Detection of soluble biomarkers of pancreatic cancer in endoscopic ultrasound-guided fine-needle aspiration samples.

BACKGROUND: 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.

Short-Term Biological Toxicity Prediction of [177Lu]Lutetium-Oxodotreotide: An Original Retrospective Analysis.

Introduction: [177Lu]Lutetium (Lu)-oxodotreotide is a radiopharmaceutical drug used as peptide receptor radionuclide therapy (PRRT) for somatostatin receptor-expressing neuroendocrine neoplasms. It provides an additional effective alternative treatment for these rare cancers. Although well tolerated, its safety profile must continue to be characterized to support its use as a first-line treatment or for additional cycles. This study evaluated factors associated with the occurrence of [177Lu]Lu-oxodotreotide induced short-term toxicity. Materials and Methods: A retrospective observational monocentric study was carried out from July 2013 to October 2021. Inclusion criteria were defined as follows: patients who received at least four cycles of [177Lu]Lu-oxodotreotide and were followed up for 6 months after the last injection. Graduated toxicity was defined using the National Cancer Institute Common Terminology Criteria for Adverse Events 5.0. Cox regression was used in the analysis. Results: Forty patients were included. The most frequent toxicities occurred during the first cycle and were graded as G1 or G2. As expected, toxicities were predominantly hematological and hepatic, with incomplete reversibility after each cycle. The following factors were significantly related to the occurrence of hematological or hepatic toxicity during PRRT: gastrointestinal primary tumor diagnosis, bone metastases, peritoneal metastases, pancreatic metastases or pulmonary metastases, and high tumor grade. Conclusion: Knowledge and consideration of these factors in adjusting [177Lu]Lu-oxodotreotide treatment regimen could help prevent or reduce the severity of these toxicities. Further studies are still warranted to refine these results and improve treatment management.

Mechanical signatures of human colon cancers.

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.

The landscape of cancer-associated fibroblasts in colorectal cancer liver metastases.

Rationale: Patients with colorectal cancer die mainly due to liver metastases (CRC-LM). Although the tumor microenvironment (TME) plays an important role in tumor development and therapeutic response, our understanding of the individual TME components, especially cancer-associated fibroblasts (CAFs), remains limited. Methods: We analyzed CRC-LM CAFs and cancer cells by single-cell transcriptomics and used bioinformatics for data analysis and integration with related available single-cell and bulk transcriptomic datasets. We validated key findings by RT-qPCR, western blotting, and immunofluorescence. Results: By single-cell transcriptomic analysis of 4,397 CAFs from six CRC-LM samples, we identified two main CAF populations, contractile CAFs and extracellular matrix (ECM)-remodeling/pro-angiogenic CAFs, and four subpopulations with distinct phenotypes. We found that ECM-remodeling/pro-angiogenic CAFs derive from portal resident fibroblasts. They associate with areas of strong desmoplastic reaction and Wnt signaling in low-proliferating tumor cells engulfed in a stiff extracellular matrix. By integrating public single-cell primary liver tumor data, we propose a model to explain how different liver malignancies recruit CAFs of different origins to this organ. Lastly, we found that LTBP2 plays an important role in modulating collagen biosynthesis, ECM organization, and adhesion pathways. We developed fully human antibodies against LTBP2 that depleted LTBP2+ CAFs in vitro. Conclusion: This study complements recent reports on CRC-LM CAF heterogeneity at the single-cell resolution. The number of sequenced CAFs was more than one order of magnitude larger compared to existing data. LTBP2 targeting by antibodies might create opportunities to deplete ECM-remodeling CAFs in CRC-LMs. This might be combined with other therapies, e.g., anti-angiogenic compounds as already done in CRC. Moreover, we showed that in intrahepatic cholangiocarcinoma, in which ECM-remodeling CAF proportion is similar to that of CRC-LM, several genes expressed by ECM-remodeling CAFs, such as LTBP2, were associated with survival.

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

Purpose: 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-ß, 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-ß inhibitors, or molecules targeting SDC mutations.

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

While 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.

Description of 22 new alpha-1 antitrypsin genetic variants.

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.