Spatial Transcriptomic Analysis Reveals Associations between Genes and Cellular Topology in Breast and Prostate Cancers.

BACKGROUND: Cancer is the leading cause of death worldwide with breast and prostate cancer the most common among women and men, respectively. Gene expression and image features are independently prognostic of patient survival; but until the advent of spatial transcriptomics (ST), it was not possible to determine how gene expression of cells was tied to their spatial relationships (i.e., topology). METHODS: We identify topology-associated genes (TAGs) that correlate with 700 image topological features (ITFs) in breast and prostate cancer ST samples. Genes and image topological features are independently clustered and correlated with each other. Themes among genes correlated with ITFs are investigated by functional enrichment analysis. RESULTS: Overall, topology-associated genes (TAG) corresponding to extracellular matrix (ECM) and Collagen Type I Trimer gene ontology terms are common to both prostate and breast cancer. In breast cancer specifically, we identify the ZAG-PIP Complex as a TAG. In prostate cancer, we identify distinct TAGs that are enriched for GI dysmotility and the IgA immunoglobulin complex. We identified TAGs in every ST slide regardless of cancer type. CONCLUSIONS: These TAGs are enriched for ontology terms, illustrating the biological relevance to our image topology features and their potential utility in diagnostic and prognostic models.

Predicting melanoma survival and metastasis with interpretable histopathological features and machine learning models.

Introduction: Melanoma is the fifth most common cancer in US, and the incidence is increasing 1.4% annually. The overall survival rate for early-stage disease is 99.4%. However, melanoma can recur years later (in the same region of the body or as distant metastasis), and results in a dramatically lower survival rate. Currently there is no reliable method to predict tumor recurrence and metastasis on early primary tumor histological images. Methods: To identify rapid, accurate, and cost-effective predictors of metastasis and survival, in this work, we applied various interpretable machine learning approaches to analyze melanoma histopathological H&E images. The result is a set of image features that can help clinicians identify high-risk-of-metastasis patients for increased clinical follow-up and precision treatment. We use simple models (i.e., logarithmic classification and KNN) and "human-interpretable" measures of cell morphology and tissue architecture (e.g., cell size, staining intensity, and cell density) to predict the melanoma survival on public and local Stage I-III cohorts as well as the metastasis risk on a local cohort. Results: We use penalized survival regression to limit features available to downstream classifiers and investigate the utility of convolutional neural networks in isolating tumor regions to focus morphology extraction on only the tumor region. This approach allows us to predict survival and metastasis with a maximum F1 score of 0.72 and 0.73, respectively, and to visualize several high-risk cell morphologies. Discussion: This lays the foundation for future work, which will focus on using our interpretable pipeline to predict metastasis in Stage I & II melanoma.

Microbial enrichment and multiplexed microfiltration for accelerated detection of Salmonella in spinach.

To attain Salmonella detection thresholds in spinach suspensions using enrichment media requires at least 24 hr. Separation and concentration of selected microorganisms via microfiltration and microfugation reduce time for sample preparation, especially when working with large volumes of vegetable suspensions. This facilitates accelerated detection of Salmonella in spinach suspensions, and may contribute to effectively monitoring this pathogen before it reaches the consumer. We report a microfiltration-based protocol for accelerated sample preparation to concentrate and recover ≤1 colony forming unit (CFU) Salmonella/g pathogen-free spinach. Store-bought samples of spinach and a spinach plant subjected to two environmental conditions (temperature and light exposure) during its production were tested. The overall procedure involves extraction with buffer, a short enrichment step, prefiltration using a nylon filter, crossflow hollow fiber microfiltration, and retentate centrifugation to bring microbial cells to detection levels. Based on 1 CFU Salmonella/g frozen spinach, and a Poisson distribution statistical analyses with 99% probability, we calculated that 3 hr of incubation, when followed by microfiltration, is sufficient to reach the 2 log concentration required for Salmonella detection within 7 hr. Longer enrichment times (5 hr or more) is needed for concentrations lower than 1 CFU Salmonella/g of ready to eat spinach. The recovered microbial cells were identified and confirmed as Salmonella using both polymerase chain reaction (PCR) and plating methods. Different environmental conditions tested during production did not affect Salmonella viability; this demonstrated the broad adaptability of Salmonella and emphasized the need for methods that enable efficient monitoring of production for the presence of this pathogen.