Exploiting De Novo Serine Synthesis as a Metabolic Vulnerability to Overcome Sunitinib Resistance in Advanced Renal Cell Carcinoma
Bibliographic record
Abstract
Summary Sunitinib, an oral tyrosine kinase inhibitor used in advanced renal cell carcinoma (RCC), exhibits significant efficacy but faces resistance in 30% of patients. Yet, the molecular mechanisms underlying this therapy resistance remain elusive. Here, we show that sunitinib induces a metabolic shift leading to increased serine synthesis in RCC cells. The activation of the GCN2-ATF4 stress response pathway is identified as the mechanistic link between sunitinib treatment and elevated serine production. Inhibiting key enzymes in the serine synthesis pathway, such as PHGDH and PSAT1, enhances the sensitivity of resistant cells to sunitinib. The study underscores the role of serine biosynthesis in nucleotide synthesis, influencing cell proliferation, migration, and invasion. Beyond RCC, similar activation of serine synthesis occurs in other cancer types, suggesting a shared adaptive response to sunitinib therapy. This research identifies serine synthesis as a potential target to overcome sunitinib resistance, offering insights into therapeutic strategies applicable across diverse cancer contexts. Graphical abstract Highlights Sunitinib induces an increase in endogenous serine production in metastatic ccRCC. The heightened serine biosynthesis promoted by sunitinib facilitates nucleotide synthesis, thereby sustaining tumor cell proliferation. Sunitinib-induced enhancement of serine biosynthesis enables cell migration and invasion. The stimulation in serine synthesis is also observed in other cancer models treated with sunitinib.
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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) | 0.006 | 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".