Mutant p53 gain-of-function and Li-Fraumeni syndrome
Li-Fraumeni syndrome (LFS) is characterized by the autosomal dominant inheritance and early onset of tumors including the soft tissue sarcoma and osteosarcoma, breast cancer, brain tumor, leukemia, and adrenocortical carcinoma. The germline mutation of the TP53 tumor suppressor gene is responsible for LFS. TP53 (p53) encodes a 53 kDa transcription factor which binds DNA as a tetramer. It is well-known for its ability to repress tumor development by regulating genes involved in cell cycle arrest, DNA damage response, apoptosis, senescence, differentiation and metabolism. Mutations in p53 not only impair its tumor-suppressor function but also transform it into an oncoprotein as a so-called gain-of-function (GOF). The high prevalence of missense mutations, particularly at certain hot-spots, suggests that mutp53s provide a selective advantage during cancer progression. Indeed, mutant p53 (mutp53) GOFs includes dysregulated metabolic pathways, upregulated histone regulators, enhanced metastasis, and increased ECM gene expression. Although a few mechanisms have been proposed to explain mutp53 GOF, the comprehensive picture of mutp53-mediated malignant transformation remains nebulous.
Figure. Mutational landscape of TP53 germline and somatic mutations in human cancer. TP53 missense mutation data is obtained from the International Agency for Research on Cancer (IARC) TP53 database (http://p53.iarc.fr/). Distributions of p53 mutations are plotted over the function of amino acid position, where left side indicating germline mutations and right side indicating somatic mutations. Horizontal axis shows the frequency of any mutation at indicated residues. Vertical axis is the schematic of the p53 protein starting with N-terminal at the top. p53 protein domain contains transcriptional activation domain I and II (TAD 1, 20–40; TAD II, 40–60), the proline domain (PP, 60–90), the sequence-specific core DNA-binding domain (DNA-binding core, residues 100–300), the linker region (L, 301–324), the tetramerization domain (Tet, 325–356), and the lysine-rich basic C-terminal domain (++, 363–393). Most common mutations on “hot spot” are indicated as bold line; R175, G245, R248, R273, R282 residue are the five common hot spots in both germline and somatic mutations (indicated as lollipop). Pie charts illustrate the tumor site distribution of five hotspot TP53 mutations (left, germline; right, somatic). Malignancies located at breast, brain, soft tissues and bone are most commonly seeing over the five hotspot germline mutations; malignancies from these tumor sites are also distributed in the same five hotspot of TP53 somatic mutations (indicated in bold).
Figure is adapted from Trends Pharmacol Sci. 2017 Oct;38(10):908-927.
Osteosarcoma-prone genetic diseases
Hereditary genetic disorders associated with predisposition to osteosarcoma are relatively rare. However, studies of these diseases have led to important insights that generalize to the broader osteosarcoma population. Eight genetic diseases that predispose to the development of osteosarcoma, collectively informing our understanding of the underlying molecular determinants: Li-Fraumeni syndrome (LFS) , Hereditary retinoblastoma (RB), Rothmund-Thomson syndrome (RTS), RAPADILINO syndrome (RAPA), Werner syndrome (WS), Bloom syndrome (BS), Diamond-Blackfan anemia (DBA), and Paget’s disease of bone (PDB).
Figure. Patient-derived iPSC disease models for osteosarcoma. (A) Patient-derived iPSCs are used to model human familial cancer syndromes and reveal a role of the mutant gene in disease development. To apply iPSC methodology to study genetic disease-associated bone malignancy, patient fibroblasts are biopsied from skin and then reprogrammed to iPSCs by the four "Yamanaka factors" (OCT4, SOX2, KLF4 and c-MYC). The iPSCs are then differentiated to MSCs and further to osteoblasts. These iPSC-derived osteoblasts can be examined for osteoblast differentiation defects and tumorigenic ability. Systematic comparison of the genome/transcriptome/interactome between mutant and wild-type osteoblasts can further elucidate pathological mechanisms. (B) Current progress of applying LFS, RB, RTS, RAPA, WS, BS, DBA and PDB patient-derived iPSCs to model disease etiology and dissect disease-associated osteosarcoma.
Figure is modified from Trends Mol Med. 2017 Aug;23(8):737-755.
Transcriptional network in bone
The human skeleton is a dynamic tissue dedicated to supporting the human frame and permitting the focused application of force that is required for locomotion and spontaneous movement. It also controls various biological processes including (i) osteoblastic lineage commitment of bone marrow multipotent mesenchymal stem cells (MSCs) into bone (osteoblasts (OBs) /osteocytes (OCs)), fat (adipocytes), and cartilage (chondrocytes); and (ii) bone remodeling, a process by which OBs (bone-forming cells) and OCs (bone-resorbing cells) work sequentially to balance bone mass OBs are specialized cells responsible for the formation and maintenance of skeletal architecture. They play a critical role in regulating production of extracellular matrix proteins (collagen, osteocalcin, and osteopontin) and matrix mineralization, and OC differentiation. The fine-tuning of osteogenesis is necessary for bone homeostasis. Dysregulation of OB-modulating bone homeostasis results in development of skeletal bone diseases, particularly osteoporosis, osteoarthritis and osteosarcoma. Therefore, defining OB characteristics and understanding the molecular mechanisms regulating OB differentiation and maturation are extremely important at both an individual and population level. Currently, we integrate hESC/iPSC-derived bone lineage differentiation and systems analysis to elucidate the essentiall transcription factors in regulating human bone development.
Figure. Characterization of hPSC-derived mesenchymal stem cells (MSCs) and osteoblasts (OBs). (A) hPSC-derived MSCs expressed CD73, CD105 and CD166 surface antigens. (B) MSC-derived bone lineage cells at distinct osteogenic differentiation stages (D0, MSCs; D15-18, pre-OBs; D21-24, maturing OBs; D27, mature OBs) were evaluated their bone forming characteristics by ARS.
Develop and refine lineage differentiation
IhESC and iPSC differentiation protocols have been established for generation of hepatic cells, intestinal tissue, thyroid follicular cells, airway epithelial cells, renal progenitor cells, retinal cells, epithelial stem cells, epidermal keratinocytes, myeloid cells, neurons, MSCs, and melanocytes, among many others. Application of these protocols to cancer patient-derived iPSCs and engineered ESCs should allow research of cancer of these organs. Despite these advances, there is a large unmet need for directed differentiation protocols for a broader set of cancer types. Currently, we collaborate with AIxMed Inc (https://www.aixmed.com/) to apply AI to refine and develop new differentiation methods (e.g., endoderm and hepatocyte differentiation) to speed up the application of hESCs and iPSCs for cancer research.
Figure. AI model for predicting hESC-derived endoderm differentiation.