Citations

Endocrine therapy remains the cornerstone of treatment for estrogen receptor alpha (ERα)-positive breast cancer, yet the relationship between individual genetic background and molecular response to ERα-targeted therapy remains incompletely understood. Using a well-characterized panel of lymphoblastoid cell lines (LCLs), we performed genome-wide pharmacogenomic expression quantitative trait locus (PGx-eQTL) analysis to identify estradiol (E2)- and tamoxifen (TAM)-induced SNP-gene pairs. PGx-eQTL signals were integrated with previously published breast cancer genome-wide association study datasets to examine their association with clinically relevant breast cancer phenotypes. We identified two ER-mediated PGx-eQTL SNP-gene pairs associated with breast cancer prognosis post-treatment with E2 or TAM. Notable loci included E2-regulated MRPL15 and TAM-regulated SYCP3, which have effects in a genotype-dependent manner, with genotype-dependent survival outcomes, from worse to better relapse-free survival. Similar endocrine-therapy effects on patient survival and breast cancer risk were observed in SIK2, post TAM-treatment, and in LSM4 with E2-treatment. Moreover, qRT-PCR validation in an independent LCL panel confirmed the genotype-specificity of these signals. Overall, we identified ER-mediated PGx-eQTL SNP-gene pairs which represent potential pharmacogenomic tools for identifying patients likely to benefit from ERα-targeted endocrine therapy, offering a foundation for more genotype-informed individualized treatment decisions in breast cancer.

Our research uncovers a role for ATR in responding to ECM stiffness and promoting epithelial-to-mesenchymal transition (EMT) and metastasis. ATR, when deubiquitinated and upregulated by USP21 under enhanced ECM stiffness conditions, phosphorylates the nuclear protein SUN2, which promotes β-catenin nuclear translocation and EMT. ATM-mediated EMT promotes polymorphonuclear myeloid-derived suppressor cell recruitment and inhibits CD103 + dendritic cells, fostering an immunosuppressive tumor milieu. ATR inhibition disrupts this malignant cascade by promoting mesenchymal-to-epithelial transition to enhance antitumor immunity and mitigate metastases. Consistently, circulating HLA-DR + dendritic cells were also enhanced following treatment with the ATR inhibitor berzosertib in patients with therapeutically resistant early-stage breast cancer. Our data suggest that ATR-targeted therapy may be optimized by considering both DNA damage–dependent and EMT-inducing effects of ATR ATR promotes metastasis and immunosuppression through EMT process

Abstract Background: PD-L1 is best known for suppressing antitumor immunity at the cell surface, yet emerging evidence suggests additional nuclear functions. Previous studies from our laboratory demonstrated that nuclear PD-L1 interacts with the cohesin complex to maintain chromosomal stability. However, the upstream regulatory mechanism governing this interaction and its immunological consequences remain unknown. Methods and Results: Using acetylation-site prediction, mutagenesis, and structural modeling, we identified lysine 178 (human) / 177 (mouse) as a conserved acetylation switch regulating PD-L1 function. Deacetylation of PD-L1 (K178R/K177R) disrupted PD-L1-PDS5B/cohesin binding without affecting subcellular localization. This led to defective sister chromatid cohesion, micronuclei formation, and cytosolic DNA accumulation. RNA-seq, qPCR, and immunoblotting demonstrated robust activation of the cGAS-STING-IFN-γ signaling axis and enhanced MHC-I antigen-presentation machinery in PD-L1 K178R cells. Functionally, K178R cells proliferated more slowly and were highly susceptible to PBMC-mediated killing in co-culture assays, with further enhancement upon anti-PD-L1 antibody treatment. In xenograft models (NSG mice), tumors expressing PD-L1 K178R grew significantly slower than WT. In immunocompetent C57BL/6 syngeneic models, PD-L1 K177R tumors showed reduced growth and heightened responsiveness to PD-L1 blockade. Structural and biochemical analyses confirmed that PD-L1 acetylation at K178/K177 stabilizes its interaction with the cohesin complex, thereby preventing chromosomal instability-induced immune activation. Conclusions: We identify a conserved PD-L1 acetylation switch that couples cohesin stability to innate immune activation. PD-L1 deacetylation promotes chromosomal instability, activates cGAS-STING-IFN-γ signaling, enhances antigen presentation, and sensitizes tumors to PD-L1 immunotherapy. These findings uncover a previously unrecognized regulatory axis linking tumor genomic integrity to immune recognition, providing a mechanistic foundation for targeting PD-L1 acetylation in cancer treatment. Citation Format: Meijie Wang, Jia Yu, Liewei Wang. A pd-l1 acetylation switch coordinates chromosome cohesion and antitumor immunity [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 7391.

