The EACR’s ‘Highlights in Cancer Research’ is a regular summary of the most interesting and impactful recent papers in cancer research, curated by the Board of the European Association for Cancer Research (EACR).
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6. Machine learning identifies experimental brain metastasis subtypes based on their influence on neural circuits
Sanchez-Aguilera, A., Masmudi-Martín, M. et al. Cancer Cell. 41 (9): 1637-1649.E11 (2023).
doi: doi.org/10.1016/j.ccell.2023.07.010.
Summary of the findings
Brain metastases represent a major source of morbidity in cancer patients, with dramatic effect in their quality of life. These people exhibit a cognitive impairment that have been traditionally thought to result from the mass effect of the tumor growth. In this paper, the authors challenge this view and show that different experimental models of brain metastasis co-opt the function of the nearby neuronal circuits in vivo in a very specific manner. They recorded the intracranial electrical activity surrounding different brain metastasis types from various primary sources and oncogenomic profiles, and found characteristic features in the local field potential (LFP). These alterations were associated to changes in the density of GABAergic inhibitory synapses, calcium activity and specific transcriptional programs. For instance, they identified deregulation of the transcription factor Egr1 with roles in synaptic plasticity and angiogenesis, as a potential link with changes in electrical activity. Using data science and machine learning strategies, the authors show that a model trained in LFP signatures surrounding the three different brain metastasis can help to disambiguate between subtypes when confronted with recordings from a different metastatic line, and to anticipate brain metastases early in advance.
Figure legend. Three brain metastatic cell lines were inoculated in the same area of the brain where they generated tumors of the same size : E0771-BrM, a breast cancer brain metastatic cell line (green); B16/F10-BrM, a melanoma brain metastatic cell line (red); and 482N1, a lung cancer brain metastatic cell line, (blue). Mice with brain metastases were evaluated with electrophysiology (local field potential, LFP) to determine the impact of the tumor on brain activity in vivo. The LFP profiles clearly reflected heterogeneity on the impact of each brain metastasis model on neural activity, being the 482N1 the model generating the highest impact on neuronal communication. The computational analysis of the electrophysiological profiles allowed identifying the Principal Components that defined the heterogeneity in the electrophysiological patterns. This information was further exploited with artificial intelligence (e.g., Decision Trees) to prove the value of brain activity as a biomarker to define whether in a brain there is a metastasis and even the subtype of metastases. In our ongoing search to define the underlying cellular and molecular causes of the differential impact on neural circuits independent on the tumor mass effect we have found that the 482N1 model specifically decrease peritumoral inhibitory synapses (iSinapsis) and calcium activity. In addition we identified 51 genes specifically upregulated in the 482N1 model, including the transcription factor Egr1. Ctx: cerebral cortex; Hippo: hippocampus; Met: metastasis.
Future impact
This work opens the field to investigate the functional fingerprints of the crosstalk between cancer cells and the surrounding neuronal circuit as a potential translational biomarker. The concepts and potential mechanisms that are shown in this work advocate for considering a systematic cognitive assessment and electroencephalographic characterization of patients with brain metastases to advance artificial intelligence application for improved diagnostic tools. In addition, the possibility that a molecular mechanism underlies the impact of brain metastases on neuronal communication also offers the opportunity to develop a new therapeutic strategy aimed to improve the quality of life of these patients.
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The Cancer Researcher is an online magazine for the cancer research community from the European Association for Cancer Research.
The EACR, a registered charity, is a global community for those working and studying in cancer research. Our mission is “The advancement of cancer research for the public benefit: from basic research to prevention, treatment and care.”
Summary of the findings
Summary of the findings
