The breast tumor immune microenvironment of DNA double-strand break repair pathogenic variant carriers is enriched with tumor-associated macrophages

Abstract

Background:

Approximately 5% of breast cancer patients have a rare pathogenic germline genetic variant that is associated with increased breast cancer risk. Mutations in more than 12 genes have been associated with hereditary breast cancer risk, many of which are involved in genome stability pathways, including DNA double-strand break (DSB) repair. We hypothesized that carriers of DSB repair-related pathogenic variants may have a distinct tumor immune environment that differs from that of non-carriers.

Methods:

We utilized tumor transcriptome data from 559 participants with invasive breast cancer from the Nurses’ Health Studies (NHS) and NHSII to infer immune-related gene expression signatures and immune cell abundance.

Results:

Thirty-three of the individuals (5.9%) had germline DSB repair-related pathogenic variants in one or more of the following genes: ATM, BARD1, BLM, BRCA1, BRCA2, BRIP1, CHEK2, FANCC, FANCM, NBN, PALB2, RAD50, RAD51C, and/or RECQL. In covariate-adjusted analyses, DSB repair-related pathogenic variant carrier status was positively associated with both a STAT1 signature (standardized β = 0.59, p = 3.5 × 10⁻³) and inferred M1 macrophage infiltration (standardized β = 0.56, p = 1.4 ×10⁻³). Further, these immune features correlated with other features related to tumor interferon response signaling suggesting this enrichment is occurring in an inflammatory context.

Conclusions:

These results indicate that breast tumors of DSB repair-related pathogenic variant carriers have distinct immune features, which may have therapeutic implications in this high-risk population.

Impact:

These results support further characterization of macrophage characteristics and abundance in the breast tumor microenvironment of DSB repair-related pathogenic variant carriers.

Introduction

An estimated 5% of breast cancer patients carry a rare germline pathogenic variant (PV) that predisposes them to breast cancer (1). PVs in specific genes are associated with higher risk of breast cancer (BRCA1 or BRCA2, 50% lifetime risk; PALB2, 32% lifetime risk) and specific tumor molecular subtypes (estrogen receptor (ER)-positive: CHEK2, ATM; ER-negative and triple-negative: BARD1, RAD51C, RAD51)(1). These genes encode proteins with functional roles in DNA double-strand break (DSB) repair pathways, affirming the role of aberrant or deficient DNA repair in carcinogenesis. Defects in DSB repair mechanisms have been shown to activate the cGAS-STING signaling pathway to generate an immune response that removes the damaged cell(2). Ideally, the adaptive immune response removes DSB-repair deficient cells; however, cancer cells with persistent DSBs can upregulate immune checkpoint proteins to evade the anti-tumor immune response and continue to proliferate in the inflammatory microenvironment(3–5). For instance, persistent DSBs lead to ATM/ATR/Chk1-dependent upregulation of PD-L1 and resistance to cell-death despite infiltrating immune cells(5). Further characterizing the tumor immune microenvironment (TIME) of individuals with DSB repair-related PVs can further delineate the mechanisms promoting carcinogenesis in this population.

Differences in TIME phenotypes have been observed across PV carriers of specific DSB-related genes. Tumor infiltrating lymphocytes (TILs), a good prognostic factor in breast cancer(6–8), are enriched in individuals with germline PVs in BRCA1. The relationship between germline BRCA2 PVs and TILs is less clear, due to limited studies that differentiate BRCA1 and BRCA2 PVs carriers, though tumors from germline BRCA2 PV carriers may have higher sensitivity to immune checkpoint inhibition(9), suggesting additional factors besides TIL abundance may influence TIME immune checkpoint signaling. ATM and PALB2 PV carriers show no evidence for increased TIL infiltration compared to non-carriers(10,11). Solinas et al. also reported null associations between PV carrier status and tumor TIL density(12), though more tumors in the combined BRCA1/2 mutant group were found to be TIL-positive.

Beyond TIL quantification methods, de-convolution of gene-expression data has been used to characterize immune-cell abundance and immune signaling pathways in the TIME(13). A recent pan-cancer study of germline genetic variation and TIME features suggests that both common and rare germline genetic variations shape the composition of the TIME(14). In breast cancer specifically, polygenic risk of autoimmune conditions is inversely associated with interferon signaling in the tumor microenvironment, suggesting genetic susceptibility can influence inflammation in the TIME(15).

