Lipidomics and metabolomics as potential biomarkers for breast cancer progression

Abstract

Breast cancer is the most prevalent cancer among women in the United States, representing ~30% of all new female cancer cases annually. For the year 2024, it is estimated that 310,720 new instances of invasive breast cancer will be diagnosed, and breast cancer will be responsible for over 42,000 deaths among women. Today, despite the availability of numerous treatments for breast cancer and its symptoms, most cancer-related deaths result from metastasis for which there is no treatment. This emphasizes the importance of early detection and treatment of breast cancer before it spreads. For initial detection and staging of breast cancer, clinicians routinely employ mammography and ultrasonography, which, while effective for broad screening, have limitations in sensitivity and specificity. Advanced biomarkers could significantly enhance the precision of early detection, enable more accurate monitoring of disease evolution, and facilitate the development of personalized treatment plans tailored to the specific molecular profile of each tumor. This would not only improve therapeutic outcomes, but also help in avoiding overtreatment and the associated side effects, thereby improving the quality of life for patients. Thus, the pursuit of novel biomarkers, potentially encompassing metabolomic and lipidomic signatures, is essential for advancing breast cancer diagnosis and treatment. In this brief review, we will provide an overview of the current translational potential of metabolic and lipidomic biomarkers for predicting breast cancer prognosis and response to therapy.

Introduction

Breast cancer is the most prevalent cancer among women in the United States, representing ~30% of all new female cancer cases annually. For the year 2024, it is estimated that 310,720 new instances of invasive breast cancer will be diagnosed, and breast cancer will be responsible for over 42,000 deaths among women¹. Today, despite the availability of numerous treatments for breast cancer and its symptoms, most cancer-related deaths are still predominantly due to metastasis². While there are treatments available that can manage metastatic breast cancer and improve quality of life, it remains incurable, and the focus is often on extending life rather than achieving a cure³. This fact emphasizes the necessity for comprehensive management strategies that prioritize prevention, early detection, and vigilant monitoring throughout the course of breast cancer. Despite clinical advances in these areas, not all cases of breast cancer can be intercepted before progressing to stage IV, whether at initial diagnosis or through recurrence⁴. This limitation highlights the complexity of breast cancer and current gaps in management. It underscores the need for continued research into effective therapies to reduce the incidence of advanced disease.

The primary breast tissue biomarkers currently used in clinical practice to guide treatment decisions include hormone receptor status (estrogen receptor and progesterone receptor) and human epidermal growth factor receptor 2 (HER2)⁵. These receptors are identified through pathological analysis and play an important role in determining the most appropriate treatment approach for patients with breast cancer⁵. Breast cancer is further classified into subtypes (Luminal A, Luminal B, HER2-enriched, and Basal-like), each with distinct biological characteristics. This classification informs prognosis and guides treatment decisions tailored to the unique features of each cancer⁶, but are not comprehensive in predicting disease progression or response to therapy across all patient subtypes⁷.

Breast cancers can also vary widely in terms of their genetic and molecular characteristics, their response to treatments, and their potential for aggressiveness and metastasis⁸. Due to this heterogeneity, there is a need for more robust and sensitive biomarkers capable of predicting the risk of breast cancer progression as well as response to therapies. Advanced biomarkers could significantly enhance the precision of early detection, enable more accurate disease monitoring, and facilitate the development of personalized treatment plans tailored to the specific molecular profile of each tumor. This would not only improve therapeutic outcomes but also help avoid overtreatment and its associated adverse effects, thereby improving patient outcomes and quality of life. Thus, pursuing novel biomarkers, potentially encompassing metabolomic and lipidomic signatures, is essential for advancing breast cancer diagnosis and treatment. The utility of metabolomics is emphasized by its proven capability to detect early metabolic signatures indicative of disease states in longitudinal studies, often well before clinical symptoms become evident. Notably, early metabolic markers have been identified for diseases such as multiple sclerosis, pancreatic cancer, type 2 diabetes, and cognitive decline^4–10. Recently, it has been demonstrated that lipids can be used as biomarkers with health-to-disease transitions as observed through comprehensive lipidomic profiling of longitudinally collected plasma samples, thus highlighting the role of lipidomic changes in diabetes, ageing, inflammation, and immune homeostasis¹¹. The demonstrated ability of metabolomics and lipidomics to predict and correlate with various disease states, and the heightened metabolic and lipid adaptations that take place in cancer cells compared to non-cancer cells suggests predicting cancer progression with metabolic and lipidomic biomarkers is feasible¹². For example, metabolomics and lipidomics have recently been employed to map out and elucidate intratumor metabolic heterogeneity in the context of gastric cancer¹³.

