Kartezio: Evolutionary Design of Explainable Pipelines for Biomedical Image Analysis

An unresolved issue in contemporary biomedicine is the overwhelming number and diversity of complex images that require annotation, analysis and interpretation. Recent advances in Deep Learning have revolutionized the field of computer vision, creating algorithms that compete with human experts in image segmentation tasks. Crucially however, these frameworks require large human-annotated datasets for training and the resulting models are difficult to interpret. In this study, we introduce Kartezio, a modular Cartesian Genetic Programming based computational strategy that generates transparent and easily interpretable image processing pipelines by iteratively assembling and parameterizing computer vision functions. The pipelines thus generated exhibit comparable precision to state-of-the-art Deep Learning approaches on instance segmentation tasks, while requiring drastically smaller training datasets, a feature which confers tremendous flexibility, speed, and functionality to this approach. We also deployed Kartezio to solve semantic and instance segmentation problems in four real-world Use Cases, and showcase its utility in imaging contexts ranging from high-resolution microscopy to clinical pathology. By successfully implementing Kartezio on a portfolio of images ranging from subcellular structures to tumoral tissue, we demonstrated the flexibility, robustness and practical utility of this fully explicable evolutionary designer for semantic and instance segmentation.Comment: 36 pages, 6 main Figures. The Extended Data Movie is available at the following link: https://www.youtube.com/watch?v=r74gdzb6hdA. The source code is available on Github: https://github.com/KevinCortacero/Kartezi

Multigenerational Effects of a Complex Human-Relevant Exposure during Folliculogenesis and Preimplantation Embryo Development: The FEDEXPO Study

International audienceAnimal toxicological studies often fail to mimic the complexity of the human exposome, associating low doses, combined molecules and long-term exposure. Since the reproductive potential of a woman begins in the fetal ovary, the literature regarding the disruption of its reproductive health by environmental toxicants remains limited. Studies draw attention to follicle development, a major determinant for the quality of the oocyte, and the preimplantation embryo, as both of them are targets for epigenetic reprogramming. The “Folliculogenesis and Embryo Development EXPOsure to a mixture of toxicants: evaluation in the rabbit model” (FEDEXPO) project emerged from consideration of these limitations and aims to evaluate in the rabbit model the impacts of an exposure to a mixture of known and suspected endocrine disrupting chemicals (EDCs) during two specific windows, including folliculogenesis and preimplantation embryo development. The mixture combines eight environmental toxicants, namely perfluorooctanesulfonic acid (PFOS), perfluorooctanoic acid (PFOA), dichlorodiphenyldichloroethylene (DDE), hexachlorobenzene (HCB), β-hexachlorocyclohexane (β-HCH), 2,2′4,4′-tetrabromodiphenyl ether (BDE-47), di(2-ethylhexyl) phthalate (DEHP) and bisphenol S (BPS), at relevant exposure levels for reproductive-aged women based on biomonitoring data. The project will be organized in order to assess the consequences of this exposure on the ovarian function of the directly exposed F0 females and monitor the development and health of the F1 offspring from the preimplantation stage. Emphasis will be made on the reproductive health of the offspring. Lastly, this multigenerational study will also tackle potential mechanisms for the inheritance of health disruption via the oocyte or the preimplantation embryo

Pre- and Postnatal Dietary Exposure to a Pesticide Cocktail Disrupts Ovarian Functions in 8-Week-Old Female Mice

International audienceFemale infertility has a multifactorial origin, and exposure to contaminants, including pesticides, with endocrine-disrupting properties is considered to be involved in this reproductive disorder, especially when it occurs during early life. Pesticides are present in various facets of the environment, and consumers are exposed to a combination of multiple pesticide residues through food intake. The consequences of such exposure with respect to female fertility are not well known. Therefore, we aimed to assess the impact of pre- and postnatal dietary exposure to a pesticide mixture on folliculogenesis, a crucial process in female reproduction. Mice were exposed to the acceptable daily intake levels of six pesticides in a mixture (boscalid, captan, chlorpyrifos, thiacloprid, thiophanate and ziram) from foetal development until 8 weeks old. Female offspring presented with decreased body weight at weaning, which was maintained at 8 weeks old. This was accompanied by an abnormal ovarian ultrastructure, a drastic decrease in the number of corpora lutea and progesterone levels and an increase in ovary cell proliferation. In conclusion, this study shows that this pesticide mixture that can be commonly found in fruits in Europe, causing endocrine disruption in female mice with pre- and postnatal exposure by disturbing folliculogenesis, mainly in the luteinisation process

