Abstract LB-212: Treehouse Childhood Cancer Project: a resource for sharing and multiple cohort analysis of pediatric cancer genomics data
Bibliographic record
Abstract
Abstract Deep sequencing of adult and pediatric tumors revealed that different cancers share common genetic mutations. Aside from sequence mutation, gene expression, copy number, and epigenetic mechanisms contribute to tumorigenesis, and integrating this information may reveal more aberrant signaling pathways than analysis of mutations alone. Significantly, agents targeting specific pathways may be effective against multiple malignancies, regardless of the mechanisms of pathway deregulation. These observations suggest that pediatric cancer patients may benefit from targeted therapies developed for adults. Since the development of pediatric-cancer-specific therapies is hindered by the limited involvement of pharmaceutical companies and small patient cohorts, repositioning drugs designed for adult tumors remains the fastest and most effective way to bring new treatment options to pediatric cancer patients While pediatric tumors have been characterized by genome-wide technologies, the data from these studies are typically under-utilized beyond the initial single cohort, single data type analyses. Consequently, we still lack a comprehensive picture of the molecular pathways that contribute to pediatric cancer in each patient, especially those that can be targeted in the clinic. Integrating multiple datasets is essential for assembling large enough patient cohorts to achieve an understanding of cancer-driving molecular aberrations in individual patients. The Treehouse Childhood Cancer Project consolidates gene expression, mutation and copy number datasets under the UCSC Cancer Genomics Browser (https://genome-cancer.ucsc.edu), and currently contains data from over 1000 pediatric tumors from TARGET and other studies. Treehouse enables mining these data alongside the data from adult cancers studied by The Cancer Genome Atlas consortium (TCGA). This is accomplished using bioinformatics tools developed for the TCGA Pan-Cancer Analysis Working Group and aimed at identifying situations where a subset of pediatric tumors may be driven by similar molecular pathways as adult tumors. We have assembled a consortium of researchers who plan to both contribute data to the Treehouse platform and apply Treehouse data in their analyses. These include John Maris (Children's Hospital of Philadelphia), Michael Taylor (Hospital for Sick Children, Toronto), Poul Sorensen (University of British Columbia), Timothy Triche (Children's Hospital Los Angeles), Soheil Meshinchi (Fred Hutchinson Cancer Research Center), Doug Hawkins (Seattle Children's Hospital), Javed Khan (NIH Center for Cancer Research), Ching Lao (Texas Children's Hospital), Leonard Sender (UC Irvine, Children's Hospital of Orange County), Alejandro Sweet-Cordero (Stanford School of Medicine), and D.W. Parsons (Baylor College of Medicine). In this submission, we demonstrate the utility of the Treehouse resource by analyzing the neuroblastoma TARGET cohort in the context of adult TCGA cancers. This work presents a proof of concept that cross-cancer multiple cohort analysis can lead to new insights into pediatric malignancies. Citation Format: Olena Morozova, Yulia Newton, Melissa Cline, Jingchun Zhu, Katrina Learned, Josh Stuart, Sofie Salama, Robert Arceci, David Haussler. Treehouse Childhood Cancer Project: a resource for sharing and multiple cohort analysis of pediatric cancer genomics data. [abstract]. In: Proceedings of the 106th Annual Meeting of the American Association for Cancer Research; 2015 Apr 18-22; Philadelphia, PA. Philadelphia (PA): AACR; Cancer Res 2015;75(15 Suppl):Abstract nr LB-212. doi:10.1158/1538-7445.AM2015-LB-212
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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 | 0.013 | 0.041 |
| Meta-epidemiology (narrow) | 0.002 | 0.002 |
| Meta-epidemiology (broad) | 0.003 | 0.002 |
| Bibliometrics | 0.007 | 0.009 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.005 | 0.002 |
| Open science | 0.006 | 0.009 |
| Research integrity | 0.002 | 0.004 |
| Insufficient payload (model declined to judge) | 0.061 | 0.036 |
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".