Skip to main content
NCBI home page
JNCI Journal of the National Cancer Institute logoLink to JNCI Journal of the National Cancer Institute
. 2026 Mar 15;118(8):1416–1424. doi: 10.1093/jnci/djag070

Landscape of somatic genetic alterations and PAM50 intrinsic subtypes in breast cancer associated with germline pathogenic variants in DNA-repair genes

Siddhartha Yadav 1,#, Sonya Reid 2,#, Binyam Yilma 3, Stamatina Fragkogianni 4, Jennifer G Whisenant 5, Anne Weidner 6, Padma Sheila Rajagopal 7, Minxuan Huang 8, Elizabeth Mauer 9, Ali T Arafa 10, Calvin Chao 11, Emily A Teslow 12, Emmanuel S Antonarakis 13, Allison Kurian 14, Fergus J Couch 15,#, Tuya Pal 16,✉,#
PMCID: PMC13453545  PMID: 41832987

Abstract

Background

The association of germline pathogenic and likely pathogenic variants (GPVs) in hereditary breast cancer genes with underlying tumor biology and clinical outcomes remains incompletely understood. This study characterized differences in somatic alterations and intrinsic subtypes between sporadic and hereditary breast cancers associated with GPVs in ATM, BRCA1, BRCA2, CHEK2, or PALB2.

Methods

This retrospective cohort study included women with breast cancer and an ATM, BRCA1, BRCA2, CHEK2, or PALB2 GPV who had undergone tumor sequencing and whole transcriptome RNA expression analysis. Clinicopathological features, intrinsic subtypes, somatic alterations, and survival were compared by GPV status and immunohistochemistry-defined subtype and to sporadic cases. All statistical significance tests were 2-sided.

Results

The 4988 women with breast cancer in this study included 98 BRCA1, 126 BRCA2, 74 PALB2, 54 ATM, and 83 CHEK2 GPVs. Compared with sporadic cases, hormone receptor–positive/(ERBB2) formerly HER2–negative tumors in BRCA1 GPVs were statistically significantly enriched for basal subtype (45.5% vs 11.4%; P < .001), while CHEK2 carriers had a higher prevalence of luminal A subtype (80.4% vs 60.3%; P = .006). In hormone receptor–positive/(ERBB2)-negative breast cancers, BRCA1 GPVs were enriched for TP53 alterations (84.6% vs 29.8%; q < 0.001), ATM GPVs with FGFR1 alterations (35.4% vs 12.7%; q = 0.04), and BRCA2 GPVs with APC alterations (10.1% vs 1.5%; q = 0.004). Conversely, BRCA2 GPVs were inversely associated with PIK3CA alterations (13.0% vs 34.1%; q = 0.005) and CHEK2 GPVs with TP53 alterations (8.0% vs 29.8%; q = 0.02).

Conclusions

The GPVs in BRCA1, BRCA2, ATM, CHEK2, and PALB2 are associated with distinct intrinsic breast cancer subtypes and somatic genomic alterations. These findings may enhance precision in risk stratification and guide personalized treatment strategies.

Introduction

Breast cancer can be broadly classified as hereditary (5%-10%) and sporadic.1 Hereditary breast cancer is primarily the result of germline pathogenic or likely pathogenic variants (GPVs) in BRCA1, BRCA2, PALB2, ATM, and CHEK2.2 The clinicopathological and molecular characteristics across genes, based on immunohistochemical and intrinsic subtype classifications, support distinct pathways to tumorigenesis. For example, triple-negative breast cancer is overrepresented in BRCA1-, BRCA2-, and PALB2-associated breast cancers yet less common with CHEK2 or ATM.3-8 In contrast, hormone receptor–positive/ERBB2 (formerly HER2)–negative breast cancer accounts for most CHEK2- and ATM-associated breast cancers yet is less common with BRCA1.3,9-11 In addition, molecular subtyping shows that most germline (g) BRCA1- and gBRCA2-associated breast cancers are basal and luminal subtypes, respectively.12,13 Although molecular subtyping refines prognostication beyond standard immunohistochemical subtype classification,13-16 comprehensive data across hereditary breast cancer genes are limited. Distinct pathways to gBRCA1- and gBRCA2 (gBRCA1/2)–associated tumorigenesis enabled development of targeted therapies such as poly(ADP-ribose) polymerase inhibitors.17-21 Consequently, mapping the somatic mutation landscape of breast cancer across GPVs could decipher underlying tumorigenesis and identify therapeutic targets and prognostic biomarkers.

Prior studies evaluating survival among patients with hereditary breast cancer have shown inconsistent results.6,22-30 Survival differences may vary by clinical subtypes, with better survival among gBRCA1/2 carriers with triple-negative breast cancer but not hormone receptor–positive and ERBB2-positive subtypes.24 Data are scarce, however, on the somatic landscape of hereditary breast cancer–associated tumors. We characterized the intrinsic subtypes and somatic mutation profiles of breast cancers with gBRCA1, gBRCA2, gPALB2, gATM, and gCHEK2 compared with sporadic cases.

Methods

Study population

For this study, we used the Tempus Database and analyzed 4988 deidentified records of patients diagnosed with breast cancer whose samples had undergone comprehensive genomic profiling with the Tempus xT and xR next-generation sequencing assays (Tempus AI, Inc). A total of 4835 samples (including 282 with hereditary breast cancer GPVs) were clinically sequenced, while 153 were sequenced through a research study that enrolled women with known GPVs recruited through the Inherited Cancer Registry31 and Black Women With Breast Cancer: Etiology, Survival and Treatment Outcomes32 institutional review board–approved studies at Vanderbilt University Medical Center (Table S1). Race and ethnicity were self-reported. As expected from a predominantly clinically sequenced cohort, the majority of cases were metastatic (approximately 80%).

DNA and RNA sequencing

Next-generation sequencing was conducted using the Tempus xT assay, as previously described.33 Tempus xT is a targeted, tumor-normal-matched DNA panel that detects single-nucleotide variations (formerly single-nucleotide polymorphisms), insertions and deletions (indels), and copy number variants in 648 genes as well as chromosomal rearrangements in 22 genes with high sensitivity and specificity.34

RNA sequencing was conducted using the Tempus xR assay. Tempus xR is a whole-transcriptome next-generation sequencing assay spanning a 39-megabase target region of the human genome that quantifies transcript-level and gene-level expression and identifies transcriptional evidence of chromosomal rearrangements resulting in the expression of fusion RNA species.35,36 Briefly, a hybrid capture-based protocol was used to address RNA fragmentation in formalin-fixed, paraffin-embedded samples that were sectioned, reviewed, and subjected to RNA extraction. The extracted RNA was quantified and assessed for quality, then used for library preparation with at least 150 ng of complementary DNA required for hybridization and sequencing on an Illumina NovaSeq 6000 sequencer. Sequencing data were processed and deduplicated samples were converted back to FASTQ files and quantified using Kallisto with the Ensembl GRCH37 reference transcriptome.34,35,37 Intrinsic subtyping methods are included in Supplementary Methods. Data from RNA sequencing was used for PAM50 subtyping of the tumors.

