Abstract A2-14: Integrated genetic and epigenetic analysis of model and patient acute myeloid leukemias
Bibliographic record
Abstract
Abstract Next generation DNA sequencing has provided significant insights into the genetic determinants of acute myeloid leukemia (AML). Large scale sequencing studies of AML patient cohorts have revealed a remarkable level of genetic heterogeneity between patients who nevertheless have the same disease phenotype. As a solution to the problem of extensive genetic diversity between patients, we have modified a previously published model system in order to generate multiple human leukemias from CD34+ cord blood cells from a single healthy donor. A human MLL-AF9 (MA9) fusion gene is retrovirally introduced into the donor cells which are then cultured for 30 days before being transplanted into immunocompromised (NSG) mice that subsequently develop either AML or acute B-cell lymphoblastic leukemia (B-ALL) after ~24 weeks. We have now generated 22 leukemias from 4 single donors and have performed RNA-seq on the samples during their step-wise leukemic transformation (i.e. CD34+ cells, CD34+ cells with MA9, and the resulting leukemias). We have compared these data to RNA-seq data we have generated for several pediatric AML patients with MA9 translocations, as well as normal blood cells and other tissues. This analysis has revealed 39 candidate genes with an expression pattern highly specific for MA9 AMLs. Interestingly, we can find no evidence for secondary mutations acquired by the human leukemias in our model system, suggesting the human MA9 translocation is sufficient to generate leukemias in this context. To understand the epigenetic impact of the MA9 fusion gene, we have examined the DNA methylation changes at each stage in our model system using a capture survey approach (e.g. Human Methyl-Seq; Agilent) and used this same approach for several primary patient samples with MLL translocations. These data have been correlated with gene expression changes within the model system and have revealed a number of specific changes with relevance for the process of transformation. Lastly, functional assessment of specific candidate genes through shRNA knock-down experiments has shown that at least some of these candidate genes, which are known oncogenes in other tumor types, are essential for MA9 AML. In summary, the combination of RNA-seq data from patient and single donor model AMLs has highlighted consistent genetic changes associated with this AML sub-group, and has also revealed novel potential therapeutic targets. Citation Format: Frederic Barabe, Magalie Celton, Audrey Forest, Anne Bergeron, Radia Johnson, Laurine Gil, Angélique Bellemare-Pelletier, Sonia Cellot, Josee Hebert, Etienne Gagnon, Brian T. Wilhelm. Integrated genetic and epigenetic analysis of model and patient acute myeloid leukemias. [abstract]. In: Proceedings of the AACR Special Conference on Translation of the Cancer Genome; Feb 7-9, 2015; San Francisco, CA. Philadelphia (PA): AACR; Cancer Res 2015;75(22 Suppl 1):Abstract nr A2-14.
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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 | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| 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".