3D Telomeric Profiles of MGUS, MMN and Relapsed MM
Bibliographic record
Abstract
Abstract Abstract 2899 Multiple myeloma (MM) is recognized as the second most common cancer of the blood. It is a malignant disorder of plasma cells and commonly affects adults past the age of 50. Although risk factors have been established, it is currently not possible to assess the individual risk to cancer progression. Moreover, the causes of disease progression from its precursor condition, monoclonal gammopathy of undetermined significance (MGUS), to full-blown MM and its progression to relapsed MM remain elusive. We have performed previous studies on the three-dimensional (3D) nuclear organization of telomeres and found that normal and tumor cells display significant differences in their nuclear organization. These differences were objectively quantified with two software programs, TeloView and TeloScan developed by our group. We now report on a new double blinded preliminary study with 36 patients, including 20 MM, 12 MGUS and 4 relapsed MM. Using blood- and bone marrow-derived plasma cells from the respective patient groups, we have examined 3D nuclear telomeric profiles of the above patients. Plasma cells from MM, MGUS and relapsed MM exhibit specific 3D telomeric signatures. MM have the highest telomere numbers, followed by MGUS, while relapsed MM presents with the lowest numbers of telomeres and the shortest telomeres. Additional telomere parameters, such as cell cycle distribution profiles (a/c ratio), telomere aggregate numbers, distances from nuclear centre and also exhibited significance (p< 0.001). Within the MGUS and MM patient groups we studied are patients whose 3D telomeric profiles indicate the beginning of a new signature that resembles signatures of MM and relapsed MM respectively. Blood and bone marrow gave us comparable results opening the future opportunity to base diagnostics and monitoring on blood samples, sparing the patient form invasive bone marrow sampling with potentially adverse effects. Based on our current preliminary data, we propose the following 3D telomeric criteria that define individual signatures of MGUS and MM and progression within each of the groups: telomere numbers, telomere sizes, presence of telomeric aggregates, telomeres per nuclear volume, and a/c ratios. The clinical significance of these findings is the early identification of individuals with high risk of progression. This opens the possibility for better monitoring and early intervention with newer treatments with an acceptable efficacy and low toxicity ratio. Disclosures: No relevant conflicts of interest to declare.
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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 | 0.001 |
| 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".