The Use of Targeted Agents in the Treatment of Gynecologic Cancers

TIGIT: A potential immunotherapy target for gynecological cancers

Gynecological cancer represents a significant global health challenge, and conventional treatment modalities have demonstrated limited efficacy. However, recent investigations into immune checkpoint pathways have unveiled promising opportunities for enhancing the prognosis of patients with cancer. Among these pathways, TIGIT has surfaced as a compelling candidate owing to its capacity to augment the immune function of NK and T cells through blockade, thereby yielding improved anti-tumor effects and prolonged patient survival. Global clinical trials exploring TIGIT blockade therapy have yielded promising preliminary findings. Nevertheless, further research is imperative to comprehensively grasp the potential of TIGIT-based immunotherapy in optimizing therapeutic outcomes for gynecological cancers. This review primarily delineates the regulatory network and immunosuppressive mechanism of TIGIT, expounds upon its expression and therapeutic potential in three major gynecological cancers, and synthesizes the clinical trials of TIGIT-based cancer immunotherapy. Such insights aim to furnish novel perspectives and serve as reference points for subsequent research and clinical application targeting TIGIT in gynecological cancers.

Hormone regulation of thrombospondin-1 mRNA in porcine granulosa cells in vitro

Thrombospondin-1 (THBS1) is involved in the process of angiogenesis and is down-regulated by insulin-like growth factor 1 (IGF1) in porcine granulosa cells (GC), but what other hormones regulate GC THBS1 and its role in follicular growth is unclear. Thus, six experiments were conducted to determine the influence of other hormones on THBS1 gene expression in porcine GC, and to determine if THBS1 mRNA changes during follicular development. For Exp. 1-5, small (1-5 mm) follicles from ovaries of abattoir gilts were aspirated, GC collected and treated with FSH, IGF1, fibroblast growth factor 9 (FGF9), Sonic hedgehog (SHH), estradiol, cortisol, and/or prostaglandin E2 (PGE2). FSH, IGF1 and FGF9 each decreased (P < 0.05) THBS1 mRNA abundance. Alone, PGE2 increased (P < 0.05) THBS1 mRNA abundance. PGE2 significantly attenuated the FSH-induced inhibition of THBS1 mRNA expression. Estradiol, cortisol, and SHH had no effect on THBS1 mRNA abundance. In Exp. 6, small (1-3 mm), medium (4-6 mm) and large (7-14 mm) follicles were aspirated to measure abundance of THBS1 mRNA in GC which did not differ (P > 0.10) between small and medium-sized follicles but was threefold greater (P < 0.05) in large compared to small or medium follicles. We hypothesize that the inhibitory effects of FSH, IGF1 and FGF9 on the antiangiogenic gene THBS1 could contribute to promoting angiogenesis in the developing follicle, while stimulation of THBS1 mRNA by PGE2 may help reduce angiogenesis during the preovulatory period when PGE2 and THBS1 mRNA are at their greatest levels.

Stochastic model of ERK-mediated progesterone receptor translocation, clustering and transcriptional activity

Progesterone receptor (PR) transcriptional activity is a key factor in the differentiation of the uterine endometrium. By consequence, progestin has been identified as an important treatment modality for endometrial cancer. PR transcriptional activity is controlled by extracellular-signal-regulated kinase (ERK) mediated phosphorylation, downstream of growth factor receptors such as EGFR. However, phosphorylation of PR also targets it for ubiquitination and destruction in the proteasome. Quantitative studies of these opposing roles are much needed toward validation of potential new progestin-based therapeutics. In this work, we propose a spatial stochastic model to study the effects of the opposing roles for PR phosphorylation on the levels of active transcription factor. Our numerical simulations confirm earlier in vitro experiments in endometrial cancer cell lines, identifying clustering as a mechanism that amplifies the ability of progesterone receptors to influence gene transcription. We additionally show the usefulness of a statistical method we developed to quantify and control variations in stochastic simulations in general biochemical systems, assisting modelers in defining minimal but meaningful numbers of simulations while guaranteeing outputs remain within a pre-defined confidence level.