Decoding Spatial Tissue Architecture: A Scalable Bayesian Topic Model for Multiplexed Imaging Analysis.

Recent progress in multiplexed tissue imaging is advancing the study of tumor microenvironments to enhance our understanding of treatment response and disease progression. Cellular neighborhood analysis is a popular computational approach for these complex image data. Despite its popularity, there are significant challenges, including high computational demands that limit feasibility for large-scale applications and the lack of a principled strategy for integrative analysis across images. This absence hampers the precise and consistent identification of spatial features and tracking of their dynamics over disease progression. To overcome these challenges, we introduce SpaTopic, a spatial topic model designed to decode high-level spatial architecture across multiplexed tissue images. This algorithm integrates both cell type and spatial information within a topic modelling framework, originally developed for natural language processing and adapted for computer vision. Spatial information is incorporated into the flexible design of documents, representing densely overlapping regions in images. The model employs an efficient collapsed Gibbs sampling algorithm for both statistical and computational inference. We benchmarked the performance against five state-of-the-art algorithms through various case studies using different single-cell spatial transcriptomic and proteomic imaging platforms across different tissue types. Our findings demonstrate that SpaTopic consistently identifies biologically and clinically significant spatial "topics" such as tertiary lymphoid structures (TLSs) and tracks dynamic changes in spatial features over disease progression. Its computational efficiency and broad applicability across various molecular imaging platforms will enhance the analysis of large-scale tissue imaging datasets.

Spatial Immunophenotyping from Whole-Slide Multiplexed Tissue Imaging Using Convolutional Neural Networks.

The multiplexed immunofluorescence (mIF) platform enables biomarker discovery through the simultaneous detection of multiple markers on a single tissue slide, offering detailed insights into intratumor heterogeneity and the tumor-immune microenvironment at spatially resolved single cell resolution. However, current mIF image analyses are labor-intensive, requiring specialized pathology expertise which limits their scalability and clinical application. To address this challenge, we developed CellGate, a deep-learning (DL) computational pipeline that provides streamlined, end-to-end whole-slide mIF image analysis including nuclei detection, cell segmentation, cell classification, and combined immuno-phenotyping across stacked images. The model was trained on over 750,000 single cell images from 34 melanomas in a retrospective cohort of patients using whole tissue sections stained for CD3, CD8, CD68, CK-SOX10, PD-1, PD-L1, and FOXP3 with manual gating and extensive pathology review. When tested on new whole mIF slides, the model demonstrated high precision-recall AUC. Further validation on whole-slide mIF images of 9 primary melanomas from an independent cohort confirmed that CellGate can reproduce expert pathology analysis with high accuracy. We show that spatial immuno-phenotyping results using CellGate provide deep insights into the immune cell topography and differences in T cell functional states and interactions with tumor cells in patients with distinct histopathology and clinical characteristics. This pipeline offers a fully automated and parallelizable computing process with substantially improved consistency for cell type classification across images, potentially enabling high throughput whole-slide mIF tissue image analysis for large-scale clinical and research applications.

Ovarian function in female survivors of high-risk neuroblastoma.

INTRODUCTION: Data on ovarian function in neuroblastoma survivors are limited. We sought to determine the prevalence of ovarian dysfunction in a cohort of high-risk neuroblastoma survivors and compare outcomes among survivors treated with and without autologous stem cell rescue (ASCR) preceded by myeloablative chemotherapy. METHODS: Retrospective review of female survivors of high-risk neuroblastoma ≥5 years from diagnosis, diagnosed between 1982 and 2014, and followed in a tertiary cancer center. Participants were divided into two groups: individuals treated with conventional chemotherapy ± radiation ("non-ASCR") (n = 32) or with chemotherapy ± radiation followed by myeloablative chemotherapy with ASCR ("ASCR") (n = 51). Ovarian dysfunction was defined as follicle-stimulating hormone ≥15 mU/mL, while premature ovarian insufficiency (POI) was defined as persistent ovarian dysfunction requiring hormone replacement therapy. Poisson models were used to determine prevalence ratios of ovarian dysfunction and POI. RESULTS: Among 83 females (median attained age: 19 years [range, 10-36]; median follow-up: 15 years [range, 7-36]), 49 (59%) had ovarian dysfunction, and 34 (41%) developed POI. Survivors treated with ASCR were 3.2-fold more likely to develop ovarian dysfunction (95% CI: 1.8-6.0; p < 0.001) and 4.5-fold more likely to develop POI (95% CI: 1.7-11.7; p = 0.002) when compared with those treated with conventional chemotherapy, after adjusting for attained age. Two participants in the non-ASCR group and six in the ASCR group achieved at least one spontaneous pregnancy. DISCUSSION: Ovarian dysfunction is prevalent in female high-risk neuroblastoma survivors, especially after ASCR. Longitudinal follow-up of larger cohorts is needed to inform counseling about the risk of impaired ovarian function after neuroblastoma therapy.