Recent studies have significantly advanced our understanding of the metabolic landscape in breast cancer, highlighting the complexity and clinical relevance of metabolic stratification. Studies have identified distinct metabolic subtypes of human breast tumors, underscoring their potential therapeutic implications and clinical relevance¹⁴. This stratification could guide personalized treatment strategies, improving patient outcomes. A recent study further explored the metabolic heterogeneity within cancers, emphasizing the diverse metabolic adaptations that tumors undergo to survive and proliferate¹⁵. Another study utilized spatially resolved multiomics to reveal cell-specific metabolic remodeling in gastric cancer, demonstrating the intricate metabolic interactions at the cellular level, which may also be applicable to breast cancer research¹³. Recent work has highlighted the burgeoning field of spatial metabolomics, suggesting that it could drive innovation in cancer research by providing more detailed metabolic maps of tumors¹⁶. A recent study investigated metastatic triple-negative breast cancer, showing how these tumors adapt their metabolism to different tissues while retaining essential metabolic signatures, which is crucial for understanding metastasis¹⁷. Finally, the role of metabolites in the tumor microenvironment has recently been explored, detailing how metabolic interactions between cancer cells and their surroundings can influence systemic metabolism, potentially offering new avenues for therapeutic intervention¹⁸. Together, these studies underscore the importance of metabolic profiling in cancer.

In this brief review, we will provide an overview of the current translational potential of metabolic and lipidomic biomarkers for predicting breast cancer prognosis and response to therapy.

Metabolomic biomarkers of breast cancer progression

Metabolic processes shape the functional dynamics of cancer cells and their progression¹⁹. Metabolomics, the study of these processes, offers a precise method to understand how physiological conditions relate to both external influences and diseases. Metabolites are particularly useful in this context because they act as immediate indicators of disease processes and can serve as direct biomarkers reflecting pathogenic activities. Metabolomics provides an intricate and comprehensive view of biological processes and metabolic pathways, positioning it as a potentially pivotal tool in precision medicine for offering a precise and objective molecular-level analysis²⁰. This omic technique further provides direct readouts, through metabolic maps and profiles, that have the potential to predict metabolic states ranging from the cellular level to the organ of interest¹⁶.

Recent innovations in highly sensitive, low-input assays allow for detailed profiling of the metabolome and lipidome from minimal amounts of biological samples, which is revolutionizing the use of lipidomics and metabolomics as biomarkers for tracking the progression of diseases such as breast cancer²¹. In cancer, fundamental shifts in metabolic pathways have been demonstrated to promote cancer progression in mouse and patient samples²². For instance, cancer cells have been shown to capitalize on glucose uptake and downstream shifts in metabolic processes to provide maximal cellular fuel to maintain cancer cell growth and proliferation²³. Furthermore, tumors are dependent on metabolites to sustain growth and survival, and are able to adapt nutrient usage in response to the nutrient availability¹⁸.

Amino acid metabolism has also been shown to be altered in cancer cells compared to non-cancer cells²⁴. Cancer cells require and exploit glutamate as it is a precursor molecule for various growth signaling pathways and thus is critical for cell proliferation²⁴. Proline catabolism, by the enzyme proline dehydrogenase (PRODH), has been shown to play a crucial role in supporting the growth and spread of metastatic breast cancer cells, as demonstrated in both 3D culture systems and in vivo mouse models²⁵. Elevated PRODH expression and proline catabolic activity were observed in metastatic tumors compared to primary tumors, indicating its potential as a biomarker and/or target for breast cancer progression²⁵.

Additional metabolite markers, including lactate, β-hydroxybutyrate, acetoacetate, glycoproteins, pyruvate, glutamate, and mannose, have been shown to be increased in early cases of breast cancer^26–28. The presence of altered metabolic pathways for early breast cancer detection has also been noted in patient samples, notably those involving taurine, hypotaurine, and the metabolism of amino acids such as alanine, aspartate, and glutamate, thus highlighting these metabolites as potential biomarkers of breast cancer progression²⁸. Redox imbalance is also a hallmark of metabolic adaptations as cancer cells tend to experience higher levels of oxidative stress and mitochondrial dysfunction^29,30. Studies have shown that breast cancer cells upregulate antioxidant pathways to maintain redox balance and sustain cell survival³¹.

