Purpose: Li-Fraumeni Syndrome (LFS) is a genetic disorder associated with a significant risk of early-onset cancer. This condition affects 1 in 5000 individuals and is largely driven by germline mutations in the TP53 tumor suppressor gene, which has a broad spectrum of functions including metabolic regulation. While LFS is highly penetrant, there is a wide degree of variability in clinical phenotype, including age of onset and tumor type. This variability suggests a role for patient-specific genetic factors, such as the type of TP53 mutation, which may define each individual's cancer risk and response to therapy. There remains a significant clinical need for better prognostication of LFS patients to predict disease outcomes and improve treatment options. An emerging body of literature is focused on the identification of non-invasive biomarkers to stratify patient populations. To this end, dermal skin fibroblasts (DSFs) have been shown to contain patient-specific disease correlates in a variety of conditions. Importantly, it has been shown that different TP53 mutations may underlie differential metabolic patterns in LFS fibroblasts; hence, we hypothesize that the metabolic signatures of DSFs from LFS patients can be utilized as prognostic biomarkers of cancer risk and response to treatment.

Methods and Results: To understand the phenotypic diversity of LFS fibroblasts, our lab created a mouse xenograft model and co-cultured human LFS-derived DSFs with a sarcoma cancer cell line. LFS fibroblasts initiated earlier tumor onset in the mice compared to DSFs from healthy individuals, suggesting that these fibroblasts may secrete tumorigenic factors. Moreover, this effect was abrogated by exposure to rapamycin, an inhibitor of the mTORC1 protein kinase, suggesting that mTORC1 activity may govern the paracrine activity of these divergent fibroblast phenotypes. Next, to explore the role of mTORC1 in LFS, we used inducible expression of mutant p53 in DSFs. p53 mutants promoted mTORC1 hyperactivation, leading to increased anabolic activity, basal respiration and ATP production, suggesting that mTORC1 may alter fibroblast metabolism in a p53-dependent manner. Overall, these data provide evidence for divergent metabolic profiles of LFS skin fibroblasts which may reflect LFS phenotype variability. Ongoing work in our lab aims to further characterize the metabolic profiles of LFS fibroblasts through RNA sequencing and metabolomic profiling. Machine learning techniques will then be employed to identify molecular signatures correlating with clinical features such as age of tumor onset.

Significance: This work will advance our understanding of how metabolism may underpin the clinical heterogeneity of LFS. The discovery of metabolic biomarkers will provide prognostic information with the potential to improve the early detection and treatment of cancers in LFS patients.

Citation Format: Pamela Psarianos, Camilla Giovino, Sangeetha Paramathas, Nish Patel, Rajesh Gupta, Ran Kafri, David Malkin. Characterizing the metabolic landscape of dermal fibroblasts in Li-Fraumeni Syndrome for the prediction of cancer risk and drug response [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2021; 2021 Apr 10-15 and May 17-21. Philadelphia (PA): AACR; Cancer Res 2021;81(13_Suppl):Abstract nr 2591.