RICHRBT is about to start the installation of the first of the four solar power plants in its project pipeline in Romania 0 saw in time the opportunity for a bigger piece of the action active in civil and industrial construction announced via domestic media outlets that it would begin works in September on the first solar power plant in a portfolio of four units The facility in the Bolintin-Deal commune in Giurgiu county would have 40 MW The firm scheduled the start of installation of the three remaining ones for early next year An 80 MW photovoltaic facility is planned to be built in Joiţa commune The last solar park would be in Bolintin-Vale All the sites are in the Giurgiu area in the Muntenia region and are just a few kilometers from each other RICHRBT pointed to the significance of its project pipeline for the local economy in terms of job creation and attracting investments in renewables “In the next three years we want to establish another 12 parks because we want to reach a total capacity of 900 MW by 2027 I hope we have the appropriate support from a legislative point of view and from the Romanian Government,” said Reman Henk Jonah Rutger He said the firm is committed to continuing to invest in sustainable and innovative projects As for other developments in the sector in Romania, Greece-based Public Power Corp. (PPC) began the construction of a 140 MW wind farm There are more acquisitions of mature projects as well The EUR 3.25 million deal was actually agreed three years ago, Economica.net reported Earlier, Econergy received the approval for the connection of its solar power project for 34 MW in peak capacity to the transmission grid The Mircea Vodă site is in Dobrogea or Dobruja A subsidiary of the Israeli company also got network connection terms for a 27 MW solar park in Dobrogea It expects it would bring both facilities online next year Shikun and Binui Energy secured a EUR 49 million loan a month ago for a 101 MW photovoltaic endeavor in the northwest It also has a new transmission network connection approval for its proposed Gold-Wind wind farm of 376 MW near Constanța its target completion date is by the end of 2028 recently bought a 46 MW photovoltaic park in Constanța county and obtained the connection approval for a 245 MW solar power plant in Giurgiu county The latter is called Tălgat and it is slated to be completed by the end of 2028 Bellini has been present in the market since early 1990s Be the first one to comment on this article 05 May 2025 - The delegations from the two countries met on the sidelines of the 10th summit meeting of the Three Seas Initiative 05 May 2025 - The Trebinje 3 photovoltaic plant would have an installed capacity of 53.63 MW and an estimated annual production of 85.5 GWh 05 May 2025 - VDE Renewables found that SolarEdge’s advanced safety capabilities minimize photovoltaic system risks and effectively prevent fire hazards 02 May 2025 - The project is located in Constanța county recognized for its superior yields in green energy production © CENTER FOR PROMOTION OF SUSTAINABLE DEVELOPMENT 2008-2020 website developed by ogitive Metrics details Metastatic cancer is a major cause of death and is associated with poor treatment efficacy A better understanding of the characteristics of late-stage cancer is required to help adapt personalized treatments pan-cancer study of metastatic solid tumour genomes including whole-genome sequencing data for 2,520 pairs of tumour and normal tissue and surveying more than 70 million somatic variants The characteristic mutations of metastatic lesions varied widely with mutations that reflect those of the primary tumour types and with high rates of whole-genome duplication events (56%) Individual metastatic lesions were relatively homogeneous with the vast majority (96%) of driver mutations being clonal and up to 80% of tumour-suppressor genes being inactivated bi-allelically by different mutational mechanisms Although metastatic tumour genomes showed similar mutational landscape and driver genes to primary tumours we find characteristics that could contribute to responsiveness to therapy or resistance in individual patients We implement an approach for the review of clinically relevant associations and their potential for actionability we identify genetic variants that may be used to stratify patients towards therapies that either have been approved or are in clinical trials This demonstrates the importance of comprehensive genomic tumour profiling for precision medicine in cancer a better understanding of metastatic cancer genomes will be highly valuable to improve on adapting treatments for late-stage cancers Only tumour types with more than ten samples are shown (n = 2,350 independent patients) and are ranked from the lowest to the highest overall SNV mutation burden (TMB) we found extensive variation in the mutational load of up to three orders of magnitude both within and