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Article

Revealing the Angiogenic Signature of FH-Deficient Breast Cancer: Genomic Profiling and Clinical Implications

by
Liat Anabel Sinberger
1,
Noa Keren-Khadmy
2,
Assaf Goldberg
1,
Tamar Peretz-Yablonski
3,
Amir Sonnenblick
2,4,* and
Mali Salmon-Divon
1,5,*
1
Department of Molecular Biology, Ariel University, Ariel 4077625, Israel
2
Institute of Oncology, Tel Aviv Sourasky Medical Center, Tel Aviv 6423906, Israel
3
Hadassah Medical Center, Faculty of Medicine, Hebrew University of Jerusalem, Jerusalem 9112001, Israel
4
Sackler Faculty of Medicine, Tel Aviv University, Tel Aviv 6997801, Israel
5
Adelson School of Medicine, Ariel University, Ariel 4077625, Israel
*
Authors to whom correspondence should be addressed.
Cancers 2025, 17(18), 2942; https://doi.org/10.3390/cancers17182942
Submission received: 1 August 2025 / Revised: 2 September 2025 / Accepted: 4 September 2025 / Published: 9 September 2025
(This article belongs to the Section Cancer Therapy)
Browse Figures
Review Reports Versions Notes

Simple Summary

Breast cancer is a complex disease influenced not only by genes but also by the way cancer cells generate and use energy. One rare change is the loss of a gene called fumarate hydratase (FH), which normally helps cells produce energy efficiently. We examine how this change influences how tumors grow and respond to treatment. By studying thousands of breast cancer samples, we found that tumors missing FH tend to create a special environment that helps them grow and survive, especially by encouraging new blood vessel formation. We also describe a patient with this FH change who had a remarkable and long-lasting response to a therapy that blocks blood vessel growth (known as anti-VEGF treatment). These findings suggest that identifying this rare energy-related alteration may help clinicians determine which patients are most likely to benefit from this therapy. Such insights could contribute to advancing more personalized approaches to breast cancer care.

Abstract

Background: Fumarate hydratase (FH) deficiency is a rare metabolic alteration in breast cancer that may drive tumor progression through angiogenic remodeling. However, its role in shaping the tumor microenvironment remains poorly defined, limiting our understanding of metabolism-driven angiogenesis and its therapeutic significance. Methods: We analyzed genomic and transcriptomic profiles from thousands of breast cancer samples, including the TCGA cohort, to identify FH mutations and copy number alterations. Differential expression, pathway enrichment, and weighted gene co-expression network analysis (WGCNA) were performed to characterize metabolic and signaling changes. Clinical relevance was examined in a triple-negative breast cancer patient with an FH mutation treated with bevacizumab. Results: FH alterations were enriched in larger, primary tumors and in older patients. FH-deficient tumors displayed metabolic reprogramming, with reduced oxidative phosphorylation and TCA cycle activity, accompanied by upregulation of angiogenesis, VEGF signaling, and epithelial–mesenchymal transition pathways. WGCNA identified 11 hub genes (including CDH5, CLDN5, VWF, and PECAM1) linked to a pro-angiogenic microenvironment. A clinical case illustrated a durable and exceptional response to bevacizumab-based therapy in an FH-mutant patient. Conclusions: FH deficiency promotes an angiogenic tumor microenvironment and may serve as a predictive biomarker for VEGF-targeted therapies. These findings underscore the therapeutic potential of exploiting metabolic vulnerabilities to inform precision oncology.

1. Introduction

Targeting tumor angiogenesis represents a key therapeutic approach in the treatment of advanced breast cancer (BC), most notably through anti-VEGF therapies [1,2]. The role of bevacizumab in the treatment of stage IV BC has been controversial due to mixed evidence regarding its efficacy. Bevacizumab was initially granted accelerated approval for use in combination with paclitaxel for first-line treatment of HER2-negative metastatic BC based on improvements in progression-free survival (PFS) observed in clinical trials [3]. However, subsequent studies failed to demonstrate a significant improvement in overall survival (OS) with bevacizumab, and safety concerns, including risks of hypertension, proteinuria, and thromboembolic events, led the FDA to revoke its approval for BC in 2011 [4]. This highlights the complexity of anti-VEGF effects in BC and the need for better predictive biomarkers to identify patients who might benefit from bevacizumab therapy.
Emerging evidence suggests that alterations in metabolic enzymes may contribute to angiogenic signaling in cancer, implicating fumarate hydratase (FH) as a potential contributor in this process. FH is an enzyme in the tricarboxylic acid (TCA) cycle that catalyzes the conversion of fumarate to malate. The gene responsible for encoding FH is located on chromosome 1q42.2. Recent research has identified FH as a tumor suppressor gene involved in cancer development and progression, highlighting its potential as a therapeutic target [5,6,7].
Heterozygous germline mutations in the FH are associated with hereditary leiomyomatosis and renal cell carcinoma (HLRCC), an autosomal dominant cancer syndrome that significantly increases the risk of developing type II papillary kidney cancer. This form of cancer is known for its aggressive growth and early metastasis [5,8,9].
Genetic alterations resulting in FH loss of function lead to the accumulation of fumarate, causing dysregulation of cellular metabolism and signaling pathways. In HLRCC, FH-deficient cells, defined by reduced or absent FH activity, show impaired oxidative phosphorylation and transition to aerobic glycolysis. These cells exhibit reduced levels of AMPK, p53, and DMT1, resulting in low cellular iron levels. Additionally, the elevation of fumarate stabilizes HIF-1α (hypoxia-inducible factor 1α) and increases VEGF (vascular endothelial growth factor) expression, facilitating rapid cell growth. The effects of FH deficiency suggest several drug development avenues, including VEGF inhibitors, DNMT (DNA methyltransferases) inhibitors, PARP inhibitors, and LDHA (lactate dehydrogenase) inhibitors [5,6,7].
In vivo studies of kidney-specific Fh1 deletion have shown that FH loss induces stabilized levels of HIF-1α and NRF2, which are involved in cell proliferation and angiogenesis [10,11,12].
Advances in genetic testing have improved the ability to predict the prevalence of FH germline mutations in the population. Recent studies have estimated that pathogenic or likely pathogenic FH variants occur in 1 in 901 to 1 in 1252 individuals. Additionally, 1.05% of breast cancer (BC) patients (389 out of 36,966) have been found to carry FH pathogenic or likely pathogenic variants [13,14].
In this study, we present a novel treatment approach in a BC patient with FH mutations using VEGF inhibitors. We also investigate how FH alteration affects BC tumors to better understand the mechanism behind VEGF inhibitor therapy.

