Spatial omics in biomarker discovery in breast cancer: a narrative review

Introduction

Breast cancer is the most common cancer in women worldwide and is recognized as one of the most heterogeneous forms of cancer (1,2), which poses significant challenges for the development of effective therapies. This biological complexity is seen not only among different breast cancer types but also between tumors of the same subtype and even within a single tumor, encompassing individual cancer cells and their surrounding tumor microenvironment (TME) (1). Although substantial advancements in research have improved treatment options and survival rates, a deeper understanding of the molecular heterogeneity of breast cancer is necessary to enable personalized therapeutic approaches, including surgery, radiation, and systemic therapy. While various biomarkers such as estrogen receptor (ER), progesterone receptor (PR) and human epidermal growth factor receptor 2 (HER2) are currently used for prognostic purposes and patient stratification, they reflect only a fraction of the molecular heterogeneity of breast cancer. The scarcity of effective biomarkers correlates with limited treatment options and poor outcomes in many cases (3). New therapeutic approaches, such as immunotherapy, have further increased the demand in terms of developing specific biomarkers for treatment stratification. Despite its success in treating certain breast cancer subtypes, the effectiveness of immunotherapy is limited, benefiting only a subset of patients (4). The complexity and heterogeneity of breast cancer may add to this limitation, making it challenging to identify which patients would benefit from treatment (5). To overcome these challenges, it is essential to identify specific biomarkers that help recognize individuals who are more likely to respond to immunotherapy.

Understanding the vast molecular diversity of breast cancer often calls for large-scale omics approaches that traditionally rely on bulk or single-cell omics methodologies. However, despite significant research efforts with these technologies, a limited number of new molecules and biomarkers have been identified to serve as targets for novel drug development, patient treatment stratification, and prognostication (6,7). One of the major limitations of bulk and single-cell omics is that they fail to capture the tissue architecture and spatial interactions between breast cancer cells and their associated TME, particularly the immune TME (iTME). The iTME has been shown to play a key role in breast cancer development, progression, and treatment response, highlighting the importance of exploring these spatial interactions (8-10). To fully understand the complexity of breast cancer and unveil novel molecular determinants with utility in patient management and drug development, it is essential to gather information on molecular changes in cancer cells as well as the associated adaptive changes in the TME in the context of the tissue architecture. Biomarkers or spatial biomarkers offer insights into the spatial relationships and arrangements of various cells, biomolecules, and structures within tissue samples, and have the potential to revolutionize treatment approaches. These may be critical for improving treatment outcomes and developing personalized management strategies that are required for the heterogeneous nature of breast cancer (11).

Omics technologies have advanced through several stages in the past decades, starting with first-generation sequencing, enabling the Human Genome Project (12). Further innovation with the next-generation sequencing (13,14) helped lower the costs and increase the use of omics in research. Eventually, single-cell RNA sequencing (scRNA-seq) emerged (15), bringing single-cell resolution and deepening our understanding of cell heterogeneity and the complexity of tissues such as tumors. More recently, spatial transcriptomics and proteomics have combined sequencing and in situ imaging, allowing for the mapping of gene and protein expression within the spatial context (16,17). The progression of these techniques has laid the groundwork for current spatial multi-omics approaches, putting this technology at the forefront of biomarker discovery in breast cancer.

The recent advancements in spatial omics and large-scale multiplex technologies have opened up new opportunities for mapping breast cancer and the molecular and cell phenotypic changes within the TME, down to single-cell or subcellular resolution (18). Technologies such as spatial transcriptomics and spatial proteomics can facilitate a better understanding of breast cancer heterogeneity, the mechanisms behind early tumor progression, and the interactions between cancer cells and the local immune system, potentially revealing novel vulnerabilities that could be targeted to improve patient outcomes (19). Identifying spatially resolved biomarkers using these approaches is becoming increasingly significant in oncology (20). Traditionally, the analysis of biological molecules in a spatial context has been performed using single-plex methodologies like immunohistochemistry or in situ hybridization that allow the analysis of only one molecule at a time (21,22). Modern high-plex spatial technologies such as MERSCOPE (Vizgen), GeoMx and CosMx (Bruker Spatial Biology), PhenoCycler (Akoya Biosciences), Visium and Xenium (10x Genomics) enable the detection of hundreds or even thousands of molecules, supporting high-resolution mapping of biological molecules in the analyzed tissue (23). Linking molecular data from these technologies to patient clinical parameters allows for new perspectives in biomarker and drug discovery. However, as these technologies are relatively new and costly, only a limited number of studies have explored their applications in breast cancer to date. Thus, we aimed to investigate the existing peer-reviewed literature landscape pertaining to the application of spatial transcriptomics and spatial proteomics focused on identifying tumor-associated molecular elements relevant to clinical variables in breast cancer. We present this article in accordance with the Narrative Review reporting checklist (available at https://abs.amegroups.com/article/view/10.21037/abs-25-24/rc).

