Differential positioning and close spatial proximity of translocation‐prone genes in nonmalignant B‐cells from multiple myeloma patients
Bibliographic record
Abstract
Accumulating evidence suggests that spatial proximity of potential chromosomal translocation partners influences translocation probability. It is not known, however, whether genome organization differs in nonmalignant cells from patients as compared to their cellular counterparts from healthy donors. This could contribute to translocation potential causing cancer. Multiple myeloma is a hematopoietic cancer of the B-lineage, characterized by karyotypic instability, including chromosomal translocations involving the IGH locus and several translocation partners. Utilizing 3-D FISH and confocal imaging, we investigate whether nuclear spatial positioning of the translocation-prone gene loci, IGH, FGFR3, and CCND1 differs in nonmalignant cell subsets from multiple myeloma patients as compared to positioning in their corresponding healthy donor cell subsets. 3-D analysis software was used to determine the spatial proximity of potential translocation pairs and the radial distribution of each gene. We observed that in all cell subsets, the translocation-prone gene loci are intermediately located in the nucleus, while a control locus occupies a more peripheral position. In nonmalignant B-cells from multiple myeloma patients, however, the translocation-prone gene loci display a more central nuclear position and close spatial proximity. Our results demonstrate that gene positioning in nonmalignant B-cells from multiple myeloma patients differs from that in healthy donors, potentially contributing to translocation probability in patient cells. We speculate that genome reorganization in patient B-cells may closely reflect gene positioning at the time the multiple myeloma-specific translocation initially formed, thus influencing translocation probability between proximal loci in the B-cell population from which the malignancy emerged.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 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) | 0.000 | 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 teacher head, 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".