2. Materials and Methods

2.1. Data Resource and Study Design

We collected clinical and genomic aberration data for 10,953 cancer patients from the TCGA pan-cancer study (Supplementary Table S1) and an additional 9163 BC samples from twelve studies (Table 1) using cBioPortal for Cancer Genomics [15,16].
Normalized gene expression profiles, probe annotation, and clinical information for 1073 samples from the TCGA dataset [29,30] were downloaded using the MetaGxBreast (version 1.12.0) R package [31]. In addition, information on somatic non-synonymous mutations, structural variance (SV), copy number alterations (CNAs), putative arm-level copy number (888 samples), and protein expression z-scores measured by mass spectrometry (105 samples) was downloaded from cBioPortal for Cancer Genomics.
Data processing was conducted using R studio software (version 4.2.2) [32]. TCGA samples were categorized based on FH RNA and protein expression levels.
We defined wild-type (WT) samples as those without FH mutations or SV and with diploid FH status, and “FH aberration” as samples with a combination of CNA shallow deletion and/or 1Q arm loss, and/or samples with FH mutation.
All clinical information in the Case Study Section was collected and published following ethical approval (Helsinki ethics approval number TLV-19-449), with informed consent obtained from the patient for use of their anonymized data.
The study design is presented in Figure 1.

2.2. Calculation of Angiogenesis Score

Angiogenesis scores were calculated for each TCGA breast cancer sample using the GSVA R package [33] (version 2.2.0) based on the HALLMARK_ANGIOGENESIS gene set obtained from the MSigDB database [34,35] R package (v25.1.1).

2.3. Differentially Expressed Gene (DEG) Identification

Differential gene expression analysis was conducted using limma [36] (version 3.54.1) and edgeR [37] (version 3.40.2) R packages. DEGs were identified across the various groups, with statistical significance defined as a false discovery rate (FDR) ≤ 0.05 and a fold change ≥ 2.

2.4. Estimation of Tumor-Infiltrating Immune Cells

Tumor-infiltrating immune cell proportions in TCGA samples were assessed using data from the TIMER3 database [38,39] (https://compbio.cn/timer3/ accessed on 21 August 2025). We analyzed the infiltration estimates derived from the CIBERSORT deconvolution method [40] employing the absolute score as the quantitative metric.

2.5. Weighted Correlation Network Analysis (WGCNA)

Gene expression profiles from TCGA samples were subjected to signed network analysis using the WGCNA [41,42] R package (version 1.70.3). The network was built with a power (β) of 12, the default parameter for signed networks, ensuring a scale-free topology model fit of 0.8 (R2 > 0.8) [43] (Supplementary Figure S1). Modules with high similarity were merged using a cutline of 0.25 (the recommended threshold, corresponding to eigengene correlation > 0.75 [43], Supplementary Figure S1). Correlated modules were identified based on their association with FH mRNA and protein expression traits. For the selected modules, we calculated module membership (MM) and gene significance (GS).

2.6. Identifying Hub Genes

Hub genes are defined as those within candidate modules that show strong correlations with specific traits in the WGCNA analysis. Identification of hub genes was performed as previously described [44]. Briefly, unless stated otherwise, the top 10% of genes in a candidate module with module membership (MM)  >  0.8 and gene significance (GS)  >  0.2 were considered hub genes [45]. These hub genes were used as input to perform protein–protein interaction (PPI) analysis using the Search Tool for Retrieval of Interacting Genes/Proteins (STRING) [46] database. Genes with the highest connectivity in each PPI network were identified as key hub genes, as they likely play critical roles in the module [45].

2.7. Pathways Enrichment Analysis

Gene set enrichment analysis (GSEA) was performed through the WebGestalt 2024 [47] tool against the Hallmark pathways gene sets obtained from the MSigDB database [35,48] using the list of differentially expressed genes (DEGs) as input. Pathways with FDR  ≤  0.05 are shown.
Overrepresentation analysis (ORA) was performed using GeneAnalytics [49] and Metascape [50] using genes from WGCNA-selected modules. Pathways with FDR  ≤  0.05 are shown.

