Poor therapeutic response in subsets of breast cancer (BC) patients poses an ongoing challenge. Here, we present a biomarker-guided characterization of 40 patient-derived BC organoids, with the aim of modeling resistant disease with greater fidelity and developing an in vitro system grounded in clinical data for testing alternative treatment strategies. We utilize patient data from the I-SPY2 clinical trial (NCT01042379) to develop predictive models of response to a range of therapies, using only organoid-detectable biomarkers as input, and validate a model predicting response to veliparib-platinum chemotherapy (VP) in triple-negative BC (TNBC) organoids. A drug screen in VP-resistant TNBC organoids reveals combination treatments that overcome resistance to cisplatin, including pro-apoptotic therapies. Another class of hits, HSP90 inhibitors, links organoid drug sensitivity to improved recurrence-free survival in a biomarker-defined patient subset. These findings establish organoid-based functional modeling as a bridge between clinical biomarkers and precision treatment strategies in breast cancer.

The Women Informed to Screen Depending on Measures of risk (WISDOM) Study (clinicaltrials.gov NCT02620852, 2016-08-31) is pragmatic randomized controlled trial evaluating risk assessment to inform the age to start, frequency and modality of breast cancer screening and risk reduction. The WISDOM Study uses a preference-tolerant design, enabling women who decline randomization to self-select their screening arm. The risk-based arm included comprehensive breast cancer risk assessment using the Breast Cancer Surveillance Consortium (BCSC) model and genetics. From August 2016 to February 2023, WISDOM enrolled a large, diverse, nationwide cohort of 46,403 participants - 77% non-Hispanic (NH) White, 9% Hispanic, 6% (NH) Black, and 5% (NH) Asian; demographic diversity increased over time. Mean age at baseline was 54 years. 28,372 (61%) were randomized, while 89% of those who self-selected their screening arm chose risk-based screening. The preference-tolerant design captured real-world screening preferences while maintaining scientific rigor and demonstrated strong preference for risk-based screening.

Importance Although adding immune checkpoint inhibitors to neoadjuvant chemotherapy improves outcomes in high-risk early-stage breast cancer, opportunities remain to further enhance response. Dual checkpoint blockade offers a potential strategy to further enhance efficacy. Objective To evaluate the combination of anti−programmed cell death 1 protein (PD-1) cemiplimab and anti−lymphocyte activation gene 3 (LAG-3) added to neoadjuvant therapy in ERBB2 -negative early-stage, high-risk breast cancer. Design, Setting, and Participants The I-SPY2 (Investigation of Serial Studies to Predict Your Therapeutic Response With Imaging and Molecular Analysis 2) is an ongoing randomized clinical platform trial being conducted at multiple US clinical sites including patients with early-stage (II or III) ERBB2 -negative, high-risk breast cancer. Participants, continuously enrolled since 2010, were adaptively randomized from February 2, 2020, to December 9, 2021, to one of several experimental neoadjuvant therapies or control groups based on receptor subtypes defined by hormone receptor (HR), ERBB2 status, and MammaPrint (Agendia Inc) molecular risk, categorized as high (MP1) or ultrahigh (MP2). Data were analyzed from January 1, 2022, to August 5, 2025. Interventions Both groups received weekly paclitaxel for 12 weeks, then doxorubicin and cyclophosphamide followed by surgery; concomitant with paclitaxel, the intervention group also received 4 doses of cemiplimab and fianlimab (PCF) every 3 weeks. Main Outcomes and Measures Pathologic complete response (pCR). Treatments graduated when they achieved 85% bayesian probability of success in a subtype-specific phase 3 trial. Pathway-specific biomarkers were assessed for response prediction. Results A total of 78 participants (mean [SD] age, 47 [39-54] years) were randomized to the intervention group, with 350 participants (mean [SD] age, 48 [39-57] years) randomized to the historical control population. PCF graduated in all clinical signatures, with pCR rates vs control of 44% (95% CI, 34%-53%) vs 21% (95% CI, 17%-25%) in all ERBB2 , 53% (95% CI, 39%-67%) vs 29% (95% CI, 22%-36%) in triple-negative, and 36% (95% CI, 23%-49%) vs 14% (95% CI, 9%-19%) in HR-positive and ERBB2 -negative disease. Among the total participants, 16 (21%) experienced adrenal insufficiency, including hypophysitis (11% grade 3 or 4), mostly occurring after immunotherapy completion. PCF was found to be highly effective in the subset of patients with immune signature positive status (ImPrint positive). Conclusions and Relevance In this randomized clinical trial, the combination of PD-1 and anti−LAG-3 inhibition with standard NAC was effective in early-stage ERBB2 -negative breast cancer, particularly in patients displaying a positive ImPrint immune signature. These results warrant further definitive trials. Trial Registration ClinicalTrials.gov Identifier: NCT01042379

