Characteristics of fusion genes in breast cancer

Abstract

Fusion genes, arising from aberrant genomic rearrangements, represent critical oncogenic drivers with distinct oncogenic functions. Although relatively uncommon in breast cancer, accumulating evidence suggests that fusion genes contribute to tumor initiation, progression, and therapeutic resistance. This review first summarizes the molecular mechanisms underlying fusion gene formation, their frequency and subtype distribution, and advances in detection technologies in breast cancer. We then discuss how fusion genes reprogram oncogenic signaling pathways and mediate resistance to conventional and targeted therapies. Finally, we evaluate their translational potential as diagnostic biomarkers and therapeutic targets, emphasizing opportunities for precision oncology. By integrating current insights, this review underscores the multifaceted roles of fusion genes in breast cancer biology and highlights their promise for guiding the development of more effective, personalized treatment strategies.

Introduction

Fusion genes, which can give rise to chimeric or hybrid RNAs, are formed by chromosomal rearrangement, RNA splicing or read-through, and result in the combination of two independent genes [1]. Recent advances in genomic technologies have underscored their significance and widespread occurrence in solid tumors. Fusion genes serve as the sole oncogenic driver in over 1% of cases and are implicated in the development of 16.5% of human cancers [2]. Breast cancer constitutes nearly one-quarter of all cancer cases and one-sixth of cancer deaths in women globally [3]. Recent reports have identified fusion genes as important contributors to breast cancer development, especially within rare histological subtypes [4, 5], such as inflammatory breast cancer (IBC) [4], secretory breast cancer (SBC) [6], and breast adenoid cystic carcinomas (BACC) [7]. Moreover, many fusion genes are associated with more aggressive phenotypes and therapeutic resistance in breast cancer. Therefore, the detection and prediction of fusion genes hold immense significance in cancer diagnosis and treatment in the clinic, particularly in facilitating accurate identification of cancer subtypes and developing personalized treatment strategies.

Compared with prototypical fusion-driven malignancies such as hematologic cancers and lung cancer, the epidemiological patterns, biological functions and clinical relevance of gene fusions in breast cancer remain considerably uncertain. Large-scale pan-cancer sequencing efforts have uncovered numerous fusion transcripts; however, many of these occur at low frequency or lack concordance across breast cancer cohorts, and frequently lack DNA-level support or functional validation. These observations have raised concerns that a substantial subset may represent transcriptional noise or technical artefacts. Moreover, numerous fusion events are detectable only at the RNA level without evidence of protein expression or functional output, limiting confidence in their designation as driver events or actionable therapeutic targets. The unique transcriptional heterogeneity of breast cancer, together with stromal contamination and low tumour purity, further diminishes the signal-to-noise ratio of RNA-based fusion detection and increases susceptibility to read-through transcripts and alignment artefacts.

Collectively, these technical and biological confounders contribute to a fragmented “detection–interpretation–translation” continuum: despite emerging evidence that fusion events may possess biological and clinical significance, only a minority can currently be defined as bona fide drivers or clinically actionable alterations. Yet, the existence of subtype-defining fusions (e.g., ETV6–NTRK3 in SBC) and functionally implicated fusions associated with therapeutic resistance or invasion (e.g., ESR1-related fusions) underscores the need to distinguish statistically inferred fusion transcripts from biologically consequential fusion events.

Accordingly, research on gene fusions in breast cancer is transitioning from a predominantly discovery-oriented phase toward mechanistic and translational investigation. This review synthesizes current knowledge on fusion formation, detection strategies, oncogenic mechanisms and therapeutic implications in breast cancer (Fig. 1), and discusses their emerging roles in precision diagnostics and targeted therapy, as well as prevailing obstacles and future directions for clinical translation.

Fig. 1.

Overview of the formation and distribution, detection methods, oncogenic mechanisms, and therapeutic implications of fusion genes in breast cancer

The formation and distribution of fusion genes in breast cancer

Formation

Fusion genes are typically generated by chromosomal rearrangement, splicing, and read-through transcripts.

Chromosomal rearrangement

Chromosomal rearrangement results from DNA damage caused by radiation, chemical exposure, or other external factors. In breast cancer, genomic instability associated with HER2 overexpression and BRCA1 deficiency has been linked to an increased frequency of chromosomal rearrangements [8, 9]. The main types are as follows:

Translocation: an exchange of segments between two nonhomologous chromosomes. For example, ETV6-NTRK3 [6], MYB-NFIB [10], and BCL2L14-ETV6 [11].

Inversion: a segment of a chromosome breaks off, rotates 180 degrees, and then reattaches to the same position. For example, PPP6R3-TENM4-NRG1 [12].

Insertion: a chromosomal fragment is inserted into another genomic location, resulting in the juxtaposition of previously separate genes. For example, ESR1-CCDC170 [13].

Deletion: the loss of a chromosomal segment that brings previously separated genomic regions into proximity, thereby generating fusion genes. For example, HER2–GRB7 and TRPS1-PLAG1 [14].

Tandem duplication: a segment of DNA is duplicated, with the duplicated copies positioned adjacent to each other on the same chromosome. For example, KIAA1549-BRAF [15].

Chromothripsis: extensive and chaotic chromosomal rearrangements occurring in a single catastrophic event. Chromothripsis frequently affects chromosomes 11 and 17 in breast cancer. A fusion event involving chromosome 17 has been reported to promote NF1 inactivation, thereby contributing to tumor initiation and progression [16].

Splicing and read-through

Cis-splicing occurs within the same pre-mRNA molecule, for example, the RBM6-RBM5 fusion has been implicated in cis-splicing, which correlates with increased transcriptional activity of RBM6 [17]. Trans-splicing involves two different RNA molecules, such as ESR1-CCDC170 [13]. Read-through transcripts occur when RNA polymerase fails to stop at a gene’s termination site, producing RNA that spans multiple genes, potentially creating fusion transcripts with novel functions or implications in diseases. Examples include SCNN1A-TNFRSF1A, CTSD-IFITM10 [18], MFGE8-HAPLN3 [19], RAD51AP1-DYRK4 [20], EEF1DP3-FRY and PPP1R1B-STARD3 [21].

Distribution

At the tissue level, breast tumors harbor more fusion events than adjacent non-tumor tissues, suggesting that fusion formation is associated with tumorigenesis [11, 19]. At the chromosomal level, fusion genes in breast cancer are more commonly found on chromosomes 1, 2, 17, and 19, while they are rare on chromosomes 4, 9, 13, 15, 20 and 21 [22]. Jiang et al. proposed the Fudan classification of triple-negative breast cancer (TNBC), which includes four subtypes: luminal androgen receptor (LAR), immunomodulatory (IM), basal-like immune-suppressed (BLIS), and mesenchymal-like (MES) [23]. Based on this classification, Wang et al. found that fusion genes in the IM subtype are enriched on chromosome 11, while those in the LAR subtype are enriched on chromosomes 17 and 19. Fusion events in the MES subtype are enriched on chromosomes 7, 9, and 15, but are rare on chromosomes 2 and 3 [19]. The association of the fusion gene distribution patterns with specific molecular subtypes indicates that fusion genes may play important roles in shaping the characteristics of breast cancer subtypes.

