High resolution analysis of follicular lymphoma genomes reveals somatic recurrent sites of copy‐neutral loss of heterozygosity and copy number alterations that target single genes
Bibliographic record
Abstract
A multiplatform approach, including conventional cytogenetic techniques, BAC array comparative genomic hybridization, and Affymetrix 500K SNP arrays, was applied to the study of the tumor genomes of 25 follicular lymphoma biopsy samples with paired normal DNA samples to characterize balanced translocations, copy number imbalances, and copy-neutral loss of heterozygosity (cnLOH). In addition to the t(14;18), eight unique balanced translocations were found. Commonly reported FL-associated copy number regions were revealed including losses of 1p32-36, 6q, and 10q, and gains of 1q, 6p, 7, 12, 18, and X. The most frequent regions affected by copy-neutral loss of heterozygosity were 1p36.33 (28%), 6p21.3 (20%), 12q21.2-q24.33 (16%), and 16p13.3 (24%). We also identified by SNP analysis, 45 aberrant regions that each affected one gene, including CDKN2A, CDKN2B, FHIT, KIT, PEX14, and PTPRD, which were associated with canonical pathways involved in tumor development. This study illustrates the power of using complementary high-resolution platforms on paired tumor/normal specimens and computational analysis to provide potential insights into the significance of single-gene somatic aberrations in FL tumorigenesis.
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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 | 0.000 |
| 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".