Background:Circulating tumor DNA (ctDNA) is an emerging biomarker of treatment response and recurrence risk, while residual cancer burden (RCB) after neoadjuvant treatment (NAT) is a well-established risk factor for distant recurrence. Here, we examined the association between high ctDNA concentration at diagnosis and risk of distant recurrence after neoadjuvant treatment (NAT), in the context of RCB. Methods:The study included 712 patients with high-risk breast cancer in the neoadjuvant I-SPY2 trial. Tumor- informed ctDNA test results at diagnosis were used to stratify patients into ctDNA-negative and ctDNA-positive groups. For this analysis, the ctDNA-positive group was divided into tertiles (low, intermediate, high) based on ctDNA concentration reported as mean tumor molecules per mL [MTM/mL] of plasma. Correlations between MTM/mL at diagnosis and ctDNA dynamics during NAT, residual cancer burden (RCB), and distant recurrence-free survival (DRFS) were examined across all subtypes. Results:In all subtypes, high ctDNA concentration at diagnosis was associated with worse DRFS, whereas low ctDNA concentration or ctDNA-negative status was associated with improved DRFS, even with high tumor burden after NAT (RCB-II/RCB-III). We also found that patients with high ctDNA concentration, regardless of subtype, were less likely to experience early ctDNA clearance; however, those who did had a significantly higher likelihood of achieving a favorable response (RCB-0/RCB-I) than those with late or no ctDNA clearance. Furthermore, across all subtypes, patients with early ctDNA clearance, including those with substantial residual cancer (RCB-II/RCB-III) after NAT, had improved DRFS, irrespective of the ctDNA concentration at diagnosis. Conclusions:Across all subtypes, pathologic response and ctDNA clearance reduce the risk of distant recurrence associated with high ctDNA concentration at diagnosis. ctDNA concentration at diagnosis and ctDNA clearance dynamics during NAT may facilitate the prediction of treatment response and further stratify the risk of metastatic recurrence in non-responders. Trial Registration: NCT01042379.

