Polygenic risk score (PRS) models effectively predict breast cancer (BC) risk in European-ancestry women but have limited accuracy for African-ancestry women, particularly for aggressive subtypes. We developed PRS models for overall BC, estrogen receptor (ER)-positive, ER-negative and triple-negative BC (TNBC) in African-ancestry women using data from the African Ancestry Breast Cancer Genetics consortium (17,391 cases and 18,800 controls). We applied several PRS methods and integrated information across ancestries and BC subtypes. The best models for overall, ER-positive, ER-negative and TNBC showed an area under the receiving operating curve of 0.612, 0.621, 0.611 and 0.639, respectively, and maintained predictive accuracy in external validation studies with area under the receiving operating curves of 0.612, 0.640, 0.605 and 0.652. We further introduce a parsimonious 162-variant PRS for TNBC with comparable accuracy (0.626). These findings demonstrate markedly improved PRS accuracy for BC risk prediction in African-ancestry women. Using these PRS models for screening will help promote more equitable cancer prevention efforts.

Genome-wide association studies (GWAS) have identified over 200 genetic risk loci for breast cancer, yet the target genes in these loci remain largely unknown. To address this knowledge gap, we conducted a series of multi-ancestry transcriptome-wide association studies (TWAS) to discover potential breast cancer susceptibility genes. We developed and validated ancestry-specific genetic models to predict levels of gene expression, alternative splicing, and 3' UTR alternative polyadenylation, using genomic and transcriptomic data from normal breast tissue samples of 652 females of African, Asian, or European ancestry. These models were then applied to GWAS data of 178,534 breast cancer cases and 248,300 controls from these ancestry groups for association analyses. We identified 290 genes associated with breast cancer risk, including 103 previously unreported in TWAS and 46 located at least 500Kb away from any previously identified risk variants. Among them, 39 genes exhibited distinct associations with breast cancer risk by estrogen receptor status. The identified genes were enriched in pathways related to homologous recombination, apoptosis, p53, PI3K/AKT/mTOR, estrogen, and IL-2/STAT5 signaling. Single-cell RNA sequencing and in vitro experiment data provided additional functional evidence for 169 genes. Our study uncovered large numbers of candidate breast cancer susceptibility genes and contributed valuable insights into the genetics and biology of this common cancer.

Compared to European American women, African American women are more likely to be diagnosed with triple-negative breast cancer (TNBC). This difference may be partially due to genetic factors. This study aims to investigate associations of African ancestry and risk variants with TNBC among African American women. We used data from 2,335 TNBC cases, 8,159 estrogen receptor (ER)-positive cases, and 9,814 controls included in the African-ancestry Breast Cancer Genetics (AABCG) Consortium. The proportion of African ancestry (

Supplementary Table S1. Summary of clinical information. Supplementary Table S2. Summary of sample characteristics. Supplementary Table S3. Summary of whole-genome sequencing and fragment analyses. Supplementary Table S4. Summary of protein analyses. Supplementary Table S5. Summary of machine learning models and scores. Supplementary Table S6. Summary of performance for ovarian cancer detection.

Genome‐wide association studies (GWAS) have identified more than 200 risk loci for breast cancer. However, target genes and their encoded proteins in these loci remain largely unknown. In this study, we utilized genetic prediction models for 1349 circulating proteins derived from individuals of African ( n = 1871) and European ( n = 7213) ancestry to investigate genetically predicted protein levels in association with breast cancer risk among females of African ( n = 40,138), Asian ( n = 137,677), and European ( n = 247,173) ancestry. We identified 51 blood protein biomarkers associated with breast cancer risk, overall or by subtypes, at a false discovery rate (FDR) < 0.05, including 27 proteins encoded by genes located at least 1 Mb away from any of the known risk loci identified in GWAS. Of them, 32 proteins showed significant associations with breast cancer risk at the Bonferroni‐corrected significance level ( p < 2.45 × 10 −4 ). Of the 24 proteins located at GWAS‐identified risk loci, associations for 14 proteins were significantly attenuated after adjustment for the index risk variant of each respective locus, suggesting that these proteins may be target proteins for the risk loci. Encoding gene expression levels in normal breast tissue could be genetically predicted for 23 of the 51 identified proteins, and 13 encoding genes were associated with breast cancer risk in the same direction ( p < .05). Our study identified potential protein targets of GWAS risk loci and biomarkers for breast cancer risk and provided additional insights into breast cancer genetics and etiology.

