ReviewBlood advances2025
Audit of B-cell cancer genes.
Review in Blood advances, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 5 papers.
What it found
Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.
The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.
Who cites it
5 citing papers in PubMed.
- Analysis of cell-free DNA in lymphomas: from sample collection to genotyping and minimal residual disease monitoring.Blood advances · 2026Review
- Article
- HNRNPU mutations redirect cell cycle control to E2F in MYC-driven lymphomas.Blood advances · 2026Article
- Lamin B1 safeguards the B cell genome and shapes lymphoma outcome.HemaSphere · 2026Article
- DNA Methylation Epitypes of Burkitt Lymphoma with Distinct Molecular and Clinical Features.Blood cancer discovery · 2025Article
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
4 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
abstractComprehensive 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.
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What OpenQuestion holds
Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.