2.8. Statistical Analysis

Statistical analyses were performed utilizing the R 4.2.2 statistical framework. Differences in categorical variables among groups were analyzed using Fisher’s exact test. Continuous parameters among distinct groups were analyzed using the nonparametric Kruskal–Wallis test, followed by the Dunn post hoc test. Kaplan–Meier survival curves were generated and visualized using the R packages Survival [51] (Version 3.5.3) and Survminer [52] (Version 0.4.9), with p-values calculated by Cox regression analysis. Plots were generated using the ggplot2 (Version 3.4.1) [53] R package.

3. Results

3.1. Prevalence of FH Alterations in Cancer Patients

We analyzed FH aberrations across different cancer types using data from pan-cancer studies and breast cancer (BC) studies. In pan-cancer studies, the mean prevalence rates were 0.54% for FH mutations, 7.75% for copy number alteration (CNA) deletions (including shallow and deep deletions), and 5% for 1q arm loss (Supplementary Table S1). In BC, we examined 9163 primary and metastatic samples from twelve studies (Table 1) and found mean prevalence rates for FH mutations of 0.36% (95% CI: 0.29–0.43% across 13 studies), significantly below the pan-cancer prevalence mean (one-sample t-test, t(12) = −5.4, p < 0.001), 1.72% for CNA deletions, and 1.13% for 1q arm loss. While the estimate was consistent across studies, the rarity of FH-deficiency reduces statistical power and warrants cautious interpretation.
To understand how FH deficiency affects clinical features in BC, we classified samples based on mutation, CNA status, and 1q arm level. We then examined the clinical properties of 94 samples with FH aberrations compared to 9069 WT samples (Supplementary Table S2). Our analysis revealed that FH aberrations occurred more frequently in primary samples than in metastatic samples (Fisher’s exact test, N = 9074, odds ratio (OR) = 1.8, p-value < 0.05). In primary samples, these aberrations were more commonly found in tumors larger than 2 cm (Fisher’s exact test, N = 990, OR = 2.32, p-value < 0.05) and in older patients (Kruskal–Wallis test, N = 2301, p < 0.05).
Table 2 describes the mutational landscape of identified FH mutations classified as likely oncogenic according to the OnkoKB dataset [54] in 11 BC-mutated samples from nine patients. FH mutations were identified in both metastatic and primary BC samples, encompassing a range of mutation types and variant classes. The most recurrent alteration was a fusion event involving FH, observed in combination with PDE1C, RGS7, and MIR-1273E/1273E genes, predominantly in metastatic samples. Additionally, primary tumor samples harbored frameshift insertion (L208Vfs9), nonsense mutation (Q237*), and splice region variant (X186_splice), while metastatic samples harbored missense mutation (M336K) and frameshift deletion (P63Ifs*9), indicating potentially diverse mechanisms of FH inactivation. Collectively, these findings suggest that FH alterations in BC are rare and span a spectrum of structural and point mutations.

3.2. Effect of FH Deficiency on Primary Breast Cancer Tumors

Previous studies have shown that FH-deficient renal cancer cells exhibit impaired oxidative phosphorylation and increased aerobic glycolysis. This leads to dysregulated gene expression in pathways that promote tumor growth, including angiogenesis and invasion [5,55,56]. To investigate the effect of FH deficiency on primary BC tumors, we analyzed data from the TCGA dataset. We explored the correlation between FH mRNA and protein expression, finding a significant positive correlation (N = 105, R = 0.62, p < 2.2 × 10−16, Figure 2a). Given the low number of samples having known FH aberrations and the limited availability of protein expression data in the dataset, we classified BC samples based on their FH RNA expression levels. We defined the “FH-deficient” group as the 25% of samples with the lowest FH expression, with the rest defined as “high-FH”. When assessing the prevalence of FH aberrations and mutations across these groups, we observed that such aberrations were significantly more frequent in the FH-deficient expression group (Fisher’s exact test, N = 1059, p = 6.82 × 10−14, OR = 19.5, Figure 2b).
To account for potential confounding by clinical variables, we calculated an angiogenesis score for each sample and performed both univariate and multivariate linear regression analyses, with angiogenesis score as the dependent variable and FH expression, age, tumor size, and PAM50 subtype as predictors (Supplementary Table S3). The multivariate analysis demonstrated that FH expression retained a highly significant effect on the angiogenesis score (p = 1.97 × 10−14), independent of age, tumor size, or molecular subtype.

3.3. Molecular and Clinical Consequences of FH Deficiency in Breast Cancer

To investigate the impact of FH deficiency on molecular signaling, we conducted differential gene expression (DGE) analysis comparing FH-deficient and high-FH expression groups. We identified 24 downregulated and 54 upregulated genes (logFC = 1, FDR ≤ 0.05). Among them, SCUBE2 has been reported as upregulated in BC metastasis [57], while CA9, PRAME, and ELF5 are downregulated in BC metastasis [57,58]. CHAD has been reported to play a role in focal adhesion [59] (Figure 3a, Supplementary Table S4). These results suggest that FH deficiency may influence breast cancer progression by modulating genes associated with promoting metastasis and cell adhesion.
GSEA against the MSigDB Hallmark collection [35] based on the DEGs revealed the expected downregulation of oxidative phosphorylation and the TCA cycle. In contrast, angiogenesis and epithelial–mesenchymal transition (EMT) pathways were upregulated (Figure 3b), suggesting a potential benefit from targeting the VEGF pathway in breast cancer patients with FH aberrations.
Previous studies have demonstrated that metabolic reprogramming can reshape the tumor microenvironment (TME) and alter the composition of tumor-infiltrating immune cells [60]. To investigate this, we applied the CIBERSORT algorithm [40] to deconvolute bulk RNA expression data and estimate 22 subsets of tumor-infiltrating lymphocytes (TILs). Comparison of FH-deficient and high-FH breast tumors (Supplementary Table S5) revealed a significant increase in activated mast cells, which are implicated in tumor proliferation and angiogenesis [61], with an additional increase detected in resting memory CD4+ T cells (Figure 3c).
To explore the prognostic significance of FH deficiency, we analyzed OS and recurrence outcomes stratified by molecular subtype. Kaplan–Meier and Cox regression analyses stratified by PAM50 subtypes demonstrated that FH-deficient tumors were associated with significantly worse overall survival in the basal subtype (HR = 6.0, 95% CI 2.09–17.23, p < 0.001, Figure 3d), while no consistent associations were observed in luminal and HER2-positive subtypes. Disease-free survival did not differ significantly across groups (Supplementary Table S6).

