The molecular pathology of cancer: from pan‐genomics to post‐genomics
Bibliographic record
Abstract
As the cancer genomics of most major cancer types have been comprehensively catalogued over the past decade through a variety of national and international efforts, the delineation of cancer subtypes has been refined, and our understanding of critical cancer drivers and of the potentially targetable vulnerabilities that they create has grown tremendously. The 2018 Annual Review Issue of the Journal of Pathology provides in-depth assessments of how these pan-genomic approaches have enabled advances in cancer classification, targeted therapy selection, and assessment of cancer progression, all of which are now genomically informed, using several cancer types as examples. Beyond these areas of by now conventional pan-genomic tumour analysis, there are also reviews of diverse 'post-genomic' areas, such as the analysis of circulating free tumour DNA in plasma, concurrent germline cancer predisposition profiling in the setting of apparently sporadic cancer, genetic alterations in epigenetic control and DNA repair, proteomics of tumour heterogeneity, computational pathology, and the roles of the cellular stress response and the microbiome in human cancers. As we are able to derive more and more biologically useful information from diverse human biospecimens, these many advances are informing and transforming the practice of cancer pathology. Copyright © 2018 Pathological Society of Great Britain and Ireland. Published by John Wiley & Sons, Ltd.
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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 | 0.000 |
| 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 | 0.001 | 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".