Somatic variations included in the analysis were classified as likely pathogenic or pathogenic. The determination of pathogenic and likely pathogenic variants was based on a weighted-heuristic model informed by established guidelines, including those of Association for Molecular Pathology/American Society of Clinical Oncology/College of American Pathologists/ClinGen Somatic Working Group and American College of Medical Genetics and Genomics (ACMG). Variant detection, visualization, and reporting were performed as previously described.34

Germline findings from tumor/normal-matched Tempus xT analyses are limited to single-nucleotide variations and small indels.38 The incidental germline reporting used is not from a validated germline panel, and additional germline panel testing may be indicated. Incidental germline findings from Tempus xT sequencing data are reported according to recommendations by the ACMG, National Comprehensive Cancer Network Genetic/Familial High-Risk Assessment guidelines, and other literature.39,40 The classification of germline alterations broadly followed the 5-tier system from the ACMG.41 Using this system, gBRCA1, gBRCA2, gATM, gPALB2, and gCHEK2 were derived from patients whose tumors were sequenced using the Tempus xT assay or using a validated germline test. Six patients with GPVs in more than 1 gene of interest were excluded. Tumors without GPVs in any of the 5 genes of interest were classified as sporadic. In addition, because there was a small number of tumors from women with a mutation in the other hereditary breast cancer gene tested for through the Tempus analyses (n = 29), these tumors were included in the sporadic group, with genes and numbers with GPVs in parentheses: TP53 (7), PTEN (6), RAD51C (5), RAD51D (5), NF1 (4), BARD1 (1), CDH1 (1), and STK11 (0).

Comparator group definition

Breast cancer cases were classified into 3 clinical subtypes (hormone receptor positive [either estrogen receptor or progesterone receptor positive]/ERBB2 negative, triple-negative, and ERBB2 positive) based on the clinically reported receptor status. Patients were also classified into the 4 intrinsic subtypes (luminal A, luminal B, basal, and ERBB2 enriched). Intrinsic subtype distribution was evaluated by GPV carrier status within each clinical subtype and compared between sporadic and each of the 5 GPV groups. Similarly, pairwise comparisons of somatic alterations between the sporadic group and each of the 5 GPV groups were performed within each clinical and intrinsic subtype of breast cancer and the overall study population.

Statistical methods and survival analyses

Statistical comparisons were conducted using the Kruskal-Wallis rank sum test and the Pearson χ2 test for continuous variables and categorical variables, respectively. The Fisher exact test was applied when expected cell counts were below 5. An ɑ level of .05 was used to determine statistical significance, with P values below this threshold deemed statistically significant. All tests of significance were 2-sided. Where necessary, adjustments for multiple comparisons were made using the false discovery rate, and only results with false discovery rate–adjusted q values below 0.05 were considered statistically significant.

Exploratory analyses of overall survival were conducted in a subset of patients with metastatic/late-stage tumors only, based on GPV carrier status and the clinical or intrinsic subtype of breast cancer. Overall survival was defined as the duration from diagnosis to death from any cause. To mitigate immortal time bias, a risk set adjustment method was employed, ensuring that patients were considered at risk based on the corrected study entry time. The study entry was marked by the date of diagnosis or the first sequencing date, whichever was later. Patients were censored at their last follow-up date if they had not died. Cox proportional hazards models, stratified by germline mutations, were used, and the Wald test assessed the statistical significance of the hazard ratio at a 5% significance level. All survival analyses included 95% confidence intervals (CIs) for the hazard ratio. Survival analysis results were reported only for groups with more than 5 events for overall survival to maintain statistical validity.

Ethical approval

All patient-level data were deidentified in accordance with the US Health Insurance Portability and Accountability Act. Tempus AI has been granted an institutional review board exemption (Advarra Pro00072742) permitting the use of deidentified clinical, molecular, and multimodal data to derive or capture results, insights, or discoveries.

Results

The median age at diagnosis of the cohort was 56 years (interquartile range [IQR] = 47-65 years). Approximately 73% of the study population was White, 14% was Black, and 16% was Hispanic. Distribution of PAM50 findings, disease stage, and race by hormone receptor status is included in Table S2. There were 98 gBRCA1, 126 gBRCA2, 74 gPALB2, 54 gATM, and 83 gCHEK2 carriers. Among gBRCA1 and gBRCA2 carriers, median ages at diagnosis were 47 years (IQR = 37-58 years) and 49 years (IQR = 39-58 years), respectively, vs 57 years (IQR = 47-65 years) in the sporadic group. Approximately 65% of gBRCA1-associated tumors were triple negative vs 5% in gCHEK2 and 22% in the sporadic group (Table 1). Type of GPV is included in Table S3.

Table 1.

Demographics and clinical characteristics of the study population.

Overall (n = 4988) Sporadica (n = 4553) BRCA1 (n = 98) BRCA2 (n = 126) PALB2 (n = 74) ATM (n = 54) CHEK2 (n = 83)
Age at diagnosis, y
 Median (IQR) 56 (47-65) 57 (47-65) 47 (37-58) 49 (39-58) 53 (46-60) 53 (46-60) 57 (49-64)
Race, No. (%)b
 Asian 161 (4.6) 146 (4.7) 4 (5.1) 6 (6.5) 3 (4.8) 1 (2.6) 1 (1.6)
 Black 487 (14.0) 447 (14.2) 14 (17.7) 17 (18.3) 3 (4.8) 4 (10.6) 2 (3.2)
 White 2532 (72.9) 2277 (72.5) 54 (68.3) 62 (66.7) 51 (82.2) 32 (84.2) 56 (88.9)
 Other 294 (8.5) 269 (8.6) 7 (8.9) 8 (8.6) 5 (8.1) 1 (2.6) 4 (6.3)
 Unknown 1514 1414 19 33 12 16 20
Ethnicity, No. (%)c
 Hispanic or Latino 318 (15.8) 293 (15.6) 4 (16.7) 11 (20.7) 4 (26.7) 2 (12.5) 4 (21.0)
 Not Hispanic or Latino 1692 (84.2) 1590 (84.4) 20 (83.3) 42 (79.3) 11 (73.3) 14 (87.5) 15 (79.0)
 Unknown 2978 2670 74 73 59 38 64
Tumor sequencing source, No. (%)
 Locoregional site 942 (18.9) 753 (16.6) 50 (51.0) 45 (35.7) 44 (59.5) 15 (27.8) 35 (42.2)
 Metastatic site 4046 (81.1) 3800 (83.8) 48 (49.0) 81 (64.3) 30 (40.5) 39 (72.2) 48 (57.8)
Clinical subtype, No. (%)d
 Hormone receptor positive/ERBB2 negative 2495 (64.4) 2271 (64.6) 26 (30.2) 69 (68.3) 48 (72.7) 31 (75.6) 50 (76.9)
 Triple negative 854 (22.0) 757 (21.5) 56 (65.1) 24 (23.7) 13 (19.7) 1 (2.4) 3 (4.6)
 ERBB2 positive 527 (13.6) 489 (13.9) 4 (4.6) 8 (7.9) 5 (7.6) 9 (21.9) 12 (14.5)
 Unknown 1112 1036 12 25 8 13 18
Open in a new tab
a

