Moving From Mutation to Actionability
Bibliographic record
Abstract
The diffusion of high-throughput next-generation sequencing technologies has sustained massive parallel sequencing of tumor tissue providing a deep insight into tumor biology and advancement of personalized medicine. A substantial number of targeted agents have been investigated in gynecologic cancer and some have received U.S. Food and Drug Administration approval, like PARP inhibitors in ovarian cancer, bevacizumab in ovarian and cervical cancers, and pembrolizumab in microsatellite-unstable or mismatch repair-deficient endometrial cancer. To improve effectiveness of targeted therapy, identification of predictive biomarkers able to guide the selection of the correct drug for the correct patient is crucial. Different limitations must be addressed to favor a more rapid implementation of a genotyping approach in treatment selection, such as the possibility to easily assess tumor heterogeneity and clonal evolution along the disease trajectory and the need for innovative trial designs like adaptive or basket trials incorporating molecular features as selection criteria. A deep dive into the genomic features of exceptional responders may also favor better understanding of tumor biology, mechanism of action of a specific target agent, and identification or predictive biomarkers for subsequent tailored studies.
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 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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| 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".