Citations

Abstract Background For many evidence criteria within v3.0 of the ACMG/AMP guidelines, methodologies have been developed to empower their use outside the stipulated evidence strengths. However, no such methodology has been established for case-control data (PS4). With the release of large-scale unselected case-control datasets and expansion of nationally-collected laboratory datasets enriched for pathogenic variant carriers, there is potential to combine datasets across ascertainment contexts in a more quantitative manner using novel likelihood ratio tools. Methods Using our published PS4-LR-Calculator, we calculated a combined log likelihood ratio (PS4-LLR) across five datasets (three unselected, and two enriched), and estimated enrichment of pathogenic variants in clinically-ascertained laboratory data using truncating variant prevalence. Results Data were combined for 10,817 missense variants from 325,345 female breast cancer patients and 671,006 controls of Western European ancestry for five breast cancer susceptibility genes ( BRCA1, BRCA2, PALB2, ATM, CHEK2 ). A combined LLR was produced for 4,690 missense variants; 927 variants received evidence towards pathogenicity (LLR≥ 1), and 3,242 received evidence towards benignity (LLR≤ -1). Conclusion This flexible, variant-level methodology combines nationally-collected ‘enriched’ datasets with unselected case-control cohorts, expanding the available information for case-control analysis, boosting power, enabling exploration of atypical penetrance and empowering variant classification.

Abstract Inherited susceptibility plays a critical role in prostate cancer (PCa) risk. Using large biobank and case-control datasets, we evaluated the contribution of both common variants and rare germline pathogenic variants (PVs) to overall PCa risk. The Prostate Cancer Exome Sequencing Consortium currently includes 427,388 male participants (51,452 PCa cases and 375,936 controls) with whole-exome sequencing data from ten biobanks and studies: UK Biobank (14,669 cases/195,600 controls), All of Us Research Program (7,577/75,226), African Ancestry Prostate Cancer Consortium (7,176/4,675), Mayo Clinic Biobank (6,031/15,084), Mass General Brigham Biobank (3,393/14,095), Geisinger’s MyCode Community Health Initiative (3,026/15,130), UCLA ATLAS Precision Medicine Biobank (2,850/16,904), Penn Medicine Biobank (2,598/16,255), Colorado Center for Personalized Medicine (2,269/13,399), and Malmo Diet and Cancer (1,863/9,568). Based on self-reported race/ethnicity and estimated genetic ancestry, the cases comprise approximately 79% European, 18% African, and 3% other ancestry populations. Single-variant association analyses tested all variants on chromosomes 1-22 and X with a minor allele count ≥ 5. In gene-based analyses, PVs were defined as rare variants (minor allele frequency [MAF] < 1% in controls) that had either a Variant Effect Predictor (VEP) impact score of “high” or a pathogenic or likely pathogenic ClinVar classification. Associations were estimated using Firth logistic regression, adjusting for age and the top ten genetic principal components. Results from individual studies were combined using fixed-effect meta-analysis. In single-variant association analyses, 496 variants reached genome-wide significance (p<5×10-8; MAF>0.02%). Among these, 458 (92%) variants mapped to previously known risk regions, including three rare PVs in HOXB13 (rs138213197), CHEK2 (rs555607708), and FAM111A (rs533676902). Characterization of the remaining 38 variants is ongoing. Gene-based analyses identified significant associations (p<2.4×10-6) for eight genes: HOXB13 (OR=3.7, 95% CI=3.3-4.2), BRCA2 (OR=2.0, 95% CI=1.7-2.3), CHEK2 (OR=1.6, 95% CI=1.5-1.8), ATM (OR=1.6, 95% CI=1.4-1.9), FAM111A (OR=1.4, 95% CI=1.3-1.5), BIK (OR=1.4, 95% CI=1.2-1.6), SAMHD1 (OR=2.1, 95% CI=1.6-2.7), and SMOC2 (OR=3.2, 95% CI=2.0-5.1). All genes except SMOC2 have been previously implicated in PCa susceptibility. Among cancer predisposition and DNA repair genes, nominal associations were also observed for XRCC2 (OR=1.6, 95% CIs=1.2-2.3) and BRCA1 (OR=1.2, 95% CI=1.0-1.4), whereas the association was not significant for PALB2 (OR=1.2, 95% CI=0.9-1.5). These findings reinforce the role of rare germline PVs, particularly in cancer predisposition and DNA repair genes, in PCa susceptibility. As additional studies are incorporated into the Consortium, we expect this work to provide a more comprehensive characterization of the genetic architecture of PCa. Citation Format: Yifan Zhang, Shuyan Cheng, Nicholas Boddicker, Matthew Lebo, Alexander S. Berry, Roni Haas, Ryan Hausler, Tokhir Dadaev, Heena Desai, Alex A. Rodriguez, Ravi K. Madduri, Andrew Hill, Xin Sheng, Susan M. Gundell, Mine Cicek, Penn Medicine Biobank, Olle Melander, Chris R. Gignoux, Isla P. Garraway, Bogdan Pasaniuc, Paul C. Boutros, Matt Oetjens, Adam S. Kibel, Robert J. Klein, Zsofia Kote-Jarai, Fergus J. Couch, Kara N. Maxwell, Burcu F. Darst, David V. Conti, Christopher A. Haiman, Fei Chen. Genetic risk of prostate cancer: Insights from the Prostate Cancer Sequencing Consortium [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2026; Part 2 (Late-Breaking, Clinical Trial, and Invited Abstracts); 2026 Apr 17-22; San Diego, CA. Philadelphia (PA): AACR; Cancer Res 2026;86(8_Suppl):Abstract nr LB390.

