Duncan, L. M. | Search bioRxiv

bioRxiv2021

Combined tumor and immune signals from genomes or transcriptomes predict outcomes of checkpoint inhibition in melanoma

Cancer immunotherapy with checkpoint blockade (CPB) leads to improved outcomes in melanoma and other tumor types, but a majority of patients do not respond. High tumor mutation burden (TMB) and high levels of tumor-infiltrating T cells have been associated with response to immunotherapy, but integrative models to predict clinical benefit using DNA or RNA alone have not been comprehensively explored. We sequenced DNA and RNA from melanoma patients receiving CPB, and aggregated previously published data, yielding whole exome sequencing data for 189 patients and bulk RNA sequencing data for 178 patients. Using these datasets, we derived genomic and transcriptomic factors that predict overall survival (OS) and response to immunotherapy. Using whole-exome DNA data alone, we calculated T cell burden (TCB) and B cell burden (BCB) based on rearranged TCR/Ig DNA sequences and found that patients whose melanomas have high TMB together with either high TCB or high BCB survived longer and had higher response rates as compared to patients with either low TMB or TCB/BCB. Next, using bulk RNA-Seq data, differential expression analysis identified 83 genes associated with high or low OS. By combining pairs of immune-expressed genes with tumor-expressed genes, we identified three gene pairs associated with response and survival (Bonferroni P<0.05). All three gene pair models were validated in an independent cohort (n=180) (Bonferroni P<0.05). The best performing gene pair model included the lymphocyte-expressed MAP4K1 (Mitogen- Activated Protein Kinase Kinase Kinase Kinase 1) combined with the transcription factor TBX3 (T-Box Transcription Factor 3) which is overexpressed in poorly differentiated melanomas. We conclude that RNA-based (MAP4K1&TBX3) or DNA-based (TCB&TMB) models combining immune and tumor measures improve predictions of outcome after checkpoint blockade in melanoma.

genomics↗

bioRxiv2021

Protease-activatable biosensors of SARS-CoV-2 infection for cell-based drug, neutralisation and virological assays

Efforts to define serological correlates of protection against COVID-19 have been hampered by the lack of a simple, scalable, standardised assay for SARS-CoV-2 infection and antibody neutralisation. Plaque assays remain the gold standard, but are impractical for high-throughput screening. In this study, we show that expression of viral proteases may be used to quantitate infected cells. Our assays exploit the cleavage of specific oligopeptide linkers, leading to the activation of cell-based optical biosensors. First, we characterise these biosensors using recombinant SARS-CoV-2 proteases. Next, we confirm their ability to detect viral protease expression during replication of authentic virus. Finally, we generate reporter cells stably expressing an optimised luciferase-based biosensor, enabling viral infection to be measured within 24 h in a 96- or 384-well plate format, including variants of concern. We have therefore developed a luminescent SARS-CoV-2 reporter cell line, and demonstrated its utility for the relative quantitation of infectious virus and titration of neutralising antibodies. Author summaryTechniques for measuring infection with SARS-CoV-2 in the laboratory are laborious and time-consuming, and different laboratories use different approaches. There is therefore no generally agreed way to quantitate neutralising antibodies against SARS-CoV-2, which block infection with the virus and protect people from COVID-19. In this study, we describe a new way to measure SARS-CoV-2 infection, which is much simpler and faster than existing methods. It relies on the production of a specific protease enzyme by the virus, which is able to cleave and activate an engineered protein biosensor in infected cells. This biosensor emits light in the presence of viral infection, and the amount of light released is used as a readout for the amount of infectious SARS-CoV-2 present. The signal is very sensitive, so the number of infected cells required is very small, and the method can be scaled-up to test many samples at once. In particular, we demonstrate how it can be used to detect different variants of SARS-CoV-2, and quantitate neutralising antibodies against these viruses.

microbiology↗