3.4. Weighted Correlation Network Analysis

We applied weighted correlation network analysis (WGCNA) to all TCGA samples with FH expression data to identify gene modules associated with FH mRNA and protein expression (Figure 4a).
Our analysis identified a positive correlation of the MEdarkgrey module (correlation = 0.64, p-value = 2 × 10−126) and a negative correlation of the MEgreen module (correlation = −0.35, p-value = 3 × 10−32) to FH mRNA expression. Additionally, we observed a negative correlation between the MEpink module and FH protein expression (correlation = −0.24, p-value = 1 × 10−15) (Figure 4a).
Overrepresentation analysis (ORA) of the MEdarkgrey genes indicated enrichment of the TCA cycle pathway (Figure 4b). ORA of MEgreen genes with high module membership (MM > 0.7) showed enrichment in pathways related to blood vessel development, cell adhesion, and VEGF signaling (Figure 4b). Similarly, the MEpink module genes were enriched for pathways involved in blood vessel development and cell adhesion (Figure 4b).

3.5. Identifying Key Hub Genes

Key hub genes are those highly connected in protein–protein interaction (PPI) networks and may serve as potential biomarkers or predictive markers [45,62]. In the MEgreen module, we identified seven hub genes: CDH5, MMRN2, ADCY4, CLDN5, VWF, CD34, and PECAM1 (Figure 5a,b). These genes, which were upregulated in the FH-deficient group, play roles in cell adhesion, blood vessel development, and cell migration (Figure 5c,d).
In the MEpink module, we identified four key hub genes: FBN1, TIMP2, CDH11, and PDGFRA (Figure 5e). These genes are negatively correlated to FH protein expression and are involved in angiogenesis (Figure 5f,g). All together these results support VEGF inhibition as a targeted therapeutic strategy for FH-deficient BC tumors.

3.6. Case Study

To highlight the potential clinical significance of these findings, we present a case of a 71-year-old woman with a medical history of type II diabetes and hysterectomy (uterine fibroids–myoma) who presented in June 2021 with a new firm mass above her left breast. CT imaging revealed a heterogeneous mass in the chest wall, measuring 6 cm, involving the pectoralis muscle, with metastases to the lungs and regional lymph nodes. Biopsy of the breast lump confirmed invasive ductal carcinoma (IDC) and triple-negative GATA3 positive, with a Ki-67 index of 50.
Next-generation sequencing (NGS) testing revealed a tumor mutational burden (TMB) of 0 and microsatellite stability (MSS). Somatic mutations were identified in PTEN, MLL2, and TP53. In addition, an FH mutation (H318Y, 952C > T) was detected with a variant allele frequency of 56.2% and has been reported in the ClinVar database as a likely pathogenic or pathogenic germline mutation (submitted by an expert panel or multiple submitters). Her CPS PDL1 status was below 10, and no germline BRCA mutations were identified. It was therefore decided, with the diagnosis of stage 4—triple negative breast cancer, to treat the patient with palliative chemotherapy with paclitaxel. Based on international guidelines and the potential benefit of VEGF inhibition due to the FH mutation, the tumor board suggested adding the anti-VEGF bevacizumab to the protocol. She received paclitaxel 80 mg/m2 weekly and bevacizumab 10 mg/kg bi-weekly, with a dramatic long-lasting response (Figure 6). Due to cumulative toxicity and neuropathy, paclitaxel doses were reduced and subsequently switched to capecitabine (Xeloda) 1500 mg BID (days 1–14 of a 21-day cycle), while bevacizumab was continued with no evidence of progression. The patient remained on this regimen until June 2024, achieving a progression-free survival of two years, at which point disease progression was observed in the left breast and lungs.

