3D Telomere Dynamics In Hodgkin's Lymphoma
Bibliographic record
Abstract
Abstract Abstract 745 Introduction: Innovative 3D telomere q-FISH allows a mechanistic understanding of the transition from the mononuclear Hodgkin (H) to the multinuclear Reed-Sternberg (RS) cell in in Hodgkin's lymphoma (HL) derived cell lines and diagnostic patient biopsies (Leukemia. 2009; 23:565-573). In RS-cells the telomere protecting shelterin complex appears to be disrupted and deregulation of DNA repair mechanisms is observed. These changes occur in both, classical EBV negative and EBV-associated, LMP1 expressing HL (Lab Invest. 2010; 90:611-619). However, it is not known whether the 3D telomere profile at diagnostic biopsy is different in patients entering rapid remission after initiation of standard chemotherapy (ABVD) compared to that-one of patients with relapsing or refractory disease. In order to answer this question we analyzed by 3D telomere q-FISH diagnostic biopsies of HL patients entering rapid complete remission and compared them to diagnostic biopsies of patients with relapsing or refractory disease. Patients and methods: Rapid remission group (after 1–4 cycles of ABVD): 7 diagnostic biopsies of 7 patients, 19–57 years old, 5 male, 3 LMP1 expressing, 4 nodular sclerosis subtype, 3 mixed cellularity subtype, stages IA, IIA, IIIA × 2, IIIB x2, IVA. Relapse group: 7 diagnostic biopsies of 4 patients, 40–77 years old, 2 male, 1 LMP1 expressing, 3 nodular sclerosis subtype, 1 mixed cellularity subtype, stages IIA bulky, IIIB × 2, IVB; first remission after 6–8 cycles of ABVD in 3 patients, 1 patient died from progressive disease after 11 months. 3D telomere q-FISH was performed as described (Lab Invest. 2010; 90:611-619) and statistical analysis was performed using nested or two-way analysis of variance. Results: Bi- or multinuclear RS-cells of all patients from both groups showed a significant increase of very short telomeres and telomere aggregates when compared to the mononuclear precursor H-cells. However, most importantly, all diagnostic biopsies of the relapse group contained a very high percentage of very small telomeres, including so-called “t-stumps”, in both, H-cells (76,8 ± 11,8%) and RS-cells (87,9 ± 7,3%). Compared to the percentage of very small telomeres identified in both, H-cells (33,7 ± 9,4%) and RS-cells (54,6 ± 15,0%) of the rapid remission group, this increase is highly significant (p <0.001). Moreover, analogous findings are observed for the number of telomere aggregates. In the relapse group the average numbers of telomere aggregates per cell were 4,3 ± 2,4 aggregates per H-cell and 5,4 ± 3.0 aggregates per RS-cell, compared to 1,2 ± 0,7 aggregates per H-cell and 3,3 ± 1,1 aggregates per RS-cell in the rapid remission group. Discussion: The 3D nuclear telomere organization of H- and RS- cells in diagnostic biopsies of relapsing or refractory HL is characterized by both, H- and RS-cells with abundant “t-stumps” and numerous telomere aggregates. Very short telomeres, including t-stumps, and telomere aggregates, both, are characteristics of aggressiveness in cancer biology (Mol Cell. 2007;28:315-327; J Cell Biochem. 2010; 109:1095-1102). Thus, H- and RS-cells of refractory or relapsing HL show significant differences in the 3D telomere dynamics already at first, diagnostic biopsy when compared to H- and RS-cells of HL entering rapid remissions. Disclosures: No relevant conflicts of interest to declare.
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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 | 0.000 |
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".