Asparaginase-like protein 1 expression in curettage independently predicts lymph node metastasis in endometrial carcinoma: a multicentre study

Asparaginase-like protein 1 as a prognostic tissue biomarker in clinicopathologically and molecularly characterized endometrial cancer

OBJECTIVE

Prognostic stratification of endometrial cancer involves the assessment of stage, uterine risk factors, and molecular classification. This process can be further refined through annotation of prognostic biomarkers, notably L1 cell adhesion molecule (L1CAM) and hormonal receptors. Loss of asparaginase-like protein 1 (ASRGL1) has been shown to correlate with poor outcome in endometrial cancer. Our objective was to assess prognostication of endometrial cancer by ASRGL1 in conjunction with other available methodologies.

STUDY DESIGN

This was a retrospective study of patients who underwent primary treatment at a single tertiary center. Tumors were molecularly classified by the Proactive Molecular Risk Classifier for Endometrial Cancer. Expression of ASRGL1, L1CAM, estrogen receptor, and progesterone receptor was determined by immunohistochemistry. ASRGL1 expression intensity was scored into four classes.

RESULTS

In a cohort of 775 patients, monitored for a median time of 81 months, ASRGL1 expression intensity was related to improved disease-specific survival in a dose-dependent manner (P < 0.001). Low expression levels were associated with stage II-IV disease and presence of uterine factors, i.e. high grade, lymphovascular space invasion, and deep myometrial invasion (P < 0.001 for all). Among the molecular subgroups, low expression was most prevalent in p53 abnormal carcinomas (P < 0.001). Low ASRGL1 was associated with positive L1CAM expression and negative estrogen and progesterone receptor expression (P < 0.001 for all). After adjustment for stage and uterine factors, strong ASRGL1 staining intensity was associated with a lower risk for cancer-related deaths (hazard ratio 0.56, 95 % confidence interval 0.32-0.97; P = 0.038). ASRGL1 was not associated with the outcome when adjusted for stage, molecular subgroups, L1CAM, and hormonal receptors. When analyzed separately within the different molecular subgroups, ASRGL1 showed an association with disease-specific survival specifically in "no specific molecular profile" subtype carcinomas (P < 0.001). However, this association became nonsignificant upon controlling for confounders.

CONCLUSIONS

Low ASRGL1 expression intensity correlates with poor survival in endometrial cancer. ASRGL1 contributes to more accurate prognostication when controlled for stage and uterine factors. However, when adjusted for stage and other biomarkers, including molecular subgroups, ASRGL1 does not improve prognostic stratification.

Glutamine and amino acid metabolism as a prognostic signature and therapeutic target in endometrial cancer

INTRODUCTION

Endometrial cancer (EC) is the most common female reproductive system cancer in developed countries with growing incidence and associated mortality, which may be due to the growing prevalence of obesity. Metabolism reprogramming including glucose, amino acid, and lipid remodeling is a hallmark of tumors. Glutamine metabolism has been reported to participate in tumor proliferation and development. This study aimed to develop a glutamine metabolism-related prognostic model for EC and explore potential targets for cancer treatment.

METHOD

Transcriptomic data and survival outcome of EC were retrieved from The Cancer Genome Atlas (TCGA). Differentially expressed genes related to glutamine metabolism were recognized and utilized to build a prognostic model by univariate and multivariate Cox regressions. The model was confirmed in the training, testing, and the entire cohort. A nomogram combing prognostic model and clinicopathologic features was established and tested. Moreover, we explored the effect of a key metabolic enzyme, PHGDH, on the biological behavior of EC cell lines and xenograft model.

