Novel precision medicine approaches and treatment strategies in hematological malignancies
Bibliographic record
Abstract
Genetic testing has been applied for decades in clinical routine diagnostics of hematological malignancies to improve disease (sub)classification, prognostication, patient management, and survival. In recent classifications of hematological malignancies, disease subtypes are defined by key recurrent genetic alterations detected by conventional methods (i.e., cytogenetics, fluorescence in situ hybridization, and targeted sequencing). Hematological malignancies were also one of the first disease areas in which targeted therapies were introduced, the prime example being BCR::ABL1 inhibitors, followed by an increasing number of targeted inhibitors hitting the Achilles' heel of each disease, resulting in a clear patient benefit. Owing to the technical advances in high-throughput sequencing, we can now apply broad genomic tests, including comprehensive gene panels or whole-genome and whole-transcriptome sequencing, to identify clinically important diagnostic, prognostic, and predictive markers. In this review, we give examples of how precision diagnostics has been implemented to guide treatment selection and improve survival in myeloid (myelodysplastic syndromes and acute myeloid leukemia) and lymphoid malignancies (acute lymphoblastic leukemia, diffuse large B-cell lymphoma, and chronic lymphocytic leukemia). We discuss the relevance and potential of monitoring measurable residual disease using ultra-sensitive techniques to assess therapy response and detect early relapses. Finally, we bring up the promising avenue of functional precision medicine, combining ex vivo drug screening with various omics technologies, to provide novel treatment options for patients with advanced disease. Although we are only in the beginning of the field of precision hematology, we foresee rapid development with new types of diagnostics and treatment strategies becoming available to the benefit of our patients.
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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 | 0.003 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.002 | 0.004 |
| Insufficient payload (model declined to judge) | 0.005 | 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".