Individual, interpersonal, and community factors associated with health outcomes in patients with type 2 diabetes mellitus treated at federally qualified health centers.

Purpose: To examine factors of influence in diabetes management and their association with self-reported health outcomes in patients with type 2 diabetes treated at Federally Qualified Health Centers (FQHCs). Methods: This cross-sectional study examined data from the 2014 Health Center Patient Survey (HCPS). Predictor variables were categorized across three levels of the National Institute on Minority Health and Health Disparities research framework. Outcome variables retrieved from HCPS included self-reports of blood glucose levels, and diabetes-related emergency department (ED)/hospital visits during past year. Results: A total of 936 patients with diabetes were included. Most (65%) participants received a diabetes self-management plan. During the previous year, 72% received > = 2 A1C checks, 52% reported high blood glucose levels, and 12% visited an ED/hospital. Multivariable results showed that insulin use and receiving a self-management plan were associated with high blood glucose levels and ED/hospital visits. Community factors of being unable to get medications and receiving a specialist foot exam were respectively associated with high blood glucose levels and ED/hospital visits. Conclusion: Different factors were associated with health outcomes in patients with diabetes treated at FQHCs. Identifying these factors can help with targeted screening and follow-up and assessing potential interventions to improve health outcomes. Supplementary Information: The online version contains supplementary material available at 10.1007/s40200-024-01388-5.

BioFusionNet: Deep Learning-Based Survival Risk Stratification in ER+ Breast Cancer Through Multifeature and Multimodal Data Fusion.

Breast cancer is a significant health concern affecting millions of women worldwide. Accurate survival risk stratification plays a crucial role in guiding personalised treatment decisions and improving patient outcomes. Here we present BioFusionNet, a deep learning framework that fuses image-derived features with genetic and clinical data to obtain a holistic profile and achieve survival risk stratification of ER+ breast cancer patients. We employ multiple self-supervised feature extractors (DINO and MoCoV3) pretrained on histopathological patches to capture detailed image features. These features are then fused by a variational autoencoder and fed to a self-attention network generating patient-level features. A co-dual-cross-attention mechanism combines the histopathological features with genetic data, enabling the model to capture the interplay between them. Additionally, clinical data is incorporated using a feed-forward network, further enhancing predictive performance and achieving comprehensive multimodal feature integration. Furthermore, we introduce a weighted Cox loss function, specifically designed to handle imbalanced survival data, which is a common challenge. Our model achieves a mean concordance index of 0.77 and a time-dependent area under the curve of 0.84, outperforming state-of-the-art methods. It predicts risk (high versus low) with prognostic significance for overall survival in univariate analysis (HR=2.99, 95% CI: 1.88-4.78, p 0.005), and maintains independent significance in multivariate analysis incorporating standard clinicopathological variables (HR=2.91, 95% CI: 1.80-4.68, p 0.005).

An RNA origami robot that traps and releases a fluorescent aptamer.

RNA nanotechnology aims to use RNA as a programmable material to create self-assembling nanodevices for application in medicine and synthetic biology. The main challenge is to develop advanced RNA robotic devices that both sense, compute, and actuate to obtain enhanced control over molecular processes. Here, we use the RNA origami method to prototype an RNA robotic device, named the "Traptamer," that mechanically traps the fluorescent aptamer, iSpinach. The Traptamer is shown to sense two RNA key strands, acts as a Boolean AND gate, and reversibly controls the fluorescence of the iSpinach aptamer. Cryo-electron microscopy of the closed Traptamer structure at 5.45-angstrom resolution reveals the mechanical mode of distortion of the iSpinach motif. Our study suggests a general approach to distorting RNA motifs and a path forward to build sophisticated RNA machines that through sensing, computing, and actuation modules can be used to precisely control RNA functionalities in cellular systems.

Cryo-EM structure and functional landscape of an RNA polymerase ribozyme.

