Genome-wide identification of novel regions of DNA copy number alterations in natural killer cell lymphoma by array comparative genomic hybridization
Bibliographic record
Abstract
Introduction: Natural killer (NK) cell lymphoma encompasses a related spectrum of diseases and is among the most aggressive of all lymphoid malignancies. The nasal type is characterized by necrotic lesions in the nasal cavity, nasopharynx, or palate, whereas patients suffering from the aggressive type might die within a short time from disseminated disease or multi-organ failure. It is a relatively rare disease and is generally more common in Asians than in other races. Since it has not been well characterized until recently, the identification of genetic alterations would provide important insights into the genomic mechanism of lymphomagenesis. Hypotheses: NK cell lymphoma pathogenesis occurs via multiple genomic alterations and the critical alterations will be present in multiple NK cell lines. Objective: Alignment of high resolution genomic profiles from multiple NK cell lines will reveal minimal regions of alteration important in the pathogenesis of NK-cell lymphoma. Experimental Approach: To determine segmental DNA copy number changes across an entire genome, whole genome tiling path array is employed to evaluate segmental DNA gains and losses which may contain oncogenes and tumor suppressors. Currently, the whole genome has been arrayed as 26,819 bacterial artificial chromosome (BAC) derived amplified fragment pools spotted in duplicate (53,638 elements) resulting in tiling resolution with complete coverage of the sequenced human genome. Results: Whole genome array CGH was used to generate high resolution segmental copy number profiles of seven NK cell lymphoma cell lines (HANK1, KHYG-1, NK-92, NK-YS, SNK1, SNK6, and KAI3). Alignment of these profiles with the human genome map has resulted in fine-mapping the regional losses of 6q. Numerous novel regions, including gain of 5p15.33, and loss of 4q21.23-q32.1, 9p21.1-p22.3, and 12q24.32-q24.33 were also identified in at least five (>70%) of these cell lines. Conclusions: The generation of the high resolution segmental copy number profiles by array CGH allows the identification of genomic alterations of NK-cell lymphomas as well as the candidate genes involved in the pathogenesis of NK-cell lymphoma. Novel candidate oncogenes and tumor suppressor genes identified in this study will provide further insights into the genetic basis of lymphomagenesis. Acknowledgements: This work was supported by funds from Genome Canada/British Columbia. WCC is supported by the Director’s Challenge grant (U01-CA 84967). We would like to thank Spencer Watson for BAC array production and Magan Trottier for technical assistance.
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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) | 0.001 | 0.000 |
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".