Methods

We performed a comprehensive literature search in PubMed using the terms “spatial AND (transcriptomics OR proteomics) AND (breast cancer)” to identify peer-reviewed, English-language articles published between January 1, 2000 and January 1, 2025. Studies were included if they (I) were primary research articles, (II) employed spatial transcriptomics or proteomics technologies, and (III) investigated correlations or biomarkers associated with clinical variables or outcomes in human breast cancer. We excluded studies conducted in organoid or animal models, as well as those utilizing mass spectrometry or immunohistochemistry-based approaches. A total of 433 articles were identified. Two independent reviewers screened the abstracts for relevance, resulting in the inclusion of 10 studies for this review (Table 1). The search strategy is outlined in Table 2.

Prognostic biomarkers

Spatial omics technologies are revolutionizing our understanding of how the organization of cells and the TME influence cancer progression and outcomes. Unlike bulk molecular profiling, these technologies preserve the tissue architecture and allow for high-resolution characterization of the tumor tissue and the surrounding cells of the TME. Identifying spatially resolved prognostic biomarkers in breast cancer is essential for patient stratification and the development of personalized treatment strategies, particularly in the context of tumor heterogeneity.

One example of this approach is the study by Lv et al. (2021), which employed spatial transcriptomics using Visium (10x Genomics) to investigate invasive micropapillary carcinoma (IMPC) (Table 1) (24). IMPC is an aggressive subtype of breast cancer associated with higher clinical stage and histological grade at presentation, increased frequency of lymphovascular invasion and lymph node involvement, and poorer oncologic outcomes (24,33). By analyzing spatial gene expression in IMPC samples of patients who underwent a modified radical mastectomy, the study identified sterol regulatory element-binding protein 1 (SREBF1) and fatty acid synthase (FASN) as potential prognostic biomarkers (24). Both SREBF1 and FASN have a role in cancer development and progression through the regulation of lipid metabolism (34,35). The study found that the overexpression of these genes was associated with higher rates of lymph node metastasis and worse disease-free survival (24). Transcriptional activity in IMPC regions was enriched for factors linked to tumor growth and metastasis, showing that metabolic reprogramming of cancer cell subpopulations plays an important part in IMPC (24). Distinct microenvironmental features were seen in the spatial context of the analyzed tissues. Tumor-adjacent stromal regions showed distinct gene expression signatures of immune-related genes (such as immunoglobulins), whereas the distal stromal regions were enriched in metabolic pathways such as oxidative phosphorylation (24). These findings underscore the complex interactions between tumor and stroma. Compared to invasive ductal carcinoma-not otherwise specified regions, the IMPC regions exhibited significantly lower expression of immune cell markers such as CD45, CD3, and CD8 (24). The low expression of these immune elements suggests a less active immune microenvironment that may contribute to the aggressive behavior of IMPC. These spatial transcriptomic observations support the role of SREBF1 and FASN as prognostic biomarkers with potential clinical utility in this disease, offering additional support for the use of spatial omics in the more precise identification of prognostic biomarkers in breast cancer, especially for challenging subtypes like IMPC.