Abstract Artificial intelligence (AI)-scores estimated from digital mammograms predict future breast cancer (BC) risk. MIRAI is a deep learning BC risk model that provides a continuous 5-year risk of overall BC from four screening full field digital mammograms. Common germline genetic variation in the form of a polygenic risk score (BC-PRS) is associated with increased BC risk and may improve MIRAI’s 5-year risk prediction. Our goal was to develop an updated MIRAI 5-year risk model that incorporates the BC-PRS (MIRAI+PRS) and to evaluate its discriminatory accuracy and calibration for both overall and invasive BC compared to MIRAI alone. We developed the MIRAI+PRS model by multiplying each woman’s 5-year MIRAI risk estimate by their relative risk based on their BC-PRS relative to the population mean. We evaluated the models within the Mayo Clinic Biobank mammography cohort, comprised of 12,307 women without a prior history of BC; 176 invasive and 250 overall BC were diagnosed within 5 years. MIRAI was estimated on screening mammograms closest to enrollment but at least 6 months prior to BC. Discriminatory accuracy, assessed by C-statistic, was high and similar for MIRAI+PRS vs. MIRAI models, for overall BC and invasive BC (Table). Calibration assessed by observed to expected (O/E) ratios was also similar for MIRAI+PRS compared to MIRAI predictions for BC outcomes (Table), although there was improvement in decile-specific O/E ratios across the lowest risk deciles (<1.67%) for MIRAI+PRS. Calibration for invasive cancer was poor for MIRAI with or without PRS. In summary, the MIRAI+PRS risk model did not result in significant difference of discriminatory accuracy or overall calibration compared to the MIRAI model, but there was evidence for improved calibration for women with 5-year risk below 1.67%. For invasive BC, the model had poor calibration regardless of whether PRS was included, underscoring the importance of training AI models for BC outcomes that are associated with a clinical intervention. Citation Format: Christopher G. Scott, Peter Kraft, Imon Banerjee, Ramon Correa Medero, Aaron D. Norman, Fergus J. Couch, Karla Kerlikowske, Stacey J. Winham, Celine M. Vachon. Development and evaluation of a MIRAI 5-year risk model with a breast cancer polygenic risk score [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2026; Part 1 (Regular Abstracts); 2026 Apr 17-22; San Diego, CA. Philadelphia (PA): AACR; Cancer Res 2026;86(7 Suppl):Abstract nr 2779.

