A metabolite can predict cancer or ASD

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In a new study published in American Journal of Human Genetics, a team of researchers led by Charis Eng, M.D., Ph.D., Chair of Cleveland Clinic’s Genomic Medicine Institute, identified a metabolite that may predict whether individuals with PTEN mutations will develop cancer or autism spectrum disorder (ASD).

Germline mutations of the tumor suppressor gene PTEN are associated with a spectrum of rare genetic disorders that increase the risk of certain cancers, cognitive and behavioral deficits, benign growths and tumors (i.e., hamartomas), and macrocephaly.

These disorders are referred to collectively as PTEN hamartoma tumor syndrome (PHTS), but clinical manifestations vary greatly among patients and often are difficult to anticipate.

For example, subsets of Cowden syndrome (CS) and Bannayan-Riley-Ruvalcaba syndrome (BRRS), two well-defined disorders on the PHTS spectrum, are characterized by either a high risk of certain cancers or ASD.

There are functional and structural differences between PTEN mutations associated with ASD and those associated with cancer. However, a biomarker that could proactively determine if a patient with CS/BRRS will develop cancer or ASD has not yet been identified.

Previous studies have established metabolic dysregulation as one of the hallmarks of cancer.

Specifically, germline variants in the SDHx genes cause an accumulation of the metabolite succinate, which has been linked to tumorigenesis.

Some patients with PTEN mutations have been found to have succinate accumulation despite the lack of SDHx mutations, suggesting that variations in metabolite levels may indicate susceptibility to cancer versus ASD.

To investigate this further, Dr. Eng’s team analyzed the metabolite levels of 511 patients with CS, BRRS, or Cowden-like syndrome compared to controls.

The results suggest that certain metabolites are associated with specific mutations and/or clinical features.

The results suggest that certain metabolites are associated with specific mutations and/or clinical features.

In particular, they discovered that decreased levels of fumarate, a metabolite formed from succinate, was more strongly associated with ASD or other developmental disorders compared to cancer in individuals with PTEN mutations.

These findings indicate that certain metabolites, such as fumarate, may serve as predictive biomarkers that could distinguish patients who will develop neurodevelopmental disorders from those who will develop cancer.

“By identifying a way to differentiate those with germline PTEN mutations who develop cancer and those who develop autism, this provides clinicians with a new tool to better tailor treatments to individual patients,” says Dr. Eng.

Dr. Eng is also the inaugural Director of the Center for Personalized Genetic Healthcare and Medical Director of the PTEN Multidisciplinary Clinic. Notably, she was the first to discover a link between mutations in PTEN and Cowden and other syndromes, and her research has formed the basis of national practice guidelines for those with PHTS and Cowden syndrome. Dr. Eng holds the Sondra J. and Stephen R. Hardis Endowed Chair in Cancer Genomic Medicine at Cleveland Clinic.

Funding: This study was supported in part by the National Cancer Institute, Ambrose Monell Foundation, American Cancer Society, Breast Cancer Research Foundation, Doris Duke Distinguished Clinical Scientist Award, the National Institutes of Health, and the clinical research infrastructure enabled by the Zacconi Program of PTEN Research Excellence.


Changes in metabolism are one of the emerging hallmarks of cancer cells (1).

Although many signaling pathways that are affected by genetic mutations in cancer influence metabolism (2), metabolic alterations are more than just an epiphenomenon (3).

Alterations in cellular metabolism sustain rapid production of adenosine triphosphate (ATP) and increased biosynthesis of macromolecules, including nucleotides, lipids and amino acids, and also help maintain cellular redox state (24).

As such, a rewired metabolism is essential to meet the needs of tumors for rapid cell growth and proliferation.

Both intrinsic and extrinsic mechanisms contribute to the characteristic metabolic alterations in cancer cells. Many different oncogenic as well as tumor suppressor signaling pathways influence metabolism, such as hypoxia-inducible factor 1 (HIF1), p53 and MYC.

In addition, cancer metabolism is influenced by the tumor microenvironment, for example the interaction with surrounding cells and the variation in availability of nutrients and oxygen, as extensively reviewed elsewhere (2512).

These mechanisms affect pathways involved in central carbon metabolism, such as glycolysis and the tricarboxylic acid (TCA) cycle, amongst others.

As a result, cancer cells have an increased consumption of glucose and glutamine to satisfy their altered metabolic needs. The fact that cancer cells can become addicted to specific metabolic pathways has led to the recent development of novel drugs that target these metabolic vulnerabilities (1314).

Resistance to therapeutic agents, either intrinsic or acquired, is currently a major problem in the treatment of cancers and occurs in virtually every type of anti-cancer therapy (1516).

Although increased knowledge about the molecular mechanisms of cancer has led to the development of novel targeted therapeutic compounds that increase progression-free survival, this does not always translate in overall survival benefits due to development of resistance (17).

Acquired drug resistance can result from the acquisition of mutations causing decreased drug binding, increased activity of the drug target or the upregulation of multi-drug resistance transporters.

Acquired resistance can also be the result of various adaptive responses that occur downstream of the drug target and that help cancer cells withstand the effects of the drug [reviewed in (18)].

Examples of such mechanisms are the upregulation of cellular pro-survival pathways, including the activation of DNA repair mechanisms (19), the upregulation of anti-apoptotic proteins (2021) or autophagy (22).

Another mechanism of resistance, that is frequently observed with kinase inhibitor therapy, is the so-called “oncogenic bypass,” in which the target pathway is activated through an alternative kinase, even when the primary kinase remains inhibited (2326).

Although adaptive resistance can be targeted to improve drug efficacy, heterogeneity and adaptability of cancer cells often leads to new forms of adaptive resistance (27). Therefore, understanding how resistance can be prevented, targeted, and predicted becomes increasingly important to improve cancer therapy.