There were 29 women with a mutation in a hereditary breast cancer gene aside from the 5 studied were included in the sporadic group with gene (numbers) as follows: TP53 (7), PTEN (6), RAD51C (5), RAD51D (5), NF1 (4), BARD1 (1), CDH1 (1), and STK11 (0).

b

Cases with Unknown race were subtracted from the total n, and the resulting number was used as the denominator for all percentages.

c

Cases with Unknown ethnicity were subtracted from the total n, and the resulting number was used as the denominator for all percentages.

d

Cases with Unknown clinical subtype were subtracted from the total n, and the resulting number was used as the denominator for all percentages.

Distribution of intrinsic PAM50 subtype of breast cancer by GPV carrier status

Subtype distribution among sporadic tumors included luminal A in 46.6%, luminal B in 17.2%, basal in 25.6%, and ERBB2 enriched in 11.2%. In contrast to the sporadic group, a statistically significantly higher proportion of basal subtype was noted in gBRCA1 (25% vs 74.7%; P < .001) and luminal A subtype in gATM (46% vs 62%; P = .04) and gCHEK2 (46% vs 75.0%; P < .001) (Figure 1, A and Table S2). Among hormone receptor–positive/ERBB2-negative tumors, compared with sporadic tumors, basal subtype was statistically significantly enriched with gBRCA1 (11.4% vs 45.5%; P < .001) and luminal A with gCHEK2 (60.3% vs 80.4%; P = .006) and a lower proportion of luminal A with gBRCA1 (60.3% vs 22.7%; P < .001) (Figure 1, B and Table S4). Among triple-negative breast cancer cases, 91.5% of gBRCA1-associated tumors were basal compared with 80.6% in sporadic tumors (P = .06) (Figure 1, C and Table S4). Among locoregional tumors, basal subtype was enriched with gBRCA1 and luminal A with gCHEK2, overall and with hormone receptor–positive/ERBB2-negative cases (Table S5). Among Black women, 36.4% of sporadic tumors were basal compared with 21.4% among White women (Table S6). A trend toward enrichment of basal subtype was noted among Black BRCA1 carriers vs sporadic cases (66.7% vs 36.4%; P = .06).

Figure 1.

For image description, please refer to the figure legend and surrounding text.

Open in a new tab

Distribution of PAM50-based intrinsic subtypes by germline pathogenic variant carrier status in the (A) overall study population and among (B) hormone receptor–positive/ERBB2-negative and (C) triple-negative breast cancer subsets.

Landscape of somatic genetic alterations by GPV carrier status and breast cancer clinical subtype

Among sporadic tumors, TP53 alterations were most common (39%), followed by 10% or greater frequency in PIK3CA (29%), CCND1 (14%), FGFR1(11%), and ERBB2 (10.0%) (Figures S1 and S2). The most frequent somatic alterations by GPV status overall and among hormone receptor–positive/ERBB2-negative cases were TP53 in gBRCA1, gBRCA2, and gPALB2 carriers and PIK3CA in gATM and gCHEK2 carriers (Figure S1). Somatic alteration frequencies along with odds ratios by gene are included in Table S7.

In hormone receptor–positive/ERBB2-negative cases, compared with sporadic tumors (Figure 2 and Table S8), TP53 alterations were enriched in gBRCA1 carriers (29.8% vs 84.6%; q < 0.001), FGFR1 in gATM carriers (12.7% vs 35.4%; q = 0.04), and APC in gBRCA2 carriers (1.5% vs 10.1%; q = 0.004). In contrast, a lower prevalence of PIK3CA alterations was noted in gBRCA2 carriers (34.1% vs 13.0%; q = 0.005) and of TP53 alterations in gCHEK2 carriers (29.8% vs 8.0%; q = 0.02). Compared with sporadic tumors, significantly fewer TP53 (29.8% vs 6.5%) alterations were noted in gATM carriers and CCND1 (17.2% vs 4.3%) and FGFR1 (12.7% vs 4.3%) alterations in gBRCA2 carriers, but these values did not meet the threshold for statistical significance after adjusting for multiple testing (Table S8).

Figure 2.

For image description, please refer to the figure legend and surrounding text.

Open in a new tab

Landscape of somatic alterations by germline pathogenic carrier status in (A) hormone receptor–positive/ERBB2-negative and (B) triple-negative breast cancer.

Among triple-negative breast cancer cases, gBRCA1-associated tumors had a significantly higher proportion of somatic TP53 (68.2% vs 94.6%; q < 0.001) and KMT2D (2.1% vs 12.5%; q = 0.01) alterations compared with sporadic tumors (Figure 2 and Table S8). Of note, among ERBB2-positive breast cancer cases, all gBRCA1 carriers had TP53 alterations vs 54.0% in sporadic tumors and 8.3% in gCHEK2 carriers. Among sporadic ERBB2-positive breast cancer cases, ERBB2 alterations were most common (64.8%), but no gPALB2-associated tumors and only 25% of gBRCA1-associated tumors had ERRB2 alterations.