Characterizing the immune cell composition and signaling pathways active in tumors of breast cancer PV carriers may reveal common features underlying their relationship with the TIME. Here, we investigated the breast tumor immune profiles of 33 DSB repair-related PV carriers and 526 non-carriers in the Nurses’ Health Study (NHS; RRID: SCR_019144) and NHSII.

Methods

Data source and covariates

NHS and NHSII are longitudinal cohort studies of US female registered nurses described in detail previously(16). NHS enrolled 121,700 participants ages 30–55 years in 1976, and NHSII enrolled 116,429 participants ages 25–42 years in 1989. Participants reporting an incident breast cancer diagnosis were asked to review their medical records and cases with pathology information were confirmed by medical review (>99%)(17–20). Formalin-fixed paraffin-embedded (FFPE) tumor blocks were collected prior to treatment and were later used for microarray analysis of tumor RNA expression and immunohistochemistry (IHC) analyses(21). Blood samples for 32,826 participants of NHS and 29,611 participants of NHSII were collected from 1989 to 1990 and from 1996 to 1999, respectively.

Age at diagnosis was retrieved from the NHS and NHSII questionnaires. ER and human epidermal growth factor receptor 2 (HER2) status were determined from pathologist assessment of tissue microarrays (TMAs) (20,22).Tumor grade, stage at diagnosis, and tumor morphology were obtained from pathology reports. Genetic principal components (PCs) for population substructure were based on genotyping data collected previously(23).

All participants provided written informed consent and the study was approved by the institutional review boards (IRB) of the Brigham and Women’s Hospital and Harvard T.H. Chan School of Public Health. The study was conducted in accordance with recognized ethical guidelines (Declaration of Helsinki, CIOMS, Belmont Report, U.S. Common Rule). The study protocol was approved by the institutional review boards of the Brigham and Women’s Hospital and Harvard T.H. Chan School of Public Health, and those of participating registries as required.

Gene expression data and immune-related molecular features

RNA extraction from FFPE tissue blocks and gene expression profiling have been described previously (17,20,21,24). In brief, RNA was extracted from 1.0 or 1.5 mm cores from FFPE tissue blocks using the Qiagen AllPrep RNA isolation kit. Gene expression profiling was performed in two batches using Affymetrix Glue Grant Human Transcriptome Arrays (HTA 3.0 pre-release version 2012–2014, and HTA 2.0 Affymetrix array 2015–2018; Affymetrix, Santa Clara, CA, USA) by the Molecular Biology Core Facilities, Dana-Farber Cancer Institute (Boston, MA). Gene expression data were normalized and summarized using Robust Multiarray Average (Affymetrix Power Tools v1.18.0). Data quality was evaluated using APT probeset summarization-based metrics with AUC <0.55 excluded from further analysis. Samples that failed non-outlier analysis by arrayQualityMetrics v3.24.0 were also excluded(25). Batch effects were corrected with the ComBat package(26). Unannotated probes, probes from the Y chromosome, low-expression probes were removed from analysis. 869 tumor tissue samples and 17,787 gene-level annotated probesets of coding and non-coding RNAs remained for CIBERSORTx(27) deconvolution analysis and expression signature scores. CIBERSORTx uses a support vector regression approach based on prior training for leukocyte subsets to estimate both relative and absolute quantities of infiltrating cell types. The LM22 deconvolution matrix was used as reference to estimate the absolute abundance for 22 tumor infiltrating immune cells. Three cell types returned scores of zero (>99%) and were omitted from further study (naïve CD4+ T cells, activated CD4+ memory T cells, and gamma-delta T cells). Gene expression signature scores for 68 immune features were computed as in Wolf et al. (28) and Amara et al. (29) In brief, signature scores were calculated as mean, median, PCA, and weighted mean values based on their original methods and were mean-centered with deviations scaled to one. Gene expression data are available from the Gene Expression Omnibus (GEO: GSE115577). Cell enrichment values and gene signature scores are referred to subsequently as immune features.

