Wang, Liewei

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: Second generation androgen receptor signaling inhibitors (ARSIs), such as abiraterone, have demonstrated improved survival in metastatic castration-resistant prostate cancer (mCRPC) patients, however, acquired resistance to these agents often arises. Novel and alternative therapies are needed to combat the ARSI resistant tumor. In the last two decades patient-derived xenograft (PDX) and PDX organoids have become pivotal models in anticancer drug development and biomarker discovery due to their ability to faithfully recapitulate the complex and heterogeneous tumor biology. Methods: We analyzed RNAseq data from mCRPC patients and corresponding PDX models (PROMOTE study, NCT #01953640) and employed L1000 Chem Perturbation down/up signatures database to find candidate drugs to overcome abiraterone resistance by reverse signature search. We then performed proliferation assay using 49 compounds in three PROMOTE-PDX organoid models (2 abiraterone-resistant and 1 abiraterone-sensitive) using our semi high-throughput drug screening platform. Results: Overall, we identified a total of 32 compounds with IC50<10µM in at least one of the models, among which, 5 agents are active in all three models and 5 in at least two of the models. (Table 1). Within active compounds we find selective kinase inhibitors (BRAF, JAK2, AKT, FLT3) and histone deacetylase inhibitors (belinostat and pracinostat). Particularly interesting are two drugs that are only active in the abiraterone resistant setting but not the sensitive PDX. Conclusions: In summary, we leveraged RNAseq data from mCRPC patients and PDX models and performed a high-throughput drug screen in PDX-derived organoid models to identify potential drugs that could overcome abiraterone-resistance in lethal advanced prostate cancer. These data highlight potential drug combinations or subsequent therapeutic strategies for these patients. Citation Format: Irene Marín-Goñi, Huanyao Gao, Joachim L. Petit, Shreya Indulkar, Adam M. Kase, Cassandra N. Moore, Krishna R. Kalari, Michael T. Barrett, Richard Weinshilboum, Winston Tan, John A. Copland, Liewei Wang. Overcoming resistance in mCRPC: drug discovery using PDX models and high-throughput drug screening [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 5658.

Most patients with metastatic prostate cancer eventually develop resistance to primary androgen deprivation therapy. To identify predictive biomarker for Abiraterone acetate/prednisone resistance, we screened alternative splice variants between responders and non-responders from the PROMOTE clinical study and pinned down the most significant variant, CENPK–delta8. Through preclinical patient-derived mouse xenograft (PDX) and 3D organoids obtained from responders and non-responders, as well as in vitro models, aberrant CENPK–delta8 expression was determined to link to drug resistance via enhanced migration and proliferation. The FLNA and FLOT1 were observed to specifically bind to CENK–delta8 rather than wild-type CENPK, underscoring the role of CENPK–delta8 in cytoskeleton organization and cell migration. Our study, leveraging data from the PROMOTE study, TCGA, and TCGA SpliceReq databases, highlights the important function of alternative splice variants in drug response and their potential to be prognostic biomarkers for improving individual therapeutic outcomes in precision medicine.

Purpose The majority of patients with cancer receive treatments that are minimally informed by omics data. We propose a precision medicine computational framework, PANOPLY (Precision Cancer Genomic Report: Single Sample Inventory), to identify and prioritize drug targets and cancer therapy regimens. Materials and Methods The PANOPLY approach integrates clinical data with germline and somatic features obtained from multiomics platforms and applies machine learning and network analysis approaches in the context of the individual patient and matched controls. The PANOPLY workflow uses the following four steps: selection of matched controls to the patient of interest; identification of patient-specific genomic events; identification of suitable drugs using the driver-gene network and random forest analyses; and provision of an integrated multiomics case report of the patient with prioritization of anticancer drugs. Results The PANOPLY workflow can be executed on a stand-alone virtual machine and is also available for download as an R package. We applied the method to an institutional breast cancer neoadjuvant chemotherapy study that collected clinical and genomic data as well as patient-derived xenografts to investigate the prioritization offered by PANOPLY. In a chemotherapy-resistant patient-derived xenograft model, we found that that the prioritized drug, olaparib, was more effective than placebo in treating the tumor ( P < .05). We also applied PANOPLY to in-house and publicly accessible multiomics tumor data sets with therapeutic response or survival data available. Conclusion PANOPLY shows promise as a means to prioritize drugs on the basis of clinical and multiomics data for an individual patient with cancer. Additional studies are needed to confirm this approach.

Studies assessing the effect of bevacizumab (BEV) on breast cancer (BC) outcome have shown different effects on progression‐free and overall survival, suggesting that a subgroup of patients may benefit from this treatment. Unfortunately, no biomarkers exist to identify these patients. Here, we investigate whether single nucleotide polymorphisms (SNPs) in VEGF pathway genes correlate with pathological complete response (pCR) in the neoadjuvant GeparQuinto trial. HER2‐negative patients were randomized into treatment arms receiving either BEV combined with standard chemotherapy or chemotherapy alone. In a pre‐planned biomarker study, DNA was collected from 729 and 724 patients, respectively from both treatment arms, and genotyped for 125 SNPs. Logistic regression assessed interaction between individual SNPs and both treatment arms to predict pCR. Five SNPs may be associated with a better response to BEV, but none of them remained significant after correction for multiple testing. The two SNPs most strongly associated, rs833058 and rs699947, were located upstream of the VEGF‐A promoter. Odds ratios for the homozygous common, heterozygous and homozygous rare rs833058 genotypes were 2.36 (95% CI, 1.49–3.75), 1.20 (95% CI, 0.88–1.64) and 0.61 (95% CI, 0.34–1.12). Notably, some SNPs in VEGF‐A exhibited a more pronounced effect in the triple‐negative subgroup. Several SNPs in VEGF‐A may be associated with improved pCR when receiving BEV in the neoadjuvant setting. Although none of the observed effects survived correction for multiple testing, our observations are consistent with previous studies on BEV efficacy in BC. Further research is warranted to clarify the predictive value of these markers.