Landscape of somatic genetic alterations by GPV carrier status and breast cancer intrinsic PAM50 subtype

Compared with the sporadic group, gBRCA2-associated luminal A tumors were enriched for APC alterations (1.7% vs 12.0%; q = 0.01) and gBRCA1-associated basal tumors for TP53 (73.1% vs 96.7%; q = 0.001) and KMT2D (2.2% vs 11.3%; q = 0.02) alterations. In contrast, PIK3CA alterations were significantly lower in gBRCA2-associated vs sporadic luminal A tumors (41.4% vs 18.0%; q = 0.02). Among gATM-associated luminal A tumors, 10.3% had TP53 alterations and 31.0% had CCND1 alterations vs 27.7% and 16.6% of sporadic tumors, respectively. Similarly, although not statistically significant (q = 0.06), gPALB2-associated basal tumors were enriched for PTEN alterations (12.3% vs 43.7%) (Table S9).

Landscape of somatic genetic alterations by GPV carrier status and breast cancer stage at sequencing

Among both locoregional and metastatic tumors, TP53 alterations were present in more than 35% of sporadic and more than 90% of gBRCA1-associated tumors and less than 12% of gCHEK2-associated tumors (Table S10). PIK3CA alterations were present in 23% and 30% of sporadic locoregional and metastatic tumors, respectively, and in 6% of gBRCA1 and less than 14% of gBRCA2 carriers with locoregional or metastatic tumors. ESR1 alterations were present in less than 3% of locoregional tumors, irrespective of GPV status, whereas among metastatic tumors, ESR1 alterations were identified in 8.8% of sporadic tumors; 8.6% of gBRCA2-associated tumors; more than 18% of gPALB2-, gCHEK2-, and gATM-associated tumors; and no gBRCA1-associated tumors. CCND1 and FGFR1 alterations among gATM carriers were present in less than 10% of locoregional but more than 30% of metastatic tumors.

Overall survival

Among metastatic breast cancer cases, overall survival did not differ in clinical or PAM50 subtypes by GPV status compared with sporadic tumors, except a trend toward improved overall survival was noted for gCHEK2 carriers within luminal A (hazard ratio = 0.55, 95% CI = 0.28 to 1.05) and hormone receptor–positive/ERBB2-negative (hazard ratio = 0.52, 95% CI = 0.19 to 1.39) subtypes (Figure 3).

Figure 3.

For image description, please refer to the figure legend and surrounding text.

Open in a new tab

Forrest plot of overall survival for the (A) overall cohort; clinical subtypes, including (B) hormone receptor–positive/ERBB2-negative, (C) triple-negative, and (D) ERBB2-positive breast cancer; and intrinsic subtypes, including (E) luminal A, (F) luminal B, (G) basal, and (H) ERBB-enriched breast cancer.

Discussion

In this study of almost 5000 women, we identified distinct intrinsic subtypes and somatic genomic alterations in breast cancers from the 435 women with gBRCA1, gBRCA2, gATM, gCHEK2, and gPALB2. This effort represents 1 of the largest such studies to date focused on hereditary breast cancer tumorigenesis, with representation of women with both early-stage and late-stage disease. Our findings provide novel insights into genotype-phenotype correlations as well as temporal evolution of somatic mutations to identify early genomic events that may serve as predictive markers to guide precision oncology treatment of hereditary breast cancer carriers.

In comparing clinical-based with molecular subtyping–based classifications, we observed that among hormone receptor–positive/ERBB2-negative breast cancers, basal tumors were enriched in gBRCA1-associated vs sporadic cases. These findings reinforce the potential clinical utility of intrinsic subtyping to identify and characterize hormone receptor–positive basal-type tumors in GPVs, which may be more resistant to endocrine therapy and more sensitive to chemotherapy and immunotherapy.42-45 Importantly, basal subtype in gBRCA1 carriers with hormone receptor–positive/ERBB2-negative breast cancer was enriched in locoregional tumors, emphasizing the potential need to select adjuvant treatment for gBRCA1 carriers considering both intrinsic and clinical subtypes. Specifically, these hormone receptor–positive tumors, which are of basal subtype, may be endocrine resistant. In contrast, our findings that gPALB2 carriers with triple-negative breast cancer more often exhibited luminal A subtypes is hypothesis generating and represents a biologically distinct subset characterized by luminal differentiation despite hormone receptor negativity. Although these features may have prognostic and therapeutic implications, they do not currently alter standard treatment recommendations outside clinical trials. Future studies should focus on biomarker-driven therapeutic strategies tailored to this unique triple-negative breast cancer subgroup. Consequently, these findings highlight the potential value of intrinsic subtyping in guiding treatment and will be explored in future efforts.

Prior studies have shown the co-occurrence of TP53 somatic mutations among gBRCA1 carriers and the mutual exclusivity between gBRCA1/2 carriers and PIK3CA somatic mutations,46-48 consistent with our current findings, which support distinct pathways to tumorigenesis among hereditary breast cancer carriers. Moreover, the high rates of TP53 alterations and basal subtype among gBRCA1 carriers with hormone receptor–positive/ERBB2-negative disease may portend endocrine insensitivity. TP53 alterations in breast cancer are associated with worse prognosis, and endocrine resistance has been observed among patients with TP53 somatic alterations independent of the 21-gene recurrence score.49 Although 21-gene recurrence scores are higher in gBRCA1/2 carriers with hormone receptor–positive/ERBB2-negative breast cancer,50 it is unclear whether this assay can appropriately stratify gBRCA1/2 carriers for benefit from adjuvant chemotherapy.

Our findings offer downstream context for therapeutic decision making within hormone receptor–positive breast cancers among patients with gBRCA1/2. The absence of ESR1 alterations in gBRCA1 carriers is consistent with observations that TP53 and ESR1 alterations are likely epistatic.51 Among women with hormone receptor–positive/ERBB2-negative breast cancer, there were fewer PIK3CA, PTEN, and AKT1 alterations in gBRCA2 carriers vs sporadic cases, suggesting that the PI3K-AKT-PTEN pathway may not drive endocrine resistance in gBRCA2 carriers. Given US Food and Drug Administration–approved treatment options based on alterations in ESR52 or PI3K-AKT-PTEN pathways,53-55 studies in gBRCA1/2 carriers with hormone receptor–positive/ERBB2-negtive breast cancer are needed. Prior studies suggest an inferior response to frontline cyclin-dependent kinase (CDK) 4/CDK6 inhibitors in gBRCA1/2-associated breast cancer,56-59 and a clinical trial in gBRCA1, gBRCA2, and gPALB2 carriers with metastatic breast cancer comparing the combination of an oral selective estrogen receptor down-regulator with a poly(ADP-ribose) polymerase inhibitor against the standard-of-care combination of endocrine therapy and CDK4/CDK6 inhibitors is ongoing (ClinicalTrials.gov identifier NCT06380751). Based on the unique biology of hormone receptor–positive/ERBB2-negative tumors associated with gBRCA1/2 and gPALB2 and concerns about their endocrine sensitivity, it remains critical to report outcomes by hereditary breast cancer GPV status in endocrine therapy trials among patients with metastatic breast cancer.