Importance:Individual breast cancer risk can guide screening initiation, frequency, use of supplemental imaging, and preventive measures to improve breast cancer screening by shifting resources from low-risk women to high-risk women. Objective:To determine whether risk-based breast cancer screening is a feasible alternative to annual mammography. Design, Setting, and Participants:Parallel-group, pragmatic, multicenter randomized clinical trial comparing risk-based (n = 14 212) with annual (n = 14 160) breast cancer screening. Women aged 40 to 74 years without prior diagnoses of breast cancer or ductal carcinoma in situ, or prophylactic bilateral mastectomy, were recruited from all 50 US states from September 2016 to February 2023, with follow-up through September 5, 2025 (median follow-up, 5.1 years). Statistical analysis was conducted between July and November 2025. All study procedures were conducted via an online platform. Women who declined randomization were enrolled in an observational cohort. Interventions:Risk assessment included sequencing of 9 susceptibility genes, polygenic risk score, and the Breast Cancer Surveillance Consortium version 2 model. The risk-based group received 1 of 4 recommendations: (1) highest risk (≥6% 5-year risk, high-penetrance pathogenic variant): alternating mammography and magnetic resonance imaging (MRI) every 6 months and counseling; (2) elevated risk (top 2.5 risk percentile by age): annual mammography and risk-reduction counseling; (3) average risk: biennial mammography; and (4) low risk (aged 40-49 years and <1.3% 5-year risk): no screening until risk is 1.3% or greater or age 50 years. Main Outcomes and Measures:The coprimary outcomes included noninferiority for stage ≥IIB cancers and superiority in reducing biopsy rates. Secondary outcomes included identification of stage ≥IIA cancers, mammogram rates, uptake of prevention strategies in higher risk cohorts, preference for screening group in the observational cohort, ductal carcinoma in situ, MRI, and stage-specific cancer rates. Results:A total of 28 372 women were randomized. The mean (SD) age was 54 (9.6) years and the majority were non-Hispanic White (77%). The rate of stage ≥IIB cancers was noninferior in the risk-based compared with the annual group (risk-based: 30.0 [95% CI, 16.3-43.8] vs annual: 48.0 [95% CI, 30.1-65.5] per 100 000 person-years; rate difference, -18.0 per 100 000 person-years [95% CI, -40.2 to 4.1]). The rate of breast biopsies was not lower in the risk-based group (rate difference, 98.7 per 100 000 person-years [95% CI, -17.9 to 215.3]) despite fewer mammograms (rate difference, -3835.9 [95% CI, -4516.8 to -3154.9]). The cumulative incidence of cancer, biopsy, mammogram, and MRI increased as risk category increased. In the observational cohort, 89% of participants (15 980/18 031) chose risk based. Conclusions:Risk-based breast cancer screening that includes population-based genetic testing safely stratified risk and screening intensity, but did not reduce biopsy rates. Trial Registration:ClinicalTrials.gov Identifier: NCT02620852.

From extrachromosomal DNA to neo-peptides, reprogramming of cancer genomes leads to the emergence of cancer state-specific molecules. Here, we systematically identify and characterize a large repertoire of orphan non-coding RNAs (oncRNAs), a class of cancer-emergent small RNAs, across 32 tumor types. We show that oncRNA binary presence-absence patterns represent a digital molecular barcode that captures cancer type and subtype identities. Importantly, this barcode is partially accessible from the cell-free space as cancer cells secrete a subset of oncRNAs. Leveraging large-scale in vivo genetic screens in xenografted mice, we functionally identify driver oncRNAs in multiple tumor types. In a retrospective study across 192 breast cancer patients, we show that oncRNAs are reliably detected in blood and that changes in cell-free oncRNA burden predict both short-term and long-term clinical outcomes. Together, we establish that oncRNAs have potential roles in tumor progression and clinical utility in liquid biopsies for tumor-naive minimum residual disease monitoring.

Dynamic biomarkers of therapy response are critical for precision oncology but often rely on serial tissue biopsies, which are invasive and not always feasible. In contrast, peripheral blood offers a minimally invasive, dynamic window into the evolving systemic immune landscape. Leveraging this, we performed RNA sequencing on 546 peripheral blood samples from 160 patients with high-risk stage II/III human epidermal growth factor receptor 2 (HER2)-negative breast cancer treated with either chemotherapy alone or in combination with immunotherapy (chemoimmunotherapy). Our analysis uncovered immune correlates of tumor subtype and treatment response. For example, samples from patients with triple-negative breast cancer exhibited elevated T cell receptor clonality and robust immune activation profiles. Among patients receiving chemoimmunotherapy, early responders demonstrated high baseline T cell receptor diversity, followed by rapid clonal expansion and activation of T cells after just one treatment cycle. We developed a multiparametric peripheral immune biomarker that integrated baseline and early on-treatment features to predict response to pembrolizumab, which was successfully validated in an independent cohort of 59 patients with breast cancer treated with neoadjuvant dostarlimab. These findings reveal the potential of blood-based immune monitoring to predict immunotherapy benefit, offering an accessible tool for tailoring treatment strategies in breast cancer.