across cancer types The variation for MNVs was even greater, with lung (median of 821) and skin (median of 764) tumours having five times the median MNV counts of any other tumour type. This can be explained by the well-known mutational effect of UV radiation (CC>TT) and smoking (CC>AA) mutational signatures, respectively (Extended Data Fig. 2) Although only dinucleotide substitutions are typically reported as MNVs 10.7% of the MNVs involve three nucleotides and 0.6% had four or more nucleotides affected CNS tumours also have a higher mutational load that is explained by the different age distributions of the cohorts Proportion of samples with amplification and deletion events by genomic position pan-cancer The inner ring shows the percentage of tumours with homozygous deletion (orange) LOH and significant loss (copy number < 0.6× sample ploidy; dark blue) and near copy neutral LOH (light blue) Outer ring shows percentage of tumours with high level amplification (>3× sample ploidy; orange) moderate amplification (>2× sample ploidy; dark green) and low level amplification (>1.4× amplification; light green) The scale on both rings is 0–100% and inverted for the inner ring The most frequently observed high-level gene amplifications (black text) and homozygous deletions (red text) are shown Proportion of tumours with a WGD event (dark blue) Sample ploidy distribution over the complete cohort for samples with and without WGD although the involvement of individual genes has not been established the mechanism for LOH in TP53 is highly specific to tumour type with ovarian cancers exhibiting LOH of the full chromosome 17 in 75% of samples whereas in prostate cancer (also 70% LOH for TP53) this is nearly always caused by highly focal deletions homozygous deletions are nearly always restricted to small chromosomal regions Not a single example was found in which a complete autosomal arm was homozygously deleted Homozygous deletions of genes are also surprisingly rare: we found only a mean of 2.0 instances per tumour in which one or several consecutive genes are fully or partially homozygously deleted Loss of chromosome Y is a special case and is deleted in 36% of all male tumour genomes but varies strongly between tumour types from 5% deleted in CNS tumours to 68% deleted in biliary tumours (Extended Data Fig we identified a recurrent in-frame deletion of the complete exon 3 in 12 samples these deletions were homozygous but thought to be activating as CTNNB1 normally acts as an oncogene in the WNT and β-catenin pathway and none of these nine colorectal samples had any APC driver mutations We also identified several significantly deleted genes not previously reported including MLLT4 (n = 13) and PARD3 (n = 9) is also highly amplified in our cohort with 20 out of 22 amplified samples found in colorectal cancer representing 5.4% of all colorectal samples The most prevalent somatically mutated oncogenes (a) TSGs (b) and germline predisposition variants (c) the heat map shows the percentage of samples in each cancer type that are found to have each gene mutated; absolute bar chart shows the pan-cancer percentage of samples with the given gene mutated; relative bar chart shows the breakdown by type of alteration the final bar chart shows the percentage of samples with a driver in which the gene is biallelically inactivated and for germline predisposition variants (c) the final bar chart shows the percentage of samples with loss of wild type in the tumour Violin plot showing the distribution of the number of drivers per sample grouped by tumour type (number of patients per tumour type is provided) Black dots indicate the mean values for each tumour type Relative bar chart showing the breakdown per cancer type of the type of alteration we also found twofold more amplification drivers in samples with WGD events despite amplifications being defined as relative to the average genome ploidy The 189 germline variants identified in 29 cancer predisposition genes (present in 7.9% of the cohort) consisted of 8 deletions and 181 point mutations (Fig. 3c, Supplementary Table 6) The top five affected genes (containing nearly 80% of variants) were the well-known germline drivers CHEK2 The corresponding wild-type alleles were found to be lost in the tumour sample in more than half of the cases indicating a high penetrance for these variants or a point mutation in combination with LOH (41%) the highest observed in any large-scale WGS cancer study the biallelic inactivation rate is almost 100%—TP53 (93%) PTEN (92%) and SMAD4 (96%)—which suggests that biallelic genetic inactivation of these genes is a strong requirement for metastatic cancer the other allele may also be inactivated by non-mutational epigenetic mechanisms or tumorigenesis may be driven via a haploinsufficiency mechanism We examined the pairwise co-occurrence of driver gene mutations per cancer type and found ten combinations