Personalized Cancer Therapy Study
This study aims to understand how personalized cancer treatments work, including how well they treat cancer and what side effects they cause. Researchers will look at your medical records to see if your doctors chose treatments based on the genetic makeup of your tumor, and how you responded. They will also collect information about the tests and treatments you received. Optional research tests may be done on tissue, blood, or urine samples that are already being discarded or collected for this study. The study is looking for 10,000 participants of all ages (7 years and older) and genders. Success will be measured by comparing tumor biomarker profiling (looking at specific substances in your tumor) to how well you respond to treatment, over 4 years. The current recruitment status is unclear.
- Study design
- This is an observational study, meaning researchers will collect information from medical records and optional samples. It plans to include 10,000 participants.
- What's involved
- Not specified in the trial record.
- Compensation
- Not stated in the trial record.
- Follow-up
- Your treatment outcome will be measured for 4 years.
AI-generated from the public study record. Only the study team can confirm whether you're eligible — confirm details with them before making decisions.
Study of Personalized Cancer Therapy to Determine Response and Toxicity
At a glance
Conditions
Where it's being run
3 sites across 1 statesStudy leadership
- Shumei Kato, MD · PRINCIPAL_INVESTIGATOR · University of California, San Diego
Who to contact
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Do you actually qualify for this trial?
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Inclusion
Exclusion
What this trial measures
- Comparison of Tumor Biomarker Profiling to Treatment Outcome4 years
Tumor molecular profiles will be correlated to treatment outcome, assessed by measures including the response rate, the rate of stable disease (SD)\>6months/partial response (PR)/complete response (CR), progression-free survival (PFS), PFS ratio (comparison of the PFS used after molecular profiling to PFS on prior treatment), time to treatment failure, and overall survival. Logistic regression models (univariable and multivariables) will be used when the outcome variable is dichotomous. Kaplan-meier curves will be used for time-to event outcomes, and comparisons will be done with the log-rank test and Cox regression models.