Supplementary Figure S1. Evaluation of screening model DELFI-Pro scores and comorbidities in individuals without cancer. Supplementary Figure S2. DELFI-Pro score evaluation in available clinical characteristics of patients with cancer. Supplementary Figure S3. Stability analysis across fold repeats and collection source. Supplementary Figure S4. Detection of ovarian cancer subtypes using DELFI-Pro screening model. Supplementary Figure S5. ROC analyses of asymptomatic individuals in the using screening or diagnostic models in the Discovery Cohort. Supplementary Figure S6. Performance of ichorCNA and median cfDNA fragment lengths in the Discovery Cohorts. Supplementary Figure S7. Detection of ovarian cancer subtypes using DELFI-Pro at high specificity. Supplementary Figure S8. Genome-wide fragmentation profiles are altered in patients in the Validation Cohort with ovarian cancer. Supplementary Figure S9. Analyses of chromosomal changes in Discovery and Validation cohorts. Supplementary Figure S10. Performance of DELFI-Pro for detection of ovarian cancer in the Validation Cohort. Supplementary Figure S11. Correlation of rank ordered DELFI-Pro scores for the Screening and Diagnostic models. Supplementary Figure S12. Assessment of DELFI-Pro in women with benign lesions. Supplementary Figure S13. Performance of DELFI-Pro for distinguishing ovarian cancer from benign masses. Supplementary Figure S14. Performance of DELFI-Pro for distinguishing ovarian cancer subtypes from benign masses. Supplementary Figure S15. Performance of DELFI-Pro for distinguishing ovarian cancer subtypes from benign masses in Validation Cohort. Supplementary Figure S16. Evaluation of overall tumor burden using the sum of reported lesion diameters. Supplementary Figure S17. Evaluation of CA1-25 and HE4 blood concentrations measured at different centers.

Immunosuppressive cells, such as myeloid-derived suppressor cells (MDSCs), prevent tumor infiltrating-lymphocytes (TILs) from mounting an anti-tumoral immune response in solid tumors, including breast cancer. Even though genetic factors, such as race and ethnicity, are suspected to contribute to immunosuppression, social determinants of health—non-medical factors including economic stability, education access and quality, health care access and quality, neighborhood and built environment, and social and community context—may also contribute to observed differences in tumor growth and therapeutic response across different racial and ethnic groups. We hypothesized that the combined effects of Hispanic/Latina origin and higher social deprivation contribute to an increased proportion of immunosuppressive cells in circulation. In this study, our IRB-approved cohort consisted of 131 patients with varied histologic subtypes and stages of breast cancer including 71 Hispanic/Latina (H/L), 37 Non-Hispanic White (NHW), 15 Asian, and 8 African American/Black (AAB) individuals. To assess the proportion of immunosuppressive cells in circulation, we performed flow cytometry analysis of peripheral blood mononuclear cell (PBMC) samples obtained from our cohort. To study social determinants of health across racial and ethnic groups, we utilized the Social Deprivation Index (SDI) as a proxy using each patient’s ZIP Code and stratified SDI values into quartiles (where lower quartile indicates less social deprivation). Preliminary data suggested that H/L groups have a greater percentage of circulating suppressive granulocytic (G)-MDSCs (0.31% vs. 0.14%) compared to NHW groups. A similar trend was noted for regulatory T cells with 1.51% in H/L and 1.22% in NHW. This finding also appeared to continue when further adding social deprivation, where groups with both higher social deprivation and H/L origin demonstrated a trending increase in G-MDSCs and monocytic (M)-MDSCs. Preliminary differences in circulating immune cells in in the H/L group could be driven by genetic differences in one or more of the ancestral components and/or by specific exposures that may be more prevalent among H/L women, such as stress, environmental exposures, or others. We are investigating additional data from patient surveys to further inform the observed correlations between SDOH and immune cell differences. Studies are also underway to examine differences in intratumoral immune suppressive cell types in a subset of 13 tumor specimens from 3 H/L and 9 NHW patients with early-stage hormone receptor positive breast cancer. These findings demonstrate the importance of further investigation of the multi-level determinants of response to treatment, especially in patients receiving immune checkpoint inhibitors across diverse racial and socioeconomic populations. Elexa P. Rallos, Sabrina Carrel, Michelle Li, Batul Al-Zubeidy, Edgar Gonzalez, Aaron G. Baugh, Matthew Jacobo, Cheol Park, Robert Hsu, Dominic Zavala, Steven Do, Michael F. Press, Ming Li, Anastasia Martynova, Priya Jayachandran, Daphne Stewart, Chanita Hughes-Halbert, Mariana C. Stern, Evanthia T. Roussos Torres. The relationship between social deprivation and immune suppression in Hispanic/Latina patients with breast cancer [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2025; Part 1 (Regular Abstracts); 2025 Apr 25-30; Chicago, IL. Philadelphia (PA): AACR; Cancer Res 2025;85(8_Suppl_1):Abstract nr 7100.