Our findings also offer context for the evolution of hormone receptor–positive breast cancers, which are the predominant subtype seen among patients with gATM and gCHEK2.60,61 Among gATM and gCHEK2 carriers, the frequency of TP53 mutations was lower vs sporadic cancers, consistent with prior studies.60-62 These findings suggest that these genes may be epistatic with TP53, with potential functional redundancy.63 Comparing somatic mutations across GPVs showed a similar frequency of TP53 mutations in locoregional and metastatic cases, suggesting that these mutations occur early. Similarly, PIK3CA mutations were prevalent with gATM and gCHEK2 GPVs in both early- and late-stage cases. In contrast, ESR1 mutations were observed in gATM-associated metastatic but not locoregional cases, potentially selected by adjuvant endocrine therapy that prompts ESR1 mutations to emerge as a resistance mechanism.

Among metastatic breast cancer cases, no differences in overall survival were observed by GPV status vs sporadic cases, except with gCHEK2: There was a trend toward improved overall survival in luminal A and hormone receptor–positive cases. Prior studies of gCHEK2 carriers have reported mixed results, with some reporting no differences and others poorer survival.22,25,27,64 Prior studies focused on survival have not focused exclusively on metastatic cases, however, which could be the reason for the longer survival observed in our study.

Strengths and limitations

This study is among the largest of hereditary breast cancer GPV carriers (n = 435), investigating somatic mutation profiles and intrinsic subtypes with robust clinical data and a large comparator group of sporadic cancers. Other studies published to date have included 45265 and 36846 predominantly metastatic hereditary breast cancer carriers. In contrast to prior efforts and unique to our cohort, our participants were enriched for early-stage breast cancers (approximately 30% of the hereditary breast cancer carriers), enabling us to explore somatic mutation profiles that have predominantly been reported in metastatic cases, providing additional temporal context of mutational profiles in locoregional vs metastatic cases. Our study is among the first to systematically compare across multiple hereditary breast cancer genes together within a unified design to enable direct comparisons between DNA repair pathways, such as homologous recombination deficiency (eg, gBRCA1, gBRCA2, gPALB2) vs DNA damage signaling (eg, gATM, gCHEK2) to further accentuate both shared and divergent somatic alterations across gene groups. Despite these strengths, some limitations remain. We had limited power to evaluate survival differences within subtypes and lacked serial samples for individual patients, precluding our reporting on tumor progression in the same individual to compare early-stage vs metastatic somatic changes. In addition, our study is representative of patients with breast cancer who undergo matched tumor-normal tumor testing enriched for metastatic cases because this is the population for whom tumor testing is clinically indicated. Consequently, somatic mutation frequencies and distribution of intrinsic subtypes in our study may not reflect the broader population of patients with breast cancer in the United States.

In addition, we may not have captured all potential germline findings in the clinically tested samples. Given the way germline data were captured, there is a small likelihood that germline variants such as large rearrangements or indels were missed, which would be expected to account for less than 10% of GPVs and affecting sporadic group findings minimally. Finally, given data constraints, we could not conduct loss-of-heterozygosity and homologous repair deficiency analyses.

Conclusions

Our findings demonstrate the distinct intrinsic subtypes and somatic genomic alterations in breast cancers from women with gBRCA1, gBRCA2, gATM, gCHEK2, and gPALB2. The heterogeneity of subtypes and somatic genomic alterations among these germline etiologies is critical both for elucidating pathways to tumorigenesis for each hereditary breast cancer gene and for highlighting potential areas where treatment differences may be impactful. We identified enrichment of TP53 mutations in gBRCA1 yet rarely in gATM or gCHEK2, where an increased frequency of PIK3CA mutations was seen across locoregional and metastatic hormone receptor–positive cases. In contrast, ESR1 mutations were enriched in gATM but only in metastatic cases. These genotype-phenotype correlations have tremendous potential to refine and improve upon existing prognostic and predictive markers and treatment targets.

Supplementary Material

djag070_Supplementary_Data
djag070_supplementary_data.docx (5.6MB, docx)

Acknowledgments

The authors acknowledge and thank the members of the Vanderbilt-Ingram Cancer Center SWERV writing core for assistance in the development of manuscript figures (NCI 5P30CA068485-29). The funder had no role in the design of the study; the collection, analysis, or interpretation of the data; or the writing of the manuscript and decision to submit it for publication.

Contributor Information

Siddhartha Yadav, Department of Oncology, Mayo Clinic, Rochester, MN, United States.

Sonya Reid, Division of Hematology and Oncology, Vanderbilt University Medical Center, Nashville, TN, United States.

Binyam Yilma, Tempus AI, Chicago, IL, United States.

Stamatina Fragkogianni, Tempus AI, Chicago, IL, United States.

Jennifer G Whisenant, Division of Hematology and Oncology, Vanderbilt University Medical Center, Nashville, TN, United States.

Anne Weidner, Division of Hematology and Oncology, Vanderbilt University Medical Center, Nashville, TN, United States.

Padma Sheila Rajagopal, Cancer Data Science Laboratory, National Cancer Institute, Bethesda, MD, United States.

Minxuan Huang, Tempus AI, Chicago, IL, United States.

Elizabeth Mauer, Tempus AI, Chicago, IL, United States.

Ali T Arafa, Masonic Cancer Center, University of Minnesota, Minneapolis, MN, United States.

Calvin Chao, Tempus AI, Chicago, IL, United States.

Emily A Teslow, Tempus AI, Chicago, IL, United States.

Emmanuel S Antonarakis, Masonic Cancer Center, University of Minnesota, Minneapolis, MN, United States.

Allison Kurian, Department of Medicine, Stanford University School of Medicine, Stanford, CA, United States.

Fergus J Couch, Department of Laboratory Medicine and Pathology, Mayo Clinic, Rochester, MN, United States.

Tuya Pal, Department of Medicine, Vanderbilt University Medical Center, Nashville, TN, United States.

