Major milestones in translational oncology
Bibliographic record
Abstract
Translational oncology represents a bridge between basic research and clinical practice in cancer medicine. Today, translational research in oncology benefits from an abundance of knowledge resulting from genome-scale studies regarding the molecular pathways involved in tumorigenesis. In this Forum article, we highlight the state of the art of translational oncology in five major cancer types. We illustrate the use of molecular profiling to subtype colorectal cancer for both diagnosis and treatment, and summarize the results of a nationwide screening program for ovarian cancer based on detection of a tumor biomarker in serum. Additionally, we discuss how circulating tumor DNA can be assayed to safely monitor breast cancer over the course of treatment, and report on how therapy with immune checkpoint inhibitors is proving effective in advanced lung cancer. Finally, we summarize efforts to use molecular profiling of prostate cancer biopsy specimens to support treatment decisions. Despite encouraging early successes, we cannot disregard the complex genetics of individual susceptibility to cancer nor the enormous complexity of the somatic changes observed in tumors, which urge particular attention to the development of personalized therapies.
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 | 0.000 | 0.001 |
| 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 | 0.001 | 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".