Metastasis is a complex multistep process that involves critical interactions between cancer cells and a variety of stromal components in the tumor microenvironment, which profoundly influence the different aspects of the metastatic cascade and organ tropism of disseminating cancer cells. Ovarian cancer is the most lethal gynecological malignancy and is characterized by peritoneal disseminated metastasis. Evidence has demonstrated that ovarian cancer possesses specific metastatic tropism for the adipose-rich omentum, which has a pivotal role in the creation of the metastatic tumor microenvironment in the intraperitoneal cavity. Considering the distinct biology of ovarian cancer metastasis, the elucidation of the cellular and molecular mechanisms underlying the reciprocal interplay between ovarian cancer cells and surrounding stromal cell types in the adipose-rich metastatic microenvironment will provide further insights into the development of novel therapeutic approaches for patients with advanced ovarian cancer. Herein, we review the biological mechanisms that regulate the highly orchestrated crosstalk between ovarian cancer cells and various cancer-associated stromal cells in the metastatic tumor microenvironment with regard to the omentum by illustrating how different stromal cells concertedly contribute to the development of ovarian cancer metastasis and metastatic tropism for the omentum.

The AT-rich interacting domain-containing protein 1A gene (ARID1A) encodes ARID1A, a member of the SWI/SNF chromatin remodeling complex. Mutation of ARID1A induces changes in expression of multiple genes (CDKN1A, SMAD3, MLH1 and PIK3IP1) via chromatin remodeling dysfunction, contributes to carcinogenesis, and has been shown to cause transformation of cells in association with the PI3K/AKT pathway. Information on ARID1A has emerged from comprehensive genome-wide analyses with next-generation sequencers. ARID1A mutations have been found in various types of cancer and occur at high frequency in endometriosis-associated ovarian cancer, including clear cell adenocarcinoma and endometrioid adenocarcinoma, and also occur at endometrial cancer especially in endometrioid adenocarcinoma. It has also been suggested that ARID1A mutation occurs at the early stage of canceration from endometriosis to endometriosis-associated carcinoma in ovarian cancer and also from atypical endo-metrial hyperplasia to endometrioid adenocarcinoma in endometrial cancer. Therefore, development of a screening method that can detect mutations of ARID1A and activation of the PI3K/AKT pathway might enable early diagnosis of endometriosis-associated ovarian cancers and endometrial cancers. Important results may also emerge from a current clinical trial examining a multidrug regimen of temsirolimus, a small molecule inhibitor of the PI3K/AKT pathway, for treatment of advanced ovarian clear cell adenocarcinoma with ARID1A mutation and PI3K/AKT pathway activation. Also administration of sorafenib, a multikinase inhibitor, can inhibit cancer proliferation with PIK3CA mutation and resistance to mTOR inhibitors and GSK126, a molecular-targeted drug can inhibit proliferation of ARID1A-mutated ovarian clear cell adenocarcinoma cells by targeting and inhibiting EZH2. Further studies are needed to determine the mechanism of chromatin remodeling dysregulation initiated by ARID1A mutation, to develop methods for early diagnosis, to investigate new cancer therapy targeting ARID1A, and to examine the involvement of ARID1A mutations in development, survival and progression of cancer cells.

A) Diagram shows the single-cell RNA sequencing (scRNA-seq) and analysis workflow. We collected and processed normal fallopian tube tissues from ten cancer patients. All cells (directly sorted or maintained then sorted) were processed by using the Smart-Seq2 protocol. After the initial filtering, there were 3,877 good-quality cells left for downstream analysis. We compared the cells from three conditions to select the optimal condition for scRNA-seq. Cells from cultured or cryopreserved conditions, as well as cells carrying copy number variations and non-epithelial cells, were filtered out, which left 2,132 fresh FTE cells. Next, we used differential expression-based clustering to identify secretory subtypes.