A biology-driven deep generative model for cell-type annotation in cytometry

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

Un modele d'intelligence artificielle basé sur la biologie pour l'annotation automatique en cytométrie

International audienceAbstract 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

ROSALINE: a phase II, neoadjuvant study targeting ROS1 in combination with endocrine therapy in invasive lobular carcinoma of the breast.

Invasive lobular carcinoma (ILC) is the most common histologic subtype of breast cancer after invasive ductal carcinoma (i.e., no special type [NST]). ILC differs from NST in clinical presentation, site-specific metastases and response to conventional therapies. Loss of E-cadherin protein expression, due to alterations in its encoding gene is the most frequent oncogenic event in ILC. Synthetic lethality approaches have shown promising antitumor effects of ROS1 inhibitors in models of E-cadherin-defective breast cancer in studies and provide the rationale for testing their clinical activity in patients with ILC. Entrectinib is a tyrosine kinase inhibitor targeting TRK, ROS1 and ALK tyrosine kinases. Here, the authors present ROSALINE (NCT04551495), a phase II study testing neoadjuvant entrectinib and endocrine therapy in women with estrogen receptor-positive, HER2-negative early ILC

Comparison of Management and Outcomes in ERBB2 -Low vs ERBB2 -Zero Metastatic Breast Cancer in France

International audienceImportance ERBB2 -low (ie, ERBB2 immunohistochemistry score of 1+ or 2+ in the absence of ERBB2 gene amplification) breast cancer (BC) is a new entity, with emerging dedicated treatments. Little is known about its prognosis and response to conventional therapy compared with ERBB2 -zero breast tumors (ie, those with an immunohistochemistry score of 0). Objective To compare the outcomes for patients with ERBB2 -low metastatic BC (MBC) with those of patients with ERBB2 -zero MBC. Design, Setting, and Participants This cohort study was conducted from the Epidemiological Strategy and Medical Economics MBC platform and included patients with MBC treated between 2008 and 2016 in 18 French comprehensive cancer centers. The data analysis was conducted from July 16, 2020, to April 1, 2022. Main Outcomes and Measures The main outcome was overall survival (OS), and the secondary outcome was progression-free survival under first-line treatments (PFS1). Results The median (range) age was 60.0 (22.0-103.0) years. Among 15 054 patients with MBC, 4671 (31%) had ERBB2 -low MBC and 10 383 (69%) had ERBB2 -zero MBC. The proportion of ERBB2 -low cancers was higher among patients with hormone receptor–positive MBC than those with hormone receptor–negative disease (4083 patients [33.0%] vs 588 patients [21.0%]). With a median follow-up of 49.5 months (95% CI, 48.6-50.4 months), the median OS of the ERBB2 -low group was 38.0 months (95% CI, 36.4-40.5 months) compared with 33.9 months (95% CI, 32.9-34.9 months) for the ERBB2 -zero group ( P < .001). After adjustment for age, visceral metastases, number of metastatic sites, de novo disease, period of care, and hormone receptor status, patients with ERBB2 -low MBC had slightly better OS compared with patients with ERBB2 -zero MBC (adjusted hazard ratio, 0.95; 95% CI, 0.91-0.99; P = .02). In contrast, PFS1 did not differ by ERBB2 status (adjusted hazard ratio, 0.99; 95% CI, 0.95-1.02; P = .45). No significant differences in OS and PFS1 were observed in multivariate analyses by hormone receptor status and types of frontline treatment. Conclusions and Relevance In this large cohort study, patients with ERBB2 -low MBC had a slightly better OS than those with completely ERBB2 -zero tumors, but identical PFS1, which could help guide treatment selection