Genetic profiling in non-small cell lung cancer. To predict response to immunotherapy
Examensarbete för masterexamen
Biotechnology (MPBIO), MSc
Genetic profiling in non-small cell lung cancer To predict response to immunotherapy JOHANNA SVENSSON Department of Life Sciences Chalmers University of Technology Abstract Introduction. Treatment of non-small cell lung cancer (NSCLC) was revolu tionised with immunotherapy. Particularly important is immune checkpoint blockade (ICB) targeting PD-1/PD-L1; nevertheless, two thirds are unresponsive to ICB. Better biomarkers are warranted besides the FDA approved tumour mutational burden (TMB). Genetic variants in a few selected genes have been suggested to predict response to ICB alone or in combinations as co-variants in both blood an tissue. This study aims to interpret variants in both blood plasma and tissue, and in addition analyse mutational signatures of the tumours, that might be used as biomarkers. Material and Methods. The prospective study cohort includes n=50 stage III-IV NSCLC patients that received ICB as first- or second line of treatment. Blood and tumour tissue was sequenced with next-generation sequencing (NGS) with a panel of 591 cancer-associated genes. A comprehensive variant interpretation and classification approach was used to subclass somatic variants into 6 different categories based on standard workflows, in combination with several databases and prediction tools. In addition, mutational signatures were extracted using SigProfiler tools and analysed. For n=26 patients variants were also monitored in blood during treatment with ICB using ultrasensitive methods for variant identification. Results and Discussions. In total 859 true variants were identified. These included 40 pathogenic, 96 likely pathogenic and 685 variants of unknown significance (VUS). The VUS:es were further subclassed into different categories to identify those with higher or lower driver properties and probability of pathogenicity. By using this approach 34 VUS++ and 75 VUS+ were identified. Frequently mutated genes, number of variants in different classes and their pathogenicity were related to ICB response, as was mutational signatures and levels of ctDNA at various timepoints. Conclusion. Understanding the genetic landscape and identifying biomarkers of ICB are key considerations in development of personalised treatment. The approach of a thorough classification including subclassification of the VUS:es led to identification of variants that can potentially function as biomarkers, in combination with other. Mutational signature analysis lead to differentiation of tumour types. The analysis of the combination of mutational signatures and genetic variants further enhanced refinement of biomarkers of response to ICB. Monitoring variants in ctDNA is a molecular tool for early identification of response or progress during treatment in NSCLC.
Biomarker , ctDNA , ICB , Mutational signature , NSCLC , Variant classification