Abstract Background: MHC class II molecules are normally restricted to professional antigen-presenting cells. Prior TNBC studies have focused on immune-compartment MHC-II. Using spatial transcriptomics, we recently identified aberrant tumor-cell MHC-II expression. Here, we characterized the immune architecture surrounding HLA-DRA-expressing tumor cells. Methods: High-plex single-cell spatial transcriptomics (CosMxTM SMI) was performed on treatment-naïve TNBC (Mayo TMA, n=65) and two neoadjuvant pembrolizumab cohorts (Mayo n=8; Emory n=4). Spatial neighborhoods were mapped relative to HLA-DRA-high tumor cells. Differential expression and adaptive immune gene-set scores were evaluated. Clinical relevance was assessed using FinXX (n=114), I-SPY2 (n=364), and Caris CODEai (n=3,662). Results: HLA-DRA-high tumor regions showed significant enrichment of adaptive immune subsets within ≤50 μm, including B cells, CD8+ T cells, NK cells, macrophages, and plasmacytoid dendritic cells. In pembrolizumab-treated cohorts, responders exhibited higher proportions of HLA-DRA-high tumor cells and greater numbers of B cells, plasmablasts, CD4+ and CD8+ T cells, and macrophages near tumor cells. Responders’ tumors showed upregulation of antigen-presentation machinery, B-cell/plasma-cell programs, CXCL13, and NOTCH3/DLL1. Across I-SPY2 and FinXX, higher HLA-DRA correlated with pathologic complete response and improved survival. In CODEai TNBC, high HLA-DRA expression correlated with higher CXCL13 (median TPM 7.5 vs. 1.1, q<0.05) and longer overall survival (24.2 vs. 18.5 months, HR 0.77, 95% CI 0.71-0.83, p<0.0001). This association was TNBC-specific. High CXCL13 also predicted improved survival (26.6 vs. 16.5 months, HR 0.64, 95% CI 0.59-0.69, p<0.0001). Conclusions: Tumor-cell HLA-DRA expression defines a highly organized adaptive immune niche enriched with B cells and activated T-cell populations in close proximity. Adaptive immune programs—including enhanced antigen presentation and CXCL13-mediated B-cell/plasma-cell pathways—consistently associate with improved outcomes across TNBC cohorts treated with chemotherapy and immune checkpoint blockade. Aberrant tumor-cell MHC-II expression may contribute to antitumor immunity and warrants further investigation as a potential therapeutic target. Citation Format: Yi Liu, Sachin Kumar Deshmukh, Thiti Susiriwatananont, Panuch Eiamprapaporn, Bogang Wu, Heikki Joensuu, Roberto A. Leon-Ferre, David Zahrieh, Judy C. Boughey, James Newell Ingle, Fergus J. Couch, Sharon Wu, Shipra Gandhi, Maryam Lustberg, George W. Sledge, Matthew P. Goetz, Keith L. Knutson, E Aubrey Thompson, Jodi Carter, Saranya Chumsri. Spatially resolved tumor-cell MHC class II shapes adaptive immunity and therapeutic response in triple-negative breast cancer [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2026; Part 1 (Regular Abstracts); 2026 Apr 17-22; San Diego, CA. Philadelphia (PA): AACR; Cancer Res 2026;86(7 Suppl):Abstract nr 1200.

ABSTRACT The normal adult male breast has not been characterized at single-cell resolution, leaving the cellular basis of male breast cancer (MBC) biology undefined. Here we present an integrated single-cell RNA sequencing atlas of the adult human breast comprising 174,471 cells from 17 donors (3 male, 14 female), including 18,117 male-derived cells. This revealed that the male breast retains all three epithelial populations, basal (BC), luminal progenitor (LP), and luminal committed cells (LC), but with an increase in LC at the expense of BC and LP across all three male donors. Male LC were distinguished from female by elevated ESR1 and PGR mRNA, enrichment of RNA processing and ribosome biogenesis programs, reduced inflammatory cytokine and growth factor signaling, elevated estradiol gene set enrichment scores, and higher inferred activity of developmental patterning transcription factors. This pattern was observed across differential expression, gene ontology, ligand profiling, and regulon-based analyses, and was not restricted to sex chromosome-linked gene expression. This is consistent with the near-universal estrogen receptor (ER) positivity that characterizes MBC clinically. This atlas provides the first cellular and transcriptional reference for the normal male breast and a resource for investigating sex differences in mammary biology, germline susceptibility variant interpretation, and modeling breast malignancies.