4. Discussion

The mixed clinical outcomes associated with bevacizumab in metastatic BC highlight a broader issue in precision oncology: the need for reliable biomarkers to guide anti-angiogenic therapy. In this context, our study explores the potential role of FH alterations as a contributing factor to angiogenic signaling and a possible biomarker of therapeutic response. Generally, FH aberrations (mutations, CNA, and 1q arm level) prevalence in BC is lower than in other cancer types. Notably, in BC, FH aberrations were more common in primary tumor samples and appeared more frequently in older patients.
Our analysis of the TCGA dataset suggests that FH expression levels influence the metabolic and signaling pathways in primary BC tumors. The downregulation of oxidative phosphorylation, glycolysis, and TCA cycle pathways in low-FH tumors indicates a shift in energy metabolism, suggesting a distinct metabolic reprogramming characteristic of these tumors. This shift may be driven by fumarate accumulation, which stabilizes HIF-1α, creating a pseudohypoxic environment that activates genes involved in cell proliferation, angiogenesis, and other hypoxia-adaptive pathways. In addition to HIF-1α stabilization, FH deficiency–induced fumarate accumulation activates several complementary mechanisms that further promote angiogenesis. For example, fumarate-mediated KEAP1 succination activates NRF2 signaling, indirectly supporting angiogenesis. Mitochondrial dysfunction in FH-deficient cells results in excess reactive oxygen species (ROS), which reinforces HIF-1α stabilization and VEGF induction. Moreover, fumarate has emerged as an oncometabolite that inhibits α-ketoglutarate (α-KG)–dependent dioxygenases, resulting in epigenetic alterations such as histone and DNA hypermethylation. These changes have been shown to drive epithelial-to-mesenchymal transition (EMT) [63,64,65,66,67]. Concurrently, the upregulation of angiogenesis, VEGF signaling, and epithelial-to-mesenchymal transition (EMT) suggests a tumor microenvironment favoring invasion and metastasis, similar to observations in HLRCC [5]. These findings reinforce the role of FH deficiency in enhancing angiogenesis through VEGF signaling and altering cellular metabolism.
In addition, our immune deconvolution analysis revealed an elevation of activated mast cells in FH-deficient tumors. Mast cells, traditionally associated with allergic responses, have emerged in recent years as important modulators of the TME. Tumor-associated mast cells can promote angiogenesis, invasion, and immune suppression through the release of pro-angiogenic mediators [61]. Although their role in BC remains complex and context-dependent, our findings support the possibility that mast cell elevation in FH-deficient tumors contributes to the observed pro-angiogenic phenotype.
The prognostic effect of FH deficiency in TCGA samples appeared to be subtype-specific, with a marked adverse impact on OS in basal tumors. This pattern suggests that FH deficiency may have particular importance in basal BC biology. In line with this, the presented case study involved a patient with TNBC, further supporting the relevance of FH deficiency in basal-like breast cancer.
To further validate these insights, we conducted weighted gene co-expression network analysis (WGCNA), identifying modules and hub genes strongly associated with FH expression. Key pathways enriched in the FH-deficient group included blood vessel development, cell adhesion, and VEGF signaling, underscoring the role of angiogenesis and metastasis in these tumors. Several hub genes, such as CDH5, CLDN5, VWF, and PECAM1, were significantly upregulated, reflecting their established involvement in vascular development and tumor progression [68,69,70,71,72,73,74].
The phenotypic similarity between BC tumors with FH deficiency and HLRCC tumors further highlights the therapeutic potential of targeting VEGF signaling in this subgroup. As a clinical example of these findings, we described a case of a BC patient with an FH mutation successfully treated with a VEGF inhibitor, a therapy commonly used for HLRCC. However, despite these promising findings, our study has certain limitations. The rarity of FH aberrations constrained both the size of the affected patient cohort and the availability of independent datasets for external validation, resulting in reliance on retrospective, publicly available resources. In this context, the case report presented here provides a form of clinical validation, though further prospective validation is warranted. Functional validation of these pathways in experimental models, including in vitro assays, as well as prospective clinical studies, is necessary to confirm the therapeutic relevance of FH deficiency. In addition, both the low prevalence of FH alterations and the possible contribution of endothelial content to RNA expression data limit the robustness of our findings, though they highlight the need for biomarker-based patient selection in future anti-VEGF trials. Furthermore, our angiogenesis findings are based on a curated MSigDB signature, and validation across independent cohorts and breast cancer subtypes is still required. Finally, the interplay between FH-driven metabolic changes and the tumor immune microenvironment remains an important area for future investigation.

5. Conclusions

Our findings suggest that FH aberrations, though rare, define a distinct molecular subset of BC characterized by metabolic reprogramming, enhanced angiogenesis, and upregulated VEGF signaling. This molecular profile supports the rationale for exploring VEGF inhibitors as a potential targeted therapy for FH-deficient BC. The identification of FH expression as a potential biomarker could help personalize anti-angiogenic treatment strategies in breast cancer patients, particularly those who currently lack effective targeted options.

Supplementary Materials

The following supporting information can be downloaded at https://www.mdpi.com/article/10.3390/cancers17182942/s1, Table S1: FH aberrations in pan-cancer studies; Table S2: Clinical features of samples with FH aberration; Table S3: Univariate and multivariate linear models to predict angiogenesis score; Table S4: Differentially expressed genes between FH-deficient and FH-high expression groups; Table S5: Immune cell infiltration analysis across FH-deficient and high-FH expression groups; Table S6: Survival and recurrence outcomes stratified by PAM50 subtypes and FH expression groups; Figure S1: Selection of soft-thresholding power and module merging in WGCNA.

Author Contributions

Conceptualization and supervision: M.S.-D. and A.S. Methodology: L.A.S., M.S.-D., and A.S. Formal analysis: L.A.S., N.K.-K., A.G. Investigation: L.A.S., T.P.-Y., M.S.-D., A.S. Writing—original draft: L.A.S. Writing—review and editing: M.S.-D., A.S. Approval of final manuscript: all authors. All authors have read and agreed to the published version of the manuscript.

