dataesr/scientific-paragraphs-categorization huggingface dataset:
We describe some properties of those mutations here. The median tumor depth reported by MC3 from these variants is 12 reads (+/-3 median absolute difference). The median tumor depth reported by PCAWG in this region is 39 reads (+/-35. 6 median absolute difference), suggesting wide variance of tumor read depths that were removed. However, the mode tumor depth of the PCAWG variants was 13, justifying this removal of variants with low read support. Finally, we determined how many of these poorly-covered variants originated from cancer driver genes. We observed 126 mutations from the MC3 file, and 156 cancer mutations were eliminated at this stage in the comparison. Overlapping mutations. After reducing the variants to be within exome sequencing target region, within same exon definitions, and having enough sequencing depth, the remaining variants from ICGC and PCAWG were stored in a SQLite database to enable fast lookup.
Filter flags include: "common_in_exac", "gapfiller", "native_wga_mix", "nonpreferredpair", "oxog", "StrandBias", and "wga". We pre-defined the exclusion of variants in MC3 flagged as OxoG along with the inclusion of all PASS variants. The comprehensive filter analysis resulted in two major clusters of variant recoverability (Supplementary Fig. 3 ). Here, we observed the computational trade-off of identifying more matched variants at the cost of more unique MC3 calls. Below, we highlight five strategies considered for analysis (Supplementary Data 2). 1. Only consider variants labeled PASS by the MC3 filter column. 2. Only remove variants labeled OxoG by MC3. 3. Prioritize G1 (samples in the most recoverable quadrant, MC3 and PCAWG samples with greater than or equal to 80% from both efforts.
) 4. Prioritize total number of matched variants. 5. Maximize total number of samples in the most recoverable quadrant (Fig. 2b ) while maximizing the difference between unique MC3 variants and matched variants thus generating fewer unique calls. After considering complexity, we chose to move forward with strategy 2 for the entirety of this study due to its simplicity and relative similarity to other filtering schemes. We recognize that by selecting a single filtering strategy, we are limiting the data slightly and likely introducing some false positive variant calls. However, this strategy allowed us to maintain larger sample sizes and to capture ~15,000 more matched variants than the PASS only strategy at the cost of ~3500 unique mutations calls for MC3. Assessing mutations per megabase and cancer type concordance.
Mutations per megabase data were collected from the broader TCGA dataset and reduced following the same methods outlined previously  28. Briefly, this systematically removed hypermutators from the dataset. This resulted in a set of 625 samples from the MC3/PCAWG dataset studied here and 8852 TCGA samples. Both Pearson and Mann-Whitney correlations statistics were performed to assess the association of non-silent mutations per megabase and concordance statistics. Simulation of sequencing noise and recoverability. Fan et al. benchmarked the sensitivity of MuSE at recovering somatic variants across 24 combinations of VAF and read depth  3. When simulating the recovery of PCAWG variants in MC3 we assumed that the VAF observed in PCAWG was the true VAF. We matched the observed VAF of each variant to the closest VAF reported in Fan et al.
Both consortia considered clonality in their comprehensive characterization of the somatic mutations. Locations of these files are provided in the data availability section. Here, we provide a brief summary of the strategies used to compile these resources. First, the PanCancer Atlas working-groups used MC3 mutations to predict subclonal structures using ABSOLUTE  38. This tool uses copy number, recurrent karyotype, and mutation data to calculate copy number purity and cluster identification. Furthermore, the PanCancer Atlas working group only made the distinction of clonal and subclonal mutations and did not attempt to further assign sub-clonal mutations to other likely heterogeneous clusters. PCAWG, on the other hand, used a consensus calling approach incorporating 11 different clustering tools.
Here, we evaluated cluster-ID which represents those mutations that are clonal (ID = 1), with other clusters representing mutations that are subclonal (ID = 2 through 4). For this analysis, we restricted our data to SNVs to be consistent with calls made by the PanCancer Atlas calls of MC3 mutations. Fraction of private variation explained. In Supplementary Fig. 7  we provide a breakdown of different sources of variant described in our analysis using publicly available data. For MC3 all private variants were classified as into 3 variant types (Indel, MissensePlus, and Other). Specifically, indels are comprised of: "Frame_-Shift_Del", "In_Frame_Ins", "Frame_Shift_Ins", and "In_Frame_Del". MissensePlus variants are comprised of: "Missense_Mutation", "Nonsense_Mutation", "Non-stop_Mutation", "Splice_Site".