The emergence of an RNA replicase capable of self-replication is considered an important stage in the origin of life. RNA polymerase ribozymes (PR) - including a variant that uses trinucleotide triphosphates (triplets) as substrates - have been created by in vitro evolution and are the closest functional analogues of the replicase, but the structural basis for their function is poorly understood. Here we use single-particle cryogenic electron microscopy (cryo-EM) and high-throughput mutation analysis to obtain the structure of a triplet polymerase ribozyme (TPR) apoenzyme and map its functional landscape. The cryo-EM structure at 5-Å resolution reveals the TPR as an RNA heterodimer comprising a catalytic subunit and a noncatalytic, auxiliary subunit, resembling the shape of a left hand with thumb and fingers at a 70° angle. The two subunits are connected by two distinct kissing-loop (KL) interactions that are essential for polymerase function. Our combined structural and functional data suggest a model for templated RNA synthesis by the TPR holoenzyme, whereby heterodimer formation and KL interactions preorganize the TPR for optimal primer-template duplex binding, triplet substrate discrimination, and templated RNA synthesis. These results provide a better understanding of TPR structure and function and should aid the engineering of more efficient PRs.

Breast cancer histopathology image-based gene expression prediction using spatial transcriptomics data and deep learning.

Tumour heterogeneity in breast cancer poses challenges in predicting outcome and response to therapy. Spatial transcriptomics technologies may address these challenges, as they provide a wealth of information about gene expression at the cell level, but they are expensive, hindering their use in large-scale clinical oncology studies. Predicting gene expression from hematoxylin and eosin stained histology images provides a more affordable alternative for such studies. Here we present BrST-Net, a deep learning framework for predicting gene expression from histopathology images using spatial transcriptomics data. Using this framework, we trained and evaluated four distinct state-of-the-art deep learning architectures, which include ResNet101, Inception-v3, EfficientNet (with six different variants), and vision transformer (with two different variants), all without utilizing pretrained weights for the prediction of 250 genes. To enhance the generalisation performance of the main network, we introduce an auxiliary network into the framework. Our methodology outperforms previous studies, with 237 genes identified with positive correlation, including 24 genes with a median correlation coefficient greater than 0.50. This is a notable improvement over previous studies, which could predict only 102 genes with positive correlation, with the highest correlation values ranging from 0.29 to 0.34.

RNA target highlights in CASP15: Evaluation of predicted models by structure providers.

The first RNA category of the Critical Assessment of Techniques for Structure Prediction competition was only made possible because of the scientists who provided experimental structures to challenge the predictors. In this article, these scientists offer a unique and valuable analysis of both the successes and areas for improvement in the predicted models. All 10 RNA-only targets yielded predictions topologically similar to experimentally determined structures. For one target, experimentalists were able to phase their x-ray diffraction data by molecular replacement, showing a potential application of structure predictions for RNA structural biologists. Recommended areas for improvement include: enhancing the accuracy in local interaction predictions and increased consideration of the experimental conditions such as multimerization, structure determination method, and time along folding pathways. The prediction of RNA-protein complexes remains the most significant challenge. Finally, given the intrinsic flexibility of many RNAs, we propose the consideration of ensemble models.

MCP-1 expression in breast cancer and its association with distant relapse.

BACKGROUND: Distant relapse of breast cancer complicates management of the disease and accounts for 90% of breast cancer-related deaths. Monocyte chemoattractant protein-1 (MCP-1) has critical roles in breast cancer progression and is widely accepted as a pro-metastatic chemokine. METHODS: This study explored MCP-1 expression in the primary tumour of 251 breast cancer patients. A simplified 'histoscore' was used to determine if each tumour had high or low expression of MCP-1. Patient breast cancers were retrospectively staged based on available patient data. p < 0.05 was used to determine significance and changes in hazard ratios between models were considered. RESULTS: Low MCP-1 expression in the primary tumour was associated with breast cancer-related death with distant relapse in ER- breast cancers (p < 0.01); however, this was likely a result of most low MCP-1-expressing ER- breast cancers being Stage III or Stage IV, with high MCP-1 expression in the primary tumour significantly correlated with Stage I breast cancers (p < 0.05). Expression of MCP-1 in the primary ER- tumours varied across Stage I, II, III and IV and we highlighted a switch in MCP-1 expression from high in Stage I ER- cancers to low in Stage IV ER- cancers. CONCLUSION: This study has emphasised a critical need for further investigation into MCP-1's role in breast cancer progression and improved characterisation of MCP-1 in breast cancers, particularly in light of the development of anti-MCP-1, anti-metastatic therapies.

Tertiary structure of single-instant RNA molecule reveals folding landscape.

