Chromosomal Rearrangements and Their Associated Neo-Antigens as Predictors of Response and Survival to PD-1 Inhibition in Mesothelioma


ASPIRE Award (2020-Present)

Aaron Mansfield, MD, Mayo Clinic

Aaron Mansfield, MD

Immune checkpoint inhibitors (ICIs) are thought to be particularly effective against tumors that contain a relatively high number of mutations in their DNA, such as melanoma and lung cancer. Mutations can lead to the production of neoantigens, which, in turn, can be recognized and eradicated by stimulation of the immune system by ICIs. Some tumor types, however, have been shown to respond well to ICIs despite appearing to have a relatively low mutational burden. A team led by Aaron Mansfield at the Mayo Clinic showed that mesothelioma, an ICI-responsive lung cancer with relatively few traditional mutations, instead contains numerous chromosomal rearrangements, a type of DNA alteration that is not readily detectable by traditional sequencing methods.

In this project, Mansfield’s team is analyzing tumor samples from mesothelioma patients using mate-pair sequencing (MPseq) to identify chromosomal rearrangements that are missed by conventional next-generation sequencing techniques. Their hypothesis is that the chromosomal rearrangements may induce the expression of neoantigens and that the frequency of these lesions may correlate with patient response to immune checkpoint inhibition. Early results suggest that MPseq is over 300-fold more sensitive for detecting chromosomal rearrangements than next-generation sequencing. The investigators are also determining whether the neoantigens resulting from the rearrangements are expressed and whether patient T cells are responsive to the identified neoantigens. Results from a recent clinical trial suggest that the combination of two ICIs, nivolumab and ipilimumab, significantly extends overall survival for mesothelioma, an outcome that is likely to change the standard of care. This finding highlights the importance of understanding which patients are likely to benefit from ICI therapy and of determining genomic predictors of response.

PUBLISHED RESEARCH

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Dolezal JM, Srisuwananukorn A, Karpeyev D, Ramesh S, Kochanny S, Cody B, Mansfield AS, Rakshit S, Bansal R, Bois MC, Bungum AO, Schulte JJ, Vokes EE, Garassino MC, Husain AN, Pearson AT. Uncertainty-informed deep learning models enable high-confidence predictions for digital histopathology. Nat Commun. 2022.

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