By far the most common medical use of lithium is as a first line therapy for bipolar disorder, including associated depression as well as mania (1). A comprehensive review of the literature confirms that lithium is also effective against unipolar depression with unique anti-suicidal effectiveness, and may also be useful against cancer and neurodegenerative disease (2, 3).

One line of evidence for the possible use of lithium as an anticancer agent is epidemiological. A retrospective study showed that psychiatric patients undergoing lithium therapy for bipolar disorder had a much lower incidence of cancer than a matched group not receiving lithium therapy (4). More recent studies of similar design, one conducted nationwide across Sweden, and another across Taiwan, achieved the same result (5, 6). On the other hand another nationwide study, this time from Denmark, showed no correlation of lithium with colorectal adenocarcinoma (7). On closer look, the Denmark study does not contradict the Swedish study. The Swedish study also found that for the entire population lithium was not correlated with cancer incidence, but in addition found that bipolar individuals not treated with lithium had a higher incidence of cancer than the general population. Lithium-treated bipolar patients, on the other hand, had essentially the same cancer incidence as the general population.

One piece of experimental evidence for lithium's potential as a cancer therapeutic modality is that it was observed to inhibit prostate tumor growth (8), presumably through its ability to inhibit GSK3. A detailed study of molecular mechanisms by which lithium inhibition of GSK3-beta inhibits proliferation of prostate tumor cells in culture was presented by Sun et al. (9). The work was subsequently extended to an animal model (10). A clinical trial for the effect of lithium coupled with prostatectomy on men has been conducted but as of this writing results have not yet been published ¹.

With respect to other cancers, lithium has been found to be lethal to neuroblastoma cells but not to normal nerve cells (11). A similar effect was found in ovarian cancer cells (12), although a subsequent similar study on ovarian cancer cells suggests only a more modest benefit (13). It is not clear from our reading of the two ovarian cancer papers why the results are significantly different from each other.

With respect to colorectal cancer, one study suggests that lithium inhibits proliferation of a colorectal cancer cell line (14). Another study on colon cancer cells showed that lithium specifically induced a reversal of the epithelial-to-mesenchymal transition characteristic of the cancer cells (15).

Two studies with relatively small sample size suggested a possible link between lithium and tumors of the upper urinary tract (16, 17). However, a large-scale study involving all urinary tract cancers in Denmark over a multi-year period found no correlation with lithium use (18).

Because lithium therapy is systemic rather than topical or local, it follows that lithium might inhibit metastasis. Evidence that this is the case for colon cancer comes from observation of inhibition of metastasis-inducing factors by lithium and by observation on reduced metastasis in model animals given lithium therapy (19).

Autophagy is a key cellular process in the inhibition of cancer (20). Lithium has been shown to induce autophagy, due to its inhibition of inositol monophosphatase (21). The full range of lithium effects on autophagy is complicated (22), as might be expected because lithium has multiple targets, which themselves have multiple substrates.

A promising strategy is to combine lithium with other therapies. Many cancer therapies have side effects, so augmentation with lithium may have the dual effect of enhancing the effect of the therapy itself and also permit lower but still effective therapeutic doses. Han et al. (23) reported success in combining lithium with another GSK3 inhibitor against TP53 wild-type glioblastoma cells. Zhukova et al. (24) reported that lithium abrogated TP53-mutant radiation resistance in medulloblastomas, showing that effectiveness of radiation therapy may be enhanced by augmenting with lithium.

Because of the promising indications as cited above, lithium has been suggested as one of a number of drugs commonly used for other reasons, to be repurposed for cancer (25).

Much of lithium's known biochemical action may be summarized by noting that it inhibits some phosphate-transfer enzymes (primarily phosphatases and kinases) that have magnesium as a co-factor (2). A common underlying biophysical basis for competition between lithium and magnesium for modulating phosphate-transfer enzymes, is suggested by noting that the primary energy source for cells and the substrate for phosphorylating enzymes is not bare ATP, but rather magnesium-associated ATP (MgATP) (26). NMR studies show that lithium associates with MgATP (27). Based on this admittedly small amount of data, we consider the possibility that lithium generally associates with magnesium-phosphate complexes and thus has the potential to modulate to some extent a large number of phosphorylation reactions and ATP-splitting processes.

