Kian Behbakht

The Spatial Structure of the Tumor Immune Microenvironment Can Explain and Predict Patient Response in High-Grade Serous Carcinoma

Abstract Ovarian cancer is the deadliest gynecologic malignancy, and therapeutic options and mortality rates over the last three decades have largely not changed. Recent studies indicate that the composition of the tumor immune microenvironment (TIME) influences patient outcomes. To improve spatial understanding of the TIME, we performed multiplexed ion beam imaging on 83 human high-grade serous carcinoma tumor samples, identifying approximately 160,000 cells across 23 cell types. From the 77 of these samples that met inclusion criteria, we generated composition features based on cell type proportions, spatial features based on the distances between cell types, and spatial network features representing cell interactions and cell clustering patterns, which we linked to traditional clinical and IHC variables and patient overall survival (OS) and progression-free survival (PFS) outcomes. Among these features, we found several significant univariate correlations, including B-cell contact with M1 macrophages (OS HR = 0.696; P = 0.011; PFS HR = 0.734; P = 0.039). We then used high-dimensional random forest models to evaluate out-of-sample predictive performance for OS and PFS outcomes and to derive relative feature importance scores for each feature. The top model for predicting low or high PFS used TIME composition and spatial features and achieved an average AUC score of 0.71. The results demonstrate the importance of spatial structure in understanding how the TIME contributes to treatment outcomes. Furthermore, the present study provides a generalizable roadmap for spatial analyses of the TIME in ovarian cancer research.

Utilizing Serum-Derived Lipidomics with Protein Biomarkers and Machine Learning for Early Detection of Ovarian Cancer in the Symptomatic Population

Abstract Ovarian cancer is the fifth leading cause of cancer-related deaths among women. Most patients are diagnosed at late stage (III/IV), resulting in a 5-year survival rate below 30%. This is driven by the presentation of vague abdominal symptoms that confound diagnosis at early stages (I/II) and a shortage of robust biomarkers. We are taking a novel approach for earlier ovarian cancer detection, leveraging lipids as biomarkers. We utilized untargeted ultrahigh pressure liquid chromatography–mass spectrometry to analyze sera from two large, independent cohorts (N = 433 and N = 399) designed to reflect the symptomatic population, including individuals with benign adnexal masses, early- and late-stage ovarian cancer, gastrointestinal disorders, and otherwise healthy women seeking care for symptoms. We identified a significantly altered lipid profile in ovarian cancer and early-stage ovarian cancer specifically across both cohorts compared with controls. We also profiled select protein biomarkers (cancer antigen 125, human epididymis protein 4, β-2 folate receptor α, and mucin 1) and, utilizing machine learning–based modeling, identified a proof-of-concept multiomic model consisting of less than 20 top-performing lipid and protein features. This model was trained on cohort 1 and tested on cohort 2, achieving AUCs of 92% (95% confidence interval, 87%–95%) for distinguishing ovarian cancer from controls and 88% (95% confidence interval, 83%–93%) for distinguishing early-stage ovarian cancer from controls. These findings demonstrate the clinical utility and robustness of lipids as proof-of-concept diagnostic biomarkers for early ovarian cancer within the clinically complex symptomatic population, particularly when applied in a multiomic approach. Significance: Patients with ovarian cancer endure delayed diagnosis and poor outcomes. We profiled lipids in two cohorts and integrated them with proteins in machine learning. This enabled early-stage detection in a complex range of controls.

Claudin-4 Stabilizes the Genome via Nuclear and Cell-Cycle Remodeling to Support Ovarian Cancer Cell Survival

Abstract Alterations in the interplay between the nucleus and the cell cycle during cancer development lead to a state of genomic instability, often accompanied by observable morphologic aberrations. Tumor cells can regulate these aberrations to evade cell death, either by preventing or eliminating genomic instability. In epithelial ovarian cancer, overexpression of claudin-4 significantly contributes to therapy resistance through mechanisms associated with genomic instability regulation. However, the molecular mechanisms underlying claudin-4 overexpression in epithelial ovarian cancer remain poorly understood. In this study, we modified claudin-4 expression and employed a unique claudin mimic peptide to investigate claudin-4’s function. Our findings show that claudin-4 supports ovarian cancer cell survival by stabilizing the genome through nuclear and cell-cycle remodeling. Specifically, claudin-4 induced nuclear constriction by excluding lamin B1 and promoting perinuclear F-actin accumulation, thereby altering nuclear structure and dynamics. Similarly, cell-cycle modifications due to claudin-4 overexpression resulted in fewer cells entering the S-phase and reduced genomic instability in tumors. Importantly, disrupting claudin-4’s biological effects using claudin mimic peptide and forskolin increased the efficacy of PARP inhibitor treatment, correlating with alterations in the oxidative stress response. Our data indicate that claudin-4 protects tumor genome integrity by modulating the crosstalk between the nucleus and the cell cycle, leading to resistance to genomic instability formation and the effects of genomic instability–inducing agents. Significance: High-grade serous ovarian carcinoma is marked by chromosomal instability, which can serve to promote disease progression and allow cancer to evade therapeutic insults. The report highlights the role of claudin-4 in regulating genomic instability and proposes a novel therapeutic approach to exploit claudin-4–mediated regulation.

Combining EHMT and PARP Inhibition: A Strategy to Diminish Therapy-Resistant Ovarian Cancer Tumor Growth while Stimulating Immune Activation

Abstract Despite the success of poly-ADP-ribose polymerase inhibitors (PARPi) in the clinic, high rates of resistance to PARPi presents a challenge in the treatment of ovarian cancer, thus it is imperative to find therapeutic strategies to combat PARPi resistance. Here, we demonstrate that inhibition of epigenetic modifiers euchromatic histone lysine methyltransferases 1/2 (EHMT1/2) reduces the growth of multiple PARPi-resistant ovarian cancer cell lines and tumor growth in a PARPi-resistant mouse model of ovarian cancer. We found that combinatory EHMT and PARP inhibition increases immunostimulatory double-stranded RNA formation and elicits several immune signaling pathways in vitro. Using epigenomic profiling and transcriptomics, we found that EHMT2 is bound to transposable elements, and that EHMT inhibition leads to genome-wide epigenetic and transcriptional derepression of transposable elements. We validated EHMT-mediated activation of immune signaling and upregulation of transposable element transcripts in patient-derived, therapy-naïve, primary ovarian tumors, suggesting potential efficacy in PARPi-sensitive disease as well. Importantly, using multispectral immunohistochemistry, we discovered that combinatory therapy increased CD8 T-cell activity in the tumor microenvironment of the same patient-derived tissues. In a PARPi-resistant syngeneic murine model, EHMT and PARP inhibition combination inhibited tumor progression and increased Granzyme B+ cells in the tumor. Together, our results provide evidence that combinatory EHMT and PARP inhibition stimulates a cell autologous immune response in vitro, is an effective therapy to reduce PARPi-resistant ovarian tumor growth in vivo, and promotes antitumor immunity activity in the tumor microenvironment of patient-derived ex vivo tissues of ovarian cancer.