Metabolic Biomarkers in Cancer: Lactate, Ketones, Insulin and ctDNA

By Editorial Team

Introduction: The Shift Toward Metabolic Monitoring

For decades, cancer monitoring revolved around anatomical imaging, histopathology, and a narrow set of tumour markers — CEA, CA-125, PSA. These remain clinically essential. But as metabolic oncology matures, a new layer of monitoring has emerged: one focused not on the structure of the tumour, but on the biochemical language it speaks.

Metabolic biomarkers — measurable molecules that reflect the energetic state of both tumour and host — offer a window into whether cancer cells are thriving, stressed, or responding to intervention. Among the most clinically relevant today are lactate, ketone bodies, insulin and IGF-1, and circulating tumour DNA (ctDNA) analysed through a metabolic lens.

This article examines each biomarker: its biological basis, what it reveals clinically, how it is measured, and where the evidence is still developing.

1. Lactate: The Warburg Signature in Blood

Biological Basis

Lactate is the end product of anaerobic glycolysis — the metabolic pathway in which glucose is broken down to pyruvate and then reduced to lactate, regenerating NAD+ without oxygen. In normal physiology, lactate production is a temporary response to oxygen deficit (exercise, ischaemia). In cancer, lactate is produced constitutively even in the presence of oxygen: the defining feature of the Warburg effect.

Tumours export lactate via monocarboxylate transporters (particularly MCT4), acidifying the tumour microenvironment. This has multiple oncogenic consequences:

  • Immunosuppression: lactate impairs CD8+ T-cell and NK-cell function
  • Extracellular matrix remodeling and invasion
  • Angiogenesis promotion via HIF-1α stabilisation
  • Recruitment of immunosuppressive regulatory T-cells (Tregs)

Clinical Measurement

Blood lactate is readily measured via standard point-of-care analysers (venous or arterial). Normal fasting venous lactate is typically 0.5–2.0 mmol/L. Elevations in the 2–5 mmol/L range, in the absence of exercise, sepsis, or liver disease, may reflect high tumour glycolytic burden.

Lactic acidosis (>5 mmol/L, pH <7.35) in a cancer patient — sometimes called Type B lactic acidosis — is a serious and underdiagnosed oncological emergency, most commonly seen in haematological malignancies (especially large B-cell lymphoma and acute leukaemia) but also in rapidly proliferating solid tumours.

As a Monitoring Biomarker

Lactate's utility as a routine cancer monitoring biomarker is limited by its lack of specificity — many non-malignant conditions elevate it. However, in defined contexts:

  • Serial lactate in treated haematological malignancies may reflect tumour burden changes alongside LDH
  • Intratumoral lactate imaging via hyperpolarised 13C-MRI is an emerging research tool that directly visualises glycolytic flux in real-time — currently available only in specialist research centres
  • Tumour microenvironment lactate as a measure of immune suppression is being explored as a pharmacodynamic endpoint in immunotherapy trials

Research Frontier

MCT1 and MCT4 inhibitors (e.g., AZD3965) are in clinical development, aiming to trap lactate within tumour cells. Lactate levels may eventually serve as pharmacodynamic biomarkers for these agents.

2. Ketone Bodies: Monitoring Metabolic Compliance and Tumour Response

Biological Basis

Ketone bodies — principally beta-hydroxybutyrate (BHB), acetoacetate (AcAc), and acetone — are produced in the liver from fatty acids during periods of low glucose and low insulin. They normally serve as an alternative fuel for the brain and heart.

In the oncological context, the hypothesis is that many cancer cells lack efficient ketolytic machinery (specifically the enzymes OXCT1/SCOT and BDH1) and therefore cannot utilise ketones as a fuel source. If tumours are starved of glucose while being flooded with ketones, the energetic disparity may impair tumour growth while healthy cells adapt.

Clinical Measurement

BHB is the most stable and measurable ketone body. It is measured via:

  • Finger-prick blood ketone meters (e.g., Keto-Mojo, Abbott Precision Xtra) — convenient, real-time, validated against laboratory methods
  • Serum BHB via standard laboratory venous draw
  • Urine ketone strips — measure AcAc; less reliable, particularly with deep ketosis where urinary excretion decreases

Target therapeutic ketosis in cancer studies is typically defined as BHB ≥1.0 mmol/L (nutritional ketosis). Some researchers argue for ≥2.0 mmol/L for meaningful tumour metabolic effect.

What It Tells Us Clinically

  1. Dietary adherence: BHB is the most objective measure of whether a patient is actually achieving ketosis on a ketogenic diet. Reported dietary compliance is unreliable; blood ketones are not.

