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Transposon mutagenesis disentangles osteosarcoma genetic drivers Kevin B Jones

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The genetic drivers of osteosarcoma have been difficult to identify because of the genomic complexity consistently encountered in cancer cells at diagnosis. A new study uses Sleeping Beauty transposon mutagenesis to drive osteosarcomagenesis in the mouse and identify likely drivers of the disease in humans. Osteosarcoma is a deadly and enigmatic malignancy that most often occurs in adolescents. The clinical options for treatment (and their limited success rates) have not improved in 40 years1. Progress has been slowed by a lack of understanding of the basic biology of the disease. In this issue, Branden Moriarity and colleagues2 describe how they tackled the problem of the origins of osteosarcoma using a somatic forward genetic screen with the Sleeping Beauty transposon system for tractable mutagenesis in mice. They identified some of the typical cancer-driving pathways, such as phosphoinositide 3-kinase (PI3K)-AKT-mTOR and mitogen-activated protein kinase (MAPK) signaling, but also uncovered some new pathways capable of enhancing osteosarcomagenesis, such as those comprising the axon guidance genes Sema4a and Sema6a (Fig. 1). They validated a few specific genes as well as the overall approach with additional comparative genomics analyses using both canine and human osteosarcoma data sets. Distinguishing drivers from passengers As a clinical condition, osteosarcoma is among the most narrowly patterned of all cancers. Clinical presentation in adolescents or young adults, anatomical location in the metaphyses of long bones near growth centers, radiographically aggressive and histopathologically high-grade appearance, and inexorable systemic progression in the absence of chemotherapy are remarkably consistent across cases. In striking contrast, the genetics of osteosarcoma proffer few patterns that approach similar clarity. Previous groups have profiled cytogenetic alterations3, copy number variations4, mutations in the exome5, microRNA expression6, and even mutations and rearrangements in the whole genomes of tumors7,8, but the only pattern that emerges as consistent Kevin B. Jones is at the Department of Orthopedics and the Huntsman Cancer Institute, University of Utah, Salt Lake City, Utah, USA. e-mail: [email protected]

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is the frequent loss, either early or late, of TP53. Upon the arrival of patients in the clinic, the genetic landscape of osteosarcoma is already chaotic. Despite the rapid technological developments that allow ever deeper and more efficient profiling of tumor genomes, these efforts cannot distinguish driver from passenger genetic changes. Other than judging the prevalence of any specific derangement as correlative evidence for the importance of that alteration to osteosarcomagenesis, profiling can only suggest causative relationships. These relationships can be tested one at a time with candidate gene approaches, but only a few genes have received such scrutiny. Because candidate gene sarcomagenesis experiments are laborious, they are best saved for very strong candidates. Osteosarcoma was therefore an ideal disease for the application of a strategy such as Sleeping Beauty mutagenesis, as this approach offers a clonal and even a temporal order for the accumu-

lation of chaos in cancer cells. The system allows for a rare peek into the black box of osteosarcomagenesis by marking genes with a transposon insertion that can then be identified after a tumor has developed. This provides a simple way to distinguish these early alterations from later, possibly noncausal mutational events. Cautious extrapolation from mouse to man It is important to remember that mice are not humans. These forward genetic screens are performed in the mouse and, so far, cannot be performed in any species that is genetically closer to humans. As these screens render lists of genes whose disruption or overactivation is sufficient to enhance osteosarcomagenesis, we must remember that they truly only tell us which alterations are sufficient to enhance osteosarcomagenesis in the mouse, and these data thus have important limitations. For instance, some mouse-specific idiosyncrasies cannot be avoided, such as the osteosar-

p53 loss Black box

Osteosarcomagenesis +/– p53 loss

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Axon PI3K-AKT-mTOR guidance

Trackable, transposon induced

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Figure 1 Transposon-mediated mutagenesis. Top, the genetic drivers of osteosarcoma downstream of TP53 mutation are largely unknown. Bottom, Sleeping Beauty mutagenesis as used in Moriarity et al.2 enables tracking of the initial genetic changes made to cells that then transform to become osteosarcomas.

volume 47 | number 6 | june 2015 | nature genetics

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news and views comagenic loss of Nf2 and the pocket protein redundancies in mouse preosteoblasts that de-emphasize the importance of single losses in the retinoblastoma pathway. These alterations are not shared by human osteosarcomas and remind us that other species-specific relationships between driver genes and osteosarcoma may complicate the translation of these findings to the clinic. Moriarity et al.2 go to great lengths to validate their findings across species, correlating canine and human osteosarcoma genetic profiles, as well as performing candidate gene validation in human osteoblast and osteosarcoma cell lines. Certainly, these gene lists from Sleeping Beauty mutagenesis will be used as building blocks for future investigations of osteosarcoma biology. What is fundamentally lacking in availability for correlation to the human disease is any annotated whole-genome sequencing data in a cohort large enough to match findings to outcomes such as metastatic progression and chemoresistance. No doubt these data are being generated by multiple groups. Yet, even before such data are available, the screens offer some immediately pertinent information such as the identification of PI3K-AKT-mTOR signaling as a driver of osteosarcoma. One might argue that identifying this pathway as a driver in osteosarcoma is not