Some studies have demonstrated a close association between fusion genes and specific subtypes in breast salivary gland-like tumors, which can originate not only in the breast but also in the salivary glands. For example, ETV6-NTRK3 in SBC [6], MYB–NFIB and MYBL1 fusions in BACC [7, 24], HMGA2-WIF1 in breast pleomorphic adenomas (BPA) [25] and CRTC1-MAML2 in breast mucoepidermoid carcinoma (BMEC) [25, 26]. Despite their different sites of origin, the fusion genes within these tumors may present promising targets for future diagnostic applications.

In addition to breast salivary gland-like tumors, fusion genes RAD51AP1-DYRK4 [20] and ESR1-CCDC170 [27] have been identified in 7–18% and 0–8% of Luminal B breast cancers, respectively. Fusion genes MFGE8-HAPLN3 [19], AZGP1-GJC3 [28], BCL2L14–ETV6 [11], and MAGI3-AKT3 [21] have been identified in 3.3–16.7%, 66.7%, 4.4–12.2%, and 0–7.0% of TNBC cases, respectively. Fusion genes OAZ1-CSNK1G2 and RFC4-LPP have been identified in 9.7% and 6.5% of mucinous carcinoma of the breast (MCB) [29], respectively. Fusion gene CD74-ROS1 has been reported in IBC [4]. The fusion genes mentioned above are generally recurrent events in specific breast cancer subtypes and therefore hold promise as diagnostic biomarkers. Beyond the subtype-specific fusion genes, RPS6KB1–VMP1 [30], TRMT11-GRIK2, and CCNH-C5orf30 [31] fusion genes are present across all breast cancer subtypes with frequencies of 31%, 68%, and 85%, respectively. This widespread occurrence suggests that these fusion genes are not subtype-specific and may therefore have limited utility as subtype-specific diagnostic biomarkers. Table 1 summarizes the fusion genes extensively studied in breast cancer.

Table 1.

Fusion genes widely investigated in breast cancer

Fusion gene	Histological subtype	Molecular subtype	Frequency	Formation mechanism	Carcinogenesis mechanism	Fusion type	Clinical Consequences	Ref
ETV6-NTRK3	SBC	TNBC	92% in SBC	Translocation	Chimeric protein	E5-E13	Most cases are characterized by slow-growing tumors with favorable outcomes. However, some reports indicate instances of distant metastasis, high-grade transformation, and sarcomatous dedifferentiation.	[6, 81]
MYB-NFIB	BACC	TNBC	22.6–100% in BACC	Translocation	Loss of microRNA-binding sites	E14-E9	ACCs in salivary glands are highly aggressive, while BACC, despite being triple negative and basal-like, exhibit a favorable clinical behavior with an excellent 10-year prognosis.	[7, 110]
PPP6R3-TENM4-NRG1	NA	Luminal A	0.04% in all BC subtypes	Invertion, Translocation	Loss of nuclear signalling	E1-E3-E12-E3	NRG1 nuclear signaling is involved in regulating cell apoptosis; thus, the loss of this signaling may allow cells to evade normal apoptotic pathways, promoting the survival of tumor cells.	[12]
MFGE8-HAPLN3	NA	TNBC	3.3–16.7% in TNBC	Read-through	NA	E6-E2 E5-E3 E6-E3	MFGE8 affects tumor cell survival, adhesion, and migration. The expression of HAPLN3 is significantly higher in breast cancer tissues compared to normal breast tissues. It is associated with metabolic dysregulation, migration, and invasion in TNBC.	[19]
AZGP1-GJC3	NA	TNBC	66.7% in TNBC	NA	Loss of functional domain	NA	The expression level of AZGP1 is significantly lower in invasive breast tissues compared to adjacent normal tissues. GJC3 is specifically expressed in the central nervous system and, therefore, has minimal impact on breast cancer.	[28]
ESR1-CCDC170	NA	Luminal B	0–8% in Luminal B	Tandem duplication Insertion	Truncated protein	E2–E6 E2–E7 E2–E8 E2–E10	Fusions increase cell motility and anchorage-independent growth, reduced endocrine sensitivity and enhanced xenograft tumor formation.	[13]
ESR1-YAP1	MBC	ER+	A case report	Translocation	Loss of ligand-binding domain	NA	Fusion induces TFF1 expression and is insensitive to fulvestrant.	[45]
CD74-ROS1	IBC	TNBC	A case report	NA	NA	E7-E34	The patient declined crizotinib and was unable to tolerate the side effects of palliative chemotherapy, resulting in death 4 months after diagnosis.	[4]
RAD51AP1-DYRK4	MBC	Luminal B	7–17.5% in Luminal B, 3.59% in all BC subtypes	Read-through	Loss of functional domain	E9-E2 E8-E2 E8s-E2	RAD51AP1-DYRK4 is predominantly expressed in the testes. Its ectopic expression in breast cancer is associated with elevated Ki67 levels and activation of the MEK/ERK signaling pathway, which enhances cell motility and transendothelial migration.	[20]
BCL2L14–ETV6	NA	TNBC	4.4–12.2% in TNBC	Tandem duplication	Truncated protein	E4-E2	Fusions are enriched in high-grade, necrotic, mesenchymal TNBC tumors and enhances invasiveness and paclitaxel resistance by inducing partial epithelial-mesenchymal transition.	[11]
MAGI3-AKT3	NA	TNBC	0–7% in TNBC	Translocation	Activates kinase activity Antioncogene inactivation	NA	The fusion leads to the loss of contact inhibition and lesion formation. ATP-competitive AKT inhibitors may be used in clinical trials for the treatment of fusion-positive TNBC.	[21, 44, 111]
EEF1DP3-FRY	NA	NA	6.7% in all BC subtypes	Read-through	Loss of functional domain	E2-E2	The loss of the FRY protein would decrease cellular integrity.	[21]
PPP1R1B-STARD3	NA	ER-	8.3%	Read-through	Truncated protein	E6-E2	Increase cell proliferation by activating PI3K/AKT signaling.	[21]
KDM3B-ETF1	IDC	NA	A case report	NA	Epigenetic modification	E6-E3	KDM3B-ETF1 bound and inhibited the expression of LMO2, leading to activation of Wnt/β-catenin signaling pathway in breast cancer cells.	[48]
CRTC1–MAML2	BMEC	TNBC	Three cases	Translocation	Chimeric protein	E1-E2	BMEC, aside from the CRTC1-MAML2 fusion, lacks TP53 or PIK3CA mutations or complex copy number profiles, and exhibits none or only isolated genetic abnormalities.	[25, 26]
HMGA2-WIF1	BPA	NA	A case report	NA	Oncogene overexpression Antioncogene inactivation	E5-E3	NA	[25]
CTNNB1-PLAG1	BPA	NA	A case report	NA	Promoter swap	E1-E4	NA	[25]
RPS6KB1–VMP1	NA	NA	31.4% in all BC subtypes	Tandem duplication	NA	E2-E11	The RPS6KB1–VMP1 fusion transcript is associated with shorter DFS in breast cancer patients, indicating poor prognosis. Its expression correlates with nearby genes, including MIR21 and RPS6KB1, suggesting it may mark genomic instability at the 17q23 locus.	[30]
OAZ1-CSNK1G2	MCB	Luminal A	9.7% in MCB	NA	Loss of functional domain	E1-E9	OAZ1-CSNK1G2 causes the deletion of the serine threonine kinase region of CSNK1G2 that is required for repression of ER transactivation, suggesting a potential role for this fusion in ER transactivation.	[29]
RFC4-LPP	MCB	Luminal A	6.5% in MCB	NA	NA	E3-E5	LPP is reported to mediate TGF-b-induced breast oncogenesis.	[29]
SLC2A1-FAF1	MPC	Luminal B	A case report	NA	Promoter swap	E1-E13	FAF1 encodes a protein that binds to the FAS antigen, initiating apoptosis. Its down-regulation may contribute to tumorigenesis by regulating apoptosis, NFκB activity, ubiquitination, and proteasomal degradation.	[47]
BCAS4-AURKA	MPC	Luminal B	A case report	NA	NA	NA	AURKA gene amplification, a common genetic aberration in breast cancer, encodes Aurora Kinase A, a serine-threonine kinase primarily involved in centrosome duplication, mitotic entry, and spindle assembly.	[47]
TRPS1- PLAG1	BPA	TNBC	A case report	Deletion	Promoter swap	E1-E2 E1-E3	TRPS1 is essential for the growth and differentiation of normal mammary epithelial cells; however, its role in breast cancer is still a subject of debate. Overexpression of PLAG1 can result in the dysregulation of its target gene, IGF2.	[14, 112]
HER2–GRB7	ILC	HER2-	A case report	Deletion	Loss of functional domain	E25-E12	Fusion has intact HER2 extracellular and tyrosine kinase domains and lose more than half of its C-terminal tail sequence, which contains multiple tyrosine autophosphorylation sites. The fusion of the GRB7 src homology 2 (SH2) domain would then covalently link a downstream signaling protein to HER2.	[113]
CCNH-C5orf30	NA	NA	85% in all BC subtypes	NA	Promoter swap Loss of functional domain	NA	Fusion in cancer cells may help to fend off immune responses targeting the cancers.	[31]
TRMT11-GRIK2	MBC	NA	68.33% in all BC subtypes	NA	Epigenetic modification Antioncogene inactivation	NA	The fusion may repress protein translation., accelerate cell cycle progression and promote cell migration	[31]

