Faculty Bibliography

AIMS/OBJECTIVE:Oncotype DX has played a critical role in guiding treatment decisions for hormone receptor (HR)-positive, HER2-negative early-stage breast cancer. Clinically, a subset of patients with low Oncotype recurrent score (RS) will still progress on standard therapy and ultimately develop metastasis. Our goal was to explore potential molecular mechanisms, including specific genetic alterations and pathway activity associated with disease progression. METHODS AND RESULTS/RESULTS:We retrospectively reviewed a small series of low RS breast cancers with subsequent metastasis and analysed the clinicopathological characteristics and comprehensive genomic profiling (CGP) data from tumour tissue and circulating tumour DNA (ctDNA) by liquid biopsy. RESULTS:These tumours demonstrated a range of histopathologic features and molecular profiles. Common findings included enrichment of PIK3CA and TP53 mutations and treatment-emergent ESR1 mutations, observed in both tissue and ctDNA. CDKN2A, SPEN, KIT, CTNNB1, MYC, EMSY, KMT2C, MAP3K1 gene alterations were only found in low RS group in low frequency. Copy number amplifications events were less common in low RS group. In cases with both tissue and ctDNA data, tissue CGP proved useful baseline for identifying driver mutations such as PIK3CA and for contextualizing ctDNA findings, and ctDNA analysis was adequate for disease monitoring and tracking molecular evolution over time. CONCLUSIONS:Using real-world CGP of tumour tissue and ctDNA, we identified key molecular features associated with endocrine resistance in patients with low RS who later developed metastases. PIK3CA mutation and other ER group-related mutations contributed to the low RS. Tissue CGP provides baseline for interpreting ctDNA, and ctDNA monitoring PIK3CA, TP53, ESR1 and other pathogenic or driver mutations in the early course of low RS cases may represent an excellent non-invasive option for identifying targets and early intervention to prevent disease progression, though a large validation study is needed.

BACKGROUND/UNASSIGNED:Bladder cancer is a disease characterized by complex perturbations in gene networks and is heterogeneous in terms of histology, mutations, and prognosis. Advances in high-throughput sequencing technologies, genome-wide association studies, and bioinformatics methods have revealed greater insights into the pathogenesis of complex diseases. Network biology-based approaches have been used to identify complex protein-protein interactions (PPIs) that can lead to potential drug targets. There is a need to better understand PPIs specific to urothelial carcinoma. OBJECTIVE/UNASSIGNED:This study aimed to elucidate PPIs specific to papillary and nonpapillary urothelial carcinoma and identify the most connected or "hub" proteins, as these are potential drug targets. METHODS/UNASSIGNED:A novel PPI analysis tool, Proteinarium, was used to analyze RNA sequencing data from 132 patients with papillary and 270 patients with nonpapillary urothelial carcinoma from the TCGA Cell 2017 dataset and 39 patients with papillary and 88 patients with nonpapillary urothelial carcinoma from the TCGA Nature 2014 dataset. Hub proteins were identified in distinct PPI networks specific to papillary and nonpapillary urothelial carcinoma. Statistical significance of clusters was assessed using the Fisher exact test (P<.001), and network separation was quantified using the interactome-based separation score. RESULTS/UNASSIGNED:RPS27A, UBA52, and VAMP8 were the most connected or "hub" proteins identified in the network specific to the papillary urothelial carcinoma. In the network specific to the nonpapillary carcinoma, GNB1, RHOA, UBC, and FPR2 were found to be the hub proteins. Notably, GNB1 and FPR2 were among the proteins that have existing drugs targeting them. CONCLUSIONS/UNASSIGNED:We identified distinct PPI networks and the hub proteins specific to papillary and nonpapillary urothelial carcinomas. However, these findings are limited by the use of transcriptomic data and require experimental validation to confirm the functional relevance of the identified targets.

