Genomic and proteomic profiling of GATA3 mutant metastatic hormone receptor-positive breast cancer and impact on clinical outcomes

Abstract

Purpose

GATA3 mutations are among the most common alterations in hormone receptor-positive (HR+) breast cancer (BC), yet these have no targeted therapies. MDM2 is an E3 ubiquitin ligase that targets p53 for degradation, and pre-clinical data suggests MDM2 inhibition may effectively treat GATA3^mut HR+ BC. The GATA3 co-mutational landscape has been described only in primary BC tissue, and the mechanism of MDM2-driven efficacy is incompletely understood.

Experimental design

Circulating tumor DNA (ctDNA) was assessed for GATA3 mutations via targeted sequencing. Associations with co-alterations and clinical/pathologic factors were estimated using Pearson's chi-squared test, two-sample Wilcoxon rank-sum, and multivariable logistic regression. Impact on survival was analyzed using multivariable Cox regression analysis. Tissue-based data from the Clinical Proteomic Tumor Analysis Consortium (CPTAC) database was evaluated for expression and phosphorylation of GATA3 and associated proteins.

Results

Among 609 patients with HR + /HER2− MBC, ctDNA detected non-synonymous GATA3 variants ctDNA in 69 (11%) patients, and the genomic landscape was unique from tissue-based primary BC data; GATA3^mut were not mutually exclusive from TP53^mut (p = 0.30) or PIK3CA^mut (p = 0.52) and were associated with poorer survival on endocrine monotherapy. CPTAC analysis showed no difference in GATA3 or breast cancer-associated gene abundance, however there was increased USP48 (LogFC = 0.76, FDR = 1.7 × 10^–5), which stabilizes MDM2.

Conclusion

The distinct landscape in GATA3^mut MBC ctDNA highlights critical information when assessing candidacy for targeted therapies. To our knowledge, this is the first ctDNA-based GATA3^mut landscape analysis in MBC. Furthermore, tissue-based proteomic analysis suggests mechanisms for endocrine resistance and sensitivity to MDM2 inhibition in HR+ /HER2− GATA3^mut BC.

Supplementary Information

The online version contains supplementary material available at 10.1007/s10549-025-07710-w.

Introduction

Metastatic breast cancer (MBC) remains one of the leading causes of cancer-related death, despite significant therapeutic advances [1]. Hormone receptor-positive (HR +) breast cancer is the most common subtype, and while endocrine therapy remains the backbone of HR + MBC treatment, an increasing number of targeted therapies are becoming options for patients with specific genomic variants (e.g., PIK3CA^mut, ESR1^mut, AKT^mut, PTEN^mut) [2–4].

GATA3 mutations are frequent in breast cancer, with an estimated prevalence of 10–18% in HR + MBC; the majority are loss-of-function mutations and are associated with poor response to endocrine therapy (ET) and poor survival, as described in our previous work [5]. Despite being one of the most prevalently altered genes in breast cancer, and being associated with poorer outcomes, to our knowledge there have been no attempts to therapeutically target GATA3^mut MBC [6–21]. The GATA3 protein is the most highly expressed transcription factor in breast luminal epithelial cells and is critical for mammary epithelial development and luminal identity [6–16, 22]. Functionally, it interacts with the estrogen receptor (ER) and FOXA1 to increase the transcription of genes in the ER axis, and its normal expression is associated with well-differentiated tumors with low metastatic potential. High GATA3 expression is also associated with favorable clinical prognostic features, including lymph node negativity, lower tumor grade, older age at diagnosis, and negative human epidermal growth factor receptor 2 (HER2) [23, 24]. Meanwhile, low GATA3 expression and protein dysfunction are associated with undifferentiated tumors, increased risk of metastasis, and poorer overall outcomes [12, 19, 23–30].

While GATA3 has not been pursued as a direct therapeutic target, data suggest its potential indirect druggability in GATA3^mut HR + breast cancer via inhibition of MDM2 (mouse double minute 2), an E3 ubiquitin-protein ligase that targets p53 for degradation [31]. Given the fact that MDM2 inhibition requires functional p53 (i.e., wildtype TP53), along with the fact that multiple other targeted therapies are approved or under study in the HR +/HER2− population, deeper knowledge of the genomic landscape of GATA3^mut disease will sharpen our understanding of the patient population that may benefit from targeted therapy informed by GATA3 mutational status.

