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.

Abstract Antidepressants are widely prescribed for major depressive disorder, yet only one-third of patients achieve remission after initial treatment. Previous genome-wide association studies (GWAS) of clinically assessed antidepressant response combined multiple antidepressant classes, potentially obscuring class-specific effects. This study focused on selective serotonin reuptake inhibitors (SSRIs), often first-line due to better tolerability. Data from 15 cohorts across four ancestries were integrated: European (N = 3887; 11 studies), East Asian (N = 1068; 4), African (N = 277; 1), and Admixed American (N = 250; 1). GWAS of non-remission and percentage improvement were conducted within cohorts, followed by ancestry-specific meta-analyses and trans-ancestry meta-regression. Single nucleotide polymorphism (SNP)-based heritability was estimated in European samples. Polygenic scores were used for leave-one-out prediction and to assess shared genetic architecture with psychiatric traits. Gene-level and gene-set enrichment analyses were also performed. No genome-wide significant variants were identified for either outcome in any ancestry-specific or trans-ancestry analyses. However, trans-ancestry meta-regression yielded eight independent loci with suggestive associations (p < 1 × 10 −5 ) for non-remission and 17 for percentage improvement. Gene-set analyses revealed nominal enrichment of the serotonergic synapse pathway for non-remission. SNP-based heritability estimates were not significantly different from zero for either outcome. Better SSRI response was nominally associated with lower genetic predisposition to major depressive disorder, post-traumatic stress disorder, and schizophrenia. This study represents the largest trans-ancestry GWAS of SSRI response, highlighting emerging biological signals. Limited power emphasises the need for larger and ancestrally diverse cohorts to better characterise the genetic architecture of antidepressant response.

Abstract Human genomic studies link reduced CUB domain-containing protein 1 (CDCP1) expression with myocardial recovery in heart failure. While CDCP1 regulates cardiac fibroblast proliferation in vitro, it’s in vivo role in cardiac fibrosis remains unclear. Using a Cdcp1 -knockout (KO) angiotensin II/phenylephrine mouse model, we show that Cdcp1 deletion reduces echocardiographic left ventricular mass, histologic cardiac fibrosis, and pro-fibrotic gene expression, along with decreased fibroblast activation and inflammatory markers. Spatial transcriptomics identified a pressure overload–expanded fibroblast subpopulation enriched for growth factor and TGF-β signaling (FB5), which was markedly attenuated in Cdcp1 -KO hearts, alongside reduction of a pro-inflammatory cardiomyocyte subtype (CM4). Complementary studies in human ventricular fibroblasts demonstrate that CDCP1 knockdown reduced extracellular matrix gene expression and collagen I deposition. These findings establish CDCP1 as a regulator of cardiac fibrotic remodeling in vivo and open avenues for its further investigation as a potential therapeutic target.