Supplemental Figure S1: Expression of CLDN6 by transcript in the GTEX dataset of normal tissue samples.

e13119 Background: The tumor microenvironment (TME) of primary breast tumors (PTs) and breast-to-liver metastases shows heterogeneous infiltration and interactions between tumor, immune, and stromal cells. Hepatic metastases exhibit a more immunosuppressive TME with fewer cytotoxic T cells (CTLs) relative to PTs, which may contribute to increased tumor growth and resistance to systemic therapies. To investigate the immune cell infiltration and immune-tumor interactions in PT and liver metastases, we utilized whole transcriptome analysis and immunofluorescence-based protein expression as part of spatial TME profiling. Methods: We identified 13 chemo-naïve PTs and 3 breast-to-liver metastases. Formalin-fixed paraffin-embedded (FFPE) samples were used for hematoxylin and eosin (H&E) staining and spatial transcriptomics (ST) profiling with the 10X Genomics Visium CytAssist and Visium HD platforms. The H&E slides were manually annotated in collaboration with a board-certified pathologist, and ST data were deconvoluted using breast-specific scRNA-sequenced references and canonical marker labeling. Results: Our analysis of the 13 PTs and 3 breast-to-liver metastases revealed significant heterogeneity in tumor clusters, immune cell infiltration, gene set enrichments, and gene signatures between primary and visceral TMEs. Unsupervised clustering of hormone receptor-positive (HR+) HER2-negative (HER2) samples demonstrated considerable intratumoral and intertumoral variability, with differences in the expression of tumor markers (EPCAM, ER, PR, ERBB2, KRT14, KRT18), hypoxia-related signatures (HIF1a), and pro-angiogenic drivers (VEGF). Breast-to-liver metastases showed higher expression of pathways involved in proliferation, angiogenesis, and anti-apoptosis compared to primary tumors. Hepatic metastases were enriched for HALLMARK gene sets related to proliferation and metabolic pathways, including PI3K/AKT/mTOR, mTORC1 signaling, E2F targets, MYC targets, G2M checkpoint, mitotic spindle, glycolysis, oxidative phosphorylation, and fatty acid metabolism when compared to primary tumors. In contrast, primary tumors were enriched for immune-related pathways (TNF-α, interferon-γ, inflammatory response, IL-6 JAK/STAT3), epithelial-mesenchymal transition (EMT), myogenesis, and angiogenesis gene signatures, which are consistent with their metastatic potential. Cellular profiling of liver lesions revealed higher myeloid suppressor cells (TAMs and MDSCs): CD8 + ratio with shorter cell-cell distances relative to PTs suggesting a key role in immune evasion, tumor progression, and therapeutic resistance. Conclusions: ST data from primary and metastatic breast cancer samples revealed notable tumor heterogeneity, characterized by highly active metabolic and proliferative pathways, along with a higher abundance of myeloid cells.

