Abstract 4335: The genomic landscape of childhood pre-B acute lymphoblastic leukemia
Bibliographic record
Abstract
Abstract Precursor B-cell acute lymphoblastic leukemia (pre-B ALL) is the most frequent pediatric cancer. Increased understanding of the pathobiology of B-cell ALL has led to risk-targeted treatment regimens and increased survival rates. However, the underlying causes of this pediatric cancer are still unclear. We are using next-generation sequencing technology to better understand the genomic landscape of pre-B ALL and to build a catalogue of variations involved in pediatric ALL onset and/or progression. Using a unique “quartet” design that involves matched tumor (at diagnosis) and normal (remission) samples, as well as DNA from both parents, we will be able to identify somatic mutations driving the leukemic process. Here, we report the deep-sequencing of the whole exomes, and the partial miRNomes, of over 60 childhood B-cell ALL quartets. Using ABI SOLiD technology, we captured over 4.0 Gb of sequence on average per sample with a mean coverage of 40X. Genome-wide genotyping (Illumina's Omni 2.5 array) was also for quality control and structural variant identification. For each individual, approximately 97% of the targeted region was covered α1X and 80% of the targeted bases passed our thresholds for variant calling (≥ 5X coverage, MQV ≤ 20). According to these criteria, about 25,000 SNPs were found per individual. Using this quartet design we were able to incorporate parental sequence information to reduce sequencing errors and facilitate the identification of true variants within a given family, and of leukemia-specific variants within the ALL cohort. We investigated the somatic mutation profiles of the ALL genomes and identified both recurrent and private leukemia-specific mutations, and highlighted genes/pathways with an increased burden of somatic loss of function variants. Our goal now is to validate potential driver mutations that could play a direct role in leukemogenesis through functional assessment. Ultimately, this work will provide invaluable insights to understand the genetic mechanisms underlying pediatric ALL which could lead to the development of powerful clinical tools to improve detection, diagnosis and treatment of this childhood cancer. Citation Format: {Authors}. {Abstract title} [abstract]. In: Proceedings of the 103rd Annual Meeting of the American Association for Cancer Research; 2012 Mar 31-Apr 4; Chicago, IL. Philadelphia (PA): AACR; Cancer Res 2012;72(8 Suppl):Abstract nr 4335. doi:1538-7445.AM2012-4335
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
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".