The correlation of phenotypic outcomes with genetic variation and environmental factors is a core pursuit in biology and biomedicine. Numerous challenges impede our progress: patient phenotypes may not match known diseases, candidate variants may be in genes that have not been characterized, model organisms may not recapitulate human or veterinary diseases, filling evolutionary gaps is difficult, and many resources must be queried to find potentially significant genotype–phenotype associations. Non-human organisms have proven instrumental in revealing biological mechanisms. Advanced informatics tools can identify phenotypically relevant disease models in research and diagnostic contexts. Large-scale integration of model organism and clinical research data can provide a breadth of knowledge not available from individual sources and can provide contextualization of data back to these sources. The Monarch Initiative (monarchinitiative.org) is a collaborative, open science effort that aims to semantically integrate genotype–phenotype data from many species and sources in order to support precision medicine, disease modeling, and mechanistic exploration. Our integrated knowledge graph, analytic tools, and web services enable diverse users to explore relationships between phenotypes and genotypes across species.

Expression of the c-Myc oncoprotein is affected by conserved threonine 58 (T58) and serine 62 (S62) phosphorylation sites that help to regulate c-Myc protein stability, and altered ratios of T58 and S62 phosphorylation have been observed in human cancer. Here, we report the development of 3 unique c-myc knock-in mice that conditionally express either c-Myc WT or the c-Myc T58A or c-Myc S62A phosphorylation mutant from the constitutively active ROSA26 locus in response to Cre recombinase to study the role of these phosphorylation sites in vivo. Using a mammary-specific Cre model, we found that expression of c-Myc WT resulted in increased mammary gland density, but normal morphology and no tumors at the level expressed from the ROSA promoter. In contrast, c-Myc T58A expression yielded enhanced mammary gland density, hyperplastic foci, cellular dysplasia, and mammary carcinoma, associated with increased genomic instability and suppressed apoptosis relative to c-Myc WT . Alternatively, c-Myc S62A expression reduced mammary gland density relative to control glands, and this was associated with increased genomic instability and normal apoptotic function. Our results indicate that specific activities of c-Myc are differentially affected by T58 and S62 phosphorylation. This model provides a robust platform to interrogate the role that these phosphorylation sites play in c-Myc function during development and tumorigenesis. Cancer Res; 71(3); 925-36. Ó2011 AACR.

High expression of the oncoprotein Myc has been linked to poor outcome in human tumors. Although MYC gene amplification and translocations have been observed, this can explain Myc overexpression in only a subset of human tumors. Myc expression is in part controlled by its protein stability, which can be regulated by phosphorylation at threonine 58 (T58) and serine 62 (S62). We now report that Myc protein stability is increased in a number of breast cancer cell lines and this correlates with increased phosphorylation at S62 and decreased phosphorylation at T58. Moreover, we find this same shift in phosphorylation in primary breast cancers. The signaling cascade that controls phosphorylation at T58 and S62 is coordinated by the scaffold protein Axin1. We therefore examined Axin1 in breast cancer and report decreased AXIN1 expression and a shift in the ratio of expression of two naturally occurring AXIN1 splice variants. We demonstrate that this contributes to increased Myc protein stability, altered phosphorylation at S62 and T58, and increased oncogenic activity of Myc in breast cancer. Thus, our results reveal an important mode of Myc activation in human breast cancer and a mechanism contributing to Myc deregulation involving unique insight into inactivation of the Axin1 tumor suppressor in breast cancer.T he c-Myc (Myc) oncoprotein is a pleiotropic transcription factor involved in controlling many cellular functions, including cell proliferation, cell growth, and cell differentiation, as well as pathways that regulate genome stability and cell death (1-5). High levels of Myc expression occur in a wide variety of human tumors, and animal models exhibit Myc-induced tumorigenesis in many tissues (6-8). These tumors are often dependent on continued high expression of Myc and withdrawal of Myc can induce tumor regression (8, 9), highlighting the importance of understanding how Myc expression is regulated. In breast cancer, Myc protein is reported to be overexpressed in approximately 50% to 100% of breast tumors depending on the study, whereas only approximately 16% show Myc gene amplification and 22% show increased mRNA expression (6, 10-13). Mechanisms for high Myc expression in human breast tumors lacking gene amplification or elevated mRNA expression have not been reported.Cells have evolved an elegant signaling pathway to help regulate turnover of Myc so that Myc protein levels are kept low when not needed (1,14). In this pathway, sequential and interdependent phosphorylation events on Myc at serine 62 (S62) and threonine 58 (T58) influence Myc stability. Initial phosphorylation of S62 by ERK or CDK kinases in response to mitogen signaling transiently increases Myc stability, whereas subsequent phosphorylation of T58 by GSK3β triggers dephosphorylation of S62 by protein phosphatase 2A-B56α (PP2A-B56α), ubiquitination by the SCF-Fbw7 E3 ligase, and proteasomal degradation (15, 16). Burkitt lymphoma-derived Myc mutations usually occur at or around T58, generally resulting in loss of T58 phosphorylation, elevated S...

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The principles of genetics apply across the entire tree of life. At the cellular level we share biological mechanisms with species from which we diverged millions, even billions of years ago. We can exploit this common ancestry to learn about health and disease, by analyzing DNA and protein sequences, but also through the observable outcomes of genetic differences, i.e. phenotypes. To solve challenging disease problems we need to unify the heterogeneous data that relates genomics to disease traits. Without a big-picture view of phenotypic data, many questions in genetics are difficult or impossible to answer. The Monarch Initiative (https://monarchinitiative.org) provides tools for genotype-phenotype analysis, genomic diagnostics, and precision medicine across broad areas of disease.

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The correlation of phenotypic outcomes with genetic

Background Rare diseases (RD) are a diverse collection of more than 7–10,000 different disorders, most of which affect a small number of people per disease. Because of their rarity and fragmentation of patients across thousands of different disorders, the medical needs of RD patients are not well recognized or quantified in healthcare systems (HCS). Methodology We performed a pilot IDeaS study, where we attempted to quantify the number of RD patients and the direct medical costs of 14 representative RD within 4 different HCS databases and performed a preliminary analysis of the diagnostic journey for selected RD patients. Results The overall findings were notable for: (1) RD patients are difficult to quantify in HCS using ICD coding search criteria, which likely results in under-counting and under-estimation of their true impact to HCS; (2) per patient direct medical costs of RD are high, estimated to be around three–fivefold higher than age-matched controls; and (3) preliminary evidence shows that diagnostic journeys are likely prolonged in many patients, and may result in progressive, irreversible, and costly complications of their disease Conclusions The results of this small pilot suggest that RD have high medical burdens to patients and HCS, and collectively represent a major impact to the public health. Machine-learning strategies applied to HCS databases and medical records using sentinel disease and patient characteristics may hold promise for faster and more accurate diagnosis for many RD patients and should be explored to help address the high unmet medical needs of RD patients.