Extratropical cyclones (ETCs) are the primary driver of large-scale episodic beach erosion along coastlines in temperate regions. However, key drivers of the magnitude and regional variability in rapid morphological changes caused by ETCs at the coast remain poorly understood. Here we analyze an unprecedented dataset of high-resolution regional-scale morphological response to an ETC that impacted southeast Australia, and evaluate the new observations within the context of an existing long-term coastal monitoring program. This ETC was characterized by moderate intensity  deepwater wave heights, but an anomalous wave direction approximately 45 degrees more counter-clockwise than average. The magnitude of measured beach volume change was the largest in four decades at the long-term monitoring site and, at the regional scale, commensurate with that observed due to extreme North Atlantic hurricanes. Spatial variability in morphological response across the study region was predominantly controlled by alongshore gradients in storm wave energy flux and local coastline alignment relative to storm wave direction. We attribute the severity of coastal erosion observed due to this ETC primarily to its anomalous wave direction, and call for greater research on the impacts of changing storm wave directionality in addition to projected future changes in wave heights. The result is damage to beach-front properties and infrastructure, flooding of coastal hinterland, and disturbance to beach environments and amenity. In a changing climate4, projected regional-scale changes in storminess6 will possibly threaten the future resilience of many coastal communities worldwide2. At present, our ability to model and predict the damaging impacts of present and future extreme storms at the coast remains limited. A fundamental knowledge gap has been the paucity of regional-scale observational datasets of extreme beach erosion at sufficient spatial and temporal resolution, to adequately resolve the impacts of individual storms, and to capture the sensitivity of beach response to both regional- and local-scale alongshore complexity8. Challenges include predicting the arrival of rapidly developing coastal storms (to ensure that the immediate pre-storm beach morphology is adequately quantified prior to the onset of storm conditions), as well as the practical difficulties of obtaining detailed measurements along large sections of coastline, within the short time-windows prior to and immediately following storm conditions.Extreme coastal storms such as hurricanes and extratropical cyclones (ETCs) can rapidly mobilise and redistribute vast quantities of sediment over large (>100 km) lengths of coastline, leading to erosion of beach and coastal dune systems9. While these dependencies provide a valuable framework for predicting hurricane impacts on low-lying coastlines10, the slower-moving and diffuse nature of ETCs means that coastal impacts during these events may be more a result of prolonged wave energy over several tidal cycles than the extreme and rapid increases in water levels observed during hurricanes12. In this respect it is likely that a major control of the spatially-variable morphological response to ETCs along narrow continental shelf regions (where storm surges are significantly reduced) is the degree of exposure to storm wave energy. This is particularly the case for steep, embayed coastlines such as southeast Australia13, where prominent rocky headlands both attenuate and refract storm wave energy to create distinct localized gradients in wave exposure within embayed beaches in their lee14.Regional-scale observations of hurricane impacts along the U.S. east coast indicate that spatial variability in the morphological response corresponds strongly to local maxima in storm-induced total water levels  relative to antecedent dune morphology16. The direction of storm waves generated by ETCs in this region is typically from the south and south-east, depending on synoptic evolution17. Storm surge in comparison to other ETCs  meanwhile is relatively small (maximum up to 0.7 m only), which is primarily due to the region’s steep and narrow continental shelf18. The June 2016 ETC was unusual in that an east coast low combined with a blocking high in the south Tasman Sea, which created a large (>2000 km) and relatively stable north-easterly fetch directed at the coastline for several days. While the recorded deepwater significant wave heights in the order of 6–7 m and surges of 0.2–0.5 m are considered moderate storm conditions for this coastline , the predominantly easterly wave direction for those conditions was unprecedented when compared to the available 25 years of directional wave measurements20. The damaging and widespread impacts of the June 2016 ETC event along the southeast Australia coastline were compounded by the storm’s coincidence with winter solstice spring tide, such that tidal levels over several days were up to 0.44 m higher than mean high water springs .Here we present the first detailed measurements, both in terms of their immediacy pre- and post-storm as well as their high spatial resolution, of the regional-scale morphological response to an extreme ETC coastal erosion event. Referred to locally as an ‘east coast low’, storms of this genesis are the primary cause of coastal damage in this region and can produce long duration  near hurricane-force winds, intense precipitation and large ocean waves (up to 9 m deepwater significant wave height)High resolution topographic measurements of the morphological response to this storm were quantified by repeat airborne Lidar obtained immediately before and after the ETC (see Methods) along beaches extending from 30°40’S to 33°45’S Fig. . The sur3 of sand was eroded from the subaerial beach (above mean sea level) over the full 177 km survey region, equating to average volume losses of 65 m3m−1 . The corresponding average shift in shoreline position was landwards by 22 m. Interestingly these average volume losses are similar in magnitude to those observed along the U.S. east coast in response to the 2012 Hurricane Sandy event21, highlighting the comparable morphological change that can occur during these less documented ETCs. The subaerial beach response across the entire survey region was overwhelmingly erosional , with sediment transferred predominantly offshore to the subtidal zone, rather than redistributed alongshore. This onshore-offshore transfer of sand is further confirmed at the long-term coastal monitoring site, the 3.6 km Narrabeen-Collaroy embayment located within Metropolitan Sydney  to 40 m of landward recession. A distinct sub-region of enhanced subaerial sand volume loss is evident in the central survey region between 32°07’S and 32°21’S, which is contrasted by an area of reduced volume loss immediately to the south . We attribute this to the broad scale shift in coastline orientation from easterly to more southerly at this location .To examine spatial variability in morphological response to the ETC at the regional scale, volume and shoreline changes were averaged alongshore within each embayed beach compartment defined by prominent rocky headlands, or for longer embayments, to a maximum alongshore length of 5 km. This alongshore-averaging resulted in a total of 52 individual coastal sectors for further regional-scale analysis. Figure x,10. A significant correlation is observed between subaerial volume losses and ∑Px,10 for all individual survey transects , although at a reduced magnitude compared to the regional-scale analysis (r = 0.52). This reduced positive correlation between subaerial volume losses and storm wave exposure at the local scale  is due to increased variability between volume losses at the 100 m-spacing of individual transects (see Supplementary Data 3m−1) reveals highly localized effects at specific transects, such as those in close proximity to coastal structures or complex offshore bathymetry; both features that are commonly associated with the formation of localized and highly-erosive megarips22.Considering both regional-scale and more localized  coastline exposure to storm wave energy fluxes, Fig. 23. Using the average subaerial volume loss across all five transects between surveys as a gauge of storm severity, we find that the June 2016 ETC was the most severe erosion event measured in the last four decades and 36% greater than the secondmost erosive event . Just four days later and immediately following the storm  indicate considerable regional variability close to the coast, both in terms of modal deepwater wave conditions27 as well as extreme conditions associated with storms28. In some locations, projected decreases in both modal and extreme deepwater wave energy fluxes close to the coast have been interpreted as a possible dampener to the adverse coastal effects of sea level rise, whereas the combination of increased deepwater wave energy fluxes and rising sea levels could significantly amplify the coastal risks caused by climate change6. These projected shifts are most pronounced in the Southern Hemisphere, associated with a