Author contributions

Siddhartha Yadav (Conceptualization, Data curation, Formal analysis, Writing—original draft, Writing—review & editing), Sonya Reid (Conceptualization, Data curation, Formal analysis, Writing—original draft, Writing—review & editing), Binyam Yilma (Formal analysis, Investigation, Methodology, Writing—original draft, Writing—review & editing), Stamatina Fragkogianni (Formal analysis, Investigation, Methodology, Writing—original draft, Writing—review & editing), Jennifer Whisenant (Project administration, Writing—original draft, Writing—review & editing), Anne Weidner (Data curation, Writing—original draft, Writing—review & editing), Padma Sheila Rajagopal (Formal analysis, Writing—original draft, Writing—review & editing), Minxuan Huang (Formal analysis, Writing—original draft, Writing—review & editing), Elizabeth Mauer (Formal analysis, Methodology, Writing—original draft, Writing—review & editing), Ali T. Arafa (Formal analysis, Writing—original draft, Writing—review & editing), Calvin Chao (Data curation, Formal analysis, Writing—original draft, Writing—review & editing), Emily Teslow (Data curation, Formal analysis, Writing—original draft, Writing—review & editing), Emmanuel Antonarakis (Formal analysis, Writing—original draft, Writing—review & editing), Allison Kurian (Formal analysis, Writing—original draft, Writing—review & editing), Fergus Couch (Conceptualization, Formal analysis, Investigation, Supervision, Writing—original draft, Writing—review & editing), and Tuya Pal (Conceptualization, Formal analysis, Investigation, Supervision, Writing—original draft, Writing—review & editing)

Supplementary material

Supplementary material is available at JNCI: Journal of the National Cancer Institute online.

Funding

This work was supported by the: American Society of Clinical Oncology Career Development Awards from the Conquer Cancer Foundation (S.Y. and S.R.); National Cancer Institute at the National Institutes of Health (K12CA090628, P50 CA116201, and P50 CA136393 to S.Y.; P30CA077598 to E.S.A.; R35CA253187 and R01CA225662 to F.J.C.; Breast Cancer Resource Foundation grants (S.Y., S.R., and F.J.C.); the US Department of Defense (W81XWH-22-2-0025 to E.S.A.); BRCA Research and Cure Alliance (F.J.C.); Susan G. Komen Foundation (SAC210105 to T.P.); and American Cancer Society (PF-23 to 1153194-01-CDP to A.T.A.).

Conflicts of interest

S.Y. has received research support (institutional) from AstraZeneca, Repare Therapeutics, Exelexis, and 858 Therapeutics and has served on advisory boards for AstraZeneca. B.Y., S.F., M.H., E.M., C.C., and E.A. are employees of Tempus AI. E.S.A. reports grants and personal fees from Janssen, Sanofi, Bayer, Bristol-Myers Squibb, Curium, MacroGenics, Merck, Pfizer, AstraZeneca, and Clovis; personal fees from Aadi Bioscience, Aikido Pharma, Astellas, Amgen, Blue Earth, Boundless Bio, Corcept Therapeutics, Exact Sciences, Hookipa Pharma, Invitae, Eli Lilly, Foundation Medicine, Menarini-Silicon Biosystems, Tango Therapeutics, Tempus AI, and Z-alpha; grants from Novartis, Celgene, and Orion; and has a patent for an AR-V7 biomarker technology that has been licensed to Qiagen. F.J.C. has received research funding (institutional) from AstraZeneca and GRAIL. S.R. has served on advisory boards for Astra Zeneca, Daiichi Sankyo, Gilead, Novartis, and Pfizer.

Data availability

Deidentified data used in the research were collected in a real-world healthcare setting and are subject to controlled access for privacy and proprietary reasons. When possible, derived data supporting the findings of this study have been made available within the paper and its supplementary figures and tables.