Users can sort the table by each column. There are two drop-down selection boxes where users can view mutations from a specific TCGA barcode or Hugo symbol. There is also a search bar, which results in mutations that contain the input in any columns. Controlled access files. Having worked with both the TCGA and ICGC consortia, we were privy to the controlled access data (all MC3 somatic variant calls and PCAWG germline calls). These data sets allowed for the further interrogation of unique variants called by both groups. We performed a mutation variant intersection of MC3 controlled access mutations with unique PCAWG variants in the captured exonic regions. These data can be downloaded using necessary credentials from https://gdc. cancer. gov/ about-data/publications/mc3-2017. We intersected the MC3 public MAF with the PCAWG germline call-set, donor-by-donor.
Six MC3 somatic variants were identified as germline variants in PCAWG for the same donor. Of these, five were flagged in MC3 as OxoG or nonpreferred-pair artifacts, and only one was marked PASS in MC3. This PASS variant had a depth in the matched MC3 normal of well over 100 with no alternate reads. The minimal intersection between the MC3 somatic call-set and the donormatched PCAWG germline call-set is evidence that germline contamination in MC3 calls is low. Assessment of impact on mutation signature analysis. We ran Signature-Analyzer 40 on the corpus of WES and WGS samples from our TCGA cases. This tool reports a vector of J = 7 normalized weights corresponding to mutational signatures. Once weights were computed, we used COSMIC signatures as a reference in order to synchronize labels of the fractional weights between WES and WGS data for each case to enable proper comparison.
Acknowledgements We acknowledge the contributions of the many clinical networks across ICGC and TCGA who provided samples and data to the  PCAWG Consortium  and the contributions of the Technical Working Group and the  Germline Working Group of the PCAWG Consortium  for collation, realignment, and harmonization of the variant calls of the cancer genomes used by this study. We thank the patients and their families for their participation in the individual ICGC and TCGA projects.
Data availability Somatic and germline variant calls, mutational signatures, subclonal reconstructions, transcript abundance, splice calls and other core data generated by the ICGC/TCGA Pancancer Analysis of Whole Genomes Consortium are described in another publication  10  and available for download at https://dcc. icgc. org/releases/PCAWG. Additional information on accessing the data, including raw read files, can be found at https://docs. icgc. org/pcawg/data/. In accordance with the data access policies of the ICGC and TCGA projects, most molecular, clinical and specimen data are in an open tier which does not require access approval. To access potentially identification information, such as germline alleles and underlying sequencing data, researchers will need to apply to the TCGA Data Access Committee (DAC) via dbGaP (https://dbgap.
ncbi. nlm. nih. gov/aa/ wga. cgi?page=login) for access to the TCGA portion of the dataset, and to the ICGC Data Access Compliance Office (DACO; http://icgc. org/daco) for the ICGC portion. In addition, to access somatic single nucleotide variants derived from TCGA donors, researchers will also need to obtain dbGaP authorization. Additional links to data resources used for this project can be found using the following urls: MC3 public MAF file (https://api. gdc. cancer. gov/data/1c8cfe5f-e52d-41ba-94da-f15ea1337efc), PCAWG Public MAF file (https://www. synapse. org/#!Synapse:syn7364923), bed files used for exome restrictions (MC3) (https://api. gdc. cancer. gov/data/7f0d3ab9-8bef-4e3b-928a-6090caae885b), bed files used for exome restrictions (THE BROAD) (https://api. gdc. cancer. gov/data/b1e303a5-a542-4389-8ddb-1d151218be75), wiggle files MC3 (https:// www.
synapse. org/#!Synapse:syn21785741), wiggle files PCAWG (https://www. synapse. org/#!Synapse:syn8492850), clonality files MC3 (https://www. synapse. org/#!Synapse: syn7870168), clonality files PCAWG (https://www. synapse. org/#!Synapse:syn8532460), cancer subtypes and histological data (https://www. synapse. org/#!Synapse:syn4983466), MAFit online comparison tool (https://mbailey. shinyapps. io/MAFit/), GitHub Repo (https://github. com/ding-lab/mc3_icgc_variant_pipeline), side-by-side MC3-PCAWG comparison (MAF-like) (https://www. synapse. org/#!Synapse:syn21041380). The remaining data is available within the Article, Supplementary Information or available from the authors upon reasonable request.