Hormone therapies are frequently used to reduce breast cancer risk in individuals at increased risk for primary or subsequent disease; however, tissue-level responses to these therapies are heterogeneous and incompletely understood. Background parenchymal enhancement (BPE) on breast magnetic resonance imaging (MRI) provides a non-invasive radiologic readout of breast tissue features associated with endocrine responsiveness and cancer risk. Although BPE is associated with hormonal exposure, a subset of patients with BPE do not show a response to preventive endocrine therapy and therefore may remain at increased breast cancer risk. In this study, we integrated single-nucleus RNA sequencing and spatial transcriptomics to define the determinants of endocrine responsiveness in the setting of BPE. We identify hormone-driven epithelial cells with high levels of estrogen signaling and endocrine responsiveness, together with immune-associated epithelial programs characterized by diminished luminal identity and increased expression of immune-modulatory pathways, including major histocompatibility complex (MHC) class II and CD74. Functional organoid assays validate that these epithelial states exhibit differential sensitivity to tamoxifen and demonstrate that inflammatory signals can induce immune-modulatory epithelial programs. Together, our findings identify hormone signaling and immune programs as key determinants of endocrine responsiveness in breast tissue and provide a biological basis for interpreting radiologic markers relevant to cancer prevention.

Importance:Although adding immune checkpoint inhibitors to neoadjuvant chemotherapy improves outcomes in high-risk early-stage breast cancer, opportunities remain to further enhance response. Dual checkpoint blockade offers a potential strategy to further enhance efficacy. Objective:To evaluate the combination of anti-programmed cell death 1 protein (PD-1) cemiplimab and anti-lymphocyte activation gene 3 (LAG-3) added to neoadjuvant therapy in ERBB2-negative early-stage, high-risk breast cancer. Design, Setting, and Participants:The I-SPY2 (Investigation of Serial Studies to Predict Your Therapeutic Response With Imaging and Molecular Analysis 2) is an ongoing randomized clinical platform trial being conducted at multiple US clinical sites including patients with early-stage (II or III) ERBB2-negative, high-risk breast cancer. Participants, continuously enrolled since 2010, were adaptively randomized from February 2, 2020, to December 9, 2021, to one of several experimental neoadjuvant therapies or control groups based on receptor subtypes defined by hormone receptor (HR), ERBB2 status, and MammaPrint (Agendia Inc) molecular risk, categorized as high (MP1) or ultrahigh (MP2). Data were analyzed from January 1, 2022, to August 5, 2025. Interventions:Both groups received weekly paclitaxel for 12 weeks, then doxorubicin and cyclophosphamide followed by surgery; concomitant with paclitaxel, the intervention group also received 4 doses of cemiplimab and fianlimab (PCF) every 3 weeks. Main Outcomes and Measures:Pathologic complete response (pCR). Treatments graduated when they achieved 85% bayesian probability of success in a subtype-specific phase 3 trial. Pathway-specific biomarkers were assessed for response prediction. Results:A total of 78 participants (mean [SD] age, 47 [39-54] years) were randomized to the intervention group, with 350 participants (mean [SD] age, 48 [39-57] years) randomized to the historical control population. PCF graduated in all clinical signatures, with pCR rates vs control of 44% (95% CI, 34%-53%) vs 21% (95% CI, 17%-25%) in all ERBB2, 53% (95% CI, 39%-67%) vs 29% (95% CI, 22%-36%) in triple-negative, and 36% (95% CI, 23%-49%) vs 14% (95% CI, 9%-19%) in HR-positive and ERBB2-negative disease. Among the total participants, 16 (21%) experienced adrenal insufficiency, including hypophysitis (11% grade 3 or 4), mostly occurring after immunotherapy completion. PCF was found to be highly effective in the subset of patients with immune signature positive status (ImPrint positive). Conclusions and Relevance:In this randomized clinical trial, the combination of PD-1 and anti-LAG-3 inhibition with standard NAC was effective in early-stage ERBB2-negative breast cancer, particularly in patients displaying a positive ImPrint immune signature. These results warrant further definitive trials. Trial Registration:ClinicalTrials.gov Identifier: NCT01042379.