References

  • 1. Yadav S, Couch FJ, Domchek SM.  Germline genetic testing for hereditary breast and ovarian cancer: current concepts in risk evaluation. Cold Spring Harb Perspect Med. 2024;14:a041318. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 2. Hu C, Hart SN, Gnanaolivu R, et al.  A population-based study of genes previously implicated in breast cancer. N Engl J Med. 2021;384:440-451. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 3. Hu C, Polley EC, Yadav S, et al.  The contribution of germline predisposition gene mutations to clinical subtypes of invasive breast cancer from a clinical genetic testing cohort. J Natl Cancer Inst. 2020;112:1231-1241. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 4. Gonzalez-Angulo AM, Timms KM, Liu S, et al.  Incidence and outcome of BRCA mutations in unselected patients with triple receptor-negative breast cancer. Clin Cancer Res. 2011;17:1082-1089. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 5. Rummel S, Varner E, Shriver CD, et al.  Evaluation of BRCA1 mutations in an unselected patient population with triple-negative breast cancer. Breast Cancer Res Treat. 2013;137:119-125. [DOI] [PubMed] [Google Scholar]
  • 6. Cybulski C, Kluźniak W, Huzarski T, et al. ; Polish Hereditary Breast Cancer Consortium. Clinical outcomes in women with breast cancer and a PALB2 mutation: a prospective cohort analysis. Lancet Oncol. 2015;16:638-644. [DOI] [PubMed] [Google Scholar]
  • 7. Antoniou ACCS, Heikkinen T, Barrowdale D, et al.  Breast-cancer risk in families with mutations in PALB2. N Engl J Med. 2014;371:497-506. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 8. Yadav S, Couch FJ.  Germline genetic testing for breast cancer risk: the past, present, and future. Am Soc Clin Oncol Educ Book. 2019;39:61-74. [DOI] [PubMed] [Google Scholar]
  • 9. Mavaddat NDL, Carvalho S, Allen J, et al.  Pathology of tumors associated with pathogenic germline variants in 9 breast cancer susceptibility genes. JAMA Oncol. 2022;8:e216744. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 10. Atchley DP, Albarracin CT, Lopez A, et al.  Clinical and pathologic characteristics of patients with BRCA-positive and BRCA-negative breast cancer. J Clin Oncol. 2008;26:4282-4288. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 11. Lambertini M, Ceppi M, Hamy AS, et al.  Clinical behavior and outcomes of breast cancer in young women with germline BRCA pathogenic variants. NPJ Breast Cancer. 2021;7:16. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 12. Lakhani SR, van de Vijver MJ, Jacquemier J, et al.  The pathology of familial breast cancer: predictive value of immunohistochemical markers estrogen receptor, progesterone receptor, HER-2, and p53 in patients with mutations in BRCA1 and BRCA2. J Clin Oncol. 2002;20:2310-2318. [DOI] [PubMed] [Google Scholar]
  • 13. Larsen MJ, Thomassen M, Gerdes AM, et al.  Hereditary breast cancer: clinical, pathological and molecular characteristics. Breast Cancer (Auckl). 2014;8:145-155. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 14. Prat A, Ellis MJ, Perou CM.  Practical implications of gene-expression-based assays for breast oncologists. Nat Rev Clin Oncol. 2011;9:48-57. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 15. Cancer Genome Atlas N. Comprehensive molecular portraits of human breast tumours. Nature. 2012;490:61-70. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 16. Cheang MCMM, Nielsen TO, Prat A, et al.  Defining breast cancer intrinsic subtypes by quantitative receptor expression. Oncologist. 2015;20:474-482. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 17. Tutt A, Bertwistle D, Valentine J, et al.  Mutation in Brca2 stimulates error-prone homology-directed repair of DNA double-strand breaks occurring between repeated sequences. Embo J. 2001;20:4704-4716. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 18. Farmer H, McCabe N, Lord CJ, et al.  Targeting the DNA repair defect in BRCA mutant cells as a therapeutic strategy. Nature. 2005;434:917-921. [DOI] [PubMed] [Google Scholar]
  • 19. Bryant HE, Schultz N, Thomas HD, et al.  Specific killing of BRCA2-deficient tumours with inhibitors of poly(ADP-ribose) polymerase. Nature. 2005;434:913-917. [DOI] [PubMed] [Google Scholar]
  • 20. Robson M, Im S-A, Senkus E, et al.  Olaparib for metastatic breast cancer in patients with a germline BRCA mutation. N Engl J Med. 2017;377:523-533. [DOI] [PubMed] [Google Scholar]
  • 21. Litton JK, Rugo HS, Ettl J, et al.  Talazoparib in patients with advanced breast cancer and a germline BRCA mutation. N Engl J Med. 2018;379:753-763. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 22. Morra ASM, Andrulis IL, Anton-Culver H, et al. ; ABCTB Investigators NBCS Collaborators. Association of the CHEK2 c.1100delC variant, radiotherapy, and systemic treatment with contralateral breast cancer risk and breast cancer-specific survival. Cancer Med. 2023;12:16142-16162. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 23. Mailliez AD, Lusque A, Caron O, et al.  Survival outcomes of metastatic breast cancer patients by germline BRCA1/2 status in a large multicenter real-world database. Int J Cancer. 2023;152:921-931. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 24. Kurian AW, Abrahamse P, Bondarenko I, et al.  Association of genetic testing results with mortality among women with breast cancer or ovarian cancer. J Natl Cancer Inst. 2022;114:245-253. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 25. Kurian AW, Ward KC, Howlader N, et al.  Genetic testing and results in a population-based cohort of breast cancer patients and ovarian cancer patients. J Clin Oncol. 2019;37:1305-1315. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 26. Copson ERMT, Tapper WJ, Cutress RI, et al.  Germline BRCA mutation and outcome in young-onset breast cancer (POSH): a prospective cohort study. Lancet Oncol. 2018;19:169-180. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 27. Kriege M, Hollestelle A, Jager A, et al.  Survival and contralateral breast cancer in CHEK2 1100delC breast cancer patients: impact of adjuvant chemotherapy. Br J Cancer. 2014;111:1004-1013. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 28. Yadav S, Ladkany R, Yadav D, et al.  Impact of BRCA mutation status on survival of women with triple-negative breast cancer. Clin Breast Cancer. 2018;18:e1229-e1235. [DOI] [PubMed] [Google Scholar]
  • 29. Fasching PA, Yadav S, Hu C, et al.  Mutations in BRCA1/2 and other panel genes in patients with metastatic breast cancer -association with patient and disease characteristics and effect on prognosis. J Clin Oncol. 2021;39:1619-1630. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 30. Veenstra CM, Abrahamse P, Hamilton AS, et al.  Breast, colorectal, and pancreatic cancer mortality with pathogenic variants in ATM, CHEK2, or PALB2. J Clin Oncol. 2025;43:1587-1596. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 31. Reid S, Roberson ML, Koehler K, et al.  Receipt of bilateral mastectomy among women with hereditary breast cancer. JAMA Oncol. 2023;9:143-145. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 32. Reid S, Fan R, Venton L, et al.  West African genetic ancestry and breast cancer outcomes among black women. JAMA Netw Open. 2024;7:e2449798. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 33. Ai T. Tempus xT Validation Summary. 2024. Accessed August 1, 2025. https://www.tempus.com/wp-content/uploads/2025/02/Tempus-xT_Validation.pdf? srsltid=AfmBOorAd0SXfE7ZgBouSBle0m0OJmO4rhSb-LnXoTvBkSCNCIRiH0yX
  • 34. Beaubier N, Tell R, Lau D, et al.  Clinical validation of the tempus xT next-generation targeted oncology sequencing assay. Oncotarget. 2019;10:2384-2396. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 35. Michuda J, Breschi A, Kapilivsky J, et al.  Validation of a transcriptome-based assay for classifying cancers of unknown primary origin. Mol Diagn Ther. 2023;27:499-511. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 36. Ai T. Tempus xR Validation Summary. 2023. Accessed August 1, 2025. https://www.tempus.com/wp-content/uploads/2023/06/Tempus-xR_Validation.pdf
