Bibliographic record
Abstract
Abstract We conducted a comprehensive analysis of a manually curated human signaling network containing 1634 nodes and 5089 signaling regulatory relations by integrating cancer-associated genetically and epigenetically altered genes. We find that cancer mutating genes are enriched in positive signaling regulatory loops, whereas the cancer-associated methylating genes are enriched in negative signaling regulatory loops. We further characterized an overall picture of the cancer-signaling architectural and functional organization. From the network, we extracted an oncogene-signaling map, which contains 326 nodes, 892 links and the interconnections of mutated and methylated genes. The map can be decomposed into 12 topological regions or oncogene-signaling blocks, including a few ‘oncogenic-signaling-addictive blocks' in which frequently used oncogenic signaling events are enriched.Large scale sequencing of cancer genomes has shown that there is a lot of diversity and little overlap in terms of the different types of mutated genes. This diversity is seen among different types of tumours and even between tumours that originate from the same tissue. Despite the complexity of these mutations, the cancer signalling map allows them to be divided into a few common signaling modules and therefore uncovers the underlying logic of cancer signaling. Both common and tumor-type specific signaling modules are observed. The common module contains genes that are frequently mutated in most tumors regardless of tumor-type. However, the common module is generally not sufficient for tumorigenesis, because mutations of the genes in the common module are frequently accompanied by mutations in one or two other signaling modules. Different tumor types appear to achieve tumorigenesis via distinct mechanisms, i.e., through collaboration between different signaling modules. Taking a systems-biology approach, the researchers present a network view of molecular mechanisms of cancer signaling that will shape our understanding of fundamental tumor cell biology.
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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 | 0.000 | 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 | 0.000 | 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 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".