Clues to revising the conventional diagnostic algorithm for endometriosis.

Endometriosis is a complex gynecologic disorder characterized primarily by symptoms of pelvic pain, infertility, and altered quality of life. National and international guidelines highlight the diagnostic difficulties and lack of conclusive diagnostic tools for endometriosis. Furthermore, guidelines are becoming questionable at an increasingly rapid rate as new diagnostic techniques emerge. This work aims to provide a knowledge synthesis of the relevance of various diagnostic tools and to assess areas of improvement of conventional algorithms. MEDLINE and Cochrane Library databases were searched from January 2021 to December 2023 using relevant key words. Articles evaluating the diagnostic relevance and performance of various tools were included and independently reviewed by the authors for eligibility. Included studies were assessed using the GRADE and QUADAS-2 tools. Of the 4204 retrieved articles, 26 were included. While anamnesis and clinical examination do contribute to diagnostic accuracy, their level of evidence and impact on the diagnostic process remains limited. Although imaging techniques are recommended to investigate endometriosis, ultrasonography remains highly operator dependent. Magnetic resonance imaging appears to exhibit higher sensitivities than ultrasound. However, concerns persist with regards to the terminology, anatomical definition of lesions, and accuracies of both ultrasound and magnetic resonance imaging. Recently, several biological markers have been studied and cumulative evidence supports the contribution of noncoding RNAs to the diagnosis of endometriosis. Marginal improvements have been suggested for anamnesis, clinical examination, and imaging examinations. Conversely, some biomarkers, including the saliva microRNA signature for endometriosis, have emerged as diagnostic tools which inspire reflection on the revision of conventional diagnostic algorithms.

DEBIO 1143, an IAP inhibitor, reverses carboplatin resistance in ovarian cancer cells and triggers apoptotic or necroptotic cell death.

The poor prognosis of ovarian cancer (it is the leading cause of death from gynecological cancers) is mainly due to the acquisition of resistance to carboplatin. Among the possible resistance pathways, resistance to apoptosis and especially the overexpression of inhibitor of apoptosis proteins (IAP) cIAP1 and X-linked IAP (XIAP), have been implicated. DEBIO 1143, a SMAC (second mitochondria-derived activator of caspase) mimetic, belongs to a new class of targeted agents currently being evaluated in clinical trials, which activate apoptotic cell death and block pro-survival signaling in cancer cells. Here, we demonstrate that DEBIO 1143 in vitro inhibits the cell viability of two carboplatin-sensitive cell lines (IGROV-1 and A2780S) as well as three carboplatin-resistant cell lines (A2780R, SKOV-3 and EFO-21). Of note, DEBIO 1143 is able to reverse resistance to carboplatin by inducing cell death either by apoptosis or necroptosis depending on the cell lines. To identify a biomarker able to predict the sensitivity of the cell lines to DEBIO 1143 treatment we analyzed the expression of the DEBIO 1143 targets cIAP1 and XIAP, and one of their downstream targets, caspase 9. These proteins did not constitute a marker of DEBIO 1143 sensitivity/resistance. Importantly, we confirmed these findings in vivo in SKOV-3 xenograft models where DEBIO 1143 highly potentiated carboplatin treatment.

External validation of a published nomogram for prediction of brain metastasis in patients with extra-cerebral metastatic breast cancer and risk regression analysis.

BACKGROUND: Survival of patients with metastatic breast cancer (MBC) suffering from brain metastasis (BM) is limited and this event is usually fatal. In 2010, the Graesslin's nomogram was published in order to predict subsequent BM in patients with breast cancer (BC) with extra-cerebral metastatic disease. This model aims to select a patient population at high risk for BM and thus will facilitate the design of prevention strategies and/or the impact of early treatment of BM in prospective clinical studies. PATIENTS AND METHODS: Nomogram external validation was retrospectively applied to patients with BC and later BM between January 2005 and December 2012, treated in our institution. Moreover, risk factors of BM appearance were studied by Fine and Gray's competing risk analysis. RESULTS: Among 492 patients with MBC, 116 developed subsequent BM. Seventy of them were included for the nomogram validation. The discrimination is good (area under curve = 0.695 [95% confidence interval, 0.61-0.77]). Risk factors of BM appearance are: human epidermal growth factor receptor 2 (HER2) overexpression/amplification, triple-negative BC and number of extra-cerebral metastatic sites (>1). With a competing risk model, we highlight the nomogram interest for HER2+ tumour subgroup exclusively. CONCLUSION: Graesslin's nomogram external validation demonstrates exportability and reproducibility. Importantly, the competing risk model analysis provides additional information for the design of prospective trials concerning the early diagnosis of BM and/or preventive treatment on high risk patients with extra-cerebral metastatic BC.