The rise of artificial intelligence (AI)-driven tools like ChatGPT is transforming professional fields, including pathology. This study provides early insights into how pathology trainees and practicing pathologists are integrating AI into their training and clinical practice. To assess adoption, usage patterns, perceptions, and challenges related to AI-driven tools, including large language models and vision-language models, among pathology professionals. The study also explores future directions for AI integration. A cross-sectional, anonymous survey was distributed electronically to pathology residents, fellows, and attending pathologists through the Accreditation Council for Graduate Medical Education program director registry, professional organizations, and social media (X, Reddit, LinkedIn, and The Pathologist email listserv). The survey included multiple-choice, Likert-scale, and open-ended questions on AI familiarity, usage, perceived benefits/risks, and institutional policies. Data were analyzed using descriptive and inferential statistics, with qualitative responses categorized thematically. A total of 268 respondents participated, primarily residents (41%), attendings (39%), and fellows (7%), representing 23 countries (65% from the USA). Most were affiliated with academic medical centers (72%) and aged 25-44. Whereas 73% reported some familiarity with AI, actual use was limited, 31% reported rare use and 29% no use at all, especially among residents and attendings. ChatGPT was the most used tool (84%), applied mainly for document drafting (57%), research (54%), and administrative tasks (34%). Diagnostic use was minimal. Top concerns included accuracy (81%), over-reliance (65%), and data security (63%). Only 10% reported having clear institutional AI guidelines. Familiarity was strongly associated with usage frequency (p < 0.00001). AI is increasingly used in non-diagnostic areas of pathology but adoption remains cautious. Significant gaps in clinical application, trust, and institutional support persist. Clear guidelines, targeted education, and robust validation are essential for safe, effective AI integration into pathology practice and training.

Foundation models are revolutionising pathology by leveraging large-scale, pretrained artificial intelligence (AI) systems to enhance diagnostics, automate workflows and expand applications. These models address computational challenges in gigapixel whole-slide images with architectures like GigaPath, enabling state-of-the-art performance in cancer subtyping and biomarker identification by capturing cellular variations and microenvironmental changes. Visual-language models such as CONCH integrate histopathological images with biomedical text, facilitating text-to-image retrieval and classification with minimal fine-tuning, mirroring how pathologists synthesise multimodal information. Open-source foundation models will drive accessibility and innovation, allowing researchers to refine AI systems collaboratively while reducing dependency on proprietary solutions. Combined with decentralised learning approaches like federated and swarm learning, these models enable secure, large-scale training without centralised data sharing, preserving patient confidentiality while improving generalisability across populations. Despite these advancements, challenges remain in ensuring scalability, mitigating bias and aligning AI insights with clinical decision-making. Explainable AI techniques, such as saliency maps and feature attribution, are critical for fostering trust and interpretability. As multimodal integration-combining pathology, radiology and genomics-advances personalised medicine, foundation models stand as a transformative force in computational pathology, bridging the gap between AI innovation and real-world clinical implementation.

Cancer is a complex disease with heterogeneous mutational and gene expression patterns. Subgroups of patients who share a phenotype might share a specific genetic architecture including protein-protein interactions (PPIs). We developed the Atlas of Protein-Protein Interactions in Cancer (APPIC), an interactive webtool that provides PPI subnetworks of 10 cancer types and their subtypes shared by cohorts of patients. To achieve this, we analyzed publicly available RNA sequencing data from patients and identified PPIs specific to 26 distinct cancer subtypes. APPIC compiles biological and clinical information from various databases, including the Human Protein Atlas, Hugo Gene Nomenclature Committee, g:Profiler, cBioPortal and Clue.io. The user-friendly interface allows for both 2D and 3D PPI network visualizations, enhancing the usability and interpretability of complex data. For advanced users seeking greater customization, APPIC conveniently provides all output files for further analysis and visualization on other platforms or tools. By offering comprehensive insights into PPIs and their role in cancer, APPIC aims to support the discovery of tumor subtype-specific novel targeted therapeutics and drug repurposing. APPIC is freely available at https://appic.brown.edu.