of genes that were significantly mutually exclusively mutated, and ten combinations of genes that were significantly concurrently mutated (Extended Data Fig. 10) Although most of these relationships are well established we found new positive relationship for GATA3–VMP1 (q = 6 × 10−5) and FOXA1–PIK3CA (q = 3 × 10−3) and negative relationships for ESR1–TP53 (q = 9 × 10−4) and GATA3–TP53 (q = 5 × 10−5) These findings will need further validation and experimental follow-up to understand the underlying biology Percentage of samples in each cancer type with a putative candidate actionable mutation based on data in the CGI Level A represents presence of biomarkers with either an approved therapy or guidelines and level B represents biomarkers with strong biological evidence or clinical trials that indicate that they are actionable On-label indicates treatment registered by federal authorities for that tumour type whereas off-label indicates a registration for other tumour types Break down of the actionable variants by variant type with data from 4,000 patients already available and includes data that go beyond the basic clinical and genomic data analysed in this paper such as post-biopsy treatments and responses Genomic testing of tumours faces numerous challenges in meeting clinical needs including the interpretation of variants of unknown significance the steadily expanding universe of actionable genes—often with an increasingly small fraction of patients affected—and the development of advanced genome-derived biomarkers such as tumour mutational load DNA repair status and mutational signatures Our results demonstrate that WGS analyses of metastatic cancer can provide novel and relevant insights and are instrumental in addressing some of the key challenges of precision medicine in cancer we demonstrate the importance of accounting for all types of variant including large-scale genomic rearrangements (via fusions and copy number alteration events) which account for more than half of all drivers but also activating MNVs and indels that we have shown are commonly found in many key oncogenes even with very strict variant calling criteria we could find candidate driver variants in more than 98% of all metastatic tumours including predicted putatively actionable events in a clinical and experimental setting for up to 62% of patients Although we did not find metastatic tumour genomes to be fundamentally different from primary tumours in terms of the mutational landscape or genes that drive advanced tumorigenesis we described characteristics that could contribute to responsiveness to therapy or resistance in individual patients we showed that WGD events are a more pervasive element of tumorigenesis than previously understood affecting over half of all metastatic cancers We also found metastatic lesions to be less heterogeneous than reported for primary tumours although the limited sequencing depth does not allow conclusions to be made about low-frequency subclonal variants providing enhanced cancer subtype stratification and revealing characteristic genomic differences between primary and metastatic tumours As the Hartwig Medical cohort includes a mix of treatment-naive metastatic patients and patients who have undergone (extensive) previous systemic treatments it provides unique opportunities to study responses and resistance to treatments and discover predictive biomarkers as these data are available for discovery and validation studies A detailed description of methods and validations is available as Supplementary Information No statistical methods were used to predetermine sample size and investigators were not blinded to allocation during experiments and outcome assessment Patients have given explicit consent for whole-genome sequencing and data sharing for cancer research purposes Core needle biopsies were sampled from the metastatic lesion or when considered not feasible or not safe from the primary tumour site and frozen in liquid nitrogen A single 6-μm section was collected for haematoxylin and eosin (H&E) staining and estimation of tumour cellularity by an experienced pathologist and 25 sections of 20-μm were collected in a tube for DNA isolation DNA) was stored in biobanks associated with the studies at the University Medical Center Utrecht and the Netherlands Cancer Institute To assess the effect of sequencing depth on variant calling sensitivity we downsampled the BAMS of 10 samples at random by 50% and reran the identical somatic variant calling pipeline ASCAT was run on GC-corrected data using default parameters except for gamma which was set to 1 (which is recommended for massively parallel sequencing data) We implement a simple heuristic that determines if a WGD event has occurred: major allele ploidy > 1.5 on at least 50% of at least 11 autosomes as the number of duplicated autosomes per sample (that is the number of autosomes which satisfy the