  2. Insulin suppression: BHB inversely tracks insulin. Consistently elevated BHB confirms that the systemic hormonal environment (low insulin, low IGF-1) associated with anti-tumour benefit has been achieved.

  3. Tumour metabolic response: Pilot data from GBM trials suggest that patients who achieve sustained BHB elevation show greater reductions in FDG-PET tumour uptake — though this remains correlative and the dataset is small.

Limitations

Not all patients achieve therapeutic ketosis despite adherence. Factors that impair ketogenesis include:

  • Corticosteroid use (raises blood glucose via gluconeogenesis)
  • High protein intake (excess amino acids are gluconeogenic)
  • Gut microbiome composition (emerging research suggests microbiome influences ketone production efficiency)
  • Baseline metabolic syndrome (insulin resistance impairs the switch to fat oxidation)

Repeated BHB monitoring — not a single measurement — is required for meaningful clinical interpretation.

3. Insulin and IGF-1: The Hormonal Tumour Accelerators

Biological Basis

Insulin and insulin-like growth factor 1 (IGF-1) act on overlapping receptor systems (IR and IGF-1R) that activate the PI3K/AKT/mTOR and RAS/MAPK pathways — two of the most proliferative and anti-apoptotic signalling cascades in cancer biology.

Hyperinsulinemia — chronically elevated insulin, typically driven by insulin resistance and excess caloric intake — is associated with increased cancer risk, more aggressive disease, and poorer outcomes across multiple cancer types, including:

  • Breast cancer (ER+, in particular)
  • Colorectal cancer
  • Endometrial cancer
  • Prostate cancer
  • Pancreatic cancer

IGF-1 is synthesised primarily in the liver under growth hormone stimulation and is itself a potent mitogen. Low-protein, low-calorie diets and fasting reliably reduce IGF-1. In animal models, IGF-1 reduction is one of the primary mechanisms by which caloric restriction extends lifespan and reduces tumour incidence.

Clinical Measurement

  • Fasting serum insulin (venous): Normal fasting insulin is typically 3–25 μIU/mL. Values >25 μIU/mL with normal glucose are consistent with hyperinsulinemia.
  • HOMA-IR (Homeostatic Model Assessment of Insulin Resistance): = [fasting glucose (mmol/L) × fasting insulin (μIU/mL)] / 22.5. HOMA-IR >2.5 indicates insulin resistance; >5 is severe.
  • Serum IGF-1: Age-adjusted reference ranges apply. In oncology, values in the upper quartile of the reference range may be clinically relevant even when "normal."
  • C-peptide: A co-secreted fragment of proinsulin; more stable than insulin and better reflects pancreatic insulin secretion, useful in patients on exogenous insulin therapy.

What They Reveal Clinically

As a cancer risk and progression biomarker: Elevated HOMA-IR and IGF-1 at baseline identify patients whose disease may be driven in part by hormonal-metabolic pathways — and who may disproportionately benefit from interventions targeting these pathways.

As a therapeutic monitoring tool: Serial measurements of fasting insulin and IGF-1 allow clinicians to confirm that dietary or pharmacological interventions (metformin, intermittent fasting, low-glycaemic diet) are producing the intended systemic metabolic effect. A patient on a "ketogenic diet" who shows unchanged insulin and IGF-1 has likely not achieved meaningful systemic ketosis.

In combination with cancer-specific markers: A rising PSA in the context of worsening insulin resistance warrants closer attention to metabolic optimisation alongside standard hormonal therapies. Similarly, rising CA-125 in a patient with concurrent worsening HOMA-IR may indicate that metabolic factors are contributing to disease progression.

The Metformin Evidence

Metformin lowers hepatic glucose production (primarily via AMPK activation and Complex I inhibition), reduces fasting insulin, and lowers circulating IGF-1. Large observational studies in diabetic patients show that metformin use is associated with:

  • ~25–40% reduced colorectal cancer incidence
  • Improved breast cancer survival in ER+ disease
  • Possible benefit in PDAC and lung cancer

Multiple randomised trials are ongoing (ADD-IT, MAMA, MA.32), and results are expected to determine whether insulin reduction mediates these effects or whether metformin has direct intratumoural actions.

4. Circulating Tumour DNA (ctDNA): The Metabolic Genome in Blood

Biological Basis

ctDNA consists of small fragments of tumour-derived DNA shed into the bloodstream from apoptotic and necrotic cancer cells. Unlike cell-free DNA (cfDNA) from healthy cells, ctDNA carries tumour-specific somatic mutations, methylation patterns, and copy number alterations.

