Bibliographic record
Abstract
It has been almost 15 years since the first microarray-based studies creating multigene biomarkers to subtype and predict survival of cancer patients. This Perspective looks at why only a handful of genomic biomarkers have reached clinical application and what advances are needed over the next 15 years to grow this number. I discuss challenges in creating biomarkers and reproducing them at the genomic and computational levels, including the problem of spatio-genomic heterogeneity in an individual cancer. I then outline the challenges in translating newly discovered genome-wide or regional events, like trinucleotide mutation signatures, kataegis, and chromothripsis, into biomarkers, as well as the importance of incorporating prior biological knowledge. Lastly, I outline the practical problems of pharmaco-economics and adoption: Are new biomarkers viewed as economically rational by potential funders? And if they are, how can their results be communicated effectively to patients and their clinicians? Genomic-based diagnostics have immense potential for transforming the management of cancer. The next 15 years will see a surge of research into the topics here that, when combined with a stream of new targeted therapies being developed, will personalize the cancer clinic.
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 | 0.004 | 0.006 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.000 | 0.002 |
| Scholarly communication | 0.002 | 0.004 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.003 | 0.005 |
| 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".