ScRNA-seq provides cellular-level resolution but lacks the spatial information that is essential to understand heterogeneous and complex diseases such as breast cancer (24,28). A strategy to overcome this limitation is the integration of this technology with spatial omics, a powerful approach for resolving both cellular identity and tissue organization. Liu et al. (2023) applied this combination to investigate the metabolic evolution of breast cancer during early metastasis to the axillary lymph nodes (Table 1). This work suggested that there is a switch between glycolysis and oxidative phosphorylation during the early dissemination of breast cancer cells to lymph node metastases (27). Clusters of early disseminated cancer cells exhibiting high levels of oxidative phosphorylation, marked by upregulated cytochrome C oxidase subunit 6C (COX6C) and dehydrogenase/reductase 2 (DHRS2), were identified along the leading edge of the tumor (27). Overall, the study demonstrated that higher oxidative phosphorylation activity correlates with the presence of lymph node metastasis and poorer patient survival, highlighting the potential of oxidative phosphorylation as a prognostic biomarker in breast cancer (27).

Prognostic biomarkers can also be used to guide treatment selection based on patient-specific tumor characteristics, including biomarkers used to de-escalate invasive surgical procedures. This is especially relevant for ductal carcinoma in situ (DCIS), where the ability to distinguish lesions that are likely to progress to invasive disease remains challenging (28). A study involving DCIS patients applied spatial transcriptomics (Visium; 10x Genomics) and scRNA-seq to resolve intralesional heterogeneity and identify predictive markers in DCIS (Table 1) (28). Mutations in GATA binding protein 3 (GATA3) were linked to upregulation of epithelial-to-mesenchymal transition (EMT), angiogenesis, and higher risk of relapse (28). GATA3 is a transcription factor involved in cell differentiation and has been implicated in various cancers (36). In breast cancer, it is often controversially linked to both cancer pathogenesis and better prognosis, depending on the specific cancer and the mutational context (36). Spatial analysis revealed coexisting aggressive clusters enriched for EMT pathways and non-aggressive clusters characterized by estrogen response signaling (28). These findings support a refined classification of DCIS based on molecular and spatial features, with the potential to guide clinical decision-making and treatment de-escalation, including more tailored surgical approaches in these groups of patients.

Tzeng et al. utilized spatial transcriptomics data from the STOmics database (STDS0000049), in combination with scRNA-seq data from the GEO database (GSE150660), to identify the U6 snRNA-associated Sm-like protein (LSM1) as a potential diagnostic and prognostic marker in advanced breast cancer with poor patient outcomes (Table 1). Their study suggests that alterations in LMS1 expression may contribute to breast cancer progression by modulating energy metabolism, promoting the infiltration of tumor associated macrophages (TAMs), and influencing the TME during metastatic development (29). Similarly, Li et al. explored the role of the mitochondrial calcium uniporter (MCU) protein as a biomarker in breast cancer using spatial transcriptomics and scRNA-seq data. Their analysis revealed a correlation between MCU expression and advanced clinical stages, poor overall survival (OS), and the activation of inflammation- and immune-related pathways (30). These results highlight the potential of MCU as a biomarker for monitoring the TME and evaluating immune response in breast cancer.

Collectively, the studies reviewed here provide compelling evidence that spatial omics has advanced our understanding of prognostic biomarkers in different types of breast cancer. Spatial technologies in these studies facilitated the discovery of important findings, which include the novel exploration of inter- and intra-tumoral heterogeneity of IMPC using spatial transcriptomics and providing spatial evidence supporting the “clustered metastasis of IMPC tumor cells” hypothesis (24). The use of scRNA-seq combined with spatial transcriptomics helped to characterize metabolic changes in early disseminated cancer cell clusters, results that were clinically relevant and validated in an external dataset (27). Spatial transcriptomics has allowed for the identification of a GATA3 mutation as a potential marker for DCIS classification and broader gene expression information compared to imaging mass cytometry (28). The multi-omics strategy that was used by Tzeng et al. (29) led to the identification of LSM1 as a clinically relevant marker with diagnostic and prognostic potential in the context of precision oncology. And finally, the integrative multi-omics approach with clinical validation utilized by Li et al. (30) allowed for the identification of MCU as a prognostic biomarker, correlating with worse OS, higher stages, and aggressiveness.