The folding of RNA and protein molecules during their synthesis is a crucial self-assembly process that nature employs to convert genetic information into the complex molecular machinery that supports life. Misfolding events are the cause of several diseases, and the folding pathway of central biomolecules, such as the ribosome, is strictly regulated by programmed maturation processes and folding chaperones. However, the dynamic folding processes are challenging to study because current structure determination methods heavily rely on averaging, and existing computational methods do not efficiently simulate non-equilibrium dynamics. Here we utilize individual-particle cryo-electron tomography (IPET) to investigate the folding landscape of a rationally designed RNA origami 6-helix bundle that undergoes slow maturation from a "young" to "mature" conformation. By optimizing the IPET imaging and electron dose conditions, we obtain 3D reconstructions of 120 individual particles at resolutions ranging from 23-35 Å, enabling us first-time to observe individual RNA helices and tertiary structures without averaging. Statistical analysis of 120 tertiary structures confirms the two main conformations and suggests a possible folding pathway driven by helix-helix compaction. Studies of the full conformational landscape reveal both trapped states, misfolded states, intermediate states, and fully compacted states. The study provides novel insight into RNA folding pathways and paves the way for future studies of the energy landscape of molecular machines and self-assembly processes.

hist2RNA: An Efficient Deep Learning Architecture to Predict Gene Expression from Breast Cancer Histopathology Images.

Gene expression can be used to subtype breast cancer with improved prediction of risk of recurrence and treatment responsiveness over that obtained using routine immunohistochemistry (IHC). However, in the clinic, molecular profiling is primarily used for ER+ breast cancer, which is costly, tissue destructive, requires specialised platforms, and takes several weeks to obtain a result. Deep learning algorithms can effectively extract morphological patterns in digital histopathology images to predict molecular phenotypes quickly and cost-effectively. We propose a new, computationally efficient approach called hist2RNA inspired by bulk RNA sequencing techniques to predict the expression of 138 genes (incorporated from 6 commercially available molecular profiling tests), including luminal PAM50 subtype, from hematoxylin and eosin (H&E)-stained whole slide images (WSIs). The training phase involves the aggregation of extracted features for each patient from a pretrained model to predict gene expression at the patient level using annotated H&E images from The Cancer Genome Atlas (TCGA, n = 335). We demonstrate successful gene prediction on a held-out test set (n = 160, corr = 0.82 across patients, corr = 0.29 across genes) and perform exploratory analysis on an external tissue microarray (TMA) dataset (n = 498) with known IHC and survival information. Our model is able to predict gene expression and luminal PAM50 subtype (Luminal A versus Luminal B) on the TMA dataset with prognostic significance for overall survival in univariate analysis (c-index = 0.56, hazard ratio = 2.16 (95% CI 1.12-3.06), p < 5 × 10-3), and independent significance in multivariate analysis incorporating standard clinicopathological variables (c-index = 0.65, hazard ratio = 1.87 (95% CI 1.30-2.68), p < 5 × 10-3). The proposed strategy achieves superior performance while requiring less training time, resulting in less energy consumption and computational cost compared to patch-based models. Additionally, hist2RNA predicts gene expression that has potential to determine luminal molecular subtypes which correlates with overall survival, without the need for expensive molecular testing.

Evaluation of an intensive workshop on research methods in supportive oncology.

OBJECTIVES: Since 2015, the Harvard Workshop on Research Methods in Supportive Oncology has trained early-career investigators in skills to develop rigorous studies in supportive oncology. This study examines workshop evaluations over time in the context of two factors: longitudinal participant feedback and a switch from in-person to virtual format during the COVID pandemic. METHODS: We examined post-workshop evaluations for participants who attended the workshop from 2015 to 2021. We qualitatively analyzed evaluation free text responses on ways in which the workshop could be improved and "other comments." Potential areas of improvement were categorized and frequencies were compiled longitudinally. Differences in participants' ratings of the workshop and demographics between in-person and virtual formats were investigated with t-tests and Chi-square tests, respectively. RESULTS: 286 participants attended the workshop over 8 years. Participant ratings of the workshop remained consistently high without substantial variation across all years. Three main themes emerged from the "other comments" item: (1) sense of community; (2) passion and empowerment; and (3) value of protected time. Participants appeared to identify fewer areas for improvement over time. There were no significant differences in participant ratings or demographics between the in-person and virtual formats. SIGNIFINACE OF RESULTS: While the workshop has experienced changes over time, participant evaluations varied little. The core content and structure might have the greatest influence on participants' experiences.

RNA origami scaffolds facilitate cryo-EM characterization of a Broccoli-Pepper aptamer FRET pair.

