by Keyword: Signatures
Almici, E, Arshakyan, M, Carrasco, JL, Martinez, A, Ramirez, J, Enguita, AB, Monso, E, Montero, J, Samitier, J, Alcaraz, J, (2023). Quantitative Image Analysis of Fibrillar Collagens Reveals Novel Diagnostic and Prognostic Biomarkers and Histotype-Dependent Aberrant Mechanobiology in Lung Cancer Modern Pathology 36, 100155
Fibrillar collagens are the most abundant extracellular matrix components in non-small cell lung cancer (NSCLC). However, the potential of collagen fiber descriptors as a source of clinically relevant biomarkers in NSCLC is largely unknown. Similarly, our understanding of the aberrant collagen organization and associated tumor-promoting effects is very scarce. To address these limitations, we identified a digital pathology approach that can be easily implemented in pa-thology units based on CT-FIRE software (version 2; https://loci.wisc.edu/software/ctfire) analysis of Picrosirius red (PSR) stains of fibrillar collagens imaged with polarized light (PL). CT-FIRE set-tings were pre-optimized to assess a panel of collagen fiber descriptors in PSR-PL images of tissue microarrays from surgical NSCLC patients (106 adenocarcinomas [ADC] and 89 squamous cell carcinomas [SCC]). Using this approach, we identified straightness as the single high-accuracy diagnostic collagen fiber descriptor (average area under the curve 1/4 0.92) and fiber density as the single descriptor consistently associated with a poor prognosis in both ADC and SCC inde-pendently of the gold standard based on the TNM staging (hazard ratio, 2.69; 95% CI, 1.55-4.66; P < .001). Moreover, we found that collagen fibers were markedly straighter, longer, and more aligned in tumor samples compared to paired samples from uninvolved pulmonary tissue, particularly in ADC, which is indicative of increased tumor stiffening. Consistently, we observed an increase in a panel of stiffness-associated processes in the high collagen fiber density patient group selectively in ADC, including venous/lymphatic invasion, fibroblast activation (a-smooth muscle actin), and immune evasion (programmed death-ligand 1). Similarly, a transcriptional correlation analysis supported the potential involvement of the major YAP/TAZ pathway in ADC. Our results provide a proof-of-principle to use CT-FIRE analysis of PSR-PL images to assess new collagen fiber-based diagnostic and prognostic biomarkers in pathology units, which may improve the clinical management of patients with surgical NSCLC. Our findings also unveil an aberrant stiff micro -environment in lung ADC that may foster immune evasion and dissemination, encouraging future work to identify therapeutic opportunities. (c) 2023 THE AUTHORS. Published by Elsevier Inc. on behalf of the United States & Canadian Academy of Pathology. This is an open access article under the CC BY-NC-ND license (http://creativecommo ns.org/licenses/by-nc-nd/4.0/).
JTD Keywords: biomarkers, collagen, ct-fire, lung cancer, mechanobiology, Adenocarcinoma, Association, Biomarkers, Collagen, Ct-fire, Differentiation, Expression, Extracellular-matrix, I collagen, Invasion, Lung cancer, Mechanobiology, Microenvironment, Signature, Survival, Tumor microenvironment
Narciso, Maria, Martínez, África, Júnior, Constança, Díaz-Valdivia, Natalia, Ulldemolins, Anna, Berardi, Massimiliano, Neal, Kate, Navajas, Daniel, Farré, Ramon, Alcaraz, Jordi, Almendros, Isaac, Gavara, Núria, (2023). Lung Micrometastases Display ECM Depletion and Softening While Macrometastases Are 30-Fold Stiffer and Enriched in Fibronectin Cancers 15, 2404
Mechanical changes in tumors have long been linked to increased malignancy and therapy resistance and attributed to mechanical changes in the tumor extracellular matrix (ECM). However, to the best of our knowledge, there have been no mechanical studies on decellularized tumors. Here, we studied the biochemical and mechanical progression of the tumor ECM in two models of lung metastases: lung carcinoma (CAR) and melanoma (MEL). We decellularized the metastatic lung sections, measured the micromechanics of the tumor ECM, and stained the sections for ECM proteins, proliferation, and cell death markers. The same methodology was applied to MEL mice treated with the clinically approved anti-fibrotic drug nintedanib. When compared to healthy ECM (~0.40 kPa), CAR and MEL lung macrometastases produced a highly dense and stiff ECM (1.79 ± 1.32 kPa, CAR and 6.39 ± 3.37 kPa, MEL). Fibronectin was overexpressed from the early stages (~118%) to developed macrometastases (~260%) in both models. Surprisingly, nintedanib caused a 4-fold increase in ECM-occupied tumor area (5.1 ± 1.6% to 18.6 ± 8.9%) and a 2-fold in-crease in ECM stiffness (6.39 ± 3.37 kPa to 12.35 ± 5.74 kPa). This increase in stiffness strongly correlated with an increase in necrosis, which reveals a potential link between tumor hypoxia and ECM deposition and stiffness. Our findings highlight fibronectin and tumor ECM mechanics as attractive targets in cancer therapy and support the need to identify new anti-fibrotic drugs to abrogate aberrant ECM mechanics in metastases.