Prior analyses of the GATA3 mutational landscape have been performed on tissue sequencing data from primary breast cancers, where patients have not been exposed to therapeutic pressures [18, 25, 32–38]. Liquid biopsy analysis via circulating tumor DNA (ctDNA) is a less invasive diagnostic procedure and is now in regular use in advanced breast cancer as part of the standard of care and as a tool to assess eligibility for genomically targeted clinical trials [39–41]. We aim to expand on our prior work and analyze the GATA3^mut mutational landscape based on ctDNA next generation sequencing (NGS), and furthermore, we analyze GATA3^mut proteomic data to understand the mechanism behind the efficacy of MDM2 inhibition in GATA3^mut HR +/HER2− breast cancer.

In this multi-institutional ctDNA-based study, we characterized the mutational landscape of GATA3^mut vs. GATA3^WT HR +/HER2− MBC, investigated the differences in patients’ clinicopathologic features, observed survival outcomes, and also used tissue-based data from the Clinical Proteomic Tumor Analysis Consortium (CPTAC) describe the proteomic landscape of GATA3^mut MBC. To our knowledge, our study is the first ctDNA-based analysis of the GATA3 mutational landscape in advanced breast cancer.

Materials and methods

Patient selection and genomic analysis

Patients at the Massachusetts General Hospital and at Washington University in St Louis consented to have their ctDNA data collected and medical information used for research purposes via an IRB-approved protocol. ctDNA was analyzed via Guardant360 (Guardant Health, Inc; Redwood City, CA, USA), a next generation sequencing assay that analyzed up to 74 genes during the study period. Guardant360 is a CLIA-certified, CAP-accredited, NYSDOH-approved clinical assay that detects somatic single nucleotide variants (SNVs) in 73–74 genes (panel composition changed during the course of the study), and indels, somatic copy-number gains, and fusions in a subset of these genes as previously described [42]. At allele fractions > 0.25% for mutations and fusions, > 0.20% for indels, and copy numbers > 2.24 copies, the analytical sensitivity of this assay is ≧95%, and specificity is > 99.99%. The association of GATA3^mut and co-mutations as well as number of prior therapies was estimated using Pearson's chi-squared test for categorical variables, two-sample Wilcoxon rank-sum test for continuous variables, and multivariable logistic regression. Statistical analysis was conducted using STATA, version 16—Stata (RRID:SCR_012763), StataCorp LLC—Stata (RRID:SCR_012763). The OncoKB tool was used to annotate GATA3 variants for oncogenicity [43].

Proteomic analysis

For this study, we included tissue-based data from 73 out of the 121 available CPTAC BRCA HER2-negative samples; out of this cohort, 9 samples had GATA3 mutations. We used the provided processed tables from the original study [44], specifically the median-MAD normalized log2-transformed protein and phosphosite expression tables. Differential expression was performed between mutated and unmutated samples and false discovery rate (FDR) was computed using the Benjamini–Hochberg procedure.

Survival analysis

The impact of GATA3^mut and GATA3^WT detected via ctDNA on progression-free survival (PFS) and overall survival (OS) was analyzed using multivariable Cox regression analysis adjusting for age, number of prior therapies, visceral metastases, and de novo metastases, to account for variability in other clinical factors in this retrospective real-world cohort. PFS and OS were evaluated in the overall study population, as well as in subgroups of patients that received endocrine monotherapy (GATA3^mut n = 6; GATA3^WT n = 74), ET + CDK4/6 inhibitor (GATA3^mut n = 28; GATA3^WT n = 191), and chemotherapy (GATA3^mut n = 18; GATA3^WT n = 146). Radiographic progression was determined by RECIST criteria for patients who were participants in clinical trials. If patients were receiving standard of care therapy, progression was radiographically assessed by radiologists who were not aware of the results of ctDNA sequencing or aims of this study.

All outcomes analyses were performed using STATA, version 16—Stata (RRID:SCR_012763), StataCorp LLC—Stata (RRID:SCR_012763).

