Novel insights into the disease dynamics of B‐cell lymphomas in the Genomics Era
Bibliographic record
Abstract
High-throughput sequencing has significantly contributed to revealing the molecular underpinnings of B-cell lymphomagenesis and disease progression. It is now a widely accepted concept that the diversity of clinical responses to front-line therapy and the development of relapsed/refractory disease are in part explained by 'inter-patient' genetic heterogeneity measurable by individual sets of somatic gene alterations in tumor genomes. Moreover, extensive 'intra-tumor' heterogeneity on the genotypic and phenotypic levels is the product of ongoing tumor evolution and adaptation to various selective pressures during cancer initiation, progression, and therapeutic intervention. As the management of disease progression remains one of the most significant clinical challenges, it is becoming increasingly important to delineate how B-cell lymphomas evolve over time and to develop progression-related biomarker assays. Toward this goal, recent investigations have moved from studying lymphoma biology at initial diagnosis to doing so at multiple time points during the disease course. Profiling progressed tumors, and in particular paired biopsies at initial diagnosis and disease progression of the same patients, has led to novel insights into clonal tumor evolution and tumor microenvironment dynamics. This review discusses the latest findings on genomic alterations and microenvironment biology associated with relapsed/refractory B-cell lymphomas, with a particular emphasis on alterations that are acquired or become more prevalent at disease progression. We also describe overarching tumor evolution patterns, and highlight emerging precision medicine methodologies that can aid in an improved understanding and management of relapsed/refractory disease. Copyright © 2018 Pathological Society of Great Britain and Ireland. Published by John Wiley & Sons, Ltd.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| 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".