Identification of novel regions of genomic copy number alteration in mantle cell lymphoma by array comparative genomic hybridization to a 97,299 element whole genome DNA array
Bibliographic record
Abstract
1698 Introduction: Mantle cell lymphoma (MCL) is an aggressive non-Hodgkin’s lymphoma that is incurable with current chemotherapeutic approaches and has a median survival of approximately 3 years. MCL is uniquely characterized by a t(11;14) translocation involving the IGH and BCL1 loci, resulting in over expression of Cyclin D1 and deregulated cell cycle control. However this translocation alone can not initiate lymphomagenesis and multiple secondary genomic alterations have been reported. Hypotheses: MCL pathogenesis occurs via multiple genomic alterations and the critical alterations will be present in multiple patients and detectable in MCL cell lines. Objective: Alignment of high resolution genomic profiles from multiple MCL cell lines will reveal minimal regions of alteration important in the pathogenesis of MCL. Experimental Approach: Comparative genomic hybridization (CGH) is a technique used to determine regional DNA copy number changes across an entire genome. However, conventional CGH’s resolution is limited to 10-20 Mb. A recent advancement in CGH is to improve resolution by changing the target metaphase chromosomal DNA to discrete segments of DNA spotted as a microarray. This results in resolution that is dependent on the size and spacing of the selected segments as opposed to identifying chromosomal bands on metaphase chromosomes. Currently, the whole genome has been arrayed as 32,433 bacterial artificial chromosome (BAC) derived amplified fragment pools spotted in triplicate (97,299 elements) resulting in an effective resolution of ∼0.08 Mb with complete coverage of the sequenced human genome. Results: This whole genome array was used to generate high resolution segmental copy number profiles of seven MCL cell lines (Granta-519, HBL-2, NCEB-1, Rec-1, SP49, UPN-1, and Z138C). Alignment of these profiles with the human genome map has resulted in the identification of multiple common regions of genomic alteration. These encompass both novel and previously reported regions. Conclusions: The generation of these high resolution segmental copy number profiles allows the identification of candidate genes involved in the pathogenesis of MCL as well as lead to an increased understanding of MCL. Acknowledgements: This work was supported by funds from Genome Canada/British Columbia. We would like to thank Chad Malloff and Spencer Watson for array CGH assistance and Susanne Grohmann for fluorescent in-situ hybridization.
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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 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".