strengthening of Southern Ocean westerlies and a southerly shift of Southern Hemisphere ETC storm tracks6. Our analyses highlight the additional importance of storm wave directionality and its pivotal role in modulating the degree of wave exposure along the coastal boundary. As demonstrated by the June 2016 event in southeastern Australia, storm wave directions that significantly enhance the degree of wave energy reaching the coast can lead to a major increase in episodic beach erosion, even when the absolute storm wave intensities of the event are comparably moderate. On the other hand, storm wave directions that decrease the regional wave exposure  can lead to a significant decrease in the magnitude of episodic beach erosion.Beyond the widely acknowledged likelihood that rising sea levels will impact sandy coastlines globally29. The result is slower rates of beach recovery following the event and more prolonged periods of eroded conditions30. However on inter-annual time-scales, it is observed that shifts in storm wave directionality can occur in connection with climate oscillations such as the El Niño Southern Oscillation32 and the North Atlantic Oscillation34. Such shifts in storm wave directionality over these longer time-scales can result in a regional-scale adjustment in mophodynamic behaviour35, including rotation of the beach planform at the embayment scale14.The longer-term resilience of sandy beach systems to shifts in storm wave directionality is also likely dependent on the recurrence interval of such events. For low-frequency events characterised by an anomalous storm wave direction against a unimodal storm wave climate, beach resilence along modally more-sheltered coastal sectors may be reduced relative to typical storm directions and more-exposed locations. This is because, when storm conditions subside and the wave direction rotates back to more modal conditions, subtidal storm deposits in these locations can remain stranded, as wave energy necessary to return sediment to the beachface subsequently is reduced36 as well as the predominantly shore-normal alignment of storm waves of this particular event 38 indicate a vertical accuracy of better than 0.2 m and horizontal accuracy of better than 0.5 m. The primary pre-storm Lidar flight encompassing 69% of the total 177 km of sandy coastline reported here was undertaken on 31 May 2016, five days prior to the onset of the storm. The additional survey data was derived from Lidar flights undertaken on 5 and 7 April 2016. Post-storm Lidar flights were undertaken over the two consecutive days 7 and 8 June 2016, less than 48 hours after the event had subsided.Airborne Lidar data were obtained using a Riegl Q480i Lidar coupled with a NovAtel SPAN AG62 integrated Global Navigation Satellite System (GNSS) and inertial unit and flown using a twin-engine Piper PA44 airplane at an altitude of approximately 1000 ft (300 m). At this altitude, the data point density from the Lidar measurements is approximately 1 point every 1.6 m39. Dune vegetation was removed from the data using a block-minimum and de-spiking algorithm40. Subaerial volume change δV was subsequently calculated at each transect using the equation:0 is the cross-shore position of the mean sea level contour, xdc the cross-shore position of the dune crest where negligible change is observed and z1 and z2 are the pre- and post-storm elevation data respectively. Elevation data that did not intersect with mean sea level were extended down to this level using a linear model. The lower elevation bound of Lidar surveys were approximately 0.2 m above mean sea level for pre-storm surveys and 0.7 m above mean sea level for post-storm surveys.Cross-shore transects for pre- and post-storm Lidar data processing were defined at 100 m intervals along embayed beaches of various degrees of planform curvature using a logarithmic spiral transform method41 forced with atmospheric conditions from the NOAA-NCEP Climate Forecast System Version 242. The model included three nested regular grids – global (1° resolution), Australia (0.25° resolution), south-east Australia region (0.05°) – and an irregular mesh covering the study area, reaching 100 m resolution at 10 m water depth. Sensitivity testing of the model was carried out using Waverider buoy data for comparison, and the adopted configuration included ST2 source terms, JONSWAP bottom friction, and Battjes-Janssen depth-induced wave breaking43. The model performed well against data collected by three mid-shelf (70–80 m water depth) Waverider buoys deployed within the study area , accurately reflecting the timing, duration and intensity of storm wave conditions  and 100 m resolution along the 10 m isobath  over the duration of the ETC storm. The wave energy flux towards the coast, d  by integrating Px,d over the duration of the storm event:t1 and t2 represent the up-crossing and down-crossing times respectively of the corresponding deepwater storm threshold (defined for this region31 as Hm0 = 3 m). This storm exposure metric subsequently takes into account both the magnitude of wave energy fluxes for a certain water depth as well as the event duration and has been found to be an effective means of gauging the coastal response to storm events44.Exposure to storm waves at each coastal sector  or individual survey transect  was subsequently defined at the nearest model output location along isobath of depth 45. Since 2006 these surveys have been undertaken using RTK-GNSS46. All transects extend from a landward stable benchmark until at least mean sea level. For a complete description of coastal monitoring at this site, refer to ref. Coastal monitoring at five transects along Narrabeen-Collaroy Beach commenced in April 1976 and has continued monthly up until the present date (November 2016). Between 1976 and 2006 these transects were surveyed using conventional survey techniquesPre- and post-storm bathymetric measurements for the June 2016 storm were undertaken using personal water craft (jetski) based eco soundings with RTK-GNSS positioning. The surveys were undertaken on 2 and 10 June 2016 and included 46 track lines spaced 50 m apart, with post-processing undertaken in Hypack.Supplementary Information


Prognosis of patients with advanced sarcoma after progression from FDA approved therapies remains grim. In this study, clinical outcomes of 100 patients with advanced sarcoma who received treatment on novel targeted therapy trials were evaluated. Outcomes of interest included best response, clinical benefit rate, progression-free survival (PFS) and overall survival (OS). Median patient age was 48 years (range 14–80). Patients had received a median of 2 prior lines of systemic treatment. Phase I treatments were anti-VEGF–based (n = 45), mTOR inhibitor–based (n = 15), and anti-VEGF + mTOR inhibitor–based (n = 17) or involved other targets (n = 23). Best responses included partial response (n = 4) and stable disease (n = 57). Clinical benefit rate was 36% . Median OS was 9.6 months ; median PFS was 3.5 months . RMH prognostic score of 2 or 3 was associated with lower median OS  and PFS . Receiving cytotoxic chemotherapy as part of phase I trial was also associated with shorter median OS . Patients with advanced sarcoma treated on phase I clinical trials had a clinical benefit rate of 36% and RMH score predicted survival. Sarcoma accounts for only 0.9% of adult and 12% of childhood malignancies, with approximately 13,000 adult and 1,100 pediatric cases annually in the United States15KIT receptor tyrosine kinase with imatinib mesylate for gastrointestinal stromal tumor (GIST) illustrates how this approach can potentially change outcomes even for notoriously chemotherapy-resistant sarcoma subtypes810111113The era of ‘-omics’ has helped reveal the complex biology of several sarcoma subtypes in terms of signaling pathways and molecular aberrations, thereby offering novel approaches to treatment by targeting aberrant pathwaysPhase I trials represent the most critical step in translation from bench to bedside1617182023We reviewed records of patients who were referred to the Phase I Clinical Trials Program at MDACC for refractory, relapsed, metastatic, or unresectable sarcoma. Patient characteristics and clinical outcomes were abstracted from transcribed notes in the electronic medical record system . Patient records were reviewed at the time of presentation to a phase I program. The type of investigational treatment regimens offered to patients varied throughout the study period given rapid protocol turnover. Outcomes of interest included objective response, stable or progressive disease, clinical benefit, and progression-free and overall survivals.in situ hybridization, and/or polymerase chain reaction (PCR) were obtained as indicated. For some patients, mutational analysis was performed during the latter course of phase I trials (from 2008 onwards) if additional samples were available; mutations of interest included those in KRAS, BRAF, C-KIT, EGFR, and P13KCA genes.Patients who had a biopsy at another institution had their histopathologic findings verified by an MD Anderson