Funding

This research was supported by a grant from the Israel Science Foundation (397/19), the Israel Cancer Association (ICA 20220004), and the Israeli Ministry of Health (3-18671). A.S. is supported by the Israel Science Foundation (grant number 3755/21) within the Israel Precision Medicine Partnership Program (IPMP).

Institutional Review Board Statement

The study was conducted in accordance with the Declaration of Helsinki and approved by the Institutional Review Board (or Ethics Committee) of Matarot Helsinki. Ethic code: TLV-19-449. Approved date: 10 May 2020.

Informed Consent Statement

Informed consent was obtained from the patient for use of their anonymized data.

Data Availability Statement

The TCGA dataset used in this study is publicly available through the metaGXbreast R package [31] (https://bioconductor.org/packages/release/data/experiment/html/MetaGxBreast.html, accessed on 1 January 2022) and cBioPorta [15,16] (https://www.cbioportal.org/study/summary?id=brca_tcga_pan_can_atlas_2018, accessed on 17 April 2024). Other pan-cancer and breast cancer datasets are publicly available through the cBioPortal. On request, the analyzed data generated during this study can be provided by the corresponding authors.

Conflicts of Interest

A.S. has received research grants from Novartis and Roche companies and has an advisory role at Eli Lilly, Pfizer, Novartis, Roche, MSD, and AstraZeneca. A.S. also received travel expenses from Celgene, Medison, Roche, and MSD and was part of a speaker bureau at Teva, Roche, Pfizer, Novartis, and Eli Lilly. T.P.-Y. had an advisory role at Stemline, Progenetics, Novartis, Eli Lilly, Pfizer, MSD Gilead, Rhenum, and Canabotech. T.P.-Y. did not receive any travel expenses. Other authors have no relevant financial or non-financial interests to disclose.

Abbreviations

The following abbreviations are used in this manuscript:
BCBreast cancer
CNACopy number alterations
DDDeep deletion
DEGDifferentially expressed genes
EMTEpithelial–mesenchymal transition
FHFumarate hydratase
GSGene significance
GSEAGene set enrichment analysis
HLRCCHereditary leiomyomatosis and renal cell carcinoma
IDCInvasive ductal carcinoma
MMModule membership
OROdd ratio
ORAOverrepresentation analysis
OSOverall survival
PFSProgression-free survival
PPIProtein–protein interaction
SDShallow deletion
SVStructural variants
TCATricarboxylic acid
TILTumor-infiltrating lymphocytes
TMBTumor mutation burden
TMETumor microenvironment
VEGFVascular endothelial growth factor
WGCNAWeighted correlation network analysis

