Mechanical signatures of human colon cancers

International audienceAbstract Besides the standard parameters used for colorectal cancer (CRC) management, new features are needed in clinical practice to improve progression-free and overall survival. In some cancers, the microenvironment mechanical properties can contribute to cancer progression and metastasis formation, or constitute a physical barrier for drug penetration or immune cell infiltration. These mechanical properties remain poorly known for colon tissues. Using a multidisciplinary approach including clinical data, physics and geostatistics, we characterized the stiffness of healthy and malignant colon specimens. For this purpose, we analyzed a prospective cohort of 18 patients with untreated colon adenocarcinoma using atomic force microscopy to generate micrometer-scale mechanical maps. We characterized the stiffness of normal epithelium samples taken far away or close to the tumor area and selected tumor tissue areas. These data showed that normal epithelium was softer than tumors. In tumors, stroma areas were stiffer than malignant epithelial cell areas. Among the clinical parameters, tumor left location, higher stage, and RAS mutations were associated with increased tissue stiffness. Thus, in patients with CRC, measuring tumor tissue rigidity may have a translational value and an impact on patient care

Description of 22 new alpha-1 antitrypsin genetic variants

Abstract Alpha-1 antitrypsin deficiency is an autosomal co-dominant disorder caused by mutations of the highly polymorphic SERPINA1 gene. This genetic disorder still remains largely under-recognized and can be associated with lung and/or liver injury. The laboratory testing for this deficiency typically comprises serum alpha-1 antitrypsin quantification, phenotyping according to the isoelectric focusing pattern and genotyping if necessary. To date, more than 100 SERPINA1 variants have been described and new genetic variants are frequently discovered. Over the past 10 years, 22 new genetic variants of the SERPINA1 gene were identified in the daily practice of the University Medical laboratories of Lille and Lyon (France). Among these 22 variants, seven were Null alleles and one with a M1 migration pattern (M1Cremeaux) was considered as deficient according to the clinical and biological data and to the American College of Medical Genetics and Genomics (ACMG) criteria. Three other variants were classified as likely pathogenic, three as variants of uncertain significance while the remaining ones were assumed to be neutral. Moreover, we also identified in this study two recently described SERPINA1 deficient variants: Trento (p.Glu99Val) and SDonosti (p.Ser38Phe). The current data, together with a recent published meta-analysis, represent the most up-to-date list of SERPINA1 variants available so far

Detection of soluble biomarkers of pancreatic cancer in endoscopic ultrasoundguided fine-needle aspiration samples

International audienceBackground: Biomarkers are urgently needed for pancreatic ductal adenocarcinoma (PDAC). Endoscopic ultrasound-guided fine-needle aspiration (EUS-FNA) is the cornerstone for diagnosing PDAC. We developed a method for discovery of PDAC biomarkers using the discarded EUS-FNA liquid.Methods: This retrospective study included 58 patients with suspected pancreatic lesions who underwent EUS-FNA. Protein extracts from EUS-FNA liquid were analyzed by mass spectrometry. Proteomic and clinical data were modeled by supervised statistical learning to identify protein markers and clinical variables that distinguish PDAC.Results: Statistical modeling revealed a protein signature for PDAC screening that achieved high sensitivity and specificity (0.92, 95 % confidence interval [CI] 0.79-0.98, and 0.85, 95 %CI 0.67-0.93, respectively). We also developed a protein signature score (PSS) to guide PDAC diagnosis. In combination with patient age, the PSS achieved 100 % certainty in correctly identifying PDAC patients > 54 years. In addition, 3 /4 inconclusive EUS-FNA biopsies were correctly identified using PSS.Conclusions: EUS-FNA-derived fluid is a rich source of PDAC proteins with biomarker potential. The PSS requires further validation and verification of the feasibility of measuring these proteins in patient sera

The molecular landscape and microenvironment of salivary duct carcinoma reveal new therapeutic opportunities

International audiencePurpose: Salivary duct carcinoma (SDC) is a rare and aggressive salivary gland cancer subtype with poor prognosis. The mutational landscape of SDC has already been the object of several studies, however little is known regarding the functional genomics and the tumor microenvironment despite their importance in oncology. Our investigation aimed at describing both the functional genomics of SDC and the SDC microenvironment, along with their clinical relevance. Methods: RNA-sequencing (24 tumors), proteomics (17 tumors), immunohistochemistry (22 tumors), and multiplexed immunofluorescence (3 tumors) data were obtained from three different patient cohorts and analyzed by digital imaging and bioinformatics. Adjacent non-tumoral tissue from patients in two cohorts were used in transcriptomic and proteomic analyses. Results: Transcriptomic and proteomic data revealed the importance of Notch, TGF-β, and interferon-γ signaling for all SDCs. We confirmed an overall strong desmoplastic reaction by measuring α-SMA abundance, the level of which was associated with recurrence-free survival (RFS). Two distinct immune phenotypes were observed: immune-poor SDCs (36%) and immune-infiltrated SDCs (64%). Advanced bioinformatics analysis of the transcriptomic data suggested 72 ligand-receptor interactions occurred in the microenvironment and correlated with the immune phenotype. Among these interactions, three immune checkpoints were validated by immunofluorescence, including CTLA-4/DC86 and TIM-3/galectin-9 interactions, previously unidentified in SDC. Immunofluorescence analysis also confirmed an important immunosuppressive role of macrophages and NK cells, also supported by the transcriptomic data. Conclusions: Together our data significantly increase the understanding of SDC biology and open new perspectives for SDC tumor treatment. Before applying immunotherapy, patient stratification according to the immune infiltrate should be taken into account. Immune-infiltrated SDC could benefit from immune checkpoint-targeting therapy, with novel options such as anti-CTLA-4. Macrophages or NK cells could also be targeted. The dense stroma, i.e., fibroblasts or hyaluronic acid, may also be the focus for immune-poor SDC therapies, e.g. in combination with Notch or TGF-β inhibitors, or molecules targeting SDC mutations

Machine Learning‐Assisted Evaluation of Circulating DNA Quantitative Analysis for Cancer Screening

International audienceWhile the utility of circulating cell-free DNA (cfDNA) in cancer screening and early detection have recently been investigated by testing genetic and epigenetic alterations, here, an original approach by examining cfDNA quantitative and structural features is developed. First, the potential of cfDNA quantitative and structural parameters is independently demonstrated in cell culture, murine, and human plasma models. Subsequently, these variables are evaluated in a large retrospective cohort of 289 healthy individuals and 983 patients with various cancer types; after age resampling, this evaluation is done independently and the variables are combined using a machine learning approach. Implementation of a decision tree prediction model for the detection and classification of healthy and cancer patients shows unprecedented performance for 0, I, and II colorectal cancer stages (specificity, 0.89 and sensitivity, 0.72). Consequently, the methodological proof of concept of using both quantitative and structural biomarkers, and classification with a machine learning method are highlighted, as an efficient strategy for cancer screening. It is foreseen that the classification rate may even be improved by the addition of such biomarkers to fragmentomics, methylation, or the detection of genetic alterations. The optimization of such a multianalyte strategy with this machine learning method is therefore warranted