In the PAM50 classification of breast cancer, Luminal A, Luminal B, HER2-enriched, and basal-subtypes subtypes have defined metabolic and lipidomic signatures that have been determined to have clinical relevance. In this classification system, tumors classified as Luminal A are characterized by the presence of ER and/or PR and the lack of HER2; clinically, these tumors are typically low-grade and slow-growing as they rely more on oxidative phosphorylation making the cancer type less aggressive³². This tumor subtype has moderate lipid synthesis as it affects cholesterol metabolism which can be controlled, further reflecting the subtype’s less aggressive nature³³. Like Luminal A, Luminal B tumors are ER-positive and HER2-positive, yet they are PR-negative³⁴. This tumor type has higher glycolytic activity and elevated lipid synthesis, which supports cell growth, making the subtype more aggressive in nature³⁵. The HER2-enriched subtype has an overexpression of the HER2 protein. Similar to the Luminal B subtype, HER-enriched has increased glycolytic activity as well as glutamine metabolism and increased lipid biosynthesis which contributes to the overall aggressiveness of the HER2 positive subtype³⁶. Specifically, the overexpression of HER2 can modulate lipid metabolism by promoting an upregulation in gene expression of fatty acid synthesis and uptake, including CD36, which is involved in the uptake of fatty acids and oxidized low-density lipoproteins, and the expression of which has been linked to an overall poor prognosis³⁷. Finally, the Basal-like subtype, unlike the aforementioned types, lacks the ER, PR, and HER2 receptors³⁸. This subtype is also able to readily exploit glycolytic pathways to promote cell proliferation even in normoxic conditions making it one of the most aggressive subtypes³⁹. To meet high energy demands, these cancer cells have high lipid uptake and fatty acid oxidation³⁹. Luminal A, Luminal B, HER2-enriched, and basal-subtypes have unique clinical signatures that influence prognosis and treatment triaging for breast cancer.

Distinct metabolic changes may occur in the different tissue environments to which breast cancer metastasizes¹⁷. These specific changes in metabolism could provide valuable insights, potentially serving as indicators for the prediction of breast cancer spread to specific distant metastatic sites such as lymph nodes. Furthermore, previous work has shown that patients with breast cancer undergoing neoadjuvant chemotherapy may achieve a pathologic complete response in the breast, yet not replicate the same response in the axillary lymph nodes, revealing a discordant treatment outcome depending on metastatic site⁴⁰. The unique behavior of cancer within lymph nodes, as opposed to the primary tumor site, can be linked to the specific microenvironment within the lymphatic system. This environment not only facilitates the transportation and dissemination of cancer cells but also shapes their growth, survival, and responsiveness to treatment, setting it apart from other tissue environments^41,42.

Studies have demonstrated that metabolites in the lymph node correlate with cancer progression to lymph nodes; cancer cells expressing arachidonate 15-lipoxygenase-1 can not only cause damage to the lymph endothelium, but also facilitate the entrance of tumor cells into the vessel; making it a key candidate metabolite in the progression of cancer⁴³. Similarly, studies have shown breast metastatic spread through the lymph nodes has shown an upregulation in PRODH as well as asparagine synthetase, thus proline and asparagine are metabolites of interest in progression of lymph node metastatic breast cancer²⁵. Because cancer cells in the lymph node tumor microenvironment undergo metabolomic reprograming, the altered nutrient environment presents an opportunity for the identification of metabolites as biomarkers in cancer progression^17,44. Nevertheless, the lymphatic tumor microenvironment represents an under-researched area for biomarkers pertinent to breast cancer progression. Furthermore, breast cancer cells in other distant metastatic sites (including lung, liver, brain, and bone) likely undergo metabolic alterations that could be detected by unique metabolomic/lipidomic profiling of tissues from different metastatic locations.

