Abstract 3005: International Cancer Genome Consortium
Bibliographic record
Abstract
Abstract The International Cancer Genome Consortium (ICGC) was established to bring together researchers from around the globe to comprehensively analyze the genomic, transcriptomic, and epigenomic changes in 50 different tumor types or subtypes that are of clinical and societal importance across the globe (International network of cancer genome projects. Nature 464, 993-998 (15 April 2010)). As of November 2017, the ICGC has received commitments from researchers and funding organizations in Asia, Australia, Europe, North America and South America for 90 project teams in 17 jurisdictions to study more than 25,000 tumor genomes. Processed data is available via the Data Coordination Centre (https://dcc.icgc.org/) based at the Ontario Institute for Cancer Research and is updated semi-annually. The November 2017 release (Version 26) in total comprises data from more than 17,000 cancer donors spanning 76 projects and 21 tumor sites. The Pan-Cancer Analysis of Whole Genomes (PCAWG) project of the ICGC and The Cancer Genome Atlas (TCGA) is coordinating analysis of more than 2,800 cancer genomes, with the extensive use of cloud computing. Because of the very large size of the pan-cancer dataset, PCAWG used distributed compute cloud environment spread across North America, Europe and Asia that meets the project's technical requirements and the bioethical framework of ICGC and its member projects. Each genome was characterized through a suite of standardized algorithms, including alignment to the reference genome, uniform quality assessment, and the calling of multiple classes of somatic mutations. Scientists participating in the research projects of PCAWG are now addressing a series of fundamental questions about cancer biology and evolution based on these data, and have gained new insights into the role of non-coding DNA in cancer. The first phase of ICGC, which is slated for completion in 2018, has focused on developing extensive catalogs of tumor genomic information. The proposed next phase of the consortium, ICGC-ARGO, will link genomic to extensive clinical information from clinical trials and community cohorts concerning lifestyle, environmental exposure, family history of disease, treatment and outcome data for a broad spectrum of cancers, including preneoplastic lesions. The goal will be to accelerate the translation of genomic information into the clinic to guide interventions including diagnosis, treatment, early detection and prevention. The ICGC develops policies and quality control criteria to help harmonize the work of member projects located in different jurisdictions. Data produced by ICGC projects are made rapidly and freely available to qualified researchers around the world via the data cloud and through the ICGC Data Coordination Center at (http://dcc.icgc.org). More information can be found on www.icgc.org. Citation Format: Andrew Biankin, Jennifer L. Jennings, Lincoln D. Stein. International Cancer Genome Consortium [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2018; 2018 Apr 14-18; Chicago, IL. Philadelphia (PA): AACR; Cancer Res 2018;78(13 Suppl):Abstract nr 3005.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.015 | 0.035 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.010 | 0.027 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.007 | 0.002 |
| Open science | 0.005 | 0.005 |
| Research integrity | 0.002 | 0.004 |
| Insufficient payload (model declined to judge) | 0.105 | 0.049 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".