Precision Medicine in Pediatric Oncology: Translating Genomic Discoveries into Optimized Therapies
Bibliographic record
Abstract
Abstract Survival of children with cancers has dramatically improved over the past several decades. This success has been achieved through improvement of combined modalities in treatment approaches, intensification of cytotoxic chemotherapy for those with high-risk disease, and refinement of risk stratification incorporating novel biologic markers in addition to traditional clinical and histologic features. Advances in cancer genomics have shed important mechanistic insights on disease biology and have identified “driver” genomic alterations, aberrant activation of signaling pathways, and epigenetic modifiers that can be targeted by novel agents. Thus, the recently described genomic and epigenetic landscapes of many childhood cancers have expanded the paradigm of precision medicine in the hopes of improving outcomes while minimizing toxicities. In this review, we will discuss the biologic rationale for molecularly targeted therapies in genomically defined subsets of pediatric leukemias, solid tumors, and brain tumors. Clin Cancer Res; 23(18); 5329–38. ©2017 AACR.
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.003 |
| Insufficient payload (model declined to judge) | 0.004 | 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".