Currently, changes in specific metabolites during disease progression have led to the identification of biomarkers for some, but not all cancers⁴⁴. however, no definitive metabolic biomarkers have yet been identified in breast cancer that can predict disease progression in patients⁴⁵. Prior work has shown that glucose, threonine, and beta-hydroxybutyrate are upregulated in the blood and correlate with nonspecific symptoms of breast cancer like weight loss and fatigue⁴⁶. Further studies in patient samples have shown metabolites such as glycine, taurine, lactate, and succinate are increased, and glucose and inositol are decreased in breast cancer tissues compared to non-tumorous tissue⁴⁷. A recent study has shown that metabolomics can provide a prognostic framework to identify distinct metabolomic profiles in serum samples that differentiate early from metastatic breast cancer, thereby enhancing the precision of risk stratification and therapeutic decision-making of conventional clinical methodologies⁴⁸. Another study using patient samples explored the metabolic landscape of breast cancer and subsequently identified three distinct breast cancer subtypes that correlated with tumor aggressiveness and patient outcomes; these subtypes showed pronounced dysregulation in bile-acid biosynthesis, methionine pathway, fatty acid metabolism, and glucose metabolism¹⁴.

Given that cancer is metabolically active disease, the identification of metabolites that serve not only as biomarkers but as necessary agents of cancer progression could provide the opportunity to intervene with antimetabolite therapeutics⁴⁹, such as those reviewed in Fig. 1 and Table 1. For instance, if a breast cancer patient’s lymph node metabolomic analysis indicates upregulation in folate metabolism, clinicians could tailor a treatment plan that includes methotrexate, an antimetabolite that inhibits dihydrofolate reductase, thus depleting folate, to potentially enhance the efficacy of standard chemotherapy and mitigate cancer progression⁵⁰.

Fig. 1. Representative anti‐cancer drugs targeting carbon, glucose, and fatty acid metabolism.

A Carbon metabolism has revealed various targets of interest in the folate synthesis pathway. Metabolomic techniques have led to the development of drugs like methotrexate and capecitabine to target thymidylate synthase¹⁰² and methotrexate to target key enzyme dihydrofolate reductase (DHFR)¹⁰³. B Glucose metabolism modulators target glucose transporters phloretin¹⁰⁴ and WZB117¹⁰⁵ as well as key enzymes in the glycolytic pathway like inhibition of hexokinase 2 via Genistein-27¹⁰⁶ and Resveratrol^107,108. Within the same pathway, metabolomic techniques have further pioneered anti-cancer drugs targeting glucose 6 phosphate dehydrogenase (G6PD), like Polydatin¹⁰⁹ which inhibits glycolysis, as well drugs targeting the conversion of pyruvate to lactate via the key enzyme lactate dehydrogenase (LDH) with Oxamate¹¹⁰ and FX11¹¹¹. C Fatty acid metabolism targets include Betulin which inhibits sterol regulatory element binding protein 1 (SREBP-1). At a synthesis level, key enzymes acetyl-CoA carboxylase is inhibited via Soraphen A¹¹² and fatty-acid synthase by TVB-2640^113,114. Figure generated with BioRender.com.

Table 1.

Summary of pilot and pre-clinical studies referenced in manuscript text.

Lipidomic biomarkers of breast cancer progression

Lipids, such as sterols and various forms of glycerides and phospholipids, are critical hydrophobic compounds that form the structure of cell membranes and are integral to processes like energy storage and cell signaling within the cell⁵¹. In cancer, notably in breast cancer, there is a profound remodeling of lipid metabolism, characterized by increased de novo lipogenesis and alterations in lipidomic profiles due to the rapid proliferation and metabolic demands of cancer cells, which is heightened during tumor progression^44,52. Prior work has shown that cancer cells can enhance lipolytic pathways to metabolize stored triglycerides and fatty acids, which are crucial for maintaining rapid cell division and invasion⁵³. These modifications are not merely a consequence of cancer but also contribute to tumor progression and metastasis by providing necessary components for cell membrane formation and energy production.

Furthermore, lipid metabolism in cancer is influenced by the need for various biological processes, such as the production of steroid hormones, vitamins, bile acids, and eicosanoids. This requirement is met through a combination of dietary intake and endogenous synthesis, with most cancer cells depending more heavily on exogenous lipids due to their increased metabolic requirements^54–58. Altered lipid metabolism in cancer not only supports the energy and structural needs of proliferating cells but also mediates stress responses, such as endoplasmic reticulum stress and ferroptosis, further exacerbating cancer phenotypes^53,59,60. Cancer cells also often upregulate lipolytic pathways where lipolysis and β-oxidation, may be activated in cancer cells⁶¹. This ensures that cells can then breakdown stored triglycerides and fatty acids to sustain proliferation⁶¹. Lipidomic studies in patient breast cancer samples also have revealed a correlation between lipid profiles and both the type of cancer tissue and the tumor grade. Another significant shift observed with tumor growth is the change in the balance of choline-containing compounds^62,63.

