Role of Sex in the Therapeutic Targeting of p53 Circuitry

Differences in the role of Gper1 in colorectal cancer progression depending on sex

To evaluate the role of 17β-oestradiol (E2) in the sex-dependent progression of colorectal cancer (CRC), the present study focused on E2 signalling mediated via the nuclear receptors [oestrogen receptor (ESR)1 and ESR2] and the membrane G protein-coupled oestrogen receptor 1 (Gper1) in males and females diagnosed with CRC. This study also investigated Gper1 signalling in the CRC cell lines DLD1 and LoVo, which differ in the p53 pathway. In cancer tissue, Gper1 becomes by far the most abundant E2 receptor due to an increase in Gper1 and a decrease in ESR2 expression. These changes are more prominent in males than in females. More pronounced differences in Gper1 expression between cancer and adjacent tissues were observed in males in lower stages compared with those in higher stages of disease and females. High expression of Gper1 was associated with worse survival in males without nodal involvement but not in females. The expression of E2 receptors in the CRC cell lines DLD1 and LoVo resembles that of human cancer tissue. Silencing of Gper1 (siGper1) caused an increase in the rate of metabolism in LoVo cells with wild-type tp53. In DLD1 cells with the mutated form of tp53, siGper1 did not exert this effect. High levels of Gper1 were associated with worse survival and could contribute to sex-dependent changes in the CRC prognosis. Tumour suppressor effects of Gper1 were, at least to some extent, dependent on signalling downstream of p53, which was more frequently deficient in males than in females. Overall, this suggests that up-regulation of Gper1 (or administration of a Gper1 agonist) would be more beneficial for patients with wild-type tp53.

Proteomic studies of VEGFR2 in human placentas reveal protein associations with preeclampsia, diabetes, gravidity, and labor

VEGFR2 (Vascular endothelial growth factor receptor 2) is a central regulator of placental angiogenesis. The study of the VEGFR2 proteome of chorionic villi at term revealed its partners MDMX (Double minute 4 protein) and PICALM (Phosphatidylinositol-binding clathrin assembly protein). Subsequently, the oxytocin receptor (OT-R) and vasopressin V1aR receptor were detected in MDMX and PICALM immunoprecipitations. Immunogold electron microscopy showed VEGFR2 on endothelial cell (EC) nuclei, mitochondria, and Hofbauer cells (HC), tissue-resident macrophages of the placenta. MDMX, PICALM, and V1aR were located on EC plasma membranes, nuclei, and HC nuclei. Unexpectedly, PICALM and OT-R were detected on EC projections into the fetal lumen and OT-R on 20-150 nm clusters therein, prompting the hypothesis that placental exosomes transport OT-R to the fetus and across the blood-brain barrier. Insights on gestational complications were gained by univariable and multivariable regression analyses associating preeclampsia with lower MDMX protein levels in membrane extracts of chorionic villi, and lower MDMX, PICALM, OT-R, and V1aR with spontaneous vaginal deliveries compared to cesarean deliveries before the onset of labor. We found select associations between higher MDMX, PICALM, OT-R protein levels and either gravidity, diabetes, BMI, maternal age, or neonatal weight, and correlations only between PICALM-OT-R (p < 2.7 × 10-8), PICALM-V1aR (p < 0.006), and OT-R-V1aR (p < 0.001). These results offer for exploration new partnerships in metabolic networks, tissue-resident immunity, and labor, notably for HC that predominantly express MDMX.

Clinical Informatics Approaches to Understand and Address Cancer Disparities

OBJECTIVES

Disparities in cancer incidence and outcomes across race, ethnicity, gender, socioeconomic status, and geography are well-documented, but their etiologies are often poorly understood and multifactorial. Clinical informatics can provide tools to better understand and address these disparities by enabling high-throughput analysis of multiple types of data. Here, we review recent efforts in clinical informatics to study and measure disparities in cancer.

METHODS

We carried out a narrative review of clinical informatics studies related to cancer disparities and bias published from 2018-2021, with a focus on domains such as real-world data (RWD) analysis, natural language processing (NLP), radiomics, genomics, proteomics, metabolomics, and metagenomics.

RESULTS

Clinical informatics studies that investigated cancer disparities across race, ethnicity, gender, and age were identified. Most cancer disparities work within clinical informatics used RWD analysis, NLP, radiomics, and genomics. Emerging applications of clinical informatics to understand cancer disparities, including proteomics, metabolomics, and metagenomics, were less well represented in the literature but are promising future research avenues. Algorithmic bias was identified as an important consideration when developing and implementing cancer clinical informatics techniques, and efforts to address this bias were reviewed.

CONCLUSIONS

In recent years, clinical informatics has been used to probe a range of data sources to understand cancer disparities across different populations. As informatics tools become integrated into clinical decision-making, attention will need to be paid to ensure that algorithmic bias does not amplify existing disparities. In our increasingly interconnected medical systems, clinical informatics is poised to untap the full potential of multi-platform health data to address cancer disparities.