Abstract Background Artificial intelligence (AI)–based, mammography breast cancer risk prediction models show improved discriminatory accuracy relative to clinical risk models. However, data on their calibration are limited. This study compared model performance of 3 clinical breast cancer risk models: Gail, Tyrer–Cuzick v8, and Breast Cancer Surveillance Consortium v3 with the MIRAI AI-risk model. Methods Digital mammograms were ascertained from a screening mammography cohort of 12 308 women within the Mayo Clinic Biobank with 250 incident breast cancers (176 invasive) within 5 years. We predicted 5-year breast cancer risk, estimated discriminatory accuracy (C-index), and calibration (observed-to-expected ratio) of both overall and invasive breast cancer and compared estimates using bootstrapping approaches. Results MIRAI demonstrated similar or improved discriminatory accuracy of overall breast cancer (C-index = 0.71, 95% confidence interval [CI] = 0.68 to 0.74) and invasive breast cancer (C-index = 0.71, 95% CI = 0.67 to 0.75) compared with clinical models (overall breast cancer: C-index = 0.59-0.68; invasive breast cancer: C-index = 0.60-0.68). MIRAI’s calibration for risk of overall breast cancer (observed-to-expected ratio = 0.96, 95% CI = 0.85 to 1.08) was improved compared with Gail (observed-to-expected ratio = 1.22, 95% CI = 1.07 to 1.38) and Breast Cancer Surveillance Consortium (observed-to-expected ratio = 1.38, 95% CI = 1.22 to 1.56) but similar to TC with volumetric percent density and polygenic risk score (observed-to-expected ratio = 0.99, 95% CI = 0.87 to 1.13). However, for low-risk women (approximately 50%), MIRAI overestimated risk of overall breast cancer. MIRAI also overestimated risk of invasive breast cancer across the risk spectrum (observed-to-expected ratio = 0.68, 95% CI = 0.58 to 0.78), whereas clinical models had good calibration (observed-to-expected ratio = 0.86–0.99). Conclusion MIRAI demonstrated stronger discriminatory accuracy than clinical models for 5-year overall and invasive breast cancer risk prediction but overestimated risk for both breast cancer endpoints. AI-based risk models should consider discriminatory accuracy and calibration for invasive cancer before implementation.

PURPOSE To compare mortality risk among long-term survivors of high-grade serous ovarian cancer (HGSOC) with that of the general US population and to examine how mortality and recurrence trends have evolved under changing treatment approaches. MATERIALS AND METHODS This is a retrospective cohort study including patients with histologically confirmed HGSOC treated at a single referral cancer center from 1991 to 2022. Mortality risk, expressed as standardized mortality ratios (SMRs, ratio of observed deaths in the study population to expected deaths in an age-matched US population), was assessed at diagnosis and yearly on patients without events (disease persistence, recurrence, or death from any cause). RESULTS A total of 2,074 consecutive patients were included. The median follow-up was 12.5 years (IQR, 11.7-13.8). Most patients were stage III (1,396, 69.2%), 1,299 (62.8%) underwent primary cytoreductive surgery, and 1,688 (87.4%) received platinum-taxane as first-line therapy. At diagnosis, the mortality rate was 7.40 times higher than that in the general population (95% CI, 7.04 to 7.77). By 7 event-free years, the 95% CI for the SMR included 1 for the first time, and at 10 years, the SMR was 1.05 (95% CI, 0.72 to 1.49), indicating no statistically significant excess mortality compared with the general population. In those event-free at 10 years, the 5-year cumulative incidence of ovarian cancer–related deaths (8.2% [95% CI, 4.2 to 16.0]) was comparable with that of non–ovarian cancer–related deaths (6.3% [95% CI, 2.9 to 13.8]). CONCLUSION In this cohort, mortality risk steadily declined for patients who remained event-free, approaching that of the general population by 7 years after diagnosis. These results emphasize the importance of individualized, long-term follow-up to address both recurrence risk and survivorship needs.

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.

PURPOSE BRCA carriers face high risks of developing both breast and ovarian/fallopian tube cancers (hereafter referred to as ovarian ). Among BRCA carriers with ovarian cancer, it is not clear whether the risk of breast cancer is sufficiently high that risk-reducing mastectomy should be offered. This study aimed to assess the risk of breast cancer BRCA carriers after a diagnosis of ovarian cancer. METHODS We included women with a pathogenic/likely pathogenic variant in BRCA1 or BRCA2 , a diagnosis of ovarian cancer, and no other cancer history and no risk-reducing bilateral mastectomy. Women were followed for incident breast cancer from the date of ovarian cancer diagnosis or the date of baseline questionnaire, whichever came last. The 5-, 10-, and 15-year cumulative risks of breast cancer were compared for women with ovarian cancer and an age-matched set of control women without ovarian cancer. RESULTS A total of 960 participants with ovarian cancer were identified (814 BRCA1 and 146 BRCA2 carriers). After a mean follow-up of 4.9 years, 41 women (4.3%) developed breast cancer, at a mean age at diagnosis of 57.5 years (range, 39-74). Actuarial cumulative breast cancer risks after ovarian cancer were 4.4%, 8.9%, and 11.5% at 5, 10, and 15 years, respectively. Only three breast cancer–related deaths occurred. Among 741 age-matched BRCA carriers without ovarian cancer, actuarial cumulative risks of breast cancer were 20.9%, 38.6%, and 47.2% at 5, 10, and 15 years, respectively. The hazard ratio for breast cancer, after an ovarian cancer diagnosis, compared with no ovarian cancer, was 0.18 ([95% CI, 0.12 to 0.27]; P < .0001). CONCLUSION After ovarian cancer, BRCA carriers have a relatively low risk of breast cancer. Risk-reducing mastectomy should not be recommended routinely, but might be considered for long-term survivors. Magnetic resonance imaging surveillance and/or mammography is a realistic alternative.

