Harmonisation of biobanking standards in endometrial cancer research

Background: Endometrial cancer is the most common gynaecological cancer and its incidence is predicted to escalate by 50–100% in 2025 with a parallel increase in associated mortality. Variations in the collection, processing and storage of biospecimens can affect the generalisability of the scientific data. We aimed to harmonise the collection of biospecimens, clinical data relevant to endometrial cancer and to develop standard operative procedures for the collection, processing and storage of endometrial cancer biospecimens. Methods: We designed research tools, which were evaluated and revised through three consensus rounds – to obtain local/regional, national and European consensus. Modified final tools were disseminated to a panel (n=40) representing all stakeholders in endometrial cancer research for consensus generation. Results: The final consensus demonstrated unanimous agreement with the minimal surgical and patient data collection tools. A high level of agreement was also observed for the other remaining standard tools. Conclusions: We here present the final versions of the tools, which are freely available and easily accessible to all endometrial cancer researchers. We believe that these tools will facilitate rapid progress in endometrial cancer research, both in future collaborations and in large-scale multicentre studies

Genetic overlap between endometriosis and endometrial cancer: evidence from cross-disease genetic correlation and GWAS meta-analyses.

Epidemiological, biological, and molecular data suggest links between endometriosis and endometrial cancer, with recent epidemiological studies providing evidence for an association between a previous diagnosis of endometriosis and risk of endometrial cancer. We used genetic data as an alternative approach to investigate shared biological etiology of these two diseases. Genetic correlation analysis of summary level statistics from genomewide association studies (GWAS) using LD Score regression revealed moderate but significant genetic correlation (rg  = 0.23, P = 9.3 × 10-3 ), and SNP effect concordance analysis provided evidence for significant SNP pleiotropy (P = 6.0 × 10-3 ) and concordance in effect direction (P = 2.0 × 10-3 ) between the two diseases. Cross-disease GWAS meta-analysis highlighted 13 distinct loci associated at P ≤ 10-5 with both endometriosis and endometrial cancer, with one locus (SNP rs2475335) located within PTPRD associated at a genomewide significant level (P = 4.9 × 10-8 , OR = 1.11, 95% CI = 1.07-1.15). PTPRD acts in the STAT3 pathway, which has been implicated in both endometriosis and endometrial cancer. This study demonstrates the value of cross-disease genetic analysis to support epidemiological observations and to identify biological pathways of relevance to multiple diseases

Identification of nine new susceptibility loci for endometrial cancer

Endometrial cancer is the most commonly diagnosed cancer of the female reproductive tract in developed countries. Through genome-wide association studies (GWAS), we have previously identified eight risk loci for endometrial cancer. Here, we present an expanded meta-analysis of 12,906 endometrial cancer cases and 108,979 controls (including new genotype data for 5624 cases) and identify nine novel genome-wide significant loci, including a locus on 12q24.12 previously identified by meta-GWAS of endometrial and colorectal cancer. At five loci, expression quantitative trait locus (eQTL) analyses identify candidate causal genes; risk alleles at two of these loci associate with decreased expression of genes, which encode negative regulators of oncogenic signal transduction proteins (SH2B3 (12q24.12) and NF1 (17q11.2)). In summary, this study has doubled the number of known endometrial cancer risk loci and revealed candidate causal genes for future study

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

OBJECTIVE: Correct preoperative identification of high-risk patients is important to optimise surgical treatment and improve survival. We wanted to explore if asparaginase-like protein 1 (ASRGL1) expression in curettage could predict lymph node metastases and poor outcome, potentially improving preoperative risk stratification. DESIGN: Multicentre study. SETTING: Ten hospitals in Norway, Sweden and Belgium. POPULATION: Women diagnosed with endometrial carcinoma. METHODS: ASRGL1 expression in curettage specimens from 1144 women was determined by immunohistochemistry. MAIN OUTCOME MEASURES: ASRGL1 status related to disease-specific survival, lymph node status, preoperative imaging parameters and clinicopathological data. RESULTS: ASRGL1 expression had independent prognostic value in multivariate survival analyses, both in the whole patient population (hazard ratio (HR) 1.63, 95% CI 1.11-2.37, P = 0.012) and in the low-risk curettage histology subgroup (HR 2.54, 95% CI 1.44-4.47, P = 0.001). Lymph node metastases were more frequent in women with low expression of ASRGL1 compared with women with high ASRGL1 levels (23% versus 10%, P < 0.001), and low ASRGL1 level was found to independently predict lymph node metastases (odds ratio 2.07, 95% CI 1.27-3.38, P = 0.003). CONCLUSIONS: Low expression of ASRGL1 in curettage independently predicts lymph node metastases and poor disease-specific survival. TWEETABLE ABSTRACT: Low ASRGL1 expression in curettage predicts lymph node metastasis and poor survival in endometrial carcinoma.status: publishe

