Mechanisms of oncogene induced senescence in MAPK- driven cancer development
Bibliographic record
Abstract
Cancer is caused by the accumulation of genetic mutations that promote the abnormal growth of cells.Oncogene Induced Senescence (OIS), a tumour suppressive mechanism, provides a robust barrier to proliferation promoted by commonly mutated oncogenes, such as Raf or Ras, and the bypass of this barrier is a critical event on the path to malignancy.The mechanisms involved in OIS bypass are not yet fully understood.Many questions remain such as whether the timing of genetic mutations is relevant, whether additional mutations can permit escape from OIS, what cellular processes are required to establish OIS, and how senescent tumour cells can contribute to the tissue microenvironment.Using a Flp-activated Braf allele paired with a Cre-conditionally null p53 allele, p53 was ablated at six independent timepoints following the initiation of Braf V600E lung adenomas in the mouse lung, allowing for temporal dissection of tumour progression and OIS.Using this dualrecombinase system, it was determined that p53 loss after OIS is established is not sufficient to permit malignant adenocarcinoma (LUAD) development.Braf V600E adenomas are stably restrained from malignancy by OIS by approximately 24 weeks after Braf V600E expression, and several senescence and SASP markers can be detected in those adenomas.Interestingly, the length of time until OIS establishment could be modulated by the initiating viral titres of adenoviral-Flp.Lower initiating adenoviral titre produced lower tumour density in the lung that was correlated with smaller, more proliferative tumours.Lower-density tumour environments also permitted bypass of OIS and LUAD development at 24-32 weeks, suggesting that higher proliferation is due to delay in OIS.mutations in MAPK-related factors.Elucidating the mechanisms underlying the establishment and maintenance of OIS will help us understand how cells might bypass or reverse these intrinsic barriers to become malignant.In addition, many common cancer chemotherapies induce senescence in tumour cells, making understanding senescence critical to the clinic (Ewald et al., 2010).In particular, this work helps to uncover the mechanisms that underlie OIS using both in vivo mouse models and an in vitro genetic screen. CancerOverview Cancer is a disease resulting from unrestricted cell proliferation or survival caused by genetic mutations.Cancerous cells acquire a number of common characteristics described by Hanahan & Weinberg in 2000 as the "Hallmarks of Cancer", which are: sustaining proliferative signaling, evading growth suppressors, resisting cell death, enabling replicative immortality, inducing angiogenesis, and activating invasion and metastasis (Hanahan and Weinberg, 2000).Mutations underlying cancer development fall into two general categories: oncogenes, and tumour suppressor genes (TSGs).Typically, as tumours evolve, they accumulate mutations in a stepwise fashion, leading to an increasingly aggressive disease (Fearon and Vogelstein, 1990;Arends, 2000;McGranahan and Swanton, 2017).The large diversity of cancer types and aggressiveness can be partially attributed to the variety of different genetic mutations that can cause cancer, as well as the underlying genetic instability of cancer that contributes to these hallmark characteristics (Negrini et al., 2010;McGranahan and Swanton, 2017).
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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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 0.001 |
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".