BACKGROUND:The 70-gene signature (70-GS) has been shown to identify women at low-risk of distant recurrence who can safely forgo adjuvant chemotherapy. Incorporating this GS into the well-validated and widely used PREDICT breast cancer model could improve the model's ability to estimate breast cancer prognosis, and thereby further reduce overtreatment and its long-term impact on patients' quality of life. We incorporated the 70-GS into PREDICT-v2.3 and assessed the new PREDICT-GS model's ability to predict 5-year risk of breast cancer death. METHODS:Data from the MINDACT trial (N = 5920) was used to estimate the 70-GS's prognostic effect (coefficient = 0.70), which was then incorporated into PREDICT-v2.3. Netherlands Cancer Registry (NCR) data (N = 3323) was used to assess PREDICT-GS's discrimination (area under curve (AUC)), calibration and clinical utility. RESULTS:Compared to PREDICT-v2.3 (AUC: 0.71 (95 % CI: 0.63-0.79)), PREDICT-GS (AUC: 0.76 (95 % CI: 0.69-0.83)) had better discrimination. Both models tended to overestimate the 5-year risk of breast cancer death in the NCR cohort, but the absolute overestimation was smaller for PREDICT-GS. Regarding clinical utility, only at the 10 % decision threshold did we find modest improvement: four extra patients per 1000 tests were correctly classified as not needing chemotherapy by PREDICT-GS compared to PREDICT-v2.3. CONCLUSION:Extending PREDICT-v2.3 with 70-GS led to modest improvement in its ability to predict 5-year risk of breast cancer death. Future research should focus on assessing the added value of the 70-GS for longer-term prediction of recurrence and death with the incorporation of quality of life in risk prediction tools.

Deep learning models applied to digital histology can predict gene expression signatures (GES) and offer a low-cost, rapidly available alternative to molecular testing at the time of diagnosis. We optimized transformer-based models to infer GES results and applied this approach to pre-treatment H&E-stained biopsies from 1,940 breast cancer patients treated with neoadjuvant chemotherapy in clinical trial and real-world cohorts. The most predictive histology-derived GES for pathologic complete response (pCR) in the I-SPY2 trial was validated in four external cohorts: CALGB 40601, CALGB 40603, a trial of durvalumab plus CT, and standard-of-care CT-treated patients from the University of Chicago. Among HER2-negative patients, a transformer-based model trained using a signature composed of estrogen-regulated genes, proliferation, apoptosis, and interferon response genes predicted pCR with an AUC of 0.794, outperforming models based on clinical features alone (AUC 0.704, p = 0.001), pathologist TIL assessment, and a model trained directly to predict response from I-SPY2 cases. Tertiles of this signature stratify patients into clinically relevant groups with increasing likelihood of complete response, with pCR rates ≥50% in the top tertile regardless of treatment or hormone receptor status. Additional transformer-based signature models predicted response to specific therapies (but not chemotherapy alone), including a HER2 signaling signature in IO-treated patients, and a claudin-low signature in bevacizumab treated patients. In HER2- cohorts with available gene expression data and histology, models trained on expression data performed similarly to digital histology predictions, but the combination of gene expression and histology outperformed histology alone. These findings suggest that histology-based GES provides additive information to RNA sequencing data and can inform precision treatment selection across breast cancer subtypes.

Despite efforts to understand breast cancer biology, metastatic disease remains a clinical challenge. Identifying suppressors of breast cancer progression and mechanisms of transition to more invasive phenotypes could provide game changing therapeutic opportunities. Transcriptional dysregulation is central to all malignancies, highlighted by the extensive reprogramming of regulatory elements that underlie oncogenic programs. Among these, super-enhancers (SEs) stand out due to their enrichment in genes controlling cancer hallmarks. To reveal novel breast cancer dependencies, we integrated the analysis of the SE landscape with master regulator activity inference for a series of breast cancer cell lines. As a result, we identified T-helper-inducing Poxviruses and Zinc-finger (POZ)/Krüppel-like factor (ThPOK, ZBTB7B), a CD4+ cell lineage commitment factor, as a breast cancer master regulator that is recurrently associated with a SE. ThPOK expression is highest in luminal breast cancer but is significantly reduced in the basal subtype. Manipulation of ThPOK levels in cell lines shows that its repressive function restricts breast cancer cells to an epithelial phenotype by suppressing the expression of genes involved in the epithelial-mesenchymal transition (EMT), WNT/β-catenin target genes, and the pro-metastatic TGFβ pathway. Our study reveals ThPOK as a master transcription factor that restricts the acquisition of metastatic features in breast cancer cells.