Genome-wide association studies (GWAS) have identified more than 200 risk loci for breast cancer. However, target genes and their encoded proteins in these loci remain largely unknown. In this study, we utilized genetic prediction models for 1, 350 circulating proteins derived from individuals of African (n= 1, 871) and European (n= 7, 213) ancestry to investigate genetically predicted protein levels in association with breast cancer risk among females of African (n=40, 138), Asian (n= 137, 677), and European (n=247, 173) ancestry. We identified 70 blood protein biomarkers associated with breast cancer risk, overall or by subtypes, at a false discovery rate (FDR) <0.05, including 37 proteins encoded by genes located at least 500kb away from any of the known risk loci identified in GWAS. Of the 33 proteins located at GWAS-identified risk loci, associations for 14 proteins were significantly attenuated after adjustment for the index risk variant of each respective locus, suggesting that these proteins may be target proteins for the risk loci. Encoding gene expression levels in normal breast tissue could be genetically predicted for 35 of the 70 identified proteins, and 17 encoding genes were associated with breast cancer risk in the same direction (P <0.05). Our study identified potential protein targets of GWAS risk loci and biomarkers for breast cancer risk and provided additional insights into breast cancer genetics and etiology. Guochong Jia, Jie Ping, Ran Tao, Jirong Long, Lili Liu, Shuai Xu, Stefan Ambs, Mollie E. Barnard, Yu Chen, Ji-Yeob Choi, Yu-Tang Gao, Montserrat Garcia-Closas, Jian Gu, Jennifer J. Hu, Motoki Iwasaki, Esther M. John, Sun-Seog Kweon, Koichi Matsuda, Keitaro Matsuo, Katherine L. Nathanson, Barbara Nemesure, Olufunmilayo I. Olopade, Tuya Pal, Sue K. Park, Boyoung Park, Michael F. Press, Maureen Sanderson, Dale P. Sandler, Song Yao, Ying Zheng, Prisca O. Adejumo, Thomas Ahearn, Abenaa M. Brewster, Anselm J. Hennis, Hidemi Ito, Michiaki Kubo, Eun-Sook Lee, Siew-Kee Low, Timothy Makumbi, Paul Ndom, Dong-Young Noh, Katie M. O'Brien, Andrew F. Olshan, Mojisola M. Oluwasanu, Min-Ho Park, Sonya Reid, Taiki Yamaji, Gary Zirpoli, Ebonee N. Butler, Wei Zheng. Integrating multi-ancestry genomic and proteomic data to identify blood risk biomarkers and target proteins for breast cancer genetic risk loci [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2025; Part 1 (Regular Abstracts); 2025 Apr 25-30; Chicago, IL. Philadelphia (PA): AACR; Cancer Res 2025;85(8_Suppl_1):Abstract nr 2286.

Supplemental Figure S2: Amino acid sequence alignment of CLDN6 (P56747) and CLDN9 (O95484) with the extracellular loops highlighted

Supplemental Figure S3. A, Efficacy of anti-CLDN6 mouse antibodies in CLDN6 positive OV90 ovarian cancer cell line xenografts. B, Efficacy in CLDN6 positive UMUC4 bladder cancer cell line xenografts. C, No efficacy in CLDN6 negative M202 melanoma cell line xenografts. All antibodies are dosed at 10 mg/kg QW IV in each study. Errors bars represent SEM of 8 replicate animals per group.

