High transposable element expression in sarcomas is associated with increased immune infiltrates and improved outcomes including after immunotherapy

Comprehensive Summary

Immune checkpoint inhibition (ICI) is a form of cancer immunotherapy which has previously been relatively unsuccessful in treating sarcomas. To investigate the impact of transposable elements (TEs) on ICI treatment in sarcomas, Nacev et al. collected DNA and RNA data from clinical biopsy samples before treatment and on-treatment. After DNA and RNA purification and sequencing, several algorithms were used to identify genomic features which differed between immune types. Statistical methods were then employed to compare and validate their findings. Their computational methods clustered two genetic groups which they termed “immune depleted” and “immune enhanced”. These two groups were significantly correlated with existing sarcoma immune classes (SIC). These two groups and their analogous SICs had significantly differing progression-free survival (PFS) and objective response rates (ORR) to the ICI immunotherapy. Further use of machine learning allowed the researchers to identify a potentially predictive value of TEs and immune type (how the person’s immune system may respond to immunotherapy).

Outcomes and Implications

This research studies the poorly understood cause of anti-tumor immunity in sarcomas. It precedes much further research into other potential biomarkers for ICI response and the improvement of ICI-based immunotherapy. Despite its initial training involving six TEs, the machine learning model has potential to be useful in patient selection for future immunotherapy clinical trials. Further research must be done to solidify the predictive capability of the TE biomarkers for immune response. However, the consideration of these new biomarkers is useful in ICI clinical trial design going forward.