Targeted DNA sequencing

Germline DNA was extracted from blood samples and a multigene amplicon-based panel was sequenced as described in Hu et al(1). In brief, dual bar-coded QIAseq (Qiagen) multiplex amplicon-based analysis of 746 targeted regions in 37 cancer predisposition genes was performed to prepare libraries. Samples were pooled and sequenced by the HiSeq 4000 system (Illumina). Variants were evaluated by the Genome Analysis Toolkit (GATK) Haplotype Caller tool and the VarDict variant caller tool. Variants met these criteria: allele frequency <0.01 in cases or <0.003 in gnomAD (or not present in gnomAD); altered allele frequency > 0.05 or < 0.95, or equal to 1; and read depth of both altered reads and reference reads > 5. Variants considered “loss of function” (nonsense, frameshift, consensus splice sites (+/− 1 or 2) in panel genes, without an entry in ClinVar were classified as PVs. Missense variants and in-frame deletions were called as variants of unknown significance and excluded, except those occurring in the first codon. Intronic and synonymous variants were considered benign. Copy number variants were evaluated individually based on log2ratios. Variants reported with reduced penetrance were excluded. Variants considered “likely pathogenic” in the ClinVar database were called “pathogenic”. PVs in 16 double-strand break (DSB)-related cancer predisposition genes were considered (ATM, BARD1, BLM, BRCA1, BRCA2, BRIP1, CHEK2, FANCC, FANCM, MRE11A, NBN, PALB2, RAD50, RAD51C, RECQL, and XRCC2) based on their functional roles in the Fanconi Anemia related signaling and Homologous Recombination pathways (30,31). No participants had PVs in MRE11A or XRCC2. The 33 individuals with PVs in fourteen genes comprised the “PV carriers” group in subsequent analyses.

Immunohistochemistry

Of the 559 participants with both gene expression data and PV sequencing in the analysis, 87 participants from the NHSII cohort had immunohistochemical evaluation of four immune cell markers from a previous nested case-control study(32). In brief, TMAs were constructed at the Dana Farber Harvard Cancer Center Tissue Microarray core facility (Boston MA). Three 0.6 mm tissue cores were taken from tumor regions on each tissue block and IHC staining was performed on TMA sections using antibodies for CD4 (Dako 7310, clone 4B12), CD8 (Dako 7103, clone C8/144B), CD20 (Dako 0755, clone L26), and CD163 (Vector Labs VP-C374, clone 10D6). Staining positivity was assessed by an automated computational image analysis system (Definiens Tissue Studio software, Munich, Germany). Mean percent positivity was calculated as the average number of positive cells over the total number of cells for three patient cores(32,33).

Statistical Analysis

Prior to statistical analyses, CIBERSORTx-based immune cell infiltration, immune-related gene expression signature scores and IHC mean percent positivity were rank-based normal-transformed to account for skewed distributions.

For clustering analysis, Pearson correlation coefficients were calculated for the 87 immune features across the 559 participants. Correlations were converted to distances using a distance metric of 1 – correlation and clustered with the complete agglomerative approach(13,29).

Linear regression models were fit to test the associations between carrier status of DSB repair-related genes with immune cell infiltration, gene expression signature scores, and mean percent positivity for IHC markers, adjusting for age at diagnosis, NHS study cohort, tumor grade (well-differentiated, moderately differentiated, poorly differentiated), tumor stage at diagnosis (stage 1, stage 2, and stages 3 and 4), dichotomous ER status (negative/low ER and high ER:ER status was quantified as a percentage of positive cells and three categories were determined: ER negative: <1%, low ER: 1–10%, high ER >10%)), the top four genetic PCs, and genotyping array. The ER variable was dichotomized based on the observation that ER-low tumors may behave more similarly to ER negative tumors than ER positive tumors in the context of immune response in early breast cancer(34). To account for multiple testing for the 87 immune feature regression models, the threshold for significance of p-values was adjusted to P ≤ 5.75 × 10⁻⁴ based on a Bonferroni-correction for 87 tests. We define nominal association as a p-value between 5.75 × 10⁻⁴ and 0.05.

Data Availability

The NHS/NHSII microarray dataset is publicly accessible in GEO (RRID: SCR_005012; GEO #; GSE11577). Code for computing the gene signatures and CIBERSORTx variables from the NHS/NHSII microarray dataset is available at https://github.com/yuxi-liu42/PRS_immune. The targeted sequencing data is controlled access and available through a signed access request with dbGaP (dbGaP (RRID: SCR_002709); accession: phs002820.v1.p1). Other individual-level data are available via collaborative request and subject to institutional approval at https://nurseshealthstudy.org/researchers.