Lipid metabolism plays a critical role in breast cancer as lipid remodeling can shape the tumor microenvironment by altering immune cell responses⁶⁴. Specifically, tumor cells can alter the tumor microenvironment by secreting signaling molecules and can exacerbate cancer progression as cancer-associated fibroblasts and immune cell function become compromised⁶⁵. The direct effect on cancer cells can disrupt many processes and specifically alter lipid metabolism in cancer cells by enhancing lipid biosynthesis by promoting an upregulation in fatty acid, cholesterol, and phospholipid synthesis to promote cell growth⁵³. The accumulation of lipid droplets in cancer cells can subsequently inactivate immune signaling molecules to foster a hospitable environment by providing the necessary energy reserves and redox balance⁶⁶.

Lipid metabolism remodeling further hinders cancer phenotypes through its direct association with myeloid-derived suppressor cells (MDSCs) and tumor-associated macrophages (TAMs) as tumor-infiltrated MDSC upregulates fatty acid uptake and oxidation⁶⁷. Myeloid cells have been deemed regulators of metastasis and cancer/tumor progression as they negatively regulate immune responses; regulation is dependent on the type of myeloid cell involved which can include and range from polymorphonuclear neutrophils, eosinophils, dendritic cells, macrophages, megakaryocytes, and basophils⁶⁷. TAMs also support tumor growth as lipid metabolism can reprogram cells to a pro-tumorigenic state⁶⁷. The lipid tumor microenvironment can also reshape how cancer cells behave and facilitate metabolic reprogramming, as lipids are a source of energy. The readily available abundance of energy provided by lipids is able to sustain cancer cell growth and proliferation as cancer cells can use fatty acids undergo beta oxidation to generate adenosine triphosphate⁶⁸. Lipids, like prostaglandins, within the TME, can further modulate an immune response due to their ability to readily suppress and alter immune function; they can further promote the formation of new blood vessels or angiogenesis to further supply nutrients necessary for tumor growth⁶⁹.

Given these significant changes in lipid metabolism, lipidomics emerges as a promising field for developing biomarkers for breast cancer progression. By analyzing the lipid profiles of cancer cells, particularly the patterns of lipid synthesis, remodeling, and breakdown, lipidomics can offer valuable insights into the metabolic state of tumors. This understanding could lead to novel therapeutic strategies targeting lipid biosynthesis and metabolism, potentially decreasing cancer progression.

Influence of environmental and lifestyle factors on metabolome and lipidome

Metabolomics and lipidomics have facilitated new insights into the connections between environmental and lifestyle factors (such as dietary factors) and disease states⁷⁰. For example, branched-chain amino acids have been associated with obesity and insulin resistance⁷¹, and physical activity and dietary-associated changes are known to induce extensive alterations to the plasma metabolome and lipidome⁷². Plasma levels are directly influenced by dietary intake as consumption of food and drinks provides the body with nutrients, fats, vitamins, mineral and proteins that can be absorbed into the bloodstream^73,74. Depending on the content of the diet, metabolic pathways can become enriched as substrates necessary to carryout processes become readily available. For example, a diet high in carbohydrates will raise plasma glucose levels as the carbohydrates provide the substrates necessary to fuel glycolytic pathways⁴⁵. This will most typically result in a higher presence of glucose related metabolites being enriched from metabolomic analysis. To a similar degree, a diet high in fat content would most likely reveal an elevated lipid and cholesterol panel as the substrates necessary for the metabolic pathways associated would be available⁷⁵. Because cancer cells can readily rewire their metabolism in cancer progression, this further contributes to the overall metabolic heterogeneity and aggressiveness of the cancer¹⁵.

