Clinical Outcomes of TP53 Mutations in Cancers

Abstract

High-throughput sequencing of cancer genomes is increasingly becoming an essential tool of clinical oncology that facilitates target identification and targeted therapy within the context of precision medicine. The cumulative profiles of somatic mutations in cancer yielded by comprehensive molecular studies also constitute a fingerprint of historical exposures to exogenous and endogenous mutagens, providing insight into cancer evolution and etiology. Mutational signatures that were first established by inspection of the TP53 gene somatic landscape have now been confirmed and expanded by comprehensive sequencing studies. Further, the degree of granularity achieved by deep sequencing allows detection of low-abundance mutations with clinical relevance. In tumors, they represent the emergence of small aggressive clones; in normal tissues, they signal a mutagenic exposure related to cancer risk; and, in blood, they may soon become effective surveillance tools for diagnostic purposes and for monitoring of cancer prognosis and recurrence.

Figure 1.

Frequency of mutation types found in the tumor-suppressor genes TP53, PTEN, RB1, and APC. Mutations are classified as substitution, deletion, insertion, or complex mutations. The “other” category contains all those mutations that fall outside the defined categories or for which there is no information on nucleotide changes. Color codes are indicated. Data was downloaded from COSMIC (cancer.sanger.ac.uk) (Forbes et al. 2015) and includes only tumor samples.

Figure 2.

Distribution of TP53 mutations in cancer genomes analyzed by exome sequencing. Figure was generated by the cBio Cancer Genomics Portal (cbioportal.org) (Cerami et al. 2012). Mutation diagram circles are colored with respect to the most frequent mutation type at that position (green, missense; red, truncating; black, in-frame deletion (del)/insertion (ins); gray, splice-site; purple, different mutation types at the same proportion). P53_TAD, P53 transactivation motif (5–29); P53, P53 DNA-binding domain (95–289); P53_tetramer, P53 tetramerization motif (318–359).

Figure 3.

Frequency of TP53 mutations in 24 cancer genomes analyzed by The Cancer Genome Atlas (TCGA) using exome sequencing. The cBio Cancer Genomics Portal (cbioportal.org) (Cerami et al. 2012) was used to interrogate TP53 mutation frequency in each tumor data set, except for esophageal carcinoma data, which was downloaded from TCGA Data Portal (tcga-data.nci.nih.gov).

Figure 4.

A Precision Medicine research strategy. As outlined in the 2011 Institute of Medicine’s National Research Council report “Toward Precision Medicine: Building a Knowledge Network for Biomedical Research and a New Taxonomy of Disease” (National Research Council 2011), Precision Medicine starts with the creation of an Information Commons that interactively houses multiple “-omics” data types (genomics, epigenomics, transcriptomics, metabolomics, proteomics) along with historical exposure and lifestyle information from individual patients. Bioinformatic integration of these data will lead to the development of a Knowledge Network that will be used to improve disease taxonomy, the application of clinical medicine and the study of molecular mechanisms of disease. An iterative process of acquiring information in individuals or cohorts of patients, making improvements in taxonomy and using that knowledge to care for patients and design new studies that further feed the Information Commons will refine the molecular taxonomic classifiers and improve clinical medicine.

Figure 5.

p53 and its functions affect multiple layers of “-omics” data in the Precision Medicine paradigm.