We evaluate therapy-induced molecular heterogeneity in longitudinal samples from high-risk, hormone-receptor positive/HER2-negative breast cancer patients with residual tumor after neoadjuvant chemotherapy from the Penelope-B trial (NCT01864746; EudraCT 2013-001040-62). Intrinsic subtypes are prognostic in pre-therapeutic (Tx) samples (n = 629, p < 0.0001) and post-Tx residual tumors (n = 782, p < 0.0001). After neoadjuvant chemotherapy, a shift of intrinsic subtypes is observed from pre-Tx luminal (Lum) B to post-Tx LumA, with reverse transition back to LumB in metastases. In a combined analysis of 540 paired pre-Tx and post-Tx samples, we identify five adaptive clusters (AC-1-5) based on transcriptomic changes before and after neoadjuvant chemotherapy. These AC-subtypes are prognostic beyond classical intrinsic subtyping, categorizing patients into groups with excellent prognosis (AC-1 and AC-2), poor prognosis (AC-3 and AC-4), and very poor prognosis (AC-5, enriched for basal-like subtype). Our analysis provides a basis for an extended molecular classification of breast cancer patients and improved identification of high-risk patient populations.

Motivation: Previously, we developed MRI-based models for the prediction of pathologic complete response (pCR) using an initial cohort of 990 patients enrolled in I-SPY. The purpose of this study is to validate the performance of the MRI model using an independent patient cohort from I-SPY 2. Goal(s): The goal is to test the robustness of the MRI-based models. Approach: Area under the receiver operating characteristic curve (AUC), PPV, and sensitivity for pCR prediction was used to evaluate performance in the subsequent cohort. Results: Overall, the sensitivity and PPV were 72% and 57%, slightly higher than the values evaluated in the initial 990 cohort. Impact: This is the first study of MRI-based predictive models that were developed and validated using two separate large cohorts from a multicenter neoadjuvant chemotherapy clinical trial.

Residual Cancer Burden (RCB) after neoadjuvant chemotherapy (NAC) is validated to predict event-free survival (EFS) in breast cancer but has not been studied for invasive lobular carcinoma (ILC). We studied patient-level data from a pooled cohort across 12 institutions. Associations between RCB index, class, and EFS were assessed in ILC and non-ILC with mixed effect Cox models and multivariable analyses. Recursive partitioning was used in an exploratory model to stratify prognosis by RCB components. Of 5106 patients, the diagnosis was ILC in 216 and non-ILC in 4890. Increased RCB index was associated with worse EFS in both ILC and non-ILC ( p = 0.002 and p < 0.001, respectively) and remained prognostic when stratified by receptor subtype and adjusted for age, grade, T category, and nodal status. Recursive partitioning demonstrated residual invasive cancer cellularity as most prognostic in ILC. These results underscore the utility of RCB for evaluating NAC response in those with ILC.