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: Tumor-infiltrating lymphocytes (TILs) in HER2+ and triple-negative breast cancer (BC) are prognostic and associated with therapy response; however, there are few data regarding distribution and significance of TIL values in ER+ HER2-negative (HER2-) MBC patients (pts) treated with CDK4/6 inhibitors (CDK4/6i). CDK4/6 inhibition may promote antitumor immunity by production of type III interferons and tumor antigen presentation, and by inhibition of regulatory T cells (Goel, Nature 2017). Further, the addition of immune checkpoint therapy to palbociclib numerically prolonged PFS in the 2nd line (2L) setting (Mayer, JCO 2024). We aimed to investigate the prognostic role of TILs derived from fresh metastatic tumor biopsies in pts with ER+HER2- MBC prior to starting CDK4/6i and endocrine therapy (ET) in the first line (1L) and 2L setting. Methods: PROMISE (NCT0281902) is a multicenter, prospective study that enrolled women commencing the CDK4/6i palbociclib (Pb) with letrozole (1L) or fulvestrant (2L) for HR+HER2- MBC. The primary goal was to identify novel genomic variants and pathways associated with PFS. Here, we performed a secondary analysis to quantitate stromal TILs using protocol mandated pretreatment tumor biopsies and determine their association with PFS. Using the Kaplan-Meier method, we reported median PFS (mPFS) and 3-year PFS percentage (%) (95% confidence intervals [CIs]) by TILs [≥20% vs &lt;20%] separately within 1L and 2L. As sample TIL values were generally concentrated at zero (27/58 = 46.6%), coupled with wide spread of sample TIL values ≥20% [range: 20%- 80%], we applied a ≥20% threshold for PFS analysis (high vs low TIL group) as described by Luen, Salgado et al. Lancet Oncol 2017. Statistical significance was based on the Wilcoxon test of the null hypothesis that there was no difference in the PFS curves by TILs. PFS is reported at median follow-up of 21 months. Results: Pretreatment tumor biopsies from 58/68 (85.3 %) pts were evaluable (42 in 1L and 16 in 2L). Median [Q1, Q2] TIL values were higher in 1L (5% [0, 10]) vs 2L (0% [0, 15]). The most common biopsy sites were bone (44.8%), lymph node (17.2%), liver (17.2%) and breast (12%). Overall, TILs frequency (&gt;20%) by the 4 most common biopsy sites were bone (2/20, 10%), lymph node (4/7, 57%), liver (2/6, 33%), and breast (1/7, 14%). In 1L, although PFS was not significantly different (P = 0.237), mPFS (44 vs 34 months) and 3-year PFS 75.0%, [95% CI: 50.3%, 100%] vs 46.4% [95% CI: 31.0%, 69.5%]) were longer in pts with high versus low TILS, respectively. In 2L, the 3-year PFS was numerically similar comparing high and low TILs groups, (25.0%, [95% CI: 4.6%, 100%] vs 22.5% [95% CI: 6.7%, 76.1%]) with no significant difference in PFS (P = 0.761). Conclusions: In pts with ER+HER2- MBC treated with Pb and ET, PFS was numerically longer comparing high vs low TIL values in 1L but not 2L settings. Future studies evaluating TILs in larger datasets are needed to validate these findings. Genomic analysis (DNA/RNA) and its association with TIL levels is underway and will be presented at the meeting. Funding: The Center for Individualized Medicine, Mayo Clinic; Mayo Clinic Breast Specialized Program of Research Excellence (SPORE P50CA116201); Mayo Clinic Cancer Center (CA15083-40A2); TEMPUS (https://www.tempus.com/) and Conquer Cancer Foundation of ASCO (Career Development Award 2018, Dr. O’Sullivan) Citation Format: C. C. O'Sullivan, A. M. Moyer, A. Nassar, D. M. Zahrieh, V. Suman, X. Tang, K. J. Thompson, R. C. Hentz, J. J. Harrington, A. Moreno-Aspitia, D. W. Northfelt, T. C. Haddad, S. Chumsri, P. P. Peethambaram, K. J. Ruddy, K. V. Giridhar, R. A. Leon-Ferre, R. Weinshilboum, L. Wang, K. R. Kalari, M. P. Goetz. Tumor-infiltrating lymphocytes in advanced ER-positive, HER2-negative breast cancer prior to treatment with palbociclib and endocrine therapy: a retrospective analysis of the PROMISE study [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 PS1-10-22.

This study uncovers a previously unrecognized role for NAV3 in TGF-β1-driven cardiac fibroblast activation. We show that NAV3 facilitates profibrotic remodeling through noncanonical signaling and cell cycle arrest, independently of SMAD2/3. These findings position NAV3 as a novel regulator of fibroblast phenotype and a potential modulator of cardiac fibrosis.

Background: Cardiac fibrosis is a critical and independent driver of heart failure (HF), characterized by excessive extracellular matrix (ECM) deposition. Decreased CDCP1 (CUB domain-containing protein 1) expression has been associated with myocardial recovery in human genome wide association studies by attenuating PDGF-BB driven cardiac fibroblast proliferation with no effect on canonical TGF-beta mediated transdifferentiation in-vitro. In-vivo, Cdcp1 deletion significantly reduced cardiac fibrosis and dysfunction in an angiotensin II/phenylephrine-induced pressure overload mouse model. However, its upstream regulators and its downstream effects on molecular signaling pathways that modulate ECM remodeling are unknown. Hypothesis: CDCP1 promotes human cardiac fibroblast mediated ECM remodeling in the HF microenvironment. Methods: Human ventricular fibroblasts (HVF) were treated with PDGF-BB (20 ng/ml), TGF-β (10 ng/ml), or Angiotensin II (100 ng/ml) for 48 hours. CDCP1 knockdown (KD) was achieved through siRNA transfection. Gene expression analysis was performed using qRT-PCR and protein quantification by Western blot. Both intact HVF and decellularized ECM were immunostained for collagen 1 to evaluate for ECM compositional changes. Results: Individual and combination treatments with fibrotic stimuli significantly increased CDCP1 expression (Figure 1A-1B). CDCP1 KD resulted in reduction of key ECM markers COL1A1, CTGF, and LOX (Figure 1C) across all fibrotic stimuli . Immunostaining of HVF after treatment with fibrotic stimuli showed reduced collagen 1 production with CDCP1 KD (Figure 1D). Western blot analysis showed increased phosphorylation of Src (tyr416) with 6h of treatment with fibrotic stimuli which was nullified by silencing of CDCP1 (Figure 1E). Conclusion: Our findings demonstrate that CDCP1 is necessary for ECM synthesis and Src signaling across multiple profibrotic pathways. Selective targeting of CDCP1 to modulate Src signaling pathways could provide novel therapeutic avenues for HF and fibrosis treatment.

