Abstract 3209: The cBioPortal for Cancer Genomics
Bibliographic record
Abstract
Abstract The cBioPortal for Cancer Genomics is an open-source software platform that enables interactive, exploratory analysis of large-scale cancer genomics data sets with a biologist-friendly interface. It integrates genomic and clinical data, and provides a suite of visualization and analysis options, including OncoPrint, mutation diagram, variant interpretation, survival analysis, expression correlation analysis, alteration enrichment analysis, cohort and patient-level visualization, among others. The public site (https://www.cbioportal.org) hosts data from more than 280 studies from diverse sources including individual labs and large consortia. All data is also available in the cBioPortal Datahub (https://github.com/cBioPortal/datahub/). Data from 40 studies, totaling more than 10,000 samples, was added in 2019, including the latest release from the Cancer Cell Line Encyclopedia and the Pediatric Preclinical Testing Consortium. The site is accessed by over 30,000 unique visitors per month. cBioPortal also supports AACR Project GENIE with a dedicated instance hosting the GENIE cohort of 80,000 clinically sequenced samples from 19 institutions worldwide (http://genie.cbioportal.org). In addition, more than 40 instances are installed locally at academic institutions and pharmaceutical/biotechnology companies. In support of these local installations, cBioPortal now has improved documentation and simplified installation via container technologies such as Docker and Kubernetes. Building on our successful refactoring of the code base, we have released a variety of new features and enhancements to cBioPortal over the past year. Most notably, we released a group comparison feature, enabling users to define groups of interest based on any clinical or genomic features. User-defined groups can be compared simultaneously across genomic and clinical data, including survival analysis and genomic alteration enrichment analysis. Additional new features include: integration of mutation annotations from dbSNP, ClinVar and gnomAD; support for waterfall plots to enable treatment response analysis; saving user preferences for chart layout on study view. The cBioPortal remains under active development. The portal is fully open source (https://github.com/cBioPortal/) under a GNU Affero GPL license. Development is a collaborative effort among groups at Memorial Sloan Kettering Cancer Center, Dana-Farber Cancer Institute, Children's Hospital of Philadelphia, Princess Margaret Cancer Centre, and The Hyve. Ongoing and future development is focused on: (1) building the open source community; (2) continued performance improvements; (3) expanding user support, documentation and training resources; (4) supporting longitudinal data analysis and visualization; (5) developing novel features to support immunogenomics and immunotherapy; (6) enhancing individual variants and overall patient interpretation; (7) supporting single cell data visualizations and analysis. Citation Format: Jianjiong Gao, Tali Mazor, Adam Abeshouse, Ino de Bruijn, Benjamin Gross, Karthik Kalletla, Priti Kumari, Ritika Kundra, Xiang Li, James Lindsay, Aaron Lisman, Pieter Lukasse, Ramyasree Madupuri, Angelica Ochoa, Oleguer Plantalech, Sander Rodenburg, Fedde Schaeffer, Robert Sheridan, Lucas Sikina, Jing Su, S. Onur Sumer, Yichao Sun, Paul van Dijk, Sjoerd van Hagen, Pim van Nierop, Avery Wang, Manda Wilson, Hongxin Zhang, Gaofei Zhao, Kelsey Zhu, Kees van Bochove, Ugur Dogrusoz, Allison Heath, Adam Resnick, Trevor J. Pugh, Chris Sander, Ethan Cerami, Nikolaus Schultz. The cBioPortal for Cancer Genomics [abstract]. In: Proceedings of the Annual Meeting of the American Association for Cancer Research 2020; 2020 Apr 27-28 and Jun 22-24. Philadelphia (PA): AACR; Cancer Res 2020;80(16 Suppl):Abstract nr 3209.
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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 | 0.003 | 0.009 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.009 | 0.009 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.005 | 0.003 |
| Open science | 0.005 | 0.007 |
| Research integrity | 0.002 | 0.004 |
| Insufficient payload (model declined to judge) | 0.062 | 0.076 |
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".