Understanding serine and glycine metabolism in cancer: a path towards precision medicine to improve patient's outcomes

Abstract

In this perspective, we highlight and reflect on the current knowledge with respect to serine/glycine metabolism in cancer, therapeutic resistance, and precision medicine opportunities for therapeutic targeting and treatment follow-up. Cancer subtypes with high mortality rates include lung cancer and glioblastomas. In order to improve future therapeutic opportunities, patient stratification need to be performed to select patients that might benefit from adjuvant serine/glycine targeting compounds. In an effort to identify the group of patients for stratification purposes, we analyzed publicly available TCGA patient datasets to test associations between serine/glycine metabolism enzyme expression and important cancer drivers in lung cancer and glioblastoma. These patients presenting serine/glycine pathway overexpression might benefit from adjuvant sertraline treatment in the future.

Fig. 1

Cancer hallmarks that are regulated and linked to cancer cell metabolic reprogramming

Fig. 2

Cancer cell metabolism intrinsic benefits and influences on the tumor microenvironment

Fig. 3

Link of serine/glycine synthesis enzyme overexpression with the expression and mutations of genetic drivers of LUAD. TCGA data of LUAD patients using TCGA, PanCancer Atlas dataset in cBioPortal, n = 503. The data was ranked according to high and low mRNA expression levels of serine/glycine synthesis enzymes, PHGDH, PSAT1, PSPH, SHMT1, and SHMT2, n = 150 high versus n = 150 low for each enzyme. The graph shows the sum of mRNA expression (high = red, low = blue) and mutation data of the main genetic drivers of LUAD in the patient groups with high expression of the different serine/glycine synthesis enzymes. The significant changes were calculated upon comparison of the mRNA expression and mutation data in the group of patients with high vs low expression of serine/glycine synthesis enzymes. Copy-number alterations (CNA) were not included due to a frequency lower than 10%. A two-tailed equal variance t-test has been performed for gene expression analysis and Fisher Exact test for mutations

Fig. 4

Link of serine/glycine synthesis enzyme overexpression and expression, mutations, and CNA of genetic drivers of GBM. TCGA data of GBM patients using TCGA, Firehose Legacy Samples with mRNA data (Agilent microarray) n = 401. The data was ranked according to high and low mRNA expression levels of serine/glycine synthesis enzymes, PHGDH, PSAT1, PSPH, SHMT1, and SHMT2, n = 150 high versus n = 150 low for each enzyme. Of note, among GBM patients with high expression of PSPH, 25% of these cases include PSPH amplifications. The graph shows the sum of mRNA expression (high = red, low = blue) and mutation/CNA data of the main genetic drivers of GBM in the patient groups with high expression of the different serine/glycine synthesis enzymes. The significant changes were calculated upon comparison of the mRNA expression and mutation/CNA data in the group of patients with high vs low expression of serine/glycine synthesis enzymes. CNA were included in those genes with a ≥ 10% frequency. A two-tailed equal variance t-test has been performed for gene expression analysis and Fisher Exact test for mutations/CNA