Individual breast cancer risk can guide screening initiation, frequency, use of supplemental imaging, and preventive measures to improve breast cancer screening by shifting resources from low-risk women to high-risk women. To determine whether risk-based breast cancer screening is a feasible alternative to annual mammography. Parallel-group, pragmatic, multicenter randomized clinical trial comparing risk-based (n = 14 212) with annual (n = 14 160) breast cancer screening. Women aged 40 to 74 years without prior diagnoses of breast cancer or ductal carcinoma in situ, or prophylactic bilateral mastectomy, were recruited from all 50 US states from September 2016 to February 2023, with follow-up through September 5, 2025 (median follow-up, 5.1 years). Statistical analysis was conducted between July and November 2025. All study procedures were conducted via an online platform. Women who declined randomization were enrolled in an observational cohort. Risk assessment included sequencing of 9 susceptibility genes, polygenic risk score, and the Breast Cancer Surveillance Consortium version 2 model. The risk-based group received 1 of 4 recommendations: (1) highest risk (≥6% 5-year risk, high-penetrance pathogenic variant): alternating mammography and magnetic resonance imaging (MRI) every 6 months and counseling; (2) elevated risk (top 2.5 risk percentile by age): annual mammography and risk-reduction counseling; (3) average risk: biennial mammography; and (4) low risk (aged 40-49 years and <1.3% 5-year risk): no screening until risk is 1.3% or greater or age 50 years. The coprimary outcomes included noninferiority for stage ≥IIB cancers and superiority in reducing biopsy rates. Secondary outcomes included identification of stage ≥IIA cancers, mammogram rates, uptake of prevention strategies in higher risk cohorts, preference for screening group in the observational cohort, ductal carcinoma in situ, MRI, and stage-specific cancer rates. A total of 28 372 women were randomized. The mean (SD) age was 54 (9.6) years and the majority were non-Hispanic White (77%). The rate of stage ≥IIB cancers was noninferior in the risk-based compared with the annual group (risk-based: 30.0 [95% CI, 16.3-43.8] vs annual: 48.0 [95% CI, 30.1-65.5] per 100 000 person-years; rate difference, −18.0 per 100 000 person-years [95% CI, −40.2 to 4.1]). The rate of breast biopsies was not lower in the risk-based group (rate difference, 98.7 per 100 000 person-years [95% CI, −17.9 to 215.3]) despite fewer mammograms (rate difference, −3835.9 [95% CI, −4516.8 to −3154.9]). The cumulative incidence of cancer, biopsy, mammogram, and MRI increased as risk category increased. In the observational cohort, 89% of participants (15 980/18 031) chose risk based. Risk-based breast cancer screening that includes population-based genetic testing safely stratified risk and screening intensity, but did not reduce biopsy rates. ClinicalTrials.gov Identifier: NCT02620852

PURPOSE:The MammaPrint (MP) prognostic assay categorizes breast cancers into high- and low-risk subgroups, and the high-risk group can be further subdivided into high-1 (MP-H1), and very high-risk high-2 (MP-H2). The aim of this analysis was to assess clinical and molecular differences between the hormone receptor-positive (HR+)/HER2-negative MP-H1, -H2, and triple-negative (TN) MP-H1 and -H2 cancers. EXPERIMENTAL DESIGN:Pretreatment gene expression data from 742 HER2-negative breast cancers enrolled in the I-SPY2 neoadjuvant trial were used. Prognostic risk categories were assigned using the MP assay. Transcriptional similarities across the four receptor and prognostic groups were assessed using principal component analyses and by identifying differentially expressed genes. We also examined pathologic complete response rates and event-free survivals by risk group. RESULTS:Principal component analysis showed that HR+/MP-H2 tumors clustered with TN/MP-H2 cancers. Only 125 genes showed differential expression between the HR+/MP-H2 and TN/MP-H2 cancers, whereas 1,465 genes were differentially expressed between HR+/MP-H2 and -H1. Gene set analysis revealed similarly high expression of cell cycle, DNA repair, and immune infiltration-related pathways in HR+/MP-H2 and TN/MP-H2 cancers. HR+/MP-H2 cancers also showed low estrogen receptor-related gene expression. Pathologic complete response rates were similarly high in TN/MP-H2 and HR+/MP-H2 cancers (42% vs. 30.5%; P = 0.11), and MP-H2 cancers with residual cancer had similarly poor event-free survival regardless of estrogen receptor status. CONCLUSIONS:In conclusion, HR+/MP-H2 cancers closely resemble TN breast cancers in transcriptional and clinical features and benefit from similar treatment strategies.