Code availability A public GitHub repository (under an MIT opensource license) at https://github. com/ ding-lab/mc3_icgc_variant_pipeline furnishes work-flows, scripts, figures, and computational tools used to assess mutation concordance between maf files. For this project we refrained from accessing alignment files, i. e. , BAM files or fastq files. Furthermore, due to the memory footprint of mutation and coverage files we have not included them in the repository. Thus, by removing core data files from the repository the software provided supplies the user with processes and decisions, not a fully automated tool for deployment on additional datasets. In addition to our analysis, the core computational pipelines used by the PCAWG Consortium for alignment, quality control and variant calling are available to the public at https://dockstore.
Radiopharmaceutical dosimetry depends on the localization in space and time of radioactive sources and requires the estimation of the amount of energy emitted by the sources deposited within targets. In particular, when computing resources are not accessible, this task can be performed using precomputed tables of specific absorbed fractions (SAFs) or S values based on dosimetric models. The aim of the OpenDose collaboration is to generate and make freely available a range of dosimetric data and tools. Methods: OpenDose brings together resources and expertise from 18 international teams to produce and compare traceable dosimetric data using 6 of the most popular Monte Carlo codes in radiation transport (EGSnrc/EGS11, FLUKA, GATE, Geant4, MCNP/MCNPX, and PENELOPE). SAFs are uploaded, together with their associated statistical uncertainties, in a relational database.
S values are then calculated from monoenergetic SAFs on the basis of the radioisotope decay data presented in International Commission on Radiological Protection Publication 107. Results: The OpenDose collaboration produced SAFs for all source region and target combinations of the 2 International Commission on Radiological Protection Publication 110 adult reference models. SAFs computed from the different Monte Carlo codes were in good agreement at all energies, with SDs below individual statistical uncertainties. Calculated S values were in good agreement with OLINDA/EXM 2. 0 (commercial) and IDAC-Dose 2. 1 (free) software. A dedicated website (www. opendose. org) has been developed to provide easy and open access to all data. Conclusion: The OpenDose website allows the display and downloading of SAFs and the corresponding S values for 1,252 radionuclides.
The OpenDose collaboration, open to new research teams, will extend data production to other dosimetric models and implement new free features, such as online dosimetric tools and patient-specific absorbed dose calculation software, together with educational resources. Thenecessi ty of dosimetry in nuclear medicine is increasing with the remarkable development of targeted radionuclide therapy  (1). Absorbed doses in nuclear medicine are usually calculated using the MIRD formalism (2), with the following time-independent formulation: Historically, SAFs and S values were computed from mathematic models and were the basis for International Commission on Radiological Protection (ICRP) reports for radiopharmaceutical dosimetry  (4). Since the publication of ICRP Publication 103 (5), the ICRP recommends using voxel-based models, such as those presented in ICRP Report 110 (ICRP 110)  (6).
This delay might be amplified in coming years as the number of models and their level of complexity increase  (10). The OpenDose project is based on the idea that a collaborative approach enables this challenge to be addressed by offering a responsive network of shared dosimetric resources and expertise from several teams involved in radiopharmaceutical dosimetry. This approach allows the production and comparison of data with different codes, increasing the robustness of the results. The OpenDose collaboration strives to produce and disseminate dosimetric data that are traceable and reproducible and include associated uncertainties  (11).
Collaboration The first meeting of the collaboration was held at the European Association of Nuclear Medicine Congress in Barcelona, Spain, in 2016. At this meeting, the structure of the OpenDose collaboration and its scientific priorities were defined and a collaboration agreement in which the teams committed to generate freely accessible dosimetric data that meet the FAIR Guiding Principles was drafted  (12). OpenDose currently includes 18 research teams at 30 institutes and has a steering committee composed of 1 representative per team. The collaboration organizes annual meetings to present results and discuss future actions.
