Cancer Therapy

Thanks to extended research from human tissue samples we have been able to make major breakthroughs in cancer research. In the twenty-first century, evidence, both epidemiologically and clinically, have supported that the changes in whole-body metabolism can affect oncogenesis, the progression of tumors, and the response of tumor to therapy. It has been observed that metabolic conditions such as hyperglycemia, obesity, hyperlipidemia, and insulin resistance are associated higher with risk of cancer development, accelerated progression of tumors, and poor clinical outcome. Due to these findings, many clinical studies indicate that statins and metformin may help in decreasing cancer-related mortality and morbidity. Phenformin is another drug used to treat diabetics that can help with anticancer effects. However, phenformin was discontinued in the late 1970s due to a high incidence of lactic acidosis. Metformin is the most commonly used antihyperglycemic agent globally. It has an optimal pharmacokinetic profile with:

·         50 – 60% of absolute oral bioavailability

·         Slow absorption

·         Negligible binding to plasma protein

·         Broad tissue distribution

·         No hepatic metabolism

·         Limited drug interactions

·         Rapid urinary interaction

It also has an exceptional safety profile as there is a low number of individuals who have side effects. Statins also have a great safety profile and is currently used by a large population.

Cancer and Cellular Metabolism

The accumulation of evidence has suggested that malignant transformation is linked to changes that affect several factors of metabolism. Metabolic rearrangements associated with cancer have been linked with the inactivation of tumor suppressor genes and activation of proto-oncogenes. However, the accumulation of metabolites such as fumarate, succinate, and 2-hydroxyglutarate (2-HG) drives oncogenesis through the signal transduction cascades. Conclusively, these observations support the notion that signal transduction and intermediate metabolism are associated.


a)       Oncogenes and Metabolism

The signaling pathways from oncogenic drivers are linked to metabolic alterations due to cancer. For example, the expression of the PKM2 (an M2 isoform of pyruvate kinase) encourages the alteration of glycolytic intermediates in the direction of anabolic metabolism while regulating both transcriptional and post-transcriptional program that leads to the addiction of glutamine.


b)      Oncosuppressors and Metabolism

There are some oncosuppressor proteins that can regulate cellular metabolism. The inactivation of tumor suppressor p53 happens in more than 50% of all neoplasms causes a variety of metabolic consequences that could potentially stimulate the Warburg effect. P53 can possibly suppress the transcription of GLUT4 and GLUT1 and stimulate the expression of apoptosis regulator (TIGAR), TP53 induced glycolysis, SCO2, glutaminase 2 (GLS2) and many other pro-autophagic factors. It also interacts physically with glucose-6-phosphate-dehydrogenase (G6PD) with RB1-inducible coiled-coil 1 (RB1CC1).

c)       Oncometabolites and Oncoenzymes

It was found that metabolites can contribute to oncogenesis when mutations such as fumarate hydratase (FH) and succinate dehydrogenase (SDH) was linked to sporadic and familial types of cancer including pheochromocytoma, leiomyoma, renal cell carcinoma, and paraganglioma. once the enzymatic activity of SDH and FH is disrupted, succinate and fumarate accumulate resulting in oncogenesis.

Targeting Cancer Metabolism

The metabolic targets for cancer therapy rewiring of cancer cells is seen as a promising source for new drug targets. Some different approaches have resulted in the identification of agents that can help with targeting glucose metabolism for cancer therapy. However, the low number of metabolic inhibitors reflect the recent rediscovery of the field. There are also some concerns about the uniformity between malignant cells and non-transformed cells that are undergoing proliferation.


a)       Targeting Bioenergetic Metabolism

Some cancer-associated alterations such as the Krebs cycle, glycolysis, glutaminolysis, mitochondrial respiration, and fatty acid oxidation have been studied as potential sites for drug therapy.


b)      Targeting Anabolic Metabolism

The anabolic metabolism in cancer cells increases the output from nucleotide, protein, and protein biosynthesis pathways to help with the generation of new biomass in rapidly proliferating cells (includes both normal and malignant). A high metabolic flux through the pentose phosphate pathway is vital to cancer cells as it generates ribose-5-phosphate and nicotinamide adenine dinucleotide phosphate (NADPH).


c)       Targeting Other Metabolic Pathways

Other pathways involved in the adaptation to metabolic stress may provide drug targets for cancer therapy. This applies to autophagy, hypoxia-inducible factors 1, and nicotinamide adenine dinucleotide metabolism. A competitor of nicotinamide phosphoribosyltransferase (NAMPT) known as FK866 has been observed to have antineoplastic effects in murine tumor models.



The extensive metabolic rewiring in malignant cells provides a large number of possible drug targets. Many agents that target metabolic enzymes are used for decades while others are being developed. Therefore, the use of metabolic modulators that could be complicated by the similarities of highly proliferating normal cells and metabolism of malignant cells, there might be a chance to harness the antineoplastic activity of these drugs clinically. While many efforts were focused on merging metabolic modulators and targeted anticancer drugs, there may be a common view that metabolism and signal transduction are mostly independent if not separate entities. More research is needed to study the extent of how the metabolic functions of oncosuppressive and oncogenic systems contribute to the biological activity.


Galluzzi L, Kepp O, Vander Heiden MG, Kroemer G. Metabolic targets for cancer therapy. Nature Reviews Drug Discovery. 2013; 12: 829-846.