Identification of KIFC1 as a putative vulnerability in lung cancers with centrosome amplification
Bibliographic record
Abstract
Abstract Centrosome amplification (CA), an abnormal increase in the number of centrosomes in the cell, is a recurrent phenomenon in lung and other malignancies. Although CA contributes to tumor development and progression by promoting genomic instability (GIN), it also induces mitotic stress that jeopardizes cellular integrity. The presence of extra centrosomes leads to the formation of multipolar mitotic spindles prone to causing lethal chromosome segregation errors during cell division. To sustain the benefits of CA, malignant cells are dependent on adaptive mechanisms to mitigate its detrimental consequences, and these mechanisms represent therapeutic vulnerabilities. We aimed to discover genetic dependencies associated with CA in lung cancer. Combining a CRISPR/Cas9 functional genomics screen with analyses of tumor genomic data, we identified the motor protein KIFC1 as a putative vulnerability specifically in lung cancers with CA. KIFC1 expression was positively correlated with CA in lung adenocarcinoma (LUAD) cell lines and with a gene expression signature predictive of CA in LUAD tumor tissues. High KIFC1 expression was associated with worse patient outcomes, smoking history, and indicators of GIN. KIFC1 loss-of-function sensitized LUAD cells to potentiation of CA and sensitization was associated with a diminished ability of KIFC1-depleted cells to cluster extra centrosomes into pseudo-bipolar mitotic spindles. Our work suggests that KIFC1 inhibition represents a novel approach for potentiating GIN to lethal levels in LC with CA by forcing cells to divide with multipolar spindles, rationalizing the clinical development of KIFC1 inhibitors and further studies to investigate its therapeutic potential.
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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 | 0.001 | 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) | 0.001 | 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".