above rule) follows a bimodal distribution with 95% of samples have either ≤6 or ≥15 autosomes duplicated samples were filtered out based on absence of somatic variants yielding a high-quality dataset of 2,520 samples Where multiple biopsies exist for a single patient the highest purity sample was used for downstream analyses (resulting in 2,399 samples) Residuals were calculated as the sum of the absolute difference between observed and fitted across the 96 buckets Signatures with <5% overall contribution to a sample or absolute fitted mutational load <300 variants were excluded from the summary plot selected because these are the only additional genes from the larger list of 152 genes with a significantly increased proportion of called germline variants with loss of wild type in the tumour sample The ploidy of each variant is calculated by adjusting the observed VAF by the purity and then multiplying by the local copy number to work out the absolute number of chromatids that contain the variant no wild type remaining) if variant ploidy > local copy number − 0.5 we also determine a probability that it is subclonal This is achieved via a two-step process involving fitting the somatic ploidies for each sample into a set of clonal and subclonal peaks and calculating the probability that each individual variant belongs to each peak Subclonal counts are calculated as the total density of the subclonal peaks for each sample Subclonal driver counts are calculated as the sum across the driver catalogue of subclonal probability × driver likelihood trinucleotide and tetranucleotide sequences of repeat count four or more To identify significantly mutated genes in our cohort we used a strict significance cut-off value of q < 0.01 Most of the deletion peaks resolve clearly to a single target gene which reflects the fact that homozygous deletions are highly focal but for amplifications this is not the case and most of our peaks have ten or more candidates to choose a single putative target gene using an objective set of automated curation rules filtering was applied to yield highly significant deletions and amplifications personal communication) in which we first calculated the number of genes with putative driver mutations in a broad panel of known and significantly mutated genes across the full cohort and then assigned the candidate driver mutations for each gene to individual patients by ranking and prioritizing each of the observed variants Key points of difference in this study were both the prioritization mechanism used and our choice to ascribe each mutation a probability of being a driver rather than a binary cut-off based on absolute ranking (2) Determine TSG or oncogene status of each significantly mutated gene using a logistic regression classification model (trained using COSMIC annotation) (4) Calculate a per-sample likelihood score (between 0 and 1) for each mutation in the catalogue as a potential driver event to ensure that only likely pathogenic and excess mutations (based on dN/dS) are used to determine the number of drivers All putative driver mutation counts reported at a per-cancer type or sample level refer to the sum of driver likelihoods for that cancer type or sample For each candidate actionable mutation in each sample we aggregated all the mapped evidence that was available supporting both on-label and off-label treatments at the A or B evidence level Treatments that also had evidence supporting resistance based on other biomarkers in the sample at the same or higher evidence level were excluded as non-actionable Samples classified as MSI in our driver catalogue were also mapped as actionable at level A evidence based on clinical annotation in the OncoKB database we reported the highest level of predicted actionability ranked first by evidence level and then by on-label vs off-label Further information on research design is available in the Nature Research Reporting Summary linked to this paper All data described in this study are freely available for academic use from the Hartwig Medical Foundation through standardized procedures and request forms that can be found at https://www.hartwigmedicalfoundation.nl/en/appyling-for-data/ Available data include germline and tumour raw sequencing data (BAM files, including non-mapped reads), annotated somatic and germline variants (VCF files with annotated SNV and indels, and pipeline output files for purity and ploidy status as well as copy number alteration and structural variants) and clinical data. Examples of output files can be found at https://resources.hartwigmedicalfoundation.nl a data request can be initiated by filling out the standard form in which intended use of the requested data is motivated an advice on scientific feasibility and validity is obtained from experts in the field that is used as input by an independent data access board who also evaluates if the intended use of the data