RESULTS

Five glutamine metabolism-related genes, including PHGDH, OTC, ASRGL1, ASNS, and NR1H4, were involved in prognostic model construction. Kaplan-Meier curve suggested that patients recognized as high risk underwent inferior outcomes. The receiver operating characteristic (ROC) curve showed the model was sufficient to predict survival. Enrichment analysis recognized DNA replication and repair dysfunction in high-risk patients whereas immune relevance analysis revealed low immune scores in the high-risk group. Finally, a nomogram integrating the prognostic model and clinical factors was created and verified. Further, knockdown of PHGDH showed cell growth inhibition, increasing apoptosis, and reduced migration. Promisingly, NCT-503, a PHGDH inhibitor, significantly repressed tumor growth in vivo (p = 0.0002).

CONCLUSION

Our work established and validated a glutamine metabolism-related prognostic model that favorably evaluates the prognosis of EC patients. DNA replication and repair may be the crucial point that linked glutamine metabolism, amino acid metabolism, and EC progression. High-risk patients stratified by the model may not be sufficient for immune therapy. PHGDH might be a crucial target that links serine metabolism, glutamine metabolism as well as EC progression.

Metabolic classifications of renal cell carcinoma reveal intrinsic connections with clinical and immune characteristics

BACKGROUND

Kidney cancer undergoes a dramatic metabolic shift and has demonstrated responsiveness to immunotherapeutic intervention. However, metabolic classification and the associations between metabolic alterations and immune infiltration in Renal cell carcinoma still remain elucidative.

METHODS

Unsupervised consensus clustering was conducted on the TCGA cohorts for metabolic classification. GESA, mRNAsi, prognosis, clinical features, mutation load, immune infiltration and differentially expressed gene differences among different clusters were compared. The prognosis model and nomograms were constructed based on metabolic gene signatures and verified using external ICGC datasets. Immunohistochemical results from Human Protein Atlas database and Tongji hospital were used to validate gene expression levels in normal tissues and tumor samples. CCK8, apoptosis analysis, qPCR, subcutaneously implanted murine models and flowcytometry analysis were applied to investigate the roles of ACAA2 in tumor progression and anti-tumor immunity.

RESULTS

Renal cell carcinoma was classified into 3 metabolic subclusters and the subcluster with low metabolic profiles displayed the poorest prognosis, highest invasiveness and AJCC grade, enhanced immune infiltration but suppressive immunophenotypes. ACAA2, ACAT1, ASRGL1, AKR1B10, ABCC2, ANGPTL4 were identified to construct the 6 gene-signature prognosis model and verified both internally and externally with ICGC cohorts. ACAA2 was demonstrated as a tumor suppressor and was associated with higher immune infiltration and elevated PD-1 expression of CD8⁺ T cells.

CONCLUSIONS

Our research proposed a new metabolic classification method for RCC and revealed intrinsic associations between metabolic phenotypes and immune profiles. The identified gene signatures might serve as key factors bridging tumor metabolism and tumor immunity and warrant further in-depth investigations.

Preoperative pelvic MRI and 2-[18F]FDG PET/CT for lymph node staging and prognostication in endometrial cancer-time to revisit current imaging guidelines?

OBJECTIVE

This study presents the diagnostic performance of four different preoperative imaging workups (IWs) for prediction of lymph node metastases (LNMs) in endometrial cancer (EC): pelvic MRI alone (IW1), MRI and [¹⁸F]FDG-PET/CT in all patients (IW2), MRI with selective [¹⁸F]FDG-PET/CT if high-risk preoperative histology (IW3), and MRI with selective [¹⁸F]FDG-PET/CT if MRI indicates FIGO stage ≥ 1B (IW4).

METHODS

In 361 EC patients, preoperative staging parameters from both pelvic MRI and [¹⁸F]FDG-PET/CT were recorded. Area under receiver operating characteristic curves (ROC AUC) compared the diagnostic performance for the different imaging parameters and workups for predicting surgicopathological FIGO stage. Survival data were assessed using Kaplan-Meier estimator with log-rank test.

