The biology behind prognostic factors of cutaneous melanoma
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
PURPOSE OF REVIEW: Cutaneous melanoma still represents a paradox among all solid tumors. It is the cancer for which the best prognostic markers ever identified in solid tumors are available, yet there is very little understanding of their biological significance. This review focuses on recent biological data that shed light on the clinico-biological correlations that support the 2010 AJCC melanoma staging system. RECENT FINDINGS: E-cadherin is a keratinocyte-melanoma adhesion molecule whose loss is required for the acquisition of an invasive phenotype. Recent data showed that this loss is mediated by the transcription factor Tbx3 which is also involved in suppressing melanocytes senescence. CCN3 is present in melanoma cells close to the epidermal-dermal interface, but not in melanoma cells that have invaded deep into the dermis. It has been recently demonstrated that CCN3 decreases the transcription and activation of matrix metalloproteinases and suppresses the invasion of melanoma cells. These results suggest that the absence of CCN3 in advanced melanoma cells contributes to their invasive phenotype and that ulceration modifies the microenvironment allowing CCN3-depleted melanoma cells to invade. SUMMARY: A major challenge is to replace outcome clustering based on artificial biomarker breakpoints by a continuous multidimensional prognostic model. Major improvement will come from shared computerized tools allowing to generate continuous likelihood scores for diagnosis, prognosis and response prediction. This will lead to the development of platforms which can be used by scientists from different fields to integrate and share high-quality data in the precompetitive setting and generate new probabilistic causal models.
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.
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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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 it