Cryogenic electron microscopy (cryo-EM) is a promising method for characterizing the structure of larger RNA structures and complexes. However, the structure of individual aptamers is difficult to solve by cryo-EM due to their low molecular weight and a high signal-to-noise ratio. By placing RNA aptamers on larger RNA scaffolds, the contrast for cryo-EM can be increased to allow the determination of the tertiary structure of the aptamer. Here we use the RNA origami method to scaffold two fluorescent aptamers (Broccoli and Pepper) in close proximity and show that their cognate fluorophores serve as donor and acceptor for FRET. Next, we use cryo-EM to characterize the structure of the RNA origami with the two aptamers to a resolution of 4.4 Å. By characterizing the aptamers with and without ligand, we identify two distinct modes of ligand binding, which are further supported by selective chemical probing. 3D variability analysis of the cryo-EM data show that the relative position between the two bound fluorophores on the origami fluctuate by only 3.5 Å. Our results demonstrate a general approach for using RNA origami scaffolds for characterizing small RNA motifs by cryo-EM and for positioning functional RNA motifs with high spatial precision.

Structure, folding and flexibility of co-transcriptional RNA origami.

RNA origami is a method for designing RNA nanostructures that can self-assemble through co-transcriptional folding with applications in nanomedicine and synthetic biology. However, to advance the method further, an improved understanding of RNA structural properties and folding principles is required. Here we use cryogenic electron microscopy to study RNA origami sheets and bundles at sub-nanometre resolution revealing structural parameters of kissing-loop and crossover motifs, which are used to improve designs. In RNA bundle designs, we discover a kinetic folding trap that forms during folding and is only released after 10 h. Exploration of the conformational landscape of several RNA designs reveal the flexibility of helices and structural motifs. Finally, sheets and bundles are combined to construct a multidomain satellite shape, which is characterized by individual-particle cryo-electron tomography to reveal the domain flexibility. Together, the study provides a structural basis for future improvements to the design cycle of genetically encoded RNA nanodevices.

An RNA Paranemic Crossover Triangle as A 3D Module for Cotranscriptional Nanoassembly.

RNA nanotechnology takes advantage of structural modularity to build self-assembling nano-architectures with applications in medicine and synthetic biology. The use of paranemic motifs, that form without unfolding existing secondary structure, allows for the creation of RNA nanostructures that are compatible with cotranscriptional folding in vitro and in vivo. In previous work, kissing-loop (KL) motifs have been widely used to design RNA nanostructures that fold cotranscriptionally. However, the paranemic crossover (PX) motif has not yet been explored for cotranscriptional RNA origami architectures and information about the structural geometry of the motif is unknown. Here, a six base pair-wide paranemic RNA interaction that arranges double helices in a perpendicular manner is introduced, allowing for the generation of a new and versatile building block: the paranemic-crossover triangle (PXT). The PXT is self-assembled by cotranscriptional folding and characterized by cryogenic electron microscopy, revealing for the first time an RNA PX interaction in high structural detail. The PXT is used as a building block for the construction of multimers that form filaments and rings and a duplicated PXT motif is used as a building block to self-assemble cubic structures, demonstrating the PXT as a rigid self-folding domain for the development of wireframe RNA origami architectures.

Pharmacists' naloxone services beyond community pharmacy settings: A systematic review.

BACKGROUND: Pharmacists' provision of naloxone services in community pharmacy settings is well-recognized. Recently, studies describing pharmacists' naloxone services in settings other than community pharmacies have emerged in the literature. There is a need to synthesize evidence from these studies to evaluate the scope and impact of pharmacists' naloxone services beyond community pharmacy settings. OBJECTIVES: The objectives of this systematic review were to a) identify pharmacists' naloxone services and their outcomes, and b) examine knowledge, attitudes, and barriers (KAB) related to naloxone service provision in non-community pharmacy settings. METHODS: Eligible studies were identified using PubMed, Web of Science, and CINAHL. Inclusion criteria were as follows: peer-reviewed empirical research conducted in the U.S. from January 2010 through February 2022; published in English; and addressed a) pharmacists' naloxone services and/or b) KAB related to the implementation of naloxone services. PRISMA guidelines were used to report this study. RESULTS: Seventy-six studies were identified. The majority were non-randomized and observational; only two used a randomized controlled (RCT) design. Most studies were conducted in veterans affairs (30%) and academic medical centers (21%). Sample sizes ranged from n = 10 to 217,469, and the majority reported sample sizes <100. Pharmacists' naloxone services involved clinical staff education, utilization of screening tools to identify at-risk patients, naloxone prescribing and overdose education and naloxone dispensing (OEND). Outcomes of implementing naloxone services included improved naloxone knowledge, positive attitudes, increased OEND, and overdose reversals. Pharmacists cited inadequate training, time constraints, reimbursement issues, and stigma as barriers that hindered naloxone service implementation. CONCLUSION: This systematic review found robust evidence regarding pharmacist-based naloxone services beyond community pharmacy settings. Future programs should use targeted approaches to help pharmacists overcome barriers and enhance naloxone services. Additional research is needed to evaluate pharmacist naloxone services by using rigorous methodologies (e.g., larger sample sizes, RCT designs).