Framework A common framework (Fig. 1 ) has been defined to harmonize the different Monte Carlo codes and different units and formats used by the teams. Within this framework, each team is responsible for its own Monte Carlo simulations but uses the same input models and delivers the data in a common output format. Therefore, each collaboration member can independently contribute to data generation. The data produced are then collected and centralized in a database along with the models and radioisotope decay data. A dedicated website allows access to the database, displays SAFs, allows the calculation of S values, and allows downloading of the results. Here we describe the details of the OpenDose framework.
S Value Calculation A Python script was developed to calculate S values from SAFs, for any radioisotope, using Equation  2. First, SAFs are extracted from the database and averaged over all Monte Carlo codes, increasing the robustness of the calculation. Then, SAFs are linearly interpolated to the energy of each radiation type of the radioisotope considered and multiplied by the corresponding yield (Bq -1 Ás 21 ) and energy (J). For b-emission spectra, SAFs are integrated over energy bins. a-particles and a-recoil nuclei are considered to deposit their energy locally. Finally, all emission contributions are summed to obtain the S value for a selected model, source, target, and radioisotope. The S value is also calculated per particle type (photon, electron, alphaparticle) to provide more insight into the S value composition.
At each step of the calculation, the uncertainty of the S value is computed as u À S ðTarget)SourceÞ Á 2 5 + i À E i Y i uðF iðTarget)Source;EiÞ ÁÁ 2 ; Eq. 3 where E i and Y i are the energy (J) and yield (Bq -1 Ás 21 ), respectively, of radiation type i (for a given isotope) and uðF iðTarget)Source;EiÞ Þ is the SAF statistical uncertainty (kg 21 ) for radiation type i. The computation is performed on demand to use the most up-to-date data (SAFs and nuclear data). No S values are stored in the database.
Verification of Produced Data To assess the variability of results among the different Monte Carlo codes, comparisons were performed using 1 model for a restricted number of sources and energies. SAFs were computed by all teams for the ICRP 110 adult female model using the Liver and Blood Vessels Trunk as sources and monoenergetic photons and electrons of 0. 05, 0. 1, 0. 2, 0. 5, 1, 2, and 5 MeV. The Liver was chosen for its relevance in radiopharmaceutical dosimetry and its compact shape, whereas the Blood Vessels Trunk was chosen to test a more spatially complex source. The SAFs for the source Liver were compared with the data published in ICRP 133  (8). For the calculation of S values from the SAFs (Eq. 2), the database must contain at least 1 value for each model, source, target, and energy combination.
To meet this requirement, simulations were run with a reduced number of 10 7 particles with Geant4 10. 5 to reach a complete dataset. S values for a source located in the Liver of the female model, 6 targets, and 3 radioisotopes commonly used in nuclear medicine ( 131 I, 177 Lu, and 90 Y) were compared with IDAC-Dose 2. 1 (9) and OLINDA/EXM 2. 0  (21).
Current Production Status Producing a complete dataset for 1 model (e. g. , the ICRP 110 adult female) requires approximately 30,000 simulations, representing approximately 750,000 CPU hours of computation. By sharing the workload and the computing resources of the teams (https://www. ziemowit. hpc. polsl. pl/en, https://cc. in2p3. fr/en, https://www. calmip. univ-toulouse. fr, and https://nci. org. au), the collaboration can produce data for a model in a few months. Some developments to take advantage of large grid infrastructures have also been made  (22). As of October 2019, the database held 11,177,347 entries, representing data spread over 2 models, 141 sources, 172 targets, 2 particle types (photons and electrons), and 91 energies produced with 9 different Monte Carlo code versions.