Evolutionary design of explainable algorithms for biomedical image segmentation

International audienceAn unresolved issue in contemporary biomedicine is the overwhelming number and diversity of complex images that require annotation, analysis and interpretation. Recent advances in Deep Learning have revolutionized the field of computer vision, creating algorithms that compete with human experts in image segmentation tasks. However, these frameworks require large human-annotated datasets for training and the resulting “black box” models are difficult to interpret. In this study, we introduce Kartezio , a modular Cartesian Genetic Programming-based computational strategy that generates fully transparent and easily interpretable image processing pipelines by iteratively assembling and parameterizing computer vision functions. The pipelines thus generated exhibit comparable precision to state-of-the-art Deep Learning approaches on instance segmentation tasks, while requiring drastically smaller training datasets. This Few-Shot Learning method confers tremendous flexibility, speed, and functionality to this approach. We then deploy Kartezio to solve a series of semantic and instance segmentation problems, and demonstrate its utility across diverse images ranging from multiplexed tissue histopathology images to high resolution microscopy images. While the flexibility, robustness and practical utility of Kartezio make this fully explicable evolutionary designer a potential game-changer in the field of biomedical image processing, Kartezio remains complementary and potentially auxiliary to mainstream Deep Learning approaches

First-in-human first-in-class phase I trial of murlentamab, an anti-Mullerian-hormone receptor II (AMHRII) monoclonal antibody acting through tumor-associated macrophage (TAM) engagement, as single agent and in combination with carboplatin (C) and paclitaxel (P) in AMHRII-expressing advanced/metastatic gynecological cancer patients (pts)

International audience2521 Background: Membranous expression of AMHRII is found in ~70% of gynecological tumors. Murlentamab (M) binds with high affinity both AMHRII (at cell membrane) and CD16 (on macrophage, via its low fucose Fc). M reprograms TAMs, restoring their antitumoral functions (phagocytosis) resulting in cytotoxic T cell reactivation. Methods: Pts with advanced/metastatic AMHRII-expressing ovarian, cervical or endometrial cancer with measurable disease and performance status ≤ 1 received M as single agent (SA) in 8 dose escalating and 2 expansion cohorts. Combination with CP was studied in 2 escalating cohorts. Safety, recommended dose determination, antitumor activity, pharmacodynamics (PD) effects (circulating immune cells and tumor microenvironment (TME) from paired biopsies) were assessed. Results: 68 heavily pretreated (median 4 prior lines) pts received M for 0.5 to 11 months (mo) (59 pts M SA and 9 pts M + CP). No dose limiting toxicity was reported. Most common toxicity was G1-2 asthenia (29 %). Eight pts (12%) had G ≥ 3 reversible toxicities (asthenia, nausea/vomiting, anorexia, arthralgia). No antidrug antibody was detected. One partial response (PR) was achieved with M SA in a granulosa pt. In CP combination, 4/9 pts (44%) responded to treatment (1 Complete Response and 3 PRs). Overall, 22/67 (33%) pts were progression-free at 4 mos. Among 17 pts treated ≥ 6 mos, 6/9 (67%) granulosa pts with M SA and 4/5 (80%) endometrium and cervix with CP combination had a longer PFS than under previous regimen. PD blood assessment of 25 pts treated with M SA showed an increase in classical monocytes, and T cells and neutrophils activation. Changes in TME under M will be presented. Conclusions: Murlentamab was very well tolerated, demonstrated immune PD effects and showed hints of antitumor activity. These results together with its innovative immunological mode of action support development of M in AMHRII-expressing cancers, in combination with chemotherapy or other immune oncology drugs. Clinical trial information: NCT02978755