It has been shown that certain metabolites, like trimethylamine N-oxide, are upregulated in patients whose diet consists of meat, fish, and eggs as these foods enrich the gut microbiome to show an upregulation in this metabolite⁷⁶. Additionally, analysis of metabolite profiles from the plasma of patients with breast cancer from the Nurses’ Health Study demonstrate many associations between metabolite subclasses and breast cancer risk, many of which could be influenced by dietary consumption⁷⁷. In summary, dietary and environmental factors are critical additional elements that can substantially affect the lipidome and metabolome, potentially influencing the progression of breast cancer. It is important to acknowledge the influence of environmental and lifestyle (including dietary) factors on metabolome and lipidome within the context of analyzing -omics profiles as an additional variable for breast cancer progression.

Patient specimens for metabolomics/lipidomics profiling

Metabolomic and lipidomic profiling techniques can analyze small quantities and volumes of both solid and liquid tissue types. This flexibility means a variety of tissue sources can be used for profiling including plasma, serum, saliva, primary tumor or metastatic site biopsies (including lymph node biopsies), and urine^78,79, among others. One study using saliva identified that patients with breast cancer had a distinct metabolite profile of volatile metabolites (including acetic, propanoic, and benzoic acids) that could potentially be used as a prognostic biomarker⁸⁰. Another study using metabolomics of salivary samples of patients with breast cancer determined that the ratios of polyamines correlated with cancer stage in patients and increased with worsened health status⁸¹. A dried blood spot technique for analyzing amino acids and acylcarnitines identified piperamide, asparagine, proline, tetradecenoylcarnitine/palmitoylcarnitine, phenylalanine/tyrosine, and the glycine/alanine ratio as potential biomarkers for breast cancer diagnosis⁸².

Plasma sampling enables the detection of various indicators of cancer, including circulating tumor cells, serum-based disease markers, circulating tumor DNA, and plasma DNA methylation patterns^83,84. These indicators can be analyzed together with metabolomic and lipidomic profiles derived from the same plasma samples for a comprehensive understanding of breast cancer progression at a molecular-level. Fine-needle aspiration (FNA) biopsies also provide sufficient sample to be able to obtain a metabolomic/lipidomic profile, and FNA techniques have been successfully applied to other -omic profilings in cancer including proteomics⁸⁵. Since FNA is commonly performed on primary breast tumors and lymph node biopsies, it represents a novel sampling method that could be used to determine the metabolic and lipidomic profile of the tissues for comparison of profiles between primary tumor and progression to lymph nodes^85–87 (Fig. 2).

Fig. 2. Clinical potential of lipidomic and metabolomic profiling in breast cancer.

As fine-needle aspirations are commonly performed on primary breast tumors and lymph node biopsies, it represents a novel sampling method that could be used to determine the metabolic and lipidomic profile of the tissues for comparison of profiles between primary tumor and progression to lymph nodes. Figure generated with BioRender.com.

Benefits and challenges of lipidomics/metabolomics profiling in breast cancer

Lipidomics and metabolomics profiling offer potential benefits in breast cancer management, specifically by opening new opportunities for personalized treatments. In the context of lipidomics and metabolomics profiling for breast cancer, several types of specimens can be considered for clinical analysis. Primary tumor samples are the most direct source of metabolic information specific to the malignancy⁸⁸. Lymph node samples, often obtained during staging procedures, provide critical insights into metastatic processes⁸⁹. Plasma represents a highly feasible option due to its non-invasive collection and potential to reflect systemic metabolic alterations associated with breast cancer. Moreover, plasma can be longitudinally sampled over time, allowing for dynamic monitoring of disease progression and response to treatment^66,81,82. Other potential sample types include urine, which offers ease of collection and potential for reflecting systemic metabolic changes, and fine-needle aspiration samples of primary tumors, lymph nodes, and larger metastases at distant sites (including liver biopsies). While each specimen type presents unique advantages and challenges, in the future routine clinical implementation will likely favor those that balance diagnostic value with practicality and patient comfort, particularly plasma and minimally invasive biopsy techniques^83,84.

By discovering the unique lipid and metabolite alterations associated with an individual’s breast cancer progression, these tools have the potential to inform tailored treatment regimens that integrate with standard oncologic care, including targeted therapy and conventional treatments like chemotherapy and immunotherapy. Despite their promise, the use of lipidomic and metabolomic profiling as prognostic and diagnostic tools in breast cancer also faces technical and methodological challenges. Immediate freezing of patient samples on dry ice is imperative to preserve the integrity of the metabolome and lipidome, and plasma must be processed without delay to prevent coagulation—prerequisites that demand meticulous handling and rapid processing^90–94. Temperature fluctuations can impair the integrity and overall accuracy of the sample analysis^91–94. The shortcomings of these tools further include the conditions at which samples must be prepared.