Results

Detection of GATA3 mutations by plasma-based genotyping in HR + MBC

Among the 647 patients with HR + MBC in the ctDNA study cohort, 609 had HR +/HER2− MBC. Among the latter, there were 69 patients (11%) in whom ctDNA analysis identified a non-synonymous GATA3 mutation. GATA3 mutations were observed in a statistically significant larger percentage of patients with documented visceral disease (76.8% vs. 62.0%; p = 0.016) and de novo metastatic disease (33.3% vs. 20.7%; p = 0.018) compared to GATA3^WT; no other distinguishing clinical characteristics, including age at metastatic diagnosis, number of prior therapies, and progesterone receptor status, were observed (see Table 1 for clinical characteristics & mutational status of the cohort). Of note, when comparing only patients with frameshift or nonsense GATA3 mutations (annotated as likely oncogenic or known oncogenic via the OncoKB tool, n = 55), observations and significance were similar to the above (Supplemental Table 1). A separate analysis showed no correlation with HER2 status and GATA3 mutations, but given the more aggressive phenotype and different treatment approach in HER2 + MBC, this subtype was otherwise excluded from this analysis. Among these 69 patients with HR +/HER2− GATA3^mut MBC, 49 (71%) patients identified as White and 14 (20%) as Black. Patients had received a median of 2 prior lines of therapy in the metastatic setting (range 0–9).

Table 1.

Patient characteristics in patients with GATA3^WT and GATA3^mut HR +/HER2− MBC

Patient characteristics	GATA3 wildtype (n = 540)	GATA3 mutation (n = 69)	p-value
Age, median (IQR)	61.3 (52.5, 69.7)	59.1 (48.6, 65.4)	0.060
De novo metastases	112 (20.7)	23 (33.3%)	0.018
Visceral metastases	335 (62.0%)	53 (76.8%)	0.016
Number of prior therapies, median (IQR)	2 (1, 4)	2 (1, 5)	0.055
TP53 co-mutation	215 (39.8%)	23 (33.3%)	0.30
PIK3CA co-mutation	213 (39.4%)	30 (43.5%)	0.52

Next, we aimed to characterize GATA3 mutations and their location within the gene. The GATA3 protein is encoded by 444 amino acids, which encompass 6 exons, 5 of which are coding; the gene also contains two transactivating domains and two Zinc finger (ZnFn) motifs. [13] ZnFn 1 stabilizes DNA binding, while ZnFn 2 binds DNA at the GATA3 motif. [45] Prior tissue-based analyses have found most GATA3 mutations to occur at the splice sites between exons 4/5 and 5/6, as well as within exon 5 (including Zn finger 2) and exon 6, the majority of which were frameshift mutations and annotated as likely oncogenic per the OncoKB tool [14, 25, 32, 33, 43, 46].

In our ctDNA analysis, among these 69 GATA.^mut patients we found 73 GATA3 mutations: 18 (26%) occurred in exon 5, all but three of which were in the second zinc finger, 8 (11%) were c.925-3_925-2 del splice site variants, and 46 (67%) were in exon 6. In terms of mutation type, 43 (62%) were frameshift mutations, 14 (20%) were missense mutations, and 5 (7%) were nonsense mutations (Fig. 1B). Comparison to breast cancer variants in the Catalog Of Somatic Mutations In Cancer (COSMIC) database, a tissue-based cancer mutation repository, also showed the majority of variants being frameshift mutations (Supplemental Fig. 1). Median variant allele fraction (VAF) of the GATA3 mutations was 1.0% (IQR 0.34–5.5%). To approximate clonality, we calculated the fraction of the alteration of interest relative to the alteration in the ctDNA sample with the highest VAF; we used 30% as a cutoff. Four patients had two GATA3 mutations with comparable VAFs (listed in parentheses), consistent with them being two clonal hits (based on above criteria to estimate clonality as ratio of VAF over highest VAF in the sample): F392L (6.40%)/P409fs (6.20%), 336* (1.29%)/435* (0.71%), W329R (0.8%)/A395 V (0.1%), and A442fs (2.9%)/R353 T (2.4%). While the Guardant360 test is not intended to report germline mutations, we set a VAF cutoff of 40% to identify potential germline GATA3 alterations, which result in HDR syndrome (hypoparathyroidism, deafness, renal dysfunction) [47, 48]. By the above criterion, no GATA3 alterations appeared to be germline (i.e., all VAFs were under 40%). Fifty-five patients (80%) were determined to have likely oncogenic variants via OncoKB, which in this dataset included all frameshift, nonsense, and splice site variants (Fig. 1B) [43].