terribly surprising. Others have suspected it and have even attempted to target the pathway with therapy in osteosarcoma8. That noted, Moriarity et al.2 have demonstrated a causal— not just correlative— relationship between this pathway and osteosarcomagenesis, as well as progression to metastasis. The experimental proof of this relationship, even in the mouse, is novel and important. Perhaps targeting PI3K-AKT-mTOR signaling deserves another, more robust attempt therapeutically in human osteosarcoma. In contrast, highlighting overactivation of the axon guidance pathway is both novel and a little surprising. Osteosarcoma is not the only cancer in which such genes have received recent attention9,10, but these genes certainly deserve closer consideration. In contrast to many previous Sleeping Beauty screens, Moriarity et al.2 were able to drive oncogenesis efficiently without a priming Trp53 mutation. This result hints at the fundamental biology of osteosarcomagenesis in the setting of mutations affecting helicases, exposure to radiation or exposure to certain bone-homing chemical carcinogens and sugg­ ests that the rapid accrual of genetic alterations will readily transform osteoblast precursors. That the tumors initiated by Sleeping Beauty alone lacked the chromothripsis common in Trp53 loss–induced osteosarcomas and

that those developing in mice with Sleeping Beauty mutagenesis on a Trp53-mutant background demonstrated reduced cytogenetic derangement in comparison to those resulting from Trp53 mutation alone argue that even the genomic instability that we consider characteristic of osteosarcoma may be only a side effect. The character of genomic changes (mutation or cytogenetic rearrangement) may not be as fundamental to osteosarcoma as the speed at which alterations occur. As any good science will inevitably do, these data present as many questions as answers, prompting promising new avenues for further investigation. COMPETING FINANCIAL INTERESTS The author declares no competing financial interests. 1. Jaffe, N. Adv. Exp. Med. Biol. 804, 1–30 (2014). 2. Moriarity, B.S. et al. Nat. Genet. 47, 615–624 (2015). 3. Bridge, J.A. et al. Cancer Genet. Cytogenet. 95, 74–87 (1997). 4. Tarkkanen, M. et al. Cancer Res. 55, 1334–1338 (1995). 5. Egas-Bejar, D. et al. Oncoscience 1, 167–179 (2014). 6. Jones, K.B. et al. Cancer Res. 72, 1865–1877 (2012). 7. Chen, X. et al. Cell Rep. 7, 104–112 (2014). 8. Perry, J.A. et al. Proc. Natl. Acad. Sci. USA 111, E5564–E5573 (2014). 9. Chédotal, A. Cell Death Differ. 12, 1044–1056 (2005). 10. Mann, M.B. et al. Nat. Genet. 47, 486–495 (2015).

Massive lineage replacements and cryptic outbreaks of Salmonella Typhi in eastern and southern Africa Thierry Wirth A new large-scale study reports the whole-genome sequences of nearly 2,000 Salmonella enterica serovar Typhi (S. Typhi) strains collected from 63 countries. A recent and dominant multidrug-resistant (MDR) lineage originating from South Asia, which is supplanting a bulk of ancestral drug-sensitive strains, is identified; the success of this lineage is likely driven by plasmid acquisitions and the chromosomal integration of resistance-conferring genes. Typhoid fever is a systemic human infectious disease caused by S. Typhi. Symptoms during the acute phase of infection comprise nausea, abdominal pain, headache and fever. According to the World Health Organization (2014), the annual toll of typhoid fever Thierry Wirth is at the Institut de Systématique, Evolution, Biodiversité (ISYEB), UMR 7205, CNRS, Muséum National d’Histoire Naturelle (MNHN), Université Pierre et Marie Curie, and Ecole Pratique des Hautes Etudes (EPHE), Sorbonne Universités, Paris Sciences et Lettres, Paris, France. e-mail: [email protected]

reaches 21 million cases and 222,000 fatalities. Transmission mainly occurs via the consumption of contaminated food and water and is mostly confined to poor countries with reduced sanitation and hygiene1. Patients respond differently to the enteric bacterial infection, and asymptomatic carriers can have an important role in the dissemination of the disease. Our knowledge of S. Typhi has recently made a quantum leap, thanks to the rise of next-generation sequencing and access to genome-wide data. Today, the latest developments consist of sophisticated analyses of large-scale data sets comprising thousands of strains2. These developments are beautifully

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exemplified on page 632 of this issue, where Vanessa Wong and colleagues3 report a study that disentangles the population dynamics, phylogeography and transmission events of an expanding S. Typhi MDR lineage. Origin, spread and demography of H58 Wong et al. assembled a total of 1,832 S. Typhi strains from 5 continents, of which nearly half belonged to a clade named H58 (refs. 4,5). The H58 lineage has a very low level of genetic diversity in comparison to the other representative lineages, with a median of only six SNPs separating any two randomly chosen H58 strains. It differs from its closest relatives by 565

Transposon mutagenesis disentangles osteosarcoma genetic drivers.

The genetic drivers of osteosarcoma have been difficult to identify because of the genomic complexity consistently encountered in cancer cells at diag...
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