Editorial: Biomarkers and therapeutic strategies in acute lymphoblastic leukemia
Bibliographic record
Abstract
Editorial on the Research Topic Biomarkers and therapeutic strategies in acute lymphoblastic leukemia Acute lymphoblastic leukemia (aLL) is a malignancy characterized by an expeditious increase in immature B-(in ~85% of cases) and T-(in ~15% of cases) lymphocytes in the blood and bone marrow.It is the most prevalent cancer in children and the primary cause of death from pediatric cancer (Hunger and Mullighan, 2015).Chemotherapy continues to be the main treatment for aLL (Lee et al., 2019).There is considerable evidence for superior outcome from multiple rounds of highly intensive chemotherapy (Pui and Evans, 2006).However, the risk of acquiring resistance and toxicity from chemotherapy could be fatal.Although novel therapies such as monoclonal antibodies have been developed, their effectiveness is greatly enhanced when used in combination with chemotherapy.Thus, there is continued interest in chemotherapy.However, some of these combinations are effective in certain patients but have no clinical benefit to others, and their non-specific effects make them intolerable to many.Thus, unnecessary toxicity-and resistance-induced patient suffering or mortality occurs frequently, and relapsed and refractory aLL continue to be a major concern.A growing number of resistance biomarkers for aLL drugs have been identified (Kang et al., 2017;Lee et al., 2019), increasing the current understanding of the molecular mechanisms by which chemotherapy resistance develops.On the other hand, the extensive genetic heterogeneity in B-and T-acute lymphoblastic leukemia precursor cells indicates a range of biomarkers that promote disease development and recurrence.The objective of this Research Topic is to bring forth recent advances in biomarkers and potential therapeutic strategies in aLL as well as their implications in managing the disease.The ultimate goal is to foster exploitation of these discoveries and provide insight into the development of more innovative and effective therapeutic approaches for aLL. Novel biomarkers and therapeuticsUtilizing novel innovative tools such as high throughput small molecular drug screening and gene expression analyses/datasets, a number of new biomarkers and potential therapeutics have been identified.For example, a large-scale screening of small molecule drugs for aLL performed by (Nevado et al.) led to the identification of synthetic oleanane
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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 | 0.019 |
| Meta-epidemiology (narrow) | 0.004 | 0.001 |
| Meta-epidemiology (broad) | 0.004 | 0.003 |
| Bibliometrics | 0.004 | 0.001 |
| Science and technology studies | 0.003 | 0.002 |
| Scholarly communication | 0.006 | 0.005 |
| Open science | 0.003 | 0.002 |
| Research integrity | 0.012 | 0.015 |
| Insufficient payload (model declined to judge) | 0.023 | 0.019 |
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".