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Figure 1. Study design and datasets.
Figure 1. Study design and datasets.
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Figure 2. FH expression in primary breast cancer tumors. (a) Scatter plot representing the correlation between FH mRNA expression and FH protein expression from 105 samples. The color of the points represents types of FH aberration (orange—1Q arm lost; light blue—FH CNA shallow deletion (SD); light gray—other FH aberrations (1Q arm gain/FH CNA gain/amplification); dark gray—WT FH). Point shape represents the FH mRNA expression group (square—FH-deficient; star—high expression). The dashed line indicates the cut-off between FH expression groups. (b) Bar graph illustrating the percentage of samples with FH CNA SD or 1Q arm lost (green) in each of the FH mRNA expression groups.
Figure 2. FH expression in primary breast cancer tumors. (a) Scatter plot representing the correlation between FH mRNA expression and FH protein expression from 105 samples. The color of the points represents types of FH aberration (orange—1Q arm lost; light blue—FH CNA shallow deletion (SD); light gray—other FH aberrations (1Q arm gain/FH CNA gain/amplification); dark gray—WT FH). Point shape represents the FH mRNA expression group (square—FH-deficient; star—high expression). The dashed line indicates the cut-off between FH expression groups. (b) Bar graph illustrating the percentage of samples with FH CNA SD or 1Q arm lost (green) in each of the FH mRNA expression groups.
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Figure 3. Transcriptomic differences based on FH expression. (a) Volcano plot of differentially expressed genes (DEGs) between FH-deficient and FH high-expression groups, showing log fold change (logFC) on the x-axis and -log10(FDR) on the y-axis. Point color indicates upregulated (red) or downregulated (blue) genes. Dashed lines mark statistical thresholds: horizontal line for FDR ≤ 0.05 and vertical lines for log fold-change > ±1 (b) Gene set enrichment analysis (GSEA) of DEGs comparing FH-deficient and FH high-expression groups. (c) Boxplots showing CIBERSORT absolute scores for immune cell subsets in TCGA breast cancer samples, stratified by FH expression (high vs. FH-deficient) (Wilcoxon test, ****: fdr < 0.0001). (d) Kaplan–Meier overall survival analysis of basal BC patients stratified by FH expression (red—high expression group; turquoise—FH-deficient group).
Figure 3. Transcriptomic differences based on FH expression. (a) Volcano plot of differentially expressed genes (DEGs) between FH-deficient and FH high-expression groups, showing log fold change (logFC) on the x-axis and -log10(FDR) on the y-axis. Point color indicates upregulated (red) or downregulated (blue) genes. Dashed lines mark statistical thresholds: horizontal line for FDR ≤ 0.05 and vertical lines for log fold-change > ±1 (b) Gene set enrichment analysis (GSEA) of DEGs comparing FH-deficient and FH high-expression groups. (c) Boxplots showing CIBERSORT absolute scores for immune cell subsets in TCGA breast cancer samples, stratified by FH expression (high vs. FH-deficient) (Wilcoxon test, ****: fdr < 0.0001). (d) Kaplan–Meier overall survival analysis of basal BC patients stratified by FH expression (red—high expression group; turquoise—FH-deficient group).
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Figure 4. WGCNA of FH expression. (a) Module–trait relationships calculated by the WGCNA algorithm. Each cell shows the correlation coefficient between a gene module and a specific trait with the corresponding p-value in parentheses. (b) Pathway enrichment analysis of genes included in the MEdarkgrey, MEgreen (genes with ModelMembership (MM) > 0.7), and MEpink modules. The point size represents the enrichment score (−log2(FDR)).
Figure 4. WGCNA of FH expression. (a) Module–trait relationships calculated by the WGCNA algorithm. Each cell shows the correlation coefficient between a gene module and a specific trait with the corresponding p-value in parentheses. (b) Pathway enrichment analysis of genes included in the MEdarkgrey, MEgreen (genes with ModelMembership (MM) > 0.7), and MEpink modules. The point size represents the enrichment score (−log2(FDR)).
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Figure 5. Hub genes in WGCNA modules. (a,b) Protein–protein interaction (PPI) network of hub genes detected in the MEgreen module according to (a) FH-deficient expression group (yes/no) and (b) FH mRNA expression (continuous values), visualized by the Cytoscape software (Version 3.10.2). Colored circles represent the connectivity degree of the hub gene. Red nodes correspond to higher degrees of connectivity. (c) Boxplot of mRNA expression of the seven key hub genes in the MEgreen module according to FH expression group (****: p-value ≤ 0.0001). (d) PPI network of the eight key hub genes in the MEgreen module visualized by STRING-DB software (Version 12.0). (e) PPI network of hub genes of the MEpink module according to FH protein expression, visualized by the Cytoscape software. Colored circles represent the connectivity degree of the hub gene. Red nodes correspond to higher degrees of connectivity. (f) Scatter plots that represent the correlation between FH protein expression (x-axis) and the expression of the four key hub genes (y-axis) in the MEpink module. For each scatter plot, Spearman correlation coefficients (R) and the corresponding p-values are presented. (g) The top five enriched pathways of the MEpink module key hub genes. The score represents −log2(FDR) of the enrichment.
Figure 5. Hub genes in WGCNA modules. (a,b) Protein–protein interaction (PPI) network of hub genes detected in the MEgreen module according to (a) FH-deficient expression group (yes/no) and (b) FH mRNA expression (continuous values), visualized by the Cytoscape software (Version 3.10.2). Colored circles represent the connectivity degree of the hub gene. Red nodes correspond to higher degrees of connectivity. (c) Boxplot of mRNA expression of the seven key hub genes in the MEgreen module according to FH expression group (****: p-value ≤ 0.0001). (d) PPI network of the eight key hub genes in the MEgreen module visualized by STRING-DB software (Version 12.0). (e) PPI network of hub genes of the MEpink module according to FH protein expression, visualized by the Cytoscape software. Colored circles represent the connectivity degree of the hub gene. Red nodes correspond to higher degrees of connectivity. (f) Scatter plots that represent the correlation between FH protein expression (x-axis) and the expression of the four key hub genes (y-axis) in the MEpink module. For each scatter plot, Spearman correlation coefficients (R) and the corresponding p-values are presented. (g) The top five enriched pathways of the MEpink module key hub genes. The score represents −log2(FDR) of the enrichment.