JTD Keywords: atomic force microscopy, basement membrane, breast-cancer, decellularization, expression, extracellular matrix, extracellular-matrix, fibronectin, intermittent hypoxia, lung carcinoma, lung metastases, melanoma, metastatic niche formation, micromechanical properties, nintedanib, signature, stiffness, tumor-growth, Colorectal-cancer progression, Lung metastases, Stiffness
Moussa, Dina G., Sharma, Ashok K., Mansour, Tamer A, Witthuhn, Bruce, Perdigão, Jorge, Rudney, Joel D., Aparicio, Conrado, Gomez, Andres, (2022). Functional signatures of ex-vivo dental caries onset Journal Of Oral Microbiology 14, 2123624
The etiology of dental caries remains poorly understood. With the advent of next-generation sequencing, a number of studies have focused on the microbial ecology of the disease. However, taxonomic associations with caries have not been consistent. Researchers have also pursued function-centric studies of the caries microbial communities aiming to identify consistently conserved functional pathways. A major question is whether changes in microbiome are a cause or a consequence of the disease. Thus, there is a critical need to define conserved functional signatures at the onset of dental caries.Since it is unethical to induce carious lesions clinically, we developed an innovative longitudinal ex-vivo model integrated with the advanced non-invasive multiphoton second harmonic generation bioimaging to spot the very early signs of dental caries, combined with 16S rRNA short amplicon sequencing and liquid chromatography-mass spectrometry-based targeted metabolomics.For the first time, we induced longitudinally monitored caries lesions validated with the scanning electron microscope. Consequently, we spotted the caries onset and, associated with it, distinguished five differentiating metabolites - Lactate, Pyruvate, Dihydroxyacetone phosphate, Glyceraldehyde 3-phosphate (upregulated) and Fumarate (downregulated). Those metabolites co-occurred with certain bacterial taxa; Streptococcus, Veillonella, Actinomyces, Porphyromonas, Fusobacterium, and Granulicatella, regardless of the abundance of other taxa.These findings are crucial for understanding the etiology and dynamics of dental caries, and devising targeted interventions to prevent disease progression.© 2022 The Author(s). Published by Informa UK Limited, trading as Taylor & Francis Group.
JTD Keywords: bacteria, biofilms, children, dental caries, generation, genomics, longitudinal model, metabolism, metabolomics, microscopy, non-invasive bioimaging, oral microbiome, plaque, restorations, signatures, Dental caries, Field-emission sem, Signatures
Solorzano, A, Eichmann, J, Fernandez, L, Ziems, B, Jimenez-Soto, JM, Marco, S, Fonollosa, J, (2022). Early fire detection based on gas sensor arrays: Multivariate calibration and validation Sensors And Actuators B-Chemical 352,
Smoldering fires are characterized by the production of early gas emissions that can include high levels of CO and Volatile Organic Compounds (VOCs) due to pyrolysis or thermal degradation. Nowadays, standalone CO sensors, smoke detectors, or a combination of these, are standard components for fire alarm systems. While gas sensor arrays together with pattern recognition techniques are a valuable alternative for early fire detection, in practice they have certain drawbacks-they can detect early gas emissions, but can show low immunity to nuisances, and sensor time drift can render calibration models obsolete. In this work, we explore the performance of a gas sensor array for detecting smoldering and plastic fires while ensuring the rejection of a set of nuisances. We conducted variety of fire and nuisance experiments in a validated standard fire room (240 m(3)). Using PLS-DA and SVM, we evaluate the performance of different multivariate calibration models for this dataset. We show that calibration models remain predictive after several months, but perfect performance is not achieved. For example, 4 months after calibration, a PLS-DA model provides 100% specificity and 85% sensitivity since the system has difficulties in detecting plastic fires, whose signatures are close to nuisance scenarios. Nevertheless, our results show that systems based on gas sensor arrays are able to provide faster fire alarm response than conventional smoke-based fire alarms. We also propose the use of small-scale fire experiments to increase the number of calibration conditions at a reduced cost. Our results show that this is an effective way to increase the performance of the model, even when evaluated on a standard fire room. Finally, the acquired datasets are made publicly available to the community (doi: 10.5281/zenodo.5643074).
JTD Keywords: Calibration, Chemical sensors, Co2, Early fire, Early fire detection, En-54, Fire alarm, Fire detection, Fire room, Fires, Gas detectors, Gas emissions, Gas sensors, Pattern recognition, Public dataset, Sensor arrays, Sensors array, Signatures, Smoke, Smoke detector, Smoke detectors, Standard fire, Standard fire room, Support vector machines, Temperature, Toxicity, Volatile organic compounds