pathologist. When biopsies were performed at MD Anderson, additional studies including cytogenetics, immunohistochemistry, fluorescent All patients provided written informed consent before enrollment in phase I trials and all trials were approved and were carried out in accordance with the guidelines by the Institutional Review Board of MD Anderson Cancer Center, which also approved and granted waivers of informed consent for this retrospective study.Eligible patients with metastatic or unresectable sarcoma, for whom approved curative therapies had failed, were included in the study. All patients had evidence of measurable disease according to the Response Evaluation Criteria in Solid Tumors (RECIST) criteria, Eastern Cooperative Oncology Group (ECOG) performance status of 0 to 2, and a life expectancy of at least 3 months. Premenopausal women were required to have negative results on a pregnancy test and patients with child-bearing potential were required to use contraception. A washout period of 4 weeks was required preceding initiation of treatment on phase I trial.Once enrolled on a phase I trial, patients were evaluated at 3- to 4-week intervals in accordance with each protocol’s cycle length. At each visit, history was updated and physical examination was performed along with a comprehensive metabolic and hematologic panel. Patients were assessed for onset of new symptoms and compliance with treatment. Computed tomography (CT) or positron emission - computed tomography (PET-CT) scans were obtained every 2 cycles of therapy, and responses were determined based on RECIST criteria based on the protocol.P-value of 0.05 or less was considered the criterion for statistical significance. Statistical analyses were performed using SAS 9.1  and S-Plus software .Descriptive statistics were used to summarize the patients’ characteristics. Clinical benefit was defined as objective response or stable disease lasting 6 months or longer. The Kaplan-Meier method was used to estimate overall survival (OS) and progression-free survival (PFS) distributions and the log rank test was used to compare OS and PFS distributions between groups. Cox proportional hazards regression analysis was used to estimate the hazard ratio with 95% confidence intervals. OS was measured from date of presentation to the Phase I Clinical Trials Program until death from any cause or last follow-up. PFS was measured from date of study enrollment until disease progression or death (whichever came first) or last follow-up. A A wide variety of sarcoma subtypes were treated . ChondroMET mutation (7 of 50 patients), HER2 amplification by fluorescence in situ hybridization (1 of 26 patients), K-RAS G12A mutation (1 of 67 patients), N-RAS Q61K mutation (1 of 45 patients), EGFR G719D mutation (1 of 55 patients), P53 mutation (7 of 36 patients), and C-KIT mutation (1 of 56 patients). No MET amplification (62 patients), PIK3CA mutation (81 patients), or BRAF mutation (71 patients) was detected.Among the 100 patients in this study, 82 died after a median follow-up of 35 months. The median OS was 9.6 months  . SurvivaThis study summarizes the clinical outcomes of patients with refractory sarcomas in phase I clinical studies after progression from standard US FDA approved therapies. As we continue to decipher the unique molecular characteristics of these subtypes, phase I trials provide an opportunity to target these molecular characteristics to potentially induce responses. Improvement in treatment of GIST represents an excellent example. Before 2000, cases of GIST were often treated using regimens similar to those for leiomyosarcoma and were highly resistant to cytotoxic chemotherapy2527However, the success in treatment of GIST has not been reproducible in all subtypes of sarcoma. For instance, despite a better understanding of the molecular pathogenesis of Ewing sarcoma, systemic chemotherapy remains the mainstay of treatment. The EWS-FLI1 fusion protein, the hallmark of Ewing sarcoma, positively regulates expression of IGF1R, which is necessary for fibroblast transformation30313334Review of literature shows that a similar analysis among sarcoma patients enrolled in phase 1 clinical trials was conducted at the Royal Marsden hospitalOur study involved a diverse range of sarcoma subtypes in patients enrolled in a wide variety of phase I trials at MD Anderson Cancer Center. Neither demographic features, translocation status, nor mutations in TP53 or MET genes predicted outcome. The choice of therapy whether one used VEGF inhibitor, mTOR inhibitor, or other targeted agents similarly failed to significantly affect outcome. Paradoxically, the use of cytotoxic chemotherapy as part of phase I treatment was associated with shorter survival. This unexpected outcome may be due to the heavily pretreated nature of our phase I population. As most of these patients had progressed on chemotherapy before referral to phase I program, reduced survival in chemotherapy-treated group may reflect acquired drug resistance by patients’ tumors that predestined phase I agents to fail. Conversely, the use of biologically targeted therapies may have offered patients a novel mechanism of action not previously encountered as part of their standard-of-care regimens. It is noteworthy that chemotherapy was used in combination with inhibitor(s) of VEGF, mTOR, and/or other targets as part of phase I trials. We closely analyzed patients with an objective response to identify any distinguishing feature. Although 3 of the 4 patients with objective response received VEGF inhibitor, there was no difference in outcome of patients treated with VEGF inhibitor in the overall sample.The initial report of the RMH prognostic score included only a few patients with sarcoma24This study has a few limitations that should be considered while interpreting its findings. First, the retrospective study design may have led to selection bias. Second, patients included were treated on phase I trials at a single institution, making the study susceptible to referral bias. Third, treatment involved inhibitors of VEGF and/or mTOR in more than three-quarters of the patients and no patients were enrolled on immunotherapy trials. While phase 1 trials are designed to evaluate toxicity not efficacy, the results shown here in should be viewed as preliminary. This is a retrospective study of sarcoma patients in several “all comer Phase 1” trials so we may not be able to derive any conclusions. Although efficacy activity is not the end point or primary objective in any Phase 1 trial, the primary motivation for patient’s participation and the treating physicians enrollment in phase I trials is the anticipation or optimism of some response/efficacy or therapeutic benefit3739In conclusion, this retrospective analysis of 100 sarcoma patients treated on phase I trials predominantly involving inhibitors of VEGF and/or mTOR demonstrated a clinical benefit rate of 36%. Higher RMH score and treatment with cytotoxic chemotherapy as part of phase I trial were associated with shorter OS.How to cite this article: Subbiah, V. et al. Evaluation of Novel Targeted Therapies in Aggressive Biology Sarcoma Patients after progression from US FDA approved Therapies. Sci. Rep.6, 35448; doi: 10.1038/srep35448 (2016).


There has been an explosion of clinical trials in an increasing variety of tumors utilizing immunotherapies as the consequence of their striking efficacy in the treatment of certain cancers, albeit in a minor population of patients . In paraCombining immunotherapy using anti-PD-L1 with antiangiogenic therapy had reciprocally beneficial effects in that immunotherapy targeted evasion from antiangiogenic therapy, while vascular normalization elicited by antiangiogenic treatment could increase lymphocyte infiltration and activation , 4. In aHEVs are morphologically and functionally distinct postcapillary venules that mediate the adhesion and transendothelial migration of circulating T- and B-cells to secondary lymphoid organs, including lymph nodes, where they encounter antigens and become activated  and refe and refeSeveral lines of evidence point to a critical role of lymphotoxin  /lymphotoxin β-receptor (LTβR) signaling in HEV formation, and recent studies revealing that EC-specific LTβR knockout animals lack fully functional HEVs, reinforce this notion  and refe. The LTβWhile tantalizing, these results raise some additional questions regarding optimization of therapeutic regimens, including those related to the stability of therapeutically induced HEVs, and whether the composition of immune infiltrates will change over time. While systemic activation of LTβR may not be suitable for the clinic due to potential immune-related side effects, the concept of therapeutic HEV induction nevertheless holds great promise to markedly improve immune responses to tumors when used in parallel with many approved immunotherapies and vascular-normalizing antiangiogenics, since naive T-cells can be locally recruited and activated by cytokines or dendritic cells within the tumor without being diluted in transit from the lymph node through the bloodstream.