inflammatory breast cancer (IBC), breast adenoid cystic carcinomas(BACC), breast pleomorphic adenomas(BPA), breast mucoepidermoid carcinoma (BMEC), mucinous carcinoma of the breast (MCB), micropapillary carcinoma (MPC), breast adenomyoepitheliomas (BAME), invasive lobular carcinoma (ILC), metastatic breast cancers (MBC), not available (NA), exon (E)

Methods to detect fusion genes

Several methods have been developed to detect fusion genes, including fluorescent in situ hybridization (FISH), reverse transcription-PCR (RT-PCR), and immunohistochemistry (IHC). FISH offers the advantage of directly visualizing fusion events at the cellular or tissue level, thereby providing valuable spatial information. However, it requires specialized equipment and expertise for interpretation [32, 33]. IHC is widely used as a screening tool because it is relatively simple and can be performed under routine laboratory conditions. However, this detection method has many limitations. Firstly, its sensitivity and specificity can be influenced by antibody quality and efficiency. Secondly, certain fusion events may not result in detectable changes in protein expression or may produce proteins that are unstable or rapidly degraded. Thirdly, IHC detects the expression of fusion proteins rather than the fusion genes themselves. As a result, it cannot directly visualize the fusion genes or identify the exact breakpoints of the fusion events within the genome [34, 35]. RT-PCR exhibits high sensitivity and can detect fusion transcripts even at low abundance, allowing for quantitative analysis. Although RT-PCR overcomes some limitations of FISH and IHC, sample preparation and PCR conditions may affect its reliability, and it lacks the ability to provide spatial information on fusion genes within cells [36](Table 2).

Table 2.

Summary of the advantages and disadvantages of the detection methods for fusion genes

Method	Advantages	Disadvantages	Knowledge gaps	Ref
FISH	Gold standard. Direct observation of fusion events at the cellular or tissue level without relying on protein expression or indirect markers.	Could not identify the fusion partner. The interpretation of FISH results is rather subjective, and requires specialized training because a considerable variability in interpretation of FISH results between readers. Require a fluorescence microscope, and the signals are labile and rapidly fade over time.	Low throughput limits large cohort studies; cannot validate rare or cryptic fusions in breast cancer; partner gene often missing; not suitable for comprehensive subtype-specific fusion profiling.	[32, 33, 86]
IHC	Cheap and fast. Familiar to most pathologists and can be used universally. The most reliable screening method for large-scale clinical practice. More stable, and tumor cells can be easily recognized on the slides regardless of the tumor cell number or specimen.	The sensitivity and specificity vary with the observers. Detecting the expression of proteins rather than the genetic rearrangements themselves.	Misses non-expressed, truncated, or low-abundance fusion proteins; limited validation for rare or newly discovered breast cancer fusions; cannot provide precise fusion partner info.	[34, 35]
RT-PCR	The most sensitive. Free from subjectivity in analysis, unlike IHC and FISH.	Not available in many routine pathology departments in China. Could not detect rare fusions types. Could not provide spatial information on fusion genes within cells.	Often limited to previously reported fusions; rare or complex breast cancer fusions may remain undetected; insufficient validation in large-scale cohorts.	[36]
NGS	Deliver high-resolution fusion gene detection with high sensitivity and specificity.	Detection sensitivity for lowly expressed fusion genes or those diluted by non-cancerous cells is low, and it is difficult to comprehensively identify structural variations.	Low-abundance or subtype-specific fusions in breast cancer remain incompletely mapped; functional significance of many detected fusions is unvalidated; challenges in distinguishing passenger vs. driver fusions.	[32, 37–39]
TGS	Shown higher sensitivity and specificity in structural variation detection.	High error rate, high sample requirements, and high cost.	Very few large-scale breast cancer cohort studies; rare, cryptic, or complex fusion events largely unexplored; functional validation lacking; limited real-world clinical correlation.	[38, 87]

