Data from Modeling Genomic Diversity and Tumor Dependency in Malignant Melanoma
Bibliographic record
Abstract
Abstract The classification of human tumors based on molecular criteria offers tremendous clinical potential; however, discerning critical and “druggable” effectors on a large scale will also require robust experimental models reflective of tumor genomic diversity. Here, we describe a comprehensive genomic analysis of 101 melanoma short-term cultures and cell lines. Using an analytic approach designed to enrich for putative “driver” events, we show that cultured melanoma cells encompass the spectrum of significant genomic alterations present in primary tumors. When annotated according to these lesions, melanomas cluster into subgroups suggestive of distinct oncogenic mechanisms. Integrating gene expression data suggests novel candidate effector genes linked to recurrent copy gains and losses, including both phosphatase and tensin homologue (PTEN)–dependent and PTEN-independent tumor suppressor mechanisms associated with chromosome 10 deletions. Finally, sample-matched pharmacologic data show that FGFR1 mutations and extracellular signal–regulated kinase (ERK) activation may modulate sensitivity to mitogen-activated protein kinase/ERK kinase inhibitors. Genetically defined cell culture collections therefore offer a rich framework for systematic functional studies in melanoma and other tumors. [Cancer Res 2008;68(3):664–73]
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 | 0.001 | 0.007 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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 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".