Updates on germline predisposition in pediatric hematologic malignancies: What is the role of flow cytometry?
Bibliographic record
Abstract
Hematologic neoplasms with germline predisposition have been increasingly recognized as a distinct category of tumors over the last few years. As such, this category was added to the World Health Organization (WHO) 4th edition as well as maintained in the WHO 5th edition and International Consensus Classification (ICC) 2022 classification systems. In practice, these tumors require a high index of suspicion and confirmation by molecular testing. Flow cytometry is a cost-effective diagnostic tool that is routinely performed on peripheral blood and bone marrow samples. In this review, we sought to summarize the current body of research correlating flow cytometric immunophenotype to assess its utility in diagnosis of and clinical decision making in germline hematologic neoplasms. We also illustrate these findings using cases mostly from our own institution. We review some of the more commonly mutated genes, including CEBPA, DDX41, RUNX1, ANKRD26, GATA2, Fanconi anemia, Noonan syndrome, and Down syndrome. We highlight that flow cytometry may have a role in the diagnosis (GATA2, Down syndrome) and screening (CEBPA) of some germline predisposition syndromes, although appears to show nonspecific findings in others (DDX41, RUNX1). In many of the others, such as ANKRD26, Fanconi anemia, and Noonan syndrome, further studies are needed to better understand whether specific flow cytometric patterns are observed. Ultimately, we conclude that further studies such as large case series and organized data pipelines are needed in most germline settings to better understand the flow cytometric immunophenotype of these neoplasms.
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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 | 0.006 | 0.021 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.004 | 0.002 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.004 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.002 | 0.005 |
| Insufficient payload (model declined to judge) | 0.003 | 0.002 |
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".