Interactive exploration of a global clinical network from a large breast cancer cohort.

Despite unprecedented amount of information now available in medical records, health data remain underexploited due to their heterogeneity and complexity. Simple charts and hypothesis-driven statistics can no longer apprehend the content of information-rich clinical data. There is, therefore, a clear need for powerful interactive visualization tools enabling medical practitioners to perceive the patterns and insights gained by state-of-the-art machine learning algorithms. Here, we report an interactive graphical interface for use as the front end of a machine learning causal inference server (MIIC), to facilitate the visualization and comprehension by clinicians of relationships between clinically relevant variables. The widespread use of such tools, facilitating the interactive exploration of datasets, is crucial both for data visualization and for the generation of research hypotheses. We demonstrate the utility of the MIIC interactive interface, by exploring the clinical network of a large cohort of breast cancer patients treated with neoadjuvant chemotherapy (NAC). This example highlights, in particular, the direct and indirect links between post-NAC clinical responses and patient survival. The MIIC interactive graphical interface has the potential to help clinicians identify actionable nodes and edges in clinical networks, thereby ultimately improving the patient care pathway.

Pregnancy, fertility concerns and fertility preservation procedures in a national study of French breast cancer survivors.

RESEARCH QUESTION: What are the real-life oncofertility practices in young women diagnosed with breast cancer? DESIGN: The FEERIC (FErtility, prEgnancy, contRaceptIon after breast Cancer in France) study is a web-based cohort study launched with the French collaborative research platform Seintinelles. The current work is based on the enrolment self-administered questionnaire of 517 patients with prior breast cancer diagnosis, free from relapse and aged 18 to 43 years at inclusion (from 12 March 2018 to 27 June 2019). RESULTS: Median age at breast cancer diagnosis was 33.6 years and 424 patients (82.0%) received chemotherapy. Overall, 236 (45.6%) patients were offered specialized oncofertility counselling, 181 patients underwent at least one fertility preservation procedure (FPP); 125 (24.2%) underwent one or more FPP with material preservation (oocytes n = 108, 20.9%; embryos n = 31, 6.0%; ovarian cryopreservation n = 6, 1.2%) and 78 patients received gonadotrophin-releasing hormone agonists (15.1%). With a median follow-up of 26.9 months after the end of treatments, 133 pregnancies had occurred in 85 patients (16.4%), including 20 unplanned pregnancies (15.0%). Most of the pregnancies were natural conceptions (n = 113, 87.6%), while 16 (12.4%) required medical interventions. For the planned pregnancies, median time to the occurrence of an ongoing pregnancy was 3 months. Patients who had an unplanned pregnancy reported lower rates of information on the consequences of the treatments on fertility (P = 0.036) at diagnosis. CONCLUSIONS: Most of the patients were not offered proper specialized oncofertility counselling at the time of breast cancer diagnosis. Naturally conceived pregnancies after breast cancer were much more frequent than pregnancies resulting from the use of cryopreserved gametes. Adequate contraceptive counselling seems as important as information about fertility and might prevent unplanned pregnancies.

Inferring Gene Networks in Bone Marrow Hematopoietic Stem Cell-Supporting Stromal Niche Populations.

The cardinal property of bone marrow (BM) stromal cells is their capacity to contribute to hematopoietic stem cell (HSC) niches by providing mediators assisting HSC functions. In this study we first contrasted transcriptomes of stromal cells at different developmental stages and then included large number of HSC-supportive and non-supportive samples. Application of a combination of algorithms, comprising one identifying reliable paths and potential causative relationships in complex systems, revealed gene networks characteristic of the BM stromal HSC-supportive capacity and of defined niche populations of perivascular cells, osteoblasts, and mesenchymal stromal cells. Inclusion of single-cell transcriptomes enabled establishing for the perivascular cell subset a partially oriented graph of direct gene-to-gene interactions. As proof of concept we showed that R-spondin-2, expressed by the perivascular subset, synergized with Kit ligand to amplify ex vivo hematopoietic precursors. This study by identifying classifiers and hubs constitutes a resource to unravel candidate BM stromal mediators.

Learning clinical networks from medical records based on information estimates in mixed-type data.

The precise diagnostics of complex diseases require to integrate a large amount of information from heterogeneous clinical and biomedical data, whose direct and indirect interdependences are notoriously difficult to assess. To this end, we propose an efficient computational approach to simultaneously compute and assess the significance of multivariate information between any combination of mixed-type (continuous/categorical) variables. The method is then used to uncover direct, indirect and possibly causal relationships between mixed-type data from medical records, by extending a recent machine learning method to reconstruct graphical models beyond simple categorical datasets. The method is shown to outperform existing tools on benchmark mixed-type datasets, before being applied to analyze the medical records of eldery patients with cognitive disorders from La Pitié-Salpêtrière Hospital, Paris. The resulting clinical network visually captures the global interdependences in these medical records and some facets of clinical diagnosis practice, without specific hypothesis nor prior knowledge on any clinically relevant information. In particular, it provides some physiological insights linking the consequence of cerebrovascular accidents to the atrophy of important brain structures associated to cognitive impairment.

MIIC online: a web server to reconstruct causal or non-causal networks from non-perturbative data.

Summary: We present a web server running the MIIC algorithm, a network learning method combining constraint-based and information-theoretic frameworks to reconstruct causal, non-causal or mixed networks from non-perturbative data, without the need for an a priori choice on the class of reconstructed network. Starting from a fully connected network, the algorithm first removes dispensable edges by iteratively subtracting the most significant information contributions from indirect paths between each pair of variables. The remaining edges are then filtered based on their confidence assessment or oriented based on the signature of causality in observational data. MIIC online server can be used for a broad range of biological data, including possible unobserved (latent) variables, from single-cell gene expression data to protein sequence evolution and outperforms or matches state-of-the-art methods for either causal or non-causal network reconstruction. Availability and implementation: MIIC online can be freely accessed at https://miic.curie.fr. Supplementary information: Supplementary data are available at Bioinformatics online.

Learning causal networks with latent variables from multivariate information in genomic data.

Learning causal networks from large-scale genomic data remains challenging in absence of time series or controlled perturbation experiments. We report an information- theoretic method which learns a large class of causal or non-causal graphical models from purely observational data, while including the effects of unobserved latent variables, commonly found in many genomic datasets. Starting from a complete graph, the method iteratively removes dispensable edges, by uncovering significant information contributions from indirect paths, and assesses edge-specific confidences from randomization of available data. The remaining edges are then oriented based on the signature of causality in observational data. The approach and associated algorithm, miic, outperform earlier methods on a broad range of benchmark networks. Causal network reconstructions are presented at different biological size and time scales, from gene regulation in single cells to whole genome duplication in tumor development as well as long term evolution of vertebrates. Miic is publicly available at https://github.com/miicTeam/MIIC.