In contrast to the above traditional techniques, next-generation sequencing (NGS) can deliver high-resolution fusion gene detection while assessing hundreds of genes in a single test, identifying both known and novel fusion genes. Several studies have provided evidence that NGS not only provides the same sensitivity and specificity as conventional diagnostic methods, but also displays multiplexing potential, allowing the screening of a larger range of actionable targets, with a small amount of tissue sample [32, 37]. Although NGS can overcome many limitations of the traditional techniques, its sensitivity may be reduced when detecting lowly expressed fusion genes or diluted by accompanying non-cancerous cells within a sample, due to the complexity of the transcriptome. Moreover, the detection of fusion genes using NGS is limited by short read lengths and PCR amplification bias, which may hinder the accurate characterization of structural variations [38, 39].

Recently, there has been considerable effort to develop algorithms and tools for identifying fusion genes from sequencing data. The first dedicated software, FusionSeq, was published in 2010 [40]. Most of the fusion detection tools have been developed in the last eight years, as shown in Table 3.

Table 3.

Software packages, algorithms and tools for identifying fusion genes from sequencing data

Tool	Description	Ref
FusionAI	A deep learning pipeline predicts fusion gene breakpoints from DNA sequences. It identifies potential breakpoints and explores genomic landscapes surrounding these fusion gene breakpoints in the human genome.	[88]
Genion	Applies filters to whole read clusters. Although this method is slower compared to filtering individual reads, it offers enhanced filtering capabilities and provides richer information for analyzing the selected candidates.	[89]
JAFFAL	An RNA-Seq analysis pipeline detects fusion genes by comparing transcriptomes, showcasing robust performance across various read lengths and types.	[90]
ChiTaH	Utilizes a reference database of 43,466 non-redundant known human chimeras to map sequencing reads and accurately identify chimeric reads.	[91]
Seekfusion	A time-efficient pipeline employs de-novo assembly and alignment approaches to accurately identify gene fusions using PCRUMI-based amplicon RNA-Seq.	[92]
MetaFusion	A flexible metacalling tool capable of merging outputs from any number of fusion callers. Across both real-world and simulated datasets, MetaFusion consistently achieves higher precision and recall compared to individual callers, reaching 100% precision. This highlights the essential role of integrated calling for high-confidence results.	[93]
CICERO	An algorithm based on local assembly integrates RNA-seq read support with comprehensive annotation for ranking candidate fusion transcripts, capable of identifying driver fusions beyond typical exon-to-exon fusion transcripts.	[94]
DEEPrior	An inherently flexible deep learning tool with two modes: Inference and Retraining. In Inference mode, it predicts the probability of a gene fusion’s involvement in oncogenesis by directly analyzing the amino acid sequence of the fused protein. The Retraining mode enables the creation of a customized prediction model using new user-provided data. It efficiently detects candidate gene fusions from long-read RNA-seq data, encompassing cDNA sequencing and direct mRNA sequencing.	[95]
LongGF	Efficiently identifies candidate gene fusions from long-read RNA-seq data, including both cDNA and direct mRNA sequencing.	[96]
AnnoFuse	Standardizes filtering and annotation for gene fusion calls from STAR-Fusion and Arriba. It merges, filters, and prioritizes putative oncogenic fusions across large cancer datasets.	[97]
Fusion-Bloom	A fusion detection method utilizing advancements in de novo transcriptome assembly and assembly-based structural variant calling technologies, namely RNA-Bloom and PAVFinder.	[98]
FusionScan	Recovers most true positives efficiently across varied sequencing depths and read lengths, with computational time comparable to other leading tools.	[99]
BreakID	Identifies gene fusion breakpoints at single nucleotide resolution using discordant read pairs and split reads as supporting evidence.	[100]
FuSeq	A fast and accurate method for discovering fusion genes utilizes quasi-mapping to rapidly map reads, extract initial candidates from split reads and fusion equivalence classes, and apply multiple filters and statistical tests to identify final candidates.	[101]
GeneFuse	Reports the genome locus, inferred protein forms, and supporting sequencing reads for each detected fusion.	[102]
ChimPipe	Combines discordant paired-end reads and split-reads to detect diverse chimeras, including polymerase read-through, demonstrating a strong balance between sensitivity and precision.	[103]
FuGePrior	Integrates advanced tools for discovering and prioritizing chimeric transcripts, incorporating filtering and processing steps informed by current gene fusion literature and an analysis of fusion structure functional reliability.	[104]
ChimeRScope	Predicts fusion transcripts using gene fingerprint profiles derived from RNA-Seq paired-end reads as k-mers.	[105]
ConfFuse	Ranks fusion candidates from defuse output by assigning each a confidence score, aiming to significantly reduce total candidates while maintaining a high recall rate for true positives.	[106]
MACHETE	Assigns statistical scores, including empirical p-values, to each putative fusion for unsupervised detection.	[107]
INTEGRATE	Integrates RNAseq with WGS offers a sensitive and specific approach for detecting high-confidence gene fusion predictions.	[108]
InFusion	Introduces unique features, such as detecting fusions in intergenic regions and identifying anti-sense transcription in chimeric RNAs using strand-specificity.	[109]

Despite the considerable advances in sequencing-based fusion gene detection and the proliferation of dedicated computational tools, several limitations remain that constrain their clinical and biological utility. Most current algorithms rely heavily on RNA-seq data, which are inherently affected by transcriptional noise, read-through transcripts, and variable tumor purity, potentially resulting in false positives or overlooked low-abundance fusions. Moreover, short-read sequencing limits the resolution of complex structural rearrangements and hampers accurate breakpoint characterization, while sequence bias in PCR and library preparation can further skew detection sensitivity. Although multiplexed NGS approaches enable broad profiling of actionable targets, many low-frequency or cryptic fusion events remain difficult to detect, particularly in heterogeneous tumor samples or metastatic lesions.

Looking forward, future efforts must prioritize the integration of multi-omics data, including long-read sequencing, single-cell transcriptomics, and proteomics, to achieve a more comprehensive and functionally validated landscape of fusion events. The development of robust, standardized pipelines that incorporate both discovery and functional assessment will be crucial for translating fusion gene findings into clinically actionable insights. In parallel, systematic benchmarking and validation across diverse breast cancer cohorts are needed to distinguish biologically relevant driver fusions from passenger events or technical artifacts. Collectively, these efforts will enhance the reliability, reproducibility, and translational impact of fusion gene detection, paving the way for precision oncology strategies that exploit fusion-driven vulnerabilities in breast cancer.