BACKGROUND:Axillary lymph node status is valuable in determining systemic and radiation therapy. Following neoadjuvant therapy for patients with clinically involved axillary nodes, the role of axillary lymph node dissection (ALND) following a positive sentinel lymph node biopsy (SLNB) is a subject of controversy. MATERIALS AND METHODS/METHODS:We retrospectively analyzed 224 neoadjuvant chemotherapy-treated node-positive breast cancer cases and evaluated the role of ALND in optimizing staging accuracy and treatment outcomes. RESULTS:About 63 (27.8%) underwent ALND based on post neoadjuvant persistent positive lymph nodes on exam /imaging. SLNBs were performed in 161 (71.9%) patients as initial surgical planning; 67 (41.6%) patients had positive SLNB results, and 51 (76.1%) underwent further ALND. In patients with 1 positive sentinel lymph node, follow-up ALND yielded additional positive lymph nodes in 10.5% of cases, whereas in patients with 2 or more positive sentinel lymph nodes, follow-up ALND yielded additional positive lymph nodes in 87.5% of cases. The presence of 2 positive macro-metastatic sentinel lymph nodes significantly predicts additional nodal involvement, especially in patients without a pathologic complete response. CONCLUSION/CONCLUSIONS:De-escalation of axillary surgery to SLNB alone in this context may be safely considered in neoadjuvant-treated clinical node positive patient with <2 positive sentinel lymph nodes. Our findings help guide surgeons to appropriately select patients who can potentially benefit from ALND for locoregional control and recommendation for adjuvant radiation.

DeepSeek has introduced its recent model DeepSeek-R1, showing divergence from OpenAI's ChatGPT, suggesting an open-source alternative to users. This paper analyzes the architecture of DeepSeek-R1, mainly adopting rule-based reinforcement learning (RL) without preliminary supervised fine-tuning (SFT), which has shown better efficiency. By integrating multi-stage training along with cold-start data usage before RL, the model can achieve meaningful performance in reasoning tasks along with reward modeling optimizing training process. DeepSeek shows its strength in technical, reasoning tasks, able to show its decision-making process through open source whereas ChatGPT shows its strength on general tasks and areas requiring creativeness. Despite the groundbreaking developments of both models, there is room for improvement in AI landscape and matters to be handled such as quality of data, black box problems, privacy management, and job displacement. This paper suggests the future of AI, expecting better performance in multi-modal tasks, enhancing its effectiveness in handling larger data sets, enabling users with improved AI landscapes and utility.

ChatGPT represents a transformative technology in healthcare, with demonstrated impacts across clinical practice, medical education, and research. Studies show significant efficiency gains, including 70% reduction in administrative time for discharge summaries and achievement of medical professional-level performance on standardized tests (60% accuracy on USMLE, 78.2% on PubMedQA). ChatGPT offers personalized learning platforms, automated scoring, and instant access to vast medical knowledge in medical education, addressing resource limitations and enhancing training efficiency. It streamlines clinical workflows by supporting triage processes, generating discharge summaries, and alleviating administrative burdens, allowing healthcare professionals to focus more on patient care. Additionally, ChatGPT facilitates remote monitoring and chronic disease management, providing personalized advice, medication reminders, and emotional support, thus bridging gaps between clinical visits. Its ability to process and synthesize vast amounts of data accelerates research workflows, aiding in literature reviews, hypothesis generation, and clinical trial designs. This paper aims to gather and analyze published studies involving ChatGPT, focusing on exploring its advantages and disadvantages within the healthcare context. To aid in understanding and progress, our analysis is organized into six key areas: (1) Information and Education, (2) Triage and Symptom Assessment, (3) Remote Monitoring and Support, (4) Mental Healthcare Assistance, (5) Research and Decision Support, and (6) Language Translation. Realizing ChatGPT's full potential in healthcare requires addressing key limitations, such as its lack of clinical experience, inability to process visual data, and absence of emotional intelligence. Ethical, privacy, and regulatory challenges further complicate its integration. Future improvements should focus on enhancing accuracy, developing multimodal AI models, improving empathy through sentiment analysis, and safeguarding against artificial hallucination. While not a replacement for healthcare professionals, ChatGPT can serve as a powerful assistant, augmenting their expertise to improve efficiency, accessibility, and quality of care. This collaboration ensures responsible adoption of AI in transforming healthcare delivery. While ChatGPT demonstrates significant potential in healthcare transformation, systematic evaluation of its implementation across different healthcare settings reveals varying levels of evidence quality-from robust randomized trials in medical education to preliminary observational studies in clinical practice. This heterogeneity in evidence quality necessitates a structured approach to future research and implementation.