Because mutations in G protein linked receptors have emerged as of interest in cancer research (28), it is significant that lithium appears to inhibit β-adrenergic and muscarinic receptor coupling to G proteins by competing with magnesium, which facilitates such coupling (29–33).

In the literature we find evidence for direct lithium inhibition of 17 human magnesium-dependent phosphate-transfer enzymes, as follows: A review by Phiel and Klein (34) identified five (IMPase, IPPase, FBPase, BPntase, and GSK3B). Testing against a panel of 80 protein kinases (35) revealed lithium sensitivity for eight more enzymes (MNK1, MNK2, smMLCK, PHK, CHK2, HIPK3, IKKϵ, and TBK1). It has long been observed that adenyl cyclase activity is inhibited by lithium (36). Of nine different adenylyl cyclases tested, two (ADCY5 and ADCY7) are strongly inhibited by lithium and one (ADCY2) is less strongly but significantly inhibited (37). With the addition of GSK3A (38), we have a list of 17 phosphate-transfer enzymes directly inhibited by lithium. An inspection of protein-protein interaction databases indicates that all 17 interact with multiple other gene products. It should be noted that 72 out of 80 kinases (33), and six out of nine adenylyl cyclases (34), screened were found not to be lithium-sensitive. Table 1 provides the names and synonyms, including both the commonly used gene and protein names, for the 17 known human lithium-sensitive enzymes.

Because lithium affects many different biological molecules and processes (2), it is essential to utilize the tools of systems biology (39) if a comprehensive understanding of lithium action and its prospects for therapy are to be obtained. Important concepts for organizing biological information in a systems context are pathways and networks. A very useful tool for obtaining data about known pathways is the KEGG database (40). An equally useful and complementary tool is the STRING database of interacting proteins (41).

In the present paper we investigate further the possible linkages between lithium and cancer by analyzing the mutual enrichment between STRING-derived interactomes of lithium-sensitive enzymes, and the KEGG pathways associated with cancer.

We used the R-package KEGGgraph (42, 43) to identify the proteins and genes associated with the cancer-relevant pathways.

The fundamental question we address is whether there is significant overlap or mutual enrichment between the interactomes of lithium-sensitive proteins and the pathways implicated in various cancers. Both the interactomes and the pathways are represented by lists of their constituent gene products (proteins). Our methods and results should be considered in the context of the publication from the American Statistical Association, “The ASA's Statement on p-values: Context, Process, and Purpose” (44) In particular the statement notes “P-values and related analyses should not be reported selectively. Conducting multiple analyses of the data and reporting only those with certain p-values (typically those passing a significance threshold) renders the reported p-values essentially uninterpretable.” We acknowledge that such selective reporting is common—indeed the ASA statement was prompted in large measure by the widespread practice of such selective reporting. In this paper we will present in graphical form all the p-values we compute, so that their significance can be judged in complete context.

For each of the 17 lithium sets, an ensemble of 1,000 null sets are generated by random selection from the human genome. Each null set is the same size as the corresponding lithium set. Then we used the R-package STRINGdb (45) to perform KEGG pathway enrichment analysis. This operation is a particular example of the powerful technique of gene-annotation enrichment analysis (46). In gene-annotation enrichment analysis a test list of genes (often derived from gene expression experiments) or proteins is compared to an organized database of gene annotations, often referred to as a gene ontology (47), an array of gene lists corresponding to different biological functions, molecular functions, or locations in the cell. Although the phrase “gene ontology” is used to describe the database, in fact the objects in the database are the gene products, or proteins, whose biological function, molecular function, and location in cell are tagged with annotations. The output of the gene/protein-annotation enrichment analysis is expressed as the likelihood that the list overlaps could have occurred by chance (p-value). A very low p-value implies that the degree of overlap is highly significant statistically and very likely is significant biologically. In our study the lists we are comparing are the proteins contained in the interactomes of lithium sensitive enzymes on the one hand, and the proteins and genes contained in the KEGG pathways associated with cancer on the other hand. For each KEGG term retrieved, a null distribution of uncorrected p-value is generated by the 1,000 null sets. This gives us a measure of the false discovery rate, since any overlap between the null sets and the KEGG pathways is purely accidental. Then the fraction of null set uncorrected p-values smaller than or equal to the lithium-sensitive set uncorrected p-value would be the empirical p-value. A flow chart for this calculation is provided in Figure 1. For a detailed discussion of empirical p-value determination see Ge et al. (48).

The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.