Variability in metabolites due to diet, medication, gut microbiome influences that differ from patient-to-patient, as well as timing of sample collection present significant confounders for analysis necessitating careful consideration of these factors in study designs⁹⁵. Diet can also influence the lipidome not only from patient-to-patient but within an individual patient as lipid composition can change based on dietary consumption. These techniques also rely on advanced instrumentation, such as high-resolution mass spectrometry, and may require collaborative efforts across multiple institutions to ensure consistency and validity of data¹¹.

Furthermore, the heterogeneity and structural complexity of metabolites, present substantial challenges in the specific detection and analysis of metabolic alterations tied to cancer progression⁹⁶. This complexity is particularly problematic in translational research involving cellular samples from patients, which exhibit significant variability, thereby complicating the application of standard metabolic methodologies. One of the most challenging aspects of lipidomic and metabolic profiling from plasma or tissue samples is the ability to differentiate between metabolites derived from immune versus cancer cells within a sample—a challenge that current and future technological advancements must address to refine metabolomics and lipidomics as diagnostic and prognostic tools in breast cancer progression. Furthermore, while lipidomic and metabolomic profiling can identify potential biomarkers, we currently lack a broad range of drugs to target these metabolic changes, underscoring the gap between biomarker discovery and therapeutic application.

Outlook

Ongoing clinical trials for metabolomic and lipidomic profiling in the identification of biomarkers have become a powerful tool for metabolic diseases. Specifically in colorectal cancer, -omic techniques have been previously utilized to identify predictive biomarkers for the prediction of colorectal cancer, thus demonstrating the feasibility of conducting such clinical trials in breast cancer¹². Clinical trials in the identification of metabolomic and lipidomic biomarkers are technically feasible as low cell number input is possible and able to offer insightful and key information. Recent advancements in metabolomic/lipidomic technologies such as spatial metabolomics, single-cell metabolic profiling (scMEP)⁹⁷, assessing metabolism by flow cytometry (Met-Flow), and nutrient uptake assays (QUAS-R)⁹⁸, along with single-cell metabolomics and SpaceM⁹⁹, have greatly improved the precision and throughput of metabolite quantification in breast cancer research^100,101. As these technologies advance, they are increasingly being combined with multiomics and imaging techniques, forming an integrated approach that deepens our understanding of metabolic functions and of identifying predictive biomarkers in breast cancer progression.

Outstanding questions

Key outstanding questions in the use of lipidomics and metabolomics as potential biomarkers for breast cancer progression include:

• What is the anticipated specificity and sensitivity of lipidomic and metabolomic biomarkers for breast cancer progression?

• Can metabolomic and lipidomic profiles be used for the early detection of breast cancer?

• What are the underlying mechanisms by which changes in the metabolome and lipidome contribute to breast cancer progression?

• How do environmental and lifestyle (including dietary) factors influence the metabolome and lipidome in the context of breast cancer?

• How can lipidomic and metabolomic biomarkers be used to inform treatment decisions in breast cancer?

• Given the heterogeneity of breast cancer, what is the extent to which personalized versus generalizable lipidomic and metabolomic biomarkers can be used to predict disease progression in diverse patient populations?

• How can lipidomic and metabolomic data be integrated with genomic, transcriptomic, and proteomic data to provide a more comprehensive understanding of breast cancer progression?

• Are there dynamic changes in the lipidome and metabolome during breast cancer treatment, and can these changes predict therapeutic response or relapse?

• How can advances in cell isolation techniques and mass spectrometry technology improve the accuracy, speed, ability to analyze low cell number metabolomics, ability to distinguish between cancer versus non-cancer (including immune cell) metabolomic/lipidomic profiles, and the cost-effectiveness of lipidomic and metabolomic profiling?

• What are the challenges in translating metabolomic and lipidomic findings from the lab to the clinic?

Author contributions

A.C. and J.M.U. wrote the review with input and clinical insights from S.M. and T.A.J. All authors reviewed the manuscript.

Competing interests

The authors declare no competing interests.