Abstract Spatial omics data allow in-depth analysis of tissue architectures, opening new opportunities for biological discovery. In particular, imaging techniques offer single-cell resolutions, ...providing essential insights into cellular organizations and dynamics. Yet, the complexity of such data presents analytical challenges and demands substantial computing resources. Moreover, the proliferation of diverse spatial omics technologies, such as Xenium, MERSCOPE, CosMX in spatial-transcriptomics, and MACSima and PhenoCycler in multiplex imaging, hinders the generality of existing tools. We introduce Sopa ( https://github.com/gustaveroussy/sopa ), a technology-invariant, memory-efficient pipeline with a unified visualizer for all image-based spatial omics. Built upon the universal SpatialData framework, Sopa optimizes tasks like segmentation, transcript/channel aggregation, annotation, and geometric/spatial analysis. Its output includes user-friendly web reports and visualizer files, as well as comprehensive data files for in-depth analysis. Overall, Sopa represents a significant step toward unifying spatial data analysis, enabling a more comprehensive understanding of cellular interactions and tissue organization in biological systems.
BackgroundAdulthood cancer results from (epi)genomic cellular changes associated with chronic inflammatory processes and maladaptive immune responses. Identifying sensors of health to disease ...transition culminating in early carcinogenesis beyond cell autonomous cues is an unmet medical need. A holistic view might help defining a high-risk model based on functional biomarkers amenable to personalized screening interventions and interceptive measures.MethodsWe conducted a prospective omics-based translational research study (PREVALUNG trial, NCT03976804) in 508 smokers with cardiovascular disease (CVD) aimed at predicting the 17 tobacco-associated cancers beyond the NLST, NELSON, PLCOm2012 screening scores. After inclusion, patients were scheduled for a low-dose chest CT-scan, and blood and feces samples were collected concomitantly. PREVALUNG omics-based sensors of health to disease transition included analytes related to inflammation, immunity, metabolism, metagenome, gut barrier, plasma proteomics, and clonal hematopoiesis. To validate our hypotheses, we investigated a Li-Fraumeni cohort of TP53 germline mutation carriers (LIFSCREEN trial, NCT01464086), and performed the same omics assessment on biological sample taken before cancer detection.ResultsWe could estimate cancer risk of CVD patients based on 33 soluble markers and 2 clinical risk factors, with an AUC of 0.78 (0.67; 0.79). To challenge this model of inflammation-related cancer, we investigated a Li-Fraumeni cohort of TP53 germline mutation carriers who have a significantly increased lifetime cancer risk affecting multiple organ sites. Here, 13 soluble markers and 8 clinical risk factors predicted cancer with an AUC of 0.82 (0.66; 0.91). The functional pathways shared by both cohorts paving the way to carcinogenesis include the neutrophil/lymphocyte ratio, Th2 immunity (CCL24, CCL26, IL-4/IL-4R, IL-5, CCL11, CCL22, CD163, IL-33/ST2), inflammasome activation (IL-1b, IL-1R2, IL-1RN), gut barrier permeability (IL-33/ST2, CD14/LBP, MAdCAM-1, CCL25), cholesterol and biliary salt circuitries, tryptophan and vitamin B3 metabolism, polyamines and ketogenesis.ConclusionsThese two studies suggest that health to cancer transition results from coordinated and cumulative pathological failures of the meta-organism, amenable to biologically-guided prophylactic measures beyond life style changes.AcknowledgementsMF, LZ and GK are supported by the SEERAVE Foundation, the European Union’s Horizon Europe research and innovation programme under grant agreement number No 101095604 project acronym: PREVALUNG-EU, project title: Personalized lung cancer risk assessment leading to stratified Interception. LIFSCREEN (NCT01464086) is funded by the French Ligue Contre le Cancer.Trial RegistrationPrevalence of Lung Cancer (PREVALUNG) study was approved according the French Jardé law; the study is referenced at the French ‘Agence Nationale de Sécurité du Médicament et des Produits de Santé’ (reference ID RCB: 2019-A00262-55) and registered on clinicaltrial.gov (NCT03976804).LIFSCREEN (ClinicalTrials.gov Identifier: NCT01464086).Ethics ApprovalPrevalence of Lung Cancer (PREVALUNG) study was approved according the French Jardé law; the study is referenced at the French ‘Agence Nationale de Sécurité du Médicament et des Produits de Santé’ (reference ID RCB: 2019-A00262-55) and registered on clinicaltrial.gov (NCT03976804).LIFSCREEN (ClinicalTrials.gov Identifier: NCT01464086).
Abstract
Cytometry enables precise single-cell phenotyping within heterogeneous populations. These cell types are traditionally annotated via manual gating, but this method lacks reproducibility and ...sensitivity to batch effect. Also, the most recent cytometers—spectral flow or mass cytometers—create rich and high-dimensional data whose analysis via manual gating becomes challenging and time-consuming. To tackle these limitations, we introduce Scyan https://github.com/MICS-Lab/scyan, a Single-cell Cytometry Annotation Network that automatically annotates cell types using only prior expert knowledge about the cytometry panel. For this, it uses a normalizing flow—a type of deep generative model—that maps protein expressions into a biologically relevant latent space. We demonstrate that Scyan significantly outperforms the related state-of-the-art models on multiple public datasets while being faster and interpretable. In addition, Scyan overcomes several complementary tasks, such as batch-effect correction, debarcoding and population discovery. Overall, this model accelerates and eases cell population characterization, quantification and discovery in cytometry.
