Genetic Mutations and Epigenetic Modifications: Driving Cancer and Informing Precision Medicine
Bibliographic record
Abstract
Cancer treatment is undergoing a significant revolution from "one-size-fits-all" cytotoxic therapies to tailored approaches that precisely target molecular alterations. Precision strategies for drug development and patient stratification, based on the molecular features of tumors, are the next logical step in a long history of approaches to cancer therapy. In this review, we discuss the history of cancer treatment from generic natural extracts and radical surgical procedures to site-specific and combinatorial treatment regimens, which have incrementally improved patient outcomes. We discuss the related contributions of genetics and epigenetics to cancer progression and the response to targeted therapies and identify challenges and opportunities for the success of precision medicine. The identification of patients who will benefit from targeted therapies is more complex than simply identifying patients whose tumors harbour the targeted aberration, and intratumoral heterogeneity makes it difficult to determine if a precision therapy is successful during treatment. This heterogeneity enables tumors to develop resistance to targeted approaches; therefore, the rational combination of therapeutic agents will limit the threat of acquired resistance to therapeutic success. By incorporating the view of malignant transformation modulated by networks of genetic and epigenetic interactions, molecular strategies will enable precision medicine for effective treatment across cancer subtypes.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.002 | 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".