is compatible with the consent given by the patients and if there would be any applicable legal or ethical constraints Upon formal approval by the data access board a standard license agreement that does not have any restrictions regarding intellectual property resulting from the data analysis needs to be signed by an official organization representative before access to the data are granted access to data is provided under a license model with the only main restriction that the data can only be used for the research detailed in the original request Raw data files will be made available through a dedicated download portal with two-factor authentication Non-privacy sensitive somatic variants can also be browsed and explored through an open access web-based interface which can be accessed at http://database.hartwigmedicalfoundation.nl/ All code used is open source and available from third parties or developed by Hartwig Medical Foundation (https://github.com/hartwigmedical/). A full list of tools and versions used including links to the source code is provided in the Supplementary Information The Cancer Genome Atlas Research Network et al The Cancer Genome Atlas Pan-Cancer analysis project The International Cancer Genome Consortium International network of cancer genome projects The landscape of genomic alterations across childhood cancers Pan-cancer genome and transcriptome analyses of 1,699 paediatric leukaemias and solid tumours Genomic evolution of breast cancer metastasis and relapse Origins of lymphatic and distant metastases in human colorectal cancer The evolutionary history of lethal metastatic prostate cancer Mutational landscape of metastatic cancer revealed from prospective clinical sequencing of 10,000 patients Integrative clinical genomics of metastatic cancer Selection and adaptation during metastatic cancer progression Clonal heterogeneity and tumor evolution: past Signatures of mutational processes in human cancer Campbell, P. J., Getz, G., Stuart, J. M., Korbel, J. O. & Stein, L. D. Pan-cancer analysis of whole genomes. Preprint at https://www.bioRxiv.org/content/10.1101/162784v1 (2017) A compendium of mutational signatures of environmental agents Tumor microsatellite instability and clinical outcome in young patients with colorectal cancer Sequencing of prostate cancers identifies new cancer genes Loss of chromosome 8p governs tumor progression and drug response by altering lipid metabolism Integrated molecular analysis of clear-cell renal cell carcinoma The somatic genomic landscape of glioblastoma Pan-cancer patterns of somatic copy number alteration Absolute quantification of somatic DNA alterations in human cancer Genome doubling shapes the evolution and prognosis of advanced cancers Universal patterns of selection in cancer and somatic tissues Recurrent MLK4 loss-of-function mutations suppress JNK signaling to promote colon tumorigenesis COSMIC: somatic cancer genetics at high-resolution Fragile sites in cancer: more than meets the eye Amplification of SOX4 promotes PI3K/Akt signaling in human breast cancer The PIAS-like coactivator Zmiz1 is a direct and selective cofactor of Notch1 in T cell development and leukemia CDX2 is an amplified lineage-survival oncogene in colorectal cancer Sabarinathan, R. et al. The whole-genome panorama of cancer drivers. Preprint at https://www.bioRxiv.org/content/10.1101/190330v2 (2017) Can APC mutation analysis contribute to therapeutic decisions in familial adenomatous polyposis Comprehensive characterization of cancer driver genes and mutations NETs: organ-related epigenetic derangements and potential clinical applications Structural alterations driving castration-resistant prostate cancer revealed by linked-read genome sequencing Comprehensive genomic characterization of squamous cell lung cancers CIViC is a community knowledgebase for expert crowdsourcing the clinical interpretation of variants in cancer Chakravarty, D. et al. OncoKB: a precision oncology knowledge base. JCO Precis. Oncol. https://doi.org/10.1200/PO.17.00011 (2017) Cancer Genome Interpreter annotates the biological and clinical relevance of tumor alterations Current perspectives on FOXA1 regulation of androgen receptor signaling and prostate cancer Jr Mutation and cancer: statistical study of retinoblastoma Pan-cancer analysis of the extent and consequences of intratumor heterogeneity Minimal functional driver gene heterogeneity among untreated metastases The Drug Rediscovery protocol facilitates the expanded use of existing anticancer drugs Nivolumab plus ipilimumab in lung cancer with a high tumor mutational burden First-line nivolumab in stage IV or recurrent non-small-cell lung cancer The International Cancer Genome Consortium Data Portal Genomic landscape of metastatic breast cancer and its clinical implications Implementation of a multicenter biobanking collaboration for next-generation sequencing-based biomarker discovery