Recent studies show that the response to widely-used first-line chemotherapy is substantially influenced by the metabolic state of the cells and that cancer cells rewire their metabolism in response to chemotherapeutic drugs.

We postulate that metabolic rewiring is a novel and important mechanism of adaptive resistance. Here, we will introduce the main features of cancer metabolism in relation to drug resistance and review specific metabolic programs and adaptations that exist in drug-resistant tumors.

We will discuss how these adaptations depend both on the drug and the origin of the tumor and how they contribute to drug resistance, focussing on widely-used chemotherapeutics, including proteasome inhibitors (multiple myeloma), EGFR inhibitors (breast cancer), cisplatin (lung cancer/ovarian cancer) and BRAF inhibitors (melanoma). Finally, we will illustrate how targeting metabolism could overcome drug resistance to standard chemotherapy.

Cancer metabolism

Changes in metabolism are essential to sustain cancer cell growth and proliferation

Glycolysis is the main pathway that is responsible for the breakdown of glucose, and converts glucose to pyruvate in several steps (Figure ​(Figure1)). Glycolysis results in the production of a limited amount of energy in the form of ATP and reducing equivalents in the form of NADH. Pyruvate can subsequently be fed into the mitochondrial TCA cycle, where it is condensed with oxaloacetate to produce citrate. A series of subsequent reactions yields reducing equivalents in the form of NADH and FADH2, which can be oxidized in the electron transport chain (ETC) complexes to ultimately produce ATP in a process called oxidative phosphorylation (OXPHOS) (2829) (Figure ​(Figure1).1). Although ATP production via OXPHOS is more efficient, the majority of cancer cells generate most of their ATP through glycolysis, even in the presence of oxygen (30). This phenomenon is known as aerobic glycolysis or “the Warburg effect” and is characterized by an increased glycolytic rate, whereby pyruvate is converted to lactate and secreted by the cell instead of being funneled into the TCA cycle.

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Figure 1
Metabolic pathways associated with cancer. Pathways involved in central carbon metabolism are presented. Metabolic enzymes that are often upregulated in cancer and serve as potential therapeutic targets are shown in purple. These metabolic pathways are involved in the synthesis of building blocks for macromolecules and redox homeostasis, needed for cell proliferation (shown in red boxes). 2PG, 2-phosphoglycerate; 3PG, 3-phoshoglycerate; ATP, adenosine triphosphate; CPT1, carnitine palmitoyltransferase I; F1,6-BP, fructose-1,6-bisphosphate; F2,6-BP, fructose-2,6-bisphosphate; F6P, fructose- 6-phosphate; FASN, fatty acid synthase; FH, fumarase; G6P, glucose-6-phosphate; GCLC, glutamate-cysteine ligase; GLS, glutaminase; Glu, glutamate; GLUT, glucose transporter type; HK2, hexokinase 2; I, complex I; IDH, isocitrate dehydrogenase; II, complex II; III, complex III; IV, complex IV; LDHA, lactate dehydrogenase A; MCT4, monocarboxylate transporter 4; ME, malic enzyme; OAA, oxaloacetate; PDH, pyruvate dehydrogenase complex; PDK1, pyruvate dehydrogenase kinase 1; PEP, phosphoenol pyruvate; PFK1, phosphofructokinase 1; PFK2, phosphofructokinase 2; PGAM1, phosphoglycerate mutase 1; PHGDH, 3-phosphoglycerate dehydrogenase; PKM2, pyruvate kinase M2; PPP, pentose phosphate pathway; R5P, ribose 5-phosphate; SDH, succinate dehydrogenase; SSP, serine synthesis pathway; TCA, tricarboxylic acid cycle; V, complex V.

Cancer cells sustain their high glycolytic rates in several ways. For example, glycolytic cancers often meet the high demand for extracellular glucose by overexpression of glucose transporters (GLUTs) (3132).

They also show higher levels of monocarboxylate transporter 4 (MCT4), which is responsible for lactate export and thereby helps both in maintaining intracellular pH and in continuing glycolysis (3334). In addition, the secretion of lactate could aid in creating an acidic extracellular tumor environment that favors tumor growth by promoting migration and invasion (3536). Interestingly, cancer cells seem to rely more on specific isoforms of glycolytic enzymes, making these promising targets to specifically inhibit glycolysis in cancer cells (Figure ​(Figure1)1) (14).

For example, the M2 isoform of pyruvate kinase (PKM2) is preferentially expressed over other isoforms in most cancer cells (37). PKM2 catalyzes the final step in glycolysis, and cancer cells are thought to regulate its activity to either increase glycolytic rates or divert glycolytic intermediates to biosynthetic pathways (38), as detailed below. Cancers can also be more dependent on isoforms of hexokinase (HK2) (3940) and lactate dehydrogenase (LDHA) (41), or overexpress an isoform of phosphoglycerate mutase (PGAM1) (4243) (Figure ​(Figure1).1). Finally, several metabolic enzymes that regulate glycolysis are highly expressed in cancer, including pyruvate dehydrogenase kinase 1 (PDK1) (44) and phosphofructokinase 2 (PFK2) (4546), allowing cancer cells to easily adapt glycolytic flux to meet their needs.


Source:
Cleveland Clinic
Media Contacts:
Alicia Reale – Cleveland Clinic
Image Source:
The image is in the public domain.

Original Research: Open access
“Distinct Alterations in Tricarboxylic Acid Cycle Metabolites Associate with Cancer and Autism Phenotypes in Cowden Syndrome and Bannayan-Riley-Ruvalcaba Syndrome”. Charis Eng et al.
American Journal of Human Genetics doi:10.1016/j.ajhg.2019.09.004.

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