MGMT, RAD17, and USP44) show prior evidence of a tumour suppressive function. Other candidate tumour suppressors, such as MAFTRR, KIAA1551, and IGF2BP2, are novel. Our study demonstrates how rare tumour suppressors can be identified through copy number meta-analysis.Homozygous deletions are rare in cancers and often target tumour suppressor genes. Here, we build a compendium of 2218 primary tumours across 12 human cancer types and systematically screen for homozygous deletions, aiming to identify rare tumour suppressors. Our analysis defines 96 genomic regions recurrently targeted by homozygous deletions. These recurrent homozygous deletions occur either over tumour suppressors or over fragile sites, regions of increased genomic instability. We construct a statistical model that separates fragile sites from regions showing signatures of positive selection for homozygous deletions and identify candidate tumour suppressors within those regions. We find 16 established tumour suppressors and propose 27 candidate tumour suppressors. Several of these genes (including  MAFTRR, KIAA1551, and IGF2BP2.Homozygous deletions are rare in cancers and often target tumour suppressor genes. Here, the authors conduct pan-cancer analyses and apply statistical modelling to identify 27 candidate tumour suppressors, including  Many of these somatic changes are neutral passenger events, yet some confer a clonal selective advantage on cancer cells and drive oncogenesis1. The genes involved can be oncogenes, which are frequently hit by activating point mutations or amplifications and behave in a dominant fashion, or tumour suppressor genes, which are recurrently targeted by inactivating mutations such as truncating point mutations or deletions and display a recessive pattern4.The genomes of cancer cells are shaped by somatic mutations, including base substitutions, small insertions and deletions, genomic rearrangements, and copy number changes5.The most frequently used approach to identify cancer genes is assessing recurrence of non-synonymous somatic point mutations above background level. This is an extensive field that has been highly successful in the past. The power to detect cancer genes varies based on sample size and background mutation frequency: for most tumour types, current sample sizes are inadequate to reliably detect rare cancer genes mutated at ≤ 5% above backgroundHomozygous deletions require two independent hits and, in addition, any homozygous deletion covering a gene that carries out an essential function or confers a survival advantage is swiftly eliminated by negative selection. Therefore, homozygous deletions are rare and often focal in cancers. We reasoned that a systematic screen for homozygous deletions in a large series of cancer samples would be a powerful orthogonal way to specifically identify tumour suppressors, hypothesising that some tumour suppressors may be more prone to inactivation by homozygous deletions than by truncating point mutations. As traditional recurrence analysis has identified most or all frequently mutated tumour suppressors, our novel candidates are likely rare cancer genes, inactivated only in specific contexts.6. However, this model system has important limitations: cancer cell lines are biased towards late stage cancers and metastases, they have accumulated mutations to adapt to growth in culture, their numbers are limited, and some cancer types do not lend themselves to growth in culture7.Tumour samples contain both tumour cells and admixed normal cells in unknown proportions, complicating the distinction between homozygous and hemizygous deletions and hampering the discovery of tumour suppressors. Cancer cell lines represent a simplified model system that does not show this normal cell admixture and a comprehensive catalogue of homozygous deletions in cancer cell lines has been constructed8. This prompted the development of several computational methods and mathematical models, leading to mature approaches that can infer tumour purity and ploidy and separate copy number profiles from tumour cells and admixed normal cells12, opening novel avenues for studying tumorigenesis and tumour evolution13. These methods are able to reliably separate homozygous from hemizygous deletions, and have been able to pinpoint driver homozygous deletions in known and newly identified tumour suppressors4.The advent of SNP array technologies brought the realisation that it is theoretically possible to infer the fraction of admixed normal cells in primary tumours from array dataHere, we perform a systematic screen for homozygous deletions over a compendium of 2218 SNP arrays across 12 cancer types, aiming to identify rare tumour suppressors. We find 96 genomic regions recurrently targeted by homozygous deletions, overlapping 16 established tumour suppressors. However, homozygous deletions also occur frequently over fragile sites, chromosomal regions of increased genomic instability. We therefore construct a novel statistical model to separate fragile sites from regions containing tumour suppressors. This analysis extends the landscape of cancer genes by identifying 27 candidate tumour suppressors, adding to the emerging evidence for several tumour suppressor genes recently proposed in the literature and highlighting several novel candidates.10 to infer tumour purity and ploidy and derive copy number profiles. ASCAT failed on 81 samples (3.7%) due to excessive noise, and these samples were excluded from further analyses. Of the 2137 cases that passed ASCAT analysis, 273 (12.8%) showed only few or no copy number aberrations, resulting in high variance tumour purity estimates. These samples were therefore excluded from the analysis of tumour purity and ploidy. As expected, the proportion of non-aberrant samples varied considerably between tumour types, with leukaemia showing the highest proportion of non-aberrant cases (55.6%), followed by lymphoma (22.5%) and sarcoma (20.1%)  and the highest in brain cancers (70%). These findings are broadly consistent with previous ploidy measurements from chromosome counts14, as well as with a previous SNP array study on an overlapping series of samples12.Most cancer types displayed extensive variation in purity and ploidy Fig. . All tumWe performed a systematic screen for homozygous deletions across our 2137 primary tumours that passed ASCAT analysis. We identified 1865 homozygous deletions in total (median length: 315 kb), involving 826 tumours, each having on average 2.0 Mb (median 524 kb) homozygously deleted , while the size distribution of the deletions is the same for both . CDKN2A was the dominant homozygously deleted tumour suppressor, with 108 homozygous deletions across nine cancer types. Virtually all of these homozygous deletions inactivate the adjacent “backup” tumour suppressor CDKN2B as well, rationalising the frequent loss of the combined locus17. Homozygous deletions of CYLD and BIRC3 (cIAP2) are found exclusively in multiple myeloma and are both linked to aberrant NF-κB signalling18. All BIRC3 deletions include its adjacent homologue BIRC2, consistent with previous observations that only loss of both was shown to induce alternative NF-κB activation in B-cells19. Similarly, we observed six homozygous deletions over VHL, five of which take out the nearby tumour suppressor FANCD2 as well. We detected 16 homozygous deletions over PTEN in multiple cancer types and 5 homozygous deletions over NF1. Homozygous deletions of RB1 were most frequently found in tetraploid sarcomas. We further observed four homozygous deletions each over TP53 (three sarcomas), CDKN2C, and FAT1, and six homozygous deletions each over MAP2K4 (three breast carcinomas) and CDH1 (five lung cancers). Homozygous deletions of SMARCB1 are found only in brain cancers, while homozygous deletions of TET1 were almost exclusively observed in lung cancer (seven out of eight cases). Interestingly, the four homozygous deletions over BRCA2 included two sarcomas, a cancer type not previously linked to BRCA2 mutations.We observed 15 peaks of homozygous deletions over 16 established tumour suppressors  fragile sites Fig. .6. Large hemizygous deletions, in contrast, are due to several other mechanisms, such as whole-chromosome or whole-arm loss6. As a result, fragile sites are characterised by frequent small deletion events, while regions containing tumour suppressors more frequently show large deletion events6. This difference was exploited to construct three metrics that discriminate fragile sites from regions containing tumour suppressors  the ratio of homozygous deletions to small hemizygous deletions over the peak ; and (R2) the ratio of large hemizygous deletions to small hemizygous deletions . Indeed, both ratios are significantly larger for the identified known tumour suppressors when compared to the known fragile sites . The densities of R1 and R2 for fragile sites (or tumour suppressors) can be estimated via a resampling and simulation approach using the known sites, which allows calculation of p-values for any peak under a fragile site null model (Methods). In addition, we leveraged tumour-type