based on fresh frozen pretreatment tumor tissue biopsies Fast and accurate short read alignment with Burrows–Wheeler transform The Genome Analysis Toolkit: a MapReduce framework for analyzing next-generation DNA sequencing data Poplin, R. et al. Scaling accurate genetic variant discovery to tens of thousands of samples. Preprint at https://www.bioRxiv.org/content/10.1101/201178v2 (2018) From FastQ data to high confidence variant calls: the Genome Analysis Toolkit best practices pipeline Strelka: accurate somatic small-variant calling from sequenced tumor-normal sample pairs Manta: rapid detection of structural variants and indels for germline and cancer sequencing applications Allele-specific copy number analysis of tumors MutationalPatterns: comprehensive genome-wide analysis of mutational processes Pathogenic germline variants in 10,389 adult cancers Recommendations for reporting of secondary findings in clinical exome and genome sequencing 2016 update (ACMG SF v2.0): a policy statement of the American College of Medical Genetics and Genomics MSIseq: software for assessing microsatellite instability from catalogs of somatic mutations GISTIC2.0 facilitates sensitive and confident localization of the targets of focal somatic copy-number alteration in human cancers A framework to rank genomic alterations as targets for cancer precision medicine: the ESMO Scale for Clinical Actionability of molecular Targets (ESCAT) Disease Ontology 2015 update: an expanded and updated database of human diseases for linking biomedical knowledge through disease data a novel duocarmycin-based HER2-targeting antibody-drug conjugate shows antitumor activity in uterine serous carcinoma with HER2/Neu Expression RNF43 and ZNRF3 are commonly altered in serrated pathway colorectal tumorigenesis SMAD3 and SMAD4 mutations in colorectal cancer Download references We thank the Hartwig Foundation and Barcode for Life for financial support of clinical studies and WGS analyses Implementation of the data portal was supported by a grant from KWF Kankerbestrijding (HMF2017-8225 We are particularly grateful to all patients nurses and medical specialists for their essential contributions that make this study possible van Snellenberg (Hartwig Medical Foundation) for operational management Dinjens for support with pathology assessments and mutation validations and J van de Werken for critically reading the manuscript These authors contributed equally: Peter Priestley Netherlands Cancer Institute/Antoni van Leeuwenhoekhuis Center for Molecular Medicine and Oncode Institute All authors provided input for improvement of the manuscript is a supervisory board member of the Hartwig Medical Foundation Publisher’s note Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations Peer review information Nature thanks Fran Supek and the other reviewer(s) for their contribution to the peer review of this work Sample workflow from patient to high-quality WGS data A total of 4,018 patients were enrolled in the study between April 2016 and April 2018 no blood and/or biopsy material was obtained mostly because conditions of patients prohibited further study participation Up to four fresh-frozen biopsies were obtained per patient and were sequentially analysed to identify a biopsy with more than 30% tumour cellularity as determined by routine histology assessment and 2,796 patients were further processed for WGS analysis 44 and 29 samples failed in either DNA isolation or library preparation and raw WGS data quality control tests but the determination of tumour purity based on WGS data (PURity & PLoidy Estimator; PURPLE) was less than 20% making reliable and comprehensive somatic variant calling impossible and were therefore excluded 2,338 pairs of tumour and normal tissue samples with high-quality WGS data were obtained which were supplemented with 182 pairs from pre-April 2016 adding up to 2,520 pairs of tumour and normal samples that were included in this study Tumour biopsies were taken from a broad range of locations Distribution of sample sequencing depth for tumour and blood reference samples (n = 2,520 independent samples for each category) The median for each is indicated by a horizontal bar mutational context or signature per individual sample for each SNV (a) Each column chart is ranked within tumour type by mutational load from low to high in that variant class MNVs are classified by the dinucleotide substitution with ‘NN’ referring to any dinucleotide combination Highly characteristic known patterns can be discerned CC>TT MNVs and COSMIC S18 for skin tumours and high rates of C>A SNVs and COSMIC S4 for lung tumours Proportion of samples by cancer type classified as microsatellite instable (MSIseq score > 4) Proportion of samples with a high mutational burden (TMB > 10 SNVs per Mb) Scatter plots of mutational load per sample for indels versus SNVs (c) MSI (MSIseq score > 4) and high TMB (>10 SNVs per