Identifying somatic fingerprints of cancers defined by germline and environmental risk factors.

Numerous studies over the past generation have identified germline variants that increase specific cancer risks. Simultaneously, a revolution in sequencing technology has permitted high-throughput annotations of somatic genomes characterizing individual tumors. However, examining the relationship between germline variants and somatic alteration patterns is hugely challenged by the large numbers of variants in a typical tumor, the rarity of most individual variants, and the heterogeneity of tumor somatic fingerprints. In this article, we propose statistical methodology that frames the investigation of germline-somatic relationships in an interpretable manner. The method uses meta-features embodying biological contexts of individual somatic alterations to implicitly group rare mutations. Our team has used this technique previously through a multilevel regression model to diagnose with high accuracy tumor site of origin. Herein, we further leverage topic models from computational linguistics to achieve interpretable lower-dimensional embeddings of the meta-features. We demonstrate how the method can identify distinctive somatic profiles linked to specific germline variants or environmental risk factors. We illustrate the method using The Cancer Genome Atlas whole-exome sequencing data to characterize somatic tumor fingerprints in breast cancer patients with germline BRCA1/2 mutations and in head and neck cancer patients exposed to human papillomavirus.

Metaplastic carcinoma of the breast: matched cohort analysis of recurrence and survival.

PURPOSE: Metaplastic breast carcinoma (MBC) is a rare subtype of breast cancer, defined as mammary carcinoma with squamous or mesenchymal differentiation, that may include spindle cell, chondroid, osseous, or rhabdomyoid differentiation patterns. The implications of MBC recurrence and survival outcomes remains unclear. METHODS: Cases were ascertained from a prospectively maintained institutional database of patients treated from 1998 to 2015. Patients with MBC were matched 1:1 to non-MBC cases. Cox proportional-hazards models and Kaplan-Meier estimates were used to evaluate outcome differences between cohorts. RESULTS: 111 patients with MBC were matched 1:1 with non-MBC patients from an initial set of 2400 patients. Median follow-up time was 8 years. Most patients with MBC received chemotherapy (88%) and radiotherapy (71%). On univariate competing risk regression, MBC was not associated with locoregional recurrence (HR = 1.08; p = 0.8), distant recurrence (HR = 1.65; p = 0.092); disease-free survival (HR = 1.52; p = 0.065), or overall survival (HR = 1.56; p = 0.1). Absolute differences were noted in 8-year disease-free survival (49.6% MBC vs 66.4% non-MBC) and overall survival (61.3% MBC vs 74.4% non-MBC), though neither of these reached statistical significance (p = 0.07 and 0.11, respectively). CONCLUSION: Appropriately-treated MBC may exhibit recurrence and survival outcomes that are difficult to distinguish from those of non-MBC. While prior studies suggest that MBC has a worse natural history than non-MBC triple-negative breast cancer, prudent use of chemotherapy and radiotherapy may narrow these differences, although studies with more power will be required to inform clinical management. Longer follow-up among larger populations may further elucidate the clinical and therapeutic implications of MBC.

Pathway Alterations in Stage II/III Primary Melanoma.

PURPOSE: Genomic classification of melanoma has thus far focused on the mutational status of BRAF, NRAS, and NF1. The clinical utility of this classification remains limited, and the landscape of alterations in other oncogenic signaling pathways is underexplored. METHODS: Using primary samples from the InterMEL study, a retrospective cohort of cases with specimens collected from an international consortium with participating institutions throughout the United States and Australia, with oversampling of cases who ultimately died of melanoma, we examined mutual exclusivity and co-occurrence of genomic alterations in 495 stage II/III primary melanomas across 11 cancer pathways. Somatic mutation and copy number alterations were analyzed from next-generation sequencing using a clinical sequencing panel. RESULTS: Mutations in the RTK-RAS pathway were observed in 81% of cases. Other frequently occurring pathways were TP53 (31%), Cell Cycle (30%), and PI3K (18%). These frequencies are generally lower than was observed in The Cancer Genome Atlas, where the specimens analyzed were predominantly obtained from metastases. Overall, 81% of the cases had at least one targetable mutation. The RTK-RAS pathway was the only pathway that demonstrated strong and statistically significant mutual exclusivity. However, this strong mutual exclusivity signal was evident only for the three common genes in the pathway (BRAF, NRAS, and NF1). Analysis of co-occurrence of different pathways exhibited no positive significant trends. However, interestingly, a high frequency of cases with none of these pathways represented was observed, 8.4% of cases versus 4.0% expected (P < .001). A higher frequency of RTK-RAS singletons (with no other pathway alteration) was observed compared with The Cancer Genome Atlas. Clonality analyses suggest strongly that both the cell cycle and RTK-RAS pathways represent early events in melanogenesis. CONCLUSION: Our results confirm the dominance of mutations in the RTK-RAS pathway. The presence of many mutations in several well-known, actionable pathways suggests potential avenues for targeted therapy in these early-stage cases.