Research AppraisalSystematic Review

Metabolic signatures of triple-negative breast cancer

Molecular biology reportsPusalkar, Gandhar, Madamakki, Rakshitha, Chakraborty, Parna et al.17 July 2026DOI

Clinical Snapshot

15CEBM
Evidence: WeakSystematic Review

PICO Framework

P — PopulationPatients with triple-negative breast cancer (TNBC) — defined by absence of oestrogen receptor, progesterone receptor, and HER2 expression
I — InterventionMetabolomic profiling and characterisation of dysregulated metabolic pathways (glycolysis, oxidative phosphorylation, lipid metabolism, amino acid metabolism, one-carbon metabolism, pentose phosphate pathway) in TNBC
C — ComparatorNormal breast tissue, non-TNBC breast cancer subtypes, or baseline metabolic states (comparators vary across cited primary studies; not uniformly defined within this narrative review)
O — OutcomesIdentification of metabolic signatures associated with TNBC progression, therapy resistance, and potential therapeutic vulnerabilities; applicability of metabolomics-driven precision medicine and machine learning (ML)/artificial intelligence (AI) approaches to personalised TNBC treatment

Bottom Line

This narrative review provides a broad and reasonably comprehensive overview of metabolic reprogramming in TNBC, covering glycolysis, lipid metabolism, amino acid pathways, and emerging metabolomics-AI integration. However, it must be clearly understood as a hypothesis-generating educational resource rather than a source of practice-changing evidence. The review lacks the methodological rigour of a systematic review — there is no registered protocol, no systematic search strategy, no formal inclusion criteria, and no risk of bias assessment of primary studies. The evidence base is predominantly preclinical, and no quantitative synthesis is performed. Bibliographic inconsistencies in the DOI and publisher data warrant verification before citation. For Australian oncologists, none of the metabolic therapeutic targets discussed are currently TGA-approved or PBS-listed. The review is best used to inform research prioritisation and to contextualise the scientific rationale for emerging clinical trials targeting TNBC metabolism. Clinicians should await prospective, well-powered clinical trial data before considering any metabolic pathway-targeted intervention outside a trial setting. The integration of AI/ML in metabolomics, while promising, requires critical evaluation of model validity and clinical implementation feasibility before influencing practice.

Evidence: Weak

Key Findings

  • P Value: Not applicable — no original statistical analyses performed

  • Effect Size: Not applicable — no pooled quantitative effect sizes are reported; findings are qualitative summaries of primary study results

  • Primary Outcome: Narrative synthesis of dysregulated metabolic pathways in TNBC including enhanced aerobic glycolysis (Warburg effect), altered oxidative phosphorylation, upregulated de novo lipogenesis, glutamine dependency, one-carbon metabolism alterations, and pentose phosphate pathway activation — each proposed as contributing to tumour growth, survival, and therapy resistance

  • Nnt Or Sensitivity: Not applicable — no NNT, sensitivity, specificity, or hazard ratio data synthesised; review is hypothesis-generating rather than outcome-quantifying

  • Confidence Interval: Not applicable — no meta-analytic confidence intervals generated

Clinical Application

Direct clinical application is not currently feasible based on this review alone. The metabolic targets discussed (e.g., GLUT1 inhibition, fatty acid synthase inhibition, glutaminase inhibition) remain largely in preclinical or early-phase clinical trial stages. Metabolomics-based biomarker panels are not yet validated for routine clinical use in TNBC. Implementation of AI/ML-driven metabolomics in clinical workflows requires substantial infrastructure, standardisation, and prospective validation before adoption. In Australia, TNBC accounts for approximately 15–20% of all breast cancers and is managed according to NHMRC-endorsed guidelines and Cancer Australia recommendations. Current PBS-listed systemic therapies for TNBC include anthracycline- and taxane-based chemotherapy, with pembrolizumab (PD-L1 positive, early and metastatic TNBC) and olaparib (BRCA1/2-mutated, HER2-negative metastatic breast cancer) available under PBS authority prescribing. Sacituzumab govitecan has TGA approval for pre-treated metastatic TNBC. None of the metabolic targeting strategies discussed in this review are currently TGA-approved or PBS-listed for TNBC. The RACGP and COSA do not currently recommend metabolomics-based testing in routine TNBC management. This review is most relevant to Australian researchers and translational oncologists working within clinical trial frameworks (e.g., ANZGOG, BCNA-affiliated trials) exploring novel TNBC therapeutics. Women with confirmed TNBC (ER-negative, PR-negative, HER2-negative breast cancer), particularly those with early-stage or locally advanced disease where novel biomarker-driven treatment stratification is being explored. Most relevant to oncologists, medical scientists, and researchers rather than for immediate bedside application.

Abstract

Triple-negative breast cancer (TNBC), being one of the most aggressive subtypes of breast malignancies, is characterized by poor prognosis and limited treatment options. As a leading cause of cancer-related mortality among women, TNBC poses unique clinical challenges due to the absence of effective targeted therapies. Conventional treatment strategies, including chemotherapy, often suffer from significant drawbacks such as drug resistance and intolerable side effects, underscoring an urgent need for innovative approaches to improve therapeutic outcomes. A defining hallmark of TNBC is its markedly altered cellular metabolism, which not only supports tumor growth and survival but also contributes to therapy resistance. Elucidating these metabolic alterations could provide critical insights into potential vulnerabilities that may be exploited for therapeutic intervention. This review provides a comprehensive overview of the major metabolic pathways that are dysregulated in TNBC and their relevance to disease progression and therapeutic intervention. Finally, we explore recent advances in metabolomics-driven precision medicine, highlighting the integration of artificial intelligence and machine learning approaches, covering aspects such as data processing, feature selection, and model construction that can accelerate the advancement of personalized treatment strategies for TNBC.

References

  1. 1.Pusalkar, G., Madamakki, R., Chakraborty, P., Parodi, A., & Lopus, M. (2026). Metabolic signatures of triple-negative breast cancer. Molecular Biology Reports. https://doi.org/10.1155/2017/7687851
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