Genetic overlap between endometriosis and endometrial cancer: evidence from cross-disease genetic correlation and GWAS meta-analyses

Epidemiological, biological, and molecular data suggest links between endometriosis and endometrial cancer, with recent epidemiological studies providing evidence for an association between a previous diagnosis of endometriosis and risk of endometrial cancer. We used genetic data as an alternative approach to investigate shared biological etiology of these two diseases. Genetic correlation analysis of summary level statistics from genomewide association studies (GWAS) using LD Score regression revealed moderate but significant genetic correlation (rg  = 0.23, P = 9.3 × 10-3 ), and SNP effect concordance analysis provided evidence for significant SNP pleiotropy (P = 6.0 × 10-3 ) and concordance in effect direction (P = 2.0 × 10-3 ) between the two diseases. Cross-disease GWAS meta-analysis highlighted 13 distinct loci associated at P ≤ 10-5 with both endometriosis and endometrial cancer, with one locus (SNP rs2475335) located within PTPRD associated at a genomewide significant level (P = 4.9 × 10-8 , OR = 1.11, 95% CI = 1.07-1.15). PTPRD acts in the STAT3 pathway, which has been implicated in both endometriosis and endometrial cancer. This study demonstrates the value of cross-disease genetic analysis to support epidemiological observations and to identify biological pathways of relevance to multiple diseases

Identification of nine new susceptibility loci for endometrial cancer

Endometrial cancer is the most commonly diagnosed cancer of the female reproductive tract in developed countries. Through genome-wide association studies (GWAS), we have previously identified eight risk loci for endometrial cancer. Here, we present an expanded meta-analysis of 12,906 endometrial cancer cases and 108,979 controls (including new genotype data for 5624 cases) and identify nine novel genome-wide significant loci, including a locus on 12q24.12 previously identified by meta-GWAS of endometrial and colorectal cancer. At five loci, expression quantitative trait locus (eQTL) analyses identify candidate causal genes; risk alleles at two of these loci associate with decreased expression of genes, which encode negative regulators of oncogenic signal transduction proteins (SH2B3 (12q24.12) and NF1 (17q11.2)). In summary, this study has doubled the number of known endometrial cancer risk loci and revealed candidate causal genes for future study

Clinical management of breast cancer heterogeneity

Traditionally, intertumour heterogeneity in breast cancer has been documented in terms of different histological subtypes, treatment sensitivity profiles, and clinical outcomes among different patients. Results of high-throughput molecular profiling studies have subsequently revealed the true extent of this heterogeneity. Further complicating this scenario, the heterogeneous expression of the oestrogen receptor (ER), progesterone receptor (PR), and HER2 has been reported in different areas of the same tumour. Furthermore, discordance, in terms of ER, PR and HER2 expression, has also been reported between primary tumours and their matched metastatic lesions. High-throughput molecular profiling studies have confirmed that spatial and temporal intratumour heterogeneity of breast cancers exist at a level beyond common expectations. We describe the different levels of tumour heterogeneity, and discuss the strategies that can be adopted by clinicians to tackle treatment response and resistance issues associated with such heterogeneity, including a rationally selected combination of agents that target driver mutations, the targeting of deleterious passenger mutations, identifying and eradicating the 'lethal' clone, targeting the tumour microenvironment, or using adaptive treatments and immunotherapy. The identification of the most-appropriate strategies and their implementation in the clinic will prove highly challenging and necessitate the adoption of radically new practices for the optimal clinical management of breast malignancies.SCOPUS: re.jinfo:eu-repo/semantics/publishe