Abstract Introduction: Patients with ER+/HER2- breast cancer associated with germline pathogenic variants (PVs) in BRCA1, BRCA2 and CHEK2 exhibit distinct clinicopathological features compared to sporadic cases. However, the optimal endocrine therapy strategies for these patients remain undefined. The Breast Cancer Index (BCI) is a validated gene expression-based signature that stratifies patients based on the risk of overall (0-10 years) and late (post-5 years) distant recurrence and predicts the likelihood of benefit from extended endocrine therapy (EET) in early-stage, HR+ breast cancer. Here we utilize BCI to investigate endocrine sensitivity and the predicted EET benefit in a cohort of ER+/HER2- breast cancer patients with BRCA1, BRCA2 and CHEK2 PVs. Methods: BRCA1, BRCA2 and CHEK2 PV carriers with ER+/HER2- breast cancer diagnosed at Mayo Clinic between 1996 and 2024 at Mayo Clinic were identified from a registry tracking germline test result. For comparison, we utilized the BCI Registry, a prospective, multi-institutional tumor registry of patients with early-stage ER+/HER2- breast cancer undergoing BCI testing, regardless of germline PV status. The analysis was restricted to women with invasive, early-stage locoregional breast cancer and available pre-treatment tumor specimens. BCI testing was performed by RT-PCR blinded to clinical outcome. BCI prognostic and BCI predictive categories were compared between the Mayo Clinic germline PV cohort and the BCI Registry using descriptive statistics and Fisher’s exact test. Results: The final analysis included 123 PVcarriers (BRCA1: 36; BRCA2: 40; CHEK2: 47) from the MayoClinic cohort and 2,563 women from the BCI Registry. The proportion of patientsdiagnosed before age 50 was higher among PV carriers—52.8% in BRCA1,50.0% in BRCA2, and 21.3% in CHEK2—compared to 7.0% in the BCIRegistry. PV carriers were also more likely to be premenopausal andnode-positive at diagnosis. Based on BCI score, a significant higher proportionof BRCA1 (75.0%, p=0.002) andBRCA2 (70.0%, p=0.01) PV carriers wereclassified as high risk, compared to 48.5% of patients in the BCI Registry, butno significant difference was seen for CHEK2 PV carriers (46.8%,p=0.88). A significantly higher proportion of BRCA1 PV carriers werepredicted to benefit from EET compared to the BCI Registry cohort (77.8% vs.33.9%, p&lt;0.001), whereas no significant differences in BCI Predictivecategories were observed for BRCA2 or CHEK2 PV carriers. Overall,compared to breast cancer cases in the BCI registry, a significant differencein the distribution of the combined BCI prognostic and predictive categorieswas noted for BRCA1 (p&lt;0.001)and BRCA2 (p=0.02) PV carriers,but not for CHEK2 PV carriers (p=0.66). In particular, BRCA1 andBRCA2 PV carriers were enriched for high-risk tumors with a high likelihoodof EET benefit (66.7% and 37.5%, respectively vs 25.1%, p&lt;0.001 and p=0.02),whereas only a small non-significant increase was observed for CHEK2 PVcarriers (29.8% vs 25.1%, p=0.66) compared to breast cancer cases in the BCIRegistry. Conclusions: Genomicclassification by BCI reveals distinct patterns of endocrine sensitivity andpredicted EET benefit among germline PV carriers, particularly in BRCA1and BRCA2 PV carriers. These findings warrant further investigation andmay inform personalized endocrine therapy strategies for women with germlinePV-associated ER+/HER2- breast cancer. Citation Format: S. Yadav, B. O'Neal, S. Yasir, N. Siuliukina, A. Karki, Y. Zhang, A. K. Anderson, K. Treuner, M. P. Goetz, F. J. Couch. Endocrine Sensitivity and Predicted Benefit of Extended Endocrine Therapy based on Breast Cancer Index (BCI) in BRCA1, BRCA2 and CHEK2 Pathogenic Variant Carriers with ER+/HER2- Breast Cancer [abstract]. In: Proceedings of the San Antonio Breast Cancer Symposium 2025; 2025 Dec 9-12; San Antonio, TX. Philadelphia (PA): AACR; Clin Cancer Res 2026;32(4 Suppl):Abstract nr PS2-08-25.