Clinicopathologic Subtypes of Mantle Cell Lymphoma (MCL) Show Distinct Patterns of Genetic Copy Number Alteration.
Bibliographic record
Abstract
Abstract Introduction: MCL is an aggressive NHL characterized by a t(11;14)(q13;q32). However this translocation alone cannot initiate lymphomagenesis and multiple secondary genomic alterations are necessary to induce the neoplastic phenotype. In addition, three distinct clinicopathologic subtypes of MCL (classic, blastoid, and leukemic) have been identified with differing prognosis. Secondary genomic alterations associated with each subtype have been poorly defined. Experimental Design: Genomic profiles from a panel of MCL samples comprised of 20 classic, 5 leukemic (no nodal involvement), and 12 blastoid cases, were created using a tiling-resolution BAC array CGH platform. Alignment of profiles to identify regions of recurrent alteration was accomplished using a custom developed software package (SeeGH). In addition, specific genes were analyzed for copy number status. Statistically significant distributions were determined using Chi square and Fisher’s exact analyses. Results: Twenty two regions of recurrent alteration were identified that were statistically different between subtypes (partial data in Table 1). As expected chromosome 9 losses were more prevalent in blastoid MCL. 13q34 losses were prevalent within classic and blastoid MCL, as opposed to leukemic MCL, which showed gains of this region. Analysis of biological pathways revealed that apoptotic signaling is not disrupted via copy number in a large portion of MCL cases (21%), while chromosomal instability genes show copy number disruption in 67% of MCL cases. Surprisingly, the glioblastoma pathway was preferentially disrupted in blastoid MCL (p=1.09x10−5), affecting all cases; whereas the p53 pathway was disrupted via copy number more often in leukemic MCL (p=0.0207). Conclusions: Multiple genomic regions and cancer related genes show a differential pattern of altered copy number distribution between MCL subtypes. These regions and genes likely contribute to the different clinicopathologic subtypes of MCL cases. Differential regions in MCL subtypes +/− chr. band size (Mb) clas% leuk% blast% enriched in deficient in − 1q25.2-q31.2 15.50 0 20 33 blast (p=0.03) class (p=0.01) − 2q26.3 2.91 5 0 33 blast (p=0.03) + 3cent-p12.1 4.18 0 60 25 leuk (p=0.02) class (p=0.005) + 7q11.23 3.24 0 0 33 blast (p=0.008) class (p=0.04) − 9p21.3 1.02 10 20 83 blast (p=0.00001) class (p=0.002) − 9q21.13 1.10 5 40 75 blast (p=0.0003) class (p=0.0002) − 9q21.2-q31.1 22.87 5 40 67 blast (p=0.001) class (p=0.0007) − 9q31.1 0.83 0 40 67 blast (p=0.0004) class (p=0.00006) − 9q34.12-qter 7.27 0 20 42 blast (p=0.009) class (p=0.005) + 10p12.2-p12.31 0.63 5 0 42 blast (p=0.009) − 13q34 1.69 40 0 75 blast (p=0.03) leuk (p=0.05) − 17p13.3-pter 2.28 5 60 25 leuk (p=0.04) − 17p13.1 1.21 5 60 25 leuk (p=0.04)
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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 | 0.001 |
| 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 | 0.001 |
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".