Audit of B-cell cancer genes
Bibliographic record
Abstract
ABSTRACT: Comprehensive genetic analysis of tumors with exome or whole-genome sequencing has enabled the identification of the genes that are recurrently mutated in cancer. This has stimulated a series of exciting advances over the past 15 years, guiding us to new molecular biomarkers and therapeutic targets among the common mature B-cell neoplasms. In particular, diffuse large B-cell lymphoma (DLBCL), follicular lymphoma (FL), and Burkitt lymphoma (BL) have each been the subject of considerable attention in this field. Currently, >850 genes have been reported as targets of protein-coding mutations in at least 1 of these entities. To reduce this to a manageable size, we describe a systematic approach to prioritize and categorize these genes, based on the quality and type of supporting data. For each entity, we provide a list of candidate driver genes categorized into Tier 1 (high-confidence genes), Tier 2 (candidate driver genes), or Tier 3 (lowest-confidence genes). Collectively, this reduces the number of high-confidence genes for these 3 lymphomas to a mere 144. This further affirms the substantial overlap between the genes relevant in DLBCL and each of FL and BL. These highly curated and annotated gene lists will continue to be maintained as a resource to the community. These results emphasize the extent of the knowledge gap regarding the role of each of these genes in lymphomagenesis. We offer our perspective on how to accelerate the experimental confirmation of drivers using a variety of model systems, using these lists as a guide for prioritizing genes.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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) | 0.001 | 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".