Abstract Background: Immune checkpoint inhibitors (ICIs) are one of the major therapeutic advancements in cancer treatment. Anti-programmed cell death protein 1 (Anti-PD1)/PD-L1 ICIs have improved ...progression-free survival in patients with metastatic triple negative breast cancer (TNBC) and pathologic complete response (pCR) and event-free survival in patients with early TNBC. Nevertheless, some patients treated with anti-PD(L)1 ICIs experience recurrence or do not achieve sustained clinical benefit. In addition, very interesting data show the existence of a subgroup of patients with exceptional tumour responses to monotherapy with anti-PD(L)1 ICIs. These exceptional responses are observed in various tumour types, such as colorectal or breast cancer. Although in colorectal cancer, the determinants for this extreme sensitivity to immunotherapy treatment are identified (existence of MSI) in breast cancer, this is not the case. There is still a lack of knowledge about predictive biomarkers and mechanisms of action for ICIs. Trial Design: POP-Durva (NCT05215106) is a prospective, one-arm-only study aiming at determining the pCR rate after two administrations of Durvalumab monotherapy in patients with stage I TNBC. This window-of-opportunity study will recruit 195 consecutive cases of stage I TNBC (ER < 1%, PR < 1%, HER2 negative) and TILs≥5%, eligible for short-term treatment with durvalumab. Study treatment consists of two administrations of intravenous Durvalumab monotherapy, 10mg/kg, at two weeks intervals. After study treatment, patients will receive a standard treatment strategy (surgery or neoadjuvant systemic treatment) as per physician choice. The primary endpoint is the pCR rate after treatment with Durvalumab, defined as the absence of invasive disease in the breast and negative axillary nodes (ypT0/yTis ypN0). Assessment of the primary endpoint will be performed at the surgery or at the biopsy at the end of treatment) for patients undergoing neoadjuvant treatment. For patients in whom neo-adjuvant therapy is the first standard treatment strategy (i.e. after study treatment) a breast ultrasound-guided biopsy is mandatory at the end of the treatment visit. If the biopsy-proven residual disease is demonstrated, patients can receive standard neoadjuvant therapy at the discretion of the treating investigator. Patients with biopsy-proven residual disease will be considered as having no pCR. Those with a complete response may proceed directly to surgery. The expected pCR rate with Durvalumab monotherapy is 20%. The sample size of 195 patients will allow us to estimate this expected pCR rate with a 95% confidence interval of a precision of 6.2%. Secondary objectives are objective response rate and safety. Exploratory objectives are to: a. describe immune cell dynamics associated with exceptional responses to ICI (using spectral cytometry analysis), b. describe somatic genetic contributions to the determination of immune responsiveness (using whole exome sequencing (WES) and in a subset of patient's single-cell RNA-seq), c. characterize tumour cells – immune cells' interaction/spatial distribution(using imaging mass cytometry), d. explore the association between gut microbiome composition/signatures predictive of response to ICI (using 16s rRNA sequencing) and e. identify predictive tissue/blood-based biomarkers of response and to anti-PD(L)1 ICIs therapy. A total of two dedicated FFPE samples and two fresh biopsies will be collected at the time of inclusion and at the end of treatment biopsy or on the surgical specimen. In addition, we will collect stool samples pre and at the completion of all Durvalumab treatments and blood samples at the time of inclusion, during treatment (before each Durvalumab administration) and at the end of treatment for serum and plasma extraction and whole blood to serve as a control for WES. Citation Format: Joana Mourato Ribeiro, Isabelle Pic, Quentin Blampey, Elie Rassy, Nusaibah Ibrahimi, Laura Salabert, Olivier Trédan, Monica Arnedos, Semih Dogan, Kamar Serhal Serhal, Clementine Mahaut, Alessandro Viansone, Salim Laghouati, Barbara Pistilli, Charles-Antoine Dutertre, Magali Lacroix-Triki, Jean-Yves Scoazec, Lisa Derosa, Laurence Zitvogel, Corinne Balleyguier, Nadege Bercovici, Paul-Henry Cournede, Stefan Michiels, Fabrice André. Short-term Pre-OPerative Durvalumab (MEDI 4736) in early small triple negative breast cancer patients (POP-Durva) abstract. In: Proceedings of the 2023 San Antonio Breast Cancer Symposium; 2023 Dec 5-9; San Antonio, TX. Philadelphia (PA): AACR; Cancer Res 2024;84(9 Suppl):Abstract nr PO2-18-11.
Data-driven models for glucose level forecast often do not provide meaningful insights despite accurate predictions. Yet, context understanding in medicine is crucial, in particular for diabetes ...management. In this paper, we introduce HAD-Net: a hybrid model that distills knowledge into a deep neural network from physiological models. It models glucose, insulin and carbohydrates diffusion through a biologically inspired deep learning architecture tailored with a recurrent attention network constrained by ODE expert models. We apply HAD-Net for glucose level forecast of patients with type-2 diabetes. It achieves competitive performances while providing plausible measurements of insulin and carbohydrates diffusion over time.
Cytometry enables precise single-cell phenotyping within heterogeneous populations. These cell types are traditionally annotated via manual gating, but this method suffers from a lack of ...reproducibility and sensitivity to batch-effect. Also, the most recent cytometers - spectral flow or mass cytometers - create rich and high-dimensional data whose analysis via manual gating becomes challenging and time-consuming. To tackle these limitations, we introduce Scyan (https://github.com/MICS-Lab/scyan), a Single-cell Cytometry Annotation Network that automatically annotates cell types using only prior expert knowledge about the cytometry panel. We demonstrate that Scyan significantly outperforms the related state-of-the-art models on multiple public datasets while being faster and interpretable. In addition, Scyan overcomes several complementary tasks such as batch-effect removal, debarcoding, and population discovery. Overall, this model accelerates and eases cell population characterisation, quantification, and discovery in cytometry.