Ki-67 immunohistochemistry (IHC), a commonly used assay for breast cancer risk prognostication, has significant interlaboratory heterogeneity. This study assessed the impact of antibody clones by comparing the Ki-67 IHC MIB-1 pharmDx assay (Dako Omnis; Agilent Technologies) with clones MIB-1 (Dako Autostainer Link 48 platform), K2 (Leica BOND-III platform), and 30-9 (Ventana BenchMark ULTRA platform) used in Ki-67 laboratory-developed IHC tests. Breast cancer tissue microarrays were processed and stained in 2 central laboratories per the manufacturer's instructions. Digitized images were assessed by 5 pathologists (L.K.C., X.L., M.F.P., S.S., K.C.S.) before and after training on the reference scoring algorithm. Results were compared with results obtained by an artificial intelligence software for biomarker assessment (Mindpeak Breast Ki-67). Positive percent agreement (sensitivity), negative percent agreement (specificity), and overall percent agreement were calculated against the reference assay for the 20% cutoff, with exploratory analysis conducted for 5%, 10%, and 30% cutoffs. At the 20% cutoff, none of the laboratory-developed IHC tests achieved high overall agreement with the reference assay (predetermined as ≥85%). Both clones MIB-1 on Dako Autostainer Link 48 (sensitivity = 24.8% [95% CI, 20.2-29.9]; specificity = 99.5% [95% CI, 98.6-99.8]) and K2 on Leica BOND-III (sensitivity = 25.1% [95% CI, 20.5-30.3]; specificity = 100% [95% CI, 99.4-100]) had specificity comparable to that of the reference test, whereas clone 30-9 (Ventana BenchMark ULTRA) differed substantively in specificity from the reference assay in these measures (sensitivity = 99.3% [95% CI, 97.6-99.8]; specificity = 53.6% [95% CI, 49.6-57.5]). Intraclass correlation coefficient (ICC) for each pathologist ranged from 0.6 to 0.8, indicating good consistency across pathologists with little variability (variance component = 7.1 [95% CI, 2.1-29.7]). Training did not substantively alter within-assay or within-pathologist agreement. Ki-67 artificial intelligence analysis (ICC, 0.7; 95% CI, 0.4-0.9) was comparable to pathologists’ assessment (ICC, 0.6-0.8). Commonly used IHC assays for Ki-67 assessment in breast cancer can significantly vary. Pathologists should be aware of variables that may impact Ki-67 interpretation and look to standardize biomarker assessments for early breast cancer patient care.

Supplemental Figure S6: In vivo efficacy of a range of doses of CLDN6-23-ADC in M202 xenograft models. ADC dosing is IV QW as indicated by the arrows

Supplemental Figure S5: Induction of apoptosis following 48 hr treatment of CLDN6 positive OVCA429 and CLDN6 negative M202 cells with a range of concentrations (50 μg/ml - 0.3658μg/ml) of CLDN6-23-ADC compared to control IgG ADC.

Abstract Ovarian cancer is a leading cause of death for women worldwide, in part due to ineffective screening methods. In this study, we used whole-genome cell-free DNA (cfDNA) fragmentome and protein biomarker [cancer antigen 125 (CA-125) and human epididymis protein 4 (HE4)] analyses to evaluate 591 women with ovarian cancer, with benign adnexal masses, or without ovarian lesions. Using a machine learning model with the combined features, we detected ovarian cancer with specificity >99% and sensitivities of 72%, 69%, 87%, and 100% for stages I to IV, respectively. At the same specificity, CA-125 alone detected 34%, 62%, 63%, and 100%, and HE4 alone detected 28%, 27%, 67%, and 100% of ovarian cancers for stages I to IV, respectively. Our approach differentiated benign masses from ovarian cancers with high accuracy (AUC = 0.88, 95% confidence interval, 0.83–0.92). These results were validated in an independent population. These findings show that integrated cfDNA fragmentome and protein analyses detect ovarian cancers with high performance, enabling a new accessible approach for noninvasive ovarian cancer screening and diagnostic evaluation. Significance: There is an unmet need for effective ovarian cancer screening and diagnostic approaches that enable earlier-stage cancer detection and increased overall survival. We have developed a high-performing accessible approach that evaluates cfDNA fragmentomes and protein biomarkers to detect ovarian cancer.