RESULTS

MRI and [¹⁸F]FDG-PET/CT staging parameters yielded similar AUCs for predicting corresponding FIGO staging parameters in low-risk versus high-risk histology groups (p ≥ 0.16). The sensitivities, specificities, and AUCs for LNM prediction were as follows: IW1-33% [9/27], 95% [185/193], and 0.64; IW2-56% [15/27], 90% [174/193], and 0.73 (p = 0.04 vs. IW1); IW3-44% [12/27], 94% [181/193], and 0.69 (p = 0.13 vs. IW1); and IW4-52% [14/27], 91% [176/193], and 0.72 (p = 0.06 vs. IW1). IW3 and IW4 selected 34% [121/361] and 54% [194/361] to [¹⁸F]FDG-PET/CT, respectively. Employing IW4 identified three distinct patient risk groups that exhibited increasing FIGO stage (p < 0.001) and stepwise reductions in survival (p ≤ 0.002).

CONCLUSION

Selective [¹⁸F]FDG-PET/CT in patients with high-risk MRI findings yields better detection of LNM than MRI alone, and similar diagnostic performance to that of MRI and [¹⁸F]FDG-PET/CT in all.

KEY POINTS

• Imaging by MRI and [¹⁸F]FDG PET/CT yields similar diagnostic performance in low- and high-risk histology groups for predicting central FIGO staging parameters. • Utilizing a stepwise imaging workup with MRI in all patients and [¹⁸F]FDG-PET/CT in selected patients based on MRI findings identifies preoperative risk groups exhibiting significantly different survival. • The proposed imaging workup selecting ~54% of the patients to [¹⁸F]FDG-PET/CT yield better detection of LNMs than MRI alone, and similar LNM detection to that of MRI and [¹⁸F]FDG-PET/CT in all.

Patient-derived organoids reflect the genetic profile of endometrial tumors and predict patient prognosis

Background

A major hurdle in translational endometrial cancer (EC) research is the lack of robust preclinical models that capture both inter- and intra-tumor heterogeneity. This has hampered the development of new treatment strategies for people with EC.

Methods

EC organoids were derived from resected patient tumor tissue and expanded in a chemically defined medium. Established EC organoids were orthotopically implanted into female NSG mice. Patient tissue and corresponding models were characterized by morphological evaluation, biomarker and gene expression and by whole exome sequencing. A gene signature was defined and its prognostic value was assessed in multiple EC cohorts using Mantel-Cox (log-rank) test. Response to carboplatin and/or paclitaxel was measured in vitro and evaluated in vivo. Statistical difference between groups was calculated using paired t-test.

Results

We report EC organoids established from EC patient tissue, and orthotopic organoid-based patient-derived xenograft models (O-PDXs). The EC organoids and O-PDX models mimic the tissue architecture, protein biomarker expression and genetic profile of the original tissue. Organoids show heterogenous sensitivity to conventional chemotherapy, and drug response is reproduced in vivo. The relevance of these models is further supported by the identification of an organoid-derived prognostic gene signature. This signature is validated as prognostic both in our local patient cohorts and in the TCGA endometrial cancer cohort.

Conclusions

We establish robust model systems that capture both the diversity of endometrial tumors and intra-tumor heterogeneity. These models are highly relevant preclinical tools for the elucidation of the molecular pathogenesis of EC and identification of potential treatment strategies.

To study the biology of cancer and test new potential treatments, it is important to use models that mimic patients’ tumors. Such models have largely been lacking in endometrial cancer. We therefore aimed to developing miniature tumors, called “organoids”, directly from patient tumor tissue. Our organoids maintained the characteristics and genetic features of the tumors from which they were derived, would grow into endometrial tumors in mice, and exhibited patient-specific responses to chemotherapy drugs. In summary, we have developed models that will help us better understand the biology of endometrial tumors and can be used to potentially identify new effective drugs for endometrial cancer patients.

Berg et al. establish a panel of patient-derived endometrial cancer organoids and xenograft models. They show that their models recapitulate the genetic profile of the donor tumor and can be used for drug testing and development of a prognostic gene signature.