Abstract Background: Approximately 70% of breast cancers are estrogen receptor-positive (ER+), and endocrine therapies, including selective estrogen receptor modulators (SERMs) and aromatase inhibitors (AIs), are standard-of-care. However, interpatient variability in treatment response and relapse remains substantial, suggesting underlying germline pharmacogenomic factors that modify ER signaling. Here, we mapped ER pharmacogenomic eQTLs to identify germline variants that modulate transcriptional responses to estradiol (E2) and tamoxifen (Tam). Methods: We treated 30 genotyped lymphoblastoid cell lines (LCLs) with vehicle, E2, Tam, or E2+Tam and performed RNA-seq to quantify ligand-induced expression fold changes. Using MatrixEQTL, we identified SNP-gene PGx-eQTLs (cis, ±200 kb), requiring evidence of ER transcriptional specificity via (i) reversal by dual treatment, and (ii) overlap with ER ChIP-seq binding annotations. Clinical relevance was assessed by linkage to breast cancer GWAS datasets, including NSABP, MA.27, and M3. Chromatin regulatory states were annotated (ChromHMM), and transcription factor motif enrichment was performed from SNP-centered ±200 bp regions. Results: Distinct PGx-eQTL architectures were identified for E2 and Tam, with partial overlap in ligand-responsive loci. ER PGx-eQTL variants were predominantly enriched in enhancer chromatin and co-localized with canonical ER-binding motifs. Integrative analysis with GWAS of breast cancer outcomes highlighted multiple drug-response-associated loci, including rs311392-MRPL15 and rs7953325-SYCP3. High MRPL15 expression was linked to shorter relapse-free survival, whereas high SYCP3 expression correlated with improved outcomes. Additional loci derived from a GWAS catalog for cancer and hormone phenotypes included rs2850247-SIK2 and rs11673604-LSM4, where higher SIK2 expression was associated with improved relapse-free survival and higher LSM4 expression with poorer outcomes. Conclusions: We identified ligand-dependent ER PGx-eQTLs that mechanistically connect germline regulatory variants to transcriptional response heterogeneity and clinical endocrine therapy outcomes. These SNP-gene relationships represent potential biomarkers for precision endocrine therapy selection and may reveal endocrine-dependent therapeutic vulnerabilities. Citation Format: Martin Meng, Huanyao Gao, August John, Arnab Ghosh, Shreya Indulkar, Liewei Wang, . Estrogen receptor-mediated pharmacogenomic eQTLs as predictors of endocrine therapy response in 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 3610.

Abstract Background: The increasing volume and diversity of clinical genomic reports pose a significant challenge across healthcare institutions for the interpretation and proper implementation of precision oncology research. Reports from multiple different vendors (e.g., Invitae, Ambry Genetics, Foundation Medicine) are typically PDFs and exhibit substantial heterogeneity in panel design, gene coverage, and reporting standards, which hinders efficient retrospective data mining and patient cohort identification within the electronic medical records. We sought to develop a framework to interrogate all of these reports. Methods: We extracted genomic reports from patients with breast cancer treated with neoadjuvant chemotherapy and developed MolHarmonizer, a novel, scalable framework leveraging Python and Gemini LLMs, designed to process and harmonize genomic data from disparate multi-vendor reports. Gemini LLMs are employed explicitly for robust information extraction, normalization, and structuring of key genomic features, transforming unstructured data into a unified, queryable dataset. Results: Our MolHarmonizer framework successfully processed 1,147 genomic reports from 1703 breast cancer patients (2006-2023) from 23 different companies, demonstrating robust capability to extract and standardize critical actionable biomarkers. Data sources included Invitae (n=554), Ambry Genetics (n=189), Natera (n=95), Mayo Clinic (n=88), Tempus (n=63), Guardant Health (n=47), and Foundation Medicine (n=37), with others contributing less than 20 reports. Of the samples, 827/1147 (72.1%) were germline (blood/saliva). A majority of the patients were tested using the panels due to a personal/family history (n=925). Overall, 413/1147 (36.0%) reports identified at least one mutation. For breast cancer, 75 reports showed BRCA1/2 mutations (37 BRCA1, 37 BRCA2, and one patient with both BRCA1 and 2). Other mutations identified included: PIK3CA (n=40), TP53 (n=96), PTEN (n=21), ESR1 (n=13) and AKT1/2 (n=8). Conclusion: MolHarmonizer, a powerful framework leveraging Gemini LLMs, effectively addresses genomic data heterogeneity by automating biomarker extraction and harmonization. This enables rapid cohort identification and deep retrospective analyses for clinical insights, biomarker discovery, understanding disease history, facilitating novel pattern discovery, e.g., predicting BRCA1 mutations from WSI, and accelerating research within our neoadjuvant BC cohort. Future plans include expanding to include over 20,000 breast cancer patients, developing a user-friendly chatbot, and ensuring inter-institutional adaptability for a variety of complex diseases. Citation Format: Krishna Rani Kalari, Xiaojia Tang, Thanmayee Boyapati, Tanya L. Hoskin, Sumathilatha Myla, Sumedha G. Penheiter, Richard M. Weinshilboum, Liewei Wang, Hamid R. Tizhoosh, Karthik Vikram Giridhar, Matthew P. Goetz, Judy C. Boughey. Development of an LLM framework for analysis of heterogeneous breast cancer patients genomic reports [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 2756.

