A Multiplexed Approach to Assess Small Cell Lung Cancer Subtype Heterogeneity in Primary and Patient-Derived Tumor Samples.

Unlocking the heterogeneity of cancers is crucial for developing therapeutic approaches that effectively eradicate disease. As our understanding of markers specific to cancer subclones or subtypes expands, there is a growing demand for advanced technologies that enable the simultaneous investigation of multiple targets within an individual tumor sample. Indeed, multiplex approaches offer distinct benefits, particularly when tumor specimens are small and scarce. Here we describe the utility of two fluorescence-based multiplex approaches; fluorescent Western blots, and multiplex immunohistochemistry (Opal™) staining to interrogate heterogeneity, using small cell lung cancer as an example. Critically, the coupling of Opal™ staining with advanced image quantitation, permits the dissection of cancer cell phenotypes at a single cell level. These approaches can be applied to patient biopsies and/or patient-derived xenograft (PDX) models and serve as powerful methodologies for assessing tumor cell heterogeneity in response to therapy or between metastatic lesions across diverse tissue sites.

Early immune pressure initiated by tissue-resident memory T cells sculpts tumor evolution in non-small cell lung cancer.

Tissue-resident memory T (TRM) cells provide immune defense against local infection and can inhibit cancer progression. However, it is unclear to what extent chronic inflammation impacts TRM activation and whether TRM cells existing in tissues before tumor onset influence cancer evolution in humans. We performed deep profiling of healthy lungs and lung cancers in never-smokers (NSs) and ever-smokers (ESs), finding evidence of enhanced immunosurveillance by cells with a TRM-like phenotype in ES lungs. In preclinical models, tumor-specific or bystander TRM-like cells present prior to tumor onset boosted immune cell recruitment, causing tumor immune evasion through loss of MHC class I protein expression and resistance to immune checkpoint inhibitors. In humans, only tumors arising in ES patients underwent clonal immune evasion, unrelated to tobacco-associated mutagenic signatures or oncogenic drivers. These data demonstrate that enhanced TRM-like activity prior to tumor development shapes the evolution of tumor immunogenicity and can impact immunotherapy outcomes.

A single-cell RNA expression atlas of normal, preneoplastic and tumorigenic states in the human breast.

To examine global changes in breast heterogeneity across different states, we determined the single-cell transcriptomes of > 340,000 cells encompassing normal breast, preneoplastic BRCA1+/- tissue, the major breast cancer subtypes, and pairs of tumors and involved lymph nodes. Elucidation of the normal breast microenvironment revealed striking changes in the stroma of post-menopausal women. Single-cell profiling of 34 treatment-naive primary tumors, including estrogen receptor (ER)+ , HER2+ , and triple-negative breast cancers, revealed comparable diversity among cancer cells and a discrete subset of cycling cells. The transcriptomes of preneoplastic BRCA1+/- tissue versus tumors highlighted global changes in the immune microenvironment. Within the tumor immune landscape, proliferative CD8+ T cells characterized triple-negative and HER2+ cancers but not ER+ tumors, while all subtypes comprised cycling tumor-associated macrophages, thus invoking potentially different immunotherapy targets. Copy number analysis of paired ER+ tumors and lymph nodes indicated seeding by genetically distinct clones or mass migration of primary tumor cells into axillary lymph nodes. This large-scale integration of patient samples provides a high-resolution map of cell diversity in normal and cancerous human breast.

Shape variation analyzer: a classifier for temporomandibular joint damaged by osteoarthritis.

We developed a deep learning neural network, the Shape Variation Analyzer (SVA), that allows disease staging of bony changes in temporomandibular joint (TMJ) osteoarthritis (OA). The sample was composed of 259 TMJ CBCT scans for the training set and 34 for the testing dataset. The 3D meshes had been previously classified in 6 groups by 2 expert clinicians. We improved the robustness of the training data using data augmentation, SMOTE, to alleviate over-fitting and to balance classes. We combined geometrical features and a shape descriptor, heat kernel signature, to describe every shape. The results were compared to nine different supervised machine learning algorithms. The deep learning neural network was the most accurate for classification of TMJ OA. In conclusion, SVA is a 3D Sheer extension that classifies pathology of the temporomandibular joint osteoarthritis cases based on 3D morphology.

3D superimposition of craniofacial imaging-The utility of multicentre collaborations.

Clinical applications of 3D image registration and superimposition have contributed to better understanding growth changes and clinical outcomes. The use of 3D dental and craniofacial imaging in dentistry requires validate image analysis methods for improved diagnosis, treatment planning, navigation and assessment of treatment response. Volumetric 3D images, such as cone-beam computed tomography, can now be superimposed by voxels, surfaces or landmarks. Regardless of the image modality or the software tools, the concepts of regions or points of reference affect all quantitative of qualitative assessments. This study reviews current state of the art in 3D image analysis including 3D superimpositions relative to the cranial base and different regional superimpositions, the development of open source and commercial tools for 3D analysis, how this technology has increased clinical research collaborations from centres all around the globe, some insight on how to incorporate artificial intelligence for big data analysis and progress towards personalized orthodontics.

A web-based system for neural network based classification in temporomandibular joint osteoarthritis.

OBJECTIVE: The purpose of this study is to describe the methodological innovations of a web-based system for storage, integration and computation of biomedical data, using a training imaging dataset to remotely compute a deep neural network classifier of temporomandibular joint osteoarthritis (TMJOA). METHODS: This study imaging dataset consisted of three-dimensional (3D) surface meshes of mandibular condyles constructed from cone beam computed tomography (CBCT) scans. The training dataset consisted of 259 condyles, 105 from control subjects and 154 from patients with diagnosis of TMJ OA. For the image analysis classification, 34 right and left condyles from 17 patients (39.9 ±â€¯11.7 years), who experienced signs and symptoms of the disease for less than 5 years, were included as the testing dataset. For the integrative statistical model of clinical, biological and imaging markers, the sample consisted of the same 17 test OA subjects and 17 age and sex matched control subjects (39.4 ±â€¯15.4 years), who did not show any sign or symptom of OA. For these 34 subjects, a standardized clinical questionnaire, blood and saliva samples were also collected. The technological methodologies in this study include a deep neural network classifier of 3D condylar morphology (ShapeVariationAnalyzer, SVA), and a flexible web-based system for data storage, computation and integration (DSCI) of high dimensional imaging, clinical, and biological data. RESULTS: The DSCI system trained and tested the neural network, indicating 5 stages of structural degenerative changes in condylar morphology in the TMJ with 91% close agreement between the clinician consensus and the SVA classifier. The DSCI remotely ran with a novel application of a statistical analysis, the Multivariate Functional Shape Data Analysis, that computed high dimensional correlations between shape 3D coordinates, clinical pain levels and levels of biological markers, and then graphically displayed the computation results. CONCLUSIONS: The findings of this study demonstrate a comprehensive phenotypic characterization of TMJ health and disease at clinical, imaging and biological levels, using novel flexible and versatile open-source tools for a web-based system that provides advanced shape statistical analysis and a neural network based classification of temporomandibular joint osteoarthritis.