Posted August 2, 2023

In fiscal year 2021 (FY21), the Melanoma Research Program (MRP) funded the Melanoma Academy (1). This virtual academy allows outstanding early career investigators (called Scholars) in the field of melanoma to build strong scientific collaborations and network with a diverse group of melanoma experts. In the first year, the leadership team of Dr. David Fisher (Massachusetts General Hospital, Harvard University) and Dr. Michael Davies (University of Texas MD Anderson Cancer Center) has hosted monthly meetings, providing networking and professional development opportunities for the first cohort of Scholars. The MRP is pleased to announce three more Scholars will join the Melanoma Academy for FY22.

Dissecting the Role of Polycomb Repressive Complex 1 in Uveal Melanoma Progression

Scholar: Mathieu Bakhoum, M.D., Ph.D., Yale University
Career Guide: Marcus Bosenberg, M.D., Ph.D., Yale University

Mathieu Bakhoum, M.D., Ph.D.
Dr. Mathieu Bakhoum
(Photo Provided)

Uveal melanoma (UM) is the most common ocular cancer in adults. Approximately half of the patients diagnosed with UM will develop metastatic disease, for which there are no curative therapies. Therefore, it is critical to develop therapies that treat UM early and prevent metastasis. This requires better understanding of the molecular mechanisms of UM progression and metastasis. Dr. Mathieu Bakhoum is a physician scientist with training in retinal diseases and cancer genetics. His previous work combined genetic, epigenetic (i.e., the study of how gene expression is regulated), and functional analyses at the single-cell level to demonstrate that progression of UM is driven by a critical epigenetic regulator known as Polycomb Repressive Complex 1 (PRC1). His FY22 Scholar Award will further interrogate the role of PRC1 in UM progression, specifically that loss of PRC1 can make UM more aggressive and more likely to spread. This project will use pre-clinical models to test this hypothesis and aims to identify potential targets for new drugs to prevent the spread of UM.

Identification and Peripheral Tracking of Melanoma-Specific T Cells After Early-Stage Melanoma Diagnosis for Detection of Recurrence or Metastasis

Scholar: Noah Hornick, M.D., Ph.D., Oregon Health and Science University-Portland
Career Guide: Sancy Leachman, M.D., Ph.D., Oregon Health and Science University-Portland

Noah Hornick, M.D., Ph.D.
Dr. Noah Hornick
(Photo Provided)

The presence and characteristics of tumor infiltrating lymphocytes (TIL), a type of white blood cell, are correlated with overall patient prognosis and response to immunotherapies. There is evidence that lymphocytes present in the blood contain cells that target melanoma, similar to the TIL found within the tumor. Further, lymphocytes with tumor-killing characteristics are more likely to be detected in the peripheral blood compared with “exhausted” TILs (those that do not have the capacity to kill tumor cells). Dr. Noah Hornick proposes that identifying circulating TILs in early diagnostic specimens would provide a non-invasive surveillance strategy to monitor patients for recurrence or metastasis of their disease. As a physician scientist, Dr. Hornick will be able to identify patients with a new melanoma diagnosis and prospectively collect peripheral blood over time. By evaluating patients who progress and those who do not, Dr. Hornick will assess the ability of circulating TILs to predict the development of recurrence and metastasis. This would benefit patients by providing an easy method to detect and track tumor progression and treat the disease in earlier stages.

FIND-MEL: Developing an Application for Following Images of Nevi to Detect Melanoma

Scholar: Veronica Rotemberg, M.D., Ph.D., Sloan Kettering Institute for Cancer Research
Career Guide: Susan Swetter, M.D., Stanford Medicine and Cancer Institute

Veronica Rotemberg, M.D., Ph.D.
Dr. Veronica Rotemberg
(Photo Provided)

Some studies find that artificial intelligence (AI) is more accurate at detecting skin cancer compared to dermatologists (4), but most of the research in this area is focused on dermoscopic image analysis rather than “real world” situations, such as smartphone images. The ability to evaluate skin lesions remotely using smartphones would reduce the burden on both physicians and patients. Dr. Veronica Rotemberg is a physician scientist with the goal of developing new technologies to benefit patients. The overarching goal of her FY22 award is to develop and validate an open-source algorithm called FIND-MEL: Following Images of Nevi to Detect Melanoma, for longitudinal monitoring of suspicious lesions. This technology uses photographs that are collected in dermatology clinics to train and improve the ability of AI to identify melanomas from benign lesions remotely through a cell phone or digital camera photograph. The successful development of FIND-MEL would allow for early detection of melanomas, without an in-person dermatology exam. This would help expand access to care for patients (for example, those in rural areas, or deployed Service Members) and, consequently, prevent progression and metastasis.


1. Introducing the Melanoma Academy:

2. Bakhoum MF, Francis JH, Agustinus A, Earlie EM, Di Bona M, Abramson DH, Duran M, Masilionis I, Molina E, Shoushtari AN, Goldbaum MH, Mischel PS, Bakhoum SF, Laughney AM. Loss of polycomb repressive complex 1 activity and chromosomal instability drive uveal melanoma progression. Nat Commun. 2021 Sep 13;12(1):5402. doi: 10.1038/s41467-021-25529-z. PMID: 34518527; PMCID: PMC8438051.

3. Lucca LE, Axisa PP, Lu B, Harnett B, Jessel S, Zhang L, Raddassi K, Zhang L, Olino K, Clune J, Singer M, Kluger HM, Hafler DA. Circulating clonally expanded T cells reflect functions of tumor-infiltrating T cells. J Exp Med. 2021 Apr 5;218(4):e20200921. doi: 10.1084/jem.20200921. PMID: 33651881; PMCID: PMC7933991.

4. Combalia M, Codella N, Rotemberg V, Carrera C, Dusza S, Gutman D, Helba B, Kittler H, Kurtansky NR, Liopyris K, Marchetti MA, Podlipnik S, Puig S, Rinner C, Tschandl P, Weber J, Halpern A, Malvehy J. Validation of artificial intelligence prediction models for skin cancer diagnosis using dermoscopy images: the 2019 International Skin Imaging Collaboration Grand Challenge. Lancet Digit Health. 2022 May;4(5):e330-e339. doi: 10.1016/S2589-7500(22)00021-8. PMID: 35461690; PMCID: PMC9295694.