Nevertheless, common limitations of these studies were the small cohort sizes (11,24,27,28) and the limited spatial resolution (27,28), which may constrain the generalizability of the results and the statistical power of the findings. Addressing these issues with the creation of spatial databases and the combination of spatial omics with scRNA-seq technologies may be critical to achieve spatial omics’ full potential in biomarker discovery.

Biomarkers for treatment response

Spatial omics technologies show great promise in identifying molecular and cellular targets for novel treatment response biomarkers and drug development in both the neoadjuvant (preoperative) and adjuvant (postoperative) settings.

Neoadjuvant treatment

Neoadjuvant treatment is a cornerstone in the management of high-risk and/or advanced breast cancer, particularly in the aggressive triple-negative breast cancer (TNBC) subtype. However, predicting which patients will achieve a pathologic complete response (pCR) remains a clinical challenge. The ongoing discovery of new biomarkers to aid in treatment selection represents a major step toward solving this issue.

In a study involving patients from the TRIO-US B07 clinical trial, McNamara et al. (2021) investigated the use of spatial proteomics (GeoMx; Bruker Spatial Biology) to predict early response to neoadjuvant HER2-targeted therapy in HER2-positive breast cancer patients (Table 1). Tumor and immune cell protein profiles were evaluated at three stages: before treatment, after 14–21 days of therapy, and at surgery (11). Their analysis showed that changes in protein expression observed after the initial treatment cycle of HER2-targeted treatment were more closely associated with achieving pCR, a correlation not detected with baseline profiling and bulk transcriptomic data (11). Notably, the increased expression of various immune cell markers, including CD45, was found to positively correlate with pCR (11). Findings from this study highlighted the potential role of early changes in the proteome as potential biomarkers to guide personalized therapeutic decisions, such as treatment de-escalation.

More broadly, spatial analysis reveals therapy response patterns that transcend gene expression alone. Namely, the interaction between the immune and stromal components within a tumor, and the physical proximity of various TME cells can predict response to treatment. Donati et al. (2023) applied a Cancer Transcriptome Atlas panel (GeoMx; Bruker Spatial Biology) to compare TNBC patients who achieved pCR to those without pCR following neoadjuvant chemotherapy (Table 1). Their analysis revealed that efficient responses to neoadjuvant chemotherapy were associated with increased immune cell infiltration (referred to as “spatial contamination”) and direct interactions between tumor and immune cells, including B cells and CD8⁺ T cells (25). Enhanced activation of the interferon signaling pathways [including upregulation of signal transducer and activator of transcription 1 (STAT1) and STAT2, and interferon regulatory factor 9 (IRF9)] was observed in tumors that responded well to neoadjuvant chemotherapy (25). These findings highlight the importance of considering spatial dynamics within the TME when evaluating treatment outcomes in TNBC. These factors could serve as spatially-informed biomarkers of immune activation and therapeutic response, helping to identify patients who may benefit from treatment. Ultimately, the authors propose a multifactorial model for biomarker development in TNBC, integrating spatial immune architecture, molecular signatures, and clinical parameters (25). Combining these features through machine learning could yield more accurate predictors of response, ultimately enabling personalized neoadjuvant chemotherapy strategies for TNBC patients (25).

By integrating scRNA-seq with spatial transcriptomics, Inayatullah et al. identified subpopulations of cells in TNBC that are associated with resistance to neoadjuvant chemotherapy (Table 1). The EMT was active in these populations, and they expressed signature genes linked to poor clinical prognosis (31). As such, they hold promise as potential biomarkers for predicting neoadjuvant chemotherapy response and guiding treatment strategies in early and advanced stages of TNBC (31). Integrin beta-1 (ITGB1) and alpha-actinin-1 (ACTN1) were found to play critical roles in promoting TNBC cell survival and mediating chemoresistance (31).

