ABSTRACTBackgroundAnal squamous cell carcinoma is a rare tumor. Chemo-radiotherapy yields a 50% 3-year relapse-free survival rate in advanced anal cancer, so improved predictive markers and therapeutic options are needed.MethodsHigh-throughput proteomics and whole-exome sequencing were performed in 46 paraffin samples from anal squamous cell carcinoma patients. Hierarchical clustering was used to establish groupsde novo. Then, probabilistic graphical models were used to study the differences between groups of patients at the biological process level.ResultsA molecular classification into two groups of patients was established, one group with increased expression of proteins related to adhesion, T lymphocytes and glycolysis; and the other group with increased expression of proteins related to translation and ribosomes. The probabilistic graphical model showed that these two groups presented differences in metabolism, mitochondria, translation, splicing and adhesion processes. Additionally, these groups showed different frequencies of genetic variants in some genes, such asATM, SLFN11andDST. Finally, genetic and proteomic characteristics of these groups suggested the use of some possible targeted therapies, such as PARP inhibitors or immunotherapy.ConclusionsIn this study, a molecular classification of anal squamous cell carcinoma using high-throughput proteomics and whole-exome sequencing data was proposed. Moreover, differences between the two established groups suggested some possible therapies.