CONICET | Buscador de Institutos y Recursos Humanos

Immunotherapy is a promising therapy as it proved effective in a subset of patients. It has been observed that a high number of non-synonymous somatic mutations (NSM), called total mutational burden (TMB), correlates with the immunotherapy response in melanoma and lung. NSM can generate neoantigens, which, in turn, can be recognized by the immune system, triggering an anticancer immune response. While treating cancer with immunotherapy can be highly effective, only some patients respond to this treatment, so, there is a great interest to discriminate patients most likely to benefit from this therapy. The goal of that work is to provide the subset of genes needed to be sequenced to predict the TMB.We have worked with COSMIC whole-genome v84. After filtering, the working data involve 24726 samples from 42 cancer types. Models to predict TMB were generated for each cancer type. Models were validated with internal (1000 repetitions of bootstrapping) and external data (new samples of Cosmic v90 and 135 papers of patient cohorts).We obtained several models for each cancer type. Only 28 cancer types have good models (R2 > 0.6, and RSE < median on Mutational Burden in cancer). The internal validation was passed for 22 cancer types (R2 internal validation > R2 model - 0.1 and RSE internal validation < 1.25*RSE model). However, only models from 14 cancer types passed external validation. We compared 3 strategies to build models with genes of our selection, FoundationOne panel, and Census. For these cancer types, our models have better-predicting performance, less error, and need a smaller number of base pairs to be sequenced than currently used panel or Census. Also, we select 39 of those models according to the number of MB to be sequence as representative models to predict the TMB with good accuracy for the 14 cancer types.