In these recent applications of spatial omics in the discovery of biomarkers for neoadjuvant treatment response, the authors demonstrated the feasibility of retrospective in situ spatial proteomic analysis to characterize tumor and immune cell signaling dynamics during therapy (11). The use of digital spatial profiling enabled the identification of protein-level changes associated with pCR and their findings support the stratification of patients who benefit from escalation or de-escalation of therapy (11). The use of spatial proteomics proved to be an innovative approach to understand the organization of the TME in TNBC with the identification of tumor infiltrating lymphocytes (TILs) as functional determinants of the neoadjuvant treatment response (25). And as seen before in other studies, integrating multiple omics platforms enhances the analytical depth of the results, enabling the identification of a robust gene signature linked to treatment resistance and unfavorable clinical outcomes, with predictive value (31). However, challenges such as the limited number of samples (11,25) still persist, as well as the need for a more robust validation in clinical trials and validation in independent cohorts, emphasizing the need for further studies before the clinical application of these biomarkers can emerge (11,25,31).

Immunotherapy

The introduction of immunotherapeutic approaches such as immune checkpoint inhibitors (ICIs) has reshaped treatment paradigms and led to the improvement of patient outcomes in various cancers. Despite these advances, only a subset of breast cancer patients derive significant clinical benefit from these treatments (4). By providing high-resolution insights into the tumor-immune interface, spatial omics has emerged as a powerful tool for predicting response to immunotherapy.

The use of spatial profiling enables the characterization of specific immunophenotypes, which can be used as predictive biomarkers of immunotherapy response. For example, in HER2-positive breast cancer, biomarkers for immunotherapy have become essential to stratifying patients who are more likely to benefit from this therapeutic approach (37). Schlam et al. (2021) utilized spatial proteomics (GeoMx; Bruker Spatial Biology) alongside bulk gene expression profiling (nCounter; Bruker Spatial Biology) to compare the immunological profile of primary HER2-positive and metastatic (brain, lung, and soft tissues) breast tumors (Table 1). Their analysis revealed higher immune cell infiltration and activation signatures in primary tumors, while metastatic tumors exhibited significantly lower immune presence and activity (32). The study concluded that stromal and immune TME cells are more active in primary compared to metastatic sites and suggested that, compared to patients with advanced HER2-positive breast cancer, those with early-stage disease might see more benefits from ICIs (32).

Immune checkpoint markers, such as programmed death-ligand 1 (PD-L1), cytotoxic T-lymphocyte-associated protein 4 (CTLA4), lymphocyte-activation gene 3 (LAG3), and B7 homolog 3 (B7-H3), as well as tumor inflammation signatures (TIS, an 18-gene signature that captures key aspects of antitumor immune activity) were associated with “immunologically hot” tumors (32). These tumors exhibit greater immune cell infiltration, suggesting that they are more likely to respond favorably to immunotherapy (32). In contrast, spatially-defined immune exclusion patterns, particularly in metastatic lesions, were linked to poorer responses to immunotherapy (32). Immune exclusion refers to a scenario where immune cells are present in the TME, but they are either not able to infiltrate or interact with the cancer cells in the tissue, resulting in deficient immune monitoring and consequently an inadequate immune response within the tumor (32). These observations highlight the importance of not only the presence but also the spatial distribution and density of immune cells within the tumor tissue. The presence of a favorable immune architecture in early-stage tumors may improve responsiveness to ICIs, underscoring the clinical application of spatial profiling to inform treatment options for patients, thereby improving therapeutic outcomes.

Tertiary lymphoid structures (TLS) have recently emerged as potential biomarkers to inform immunotherapy response in many cancer types (38). Despite this promise, identifying these lymphocyte aggregates within tumor tissue samples remains a challenge in clinical settings (38). Wang et al. applied spatial transcriptomics to explore how the spatial organization of TNBC influences the response to immunotherapy (Table 1). Leveraging the original spatial transcriptomics platform, they characterized TLS-rich regions based on their transcriptional profiles, which closely corresponded to areas previously annotated by pathologists (26). The resulting 30 TLS-related gene signature encompassed a range of diverse immune-related processes such as the B and T cell function, lymphoid structures development, immunoglobulin activity, and immune signaling (26). Validation across multiple publicly available breast cancer and non-breast cancer datasets revealed that the enrichment of TLS signature-genes was associated with improved prognosis and greater chances of achieving pCR in various cancer types, namely in early-stage TNBC (26). These findings support the potential of TLS profiling as a tool for identifying patients who are more likely to benefit from specific immunotherapeutic strategies.

