Unique Transcriptional Profiles Underlie Osteosarcomagenesis Driven by Different p53 Mutants.

Missense mutations in the DNA binding domain of p53 are characterized as structural or contact mutations based on their effect on the conformation of the protein. These mutations show gain-of-function (GOF) activities, such as promoting increased metastatic incidence compared with p53 loss, often mediated by the interaction of mutant p53 with a set of transcription factors. These interactions are largely context specific. To understand the mechanisms by which p53 DNA binding domain mutations drive osteosarcoma progression, we created mouse models, in which either the p53 structural mutant p53R172H or the contact mutant p53R245W are expressed specifically in osteoblasts, yielding osteosarcoma tumor development. Survival significantly decreased and metastatic incidence increased in mice expressing p53 mutants compared with p53-null mice, suggesting GOF. RNA sequencing of primary osteosarcomas revealed vastly different gene expression profiles between tumors expressing the missense mutants and p53-null tumors. Further, p53R172H and p53R245W each regulated unique transcriptomes and pathways through interactions with a distinct repertoire of transcription factors. Validation assays showed that p53R245W, but not p53R172H, interacts with KLF15 to drive migration and invasion in osteosarcoma cell lines and promotes metastasis in allogeneic transplantation models. In addition, analyses of p53R248W chromatin immunoprecipitation peaks showed enrichment of KLF15 motifs in human osteoblasts. Taken together, these data identify unique mechanisms of action of the structural and contact mutants of p53. SIGNIFICANCE: The p53 DNA binding domain contact mutant p53R245W, but not the structural mutant p53R172H, interacts with KLF15 to drive metastasis in somatic osteosarcoma, providing a potential vulnerability in tumors expressing p53R245W mutation.

A pedigree-based prediction model identifies carriers of deleterious de novo mutations in families with Li-Fraumeni syndrome.

De novo mutations (DNMs) are increasingly recognized as rare disease causal factors. Identifying DNM carriers will allow researchers to study the likely distinct molecular mechanisms of DNMs. We developed Famdenovo to predict DNM status (DNM or familial mutation [FM]) of deleterious autosomal dominant germline mutations for any syndrome. We introduce Famdenovo.TP53 for Li-Fraumeni syndrome (LFS) and analyze 324 LFS family pedigrees from four US cohorts: a validation set of 186 pedigrees and a discovery set of 138 pedigrees. The concordance index for Famdenovo.TP53 prediction was 0.95 (95% CI: [0.92, 0.98]). Forty individuals (95% CI: [30, 50]) were predicted as DNM carriers, increasing the total number from 42 to 82. We compared clinical and biological features of FM versus DNM carriers: (1) cancer and mutation spectra along with parental ages were similarly distributed; (2) ascertainment criteria like early-onset breast cancer (age 20-35 yr) provides a condition for an unbiased estimate of the DNM rate: 48% (23 DNMs vs. 25 FMs); and (3) hotspot mutation R248W was not observed in DNMs, although it was as prevalent as hotspot mutation R248Q in FMs. Furthermore, we introduce Famdenovo.BRCA for hereditary breast and ovarian cancer syndrome and apply it to a small set of family data from the Cancer Genetics Network. In summary, we introduce a novel statistical approach to systematically evaluate deleterious DNMs in inherited cancer syndromes. Our approach may serve as a foundation for future studies evaluating how new deleterious mutations can be established in the germline, such as those in TP53.