Abstract Liver and lung metastases are major drivers of breast cancer-related morbidity, characterized by poor therapeutic outcomes and diminished overall survival. These dynamic tumor microenvironments (TMEs) display distinct, site-specific cellular architectures, including cytotoxic lymphocytes, immune suppressor cells, and stromal components, which together facilitate tumor growth and metastatic spread. Varied responses to immune checkpoint inhibition have also been observed in patients with differential tumor burden, improved responses in those with higher lung metastatic burden and poor responses in those with higher liver metastatic burden. Specific immune populations driving observed differential responses have not been fully characterized. Utilizing syngeneic orthotopic mouse models and FFPE tissue samples from patients with primary and metastatic breast cancer, we investigated the organ-specific cellular composition, cell-cell interactions, and molecular pathways underlying tumor progression, immune escape, and site-specific metastasis. Flow cytometry analysis of orthotopic murine primary breast, breast-to-liver, and breast-to-lung tumors demonstrated that CD3+ lymphocytes initially accumulate in primary tumors soon after tumor inoculation and decline significantly over time. Despite this overall lymphocyte depletion within TMEs, the lungs consistently exhibit the highest lymphocyte levels. In contrast, myeloid-derived suppressor cells (MDSCs) progressively infiltrate both primary and distant sites, exerting immunosuppressive functions that likely promote tumor progression and inhibit immune responses. Both granulocytic and monocytic MDSCs suppress CD8+ T cell proliferation through distinct organ-specific mechanisms, with the strongest suppressive effects observed in the liver TME. Validation of these findings in patient specimens using high-resolution spatial transcriptomics from breast and distant breast metastases using 10X Genomics (Visium and Visium-HD platforms) revealed that liver metastases harbor lower immune-to-stromal ratio with greater MDSCs: CD8+ colocalization, and higher expression of genes associated with proliferation, angiogenesis, anti-apoptosis and antiinflammation relative to primary breast tumors. In addition, spatiotemporal plasticity of breast TMEs is specific to the disease site, and investigation of how these observations could impact tumor progression and response to immune checkpoint inhibition is underway. In summary, tissue-specific programming of MDSCs in breast-to-liver metastasis compared with primary and breast-to-lung lesions leads to reduced T cell infiltration and function, and could contribute to a lack of response to checkpoint inhibition and could guide therapeutic options for patients. Citation Format: Batul Al-zubeidy, Edgar Gonzalez, Aaron Baugh, Dominic Zavala, Mathew Jacobo, Michael Press, Evanthia Roussos Torres. Myeloid suppression within metastatic tumor microenvironments in advanced breast cancer—friend or foe to checkpoint inhibition? [abstract]. In: Proceedings of the AACR IO Conference: Discovery and Innovation in Cancer Immunology: Revolutionizing Treatment through Immunotherapy; 2025 Feb 23-26; Los Angeles, CA. Philadelphia (PA): AACR; Cancer Immunol Res 2025;13(2 Suppl):Abstract nr A086.

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Endometrioid and mucinous ovarian carcinomas represent nearly a fifth of ovarian cancers, but their molecular characteristics and pathologic origins are poorly understood. To identify the genomic and epigenomic alterations characteristic of these ovarian cancer subtypes and evaluate links to morphologically similar tumors from other sites, we performed a combination of sequence, copy number, mutation signature, and rearrangement analyses from tumor samples and matched normal tissues of 133 patients, as well as methylation analyses of these tumors and tissues of 150 patients from The Cancer Genome Atlas. Genomic analyses included samples from patients with ovarian endometrioid (n = 44), ovarian mucinous (n = 43), uterine endometrioid (n = 15), and gastrointestinal mucinous carcinomas (n = 31), including mucinous carcinomas of the stomach, colon, and pancreas. In addition to identifying genes previously known to be involved in these tumors, we identified alterations in RAD51C, NOTCH4, SMARCA1/4, and JAK1 in ovarian endometrioid, ESR1 in uterine endometrioid, and SMARCA4 in ovarian mucinous carcinomas. Whole-genome sequencing revealed rearrangements involving PTEN, NF1, and NF2 in ovarian endometrioid carcinomas and NF1 and MED1 in ovarian mucinous carcinomas. The number of alterations, affected genes, and genome-wide methylation profiles were not distinguishable between ovarian and uterine endometrioid carcinomas, supporting the hypothesis that these tumors share a tissue of origin. In contrast, mutation and methylation patterns in ovarian mucinous carcinomas were different from gastrointestinal mucinous carcinomas. These analyses provide insights into the genomic landscapes and origins of mucinous and endometrioid ovarian carcinomas, providing new avenues for early clinical intervention and management of patients with these cancers. Significance: Integrative multi-omic analyses support a common tissue of origin between ovarian endometrioid and uterine endometrioid carcinomas but not between ovarian mucinous and gastric or pancreatic mucinous carcinomas.