Cancer immunotherapy is ineffective in low TMB EGFRmutant lung adenocarcinoma. Qi et al. performed a comprehensive proteogenomic profiling of HLA class I-presented immunopeptides in high TMB melanoma and low TMB EGFRmutant lung cancer. Similar numbers of immunopeptides were identified from both. Variant, CG antigen, PTM, and lncRNAderived peptides were identified. A novel strategy to identify lncRNA-derived peptides was developed. The direct identification of class I-presented immunopeptides will potentially accelerate precision immunotherapy for low TMB tumors. Highlights• Proteogenomics identified~35,000 class I-presented peptides.• CG antigen and PTM peptides identified in melanoma and lung cancer.• De novo search identified variant and lncRNA-derived peptides.• A new strategy to identify class I-presented lncRNA-derived peptides developed.

Summary Principled computational approaches for tumor phylogeny reconstruction via single-cell sequencing typically aim to build the most likely perfect phylogeny tree from the noisy genotype matrix – which represents genotype calls of single cells. This problem is NP-hard, and as a result, existing approaches aim to solve relatively small instances of it through combinatorial optimization techniques or Bayesian inference. As expected, even when the goal is to infer basic topological features of the tumor phylogeny, rather than reconstructing the topology entirely, these approaches could be prohibitively slow. In this paper, we introduce fast deep learning solutions to the problems of inferring whether the most likely tree has a linear (chain) or branching topology and whether a perfect phylogeny is feasible from a given genotype matrix. We also present a reinforcement learning approach for reconstructing the most likely tumor phylogeny. This preliminary work demonstrates that data-driven approaches can reconstruct key features of tumor evolution.