Single-cell dissection of cellular components and interactions shaping the tumor immune phenotypes in ovarian cancer.

Distinct T cell infiltration patterns, i.e., immune infiltrated, excluded, and desert, result in different responses to cancer immunotherapies. However, the key determinants and biology underpinning these tumor immune phenotypes remain elusive. Here, we provide a high-resolution dissection of the entire tumor ecosystem through single-cell RNA-sequencing analysis of 15 ovarian tumors. Immune-desert tumors are characterized by unique tumor cell-intrinsic features, including metabolic pathways and low antigen presentation, and an enrichment of monocytes and immature macrophages. Immune-infiltrated and -excluded tumors differ markedly in their T cell composition and fibroblast subsets. Furthermore, our study reveals chemokine receptor-ligand interactions within and across compartments as potential mechanisms mediating immune cell infiltration, exemplified by the tumor cell-T cell cross talk via CXCL16-CXCR6 and stromal-immune cell cross talk via CXCL12/14-CXCR4. Our data highlight potential molecular mechanisms that shape the tumor immune phenotypes and may inform therapeutic strategies to improve clinical benefit from cancer immunotherapies.

PD-1/PD-L1 checkpoint inhibitors in combination with olaparib display antitumor activity in ovarian cancer patient-derived three-dimensional spheroid cultures.

Immune checkpoint inhibitors (ICIs) that target programmed cell death protein 1 (PD-1) and programmed death-ligand 1 (PD-L1) have shown modest activity as monotherapies for the treatment of ovarian cancer (OC). The rationale for using these therapies in combination with poly (ADP-ribose) polymerase inhibitors (PARP-Is) has been described, and their in vivo application will benefit from ex vivo platforms that aid in the prediction of patient response or resistance to therapy. This study examined the effectiveness of detecting patient-specific immune-related activity in OC using three-dimensional (3D) spheroids. Immune-related cell composition and PD-1/PD-L1 expression status were evaluated using cells dissociated from fresh OC tissue from two patients prior to and following 3D culture. The patient sample with the greatest increase in the proportion of PD-L1 + cells also possessed more activated cytotoxic T cells and mature DCs compared to the other patient sample. Upon cytokine stimulation, patient samples demonstrated increases in cytotoxic T cell activation and DC major histocompatibility complex (MHC) class-II expression. Pembrolizumab increased cytokine secretion, enhanced olaparib cytotoxicity, and reduced spheroid viability in a T cell-dependent manner. Furthermore, durvalumab and olaparib combination treatment increased cell death in a synergistic manner. This work demonstrates that immune cell activity and functional modulation can be accurately detected using our ex vivo 3D spheroid platform, and it presents evidence for their utility to demonstrate sensitivity to ICIs alone or in combination with PARP-Is in a preclinical setting.

Prospective Validation of an Ex Vivo, Patient-Derived 3D Spheroid Model for Response Predictions in Newly Diagnosed Ovarian Cancer.

Although 70-80% of newly diagnosed ovarian cancer patients respond to first-line therapy, almost all relapse and five-year survival remains below 50%. One strategy to increase five-year survival is prolonging time to relapse by improving first-line therapy response. However, no biomarker today can accurately predict individual response to therapy. In this study, we present analytical and prospective clinical validation of a new test that utilizes primary patient tissue in 3D cell culture to make patient-specific response predictions prior to initiation of treatment in the clinic. Test results were generated within seven days of tissue receipt from newly diagnosed ovarian cancer patients obtained at standard surgical debulking or laparoscopic biopsy. Patients were followed for clinical response to chemotherapy. In a study population of 44, the 32 test-predicted Responders had a clinical response rate of 100% across both adjuvant and neoadjuvant treated populations with an overall prediction accuracy of 89% (39 of 44, p < 0.0001). The test also functioned as a prognostic readout with test-predicted Responders having a significantly increased progression-free survival compared to test-predicted Non-Responders, p = 0.01. This correlative accuracy establishes the test's potential to benefit ovarian cancer patients through accurate prediction of patient-specific response before treatment.