Survival prediction in triple negative breast cancer using multiple instance learning of histopathological images.

Computational pathology is a rapidly expanding area for research due to the current global transformation of histopathology through the adoption of digital workflows. Survival prediction of breast cancer patients is an important task that currently depends on histopathology assessment of cancer morphological features, immunohistochemical biomarker expression and patient clinical findings. To facilitate the manual process of survival risk prediction, we developed a computational pathology framework for survival prediction using digitally scanned haematoxylin and eosin-stained tissue microarray images of clinically aggressive triple negative breast cancer. Our results show that the model can produce an average concordance index of 0.616. Our model predictions are analysed for independent prognostic significance in univariate analysis (hazard ratio = 3.12, 95% confidence interval [1.69,5.75], p < 0.005) and multivariate analysis using clinicopathological data (hazard ratio = 2.68, 95% confidence interval [1.44,4.99], p < 0.005). Through qualitative analysis of heatmaps generated from our model, an expert pathologist is able to associate tissue features highlighted in the attention heatmaps of high-risk predictions with morphological features associated with more aggressive behaviour such as low levels of tumour infiltrating lymphocytes, stroma rich tissues and high-grade invasive carcinoma, providing explainability of our method for triple negative breast cancer.

Barriers Associated with Access to Prescription Medications in Patients Diagnosed with Type 2 Diabetes Mellitus Treated at Federally Qualified Health Centers.

This study describes access to prescription medications and examines personal, financial, and structural barriers associated with access to prescription medications in patients with type 2 diabetes treated at Federally Qualified Health Centers. We used a cross-sectional design to analyze data retrieved from the 2014 Health Center Patient Survey. Adult participants who self-reported having type 2 diabetes were included in this study. Predictor variables were categorized into personal, financial, and structural barriers. Outcomes include being unable to get and delayed in getting prescription medications. Chi-square and multivariable regression models were conducted to examine associations between predictor and outcome variables. A total of 1097 participants with type 2 diabetes were included in analyses. Approximately 29% of participants were delayed, and 24% were unable to get medications. Multivariable regression results showed that personal barriers, such as federal poverty level, health status, and psychological distress were associated with being unable to get medications. Financial barriers including out-of-pocket medication cost and employment were associated with access to prescription medications. Type of health center funding program as a structural barrier was associated with access to medications. In conclusion, multi-level tailored strategies and policy changes are needed to address these barriers to improve access to prescription medications and health outcomes in underserved patient populations.

ALTEN: A High-Fidelity Primary Tissue-Engineering Platform to Assess Cellular Responses Ex Vivo.

To fully investigate cellular responses to stimuli and perturbations within tissues, it is essential to replicate the complex molecular interactions within the local microenvironment of cellular niches. Here, the authors introduce Alginate-based tissue engineering (ALTEN), a biomimetic tissue platform that allows ex vivo analysis of explanted tissue biopsies. This method preserves the original characteristics of the source tissue's cellular milieu, allowing multiple and diverse cell types to be maintained over an extended period of time. As a result, ALTEN enables rapid and faithful characterization of perturbations across specific cell types within a tissue. Importantly, using single-cell genomics, this approach provides integrated cellular responses at the resolution of individual cells. ALTEN is a powerful tool for the analysis of cellular responses upon exposure to cytotoxic agents and immunomodulators. Additionally, ALTEN's scalability using automated microfluidic devices for tissue encapsulation and subsequent transport, to enable centralized high-throughput analysis of samples gathered by large-scale multicenter studies, is shown.

Fecal DNA Virome Is Associated with the Development of Colorectal Neoplasia in a Murine Model of Colorectal Cancer.