In the context of metabolic oncology, ctDNA is of particular interest because the somatic mutations detectable in ctDNA directly predict metabolic phenotype:

  • KRAS mutations → constitutive upregulation of aerobic glycolysis (particularly in PDAC and colorectal cancer)
  • PIK3CA/PTEN mutations → mTOR-driven glucose uptake and insulin hypersensitivity
  • TP53 mutations → altered mitochondrial function and increased aerobic glycolysis
  • IDH1/IDH2 mutations → production of 2-hydroxyglutarate (2-HG), an oncometabolite that drives epigenetic dysregulation and impairs HIF-1α degradation
  • BRAF V600E → metabolic reprogramming toward glycolysis in melanoma and colorectal cancer

This means ctDNA analysis is not just a prognostic tool — it is a metabolic genotyping tool that can inform which metabolic vulnerabilities are likely to be present in a given patient's tumour.

Clinical Measurement

ctDNA is measured via liquid biopsy — a standard venous blood draw processed through commercially available platforms including:

  • Foundation Medicine FoundationOne Liquid CDx (FDA-approved companion diagnostic)
  • Guardant360 (comprehensive somatic panel)
  • Grail Galleri (multi-cancer early detection, methylation-based)
  • Inivata InVisionFirst (clinical liquid biopsy for solid tumours)

Sensitivity varies by tumour type, stage, and ctDNA fraction. Early-stage cancers shed little ctDNA and may be below the limit of detection. Late-stage, high-burden tumours are more reliably detected.

What ctDNA Reveals for Metabolic Oncology

  1. Identifying metabolic driver mutations: Before committing a patient to a metabolic intervention, knowing whether KRAS, PIK3CA, or IDH mutations are present helps predict whether glucose restriction, mTOR inhibition, or specific metabolic vulnerabilities are likely to be targetable.

  2. Monitoring clonal evolution under metabolic pressure: As tumours evolve under treatment, metabolic phenotype can shift. A clone that was glycolytic may evolve OXPHOS dependence. Serial ctDNA can identify emerging resistance mutations (e.g., KRAS G12C to G12D transition under pressure).

  3. Early response assessment: Changes in ctDNA variant allele frequency (VAF) after 4–8 weeks of intervention may identify responders before anatomical imaging shows change.

  4. Minimal residual disease (MRD) monitoring: After curative-intent therapy, ctDNA surveillance detects molecular relapse weeks to months before clinical or radiological relapse. In patients using metabolic strategies post-curative treatment, ctDNA monitoring allows early intervention.

Limitations

  • Shedding variability: Not all tumours shed ctDNA equally. Low-shedding tumours (particularly CNS tumours, low-grade prostate cancer) may have falsely negative liquid biopsies.
  • Spatial heterogeneity: ctDNA may not fully represent clonal diversity within the tumour, particularly from spatially distinct metastatic sites.
  • No direct metabolic phenotyping: ctDNA predicts likely metabolic phenotype via driver mutation inference — it does not directly measure glycolytic flux or OXPHOS activity. Combining ctDNA with functional metabolic imaging (FDG-PET, hyperpolarised 13C-MRI) provides a more complete picture.

Integrating Multiple Biomarkers: The Clinical Picture

The most powerful approach is to treat these biomarkers not in isolation but as a metabolic dashboard:

Biomarker What It Measures Optimal Value / Trend
Fasting glucose Systemic glycaemic environment <90 mg/dL fasting
Fasting insulin / HOMA-IR Insulin-driven tumour stimulation HOMA-IR <2.5
BHB (blood ketones) Ketogenic compliance and ketosis depth ≥1.0 mmol/L sustained
IGF-1 Growth factor-mediated tumour promotion Lower quartile for age
Venous lactate Tumour glycolytic burden (contextual) <2.0 mmol/L fasting
ctDNA VAF Tumour burden and clonal evolution Decreasing trend under therapy

Taken together, this dashboard can identify patients achieving the metabolic environment hypothesised to impair tumour growth, track response over time, and provide early warning of resistance or recurrence.

Conclusion: From Theory to Clinical Practice

Metabolic biomarkers in cancer are no longer purely experimental — several (fasting insulin, HOMA-IR, BHB, ctDNA) are available in routine clinical practice today. Their integration into oncological monitoring represents a meaningful expansion of the clinician's toolkit for patients pursuing metabolic co-interventions.

The field is moving rapidly toward multi-omic metabolic profiling — combining blood metabolomics, ctDNA analysis, functional imaging, and continuous glucose monitoring — to create a real-time metabolic portrait of the tumour and its host. This will ultimately enable genuinely personalised metabolic oncology.

For now, the clinically actionable step is to begin measuring these biomarkers routinely in patients for whom metabolic strategies are being considered — and to use them, not intuition, to guide, monitor, and adjust those interventions.

This article is for educational and clinical reference purposes. It does not replace individualised medical judgement.

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