Roles and oncogenic activation mechanisms of fusion genes in breast cancer

Chimeric or truncated protein expression and functional alterations

Fusion events often generate chimeric or truncated proteins with abnormal structural domains, conferring novel oncogenic properties. For instance, the ETV6–NTRK3 (Fig. 2) fusion produces a chimeric protein that binds to GRB2, thereby activating the MAPK and PI3K–AKT pathways [41, 42]. Within the JAK–STAT cascade, this fusion enhances STAT1 phosphorylation while reducing its acetylation, ultimately disrupting STAT–NF-κB interactions and promoting tumorigenesis [43].

Fig. 2.

Tumor signaling pathways regulated by breast cancer fusion proteins. FRS2, Fibroblast growth factor receptor substrate 2; SOS, Son of Sevenless; GDP, Guanosine diphosphate; RAS, Rat sarcoma protein; RAF, rapidly accelerated fibrosarcoma kinase; MEK, MAPK/ERK kinase; ERK, Extracellular signal-regulated kinase; GRB1 / GRB2, Growth factor receptor-bound protein 1/2; PI3K, Phosphoinositide 3-kinase; AKT, Protein kinase B; IRF9, Interferon regulatory factor 9; JAK, Janus kinase; STAT, Signal transducer and activator of transcription; STAT1 / STAT2, signal transducers and activators of transcription; WNT, Wingless-type signaling pathway; LRP, Low-density lipoprotein receptor-related protein; GSK-3β, Glycogen synthase kinase 3 beta; CKIα, Casein kinase I alpha; Axin, Scaffold protein that forms the β-catenin destruction complex; APC, Adenomatous polyposis coli; TCF/LEF, T-cell factor / Lymphoid enhancer factor; SNAI1 / SNAI2, Snail family transcriptional repressors

Truncated proteins also contribute to malignant phenotypes. For example, the ESR1–CCDC170 fusion truncates the N-terminus of CCDC170, facilitating interaction with the GRB1 signalosome and amplifying growth factor signaling, thereby enhancing motility and invasiveness [13]. Similarly, BCL2L14–ETV6 encodes a truncated BCL2L14 protein that drives epithelial-to-mesenchymal transition (EMT), promoting migration, invasion, and chemoresistance in triple-negative breast cancer (TNBC) cells [11].

Collectively, these findings highlight that structural alterations resulting from gene fusions can produce proteins with aberrant functions, contributing to breast cancer initiation, progression, and therapy resistance.

Activating kinase activity

Some fusions directly enhance kinase activity, driving uncontrolled proliferation. The MAGI3–AKT3 fusion constitutively activates the AKT3 kinase domain through Ser473 phosphorylation, even in the absence of growth factors. This constitutive activity stimulates downstream targets such as GSK3β, thereby promoting survival, proliferation, and resistance to apoptosis [44].

Loss of functional or ligand-binding domain

Fusion events may also impair critical protein domains, undermining normal cellular functions. For example, AZGP1–GJC3 results in the loss of the Ig-like C1-type domain of AZGP1, reducing its transport capacity and potentially enhancing invasiveness [28]. The RAD51AP1–DYRK4 fusion disrupts RAD51 interaction, impairing homologous recombination repair [20]. Similarly, PPP6R3–TENM4–NRG1 leads to the loss of the Ig-like domain of NRG1, abolishing its nuclear signaling functions [12].

Loss of ligand-binding domains is another oncogenic mechanism. The ESR1–YAP1 fusion eliminates the ligand-binding domain of ESR1, enabling constitutive transcriptional activity independent of estrogen. This drives continuous proliferation and renders tumors refractory to endocrine therapies such as selective estrogen receptor modulators (SERMs) and aromatase inhibitors [45].

Anomalous gene expression regulation

Loss of microRNA-binding site

Fusion events involving the 3′ Untranslated Region (UTR) may abolish microRNA-binding sites, thereby releasing oncogenes from inhibitory control. Under physiological conditions, miR-15a/16 and miR-150 suppress MYB expression [7, 46]. Loss of these sites due to fusion events results in MYB overexpression, which enhances apoptosis evasion, angiogenesis, and cell cycle progression [7]. Similar mechanisms are reported in MYBL1–NFIB1 and MYBL1–ACTN1 fusions [24].

Promoter swap

Promoter exchange can deregulate oncogene or tumor suppressor expression. For example, in CTNNB1–PLAG1, PLAG1 comes under the control of the CTNNB1 promoter, leading to its upregulation and enhanced proliferation [25]. Conversely, the SLC2A1–FAF1 fusion downregulates FAF1 by placing it under the SLC2A1 promoter, thereby impairing apoptosis and ubiquitin–proteasome regulation [47]. Other promoter swap-driven oncogenic events include TRPS1–PLAG1 [14] and CCNH–C5orf30 [31].

Epigenetic modification

Fusion events may also induce epigenetic reprogramming. For instance, KDM3B–ETF1 modifies histone methylation, reducing LMO2 expression and activating the WNT/β-catenin pathway to enhance proliferation and invasion [48]. The TRMT11–GRIK2 fusion disrupts tRNA m²G modification, impairing translational efficiency and protein synthesis [31].

Inactivation of tumor suppressor genes

Some fusions directly inactivate tumor suppressors. The MAGI3–AKT3 fusion disrupts the MAGI3–PTEN interaction, resulting in unchecked PI3K–AKT signaling [44]. Similarly, HMGA2–WIF1 and TRMT11–GRIK2 lead to the loss of WIF1 and GRIK2 tumor suppressor activity, respectively, facilitating oncogenesis [31, 49].

In summary, the oncogenic mechanisms of breast cancer fusion genes are diverse but remain largely derived from case studies. Current understanding is primarily based on a limited set of functionally validated fusions, including ETV6–NTRK3, ESR1–CCDC170, BCL2L14–ETV6, and MAGI3–AKT3, which exemplify how fusions can drive tumorigenesis through the production of chimeric or truncated proteins, constitutive kinase activation, loss of functional domains, or aberrant gene regulation. However, the majority of fusion events, particularly low-frequency or rare fusions, lack systematic functional characterization, leaving the full spectrum of fusion-driven oncogenesis incompletely defined. Experimental models further constrain mechanistic insights. Most studies rely on in vitro systems, such as MCF10A or BT20 cells, or overexpression/transfection approaches, which fail to capture the cellular heterogeneity, microenvironmental context, and inter-patient variability inherent to breast tumors. Consequently, the phenotypic impact of many fusions may be over- or underestimated, limiting the immediate translational relevance of these findings. Mechanistic overlap and complexity present an additional challenge. Multiple oncogenic processes—including structural alterations, kinase activation, domain loss, microRNA-binding site disruption, promoter swapping, and epigenetic modification—can co-occur within a single fusion event. These intersecting mechanisms remain poorly resolved, impeding precise modeling of fusion biology and the rational design of therapeutic interventions. Data limitations also constrain current understanding. Most analyses rely heavily on RNA-seq or limited DNA-seq datasets, with sparse validation of fusion protein expression or functional activity. As a result, some events may represent transcriptional noise or artifactual fusions rather than bona fide drivers.