specificity as a third element in our statistical model. Tumour-type specificity of homozygous deletions can be explained either by selection or by increased genomic instability in the originating tissue or cell type. However, higher genomic instability in a certain tissue or cell type would result in enrichment of both homozygous and small hemizygous deletions in a certain tumour type. Therefore, we can use differences in tumour-type specificity between homozygous and small hemizygous deletions as an indication that tissue-specificity of homozygous deletions is driven by selection , do we reject the null model and consider the presence of a tumour suppressor.Homozygous deletions over tumour suppressors are enriched due to positive selection, whereas homozygous deletions over fragile sites are enriched due to a local increase in genomic instability. Consequently, the structural signature of deletions is distinct in fragile sites, compared to regions harbouring tumour suppressors. Small hemizygous deletions reflect local fragility, as they require two DNA breakage events in close proximityors Fig. . The firs); and R the ratiVHL/FANCD2 and CDKN2C being the exceptions. Notably, VHL shows frequent biallelic inactivation by a hemizygous deletion combined with a point mutation, while homozygous deletions are rare. A possible reason is that for some tumour suppressor genes, homozygous deletions, which often also affect neighbouring genes or regulatory regions, are selected against, whereas point mutations may be better tolerated. Alternatively, a protein may have multiple functions, and while biallelic deletions abolish all functions, point mutations may abolish one while leaving others intact. Two of the established tumour suppressors, CDH1 and MAP2K4, fall within known fragile sites FRA16B and FRA17A, respectively. In both cases, however, our model is able to pick up the signature of positive selection due to a strong signal of tumour-type specificity for CDH1 and a high ratio of large to small hemizygous deletions for MAP2K4.Our model is able to reject local fragility for 13 of the 15 peaks containing known tumour suppressors Table , VHL/FAN6, as well as 9 (sub)telomeres showing enrichment of both homozygous and hemizygous deletions telomeric losses show more complex rearrangements compared to cases that do not show such losses  scores24; Fig. Hemizygous and homozygous deletions in these (sub)telomeric regions represent the signature of a prior telomere crisis triggering genomic instability. Ongoing cell division in the absence of telomerase expression leads to telomere attrition and subsequent removal of subtelomeric sequences. The exposed chromosome ends are often resolved by end-to-end chromosome fusions, with further loss of subtelomeric sequences25 and with scientific literature, aiming to identify candidate tumour suppressors. Two regions were intergenic, and for four additional regions the targets remain unknown , as well as novel candidates .In addition to the 15 regions containing established tumour suppressors, our model identified 32 regions that are unlikely to be explained by local fragility, showing a signature of positive selection , a suggested tumour suppressor gene that encodes a protein component of the BRCA1–A complex that leads the BRCA1-BARD1 heterodimer to sites of DNA double-strand breaks, targeting these for homologous recombination44. We therefore hypothesise that homozygous deletions of this region on chromosome 4 coordinately inactivate two tumour suppressor genes, involved in different DNA damage repair pathways.Our screen identified six genes involved in DNA damage response and repair. CASP3 and CASP9, or regions close by, in various cancer types. Both genes encode pro-apoptotic and anti-apoptotic splice isoforms of the respective cysteine/aspartic acid proteases46. CASP8, another member of the caspase family, is a tumour suppressor in breast cancer4. The pro-apoptotic isoforms of caspase-8 and caspase-9 induce apoptosis through cleavage of caspase-3. It is therefore likely that perturbation of any of these three caspase genes may abrogate apoptosis. We detected a peak of homozygous deletions in lung cancer close to SETD1B, a member of a family of histone methyltransferases that also includes SETD2, known to be involved in renal carcinoma. SETD1B specifically methylates H3K4, thereby playing a role in epigenetic control of transcription. We identified several large and smaller homozygous deletions targeting ARHGEF10, a Rho guanine nucleotide exchange factor regulating mitotic spindle formation that has been proposed as a tumour suppressor48. Interestingly, we also observed homozygous deletions affecting its closest paralog ARHGEF10L, suggesting that both genes may be tumour suppressors.We also find candidate tumour suppressors that are related to known cancer genes. We detected homozygous deletions affecting PDE4D, encoding phosphodiesterase 4D. PDE4D has previously been reported to be targeted by internal microdeletions that are hypothesised to function as tumour-promoting factors49. Therefore, these homozygous deletions may represent gain-of-function rather than loss-of-function mutations.Some homozygous deletions may drive oncogenesis through other mechanisms than inactivation of a tumour suppressor gene. We observed intragenic deletions of LEPROTL1, KIAA1551, MIDEAS, MAFTRR, and IGF2BP2. Together with its homologue Leptin Receptor Overlapping Transcript (LEPROT), LEPROTL1 negatively regulates leptin receptor surface expression and thus the response to leptin, a pleiotropic hormone51. Lung tissues both produce and respond to leptin, and leptin is required for proliferation of various non-small cell lung cancer cell lines, at least in part due to activation of downstream Notch and JAK/STAT signalling52. Four homozygous deletions in lung cancer suggest a role for LEPROTL1 in keeping this feedback loop in check in normal lung cells. MIDEAS is part of a recently discovered class I histone deacetylase complex, dubbed MiDAC (Mitotic Deacetylase Complex)53. The complex associates with cyclin A and is upregulated in cells blocked in mitosis54. While the co-repressor MIDEAS couples inositol phosphate signalling to activation of histone deacetylation, its precise role in cell cycle regulation is still unknown55. Two homozygous deletions each in myeloma and sarcoma targeted MAFTRR , a long intergenic noncoding RNA gene. MAFTRR was recently shown to recruit chromatin modifiers LSD1 and EZH2 to the upstream MAF oncogene in a long-distance chromatin interaction, downregulating its expression56. MAF overexpression is a frequent oncogenic event in multiple myeloma, stimulating cell cycle progression and altering bone marrow stromal interactions57. Note that one homozygous deletion in sarcoma abolishes both MAF and MAFTRR, suggesting MAFTRR may still have other functions. Aside from MAFTRR, we identify three additional lncRNA genes as potential tumour suppressors: LINC01060, LINC00375, and LINC00662. Unfortunately, their precise functions are yet to be elucidated. IGF2BP2  encodes a post-transcriptional modulator implicated in mRNA localisation, stability, and translational control. It is part of the frequently aberrated IGF/PI3K-AKT/mTOR pathway and controls the translation of mitochondrial mRNAs59. Depletion of IGF2BP2 decreases oxygen consumption while increasing mitochondrial mRNA translation and possibly mitochondrial biogenesis58. Our observation of a peak of homozygous deletions suggests it could act as a rare tumour suppressor, controlling a switch in cancer nutrient and energy metabolism.Several peaks of homozygous deletions point to genes not previously implicated as tumour suppressors, e.g., In this study, we aimed to identify rare tumour suppressors through a systematic pan-cancer analysis of homozygous deletions in primary tumours. Our screen detected 16 established tumour suppressors, 3 immune regions, 15 known (named) fragile sites, 24 additional intrachromosomal fragile sites, 9 regions of telomeric instability, and 32 regions showing signatures of positive selection for homozygous deletions . Particularly in kidney cancer, homozygous deletions are very rare: we only detect 25 in 73 cases, 3 of which overlap PTPRD.We developed a statistical model to test if enrichment of homozygous deletions in a region can be explained by local fragility alone, or whether there may be positive selection Fig. . As illuDMD (encoding dystrophin), we observe seven in sarcomas. Although DMD is the largest gene in the human genome and is part of known fragile site FRAXC, this suggests homozygous deletions of DMD may play a role in sarcoma. Indeed, DMD was recently validated as a tumour suppressor and anti-metastatic factor in myogenic sarcomas60 and dystrophin/dysferlin double mutant mice show a high incidence of rhabdomyosarcoma61. We also observe a peak of homozygous deletions targeting MXRA5, a poorly studied gene that was recently suggested to be frequently mutated in non-small cell lung carcinoma62. While we are unable to reject local fragility as the underlying cause of both peaks, this may be