Abstract Background Electronic health record (EHR)–based prognostic modeling is increasingly used in oncology, yet incorporating pharmacogenomic (PGx) knowledge derived from experimental systems into clinical prediction frameworks remains challenging. This gap is driven by fundamental mismatches between controlled drug–mutation assays and heterogeneous, incomplete real-world clinical data. Methods We propose a representation transfer framework that integrates PGx embeddings learned from large-scale in vitro pharmacogenomic screens into patient-level EHR models. A frozen pharmacogenomic encoder is used to generate interaction-aware embeddings from patient mutation profiles and administered therapies, which are aggregated into a fixed-length PGx Complementarity Representation. These representations are incorporated into multimodal survival prediction models alongside standard clinical features. Performance was evaluated using systematic modality ablation analyses, attribution analyses, and exploratory unsupervised representation analyses. Results Integrating PGx embeddings yielded consistent performance improvements across all evaluated modality combinations. Relative gains were largest in modality-sparse settings, where baseline EHR features encode limited biological context, and were attenuated—but remained significant—in biologically enriched configurations. Attribution analyses indicated that PGx embeddings contributed non-redundant predictive signal beyond standard clinical features. Exploratory unsupervised analyses further demonstrated that the learned representations exhibit interpretable association patterns aligned with known therapeutic exposures and pathway-level associations. Conclusion These findings suggest that externally learned pharmacogenomic representations can be transferred into real-world EHR models as a context-dependent, non-redundant augmentation. By framing PGx knowledge as an interaction-aware representation rather than a mechanistic model, this work provides an informatics framework for integrating experimental pharmacogenomic data into clinical prediction tasks in a reproducible and interpretable manner.

ABSTRACT Major depressive disorder (MDD) is a substantial public health challenge. Pharmacogenomics (PGx), which identifies genetic variations that predict drug treatment outcomes, may have utility for clinical practice, but adequate representation of all populations is needed. As precision medicine in psychiatry moves towards the use of Artificial Intelligence (AI) and Machine Learning (ML) to predict treatment outcomes using PGx data, representation of diverse populations will be especially important in order to mitigate algorithmic bias and achieve equitable and generalizable findings. This work sought to quantify population diversity in pharmacogenomic studies of MDD through a systematic review. Data from 390 MDD antidepressant PGx studies were extracted from 5739 articles screened. Studies summarized were predominantly conducted in Europe, East Asia, and North America. Across all global studies, the study population comprised 57.3% White, 36.4% Asian, 1.7% Black, 3.5% Hispanic/Latino, and 0.1% Native American or Indigenous participants. Only sixty‐three (16.2%) studies included Black or Latino/Hispanic patients. Additionally, Black, Asian, Hispanic/Latino, and American Indian/Alaska Native populations were statistically underrepresented in U.S. study populations when compared to national census data, while Asian and Black populations were underrepresented in the United Kingdom. The overrepresentation of participants from a limited number of countries combined with the underrepresentation of Black and Hispanic/Latino populations could impact the extent to which pharmacogenomic testing and associated AI/ML‐based PGx tools could individualize antidepressant medication regimens for treating MDD.

Triple-negative breast cancer (TNBC) represents the most malignant subtype of breast cancer. The clinical application of PARP inhibitors (PARPi) is limited by the low frequency of BRCA1/2 mutations in TNBC. Here, we identified that MTAP deletion sensitized genotoxic agents in our clinical cohort of metastatic TNBC. Further study demonstrated that MTAP deficiency or inhibition rendered TNBC susceptibility to chemotherapeutic agents, particularly PARPi. Mechanistically, targeting MTAP that synergized with PARPi by disrupting the METTL16-MAT2A axis involved in methionine metabolism and depleting in vivo s-adenosylmethionine (SAM) levels. Exhausted SAM in turn impaired PARPi-induced DNA damage repair through attenuation of MRE11 recruitment and end resection by diminishing MRE11 methylation. Notably, brain metastatic TNBC markedly benefited from a lower dose of PARPi and MTAP deficiency/inhibition synergy due to the inherently limited methionine environment in the brain. Collectively, our findings revealed a feed-forward loop between methionine metabolism and DNA repair through SAM, highlighting a therapeutic strategy of PARPi combined with MTAP deficiency/inhibition for TNBC.