{'fraction_non_alphanumeric': 0.016655989750160152, 'fraction_numerical': 0.0025624599615631004, 'mean_word_length': 5.973214285714286, 'pattern_counts': {'":': 0, '<': 0, '<?xml version=': 0, '>': 0, 'https://': 0, 'lorem ipsum': 0, 'www.': 0, 'xml': 0}, 'pii_count': 0, 'substrings_counts': 0, 'word_list_counts': {'cursed_substrings.json': 0, 'profanity_word_list.json': 0, 'sexual_word_list.json': 0, 'zh_pornsignals.json': 0}}
Systematic analysis of genotype‐specific drug responses in cancer

A systematic understanding of genotype‐specific sensitivity or resistance to anticancer agents is required to provide improved patient therapy. The availability of an expansive panel of annotated cancer cell lines enables comparative surveys of associations between genotypes and compounds of various target classes. Thus, one can better predict the optimal treatment for a specific tumor. Here, we present a statistical framework, cell line enrichment analysis (CLEA), to associate the response of anticancer agents with major cancer genotypes. Multilevel omics data, including transcriptome, proteome and phosphatome data, were integrated with drug data based on the genotypic classification of cancer cell lines. The results reproduced known patterns of compound sensitivity associated with particular genotypes. In addition, this approach reveals multiple unexpected associations between compounds and mutational genotypes. The mutational genotypes led to unique protein activation and gene expression signatures, which provided a mechanistic understanding of their functional effects. Furthermore, CLEA maps revealed interconnections between TP53 mutations and other mutations in the context of drug responses. The TP53 mutational status appears to play a dominant role in determining clustering patterns of gene and protein expression profiles for major cancer genotypes. This study provides a framework for the integrative analysis of mutations, drug responses and omics data in cancers.