Results

Study population and carrier status of DSB repair-related PVs

To investigate the effect of germline DSB repair-related PV carrier status on tumor immune features, we identified individuals in NHS and NHSII who had data from both gene expression profiling of their breast tumor and targeted germline sequencing of their cancer predisposition genes (N = 559) (Figure 1). Of these 559 individuals, 33 (5.9%) had germline PVs in genes with functional roles in DSB repair. (Figure 2). Of the 33 carriers, 10 (30.3%) had PVs in BRCA2 and two (6.1%) had PVs in BRCA1. Two (6.1%) individuals were observed to have PVs in multiple genes and most carriers (29, 87.9%) had ER-positive disease. Carriers were younger at diagnosis (59 vs. 63 years; p = 0.038), as would be expected, were more likely to be from the NHSII cohort (p = 0.002), and were more likely to have poorly differentiated tumors (p = 0.009) (Table 1).

Figure 1. Study workflow for immune phenotype – PV carrier status analyses.

This flowchart depicts the workflow for participant selection. Invasive breast cancer cases from NHS/NHSII with both FFPE-derived gene expression data and germline sequencing were considered for analysis (N = 559). Gene expression data were used to quantify abundance of 19 immune cell types and 68 immune-related gene expression signatures (87 immune features). A subset of tumors in the analysis group also had IHC quantification of specific immune cell markers available for analysis (N = 87). FFPE = Formalin-Fixed Paraffin-Embedded; IHC = immunohistochemistry; NHS = Nurses’ Health Study; PVs = pathogenic variants

Figure 2. Tumor characteristics and PVs of carrier group.

PV carrier status and tumor characteristics of 33 individuals with germline PVs are shown as a comutation plot with columns as individual carriers and with rows as tumor features indicated as colored boxes. ER status (Negative = orange, Positive = green); ER Level (ER <1% = orange; ER 1–10% = blue; ER >10% = green); Tumor stage at diagnosis (categories 1 = red, 2 = yellow, and 3 or 4 = orange); Morphology (Lobular = purple, Ductal = green, Other = orange, Unknown = gray); Tumor grade (1 = yellow, 2 = red, 3 or 4 = purple; unknown = gray); and PV = blue; None = gray. ER = estrogen receptor, PV = pathogenic variant

Table 1.

Participant and breast tumor characteristics

	All participants N = 559	Non-Carrier N = 526	Carrier N = 33	P-Value*
Age at diagnosis, mean (SD)	62.61 (10.06)	62.83 (9.94)	59.09 (11.45)	0.038
NHS Cohort, N (%)				0.002
NHS	409 (73.2)	393 (74.7)	16 (48.5)
NHSII	150 (26.8)	133 (25.3)	17 (51.5)
Tumor Grade, N (%)				0.009
Well-differentiated	144 (25.8)	138 (26.2)	6 (18.2)
Moderately differentiated	295 (52.8)	282 (53.6)	13 (39.4)
Poorly differentiated	98 (17.5)	85 (16.2)	13 (39.4)
Unknown	22 (3.9)	21 (4.0)	1 (3.0)
Stage at diagnosis, N (%)				0.202
Stage 1	349 (62.4)	333 (63.3)	16 (48.5)
Stage 2	158 (28.3)	146 (27.8)	12 (36.4)
Stages 3 or 4	52 (9.3)	47 (8.9)	5 (15.2)
Tumor Morphology, N (%)				NA
Ductal	453 (81.0)	425 (80.8)	28 (84.8)
Lobular	67 (12.0)	64 (12.2)	3 (9.1)
Mixed	30 (5.4)	29 (5.5)	1 (3.0)
Unknown	9 (1.6)	8 (1.5)	1 (3.0)
ER Status, N (%)				NA
Negative	87 (15.6)	83 (15.8)	4 (12.1)
Positive	472 (84.4)	443 (84.2)	29 (87.9)
ER Level, N (%)				0.708
Negative or Low	75 (13.4)	69 (13.1)	6 (18.2)
High	356 (63.7)	336 (63.9)	20 (60.6)
Unknown	128 (22.9)	121 (23.0)	7 (21.2)
IHC Subtype, N (%)				NA
ER+/HER2+	134 (24.0)	123 (23.4)	11 (33.3)
ER+/HER2-	310 (55.5)	294 (55.9)	16 (48.5)
ER-/HER2+	34 (6.1)	33 (6.3)	1 (3.0)
ER-/HER2-	44 (7.9)	42 (8.0)	2 (6.1)
Unknown	37 (6.6)	34 (6.5)	3 (9.1)

sd = standard deviation, ER = estrogen receptor, HER2 = human epidermal growth factor receptor 2, IHC = immunohistochemistry;

^*P-Value shown for two-sample independent T-Test with equal variance (age at diagnosis) or chi-square test with continuity correction (cohort, tumor grade, stage at diagnosis, ER Level); Statistical tests for tumor morphology, ER Status and IHC Subtype were not performed due to groups with N < 5.