The collection of studies synthesized here highlights how the use of spatial omics has deepened our insights in discovering biomarkers for immunotherapy response in breast cancer. Key strengths of these studies encompass the use of paired patient samples collected at multiple disease stages, which enables a longitudinal assessment of the tumor immune microenvironment (32). Large-scale application of spatial transcriptomics to analyze a cohort of 92 patients, resulted in the detection of intra- and inter-tumoral heterogeneity that conventional bulk RNA-seq failed to identify (26). The development of spatially resolved gene expression signatures (such as TLS) with prognostic and predictive value for immunotherapy response and the identification of spatial archetypes with potential to refine patient stratification strategies have significant clinical relevance and deepen our understanding of the spatial organization of the tumor-immune interface. At the same time, some recurring limitations, such as the reduced number of samples, the low spatial resolution, and the lack of clinical trial validation, potentially limit the conclusions of the studies as well as the clinical translation of these results. Overcoming these will be pivotal for the application of spatial omics in immunotherapy response research.

Spatial omics limitations

Despite the potential of spatial omics technologies in breast cancer research and biomarker discovery, methodological and technical limitations currently restrict their widespread implementation and use in clinical settings.

First, the high financial cost and infrastructure requirements of spatial omics platforms represent major barriers. These technologies require not only costly specialized equipment but also highly trained and skilled personnel, making them less accessible to laboratories with limited resources (39-41). Parallel to examples from other omics technologies, with the further development and utilization of the spatial omics, the cost-related constraints are expected to ease, providing an opportunity for broader utilization of these technologies. A promising strategy to address this challenge at present is the creation of public databases that compile and share spatial omics data. These repositories enable the use of this data by other researchers for validation of their studies, broader exploration or enhancing the statistical power of their own research cohorts.

Secondly, the volume and complexity of imaging and sequencing data generated by these methods present significant analytical challenges. The integration of high-dimensional molecular information with spatial tissue architecture requires advanced computational tools. Existing pipelines developed for bulk or single-cell omics data require modifications to adapt to spatial data and extract biologically meaningful patterns (39-42). The need for a user-friendly bioinformatics infrastructure remains unmet.

Another important challenge is the spatial resolution, which varies across different platforms, ranging from multicellular to subcellular levels. Technologies such as MERFISH and seqFISH provide subcellular resolution but limited throughput, while other platforms like Visium (10x Genomics) and GeoMx (Bruker Spatial Biology) offer scalability but with lower spatial resolution (43). This variation influences the depth and interpretability of biological insights derived from different platforms (39).

Moreover, lack of standardized protocols for sample preparation, imaging, and data processing is still a critical challenge that contributes to inter-study variability. This undermines reproducibility and comparability across studies, leading to inconsistent expression maps and limiting meta-analyses.

Finally, the small size of most spatial omics cohorts limits the generalizability and statistical power of the results. The creation of large-scale multi-institutional initiatives such as the Human Tumor Atlas Network could be used to address this limitation by creating a harmonized spatial reference dataset and fostering data sharing (44,45). Addressing these challenges is essential to improve reproducibility and enhance biological insights to promote clinical translation.

Future applications and directions

Spatial transcriptomics and proteomics are rapidly evolving technologies that hold the potential to transform our understanding of cancer biology through insights into the spatial organization of genes and proteins in tumor tissues (43). Recent advancements in spatial resolution, sensitivity, and multiplexing capacity have enhanced the mapping and profiling of the transcriptome and proteome, allowing for a detailed dissection of tumor heterogeneity and interactions in the TME (46).

One key future direction is the integration of different omics modalities, artificial intelligence, and machine learning algorithms. These combined approaches have the potential to facilitate not only the identification of clinically relevant biomarkers but also support early diagnosis, refine tumor subtyping, and enable individualized therapeutic strategies (43,47). This underscores the central role that spatially informed approaches and computational tools will play in the future of precision oncology and personalized care.

