Response to ICI treatment of melanoma predicted by new biomarkers

Response to ICI treatment of melanoma predicted by new biomarkers

Immune checkpoints are regulators of the immune system. Antibodies (light blue and red) block the interaction between PD-L1 (red molecule) on the surface of a cancer cell (red) and the PD-1 immune checkpoint (blue molecule) on a T cell (blue ), which lead to inhibition of T cells killing tumor cells. [selvanegra/Getty Images]

According to a new study from the Wistar Institute published in Nature Communication. Researchers demonstrated that mutations in processes regulating leukocyte and T-cell proliferation show potential as biomarkers with reliable and stable prediction of response to ICI treatment in several different datasets of patients with Alzheimer’s disease. melanoma.

“ICI therapy has revolutionized the treatment of advanced melanoma; however, only a subset of patients benefit from this treatment. Despite considerable efforts, the Tumor Mutation Burden (TMB) is the only FDA-approved biomarker in melanoma. However, the mechanisms underlying the association of TMB with prolonged ICI survival are not fully understood and may depend on many confounding factors,” the researchers wrote.

“To identify more interpretable ICI response biomarkers based on tumor mutations, we are training classifiers using mutations within distinct biological processes. We evaluate a variety of feature selection and classification methods and identify key mutated biological processes that provide improved predictive ability over TMB. The main mutated processes we identify are the regulation of leukocyte and T-cell proliferation, which demonstrate stable predictive performance in different data cohorts of melanoma patients treated with ICI.

“This work aims to identify better and more biologically interpretable genomic predictors for immunotherapy responses,” noted Noam Auslander, PhD, assistant professor in the Molecular Cellular Oncogenesis Program. “We need better biomarkers to help select patients most likely to respond to ICI therapy and understand what factors can help improve responses and increase those numbers.”

Using machine learning and publicly available anonymized clinical data, the team investigated why some melanoma patients responded to ICI therapy and others did not. Andrew Patterson, graduate student and first author of the paper, details that their research process involved training machine learning models on a dataset to predict whether a patient responds to ICI therapy and then confirming that the model was able to continuously predict response or resistance. to this processing on several other datasets.

The team found that the processes regulating leukocyte and T-cell proliferation include certain mutated genes that contribute to response and resistance to ICI treatment. This knowledge could be used to identify targets to improve responses or attenuate resistance in patients with melanoma.

“We were able to better predict whether a patient would respond to ICI therapy than the current standard clinical method and extract biological information that could help better understand the mechanisms underlying response and resistance to ICI therapy,” explained Patterson.

Scientists intend to continue this work with the aim of increasing the accuracy of predictions, better understanding the biological mechanisms underlying patient resistance or responsiveness to ICI treatment, and determining whether the processes distinguished in the article may also serve as predictors of response to ICI treatment for other types of cancer. .

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