BACKGROUND/UNASSIGNED:Distributed ledger technology (DLT) enables the creation of tamper-resistant, decentralized, and secure digital ledgers. A non-fungible token (NFT) represents a record on-chain associated with a digital or physical asset, such as a whole-slide image (WSI). The InterPlanetary File System (IPFS) represents an off-chain network, hypermedia, and file sharing peer-to-peer protocol for storing and sharing data in a distributed file system. Today, we need cheaper, more efficient, highly scalable, and transparent solutions for WSI data storage and access of medical records and medical imaging data. METHODS/UNASSIGNED:WSIs were created from non-human tissues and H&E-stained sections were scanned on a Philips Ultrafast WSI scanner at 40× magnification objective lens (1 μm/pixel). TIFF images were stored on IPFS, while NFTs were minted on the Ethereum blockchain network in ERC-1155 standard. WSI-NFTs were stored on MetaMask and OpenSea was used to display the WSI-NFT collection. Filebase storage application programing interface (API) were used to create dedicated gateways and content delivery networks (CDN). RESULTS/UNASSIGNED:A total of 10 WSI-NFTs were minted on the Ethereum blockchain network, found on our collection "Whole Slide Images as Non-fungible Tokens Project" on Open Sea: https://opensea.io/collection/untitled-collection-126765644. WSI TIFF files ranged in size from 1.6 to 2.2 GB and were stored on IPFS and pinned on 3 separate nodes. Under optimal conditions, and using a dedicated CDN, WSI reached retrieved at speeds of over 10 mb/s, however, download speeds and WSI retrieval times varied significantly depending on the file and gateway used. Overall, the public IPFS gateway resulted in variably poorer WSI download retrieval performance compared to gateways provided by Filebase storage API. CONCLUSION/UNASSIGNED:Whole-slide images, as the most complex and substantial data files in healthcare, demand innovative solutions. In this technical report, we identify pitfalls in IPFS, and demonstrate proof-of-concept using a 3-layer architecture for scalable, decentralized storage, and access. Optimized through dedicated gateways and CDNs, which can be effectively applied to all medical data and imaging modalities across the healthcare sector. DLT and off-chain network solutions present numerous opportunities for advancements in clinical care, education, and research. Such approaches uphold the principles of equitable healthcare data ownership, security, and democratization, and are poised to drive significant innovation.

Wnt family member 9b (Wnt9b) has been demonstrated as a valuable marker for breast cancer diagnosis in surgical pathology. In this study, we examined the utility of Wnt9b in diagnosing metastatic breast carcinoma in cytology samples. Cell blocks from fine needle aspirations (FNA) and fluid specimens of 96 metastatic breast carcinomas and 123 primary and metastatic non-breast neoplasms from various organ systems were evaluated by Wnt9b and GATA3 immunohistochemistry (IHC). Wnt9b and GATA3 were positive in 81.3% and 92.7% of metastatic breast carcinomas, respectively. Conversely, 93.5% and 90.0% of non-breast, non-urothelial carcinomas were negative for Wnt9b and GATA3, respectively. Wnt9b expression was positive in rare gastrointestinal, gynecological, lung, pancreas, and salivary gland tumors. All twenty-eight urothelial carcinomas were negative for Wnt9b, while twenty-six (92.9%) were positive for GATA3. Wnt9b was slightly less sensitive but more specific than GATA3 in diagnosing metastatic breast cancer in cytology samples. Particularly, Wnt9b shows higher specificity in differentiating breast and urothelial primaries. The combined use of Wnt9b and GATA3 may increase diagnostic accuracy.