Researchers in Sweden have shown how data-driven AI could contribute to a better understanding of how prostate cancer develops, and even improve clinical diagnosis and treatment of the disease

Every cancer tumor is unique, with characteristics that change over time. This so-called heterogeneity is due to competing clones within a given tumor, as well as acquired mutations that increase the likelihood of metastases.

Researchers at Sweden's Science for Life Laboratory have demonstrated how data-driven AI methodology has the potential to contribute to a better understanding of these major events regarding heterogeneity in prostate tumors and in the surrounding microenvironment.

The research team from KTH and Karolinska Institutet, led by Joakim Lundeberg, Professor of Molecular Biology at KTH, used data obtained from nearly 6,750 tissue samples with spatial transcriptomics – a method that combines histology (tissue) with quantitative analysis of the active genes, which has been developed by KTH and Karolinska Institutet at SciLifeLab. The results were presented in the scientific journal Nature Communications.

The use of spatial information makes a big contribution, Lundeberg says. Analysis of prostate tumor gene activity in a tissue section dramatically increases the granularity, compared to conventional tumor analysis.

"We have demonstrated that sampling different parts of the same prostate tumor show remarkable differences on the gene activity level of the cancer cells at each site as well as the surrounding non-tumor cells, such as cells related to inflammation response likely to be linked to the outcome of the patient," he says.

AI methods to identify genetic patterns

This rich source of information enables unattended AI methods to identify genetic patterns that cannot be seen by the naked eye, he says. Thus, this massive tissue genetic analysis can serve as a basis for an AI-based clinical evaluation of cancerous tissues and provide insight into gene expression in the tumor's microenvironment.

"AI simply helps us to create a computerized tissue anatomy," he says. Further insights into the mechanisms underlying cancer are crucial for understanding the progression of tumors and how patients respond to treatment, he says.

Molecular data has been used successfully in the treatment of other forms of epithelial cancers, such as breast cancer. Co-author, Emilie Berglund, a doctoral student at KTH, says that recent studies show that it can help with prostate cancer too.

"Early remedy of primary prostate cancer is efficient, however differentiating those that will progress to aggressive cases and who will benefit from what treatment is still problematic," Berglund says. "We hope that this study makes a significant contribution to these aspects."