The study introduces deconstructSigs, a computational method that identifies mutational signatures in individual tumor samples, enabling the distinction of DNA repair deficiencies and carcinoma evolution patterns. The method uses a multiple linear regression model to determine the linear combination of pre-defined mutational signatures that best reconstructs the mutational profile of a single tumor. It is implemented as an R package, utilizing the Bioconductor library BS.genome.Hsapiens.UCSC.hg19 for mutational context information and reshape2 for plotting. The package allows users to input mutational data and apply the method to determine the contribution of each mutational process to the tumor sample. The method was tested against the WTSI Mutational Signature Framework, showing consistent results in identifying mutational signatures across various cancer types. It demonstrated the ability to detect mutational processes driven by environmental exposures, DNA repair abnormalities, and mutagenic processes in individual tumors, with implications for precision cancer medicine. The study also highlighted the importance of sufficient mutations for accurate signature identification and the potential for false positives and false negatives in the WTSI framework. In esophageal carcinoma, deconstructSigs revealed distinct mutational processes in adenocarcinomas and squamous cell carcinomas, with signature 1A being prevalent in both. Signature 2, associated with APOBEC activity, was more prominent in squamous cell carcinomas. Signature 17, of unknown etiology, was found in adenocarcinomas, contributing to early mutations and being replaced by signature 1A in later mutations. These findings underscore the utility of deconstructSigs in revealing mutational processes in individual cancers, enabling comparisons of distinct histologies and elucidating the dynamics of mutational signatures over time. The study concludes that deconstructSigs provides a powerful tool for analyzing individual tumors, allowing the detection of mutational processes active in small subsets of samples and offering insights into cancer evolution and treatment strategies. The method is available for public use and can be adapted as new mutational signatures are identified through large-scale genomics analyses.The study introduces deconstructSigs, a computational method that identifies mutational signatures in individual tumor samples, enabling the distinction of DNA repair deficiencies and carcinoma evolution patterns. The method uses a multiple linear regression model to determine the linear combination of pre-defined mutational signatures that best reconstructs the mutational profile of a single tumor. It is implemented as an R package, utilizing the Bioconductor library BS.genome.Hsapiens.UCSC.hg19 for mutational context information and reshape2 for plotting. The package allows users to input mutational data and apply the method to determine the contribution of each mutational process to the tumor sample. The method was tested against the WTSI Mutational Signature Framework, showing consistent results in identifying mutational signatures across various cancer types. It demonstrated the ability to detect mutational processes driven by environmental exposures, DNA repair abnormalities, and mutagenic processes in individual tumors, with implications for precision cancer medicine. The study also highlighted the importance of sufficient mutations for accurate signature identification and the potential for false positives and false negatives in the WTSI framework. In esophageal carcinoma, deconstructSigs revealed distinct mutational processes in adenocarcinomas and squamous cell carcinomas, with signature 1A being prevalent in both. Signature 2, associated with APOBEC activity, was more prominent in squamous cell carcinomas. Signature 17, of unknown etiology, was found in adenocarcinomas, contributing to early mutations and being replaced by signature 1A in later mutations. These findings underscore the utility of deconstructSigs in revealing mutational processes in individual cancers, enabling comparisons of distinct histologies and elucidating the dynamics of mutational signatures over time. The study concludes that deconstructSigs provides a powerful tool for analyzing individual tumors, allowing the detection of mutational processes active in small subsets of samples and offering insights into cancer evolution and treatment strategies. The method is available for public use and can be adapted as new mutational signatures are identified through large-scale genomics analyses.