Fig. 1.

GATA3 gene mutation overview—A Flowchart of patient ctDNA cohort harboring GATA3 gene mutations; B Mutational distribution within the GATA3 gene

GATA3 mutational landscape

In 3 of the 69 patients with GATA3.^mut HR +/HER2− MBC, GATA3 was the only detectable ctDNA alteration, while remaining patients had concomitant co-mutations with a median number of 4 variants (range 1–44) (Fig. 2). The most frequent co-mutations were in PIK3CA (n = 30), ESR1 (n = 26), and TP53 (n = 23), and the most frequent amplification was in CCND1 (n = 14). There were no statistically significant associations (positive or negative) between GATA3 and TP53 (Fisher exact p = 0.30), and GATA3 and PIK3CA (p = 0.52). These findings are distinct from the co-mutational landscapes based on tissue analyses of primary breast cancers, where GATA3 mutations appeared to be mutually exclusive from both TP53 and PIK3CA mutations [14, 25, 33].

Fig. 2.

CoMut plot of GATA3 and other gene mutations in our study population

In the ctDNA-detected GATA3^mut population, TP53 co-mutations (n = 23) were found with a median VAF of 1.0% (range 0.03–30.5%). Of note, plasma-based cell-free DNA assays cannot definitively distinguish tumor-derived somatic mutations from clonal hematopoiesis (CH), in which TP53 is a commonly mutated gene [49]. 55 of the 69 patients (80%) had predicted clonal GATA3 mutations (based on above criteria), and 12/23 (52%) TP53 co-mutations appeared to be clonal. Among the clonal TP53 variants, 8 (67%) had a VAF > 1%. 5/12 patients had both TP53 and GATA3 as suspected clonal variants, only 2 of whom also had a VAF > 1% (Supplemental Fig. 2).

Within the GATA3^mut population, GATA3 was the mutation with the highest VAF in 23 patients (34% of patients). Among patients where GATA3 was not the highest VAF, the other highest mutations included PIK3CA (n = 19), ESR1 (n = 7), TP53 (n = 8), BRCA1 (n = 2), BRCA2 (n = 2), and SMAD4 (n = 2).

Interestingly, there were 3 patients with germline mutations in BRCA1/2. As mentioned above, the Guardant 360 assay does not report germline mutations, but suspicion is raised when allele fraction is above 40% (and much higher than other mutations). By this criterion, 3 of the 7 (43%) patients with non-synonymous BRCA1/2 mutations were noted to have findings suspicious for germline variants, which was later confirmed on retrieval of documented genetic testing. This appears to be a new finding compared to an earlier study of GATA3 mutations in familial breast cancer, where GATA3 mutations were found in 22% (7/32) of patients without BRCA1/1 mutations and not identified in patients with germline BRCA1 or BRCA2 mutations (n = 0/23) [50].

Among the 69 patients with GATA3^mut HR +/HER2− MBC, there were 20 patients with serial sampling data. Among these, in 5 patients, GATA3 mutation(s) had not been identified on initial ctDNA sampling but rather were identified on subsequent draws. New detection on serial sampling may be due to one of several factors: emergence of a new variant, or else the variant existing in the first sample but below the limit of detection. While the numbers are relatively small, many of the GATA3 frameshift mutations appeared to increase in serial sampling in synchrony with known driver alterations such as PIK3CA or ESR1, as well as ARID1A and ATM, though disparate growth patterns were also observed in 3 patients. (Supplemental Fig. 3).

Fig. 3.

