Metastatic melanoma is the most aggressive and deadliest type of skin cancer. Over the past decades, new therapies targeting mutations in these tumors have been developed. Some of the most promising of these therapies target a protein called B-Raf (BRAF) and are called BRAF inhibitors. These inhibitors are effective initially but, over time, patients develop resistance to them, and they are no longer effective. Recognizing this challenge, we propose an innovative approach targeting an important resistance mechanism called metabolic reprogramming. Through this mechanism, cells are able to switch their mode of obtaining energy. In the case of melanoma cells, they usually obtain their energy through a pathway called glycolysis, which allows them to obtain energy quickly. However, when exposed to BRAF inhibitors, these cells have been shown to prefer a pathway called oxidative phosphorylation (OXPHOS), which has been associated with resistance to drugs and metastasis in the setting of cancer. We propose using a drug called atovaquone, which inhibits OXPHOS, against melanoma cells. Atovaquone is an FDA-approved drug with minimal side effects that has been shown to have anti-cancer effects against other types of cancers, such as ovarian and breast cancer, but has not been studied in melanoma. We will first study the effects of atovaquone by itself and then will study the effects of atovaquone in combination with BRAF inhibitors. We hypothesize that atovaquone will have anti-cancer effects against melanoma and that these effects will be stronger in combination with BRAF inhibitors. This innovative approach will study an unexplored treatment avenue and will provide insight into the effects of inhibiting the metabolic pathway of OXPHOS in melanoma.

Objectives: Our main goal is to understand how demographic factors influence the medical care patterns of individuals diagnosed with malignant melanoma. We also aim to see how these care patterns and demographics affect the chances of survival in these cases. Rationale: Research has highlighted the crucial role of factors like race and socioeconomic status in determining the severity and outcome of melanoma cases. Yet, we lack comprehensive data about how these factors impact the use of medical care before and after a new melanoma diagnosis, even though these factors are known to influence the disease’s impact. Methods: We are conducting a retrospective study using the Epic COSMOS database, containing records of over 220 million deidentified patients. We’re focusing on patients newly diagnosed with malignant melanoma of the skin. We’ll examine their interactions with healthcare providers six months, one year, two years, five years, and ten years before their diagnosis and categorize them based on their demographic characteristics. We’ll conduct similar analyses for healthcare visits to dermatologists, surgeons, and oncologists after diagnosis. Finally, we will try to understand how these patterns may affect mortality rates. Expected Results: We anticipate that patients with higher socioeconomic status, especially those who are white, will have more prediagnosis dermatology and preventive care visits, as well as more visits to dermatologists, surgeons, and oncologists after diagnosis. We also hypothesize that patients with fewer care visits both before and after diagnosis will face higher mortality rates.

When melanoma is not found early, it can get into your blood and lymphatic systems and spread to other parts of your body. When this happens, the melanoma is considered metastatic. Because the cancer has spread, surgical removal of tumors is not enough to stop the cancer from progressing, so additional medication is used to kill the cancer cells in the body. For many types of cancer, chemotherapy is used for this purpose. However, for melanoma specifically, a new medication type called immunotherapy has been improving survival compared to chemotherapy. The immunotherapy most commonly used for metastatic melanoma is called ipilimumab/nivolumab, but a new immunotherapy, nivolumab/relatlimab, has recently been approved also for the treatment of metastatic melanoma. Nivolumab/relatlimab works by targeting a particular protein on immune cells called LAG3. When nivolumab/relatlimab binds to LAG3, it activates the immune cell so that it can fight against the cancer. Current research suggests that patients with type II diabetes may have less LAG3 proteins on their immune cells and therefore less places for the nivolumab/relatlimab drug to bind to. Therefore, we hypothesize that nivolumab/relatlimab does not work as well in this population and ipilimumab/nivolumab will work better in comparison for this particular group. In order to test this hypothesis we will gather information from the chart such as treatment start, duration, any recurrences, as well as other variables to fully explore and uncover any differences in response rates between the two treatment groups. We will also collect blood samples from metastatic melanoma patients treated with ipilimumab/nivolumab and nivolumab/relatlimab to look at and compare the immune cells in each group both before and during treatment.

Metastatic melanoma remains a significant cause of death in the United States, with average survival rates below 35% just 5 years after initial diagnosis. In order to improve outcomes for patients, a growing area of research has been to focus on patient-specific biomarkers, which may enable targeted therapy or contribute to prognostic tests to determine which therapy is best for a certain patient. One important existing prognostic factor in melanoma is the sentinel lymph node biopsy (SLNB), which takes a sample of patient lymph node (LN) tissue to determine the stage of a patients’ cancer. Unfortunately, there is limited knowledge on if there are specific biomarkers present in patient LN tissue from SLNB to predict efficacy for specific melanoma treatments. This is important because recent experiments have shown that patient LN tissue is critical in mediating immune cell interactions which contribute to responses in immunotherapy treatment. These experiments suggest that there may be biomarkers present in patient LN tissue. In this study, we seek to profile patient LN tissue to determine specific biomarkers associated with response to immunotherapy in both local and metastatic melanoma. We will utilize advanced gene sequencing and imaging technology with machine learning models to determine specific immune pathways and spatial architecture of immune cells in LN tissue. This research is critical because it will definitively establish pathways and biomarkers which predict responses to immunotherapy in patient LN tissue— samples that are commonly available and extracted during routine melanoma SLNB staging procedures. In the future, researchers will be able to analyze these pathways to target specific pro- and anti-cancer pathways involved in lymph node metastasis. Melanoma physicians may be able to use information from patient SLNB to further determine which immunotherapy to give patients based on this data.