Nomogram Predicting the Likelihood of Parametrial Involvement in Early-Stage Cervical Cancer: Avoiding Unjustified Radical Hysterectomies

Background: We aimed to establish a tool predicting parametrial involvement (PI) in patients with early-stage cervical cancer and select a sub-group of patients who would most benefit from a less radical surgery. Methods: We retrospectively reviewed patients from two prospective multicentric databases—SENTICOL I and II—from 2005 to 2012. Patients with early-stage cervical cancer (FIGO 2018 IA with lympho-vascular involvement to IIA1), undergoing radical surgery (hysterectomy or trachelectomy) with bilateral sentinel lymph node (SLN) mapping with no metastatic node or PI on pre-operative imaging, were included. Results: In total, 5.2% patients (11/211) presented a histologic PI. After univariate analysis, SLN status, lympho-vascular space invasion, deep stromal invasion and tumor size were significantly associated with PI and were included in our nomogram. Our predictive model had an AUC of 0.92 (IC95% = 0.86–0.98) and presented a good calibration. A low risk group, defined according to the optimal sensitivity and specificity, presented a predicted probability of PI of 2%. Conclusion: Patients could benefit from a two-step approach. Final surgery (i.e. radical surgery and/or lymphadenectomy) would depend on the SLN status and the probability PI calculated after an initial conization with bilateral SLN mapping

Drug-target identification in COVID-19 disease mechanisms using computational systems biology approaches

Introduction: The COVID-19 Disease Map project is a large-scale community effort uniting 277 scientists from 130 Institutions around the globe. We use high-quality, mechanistic content describing SARS-CoV-2-host interactions and develop interoperable bioinformatic pipelines for novel target identification and drug repurposing. Methods: Extensive community work allowed an impressive step forward in building interfaces between Systems Biology tools and platforms. Our framework can link biomolecules from omics data analysis and computational modelling to dysregulated pathways in a cell-, tissue- or patient-specific manner. Drug repurposing using text mining and AI-assisted analysis identified potential drugs, chemicals and microRNAs that could target the identified key factors. Results: Results revealed drugs already tested for anti-COVID-19 efficacy, providing a mechanistic context for their mode of action, and drugs already in clinical trials for treating other diseases, never tested against COVID-19. Discussion: The key advance is that the proposed framework is versatile and expandable, offering a significant upgrade in the arsenal for virus-host interactions and other complex pathologies.Peer Reviewe

COVID19 Disease Map, a computational knowledge repository of virus-host interaction mechanisms.

Funder: Bundesministerium für Bildung und ForschungFunder: Bundesministerium für Bildung und Forschung (BMBF)We need to effectively combine the knowledge from surging literature with complex datasets to propose mechanistic models of SARS-CoV-2 infection, improving data interpretation and predicting key targets of intervention. Here, we describe a large-scale community effort to build an open access, interoperable and computable repository of COVID-19 molecular mechanisms. The COVID-19 Disease Map (C19DMap) is a graphical, interactive representation of disease-relevant molecular mechanisms linking many knowledge sources. Notably, it is a computational resource for graph-based analyses and disease modelling. To this end, we established a framework of tools, platforms and guidelines necessary for a multifaceted community of biocurators, domain experts, bioinformaticians and computational biologists. The diagrams of the C19DMap, curated from the literature, are integrated with relevant interaction and text mining databases. We demonstrate the application of network analysis and modelling approaches by concrete examples to highlight new testable hypotheses. This framework helps to find signatures of SARS-CoV-2 predisposition, treatment response or prioritisation of drug candidates. Such an approach may help deal with new waves of COVID-19 or similar pandemics in the long-term perspective

Drug-target identification in COVID-19 disease mechanisms using computational systems biology approaches

IntroductionThe COVID-19 Disease Map project is a large-scale community effort uniting 277 scientists from 130 Institutions around the globe. We use high-quality, mechanistic content describing SARS-CoV-2-host interactions and develop interoperable bioinformatic pipelines for novel target identification and drug repurposing. MethodsExtensive community work allowed an impressive step forward in building interfaces between Systems Biology tools and platforms. Our framework can link biomolecules from omics data analysis and computational modelling to dysregulated pathways in a cell-, tissue- or patient-specific manner. Drug repurposing using text mining and AI-assisted analysis identified potential drugs, chemicals and microRNAs that could target the identified key factors.ResultsResults revealed drugs already tested for anti-COVID-19 efficacy, providing a mechanistic context for their mode of action, and drugs already in clinical trials for treating other diseases, never tested against COVID-19. DiscussionThe key advance is that the proposed framework is versatile and expandable, offering a significant upgrade in the arsenal for virus-host interactions and other complex pathologies

MIBiG 3.0 : a community-driven effort to annotate experimentally validated biosynthetic gene clusters

With an ever-increasing amount of (meta)genomic data being deposited in sequence databases, (meta)genome mining for natural product biosynthetic pathways occupies a critical role in the discovery of novel pharmaceutical drugs, crop protection agents and biomaterials. The genes that encode these pathways are often organised into biosynthetic gene clusters (BGCs). In 2015, we defined the Minimum Information about a Biosynthetic Gene cluster (MIBiG): a standardised data format that describes the minimally required information to uniquely characterise a BGC. We simultaneously constructed an accompanying online database of BGCs, which has since been widely used by the community as a reference dataset for BGCs and was expanded to 2021 entries in 2019 (MIBiG 2.0). Here, we describe MIBiG 3.0, a database update comprising large-scale validation and re-annotation of existing entries and 661 new entries. Particular attention was paid to the annotation of compound structures and biological activities, as well as protein domain selectivities. Together, these new features keep the database up-to-date, and will provide new opportunities for the scientific community to use its freely available data, e.g. for the training of new machine learning models to predict sequence-structure-function relationships for diverse natural products. MIBiG 3.0 is accessible online at https://mibig.secondarymetabolites.org/