Translating spatial omics into clinical practice could be used in real-time decision-making in surgery and treatment (48). Intraoperative tissue profiling and adaptive treatment planning could facilitate tumour margin assessments during surgery and inform treatment decisions based on the tumor heterogeneity, bridging the gap between bench and bedside.

Furthermore, the development of large-scale spatial databases integrating data from different platforms could advance the creation of tumor atlases. These resources can facilitate meta-analyses, validation of biomarkers, and training of machine learning models for predictive oncology. Expanding access to spatial datasets would accelerate translation of spatial omics into precision oncology and individualized patient care.

Strengths and limitations

To our knowledge, this is the first narrative review that summarizes studies on the application of spatial omics technologies in breast cancer biomarker discovery. We conducted a broad, exploratory search to capture papers with different spatial omics platforms and approaches.

The narrative format allowed us to interpret and synthesize heterogeneous data. However, this review is not a systematic review or a formal assessment of risk of bias. Nonetheless, it offers a valuable overview of current evidence and identifies key gaps to inform future research.

Conclusions

Preserving tissue architecture and mapping the spatial distribution of molecular and cellular features within the TME is a unique advantage of spatial omics technologies compared to bulk and single-cell approaches. The spatial architecture is a crucial component of breast cancer pathogenesis, where the complex intratumoral interactions between cancer, stromal, and immune compartments are associated with disease progression and response to treatment.

Although current limitations such as costs, technical complexity, and small cohort sizes hinder broader adoption, ongoing advances are expected to improve scalability and accessibility. Despite these challenges, spatial omics are being increasingly applied in breast cancer research and contributing to the development of biomarkers.

This paper highlights the strong potential of spatial omics technologies, both independently and in combination with other omics modalities, to advance precision oncology and ultimately to provide clinicians with tools for individualized therapeutic strategies.

Acknowledgments

We thank Anna Bonvissuto (from Dr. Parsyan’s laboratory) for help with editing and formatting the manuscript and Mahtab Malekian Naeini for help with the initial identification of papers pertinent to the topic. Our abstract was presented in part as a poster presentation at the 2023 Canadian Surgery Forum and the 2024 Division of General Surgery Research Day, University of Western Ontario.

Footnote

Reporting Checklist: The authors have completed the Narrative Review reporting checklist. Available at https://abs.amegroups.com/article/view/10.21037/abs-25-24/rc

Peer Review File: Available at https://abs.amegroups.com/article/view/10.21037/abs-25-24/prf

Funding: This work was supported by the Lawson Internal Research Fund Studentship with endowed funds from the Marion Glenn Murray Fund and the Jack Bowen Cancer Fund (No. R-21-155; PhD scholarship to A.F.G.S.); the Small Town Cancer Fighters (PhD scholarship to A.F.G.S) , the Opportunities Fund of the Academic Health Sciences Centre Alternative Funding Plan of the Academic Medical Organization of Southwestern Ontario (AMOSO) (No. F20-007; to A.P.); Breast Cancer Canada (to A.P. and V.B.); the Clinician Scientist Award, Department of Surgery, Western University, London, Ontario, Canada (to A.P.) ; the Innovation Fund of the Alternative Funding Plan of the Academic Health Sciences Centres of Ontario (AMOSO) (No. INN24-017; to A.P.); and the Canadian Institutes of Health Research (CIHR) (No. PJT-75100; to A.A.).

Conflicts of Interest: All authors have completed the ICMJE uniform disclosure form (available at https://abs.amegroups.com/article/view/10.21037/abs-25-24/coif). The authors have no conflicts of interest to declare.

Ethical Statement: The authors are accountable for all aspects of the work in ensuring that questions related to the accuracy or integrity of any part of the work are appropriately investigated and resolved.

Open Access Statement: This is an Open Access article distributed in accordance with the Creative Commons Attribution-NonCommercial-NoDerivs 4.0 International License (CC BY-NC-ND 4.0), which permits the non-commercial replication and distribution of the article with the strict proviso that no changes or edits are made and the original work is properly cited (including links to both the formal publication through the relevant DOI and the license). See: https://creativecommons.org/licenses/by-nc-nd/4.0/.

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