C.B.P. Chaves

ESR1 overexpression is a biomarker of relapse and worse prognosis in stage I endometrioid endometrial carcinoma

Endometrial cancer (EC) is the most common pelvic gynecologic malignancy in developed countries, and its incidence is also increasing in developing countries. Endometrioid endometrial carcinoma (EEC) is the most frequent subtype. EEC is often associated with favorable clinicopathological features and a good prognosis, especially when diagnosed in stage I. Although some patients have no signs to predict locally advanced or metastatic disease, they may present tumor relapse in the future. There is no biomarker capable of predicting the relapse of stage I EEC. The present study applied a transcriptome analysis to identify differentially expressed genes in stage I EEC, comparing relapsed with non-relapsed tumors. The estrogen receptor 1 gene (ESR1) was overexpressed in EEC stage I samples from patients who developed relapse by 4.3-fold compared to non-relapsed tumors. Subsequently, an independent set of 64 stage I EEC samples was used to validate ESR1 gene overexpression in relapsed tumors and assess estrogen receptor alpha (ERα) protein levels. ESR1 was confirmed to be overexpressed in samples from relapsed tumors, and its expression level was an independent prognostic variable for disease-free (hazard ratio=7.25) and overall survival (hazard ratio=5.15). In contrast, Erα did not show different values between relapsed and non-relapsed tumors. We concluded that ESR1 overexpression is a biomarker for poor prognosis in stage I EEC.

Single-cell resolution characterization of myeloid-derived cell states with implication in cancer outcome

AbstractTumor-associated myeloid-derived cells (MDCs) significantly impact cancer prognosis and treatment responses due to their remarkable plasticity and tumorigenic behaviors. Here, we integrate single-cell RNA-sequencing data from different cancer types, identifying 29 MDC subpopulations within the tumor microenvironment. Our analysis reveals abnormally expanded MDC subpopulations across various tumors and distinguishes cell states that have often been grouped together, such as TREM2+ and FOLR2+ subpopulations. Using deconvolution approaches, we identify five subpopulations as independent prognostic markers, including states co-expressing TREM2 and PD-1, and FOLR2 and PDL-2. Additionally, TREM2 alone does not reliably predict cancer prognosis, as other TREM2+ macrophages show varied associations with prognosis depending on local cues. Validation in independent cohorts confirms that FOLR2-expressing macrophages correlate with poor clinical outcomes in ovarian and triple-negative breast cancers. This comprehensive MDC atlas offers valuable insights and a foundation for futher analyses, advancing strategies for treating solid cancers.