Can Conization Specimens Predict Sentinel Lymph Node Status in Early-Stage Cervical Cancer? A SENTICOL Group Study

Background: The prognosis of patients with cervical cancer is significantly worsened in case of lymph node involvement. The goal of this study was to determine whether pathologic features in conization specimens can predict the sentinel lymph node (SLN) status in early-stage cervical cancer. Methods: An ancillary analysis of two prospective multicentric database on SLN biopsy for cervical cancer (SENTICOL I and II) was carried out. Patients with IA to IB2 2018 FIGO stage, who underwent preoperative conization before SLN biopsy were included. Results: Between January 2005 and July 2012, 161 patients from 25 French centers fulfilled the inclusion criteria. Macrometastases, micrometastases and Isolated tumor cells (ITCs) were found in 4 (2.5%), 6 (3.7%) and 5 (3.1%) patients respectively. Compared to negative SLN patients, patients with micrometastatic and macrometastatic SLN were more likely to have lymphovascular space invasion (LVSI) (60% vs. 29.5%, p = 0.04) and deep stromal invasion (DSI) ≥ 10 mm (50% vs. 17.8%, p = 0.04). Among the 93 patients with DSI < 10 mm and absence of LVSI on conization specimens, three patients (3.2%) had ITCs and only one (1.1%) had micrometastases. Conclusions: Patients with DSI < 10 mm and no LVSI in conization specimens had lower risk of micro- and macrometastatic SLN. In this subpopulation, full node dissection may be questionable in case of SLN unilateral detection

The Clinical Impact of Low-Volume Lymph Nodal Metastases in Early-Stage Cervical Cancer: The Senticol 1 and Senticol 2 Trials

Background: With the development of the sentinel node technique in early-stage cervical cancer, it is imperative to define the clinical significance of micrometastases (MICs) and isolated tumor cells (ITCs). Methods: We included all patients who participated in the Senticol 1 and Senticol 2 studies. We analyzed the factors associated with the presence of low-volume metastasis, the oncological outcomes of patients with MIC and ITC and the correlation of recurrences and risk factors. Results: Twenty-four patients (7.5%) had low-volume metastasis. The risk factors associated with the presence of low-volume metastasis were a higher stage (p = 0.02) and major stromal invasion (p = 0.01) in the univariate analysis. The maximum specificity and sensitivity were found at a cutoff of 8 mm of stromal invasion. In multivariate analysis, the higher stage (p = 0.02) and the positive lymphovascular space invasion (p = 0.02) were significantly associated with the MIC and ITC. Patients with low-volume metastasis had similar disease-free survival (DFS) (92.7%) to node-negative patients (93.6%). The addition of adjuvant treatment in presence of low-volume metastasis did not modify the DFS. Conclusions: These results confirm our previous analysis of Senticol 1: the presence of low-volume metastasis did not decrease the DFS in early-stage cervical cancer patients

Temporary cervical sling and uterine twist before B-Lynch for massive uterine bleeding after delivery

Massive uterine bleeding occurring after delivery is in most cases unpredictable and can have fatal consequences. This article presents the technique of combining the twisting of uterus on a 90° rotation on its axis and positioning a sling around the cervix, allowing to decrease incoming blood flow from uterine and ovarian arteries. The aim of this easy-to-use procedure is to enable surgeons and anaesthesiologists to respectively ensure the presence of an experienced surgeon and to stabilise the haemodynamic of the patient. It is a modus operandi of particular interest in resources’ challenged environments

Histopathologic Validation of the Sentinel Node Technique for Early-Stage Cervical Cancer Patients

International audienceAbstract Background The sentinel lymph node (SLN) biopsy may be an alternative to systematic lymphadenectomy in early cervical cancer. The SLN biopsy is less morbid and has been shown to have high sensitivity for metastasis detection. However, the sensitivity of the SLN technique might be overevaluated because SLNs are examined with ultra-staging, and non-sentinel nodes usually are examined only with routine techniques. This study aimed to validate the negative predictive value (NPV) of the SLN technique by the ultra-staging of SLNs and non-sentinel nodes (NSLNs). Methods The SENTICOL 1 study data published in 2011 were used. All nodes (i.e., SLNs and NSLNs) were secondarily subjected to ultra-staging. The ultra-staging consisted of sectioning every 200 µm, in addition to immunohistochemistry. Moreover, the positive slides and 10% of the negative slides were reviewed. Results The study enrolled 139 patients, and SLNs were detected in 136 (97.8%) of these patiets. Bilateral SLNs were detected in 104 (76.5%) of the 136 patients. A total of 2056 NSLNs were identified (median, 13 NSLNs per patient; range 1–54). Of the 136 patients with SLNs, 23 were shown to have positive SLNs after serial sectioning and immunohistochemical staining. The NSLNs were metastatic in six patients. In the case of bilateral SLN detection, the NPV was 100%, with no false-negatives (FNs). Conclusions The pelvic SLN technique is safe and trustworthy for determining the nodal status of patients with early-stage cervical cancer. In the case of optimal mapping with bilateral detection, the NPV was found to be 100%