  • 37. Beaubier NBM, Huether R, Igartua C, et al.  Integrated genomic profiling expands clinical options for patients with cancer. Nat Biotechnol. 2019;37:1351-1360. [DOI] [PubMed] [Google Scholar]
  • 38. Yap TA, Ashok A, Stoll J, et al.  Prevalence of germline findings among tumors from cancer types lacking hereditary testing guidelines. JAMA Netw Open. 2022;5:e2213070. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 39. Daly MB, Pal T, Maxwell KN, et al.  NCCN guidelines® insights: genetic/familial high-risk assessment: breast, ovarian, and pancreatic, version 2.2024. J Natl Compr Canc Netw. 2023;21:1000-1010. [DOI] [PubMed] [Google Scholar]
  • 40. Miller DT, Lee K, Gordon AS, et al. ; ACMG Secondary Findings Working Group. Recommendations for reporting of secondary findings in clinical exome and genome sequencing, 2021 update: a policy statement of the American College of Medical Genetics and Genomics (ACMG). Genet Med. 2021;23:1391-1398. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 41. Richards S, Aziz N, Bale S, et al. ; ACMG Laboratory Quality Assurance Committee. Standards and guidelines for the interpretation of sequence variants: a joint consensus recommendation of the American college of medical genetics and genomics and the association for molecular pathology. Genet Med. 2015;17:405-424.25741868 [Google Scholar]
  • 42. Rios-Hoyo A, Cobain E, Huppert LA, et al.  Neoadjuvant chemotherapy and immunotherapy for estrogen receptor-positive human epidermal growth factor 2-negative breast cancer. JCO. 2024;42:2632-2636. [DOI] [PubMed] [Google Scholar]
  • 43. Beitsch PD, Pellicane JV, Pusztai L, et al. ; NBRST Investigators Group. MammaPrint index as a predictive biomarker for neoadjuvant chemotherapy response and outcome in patients with HR+HER2- breast cancer in NBRST. J Clin Oncol. 2023;41:521-521. [Google Scholar]
  • 44. Pusztai L, Yau C, Wolf DM, et al.  Durvalumab with olaparib and paclitaxel for high-risk HER2-negative stage II/III breast cancer: results from the adaptively randomized I-SPY2 trial. Cancer Cell. 2021;39:989-998 e5. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 45. Groenendijk FH, Treece T, Yoder E, et al.  Estrogen receptor variants in ER-positive basal-type breast cancers responding to therapy like ER-negative breast cancers. NPJ Breast Cancer. 2019;5:15. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 46. Chen C, Lin CJ, Pei YC, et al.  Comprehensive genomic profiling of breast cancers characterizes germline-somatic mutation interactions mediating therapeutic vulnerabilities. Cell Discov. 2023;9:125. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 47. Lu C, Xie M, Wendl MC, et al.  Patterns and functional implications of rare germline variants across 12 cancer types. Nat Commun. 2015;6:10086. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 48. Park S, Lee E, Park S, et al.  Clinical characteristics and exploratory genomic analyses of germline BRCA1 or BRCA2 mutations in breast cancer. Mol Cancer Res. 2020;18:1315-1325. [DOI] [PubMed] [Google Scholar]
  • 49. Grote I, Bartels S, Kandt L, et al.  TP53 mutations are associated with primary endocrine resistance in luminal early breast cancer. Cancer Med. 2021;10:8581-8594. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 50. Kurian AW, Ward KC, Abrahamse P, et al.  Predicted chemotherapy benefit for breast cancer patients with germline pathogenic variants in cancer susceptibility genes. JNCI Cancer Spectr. 2021;5:pkaa083. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 51. Li Z, Spoelstra NS, Sikora MJ, et al.  Mutual exclusivity of ESR1 and TP53 mutations in endocrine resistant metastatic breast cancer. NPJ Breast Cancer. 2022;8:62. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 52. Bidard FC, Kaklamani VG, Neven P, et al.  Elacestrant (oral selective estrogen receptor degrader) versus standard endocrine therapy for estrogen receptor-positive, human epidermal growth factor receptor 2-negative advanced breast cancer: results from the randomized phase III emerald trial. J Clin Oncol. 2022;40:3246-3256. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 53. Andre F, Ciruelos E, Rubovszky G, et al.  Alpelisib for PIK3CA-mutated, hormone receptor-positive advanced breast cancer. N Engl J Med. 2019;380:1929-1940. [DOI] [PubMed] [Google Scholar]
  • 54. Turner NC, Im SA, Saura C, et al.  Inavolisib-based therapy in PIK3CA-mutated advanced breast cancer. N Engl J Med. 2024;391:1584-1596. [DOI] [PubMed] [Google Scholar]
  • 55. Turner NC, Oliveira M, Howell SJ, et al. ; CAPItello-291 Study Group. Capivasertib in hormone receptor-positive advanced breast cancer. N Engl J Med. 2023;388:2058-2070. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 56. Collins JM, Nordstrom BL, McLaurin KK, et al.  A real-world evidence study of CDK4/6 inhibitor treatment patterns and outcomes in metastatic breast cancer by germline BRCA mutation status. Oncol Ther. 2021;9:575-589. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 57. Safonov A, Marra A, Bandlamudi C, et al.  Tumor suppressor heterozygosity and homologous recombination deficiency mediate resistance to front-line therapy in breast cancer. bioRxiv. [Preprint]. 2024 Aug 26:2024.02.05.578934. doi: 10.1101/2024.02.05.578934. PMID: 39253462; PMCID: PMC11383285. [DOI] [Google Scholar]
  • 58. Andre F, Su F, Solovieff N, et al.  Pooled ctDNA analysis of MONALEESA phase III advanced breast cancer trials. Ann Oncol. 2023;34:1003-1014. [DOI] [PubMed] [Google Scholar]
  • 59. Bruno L, Ostinelli A, Waisberg F, et al.  Cyclin-dependent kinase 4/6 inhibitor outcomes in patients with advanced breast cancer carrying germline pathogenic variants in DNA repair-related genes. JCO Precis Oncol.  2022;6:e2100140. [DOI] [PubMed] [Google Scholar]
  • 60. Mandelker D, Kumar R, Pei X, et al.  The landscape of somatic genetic alterations in breast cancers from CHEK2 germline mutation carriers. JNCI Cancer Spectr. 2019;3:pkz027. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 61. Weigelt B, Bi R, Kumar R, et al. ; kConFab Investigators. The landscape of somatic genetic alterations in breast cancers from ATM germline mutation carriers. J Natl Cancer Inst. 2018;110:1030-1034. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 62. Smid M, Schmidt MK, Prager-van der Smissen WJC, et al.  Breast cancer genomes from CHEK2 c.1100delC mutation carriers lack somatic TP53 mutations and display a unique structural variant size distribution profile. Breast Cancer Res. 2023;25:53. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 63. Cheng Q, Chen L, Li Z, et al.  ATM activates p53 by regulating MDM2 oligomerization and E3 processivity. Embo J. 2009;28:3857-3867. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 64. Cragun D, Weidner A, Tezak A, et al.  Cancer risk management among female BRCA1/2, PALB2, CHEK2, and ATM carriers. Breast Cancer Res Treat. 2020;182:421-428. [DOI] [PubMed] [Google Scholar]
  • 65. Arafa ATYS, Marshall CH, Mauer E, et al.  Germline-somatic interactions in BRCA-associated cancers: unique molecular profiles and clinical outcomes linking ATM to TP53 synthetic essentiality. Clin Cancer Res. 2025;31:1730-1745. [DOI] [PMC free article] [PubMed] [Google Scholar]

Associated Data

This section collects any data citations, data availability statements, or supplementary materials included in this article.

Supplementary Materials

djag070_Supplementary_Data
djag070_supplementary_data.docx (5.6MB, docx)

Data Availability Statement

Deidentified data used in the research were collected in a real-world healthcare setting and are subject to controlled access for privacy and proprietary reasons. When possible, derived data supporting the findings of this study have been made available within the paper and its supplementary figures and tables.

Articles from JNCI Journal of the National Cancer Institute are provided here courtesy of Oxford University Press

RESOURCES