Clinical Research Directory

C7R-GD2.CART Cells for Patients With Relapsed or Refractory Neuroblastoma and Other GD2 Positive Cancers (GAIL-N)

This study is for patients with neuroblastoma, sarcoma, uveal melanoma, breast cancer, or another cancer that expresses a substance on the cancer cells called GD2. The cancer has either come back after treatment or did not respond to treatment. Because there is no standard treatment at this time, patients are asked to volunteer in a gene transfer research study using special immune cells called T cells. T cells are a type of white blood cell that helps the body fight infection. The body has different ways of fighting infection and disease. No single way seems perfect for fighting cancers. This research study combines two different ways of fighting cancer: antibodies and T cells. Both antibodies and T cells have been used to treat patients with cancers. They have shown promise but have not been strong enough to cure most patients. We have found from previous research that we can put a new gene into T cells that will make them recognize cancer cells and kill them. In our last clinical trial we made a gene called a chimeric antigen receptor (CAR) from an antibody that recognizes GD2, a substance found on almost all neuroblastoma cells (GD2-CAR). We put this gene into the patients' own T cells and gave them back to 11 neuroblastoma patients. We saw that the cells did grow for a while, but started to disappear from the blood after 2 weeks. We think that if T cells are able to last longer they may have a better chance of killing GD2 positive tumor cells. Therefore, in this study we will add a new gene to the GD2 T cells that can cause the cells to live longer. T cells need substances called cytokines to survive and the cells may not get enough cytokines after infusion. We have added the gene C7R that gives the cells a constant supply of cytokine and helps them to survive for a longer period of time. In other studies using T cells, investigators found that giving chemotherapy before the T cell infusion can improve the amount of time the T cells stay in the body and therefore the effect the T cells can have. This is called lymphodepletion and we think that it will allow the T cells to expand and stay longer in the body, and potentially kill cancer cells more effectively. The GD2-C7R T cells are an investigational product not approved by the Food and Drug Administration. The purpose of this study is to find the largest safe dose of GD2-C7R T cells, and also to evaluate how long they can be detected in the blood and what affect they have on cancer.

Breast Phyllodes Tumors: Based on 20-year Real-world Data From China

Phyllodes tumor (PT) is a rare type of breast tumor made up of both connective tissue (stroma) and gland tissue (epithelium). It accounts for less than 1% of all breast tumors. The main symptom is a fast-growing, painless lump. Based on its appearance under a microscope, the World Health Organization (WHO) classifies PT into three grades: benign (non-cancerous), borderline (intermediate), and malignant (cancerous). The chance of the tumor coming back after treatment (recurrence) is about 7%, 17%, and 25% for each grade, respectively. The average age when patients are diagnosed is between 36 and 55 years, and the typical tumor size ranges from about 4 to 11 cm. Malignant PT can spread to other parts of the body (metastasis) in up to 6% to 62% of cases, most often to the lungs or bones through the bloodstream. Because PT is rare, large studies on its causes, diagnosis, treatment, and long-term outlook are very limited. PT can grow very quickly. Delaying diagnosis and treatment may allow the tumor to become so large that surgery is no longer an option. Standard imaging tests (ultrasound, mammogram, MRI) have limited ability to tell the difference between benign and malignant PT. Also, since these tumors are often large and vary from one area to another, a small needle biopsy may not show the whole picture. An accurate grade is usually only possible after the whole tumor is removed and examined by a pathologist. PT does not respond well to radiation or chemotherapy. Therefore, the main treatment is complete surgical removal with clear margins. The role of newer therapies such as targeted therapy or immunotherapy is still being studied. Although the WHO grade gives some idea of how the patient might do, some benign PT can come back many times or even turn malignant, while some malignant PT stay stable for a long time. The current grading system does not fully explain why individual outcomes differ. In recent years, most prediction models for PT have been built using Western patient data (for example, from the SEER database). These models have not been tested in Asian populations. They are usually based on small, single-center studies with dozens to a few hundred patients, and many have only been checked inside the same database without outside (external) validation. Most studies focus on symptoms, imaging findings, how to tell benign from malignant PT, or comparing different types of surgery. Few have looked closely at patterns of recurrence and metastasis, or risk factors for rare events like multiple recurrences, distant spread, or death. To fill these gaps, we plan to conduct a large, multi-center, retrospective (looking back at past medical records) study across 8 top hospitals in China. We will use real-world data from about 3,500 patients with breast PT diagnosed between 2001 and 2023. The main goals are to describe the clinical and pathological features, treatment patterns, and long-term outcomes of Chinese PT patients. The main outcomes we will measure are local recurrence, distant metastasis, and overall survival. Secondary outcomes include disease characteristics and current treatment practices. This study aims to provide evidence from the Chinese population to help guide personalized treatment and future updates to clinical guidelines.

AI-Assisted System for Accurate Diagnosis and Prognosis of Breast Phyllodes Tumors

Breast phyllodes tumor (PT) is a rare fibroepithelial tumor, accounting for 1% to 3% of all breast tumors, categorized by the WHO into benign, borderline, and malignant, based on histopathology features such as tumor border, stromal cellularity, stromal atypia, mitotic activity and stromal overgrowth. Malignant PTs account for 18%-25%, with high local recurrence (up to 65%) and distant metastasis rates (16%-25%). Benign PT could progress to malignancy after multiple recurrences. Therefore, Early, accurate diagnosis and identification of therapeutic targets are crucial for improving outcomes and survival rates. In recent years, there has been growing interest in the application of artificial intelligence (AI) in medical diagnostics. AI can integrate clinical information, histopathological images, and multi-omics data to assist in pathological and clinical diagnosis, prognosis prediction, and molecular profiling.AI has shown promising results in various areas, including the diagnosis of different cancers such as colorectal cancer, breast cancer, and prostate cancer. However, PT differs from breast cancer in diagnosis and treatment approach. Therefore, establishing an AI-based system for the precise diagnosis and prognosis assessment of PT is crucial for personalized medicine. The research team, led by Dr. Nie Yan, is one of the few in Guangdong Province and even nationally, specializing in PT research. Their team has been conducting research on the malignant progression, metastasis mechanisms, and molecular markers for PT. The team has identified key mechanisms, such as fibroblast-to-myofibroblast differentiation, and the role of tumor-associated macrophages in promoting this differentiation. They have also identified molecular markers, including miR-21, α-SMA, CCL18, and CCL5, which are more accurate in predicting tumor recurrence risk compared to traditional histopathological grading. The project has collected high-quality data from nearly a thousand breast PT patients, including imaging, histopathology, and survival data, and has performed transcriptome gene sequencing on tissue samples. They aim to build a comprehensive multi-omics database for breast PT and create an AI-based model for early diagnosis and prognosis prediction. This research has the potential to improve the diagnosis and treatment of breast PT, address the disparities in breast PT care across different regions in China, and contribute to the development of new therapeutic targets.