Correlation across immune features

Immune signaling cascades are comprised of many cell types and signaling molecules, and many features within the TIME are correlated. To inspect the correlation among the 87 immune features across the entire sample, we performed unsupervised hierarchical clustering of the immune features to visualize their general relationships (Supplementary Figure S1). Based on inspection of the dendrogram, we observed two main clusters, one branch which contained multiple clusters of highly correlated features previously described in Amara et al(29) such as IFN-related immune features, TGF-β features, and T Cell/B Cell related features. The second main branch had fewer clusters and several small clusters of features had inverse correlations with the TGF-β features. The second branch also included CD8 T cell related features such as CIBERSORT inferred CD8+ T cells (T.cells.CD8) and expression of CD8A mRNA (CD8A). The branch also contains several of the single-gene expression features such as the immune checkpoint proteins CTLA4, PDL1, and PD1 (CTLA4_data, PDL1_data, PD1_data).

Association of carrier status with immune features

We next examined the relationship between DSB repair PV carrier status and immune features. In the unadjusted analysis, we observed three significant associations and 23 nominal associations for carrier status and immune features (Supplementary Figure S2, Supplementary Table S1), but after adjustment for tumor features and other covariates, there were 20 nominal associations with the largest effect estimates for STAT1_19272155 (standardized β = 0.59, 95% CI = [0.20, 1.0], p = 3.5 × 10⁻³) and CIBERSORTx-inferred Macrophages.M1 (standardized β = 0.56, 95% CI = [0.22, 0.90], p = 1.4 × 10⁻³) (Figure 3, Supplementary Table S2). Several of these nominal associations were with gene expression signatures within the IFN feature cluster (STAT1_19272155, Module3_IFN_score, Interferon_Cluster_21214954).. Given these relationships between carrier status and IFN-related signaling, we also tested the association between expression-based indicators of tertiary lymphoid structures (TLSs) in breast cancer (35) and carrier status (Supplementary Figure S3, Supplementary Table S3). CXCL13 and LTB expression were positively associated with PV carrier status, but only LTB was nominally associated.

Figure 3. Adjusted effect of DSB repair-related PV carrier status on immune features.

A. The adjusted associations of carrier status with 87 immune features are shown as a forest plot. The adjusted analysis is restricted to the 419 individuals with complete data for the covariates: 394 non-carriers and 25 carriers. Immune features are denoted with their labels from Newman et al. (27) and Amara et al. (29) Point estimates and confidence intervals (95% CI) are arranged by beta coefficient point estimate. Empty circles denote a p-value > 0.05, filled-circles denote p-value ≤ 0.05. B. Boxplot showing the normalized distribution of the STAT1_19272155 gene signature by DSB repair-related PV carrier status for the full study sample. C. Boxplot showing the normalized distribution of Macrophages.M1 CIBERSORTx-inferred cell abundance by DSB repair-related PV carrier status for the full study sample. D. Boxplot showing the normalized distribution of the TAMsurr_score gene signature by DSB repair-related PV carrier status. Non-Carriers = no, PV carriers = yes. DSB = Double-strand break, PV = pathogenic variant

We observed an association of PDL1 expression with PV carrier status (PDL1_data ( standardized β = 0.39, 95% CI = [−0.02, 0.81], p = 0.06), as we would have anticipated from in vitro studies (5). We also observed a positive association between carrier status and the T cell inhibitory CTLA4 expression (CTLA4_data (standardized β = 0.54, 95% CI = [0.14, 0.95], p = 9.0 ×10⁻³). To bolster and validate findings from our gene expression-based analyses of tumor immune features, we analyzed protein expression of four cell type-specific immune markers from IHC staining of a subset of tumors from NHSII (Supplementary Table S4). While we observed positive associations of DSB repair-related carrier status with staining intensity of the four immune markers, these were not statistically significant (Supplementary Figure S4, Supplementary Table S5).