Looking forward, a multi-pronged strategy is required to advance the field. Integration of long-read sequencing, single-cell transcriptomics, and proteomic analyses will be essential to map the complete landscape of biologically relevant fusion events. Systematic functional interrogation using CRISPR-based perturbations, patient-derived organoids, and in vivo models will help resolve mechanistic ambiguities and distinguish driver fusions from transcriptional noise. In parallel, the development of standardized pipelines for fusion detection, annotation, and cross-cohort validation will be critical for reproducibility and clinical applicability. Ultimately, coupling mechanistic insights with therapeutic vulnerability—particularly in the context of subtype-specific biology, stemness, and microenvironmental interactions—will facilitate the translation of fusion gene discoveries into precision oncology strategies for breast cancer.

Therapeutic target of fusions in breast cancer

Although many fusion genes have been reported in breast cancer, few have been translated into therapeutic targets or clinical interventions. Given that different fusion genes may lead to distinct biological behaviours, more personalized treatments should be explored. The following summarizes some treatment strategies that have been extensively studied (Fig. 3), some of which directly target fusion proteins.

Fig. 3.

Therapeutic implications, mechanisms of resistance, and translational barriers of fusion genes

Targeted kinase inhibitors

Targeted kinase inhibitors are a crucial approach in cancer therapy. They work by specifically targeting and inhibiting certain tyrosine kinases, thereby disrupting the signaling pathways associated with cancer. This approach is particularly significant for cancers involving fusion genes, as the fusion gene products often lead to aberrant kinase activity, which drives tumor growth and progression.

Tyrosine receptor kinase (TRK) inhibitors

TRK inhibitors such as larotrectinib and entrectinib have shown potent activities across various cancer types harboring NTRK fusions and have received Food and Drug Administration (FDA) approval for treating solid tumors with NTRK fusions [50]. These drugs selectively bind to and block the Adenosine Triphosphate (ATP)-binding site of TRK receptors, thereby inhibiting downstream proliferative mechanisms, leading to cell cycle arrest, apoptosis, and tumor shrinkage [51, 52]. While TRK inhibitors have shown significant anti-tumor activity in clinical trials, acquired resistance limits their long-term effectiveness. Next-generation TRK inhibitors such as LOXO-195 and TPX-0005 have been developed to overcome resistance observed with first-generation TRK inhibitors [53]. However, TRK inhibitors pose safety concerns such as weight gain, dizziness, ataxia, gait disturbances, and insufficient long-term toxicity assessment [54, 55].

Rearranged during transfection (RET) inhibitors

RET fusions are sensitive to RET inhibitors. A 63-year-old elderly female with NCOA4-RET-positive breast cancer and a 36-year-old woman with CCDC6-RET-positive TNBC exhibited poor responses to multimodal therapy and developed multi-site metastases. Following treatment with RET inhibitors (cabozantinib and pralsetinib, respectively), both patients showed rapid clinical responses and improved complications [56, 57], demonstrating the effectiveness of RET inhibitors in RET fusion-positive breast cancers. Despite the favorable treatment outcomes, the elderly female experienced side effects such as dyspnea [57], while the 36-year-old woman developed Grade III bone marrow suppression and mild hepatic impairment after one treatment cycle [56]. Further assessment of drug safety is warranted.

Despite significant diversity in the C-terminal sequences and structures of transcriptionally active ESR1 fusions, RET kinase emerged as a common therapeutic vulnerability. Using a PDX model derived from a patient resistant to multiple endocrine therapies, which harbored ESR1-YAP1, researchers found that this model responded similarly to the selective RET inhibitor pralsetinib as it did to palbociclib [58]. These findings provide preclinical rationale for RET inhibitors in treating ESR1 fusion-driven ET-resistant breast cancer.

Fibroblast growth factor receptor 1–3 (FGFR1-3) inhibitors

The findings from Nicolò et al. appear to provide therapeutic options for breast cancers with FGFR fusion gene positivity, as their patient with FGFR2-KIAA1598 positive breast cancer exhibited an exceptional response following treatment with pemigatinib [59]. Chew et al. treated a FGFR3-TACC3 positive breast cancer patient with PD173074 (a FGFR1-3 inhibitor), which resulted in decreased levels of Cyclin A and pRb, along with elevated cleaved PARP, indicating G1 cell cycle arrest and apoptosis [60]. These findings suggest that FGFR1-3 inhibitors could be a therapeutic option for TNBC.

Other kinase inhibitors

HER2 inhibitors (Pertuzumab), pan-ERBB inhibitors (Afatinib), HER3-targeting monoclonal antibodies (Seribantumab), and bispecific monoclonal antibodies targeting HER2/3 (Zenocutuzumab) have all demonstrated some therapeutic efficacy against NRG1 fusions [61, 62]. PKC412 (a multi-targeted protein kinase inhibitor) [63] and IGFR1 inhibitors [41] have also shown some efficacy to ETV6-NTRK3 positive SBC. The MYB-NFIB fusion gene’s expression is modulated by AKT-dependent signaling pathways that are downstream of the IGF1 receptor. As a result, the expression levels of MYB-NFIB can be suppressed through the pharmacological inhibition of IGF1R with the targeted agent linsitinib [64].

Signal pathway inhibitors

Signal pathway inhibitors target specific signaling pathways involved in cancer cell growth and survival, disrupting critical signaling proteins and pathways, thereby inhibiting tumor cell proliferation and promoting cell death. Treatment of ETV6-NTRK3-overexpressing cells with the STAT1 inhibitor, Fludarabine, significantly suppressed NF-κB activity, induced apoptosis, decreased colony formation in soft agar, and reduced tumor size in vivo in ETV6-NTRK3-overexpressing cells. This suggests that STAT1 inhibition may hold therapeutic potential for ETV6-NTRK3-driven tumors [43]. Lei et al. targeted downstream ER signaling events by using a CDK 4/6 inhibitor, Palbociclib, which suppressed growth driven by ESR1-YAP1 in vitro, and at primary and metastatic sites in a PDX model naturally harboring the ESR1-YAP1 fusion. Brett et al. also demonstrated the beneficial effects of Palbociclib in ESR1 fusion-positive breast cancer [65].