due to lack of power, and further studies are needed to conclusively determine if these genes play a tumour suppressive role in some cancers.Our model is considerably more conservative on the X chromosome (see Methods) and we do not infer any tumour suppressors on X. Out of eight homozygous deletions affecting 63 to identify recurrent focal gains and losses64, some of which could be attributed to oncogenes and tumour suppressors. Due to our strict focus on homozygous deletions rather than focal losses in general, we aim to identify tumour suppressors recurrently targeted by two independent copy number changes. This approach is not well suited to identify haploinsufficient tumour suppressors or tumour suppressors showing mainly recurrent point mutations combined with copy number losses of the other allele, as discussed above for VHL. However, both our study (on 2218 cancers) and the Zack et al. study on a larger number of cases (4934) identify the same 13 known tumour suppressors , suggesting our focused approach is competitive at identifying tumour suppressors.A previous pan-cancer study performed GISTIC analysisTP53, found mutated in a very high proportion of cases across cancer types, but homozygously deleted in less than 0.2% of cancers in this study. Other known tumour suppressors, such as RB1 and PTEN, can readily be inactivated by both homozygous deletions and inactivating point mutations . Adjacent tumour suppressor pairs  may be especially rewarding targets for homozygous deletion in some cases, as two mutation events can abolish all tumour suppressor activity. We conjecture that this study identifies a class of predominantly rare tumour suppressors, such as CPEB3 and MGMT, that are more prone to be inactivated by homozygous deletions than point mutations, a proportion of which therefore may not be readily identifiable through mutation analysis given current sample sizes.Our results provide a view on the landscape of tumour suppressors that is complementary to sequencing screens for recurrent single-nucleotide substitutions and small insertions and deletions. Many genes frequently targeted by inactivating mutations in cancers do not seem to be frequently targeted by homozygous deletions. This is exemplified by 65, the MetaCGH database66, and from literature searches, with the aim of collecting all studies of primary tumours performed on Affymetrix 250K StyI arrays with publically available raw data. All pre-cancerous lesions and metastases were excluded. Pairwise comparison of all samples with Pearson correlation was used to identify duplicate samples (Pearson correlation above 0.8), in which case duplicates were removed. Samples were stratified into 12 broadly defined tumour types: breast cancer, ovarian cancer, colorectal cancer, hepatocellular carcinoma, renal cancer, lung cancer, cancers of the brain and nervous system, oesophageal cancer, sarcoma, multiple myeloma, leukaemia and lymphoma, each containing >50 samples. Other tumour types with fewer samples available were excluded. Our final data set included 2218 unique primary tumour samples from 27 different studies (Supplementary Table SNP array studies were selected from the CaSNP database67. GRCh37/hg19 probe annotation (version 32) was obtained from the Affymetrix website (http://www.affymetrix.com). Only probes that mapped to unique locations of the genome were retained. GC wave correction was performed as previously described68 and as implemented in ASCAT 2.210. Copy number analysis was performed using ASCAT10 version 2.2, with default parameters for tumour samples without matched normal data. The default value γ = 0.55 was used in ASCAT and has previously been shown to fit the Affymetrix data well. In order to remove any adverse influence of germline copy number variants (CNVs), all SNPs within known germline CNVs were removed prior to ASCAT analysis. Positions of known germline CNVs were obtained from the Database of Genomic Variants69, version hg19.v10, from which all CNVs identified on Affymetrix 500K and SNP6 platforms were selected. This reduced the number of analysed probes from 228,586 to 208,786.Total copy number (LogR) and B allele frequency (BAF) values were obtained from CEL files using PennCNV-AffyStep 1: Identification of hotspots of homozygous deletions: After ASCAT analysis, homozygous deletions were straightforwardly identified as regions having zero copies of both alleles in the tumour cells, each region extending from the position of the first probe being homozygously deleted to the last probe being homozygously deleted. Therefore, the reported positions of a homozygous deletion can be considered the minimal region that is homozygously deleted, as supported by the SNP array data of that sample. The genome was then segmented into bins of constant number of observed homozygous deletions by considering all start and end points of every homozygous deletion to be a breakpoint. Each bin thus defined was then associated with the total number of homozygous deletions over that region observed across all samples .To determine the significance of the number of homozygous deletions in each bin, a permutation test was performed. We initially reasoned that each observed homozygous deletion is the result of two distinct hemizygous deletion events, and we therefore treated both events separately in the simulations. We identified within each parental chromosome and within each sample all hemizygously and homozygously deleted regions. Each such region represents the result of a hemizygous deletion event, and a homozygous deletion occurs when two such regions overlap within a sample. Deletions were classified into two categories, “small” and “large”, and the positions of the small deletions were randomly assigned to a new position in the genome , while the large deletions were kept fixed in their original positions. For parental chromosomal deletions being part of a homozygous deletion, the small and large deletions are readily identified by comparing their sizes. For parental chromosomal deletions not being part of a homozygous deletions , a classifier was applied to determine whether the deletion was small or large. This classifier was a 2-component mixture model trained on the small and large deletions being a part of a homozygous deletion, and resulted in a probability of being a small deletion which was then used to decide whether the position of the deletion should be kept fixed or randomly drawn in the permutation. Finally, a Even with the larger deletions in fixed positions, this permutation strategy indicated that the incidence of simulated homozygous deletions was much higher than the observed rate of homozygous deletions across the whole genome Fig. . In addip-value was calculated as n/M, where n was the number of permutations resulting in at least as many homozygous deletions in the target bin as were observed, and M was the total number of permutations . P-values were adjusted for multiple comparisons by applying the Benjamini–Hochberg procedure. Bins were considered significant if their false discovery rate-adjusted p-value  ≤0.05. Neighbouring bins with a significant q-value were merged when they lay within 1 Mb of one another and when they had more than half of their underlying homozygous deletions in common . When they were separate peaks  they were kept as such. In line with this rule, non-adjacent bins were merged in the following regions: chr4:39.08–39.15, chr4:182.34–182.70, chr9:133.39–133.62, chr11:101.95–102.05 , in a total of 30,382 protein-coding/lincRNA/miRNA genes (ensemble GRCh37 release 85), we computed the probability of observing at least as many known tumour suppressors as we do (≥16) among the total number of genes falling within a 1 Mb window around the centre of an enrichment peak (1197).Step 2: Identifying peaks containing tumour suppressors: A statistical model was constructed quantifying the probability that the observed signature of deletions in a given peak  can be ascribed to local fragility alone . Each metric captures a largely orthogonal structural or biological feature of the peak, and the three p-values are combined into one final meta-analysis p-value  to small hemizygous deletions over the peak  are estimated via a resampling and simulation approach in which we sample 107 times with replacement from the known sites and simulate a number of homozygous, small and large hemizygous deletions according to a multinomial distribution n the cohort size and P-values for any given peak can then be calculated as P(R1 ≥ r1) and P(R2 ≥ r2) using the estimated fragile site density as a null model . As a third metric, we leveraged tumour-type specificity of homozygous deletions vs. small hemizygous deletions  was then used to combine these partially correlated p-values into a single p-value for each homozygous deletion peak70 and the Benjamini–Hochberg method was used for multiple testing correction. Only when the signature of deletions in a peak is unlikely to be explained by local fragility (q ≤ 0.05) do we consider the presence of a tumour suppressor in the region.The metrics above generated three R1 and