Sensitivity analysis for BRCA2 PV carrier status

The carrier group is comprised of individuals with a heterogeneous group of PVs (14 genes), and a majority of individuals have a PV in BRCA2 (10, 30.3%). To determine if the observations for the PV carrier group are driven by BRCA2-specific biology, we performed sensitivity analyses among PV carriers without a BRCA2 variant (Non-BRCA2 carrier group, N = 17) and those with a BRCA2 variant (BRCA2 only carrier group, N = 8), respectively (Supplementary Table S6). Excluding the BRCA2 carriers attenuated the association between inferred macrophage abundance (Macrophages_M1) and carrier status (β = 0.48 [0.08, 0.88], p =0.02), but the directionality was consistent (Supplementary Figure S5, Supplementary Tables S2, S7). The BRCA2 only subgroup exhibited a stronger effect of carrier status (standardized β = 0.76 [0.18, 1.4], p =0.01), though the confidence intervals were wider due to the reduced sample size (Supplementary Table S8).

Discussion

In this study, we found that tumors from individuals with germline PVs in one of 14 DSB repair-related genes were enriched for tumor-associated macrophages (TAMs) and interferon-related signaling compared to non-carriers. Carrier status was also associated with higher CTLA4 expression and PDL1 expression, but not PD1.

The strongest association between carrier status and an immune feature was STAT1_19272155, an expression signature associated with the response to interferon signaling (28). Both M1 macrophages and the STAT1_19272155 score clustered with interferon signaling related signatures (Interferon_19272155, IFN_21978456, Module3_IFN_score), suggesting that these features are occurring within an inflammatory TIME. TAMs are involved in carcinogenesis, metastasis, and response to treatment in breast cancer(36), making their enrichment in the carrier group of particular interest. Further studies establishing the etiologic relationship between inflammation, macrophages, and breast cancer in DSB-related PV carriers is warranted to determine if compromised DSB repair could be a cause or consequence of inflammation in PV carriers.

We also observed relationships between carrier status and immune checkpoint-related features. Both CTLA4 and PD1 mRNA expression have been correlated with TILs and BRCA1 mutation carrier status in individuals specifically with triple negative subtype disease in TCGA(8), but the present population is mostly high ER tumors and has few BRCA1 carriers, so the lack of PD1 enrichment may be due to differences in specific tumor features. It was recently reported that BRCA2 carriers had improved responses to immune checkpoint inhibition compared to BRCA1 carriers(9). Mouse models of Brca1 and Brca2 deficiency displayed differential TAM infiltration, which was confirmed in human tumor samples, suggesting that BRCA2-specific biology may be involved in TAM-related phenotypes in the TIME(9). To ensure our TAM-related observations were not driven by the high proportion of individuals with BRCA2 PVs in the carrier group compared to the few BRCA1 variants and other less frequent PVs, we performed a sensitivity analysis to measure the relationship between carrier status and immune features without BRCA2 carriers. The sensitivity analysis demonstrated that even with the removal of the BRCA2 carriers, there was still a positive effect of carrier status on M1 macrophage infiltration. When only BRCA2 carriers were considered, the effect of carrier status on the M1 macrophages and CTLA4 coefficients was even stronger, but did not reach statistical significance. Replication of this observation in studies with high numbers of tumors from BRCA2 PV-carriers would be of clinical interest.

There are limitations to the generalizability of this study. The study population consists of largely early stage and ER-positive tumors. The carrier group was small, limiting statistical power to detect associations and preventing stratification by specific predisposition genes and molecular subtypes. Also, the low number of BRCA1 carriers limited the ability to consider BRCA1-specific biology in the analysis. Studies with sequencing of predisposition genes in larger and more diverse cohort studies are needed to confirm these findings and further characterize the DSB repair-related carrier specific TIME and the functional role of the enriched macrophages, especially as treatment strategies targeting immune phenotypes are developed.

Overall, this study provides evidence for an enrichment of TAMs in invasive, treatment-naïve breast cancers of individuals with germline PVs in DSB repair-related genes, increasingly relevant as the field considers precision medicine approaches around breast cancer prevention and treatment for this high-risk population.

Abbreviations

DSB

DNA double-strand break

ER

estrogen receptor

FFPE

Formalin-fixed paraffin-embedded

HR

homologous recombination

IFN

interferon

IHC

immunohistochemistry

NHS

Nurses’ Health Study

PC

principal components

PV

pathogenic variant

TAM

tumor associated macrophage

TILs

tumor infiltrating lymphocytes

TIME

tumor immune microenvironment

TLSs

tertiary lymphoid structures

TMA

tumor microarray