GATA3 proteomic profiling using CPTAC database—A GATA3 mutation and variant distribution; B Proteomic and phosphoproteomic expression changes between GATA3^WT vs GATA3^mut samples

GATA3 mutations and proteomic correlates

To understand the functional implications of GATA3 mutations, the National Cancer Institute's tissue-based Clinical Proteomic Tumor Analysis Consortium (CPTAC) database was interrogated for HR +/HER2− breast cancers harboring GATA3 mutations, and the expression and phosphorylation of GATA3 and associated proteins were analyzed. Among 121 patients with breast cancer, 10 patients had a single GATA3 mutation. We identified 6 luminal A, 3 luminal B, and 1 HER2-enriched cases. The latter was excluded given the focus of this analysis on HR +/HER2− breast cancer. Among the remaining 9 GATA3-mutated, there were 4 frameshift, 3 missense, and 2 splice site variants. We did not detect differences in GATA3 protein abundance when comparing GATA3-mutated to HR +/HER2− unmutated samples (Fig. 3). The frameshift mutations were located in the C-terminal domain and have not been demonstrated to impact protein levels of function. [51] Similarly, there was no difference in protein abundance of breast cancer-associated genes, including ESR1, PIK3CA, FOXA1, FOXO3, or RB1 (0.2 < FDR < 0.9). Notably, GATA3 mutations were associated with a significant increase in abundance of the deubiquitinating enzyme USP48 (LogFC = 0.76, FDR = 1.7 × 10^–5), which stabilizes MDM2, and thus enhances p53 ubiquitination and degradation [52]. All above samples with GATA3 mutations had wildtype TP53.

Global differential expression analysis of phosphorylation sites revealed a significant increase of RPS6 KA3 phosphorylation levels on sites S369 and S715 (FDR = 0.014 & 0.006 respectively), which both reside on the highly conserved catalytic domain of the S6 kinase, a component of the RAS/ERK signaling pathway. Similarly, we saw increased phosphorylation on FOXO3 (Sites S7 and S12, FDR = 0.048), both suggesting increased downstream signaling, potentially via ERK. KRT18 showed significantly reduced phosphorylation at multiple sites (such as S31, FDR = 0.001); this protein is linked with the epithelial-mesenchymal transition and was explored given the association of GATA3 mutations and metastasis. In addition, the histone chaperone ANP32E, which has been shown to be inversely correlated with tumor progression and relaxation of chromatin at FOXA1 binding sites, was found to be higher in GATA3^mut cancers, which is in keeping with published literature (LogFC = 1.09, FDR = 2.2 × 10^–4) [53]. Activation of these pathways may lead to estrogen receptor signaling independence.

GATA3 mutations and clinical outcomes

Finally, we evaluated the association of ctDNA-detected GATA3 mutations with clinical outcomes in the HR +/HER2− MBC setting, stratified by type of therapy. Among patients who received endocrine monotherapy (ET; GATA3^WT, n = 74, GATA3^mut, n = 6), GATA3^mut were associated with worse progression-free survival (PFS; p = 0.061) and worse overall survival (OS; p = 0.004); sample sizes were not powered to detect statistically significant differences. There was no statistically significant difference in PFS or OS between GATA3^mut (n = 25) and GATA3^WT (n = 188) subgroups that received chemotherapy and those that received ET + CDK4/6 inhibitor treatment.

In two contrasting index patient cases, we describe this clinical story at the individual level along with ctDNA clonal pattern. One patient with known lung metastases treated with palbociclib in combination with fulvestrant, remained on this therapy for 14 months, during which GATA3 T329fs and ESR1 D538G emerged, likely both in a resistant dominant clone (Fig. 4a). The disease remained stable for over a year of therapy, after which the patient developed a new lung metastasis. In contrast, in Fig. 4d, a patient with known liver metastases was treated with a novel oral selective estrogen receptor degrader (SERD) monotherapy. The liver metastases increased in size and number after only 3 months. During this time, there was a steady increase in the allele fractions in 3 mutations: PIK3CA E542 K, ESR1 Y537 N, and GATA3 K358f in a manner suggesting the PIK3CA mutation was clonal, and the ESR1 and GATA3 variants were subclonal. No amplifications in PIK3CA were detected.

Fig. 4.