Proteasome inhibitors

Proteasome inhibitors block protein degradation by the proteasome, leading to the accumulation of proteins that control cell growth and apoptosis, thus inhibiting tumor cell proliferation and promoting cell death. Gates et al. discovered enhanced recruitment of the 26 S proteasome onto the ESR1-YAP1 transcriptional complexes. Subsequently, they demonstrated the inhibition of ESR1-YAP1-induced in vitro transcriptional activity by the proteasome inhibitor MG-132, thus suppressing the growth of breast cancer cells expressing the ESR1-YAP1 fusion gene [66]. Yusenko et al. reported another proteasome inhibitor, Oprozomib, interferes with the ability of the co-activator p300 to stimulate MYB activity and exhibits significant anti-proliferative effects on ACC cells [67].

Other therapeutic approaches

Monensin, a type of ionophore antibiotic, triggers MYB degradation and broadly suppresses its transcriptional activity in the transcriptome, impairing cell viability and sphere-forming ability [68]. Additionally, all-trans retinoic acid (ATRA) [69], miR-150 [46], and JQ1 (BET bromodomain inhibitor) [70] have shown promising therapeutic effects via suppressing MYB-NFIB mRNA and protein levels as well as MYB transcriptional activity.

This section discusses therapeutic strategies targeting fusion genes in breast cancer, focusing on kinase inhibitors, signal pathway inhibitors, proteasome inhibitors, and other novel approaches. Kinase inhibitors like TRK, RET, and FGFR inhibitors show promise in treating fusion-driven cancers but face challenges such as drug resistance and side effects. Signal pathway inhibitors, such as STAT1 and CDK 4/6 inhibitors, offer alternative strategies by disrupting downstream fusion gene activity. Proteasome inhibitors like MG-132 and Oprozomib target protein complexes linked to fusion genes, while other approaches, like Monensin and BET inhibitors, degrade oncogenic proteins like MYB. These therapies emphasize the need for personalized treatments in fusion-driven breast cancers.

Despite the therapeutic enthusiasm surrounding fusion-directed interventions, the translational landscape in breast cancer remains fragmented and clinically underpowered. Most available evidence stems from case reports, small cohorts, and pan-cancer basket trials, limiting the interpretability of fusion-specific therapeutic efficacy within breast cancer subtypes. Importantly, while NTRK, RET, and FGFR inhibitors have produced notable responses in select fusion-driven cases, the durability of benefit is frequently compromised by acquired resistance mechanisms, including secondary kinase mutations, lineage reprogramming, and activation of bypass signaling pathways. Equally underexplored are real-world patterns of toxicity, tolerability, and sequencing strategies, which are essential for defining the clinical window of targeted therapies in heavily pre-treated fusion-positive patients. Moreover, the relative contribution of fusions to oncogenic dependency in breast cancer remains uncertain. Unlike leukemias in which fusion-driven oncogenesis establishes strong target addiction, many breast cancer fusions may operate as facilitators rather than indispensable drivers, thereby attenuating sensitivity to direct kinase inhibition. Emerging downstream (e.g., CDK4/6) or protein-degradation–based interventions provide alternative avenues but currently lack prospective validation. Moving forward, a more sophisticated alignment of molecular diagnostics, standardized fusion reporting, and prospective breast cancer–specific clinical trials—combined with resistance biomarker profiling and real-world outcome analyses—will be necessary to determine the true therapeutic index of fusion-targeted strategies and to rationalize combinatorial approaches capable of overcoming resistance.

Mechanisms of Resistance and Translational Barriers: Although fusion-targeted therapies can yield robust initial responses in selected settings, acquired resistance is common, and breast cancer–specific mechanisms remain underexplored [71, 72]. At present, most mechanistic insights are extrapolated from other tumour types, highlighting the paucity of longitudinal studies and molecular profiling in breast cancer (Fig. 3).

On-target secondary mutations. Secondary mutations within the kinase domain represent a well-established mechanism of on-target resistance, diminishing drug binding and attenuating therapeutic inhibition. Such alterations have been extensively characterized in NTRK- and FGFR-driven cancers treated with TRK or FGFR inhibitors [72, 73], yet their prevalence and spectrum in breast cancer remain unclear due to limited clinical experience and restricted access to paired pre- and post-treatment specimens.

Bypass pathway activation. Reactivation of compensatory signalling circuits—including PI3K–AKT, MAPK, and JAK–STAT—can circumvent dependency on fusion-driven signalling, reducing the efficacy of monotherapy and suggesting a potential role for rational combination strategies [74, 75]. Whether bypass reprogramming follows subtype-specific or lineage-dependent patterns in breast cancer remains to be defined.

Clonal and structural heterogeneity. Both intertumoural and intratumoural heterogeneity complicate the therapeutic targeting of fusion events. Some fusions may reside within minor subclones and, while biologically actionable, may be insufficient to dictate tumour fitness. Such clonal architectures pose inherent limitations for durable therapeutic control [76].

Detection and diagnostic instability. A further translational barrier lies in detection challenges. Many fusion events are identifiable only at the RNA level, exhibit low transcript abundance, or lack readily detectable genomic rearrangements, complicating the use of standard clinical assays for patient stratification and response monitoring [77–80]. These analytical constraints highlight the need for optimized sequencing pipelines, orthogonal validation strategies, and dynamic biomarkers.

Collectively, these considerations underscore that resistance to fusion-targeted therapy is multifactorial, and that the translational trajectory from biological plausibility to clinical application in breast cancer remains immature. Systematic studies incorporating longitudinal sampling, minimal residual disease monitoring, and integrative multi-omic profiling will be required to elucidate lineage-specific vulnerabilities and rationalize next-generation therapeutic strategies.

Conclusion

Fusion genes arise primarily through chromosomal rearrangements, splicing, and read-through, which increase the diversity of genetic alterations that contribute to breast cancer heterogeneity. Chromosomal rearrangements, such as translocations or inversions, are well-characterized in specific breast cancer subtypes, while splicing and read-through events contribute to a broader spectrum of fusion gene formation. Each mechanism reflects the complexity of genomic instability in cancer cells, which drives oncogenesis through novel fusion genes that alter cellular functions.

Fusion genes are not distributed uniformly across all breast cancer subtypes, but rather show specificity for certain histological types, highlighting their diagnostic and prognostic potential. The presence of fusion genes in rare subtypes like SBC (ETV6-NTRK3) and BACC (MYB-NFIB) underscores the roles of these genetic alterations in defining disease biology and clinical behavior. The organization of fusion gene patterns within distinct subtypes could help to refine breast cancer classifications and better predict disease outcomes.