Mb) thresholds are indicated Mean mutational load versus driver rate for SNVs (f) Decreasing coverage results in an average decrease in sensitivity of 10% for SNVs the TMB between primary and metastatic cohorts across all variant types are much more comparable (e which indicates that technical differences do contribute to the reported mutational load differences between primary and metastatic tumours Prostate cancer is the most notable exception with approximately twice the TMB in all variant classes can also be observed for other tumour and mutation types For cancer types that are comparable with the PCAWG cohort the equivalent PCAWG numbers are shown by dotted lines The median for each cohort is shown by a horizontal line Proportion of male tumours with somatic loss of more than 50% of Y chromosome (dark blue) grouped by tumour type Mean rate of amplification drivers per cancer type Breakdown of the number of amplification drivers per gene by cancer type Mean rate of drivers per variant type for samples with and without WGD but not found in the COSMIC gene census or curated gene databases Gene names marked in red are novel in this study Significance (Poisson with Benjamini–Hochberg false discovery rate correction) is indicated by the intensity of shading Count of driver point mutations by variant type Known pathogenic mutations curated from external databases are categorized as hotspot mutations Mutations within five bases of a known pathogenic mutation are shown as near hotspot and all other mutations are shown as non-hotspot including a positive relationship for GATA3–VMP1(q = 6 × 10−5) and FOXA1–PIK3CA (q = 3 × 10−3) and a negative relationship for ESR1–TP53 (q = 9 × 10−4) and GATA3–TP53 (q = 5 × 10−5) Violin plot showing the percentage of point mutations per tumour purity bucket (the number of independent samples per category is indicated) that are subclonal in each purity bucket per sample Black dots indicate the mean for each bucket Percentage of driver point mutations that are subclonal in each purity bucket Approximate somatic ploidy detection cut-off of the HMF pipeline at median 106× depth coverage for each purity bucket and for sample ploidy 2 and 4 Subclonal variants with cellular fraction less than this cut-off are unlikely to be detected by our pipeline analyses .This file contains a detailed description of methods and validation results Supplementary Figure 1.Copy Number profile per cancer types Circos plots showing the proportion of samples with amplification and deletion events by genomic position per cancer type The inner ring shows the % of tumours with homozygous deletion (red) LOH and significant loss (copy number < 0.6x sample ploidy - dark blue) and near copy neutral LOH (light blue) The outer ring shows the % of tumours with high level amplification (>3x sample ploidy - orange) moderate amplification (>2x sample ploidy - dark green) and low level amplification (>1.4x amplification - light green) Scales on both rings are 0-100% and inverted for the inner ring The most frequently observed high level gene amplifications (black text) and homozygous deletions (red text) are labelled Supplementary Figure 2.Coding mutation profiles by tumour suppressor driver gene Location and driver classification of all coding mutations (SNVs and indels) in tumour suppressor genes (TSG) in the driver catalogue The lollipops on the chart show the location (coding sequence coordinates) and count of mutations for all candidate drivers The height of lollipop represents the total count of each individual variant in the cohort (log scale) The height of the solid line represents the sum of driver likelihoods for that variant the proportion that are expected to be drivers (Partially) dotted lines hence indicate variants for which driver role is uncertain Variants are unshaded if all instances of that variant are monoallelic single hits with no LOH The right column chart shows the estimated number of drivers (calculated as the sum of driver likelihoods) and passenger variants in each gene by cancer type Supplementary Figure 3.Coding mutation profiles by oncogene driver gene Location and driver classification of all coding mutations (SNVs and indels) in oncogenes (a) and tumour suppressor genes (TSG) (b) in the driver catalogue The right column chart shows the stimated number of drivers (calculated as the sum of driver likelihoods) and passenger variants in each gene by cancer type .Overview of contributing organizations and local principal investigators .Overview of cohort and sample characteristics .Pan-cancer (n = 2399 independent patients) and cancer typespecific (n per tumor type category is provided in Fig 1) dNdScv results (see Supplementary Information Detailed Methods for statistical details) .Recurring amplifications (a) and deletions (b) and associated target genes .Overview of patients with multiple biopsies Reprints and permissions Download citation