Lung cancer kills the most cancer patients in the world. Most of these patients are diagnosed late in their disease, and there is no cure. Having a chest CAT scan (CT scan) every year helps detect lung cancer early and reduces the chance of dying. When lung cancer is detected early, the patient has a higher chance to survive. Patients who are diagnosed with small lumps in their lungs, called lung nodules, have a higher chance of getting lung cancer. Having lung nodules can also require unnecessary, uncomfortable, and sometimes painful medical procedures that are not helpful for the patient. The purpose of our research is to help detect lung cancer earlier for patients with lung nodules, which could give them a better chance to beat cancer and survive. To do this, we propose to combine new medical test tests, from a blood draw and computer measurements from CAT scans. We will use simple blood draws to measure DNA materials in the blood that can help detect if lung cancer is present. We will also use computers to analyze hundreds of measurements from lung nodules in CAT scans that can tell us if the nodule is cancer. We will then combine the blood draw and computer measures from CAT scans using advanced math to detect lung cancer early more accurately without hurting the patient. Our goal is to improve early lung cancer detection so that it can be cured and help save patient lives.
The cells in the human body are constantly subjected to stress, which is linked to changes in cellular metabolism. Our research team, and others, have made connections between these cell conditions and cancer. Our central question is: Can we make a simple blood test that provides an accurate measure of ongoing cell stress and metabolic changes to gauge an individual’s risk of cancer? This test may provide more than just a snapshot measure of cancer risk. For example, the test could be used to measure how lifestyle changes modify cancer risk across the lifespan. To answer our question, we developed expertise that enables rapid measurement of signals in certain blood cells attributed to changes in cell stress and metabolism. Our study will determine if these signals can be used to quantify cancer risk. We will obtain blood samples from individuals without cancer, from individuals who have a condition known to increase their risk of cancer, and from individuals diagnosed with cancer. We will isolate certain cells from these samples and then measure the candidate signals in the cells. We anticipate our studies to reveal that the signals we are measuring will be the lowest in healthy individuals, will increase in individuals with the precancer condition, and will be highest in people diagnosed with cancer. These findings would powerfully validate our technology and suggest that individuals may benefit from our test for the early detection, and even prevention, of cancer.
The study will detect cancer of the prostate in African-American men. African American men develop prostate cancer at a young age. The cancer spreads rapidly making it difficult to treat. Our method will detect substances produced by prostate cancer. The test will examine blood collected from men who have concerns with their prostate. The study will develop the test and make it available in the clinic. The test will help African American men in the community who do not have access to medical care. Early finding of prostate cancer will provide enough time for cure and will help reduce cancer related suffering and death.
Lung cancer is the leading cause of cancer death in both the US and the world. There is an effective screening tool called low dose computed tomography (CT) scans of the lungs, which can find lung cancers earlier while curative surgery is still an option. These screening CT scans are recommended once to year for heavy current and former smokers, but only a tiny fraction of those who should be getting lung screening are receiving it, in part because of the high false positive rate with screening CT scans. When lung screening identifies an abnormal area (called a nodule) within the lung, the chances are much greater that it will turn out to be benign rather than cancer. However, to prove the nodule is benign a battery of tests and procedures are often ordered, leading to cost, inconvenience, possible complications, and worry. Our project aims to cut the obstacle of false positive results on lung cancer screening in half by developing a blood test that can be drawn in a doctor’s office after a patient is found to have a lung nodule on a screening CT scan and can help predict whether the nodule is benign or cancerous. The test is built upon a cutting-edge technology called multiplexed mass spectrometry-based plasma proteomics, which can detect the signature spectrum of hundreds of proteins within a patient’s blood plasma using just a small sample. Our test will look at the pattern of proteins to see if the pattern matches those seen in cancer patients. Our long-term goal is to develop an accessible test that will promote increased lung cancer screening uptake and lead to more lives saved.
In cancer, many processes and functions of cells are changed. One such change is the presence of errors in the DNA sequence of cancer cells. By searching for these errors in blood samples from patients, one could use these as a means to detect the disease. In early disease, the presence of these errors in blood is scarce compared to normal cells, making their detection difficult. Recently, in addition to mutations, the DNA has also been observed to be chemically changed at an early stage. One such change (DNA Methylation) is vastly different in cancer cells and it covers larger regions of DNA, making it easier to detect. Analyzing these patterns from blood could be a viable means to detecting cancer in its early stages. In this proposal, we will map out the profile of patients who develop Acute Myeloid Leukemia (AML). We will use blood samples from a large number of patients that were diagnosed with the disease. Importantly, we have identified samples from patients who have particularly aggressive forms of the disease. Our objective is to create biomarkers to identify the disease early through blood samples, differentiate aggressive disease from benign ones. This allows us to treat lethal cancers with more aggressive therapy at an earlier stage. Together, we propose an exciting opportunity to detect cancer early and identify patients who could benefit from treatment before their cancer grows beyond control.
Funded by the Dick Vitale Pediatric Cancer Research Fund
Recent advances have shown that it is possible to use a patient’s own immune system to fight cancer. Osteosarcoma, the most common bone cancer in children and young adults, is one cancer type that has not responded to immune-based treatments. Most patients who relapse die when the osteosarcoma spreads to the lung, and it is critically important to design new treatments to prevent these young lives from being lost.