Background: Cardiac fibrosis is a central pathological process that leads to worse outcomes in various cardiomyopathies. Recent evidence shows that CDCP1 (CUB domain-containing protein 1) modulates myocardial recovery by attenuating human cardiac fibroblast (HCF) proliferation. This study investigates the hypothesis that selective targeting of the CDCP1 cleavage domain can mitigate fibroblast proliferation, thereby offering a novel therapeutic approach to reducing cardiac fibrosis. Methods: A high-throughput in silico screening was performed using the Enamine compound library, focusing on structural regions that include the cleavage and adjacent sites of CDCP1 . A total of 147 candidate compounds were prioritized based on docking scores and binding stability profiles. Selected compounds were subsequently evaluated in vitro using HCF cultures seeded at 3,000 cells/well in 96-well plates. Each compound was administered at 10 μM concentration, and cell proliferation and viability were assessed by time-lapse imaging in the Incucyte® system at 24 and 96 hours. All experiments were done in triplicate with appropriate DMSO vehicle controls. Results: Compound C3 significantly reduced HCF proliferation (Fig B) to 27.3±2.4% relative to DMSO controls (99.9±0.5%, P <0.001) at 96 hours. Detailed structural modeling indicated that C3 binds to the CDCP1 cleavage site (Fig C) with a favorable Glide XP docking score of -7.66 kcal/mol. The interaction is stabilized by hydrogen bonds with Lys365 and Cys374 (Fig D), and supplementary hydrophobic interactions between aromatic rings and aliphatic groups, which contribute to a strong inhibitory effect. Conclusion: Targeting the CDCP1 cleavage domain with compound C3 effectively diminishes HCF proliferation in vitro. Therefore, these results substantiate the therapeutic potential of CDCP1 inhibition in cardiac fibrosis. Ongoing in vivo studies will explore the translational relevance of these findings.

Abstract Background Acamprosate is an FDA-approved medication for the treatment of alcohol use disorder (AUD), however, only about 50% of patients will respond to acamprosate. Previously, we conducted an acamprosate trial (n=442), which is one of the largest acamprosate clinical studies ever conducted and currently the only study with multi-omics data. To date, no biological measures are utilized to predict response to acamprosate treatment. Aims & Objectives We set out to apply our established pharmaco-omics informed genomics strategy to identify potential biomarkers associated with acamprosate treatment response. Method Our open-label acamprosate clinical trial recruited 442 patients with AUD, all of whom were treated with acamprosate for three months. The primary outcomes were 1) relapse to alcohol use and 2) relapse to heavy drinking. Alcohol consumption was measured using the self-reported timeline follow back method. Plasma protein levels were measured using Olink proximity extension immunoassays. Results Our established “Pharmaco-Omics-informed genome-wide association study (GWAS)” research strategy identified 12 proteins, including interleukin-17 receptor B (IL17RB), that were associated with acamprosate treatment response. A GWAS for IL17RB concentrations identified several genome-wide significant signals. Specifically, the top hit single nucleotide polymorphism (SNP) rs6801605 with a minor allele frequency of 38% in the European American population mapped 4 kilobase (Kb) upstream of IL17RB, and intron 1 of the choline dehydrogenase (CHDH) gene on chromosome 3 (p: 4.8E-20). The variant genotype (AA) for the SNP rs6801605 was associated with lower IL17RB protein expression. In addition, we identified a series of genetic variants in IL17RB that were associated with acamprosate treatment outcomes. Furthermore, the variant genotypes for all of those IL17RB SNPs were protective against alcohol relapse. Finally, we demonstrated that the basal level of mRNA expression of IL17RB was inversely correlated with those of nuclear factor-κB (NF-κB) subunits, and a significantly higher expression of NF-κB subunits was observed in AUD patients who relapsed to alcohol use. Discussion & Conclusions This study illustrates that IL17RB genetic variants might contribute to acamprosate treatment outcomes. This series of studies represents an important step toward generating functional hypotheses that could be tested to gain insight into mechanisms underlying variation in acamprosate treatment response phenotypes.

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.