Monrovia

Colorectal cancer (CRC) is a major health concern worldwide, ranking third in diagnosis and second in mortality. This research aims to develop a new blood test that can detect early signs of colorectal adenomas and cancer with high sensitivity and specificity, using advanced biological analyses and machine learning. Current screening methods like fecal immunochemical testing detect cancers but not precursor adenomas well, while endoscopy is invasive and costly. A non-invasive blood test could improve early detection and patient participation. The study will develop a liquid biopsy test called DENEB that measures circulating microRNA from both cell-free and exosome-derived sources in blood samples. It will proceed in four phases: profiling microRNA to select biomarker candidates, using machine learning to develop detection algorithms for adenomas and cancer separately, combining these algorithms into a single test, and validating the test in diverse patient groups. This blood test aims to detect advanced adenomas and colorectal cancer from a single blood draw, optimizing sensitivity, specificity, and cost-effectiveness. Participants will have had a colonoscopy and standard diagnostic procedures before any treatment, with at least one blood sample taken before treatment. Researchers will measure the test's sensitivity over about one year of study completion. The study will compare the new blood assay to standard care and validate its performance in multiple cohorts. Success could lead to improved early detection and prevention of colorectal cancer through a simple, non-invasive blood test.

Esophageal squamous cell carcinoma (ESCC) is a highly deadly cancer worldwide, mainly because it is often diagnosed late. Current screening methods like upper endoscopy are invasive and depend heavily on the operator, making early detection difficult. This study aims to develop a non-invasive blood test that combines cell-free microRNAs (cf-miRNAs) and exosomal microRNAs (exo-miRNAs) to detect ESCC early when it may still be curable. The study involves multiple centers and includes discovery, training, and validation phases using blood samples taken before surgery. Researchers will analyze cf-miRNAs and exo-miRNAs using techniques such as small RNA sequencing, RT-qPCR quantification, and PCR-based validation. The goal is to build a machine learning model that uses these microRNA signatures to detect ESCC with high sensitivity and specificity. Participants will provide plasma or serum samples for testing. Researchers will assess the sensitivity of the test after about one year. The study includes adults aged 18 to 90 with confirmed ESCC who have not received systemic therapy, as well as control participants without cancer. The study will monitor sample quality and other factors to ensure accurate results and aims to improve early detection of this serious cancer.

Lynch syndrome is a common inherited condition that significantly raises the risk of developing colorectal and endometrial cancers, along with other types of cancers. Despite regular cancer screening, many patients still develop colorectal cancer, highlighting the need for better prevention strategies. This study focuses on understanding immune system interactions in Lynch syndrome, particularly the role of antibodies against specific cancer-related peptides and other biological markers that may signal early cancer development. The study uses a diagnostic test called LYNX EYE, which combines analyses of blood, mucosal tissue, and hair. These tests look for microRNAs in the blood, antibodies against frame-shift peptides, bacteria in colon tissue, and exposure to environmental carcinogens through hair samples. These combined measures aim to provide a detailed profile of immune system activity and early cancer signals in people with Lynch syndrome. Participants will undergo surveillance through endoscopy and other clinical procedures as part of routine care. Researchers will collect and analyze blood samples, mucosal biopsies, and hair to study immune responses, microbiota presence, and exposure to carcinogens. The main outcome measured is the sensitivity of these biomarkers over about one year. The study seeks to identify early indicators of cancer risk and improve prevention efforts for people with Lynch syndrome.

Pancreatic ductal adenocarcinoma (PDAC) is a highly deadly cancer often detected only at advanced stages, making treatment difficult. Early detection is rare, and current blood markers like serum carbohydrate antigen 19-9 (CA19-9) are unreliable for identifying early disease. Researchers aim to validate a new method using exosome-based microRNA (miRNA) signatures for noninvasive, early detection of pancreatic cancer in a large international study. The study evaluates the PANcreatic cancer Exosome Early detectiON (PANXEON) test, which measures levels of 5 cell-free and 8 exosome-contained miRNAs in plasma using quantitative reverse transcriptase polymerase chain reaction (qRT-PCR). This diagnostic test will be applied to plasma samples from patients with pancreatic ductal adenocarcinoma and individuals without the disease to assess its ability to detect early-stage cancer. Participants will undergo blood sample collection for miRNA analysis. Researchers will monitor the sensitivity of the test over about one year to determine how accurately it detects pancreatic cancer. The study will compare miRNA levels in patients with PDAC to those in non-disease controls to confirm the test's effectiveness in early cancer identification.

Researchers are studying patients with stage II and III colorectal cancer who have undergone surgery and adjuvant chemotherapy to develop a test that predicts cancer recurrence. This test will use genome-wide DNA methylation patterns to help identify patients at risk of their cancer returning. The study aims to improve how doctors determine the need and length of chemotherapy and to guide new treatment plans based on recurrence risk. The study has three main phases. First, scientists will discover specific DNA methylation markers linked to recurrence-free survival using machine learning. Next, they will develop a clinical assay to measure these markers in tissue samples from the original tumor. Finally, the test will be validated independently to confirm its accuracy in predicting cancer recurrence after chemotherapy. Participants will have had stage II or III colorectal cancer treated with surgery and chemotherapy and must be cancer-free when joining the study. Researchers will analyze tissue samples and clinical data to assess the test's ability to predict recurrence over up to 10 years. This study focuses on recurrence-free survival as the main outcome and will provide important information for personalizing treatment in colorectal cancer patients.