86% at 5 keV to 2. 0% at 5 MeV. SAFs from this preliminary assessment were also compared with data published in ICRP 133  (8). The overall agreement between OpenDose and ICRP 133 values was good, with mean relative differences of 1. 65% for photons and 8. 2% for electrons of greater than 200 keV. However, some large differences were noted for low-energy electrons, with a maximum relative difference of 1120% for Thyroid ) Liver at 100 keV. This large difference likely was due to high statistical fluctuations in the data. Unfortunately, no information on the SAF uncertainties is given in ICRP 133. ICRP 133 values for Brain ) Liver at energies of less than 200 keV are equal to 0. 0, which may indicate a lower statistical power of the ICRP 133 simulations than of the OpenDose data. These mean relative differences did not include Liver ) Liver SAF values, for which a systematic difference of 22% was found.
This finding is explained by the modification of some target masses in ICRP 133 (Tables A. 1 and A. 2 from (8)) to add blood content; the original 1. 400 kg for the Liver in the ICRP 110 adult female model was changed to 1. 810 kg. SAF values from a selection of different Monte Carlo codes are available in Supplemental Tables 1-8 (supplemental materials are available at http://jnm. snmjournals. org), along with SAFs from ICRP 133 for comparison. The ''OpenDose mean'' gives the mean and SD of all available Monte Carlo code values. S values are available for the 2 models, 141 sources, 172 targets, and 1,252 radioisotopes considered so far within the OpenDose collaboration. Table  1  shows S values obtained for the adult female model and the comparison with IDAC-Dose 2. 1 (9) and OLINDA/ EXM 2.
Website The OpenDose website is online at www. opendose. org. It provides information on the project, allows access to the SAF database, and allows calculation of the S values for 1,252 radionuclides. Results are displayed with interactive plots and can be downloaded in a comma-separated values format. Figure  2  shows the SAF section of the website, with results from all of the Monte Carlo codes. The available data should be used for research purposes only, and the website content is licensed under a Creative Commons Attribution-ShareAlike 4. 0 International license.
DISCUSSION The generation of dosimetric data represents a computing challenge because of the resources and time required to perform simulations. The idea of a collaborative framework designed for this purpose required a preliminary verification of the precision of the dosimetric estimations of the different Monte Carlo codes used within the community. The comparison exercise proposed to members of the collaboration demonstrated that SAFs produced with different Monte Carlo codes exhibit low dispersion and are in good agreement with published ICRP 133 data. The uncertainties of the SAFs or the S values estimated by OpenDose are a good indicator of the limitations of the values themselves. For example, data for electrons of low energy show statistical limitations for targets far from the source.
This information is not present in any other published SAF data. Calculated S values also show good agreement with IDAC-Dose 2. 1, which uses the same model and radioisotope decay scheme. Open-Dose and IDAC-Dose 2. 1 S values differ only for some sources because ICRP integrated organ mass adjustments. OpenDose provides data corresponding to the models used in the Monte Carlo simulations, without any mass adjustments, to avoid any calculation approximation or radiation transport simplifications and to maintain a strict framework for data traceability and reproducibility. The decay data presented in ICRP Publication 107 do not include corresponding standard uncertainties, so the reported standard uncertainty of the S value (Eq. 3) is purely statistical. For many common radionuclides used in nuclear medicine, the uncertainties in the decay data can be the dominant source of the uncertainty of the S value and should be included in any uncertainty estimation  (24).
In the future, nuclear data with reported standard uncertainties will be used in OpenDose  (25). During the last meeting (October 2019), the steering committee decided to include, as part of the OpenDose collaboration products, 2 open-source software products currently under development. The first implements model-based dosimetry and will be integrated in a future section of the website, whereas the second implements patient-specific dosimetry in a 3DSlicer (https://www. slicer. org) extension and will be available for downloading. The future vision of OpenDose is to make the website a global resource for internal dosimetry, including dosimetric data, software, and education.
CONCLUSION The aim of the OpenDose project is to establish a long-term international collaboration to generate an open-access database for nuclear medicine dosimetry. The collaboration includes 18 teams with expertise in radiopharmaceutical dosimetry, Monte Carlo methods, and high-performance computing. The initial task set for all partners to produce a set of simulations for a specific model allowed the definition of a common computational framework and the establishment of a unified format for results. OpenDose has now produced SAFs and uncertainties for the 2 ICRP 110 adult models, covering all source and target combinations, 2 particle types (photons and electrons), and 91 energies. Data production will continue and extend to other computational models. The dedicated website is online at www.