Biomarker Expression Intensity Impacts Melanoma Treatment Decision-Making

In an interview with CancerNetwork®, Thazin Nwe Aung, PhD, an associate research scientist in Pathology at the Yale School of Medicine, discussed strategies for overcoming resistance to immunotherapy and other drugs among patients with melanoma. Aung spoke in the context of the publication of a multi-institutional prognostic study she authored in JAMA Network Open that compared pathologist-read vs artificial intelligence (AI)–driven assessments of tumor-infiltrating lymphocytes (TILs) among patients with melanoma.

Aung began by highlighting the nature of tumor cells, which can adapt to and evade treatment for melanoma and other cancers. She expressed that multimodal biomarkers could be developed to help predict response probability with immunotherapy or other treatments, as well as treatment resistance, among patients with melanoma and other disease states. Furthermore, Aung highlighted platforms that may be used to predict responses, such as transcriptomics, proteomics, and digital pathology.

Next, she highlighted key biomarkers in melanoma, emphasizing that these biomarkers are used in conjunction with TILs to develop management strategies for melanoma and other cancers. She concluded by specifying that the intensity of biomarker expression is influencing treatment decisions among these patients.

Transcript:

Resistance happens because of these tumor cells. Tumor cells adapt and evade treatment. The strategy here is multimodal biomarkers; biomarkers that could be developed using various platforms, including transcriptomics, proteomics, and digital pathology. These biomarkers can predict which patients are not likely to respond to immunotherapy or any other treatments, or [it can predict] resistance to these treatments. These patients could be redirected to alternative therapies earlier.

The current melanoma biomarkers are BRAF, NRAS, PD-L1, and tumor mutational burden. These tumor-infiltrating lymphocytes combined with clinical variables are the ones that are shaping melanoma or cancer management. Based on the measurement, or the intensity of the expression of these markers, the treatment decisions are being made.

Reference

Aung TN, Liu M, Su D, et al. Pathologist-read vs AI-driven assessment of tumor-infiltrating lymphocytes in melanoma. JAMA Netw Open. 2025;8(7):e2518906. doi:10.1001/jamanetworkopen.2025.18906