Survival analyses of GATA3 mut patients treated with ET + CDK4/6 and ET alone

Discussion

Numerous sequencing analyses have shown that GATA3 gene mutations are common in breast cancer [33–36, 54–56]. Despite this fact, the GATA3 protein has not yet been found to be therapeutically targetable, directly or indirectly. As new approaches to target GATA3^mut breast cancer are underway, it is important to further broaden our understanding of the mutational landscape of such disease. To date, the majority of large-scale genomic studies have been performed via tissue-based analyses in primary breast cancer. With this study, we add a new dimension to the GATA3^mut landscape by analyzing ctDNA in MBC and demonstrate that GATA3 mutations are detectable, as well as co-occurring potential modifiers, and cancers harboring these mutations have distinct ctDNA-based genomic characteristics compared to those identified in primary breast cancer tissue. Specifically, ctDNA analysis in the advanced breast cancer setting showed that GATA3 mutations are not mutually exclusive from TP53 or PIK3CA mutations. This discrepancy may be explained by a number of different factors: biologic differences in advanced disease, disparate clonal evolution at different metastatic sites captured in a single blood draw, differences in variant detectability via blood compared to tissue, and/or contributions from clonal hematopoiesis (unrelated to tumor evolution) in this study population, where the median age was > 55.

Our tissue-based proteomic analysis highlighted important potential contributors to estrogen pathway independence, as well as a mechanistic explanation for sensitivity to MDM2 inhibition. While GATA3, ESR1, PIK3CA, FOXA1, FOXO3, and RB1 protein abundances were not significantly reduced in GATA3^mut breast cancers, the expression of genes associated with MDM2 function and phosphorylation of genes associated with ERK signaling and aggressive phenotypes were impacted. The finding of increased abundance of USP48 protein, which was explored given the potential mechanistic link between MDM2 inhibition efficacy in GATA3^mut breast cancer, supported the hypothesis that GATA3 mutations were leading to changes in the MDM2 signaling pathway, which in turn lead to the therapeutic vulnerability of MDM2 inhibition. [31] Moreover, differential expression analysis of phosphorylation sites revealed a potential association of increased downstream signaling via ERK, which could also be leveraged for potential therapeutic inhibition.

The strong pre-clinical data around MDM2 inhibition’s potential synthetic lethality role in ER +, GATA3^mut ER + breast cancer led to the design of a phase II clinical trial using the MDM2 inhibitor milademetan plus fulvestrant in patients with ER +, HER2−, GATA3^mut advanced breast cancer (NCT05932667), where the GATA3 mutation would be identified via sequencing the tumor and/or ctDNA. Patients must have wildtype TP53, and our findings suggest screening via ctDNA may find a smaller population of candidate patients, given the lack of mutual exclusivity of GATA3 and TP53 ctDNA mutations in this population with advanced disease.

Meanwhile, 3 patients with ctDNA-detected GATA3 mutations in our study also had germline BRCA1/2 mutations, which has not been previously appreciated. [50] These findings highlight likely functional heterogeneity among mutations in different regions of the GATA3 gene. Furthermore, pathogenic germline BRCA1/2 variants qualify patients for PARP inhibitor therapy, which offers alternative targeted therapy options to patients who may otherwise be considered for a GATA3^mut targeted clinical trial [57, 58].

Our study also highlights the data we may gain from serial sampling. GATA3 mutations in 5 patients were not initially identified on blood sampling, and only through serial sampling over time were these found. This emergence of GATA3 mutations may suggest their role in modifying therapeutic response. The importance of serial testing at multiple timepoints throughout treatment is increasing in MBC, where we may identify emergence of dynamic key genomic changes that can drive both resistance and sensitivity to therapeutic agents, such as ESR1, PIK3CA, PTEN, AKT1, and others; GATA3 mutations were also shown in this dataset to co-occur with alterations in many of these genes at high frequency. GATA3 mutations did not exhibit consistent mutational dynamics over time, which may speak to multiple factors, including treatment effect and/or resistance within certain (sub)clones, but also a likely non-uniform functional nature of different GATA3 mutations, as noted above. Our findings allude to differential clonal dynamics in which GATA3 variants do not always follow mutational patterns of known driver variants such as PIK3CA, which is typically clonal, nor ESR1, which is typically subclonal (Supplemental Fig. 3). This data require further exploration in larger and prospective ctDNA-based studies.