Fusion genes could contribute to oncogenic activation through intricate mechanisms in breast cancer. Alterations in protein structure and function play a central role in transforming normal cellular processes. Chimeric proteins, generated by fusion genes, can lead to aberrant signaling, unchecked cell proliferation, and resistance to apoptosis. For example, the activation of kinase activity in fusion proteins like RET or FGFR fusions drives oncogenic signaling cascades. Additionally, structural truncations or loss of functional domains in fusion proteins can disrupt normal regulatory mechanisms, enabling malignant behavior.

Beyond protein structural changes, fusion genes can regulate gene expression in complex ways, including the loss of micro-RNA-binding sites, promoter swaps, and epigenetic modifications. These regulatory changes result in the dysregulation of oncogenes or tumor suppressor genes, enhancing the aggressiveness of the cancer. The inactivation of tumor suppressor genes, either directly or indirectly through fusion events, further accelerates tumorigenesis. The multifaceted impacts of fusion genes highlight their roles as central drivers of malignancy in breast cancer.

Advances in detection technologies have significantly improved our ability to identify fusion genes. Traditional methods such as FISH, IHC, and RT-PCR have been valuable in the clinical settings. However, the advent of high-throughput next-generation sequencing NGS and TGS has revolutionized the field by enabling comprehensive genomic profiling, allowing for the discovery of rare and novel fusion events. These methods not only facilitate rapid and accurate identification of fusion genes, but also offer insights into the molecular landscape of individual tumors, aiding in diagnosis, treatment selection, and monitoring of disease progression.

Fusion genes have emerged as attractive therapeutic targets due to their roles in driving oncogenesis. Targeted therapies, such as kinase inhibitors, have demonstrated significant efficacy in blocking the activity of fusion-driven oncogenic pathways. TRK inhibitors, for instance, have shown promise in treating tumors with NTRK fusions, while RET and FGFR inhibitors are being explored in clinical trials for breast cancers harboring respective fusion events. Additionally, inhibitors of downstream signaling pathways and proteasome inhibitors provide alternative therapeutic strategies for targeting fusion-driven malignancies. These therapies represent a key component of precision oncology, offering tailored treatments based on the specific genetic alterations present in each patient’s tumor.

Despite these advances, the translation of fusion gene discoveries into routine clinical practice still faces several challenges. One major hurdle is the development of more comprehensive genomic profiling techniques capable of identifying rare or novel fusion events. Many current detection methods may miss low-frequency or cryptic fusions that could be clinically relevant. Efforts to better understand the functional consequences of fusion events, identify oncogenic activation mechanisms, and explore the full therapeutic potential of fusion-targeted therapies will be critical in improving outcomes for breast cancer patients. Integrating fusion gene analysis into routine clinical practice, supported by comprehensive genomic profiling, holds the potential to offer more personalized and effective treatment strategies. Continuously getting insights into the biology and clinical implications of fusion genes is essential to addressing these challenges. As the field of precision oncology continues to evolve, fusion genes will likely play a key role in shaping the future of breast cancer care, bringing us closer to the goal of personalized therapies that prolong patient survival and improve their quality of life.

Comparative Feasibility: Which Therapeutic Strategies Are Most Realistic in Breast Cancer? When evaluated through the lens of clinical feasibility and developmental maturity, it becomes clear that available therapeutic strategies differ substantially in their translational realism for breast cancer. Direct kinase inhibition currently represents the most clinically actionable approach, but its applicability is restricted to a narrow subset of fusion-driven tumours that display clear oncogene addiction—most notably secretory breast carcinoma harbouring the pathognomonic ETV6–NTRK3 fusion [81, 82]. These cases provide important proof of principle, yet their rarity limits population-level impact.

By contrast, downstream pathway inhibition (for example, targeting CDK4/6, STAT, or PI3K signalling) is theoretically more broadly applicable, particularly for non-kinase fusions such as those involving ESR1 [83, 84]. Such strategies also fit more naturally within the existing treatment paradigms for hormone receptor–positive breast cancer. However, their reduced specificity raises concerns regarding biomarker-driven patient selection and pharmacodynamic precision, which could attenuate clinical benefit.

More experimental modalities—including proteasome interference and targeted protein degradation—remain largely conceptual or in early-stage preclinical development [85]. Although mechanistically compelling, these approaches currently lack a defined translational path in breast cancer, both in terms of fusion target selection and regulatory or diagnostic frameworks.

Taken together, while fusion genes illuminate key aspects of breast cancer biology, only a minority presently constitute actionable therapeutic targets. A critical distinction must therefore be made between fusions that confer realistic clinical utility and those that remain predominantly mechanistic or intellectually appealing. Such distinction is essential to prevent overinterpretation and to rationally steer future research towards settings where therapeutic benefit is most plausible.

Abbreviations

IBC

Inflammatory Breast Cancer

BACC

Breast Adenoid Cystic Carcinoma

BPA

Breast Pleomorphic Adenoma

BMEC

Breast Mucoepidermoid Carcinoma

MCB

Mucinous Carcinoma of the Breast

MPC

Micropapillary Carcinoma

BAME

Breast Adenomyoepithelioma

ILC

Invasive Lobular Carcinoma

MBC

Metastatic Breast Cancers

LAR

Luminal Androgen Receptor

IM

Immunomodulatory

BLIS

Basal-like immune-suppressed

MES

Mesenchymal-like

FISH

Fluorescence In Situ Hybridization

RT-PCR

Reverse Transcription Polymerase Chain Reaction

IHC

Immunohistochemistry

NGS

Next-Generation Sequencing

TGS

Third-generation Sequencing

TRK

Tyrosine receptor kinase

FDA

Food and Drug Administration

UTR

Untranslated Region

ATP

Adenosine Triphosphate

TNBC

Triple-Negative Breast Cancer

RET

Rearranged During Transfection

FGFR1-3

Fibroblast Growth Factor Receptor 1–3

Author contributions

Conceptualization, methodology, writing—original draft preparation, Z.G; Software, validation, investigation, original draft preparation, X.Z; Writing– review & editing, Validation, Software, M.Y; Resources, data curation, visualization, formal analysis, M.S; Formal analysis, data curation, Q.F; Methodology, validation, Y.H; Supervision, project administration, G-Q J; Conceptualization, writing—review and editing, J.W. All authors reviewed and approved the final version of the manuscript.

Funding

This study was supported by Scientific Research Preliminary Fund Project of The Second Affiliated Hospital of Soochow University (SDFEYBS2423).

Data availability

No datasets were generated or analysed during the current study.

Declarations

Ethics approval and consent to participate

Not applicable.

Consent for publication

Not applicable.

Competing interests

The authors declare no competing interests.