Pilot Study to Establish a Novel Five-Gene Biomarker Panel for Predicting Lymph Node Metastasis in Patients With Early Stage Endometrial Cancer

Introduction: In the United States and Europe, endometrial endometrioid carcinoma (EEC) is the most prevalent gynecologic malignancy. Lymph node metastasis (LNM) is the key determinant of the prognosis and treatment of EEC. A biomarker that predicts LNM in patients with EEC would be beneficial, enabling individualized treatment. Current preoperative assessment of LNM in EEC is not sufficiently accurate to predict LNM and prevent overtreatment. This pilot study established a biomarker signature for the prediction of LNM in early stage EEC.

Methods: We performed RNA sequencing in 24 clinically early stage (T1) EEC tumors (lymph nodes positive and negative in 6 and 18, respectively) from Cathay General Hospital and analyzed the RNA sequencing data of 289 patients with EEC from The Cancer Genome Atlas (lymph node positive and negative in 33 and 256, respectively). We analyzed clinical data including tumor grade, depth of tumor invasion, and age to construct a sequencing-based prediction model using machine learning. For validation, we used another independent cohort of early stage EEC samples (n = 72) and performed quantitative real-time polymerase chain reaction (qRT-PCR). Finally, a PCR-based prediction model and risk score formula were established.

Results: Eight genes (ASRGL1, ESR1, EYA2, MSX1, RHEX, SCGB2A1, SOX17, and STX18) plus one clinical parameter (depth of myometrial invasion) were identified for use in a sequencing-based prediction model. After qRT-PCR validation, five genes (ASRGL1, RHEX, SCGB2A1, SOX17, and STX18) were identified as predictive biomarkers. Receiver operating characteristic curve analysis revealed that these five genes can predict LNM. Combined use of these five genes resulted in higher diagnostic accuracy than use of any single gene, with an area under the curve of 0.898, sensitivity of 88.9%, and specificity of 84.1%. The accuracy, negative, and positive predictive values were 84.7, 98.1, and 44.4%, respectively.

Conclusion: We developed a five-gene biomarker panel associated with LNM in early stage EEC. These five genes may represent novel targets for further mechanistic study. Our results, after corroboration by a prospective study, may have useful clinical implications and prevent unnecessary elective lymph node dissection while not adversely affecting the outcome of treatment for early stage EEC.

Dynamic transcriptome profiles within spermatogonial and spermatocyte populations during postnatal testis maturation revealed by single-cell sequencing

Spermatogenesis is the process by which male gametes are formed from a self-renewing population of spermatogonial stem cells (SSCs) residing in the testis. SSCs represent less than 1% of the total testicular cell population in adults, but must achieve a stable balance between self-renewal and differentiation. Once differentiation has occurred, the newly formed and highly proliferative spermatogonia must then enter the meiotic program in which DNA content is doubled, then halved twice to create haploid gametes. While much is known about the critical cellular processes that take place during the specialized cell division that is meiosis, much less is known about how the spermatocytes in the "first-wave" in juveniles compare to those that contribute to long-term, "steady-state" spermatogenesis in adults. Given the strictly-defined developmental process of spermatogenesis, this study explored the transcriptional profiles of developmental cell stages during testis maturation. Using a combination of comprehensive germ cell sampling with high-resolution, single-cell-mRNA-sequencing, we have generated a reference dataset of germ cell gene expression. We show that discrete developmental stages of spermatogenesis possess significant differences in the transcriptional profiles from neonates compared to juveniles and adults. Importantly, these gene expression dynamics are also reflected at the protein level in their respective cell types. We also show differential utilization of many biological pathways with age in both spermatogonia and spermatocytes, demonstrating significantly different underlying gene regulatory programs in these cell types over the course of testis development and spermatogenic waves. This dataset represents the first unbiased sampling of spermatogonia and spermatocytes during testis maturation, at high-resolution, single-cell depth. Not only does this analysis reveal previously unknown transcriptional dynamics of a highly transitional cell population, it has also begun to reveal critical differences in biological pathway utilization in developing spermatogonia and spermatocytes, including response to DNA damage and double-strand breaks.