An Abscopal Effect on Lung Metastases in Canine Mammary Cancer Patients Induced by Neoadjuvant Intratumoral Immunotherapy with Cowpea Mosaic Virus Nanoparticles and Anti-Canine PD-1.

Neoadjuvant intratumoral (IT) therapy could amplify the weak responses to checkpoint blockade therapy observed in breast cancer (BC). In this study, we administered neoadjuvant IT anti-canine PD-1 therapy (IT acPD-1) alone or combined with IT cowpea mosaic virus therapy (IT CPMV/acPD-1) to companion dogs diagnosed with canine mammary cancer (CMC), a spontaneous tumor resembling human BC. CMC patients treated weekly with acPD-1 (n = 3) or CPMV/acPD-1 (n = 3) for four weeks or with CPMV/acPD-1 (n = 3 patients not candidates for surgery) for up to 11 weeks did not experience immune-related adverse events.

High-Risk HPV Screening Initiative in Kosovo-A Way to Optimize HPV Vaccination for Cervical Cancer.

Nearly all cervical cancers are caused by persistent high-risk human papillomavirus (hrHPV) infection. There are 14 recognized hrHPV genotypes (HPV 16, 18, 31, 33, 35, 39, 45, 51, 52, 56, 58, 59, 66, and 68), and hrHPV genotypes 16 and 18 comprise approximately 66% of all cases worldwide. An additional 15% of cervical cancers are caused by hrHPV genotypes 31, 33, 45, 52, and 58.

Spatial Omics Driven Crossmodal Pretraining Applied to Graph-based Deep Learning for Cancer Pathology Analysis.

Graph-based deep learning has shown great promise in cancer histopathology image analysis by contextualizing complex morphology and structure across whole slide images to make high quality downstream outcome predictions (ex: prognostication). These methods rely on informative representations (i.e.

Feasibility of Inferring Spatial Transcriptomics from Single-Cell Histological Patterns for Studying Colon Cancer Tumor Heterogeneity.

Background: Spatial transcriptomics involves studying the spatial organization of gene expression within tissues, offering insights into the molecular diversity of tumors. While spatial gene expression is commonly amalgamated from 1-10 cells across 50-micron spots, recent methods have demonstrated the capability to disaggregate this information at subspot resolution by leveraging both expression and histological patterns. However, elucidating such information from histology alone presents a significant challenge but if solved can better permit spatial molecular analysis at cellular resolution for instances where Visium data is not available, reducing study costs.

Inferring spatial transcriptomics markers from whole slide images to characterize metastasis-related spatial heterogeneity of colorectal tumors: A pilot study.

Over 150 000 Americans are diagnosed with colorectal cancer (CRC) every year, and annually over 50 000 individuals will die from CRC, necessitating improvements in screening, prognostication, disease management, and therapeutic options. Tumor metastasis is the primary factor related to the risk of recurrence and mortality. Yet, screening for nodal and distant metastasis is costly, and invasive and incomplete resection may hamper adequate assessment.

Identification of Spatial Proteomic Signatures of Colon Tumor Metastasis: A Digital Spatial Profiling Approach.

Over 150,000 Americans are diagnosed with colorectal cancer (CRC) every year, and annually >50,000 individuals are estimated to die of CRC, necessitating improvements in screening, prognostication, disease management, and therapeutic options. CRC tumors are removed en bloc with surrounding vasculature and lymphatics. Examination of regional lymph nodes at the time of surgical resection is essential for prognostication.

Mixed Effects Machine Learning Models for Colon Cancer Metastasis Prediction using Spatially Localized Immuno-Oncology Markers.

Spatially resolved characterization of the transcriptome and proteome promises to provide further clarity on cancer pathogenesis and etiology, which may inform future clinical practice through classifier development for clinical outcomes. However, batch effects may potentially obscure the ability of machine learning methods to derive complex associations within spatial omics data. Profiling thirty-five stage three colon cancer patients using the GeoMX Digital Spatial Profiler, we found that mixed-effects machine learning (MEML) methods† may provide utility for overcoming significant batch effects to communicate key and complex disease associations from spatial information.

Visualization and quantification of pancreatic tumor stroma in fresh tissue via ultraviolet surface excitation.

Significance: The study has confirmed the feasibility of using ultraviolet (UV) excitation to visualize and quantify desmoplasia in fresh tumor tissue of pancreatic adenocarcinoma (PDAC) in an orthotopic xenograft mouse model, which provides a useful imaging platform to evaluate acute therapeutic responses.

Aim: Stromal network of collagen prominent in PDAC tumors is examined by imaging fresh tissue samples stained with histological dyes. Fluorescence signals are color-transferred to mimic Masson's trichrome staining.