodology and Cox Proportional Hazards models will be used to evaluate time-to-event endpoints.
Time frame: Up to 1 year
Response based on Response Evaluation Criteria in Solid Tumors (RECIST) version (v)1.1 and immune-related-response criteria (iRRC) evaluations
Categorical data analysis and logistic regression will be used to evaluate the associations between correlative measures and clinical outcome (e.g., response, clinical benefit, time to progression, progression-free survival, and survival).
Time frame: Up to 1 year
Survival
Categorical data analysis and logistic regression will be used to evaluate the associations between correlative measures and clinical outcome (e.g., response, clinical benefit, time to progression, progression-free survival, and survival). Kaplan-Meier methodology and Cox Proportional Hazards models will be used to evaluate time-to-event endpoints.
Time frame: Up to 1 year
Time to progression
Categorical data analysis and logistic regression will be used to evaluate the associations between correlative measures and clinical outcome (e.g., response, clinical benefit, time to progression, progression-free survival, and survival). Kaplan-Meier methodology and Cox Proportional Hazards models will be used to evaluate time-to-event endpoints.
Time frame: Up to 1 year
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