Due to metabolic variations it is important to
Due to metabolic variations, it is important to consider arginine metabolism and dependency in specific contexts to identify precise patterns. This is best illustrated for ASS: while ASS deficiency correlates with worse prognosis in sarcomas, ASS levels positively correlate with a poor prognosis in gastric cancer and some breast cancers (Huang et al., 2013; Qiu et al., 2015; Shan et al., 2015). Effects on cell migration also vary, as ASS overexpression in myxofibrosarcoma inhibits migration (Huang et al., 2013), but knockdown or arginine withdrawal from gastric cancer Sal 003 inhibits cell migration (Huang et al., 2013; Shan et al., 2015). The reasons for these opposing observations is not known, nor is it clear if there are underlying expression patterns that can predict response to ASS and arginine deprivation, so this is an area of work that clearly requires additional investigation.
It is also important to note that even within breast cancer, distinct cell lines do not display the same response to arginine metabolic therapy. One study observed that MCF-7 and MCF-10A cells became quiescent upon arginine removal, but upon arginine re-addition MCF-7 recovered while MCF-10A senesced and did not recover (Chiaviello et al., 2012). A separate study noted the same effects in MCF-7 cells, whereas ZR-75-1 cells underwent cell death in the absence of arginine (Scott et al., 2000). While these are intriguing results, the mechanistic basis for these observations are not fully elucidated, although one study concluded that one determinant is expression levels of ASS, or polyamine synthesis, while another study of seven breast cancer lines implicated arginase (ARG) and nitric oxide synthase (NOS) expression (Singh et al., 2000). A clear pattern across multiple breast cell lines has yet to emerge, indicating a need to identify biomarkers that can predict tumor response to arginine metabolism therapy before such therapies can become clinically relevant.
Some therapeutic options are available to deplete arginine, but have not yet been applied to breast cancer (Qiu et al., 2015). ADI-PEG20 and rhArg-PEG both metabolize arginine, but require a deficiency in at least one urea cycle enzyme to prevent the re-synthesis of arginine (Ensor et al., 2002; Qiu et al., 2015). One study found that the arginase/ornithine decarboxylase inhibitor N-omega-hydroxy-l-arginine (NOHA) increases apoptosis of breast cancer cells that overexpress ARG (Singh et al., 2001, 2000), so inhibitors of arginine metabolism as well as arginine depletion strategies have promise for breast cancer therapy. Pyrroline-5-carboxylate reductase (PYCR1) has also been identified as a potential therapeutic target, as its depletion reduces tumor forming ability, indicating that some breast cancers may be highly dependent on proline synthesis (Possemato et al., 2011). However, as exhibited by the highly variable responses to ADI-PEG20 based on ASS status (Qiu et al., 2014), it is important to identify biomarkers to accompany each of these proposed therapeutic strategies in order to apply treatment that will elicit the most robust response.
Concluding remarks It is also worth noting that while glutamine and serine metabolism have been well studied in the context of breast cancer, the metabolism of other nonessential amino acids is a more nascent field. In a recent study that profiled metabolites in breast tumors, alanine was the most significantly altered metabolite, with especially high levels in ER-negative breast cancers and a two-fold increase in tumor over normal. This most closely correlated with downregulation of 4-aminobutyrate aminotransferase (ABAT), which converts alanine to malonate semialdehyde to feed the TCA cycle. Low ABAT expression is associated with poor prognosis, so high levels of alanine may contribute to aggressive breast cancer (Budczies et al., 2013). This illustrates the need for significant future work to determine the merit of targeting alanine metabolism for breast cancer therapy, and also indicates that potential new clinical targets can be identified through investigating metabolic pathways in cancer.