Dr. Ligon’s team analyzed osteosarcoma samples from human patients and found that while immune cells are present in osteosarcoma lung tumors, they are kept at the outside of the tumor because the tumor has several ways to “exclude” these immune cells. In collaboration with Dr. Sayour, Dr. Ligon’s team proposes to use a new immune-based therapy called an RNA nanoparticle vaccine, which may be able to reprogram the tumor and allow immune cells to kill the cancer.
Based on promising data from the lab and from treating small animals such as dogs with osteosarcoma, Dr. Ligon proposes a clinical trial of this treatment in human patients with osteosarcoma which has spread to the lungs. He proposes to establish that this treatment is safe and find the right dose for future clinical trials. He will also perform studies on blood and tumor samples to understand how the vaccine works against osteosarcoma. This clinical trial will study a new treatment for a cancer that currently is incurable and help us understand how this new treatment works to help design future studies.
Oral cavity squamous cell carcinoma (OCSCC) is the most common head and neck cancers worldwide. Finding OCSCC early, when it’s small and hasn’t spread, allows for more successful treatment, and increases patients’ survival. Unfortunately, most of the patients present at advanced stage when diagnosed. Current method for OCSCC diagnosis (which includes cutting of tissue for laboratory testing), is invasive, costly, and depends on examiner experience, underscoring the need for developing noninvasive cancer detection methods. As OCSCC grows, it accumulates mutations in genes known to play role in cancer progression. Our group and others have reported that such mutations can be detected in saliva of patients with OCSCC. However, no saliva-based screening method for early detection of cancer are currently available. Recently we have developed a method based on the targeted sequencing technology specifically designed to detect OCSCC-associated mutations in saliva and validated this assay using specimens collected in India (a country with a high incidence of OCSCC). While these findings provide the foundation for using this ultra-sensitive and cost-efficient assay in clinical settings, frequency of cancer-driving mutations may vary in patients from different ethnical backgrounds. Our proposal will leverage the unique geographic location of the University of Chicago to evaluate the performance of this test across demographically heterogeneous patient populations, as well as across diverse therapeutic approaches for treatment of OCSCC. A well-validated, saliva-based cancer detection assay with optimal analytical performance would represent a significant clinical advancement in cancer care by reducing mortality, while lowering the socio-economic burden of OCSCC.
Bob Bast Translational Research Grant*
Most women from families that have the greatest risk for breast and ovarian cancers are not aware that they are at higher risk. Even among women who are aware of this risk, no tests are available for ovarian cancer, and no tests for breast or ovarian cancer can predict when a cancer is most likely to occur. The current tests typically detect cancer when it has already spread and is very difficult to cure. There is a great need to have a test that can accurately identify women who are at higher risk for breast and ovarian cancer and can detect cancer early. Our project will develop a blood test which can predict which women are most at risk for ovarian and breast cancer. Following that, we will study whether the same blood test can predict when cancer among these women is most likely to develop, increasing the chances that a cancer is found early and significantly improving the odds of survival. The novelty of our test is that we are looking at a new class of molecules in blood using cutting-edge strategies that have never been used for cancer detection.
The overall goal of “Enhancing Lung Cancer Screening For Eligible Patients (ELFE) through human- centered intervention” is to increase the completion rates of lung cancer screening (LCS) among eligible patients. LCS is important because it can facilitate the detection of lung cancer at the earliest and most treatable stage before the cancer has spread. The goal of ELFE is two-fold: 1) interviewing patients who have completed lung cancer screening to better understand factors that served as barriers to or facilitators of LCS participation and 2) develop a clinical intervention incorporating the lessons discovered through the interviews. We will explore the use of a Pre-Visit Planner in which a licensed medical assistant will engage with patients alone or coupled with a web portal to identify patients who are eligible for LCS. ELFE is a collaboration that includes the UC Davis Comprehensive Cancer Center, UC Davis Health, and Amazon Web Services to bring innovative research and tools to patients. Using a patient-centered intervention such as what we are proposing potentially could impact clinical practice thus, reducing the mortality associated with lung cancer.
Lung cancer is the leading cancer killer in both men and women in the U.S. Early detection is the most effective way to fight against this deadly disease. In recent years, an imaging method known as low-dose CT (LDCT) scan has been studied in people at higher risk of getting lung cancer. LDCT scans can help find nodules in the lungs that may be cancer. However, majority of those nodules are actually benign, yet exposing many of those patients to a needle biopsy or other invasive procedures. Hence, there is an urgent and unmet need for an accurate and non-invasive approach to distinguish those nodules that are malignant from those that are not. In this proposal, we will develop and validate a novel method to integrate a blood test and the LDCT imaging for the early detection of lung cancer. Specifically, from blood we extract cell-free DNA, from which we develop an ultra-sensitive assay to profiles the epigenome of cell-free DNA, therefore to detect even a trace amount of tumor DNA. Using advanced machine learning algorithms on the integrated genomics and imaging data, we aim to significantly improve the accuracy of the cancer detection. For those patients with nodules identified from LDCT, we will integrate the two sources of information to determine whether the nodules are malignant or benign.
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