Whole-Genome Sequencing in Cancer
Bibliographic record
Abstract
Genome sequencing of cancer has fundamentally advanced our understanding of the underlying biology of this disease, and more recently has provided approaches to characterize and monitor tumors in the clinic, guiding and evaluating treatment. Although cancer research is relying more on whole-genome characterization, the clinical application of genomics is largely limited to targeted sequencing approaches, tailored to capture specific clinically relevant biomarkers. However, as sequencing costs reduce, and the tools to effectively analyze complex and large-scale data improve, the ability to effectively characterize whole genomes at scale in a clinically relevant time frame is now being piloted. This ability effectively blurs the line between clinical cancer research and the clinical management of the disease. This leads to a new paradigm in cancer management in which real-time analysis of an individual's disease can have a rapid and lasting impact on our understanding of how clinical practices need to change to exploit novel therapeutic rationales. In this article, we will discuss how whole-genome sequencing (WGS), often combined with transcriptome analysis, has been used to understand cancer and how this approach is uniquely positioned to provide a comprehensive view of an evolving disease in response to therapy.
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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 | 0.002 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.003 | 0.004 |
| Insufficient payload (model declined to judge) | 0.004 | 0.004 |
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".