De novo purine metabolism is a metabolic vulnerability of cancers with low p16 expression.
Naveen Kumar TanguduRaquel BujHui WangJiefei WangAidan R ColeApoorva UbovejaRichard FangAmandine AmalricBaixue YangAdam ChatoffClaudia V CrispimPeter SajjakulnukitMaureen A LyonsKristine L CooperNadine HempelCostas Andreas LyssiotisUma R ChandranNathaniel W SnyderKatherine Marie AirdPublished in: Cancer research communications (2024)
p16 is a tumor suppressor encoded by the CDKN2A gene whose expression is lost in ~50% of all human cancers. In its canonical role, p16 inhibits the G1-S phase cell cycle progression through suppression of cyclin dependent kinases. Interestingly, p16 also has roles in metabolic reprogramming, and we previously published that loss of p16 promotes nucleotide synthesis via the pentose phosphate pathway. However, the broader impact of p16/CDKN2A loss on other nucleotide metabolic pathways and potential therapeutic targets remains unexplored. Using CRISPR KO libraries in isogenic human and mouse melanoma cell lines, we determined several nucleotide metabolism genes essential for the survival of cells with loss of p16/CDKN2A. Consistently, many of these genes are upregulated in melanoma cells with p16 knockdown or endogenously low CDKN2A expression. We determined that cells with low p16/CDKN2A expression are sensitive to multiple inhibitors of de novo purine synthesis, including anti-folates. Finally, tumors with p16 knockdown were more sensitive to the anti-folate methotrexate in vivo than control tumors. Together, our data provide evidence to reevaluate the utility of these drugs in patients with p16/CDKN2Alow tumors as loss of p16/CDKN2A may provide a therapeutic window for these agents.
Keyphrases
- cell cycle
- poor prognosis
- genome wide
- induced apoptosis
- endothelial cells
- cell cycle arrest
- cell proliferation
- binding protein
- long non coding rna
- cell death
- dna methylation
- gene expression
- genome wide identification
- low dose
- oxidative stress
- systematic review
- pluripotent stem cells
- electronic health record
- data analysis