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Figure 6. CT and PET imaging of the patient with FH mutation. (a) Before treatment with paclitaxel (80 mg/m2 weekly) and bevacizumab (10 mg/kg every 2 weeks), and (b) after three months of treatment. The primary breast mass is highlighted using a blue arrow, while the metastases are highlighted in orange.
Figure 6. CT and PET imaging of the patient with FH mutation. (a) Before treatment with paclitaxel (80 mg/m2 weekly) and bevacizumab (10 mg/kg every 2 weeks), and (b) after three months of treatment. The primary breast mass is highlighted using a blue arrow, while the metastases are highlighted in orange.
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Table 1. FH aberrations in breast cancer studies.
Table 1. FH aberrations in breast cancer studies.
StudySample TypeTotal (N)FH MutationFH Structural Variance (SV)FH Copy Number Alterations (CNAs)1Q Arm Status
Number of Mutated SamplesTotal Number of SamplesNumber of Samples with SVTotal Number of SamplesAmp. *GainSD *DD *DiploidTotal Number of SamplesGainLossTotal Number of Samples
Breast Cancer (MSK, Cancer Cell 2018 [17])Metastasis10002 (0.2%)10001 (0.1%)100020001 (0.1%)9791000NANANA
Primary9184 (0.44%)9181 (0.11%)9181000917918
Breast Cancer (MSK, Nature Cancer 2020) [18]Metastasis3003003011002830NANANA
Primary80808000088
MAPK on Resistance to Anti-HER2 Therapy for Breast Cancer (MSK, Nat Commun. 2022) [19]Metastasis9109109110009091NANANA
Primary5405405400005454
Metastatic Breast Cancer (MSK, Cancer Discovery 2022) [20]Metastasis13656 (0.44%)13651 (0.07%)13652500013401365NANANA
MSK MetTropism (MSK, Cell 2021) [21]Metastasis10484 (0.38%)10481 (0.1%)104819001 (0.1%)10281048NANANA
Primary15616 (0.38%)15611 (0.06%)1561600015551561
MSK-IMPACT Clinical Sequencing Cohort (MSK, Nat Med 2017) [22]Metastasis8371 (0.12%)8371 (0.12%)83714001 (0.12%)822837NANANA
Primary5002 (0.4%)5001 (0.2%)5001000499500
Non-CDH1 Invasive Lobular Carcinoma (MSK, 2023) [23]Primary2502502500002525NANANA
China Pan-cancer (OrigiMed, Nature 2022) [24]Metastasis2502502500002525NANANA
Primary7107107100007171
Breast Invasive Carcinoma (TCGA, PanCancer Atlas) [25]Primary10524 (0.38%)10520105210166737 (3.5%)0247105262510 (1.13%)888
The Metastatic Breast Cancer Project (provisional, December 2021) [26]NA3341 (0.3%)33401564911596 (28.7%)6 (1.8%)68334NANANA
MSK-CHORD (MSK, Nature 2024) [27]Metastasis25649 (0.35%)25641 (0.04%)246442001 (0.04%)24212464NANANA
Primary285913 (0.45%)28591 (0.034%)28591400028452859
Breast Cancer (MSK, 2025) [28]Metastasis204812 (0.58%)20482 (0.1%)204838001 (0.05%)20092048NANANA
Primary18125 (0.27%)18121 (0.06)1812800018041812
* Amp—amplification; SD—shallow deletion; DD—deep deletion.
Table 2. Characterization of the mutational landscape of likely oncogenic FH mutations in breast cancer tumors.
Table 2. Characterization of the mutational landscape of likely oncogenic FH mutations in breast cancer tumors.
Patient IDSample IDStudySample TypeTMBProtein ChangeMutation TypeVariant TypeMutation StatusChr.Start PosEnd PosRefVarFraction Genome Altered
P-0022525P-0022525-T01-IM6MSK, Cell 2021 [21]P3.46L208Vfs*9Frameshift InsertionINSSomatic1241,672,019241,672,020-C0.222
P-0005712P-0005712-T01-IM5MSK, Cell 2021 [21]P3.91FH intragenicFusionDupSomatic1241,665,729NA  0.4247
P-0005712P-0005712-T01-IM5MSK, Cancer Cell 2018 [17]P0.133FH intragenicFusionDupSomatic1241,665,729NA  0.5076
P-0045182P-0045182-T01-IM6MSK, Nature 2024 [27]P1.73Q237*NonsenseSNPSomatic1241,671,932241,671,932GA0.4
Patient0707P-0707OrigiMed, Nature 2022 [24]P0.166X186_spliceSplice RegionSNPNA1241,672,089241,672,089TCNA
P-0017116P-0017116-T01-IM6MSK, Cancer Discovery 2022 [20]M8.65P63Ifs*9Frameshift DeletionDELSomatic1241,680,556241,680,562CATTTGG-0.2691
P-0004918P-0004918-T02-IM6MSK, Cell 2021 [21]M6.92RGS7-FH FusionFusionDupSomatic1241,357,653NA  0.2868
P-0000532P-0000532-T02-IM5MSK, Cancer Discovery 2022 [20]M8.81PDE1C-FH FusionFusionTransSomatic731,926,696NA  0.6164
P-0048392P-0048392-T01-IM6MSK, Nature 2024 [27]M8.65MIR-1273E/1273E-FH FusionFusionInversionSomatic1240,716,629NA  0.136
P-0000532P-0000532-T02-IM5MSK, Cancer Cell 2018 [17]M0.3PDE1C-FH FusionFusionTransSomatic731,926,696NA  0.5412
P-0008574P-0008574-T03-IM6MSK, Cell 2021 [21]M7.78M336KMissenseSNPSomatic1241,667,443241,667,443AT0.2097
P—primary; M—metastasis; Dup—duplication; Trans—translocation.
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Sinberger, L.A.; Keren-Khadmy, N.; Goldberg, A.; Peretz-Yablonski, T.; Sonnenblick, A.; Salmon-Divon, M. Revealing the Angiogenic Signature of FH-Deficient Breast Cancer: Genomic Profiling and Clinical Implications. Cancers 2025, 17, 2942. https://doi.org/10.3390/cancers17182942

AMA Style

Sinberger LA, Keren-Khadmy N, Goldberg A, Peretz-Yablonski T, Sonnenblick A, Salmon-Divon M. Revealing the Angiogenic Signature of FH-Deficient Breast Cancer: Genomic Profiling and Clinical Implications. Cancers. 2025; 17(18):2942. https://doi.org/10.3390/cancers17182942

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Sinberger, Liat Anabel, Noa Keren-Khadmy, Assaf Goldberg, Tamar Peretz-Yablonski, Amir Sonnenblick, and Mali Salmon-Divon. 2025. "Revealing the Angiogenic Signature of FH-Deficient Breast Cancer: Genomic Profiling and Clinical Implications" Cancers 17, no. 18: 2942. https://doi.org/10.3390/cancers17182942

APA Style

Sinberger, L. A., Keren-Khadmy, N., Goldberg, A., Peretz-Yablonski, T., Sonnenblick, A., & Salmon-Divon, M. (2025). Revealing the Angiogenic Signature of FH-Deficient Breast Cancer: Genomic Profiling and Clinical Implications. Cancers, 17(18), 2942. https://doi.org/10.3390/cancers17182942

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