CRISPR screens identify cholesterol biosynthesis as a therapeutic target on stemness and drug resistance of colon cancer
Bibliographic record
Abstract
Cancer stem cells (CSCs) are responsible for tumor progression, recurrence, and drug resistance. To identify genetic vulnerabilities of colon cancer, we performed targeted CRISPR dropout screens comprising 657 Drugbank targets and 317 epigenetic regulators on two patient-derived colon CSC-enriched spheroids. Next-generation sequencing of pooled genomic DNAs isolated from surviving cells yielded therapeutic candidates. We unraveled 44 essential genes for colon CSC-enriched spheroids propagation, including key cholesterol biosynthetic genes (HMGCR, FDPS, and GGPS1). Cholesterol biosynthesis was induced in colon cancer tissues, especially CSC-enriched spheroids. The genetic and pharmacological inhibition of HMGCR/FDPS impaired self-renewal capacity and tumorigenic potential of the spheroid models in vitro and in vivo. Mechanistically, HMGCR or FDPS depletion impaired cancer stemness characteristics by activating TGF-β signaling, which in turn downregulated expression of inhibitors of differentiation (ID) proteins, key regulators of cancer stemness. Cholesterol and geranylgeranyl diphosphate (GGPP) rescued the growth inhibitory and signaling effect of HMGCR/FDPS blockade, implying a direct role of these metabolites in modulating stemness. Finally, cholesterol biosynthesis inhibitors and 5-FU demonstrated antitumor synergy in colon CSC-enriched spheroids, tumor organoids, and xenografts. Taken together, our study unravels novel genetic vulnerabilities of colon CSC-enriched spheroids and suggests cholesterol biosynthesis as a potential target in conjunction with traditional chemotherapy for colon cancer treatment.
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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 | 0.000 |
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".