LOTUS-CC: Understanding Cancer Cachexia in Advanced Cancers
This observational study, called LOTUS-CC, aims to better understand cancer cachexia (pronounced ka-KEK-see-uh), which is weight and muscle loss due to cancer. It focuses on patients with advanced colorectal, lung, or pancreatic cancer that cannot be surgically removed. Researchers will collect blood and tumor samples, review medical records, have participants wear a device to track activity, and perform physical function tests. The goal is to identify different types of cachexia to help develop more effective treatments in the future. You don't need to have cachexia to join, but you should be planning to start first-line cancer treatment within six weeks or have recently started.
- Study design
- This is an observational study planning to enroll 800 participants. It aims to identify different types of cancer cachexia.
- What's involved
- You would provide blood and archived tumor samples, undergo CT or PET/CT scans, have your medical records reviewed, wear an actigraph, and complete physical function tests. This will occur at baseline and during follow-up for up to one year.
- Compensation
- Not stated in the trial record.
- Follow-up
- Participants will be assessed up to one year after study completion to track changes over time.
AI-generated from the public study record. Only the study team can confirm whether you're eligible — confirm details with them before making decisions.
LOTUS-CC: An Observational Research Study to Uncover Subtypes of Cancer Cachexia
At a glance
Conditions
Where it's being run
225 sites across 22 statesStudy leadership
- Richard F Dunne · PRINCIPAL_INVESTIGATOR · University of Rochester NCORP Research Base
Who to contact
Do you actually qualify for this trial?
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Inclusion
Exclusion
What this trial measures
- Identification of multiple distinct diagnostic subtypes within the syndrome of cancer cachexia (CC)Baseline through study completion, assessed up to 1 year follow up
Defined by host characteristics (e.g. cachexia symptoms, physical activity, physical function, blood biomarkers including hemoglobin and albumin, and body composition) at baseline and change in these factors over time in patients with cancer at high risk for CC. First, will examine the distributions of each variable and assess the need for using transformations to achieve normality for continuous variables (log transformation, quartiles, or clinically important thresholds), collapsing similar variables into single categories, and excluding conditions that are too rare (\< 1% of the cohort). Next, will assess statistical correlations using the Jaccard similarity metric for binary variables and the Pearson or Spearman correlation for continuous variables to determine highly correlated variables that may provide redundant information.