R2) were not applied to the X chromosome and tumour-type specificity was assayed only on the cancers in females. Therefore, power to separate fragile sites from tumour suppressors is considerably reduced on X. FRA16B and FRA17A were excluded for density estimation of the fragile site null model as they contain tumour suppressors CDH1 and MAP2K4, respectively. To avoid infinite values of R1 and R2, a pseudocount of 0.5 was added to each of the observed counts.To avoid biases, the two structural models  noise in the signal may result in “misclassification” of a homozygously deleted array probe as being part of the adjacent segment. We computed this probability as follows. For each homozygously deleted segment and its adjacent segments on the same chromosome, we obtained robust estimates of the mean and standard deviation of the LogR signal (LogR was preferred as it is more informative than BAF in homozygously deleted regions). The probability of an array probe on the homozygously deleted segment to be wrongly assigned to the adjacent segment was then calculated as the corresponding overlap between the normal distributions of the signal of both segments. Finally, the probability of the homozygously deleted segment extending 1, 2, 3, …, array probe positions into the adjacent segments was computed using a negative binomial distribution. Results show that homozygous deletions are unlikely to extend more than two array probe positions beyond the identified minimal segments (p = 0.015).https://github.com/jdemeul/HomDels.The R code that was used to run simulations, compute statistics, and generate figures is publically available at The Affymetrix 250K StyI array data that support the findings of this study are described in the studies listed in Supplementary Table Supplementary InformationDescription of Additional Supplementary FilesSupplementary Data 1Supplementary Data 2Supplementary Data 3


This technical note entitled “Computed tomography (CT)-guided vertebroplasty using a stereotactic guidance system [stereo-guide]” focuses on the utilization of this protractor with CT guidance in the radiology suite to perform percutaneous vertebroplasty with greater safety and accuracy. Their protractor consisted of a flat plastic block with “deeply grooved protractor markings at 5° intervals” (range from 0° to 30°). While in the CT scanner, the device was placed on the patient's back, and the needle was optimally directed through the center of the pedicle through the appropriate groove. It was successfully employed in nine patients undergoing 10 procedures involving either the thoracic or lumbar spine; there were no complications. Furthermore, the CT scanner additionally facilitated monitoring the injection of vertebroplasty material into the vertebral body; cement consolidation and the presence/absence of extravasation could be immediately assessed. The authors should be commended for devising this protractor that appears to be a useful adjunct when performing percutaneous vertebroplasty in the radiology suite utilizing CT guidance.Previously, when vertebroplasties were performed under fluoroscopic guidance either in the radiology suite by neuroradiologists/radiologists or in the operating room by spine surgeons, localizing the correct level, particularly in the thoracic spine, was often difficult. Another limitation was the inability to “accurately” document extravasation of material into the spinal canal, disc space, etc. (as compared with the use of the CT scan in the radiology suite). This, therefore, increased the risk of inadvertently injecting more material, causing further neural and/or other injuires.Although other studies have routinely employed CT-guidance in radiology suites to perform vertebroplasty/kyphoplasty (VK), one unique report focused on the safety and efficacy of utilizing the Isocentric C-arm fluoroscopic cone beam CT (Iso-C) in the operating room. In this One of the major concerns regarding the performance of vertebroplasty is the advanced age of the patients involved, and their increased number of attendant comorbidities making them poor candidates for “open” operations. Osteoporotic compression fractures of vertebral bodies occur in 5-20% of patients between the ages of 50 and 80, and are four times more frequent in females.[Vertebroplasty and kyphoplasty can also cause severe neurological injury in an estimated of 1-19% of cases utilizing polymethylmethacrylate (PMMA) or Cortoss .–5 In PatNevertheless, with malignant disease, the risks of neurological injury from VK are mitigated by the patients' limited long-term prognoses. In a series involving 74 vertebrae in 51 patients with terminal metastatic disease or multiple myeloma, 15 (29%) had incomplete/complete cord compression before vertebroplasty and sustained no further deficits; only one patient developed a new cauda equina syndrome within 48 h.[An added concern is the increased risk for adjacent-level fractures following VK. In one study, 2 of 25 patients treated with VK compared with none managed conservatively developed delayed adjacent-level fractures. In anothGiven the risks/complications associated with VK procedures utilizing any modality, we should reexamine patient selection based upon short- and long-term outcomes. In a randomized prospective study, 50 patients with acute/subacute osteoporotic compression fractures (<8 weeks old) were treated either with percutaneous vertebroplasty or conservative management. Within 1


Serum- and Glucocorticoid-inducible Kinase 1 (SGK1) is involved in the regulation of insulin secretion and may represent a candidate gene for the development of type 2 diabetes mellitus in humans.SGK) variations and insulin secretion traits. The German TUEF project provided the screening population (N = 725), and four tagging SNPs  were investigated. EUGENE2 (N = 827) served as a replication cohort for the detected associations. Finally, the detected associations were validated in the METSIM study, providing 3798 non-diabetic and 659 diabetic (type 2) individuals.Three independent European populations were analyzed for the association of SGK1 gene  compared to homozygous wild type TT carriers in the screening population. As interaction analysis for BMI×rs9402571 was significant (p = 0.04) for the endpoint insulin secretion, we stratified the TUEF cohort for BMI, using a cut off point of BMI = 25. The effect on insulin secretion only remained significant in lean TUEF participants (BMI≤25). This finding was replicated in lean EUGENE2 rs9402571 minor allele carriers, who had a significantly higher AUCIns/AUCGlc . Accordingly, the METSIM trial revealed a lower prevalence of type 2 diabetes  in rs9402571 minor allele carriers.Carriers of the minor G allele in rs9402571 had significantly higher C-peptide levels in the 2 h OGTT  and higher AUCSGK genotype associates with increased insulin secretion in lean non-diabetic TUEF/EUGENE2 participants and with lower diabetes prevalence in METSIM. Our study in three independent European populations supports the conclusion that SGK variability affects diabetes risk.The rs9402571  Type 2 diabetes arises when insulin resistance cannot be compensated for with increased insulin secretion owing to a gradual loss of pancreatic beta-cell function SGK on chromosome 6q23. SGK1 was originally identified in rodents as a serum and glucocorticoid regulated kinase −/− knockout mice fed with high-salted chow demonstrated decreased SGK1-dependent cellular glucose uptake SGK gene transcription and protein expression is strongly regulated, and SGK1 up-regulates the activity of voltage-gated K+ channels, which in turn reduces Ca++ influx and inhibits insulin release +/K+-ATPase during plasma membrane repolarisation One interesting candidate for the regulation of insulin secretory function is the serum and glucocorticoid inducible kinase SGK1, which is a ubiquitously expressed serine-threonine kinase in humans that is encoded by the gene SGK genetic variance in human physiology are rather limited. Two studies confirmed an association of SGK variability with blood pressure in a German twin population SGK genetic variance and potential associations with insulin secretory function in the German TUEF cohort and the EUGENE2 consortium , as these two European diabetes risk populations were extensively phenotyped for insulin secretion traits at the prediabetic stage. To confirm the relevance of associations found for later onset of type 2 diabetes mellitus, corresponding SGK risk alleles were further investigated in the METSIM Trial, which provides a large population-based Finnish cohort for the endpoint diabetes. Analyzing four selected tagging SNPs of SGK, the SNP rs9402571 was consistently found to be associated with altered insulin secretion in both prediabetic populations, and was further confirmed to associate with the prevalence of type 2 diabetes mellitus in the population-based cohort.Taken together, compelling evidence points to a role of this ubiquitously expressed serine/threonine kinase SGK1 in glucose metabolism, especially in the regulation of insulin secretion. So far, studies on the role of SGK