Survival analyses were consistent with published results from a subgroup analysis in the MONALEESA-7 trial assessing ribociclib + ET in premenopausal patients, where GATA3 mutations were associated with poorer survival in patients receiving ET monotherapy but not in patients receiving ribociclib + ET [59]. Our findings are also interesting considering early data suggesting GATA3 mutations may predict sensitivity to monotherapy with the CDK4/6 inhibitor abemaciclib [60]. Importantly, prognostic and predictive findings are not necessarily linked, thus the efficacy of targeted therapy in this population remains to be explored in clinical trials.

While ctDNA offers the ease of serial sampling and evaluation of tumor DNA content across metastatic sites, there are also key limitations of this tool. As described above, clonal hematopoiesis is a major potential confounder to consider in using this toolset. Furthermore, low ctDNA fractions would limit the sensitivity of the NGS assays to detect variants of interest, as well as physical barriers to collection, such as the blood–brain barrier. This study is also retrospective and does not analyze matched tissue samples collected at the time of ctDNA analysis. This is an important limitation of this study. Further confounders may include the effects of prior lines of therapy, variation in the timing and clinical contexts of sample collection, and the impacts of ongoing therapy at the time of collection.

Conclusion

Accruing knowledge on the nature of GATA3 gene mutations in breast cancer offers hope for a new arsenal of targeted therapies. To date, this transcription factor has remained an area of great interest but has not yet proven therapeutically actionable. As tumor and plasma genomic profiling are becoming available to a wider population of patients via universal genomic testing initiatives, we may further define the genomic landscape of GATA3 mutations in breast cancer. ctDNA can be used to evaluate breast cancer genomics at different time points, which we may allow real time assessment of GATA3^mut breast cancer biology and response to therapy while searching for targetable variants in this promising population.

Supplementary Information

Below is the link to the electronic supplementary material.

Author contributions

AM, MV, and YG contributed equally to conceptualizing the study, collecting and analyzing the data, and writing the manuscript. Patient samples and data were collected and/or analyzed by AM, MV, LG, AN, AD, KC, JK, EP, WH, CR, CD, LK, LS, MC, GG, and AB. The first draft of the manuscript was written by AM, MV, YG and all authors commented on previous versions of the manuscript. All authors read and approved the final manuscript.

Funding

This work was supported by Conquer Cancer Foundation Grant No. (ASCO Young Investigator Award) Grant number GR0243421.

Data availability

Data is provided within the manuscript or supplementary information files.

Declarations

Conflict of interest

AJM reports grants from the ASCO Conquer Cancer Young Investigator Award and a National Institutes of Health K12 grant (K12 CA087723). The content is solely the responsibility of the authors and does not necessarily represent the official views of the American Society of Clinical Oncology or the National Institutes of Health. AJM reports personal fees from AstraZeneca, Edgewood Oncology, Guardant Health, Illumina, Myriad Genetics, Natera, Novartis, and research funding from AstraZeneca. LG reports personal fees from Novartis, Eli Lilly, AstraZeneca, Incyte and GlaxoSmithKline. LG reports research funding to the institution from AstraZeneca. AAD reports personal fees from Pfizer and bioTheranostics. WLH reports personal fees from Novartis and Gilead. LMS declares the following relationships: Consultant/advisory board: Novartis, Daiichi Pharma, Astra Zeneca, Eli Lilly, Precede, Seagen; Institutional research support: Merck, Genentech, Gilead, Eli Lilly. LWE reports personal fees from Mersana, KisoJi Biotechnology, Gilead Sciences. RCD is an employee and shareholder of Rain Oncology. MC reports personal fees from Lily, Menarini, Polaris, AstraZeneca/Daiichi Sankyo, Syantra, Sermonix Pharmaceuticals, Celcuity, Pfizer, and research funding from Lilly, Angle, Merck, AstraZeneca, and Menarini Silicon Biosystems. AB reports personal fees from Novartis, Genentech, Pfizer, Merck, Novartis, Genentech/Roche, Radius, Innocrin, Sanofi, AstraZeneca/Daiichi Sankyo, Lilly, Gilead Sciences, Menarini, and Mersana and research funding from Genentech, Novartis, Pfizer, Merck, Sanofi, Radius Health, Immunomedics, and AstraZeneca/Daiichi Sankyo. All other authors declare no competing interests.