DOI: https://doi.org/10.1038/s41586-019-1689-y Anyone you share the following link with will be able to read this content: a shareable link is not currently available for this article Sign up for the Nature Briefing: Cancer newsletter — what matters in cancer research continues it series of innovation in terms of OOH and launches the first planning programmatic app SETI (Integrated Traffic Extended System) This instrument is the result of an ample study conducted along with D&D Research and that has set as a goal to identify the demographic profile of the people passing along the 43 transit locations in Bucharest “For the first time in Romania we have an instrument that can offer us a demographic profile, on hours, for OOH. We have installed cameras on all our screens and with the help of a perimeter measurement soft we quantify the registered traffic in the locations where we are present with TV screens,” said Dio Boaca, general director of Phoenix Media. The 30 screens owned by Phoenix Media have two or three cameras that are recording and counting in real time The SETI application counts only the people for whom there is a high probability to see the screened spot on the TV screen Only those heading towards the screen are counted Phoenix Media knows exactly which type of target they are addressing and what are the chances of the target audience to see the spots “This is the most complex traffic study in the history of Bucharest We took in account three different times of the day and we’ve interviewed both pedestrians and drivers the biggest traffic in Bucharest is recorded at the Tineretului intersection (3.312.411 general impression in Decembrie 2015); Piata Universitatii (The University Square) is present in the top busiest locations due to the fact that the people mainly use the pedestian passage and are not counted by the cameras; the Pipera area has a profile formed of : 70 percent men and the Piata Domenii (Domenii Market) and Mircea Voda are tranzited by people with medium through righ income,” explained Dan Petre The SETI application is user friendly and allows the clients to have real time access to the status of the campaign they are running. the company has offered the market many premieres and is the only one that offers personalized targeting Phoenix Media introduced in Romania the first outdoor promotional system on timeframes as well as real time transmission of messages from the mobile phone to the digital screens Romanita Oprea We use cookies for keeping our website reliable and secure providing social media features and to analyse how our website is used This website is using a security service to protect itself from online attacks The action you just performed triggered the security solution There are several actions that could trigger this block including submitting a certain word or phrase You can email the site owner to let them know you were blocked Please include what you were doing when this page came up and the Cloudflare Ray ID found at the bottom of this page One in five new buildings in Romania features premium aluminum joinery Residential projects contribute to the growth of the premium aluminum segment on the joinery market The product request for office projects remains the main engine of the aluminum joinery due to the larger funds that are allocated the residential segment saw a significant growth in 2016 The aluminum systems for windows and doors are leading in the ranking of requests from beneficiaries, who choose them due their superior quality and long shelf life. “The projects carried out by Reynaers in 2016 confirm the maturity of client behavior who are much more attentive to details and concerned with the quality of solutions they choose for their buildings,” Daniel Popa Over 40 percent of the heat loss inside a building are caused by poor isolation “Alongside this projects, this year we ended deliveries for Oregon Park for a surface of over 15,000 square meters, for the residential project Mircea Voda 39, located in the center of Bucharest and contributed to the setting up of the University of Agricultural Sciences and Veterinary Medicine of Cluj-Napoca,” Daniel Popa said. All Enel distribution companies in Romania have adopted the E-Distribuție name after Enel Distributie Banat and Enel Distribuție Dobrogea changed their names to E-Distribuție Banat and E-Distributie Dobrogea E-Distributie Muntenia was the first of the Enel distribution operators in Romania to adopt its new name The name changes of the distribution operators are in line with EU and national regulations which require integrated energy companies to ensure the legal functional and accounting separation of the distribution operations from the supply and production activities in order to prevent confusion on the consumers’ side and therefore to stimulate market competition providing social media features and to analyse how our website is used.