genetic variance and insulin secretion traits for this study. The TUEF project provided the screening population, while EUGENE2 served as a replication cohort for insulin secretion traits. METSIM is a population-based cohort providing both non-diabetic and type 2 diabetic individuals, and was employed for estimation of SGK diabetes-risk alleles. Further details on each of the three study cohorts are provided in the following, with baseline characteristics presented in Three independent European cohorts were analyzed for SGK and phenotyped by OGTT and AUCCP/AUCGlc (see below). For further investigation, individuals with one of the following criteria were excluded: taking medications known to affect glucose tolerance, severe diseases , newly diagnosed diabetes, positive GAD antibodies, and one or more missing parameters needed for AUCCP/AUCGlc calculation. This method of elimination resulted in a final study cohort of 725 non-diabetic individuals.The TUEF (Tuebingen Familiy Study) cohort includes non-diabetic individuals from southern Germany with increased risks for developing type 2 diabetes . The study protocol included standard procedures as medical history, physical examination, routine blood tests and oral glucose tolerance test with blood sampling  at 0, 30, 60, 90 and 120 min Five different European clinical diabetes centres contributed non-diabetic offspring of patients with type 2 diabetes to the EUGENE2  consortium, including the Lundberg Laboratory for Diabetes Research , the Polyclinic Mater Domini of the University Magna Graecia , the Steno Diabetes Center , the Kuopio University Hospital , and the Tübingen University Hospital . All study participants underwent a standard medical history, routine laboratory testing, assessment of social issues , and an OGTT. Informed written consent was obtained of all participants, and the local ethics committees approved the study protocol at the different centres. Further details about the EUGENE2 consortium are provided elsewhere METabolic Syndrome In Men) trial is to investigate the effects of SNPs in genes of interest on the risk of type 2 diabetes and cardiovascular disease in a random sample of Finnish men, living in the town of Kuopio , in eastern Finland The Finnish cohort comprised 4457 male participants, aged from 50 to 70 years. Among them, 43.4% had a family history of diabetes. The primary aim of the ongoing METSIM (2). Waist and hip circumferences were measured in the upright position.Body mass index (BMI) was calculated as weight divided by the square of height . Plasma insulin was determined by microparticle enzyme immunoassay  for both the TUEF and EUGENE2 cohorts. Plasma C-peptide was determined by radioimmunoassay .The OGTT was performed according to the recommendations of the World Health Organization after a 12 h fasting period CP; TUEF cohort) or insulin  plasma levels, referred to as the AUC of plasma glucose (AUCGlc), using the trapezoidal approximation rule for AUC calculation Insulin sensitivity was calculated from glucose and insulin values obtained during the OGTT, as proposed by Matsuda and DeFronzo SGK (NM_005627) gene on chromosome 6q23 was subjected to Hap Map analysis in the CEU population . Tagging SNPs were selected using the TagSNP Picker software , screening the complete SGK gene, 10 kb of the promoter, and 1.5 kb downstream of the 3′ untranslated region. Genotyping of tagging SNPs was accomplished by means of the TaqMan® assay and an ABI Prism 7500 sequence detection system . Genotyping quality was tested by including three known controls of each genotype in each assay.The t-tests. Each effect was adjusted for relevant covariates using mixed linear models. Parameters that remained non-normally distributed despite log transformation were directly analyzed by a non-parametric (Wilcoxon) test. Association with diabetes (METSIM) was tested using logistic regression analysis. Hardy-Weinberg equilibrium was tested with the χ2 test. A p-value≤0.05 was considered to be statistically significant. The software package JMP  and SPSS 14.0 for Windows  were used for statistical data analysis. The software JLIN  was used for calculation of linkage disequilibrium Unless otherwise noted, data are stated as means±SEM. Comparison between genotype groups was performed using logarithmically transformed data for non-normally distributed parameters in ANOVA and SGK gene comprises 12 exons spanning 5.6 kb on chr6q23. Analysis of a 17 kb region of the SGK locus by Tag SNP Picker revealed five tagSNPs in the HapMap, each with a minor allele frequency (MAF) of >0.05 in the CEU population. Four SNPs  were finally selected for genotyping, covering 83% of all SNPs fulfilling the inclusion criteria mentioned above. Both rs1743966 (intron 6) and rs1057293  are located within the SGK gene, while rs1763527 (promoter) and rs9402571 (1429 bp downstream of the SGK 3′UTR) tag the regions surrounding SGK. MAF and Hardy-Weinberg-Equilibrium test results of all tagging SNPs are indicated for the TUEF and EUGENE2 cohort in The human C-Peptide/AUCGlc ratios . Most consistently, two different indices of insulin secretion differed significantly among rs9402571 genotypes . Minor a2. We therefore stratified the cohort for BMI, using a cut off point of BMI = 25 to distinguish lean from overweight or obese individuals, and did a reanalysis for insulin secretion for both subgroups. The effect on AUCCP/AUCGlc was only significant in the lean TUEF subgroup  and of whether BMI was treated as a continuous or discrete variable with BMI cut-off points ranging from 24 to 28 kg/mSGK genetic variance for the endpoint diabetes (type 2) in a population-based cohort. The minor allele of rs9402571, which was found to associate with a higher insulin secretion in the screening (TUEF) as well as the replication (EUGENE2) populations, associated almost significantly  with a lower prevalence of type 2 diabetes , comparing the 659 diabetic patients with the non-diabetic 3798 METSIM participants. In contrast, there was no significantly altered odds ratio in regard to the three other genotyped SGK tagging SNPs rs1763527, rs1743966, and rs1057293  genetic variance in two independent prediabetic diabetes risk populations (TUEF and EUGENE2), including a confirmatory analysis in a third independent, older population that includes both diabetic and non-diabetic individuals (METSIM). SGK is an attractive candidate gene for type 2 diabetes mellitus onset in humans, as SGK1 exerts pleiotropic effects on glucose metabolism and insulin secretion in various cellular and animal models This is the first study to investigate the role of SGK tagging SNPs with insulin sensitivity was found in the TUEF screening population or in the EUGENE2 study. One could suggest that the SGK rs9402571 SNP was not detected in the genome-wide association (GWA) studies due to its dependence on BMI, as the GWA analyses did not stratify for BMI. Interestingly, a genome-wide linkage analysis identified the gene locus 6q23, which also includes SGK, to be associated with BMI progression in participants of the Framingham Heart Study In the screening population , we found a significant association of rs9402571 with glucose-induced insulin secretion in various estimation models based on oral glucose tolerance testing. This association was reproducible in a subgroup that was additionally phenotyped by an intravenous glucose tolerance test (data not shown). After confirming a significant interaction between BMI and rs9402571 for the endpoint insulin secretion, BMI stratification (BMI cut off: 25) was introduced in our analysis, revealing that only lean individuals were affected by the rs9402571 minor allele. This genotype-phenotype association was also restricted to lean individuals in the replication cohort from the EUGENE2 consortium (N = 827), comprising individuals from Sweden, Italy, Denmark, Finland, and Germany. Interestingly, the rs9402571 allele that associates with higher insulin secretion in TUEF and EUGENE2 accordingly associates with lower diabetes prevalence in the METSIM trial. Despite the fact that SGK1 has a regulatory function for glucose transporter expression and translocation SGK genotype may become blurred in overweight or obese individuals, as environmental factors may dominate compared to the SGK genotype. However, our analysis in the METSIM trial assumed that the protective rs9402571 SGK genotype effect obviously persists even in a population-based cohort containing individuals 20 years older than those from the TUEF and EUGENE2 studies. Therefore, although the number of study participants was limited after BMI stratification in both TUEF and EUGENE2, our findings in three independent European populations allow the assumption that the SGK rs9402571 genotype is protective against type 2 diabetes. Further studies on the role of SGK1 in human insulin secretion and diabetes onset are therefore needed.The increase in insulin secretion reported here  within lean rs9402571 G allele carriers was remarkable compared to wild type genotype, and may indicate a protective genotype against type 2 diabetes. This protective rs9402571