Alteration of the gut virome has been associated with colorectal cancer (CRC); however, when and how the alteration takes place has not been studied. Here, we employ a longitudinal study in mice to characterize the gut virome alteration in azoxymethane (AOM)-induced colorectal neoplasia and identify important viruses associated with tumor growth. The number and size of the tumors increased as the mice aged in the AOM treated group, as compared to the control group. Tumors were first observed in the AOM group at week 12. We observed a significantly lower alpha diversity and shift in viral profile when tumors first appeared. In addition, we identified novel viruses from the genera Brunovirus, Hpunavirus that are positively associated with tumor growth and enriched at a late time point in AOM group, whereas members from Lubbockvirus show a negative correlation with tumor growth. Moreover, network analysis revealed two clusters of viruses in the AOM virome, a group that is positively correlated with tumor growth and another that is negatively correlated with tumor growth, all of which are bacteriophages. Our findings suggest that the gut virome changes along with tumor formation and provides strong evidence of a potential role for bacteriophage in the development of colorectal neoplasia.

Multiplexed immunofluorescence identifies high stromal CD68+PD-L1+ macrophages as a predictor of improved survival in triple negative breast cancer.

Triple negative breast cancer (TNBC) comprises 10-15% of all breast cancers and has a poor prognosis with a high risk of recurrence within 5 years. PD-L1 is an important biomarker for patient selection for immunotherapy but its cellular expression and co-localization within the tumour immune microenvironment and associated prognostic value is not well defined. We aimed to characterise the phenotypes of immune cells expressing PD-L1 and determine their association with overall survival (OS) and breast cancer-specific survival (BCSS). Using tissue microarrays from a retrospective cohort of TNBC patients from St George Hospital, Sydney (n = 244), multiplexed immunofluorescence (mIF) was used to assess staining for CD3, CD8, CD20, CD68, PD-1, PD-L1, FOXP3 and pan-cytokeratin on the Vectra Polaris™ platform and analysed using QuPath. Cox multivariate analyses showed high CD68+PD-L1+ stromal cell counts were associated with improved prognosis for OS (HR 0.56, 95% CI 0.33-0.95, p = 0.030) and BCSS (HR 0.47, 95% CI 0.25-0.88, p = 0.018) in the whole cohort and in patients receiving chemotherapy, improving incrementally upon the predictive value of PD-L1+ alone for BCSS. These data suggest that CD68+PD-L1+ status can provide clinically useful prognostic information to identify sub-groups of patients with good or poor prognosis and guide treatment decisions in TNBC.

SP142 PD-L1 Scoring Shows High Interobserver and Intraobserver Agreement in Triple-negative Breast Carcinoma But Overall Low Percentage Agreement With Other PD-L1 Clones SP263 and 22C3.

SP142 programmed cell death ligand 1 (PD-L1) status predicts response to atezolizumab in triple-negative breast carcinoma (TNBC). Prevalence of VENTANA PD-L1 (SP142) Assay positivity, concordance with the VENTANA PD-L1 (SP263) Assay and Dako PD-L1 IHC 22C3 pharmDx assay, and association with clinicopathologic features were assessed in 447 TNBCs. SP142 PD-L1 intraobserver and interobserver agreement was investigated in a subset of 60 TNBCs, with scores enriched around the 1% cutoff. The effect of a 1-hour training video on pretraining and posttraining scores was ascertained. At a 1% cutoff, 34.2% of tumors were SP142 PD-L1 positive. SP142 PD-L1 positivity was significantly associated with tumor-infiltrating lymphocytes (P <0.01), and node negativity (P=0.02), but not with tumor grade (P=0.35), tumor size (P=0.58), or BRCA mutation (P=0.53). Overall percentage agreement (OPA) for intraobserver and interobserver agreement was 95.0% and 93.7%, respectively, among 5 pathologists trained in TNBC SP142 PD-L1 scoring. In 5 TNBC SP142 PD-L1-naive pathologists, significantly higher OPA to the reference score was achieved after video training (posttraining OPA 85.7%, pretraining OPA 81.5%, P<0.05). PD-L1 status at a 1% cutoff was assessed by SP142 and SP263 in 420 cases, and by SP142 and 22C3 in 423 cases, with OPA of 88.1% and 85.8%, respectively. The VENTANA PD-L1 (SP142) Assay is reproducible for classifying TNBC PD-L1 status by trained observers; however, it is not analytically equivalent to the VENTANA PD-L1 (SP263) Assay and Dako PD-L1 IHC 22C3 pharmDx assay.