Human aneuploid cells depend on the RAF/MEK/ERK pathway for overcoming increased DNA damage
Bibliographic record
Abstract
Abstract Aneuploidy is a hallmark of human cancer, yet the cellular mechanisms that allow cells to cope with aneuploidy-induced cellular stresses remain largely unknown. Such coping mechanisms may present cellular vulnerabilities that can be harnessed for targeting cancer cells. Here, we induced aneuploidy in non-transformed RPE1-hTERT cells and derived multiple stable clones with various degrees of chromosome imbalances. We performed an unbiased genomic profiling of 6 isogenic clones, using whole-exome and RNA sequencing. We then functionally interrogated their cellular dependency landscapes, using genome-wide CRISPR/Cas9 screens and large-scale drug screens. We found that aneuploid clones activated the DNA damage response (DDR), and were consequently more resistant to further DNA damage induction. Interestingly, aneuploid cells also exhibited elevated RAF/MEK/ERK pathway activity, and were more sensitive to several clinically-relevant drugs targeting this pathway, and in particular to genetic and chemical CRAF inhibition. CRAF activity was functionally linked to the resistance to DNA damage induction, as CRAF inhibition sensitized aneuploid cells to DNA damage-inducing chemotherapies. The association between aneuploidy, RAF/MEK/ERK signaling, and DDR was independent of p53. The increased activity and dependency of aneuploid cells on the RAF/MEK/ERK pathway was validated in another isogenic aneuploid system, and across hundreds of human cancer cell lines, confirming their relevance to human cancer. Overall, our study provides a comprehensive resource for genetically-matched karyotypically-stable cells of various aneuploidy states, and reveals a novel therapeutically-relevant cellular dependency of aneuploid cells.
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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) | 0.000 | 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 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".