The Use of Targeted Therapies for Precision Medicine in Oncology
Bibliographic record
Abstract
Precision medicine is an emerging approach for disease treatment and prevention that takes into account individual variability in genes, environment, and lifestyle to develop an individualized treatment plan. It was brought to the forefront recently as President Barack Obama launched the Precision Medicine Initiative, which aims to revolutionize medicine and move the concept of precision medicine into everyday clinical practice with near-term goals focused on cancer. Clinical applications that will benefit from precision medicine include improving patient diagnosis and prognosis, predicting treatment response, and determining predisposition to certain cancers. This information will be incorporated into an individualized patient treatment plan that will provide maximum benefit while reducing the use of drugs that have serious side effects and are unlikely to benefit the patient. In addition to improving patient survival and quality of life, there will be an overall reduction in cost for the healthcare system. Targeted therapy provides the foundation of precision medicine. Even in individuals with similar clinical cancer phenotypes, drug therapy is only effective in a subset of patients. Owing to recent advances in molecular biology, genomics, and bioinformatics, research has shown that differential drug response is often a result of differences in genetic alterations. Altered genes may contribute to cancer progression by allowing growth and spread of the malignancy. Alternatively, they may contribute to drug effectiveness if there are mutations in genes involved in drug metabolism. An in-depth understanding of the biology of the tumor, including molecular changes and altered signaling pathways will allow for the identification of patients who are likely to benefit from such treatments; it also may facilitate the development of new targeted therapies, which counter the influence of the specific molecular drivers contributing to the growth and spread of the malignancy. In this Q&A article, 5 experts discuss the applications of precision medicine …
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
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 teacher head, 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".