Publications by authors named "Steven Hart"

Germline BRCA2 loss-of function variants, which can be identified through clinical genetic testing, predispose to several cancers. However, variants of uncertain significance limit the clinical utility of test results. Thus, there is a need for functional characterization and clinical classification of all BRCA2 variants to facilitate the clinical management of individuals with these variants.

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With the increasing utilization of exome and genome sequencing in clinical and research genetics, accurate and automated extraction of human phenotype ontology (HPO) terms from clinical texts has become imperative. Traditional methods for HPO term extraction, such as PhenoTagger, often face limitations in coverage and precision. In this study, we propose a novel approach that leverages large language models (LLMs) to generate synthetic sentences with clinical context, which were semantically encoded into vector embeddings.

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  • The classification of missense variants in tumor suppressor genes related to breast cancer is complicated due to their rarity, leading to uncertainty in clinical decisions.
  • This study utilizes generative AI to create high-resolution protein structures, which are then analyzed for protein stability changes to assess loss-of-function (LOF) activity in specific domains of the protein.
  • Results show that AI-generated structures of the BRCA1-C terminal and DNA-binding domains significantly improve LOF activity prediction compared to experimental structures, indicating the potential of AI in this area of genetic research.
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  • Pathogenic variants (PVs) in certain genes like BRCA1 and BRCA2 increase breast cancer risk, but it's unclear how risk varies based on the type and location of these variants.
  • This study analyzed breast cancer risks associated with different PV types and locations using data from 12 US studies and clinical cohorts involving over 64,000 women.
  • Results showed that women with specific exon PTVs had higher breast cancer risks, lower rates of ER-negative breast cancer, and were diagnosed at younger ages compared to those with other variants, with these patterns observed across multiple cohorts.
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Context.—: Generative artificial intelligence (AI) technologies are rapidly transforming numerous fields, including pathology, and hold significant potential to revolutionize educational approaches.

Objective.

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  • Clinical genetic testing helps find cancer risks by identifying gene changes, but some of these changes are confusing because we don't know what they mean (called VUS).
  • Researchers studied a huge number of breast cancer patients and healthy people to understand these confusing gene changes better.
  • They found that their method of analyzing data closely matches what other experts say about which gene changes are harmless or harmful, giving more information about 785 unclear changes.
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Germline mutations in BRCA1 and BRCA2 (gBRCA1/2) are required for a PARP inhibitor therapy in patients with HER2-negative (HER2-) advanced breast cancer (aBC). However, little is known about the prognostic impact of gBRCA1/2 mutations in aBC patients treated with chemotherapy. This study aimed to investigate the frequencies and prognosis of germline and somatic BRCA1/2 mutations in HER2- aBC patients receiving the first chemotherapy in the advanced setting.

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Context.—: Computational pathology combines clinical pathology with computational analysis, aiming to enhance diagnostic capabilities and improve clinical productivity. However, communication barriers between pathologists and developers often hinder the full realization of this potential.

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Context.—: Artificial intelligence is a transforming technology for anatomic pathology. Involvement within the workforce will foster support for algorithm development and implementation.

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Recent advances in the field of immuno-oncology have brought transformative changes in the management of cancer patients. The immune profile of tumours has been found to have key value in predicting disease prognosis and treatment response in various cancers. Multiplex immunohistochemistry and immunofluorescence have emerged as potent tools for the simultaneous detection of multiple protein biomarkers in a single tissue section, thereby expanding opportunities for molecular and immune profiling while preserving tissue samples.

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Unlabelled: Germline BRCA2 loss-of function (LOF) variants identified by clinical genetic testing predispose to breast, ovarian, prostate and pancreatic cancer. However, variants of uncertain significance (VUS) (n>4000) limit the clinical use of testing results. Thus, there is an urgent need for functional characterization and clinical classification of all variants.

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Unlabelled: Prostate cancer risk is influenced by rare and common germline variants. We examined the aggregate association of rare germline pathogenic/likely pathogenic/deleterious (P/LP/D) variants in ATM, BRCA2, PALB2, and NBN with a polygenic risk score (PRS) on prostate cancer risk among 1,796 prostate cancer cases (222 metastatic) and 1,424 controls of African ancestry. Relative to P/LP/D non-carriers at average genetic risk (33%-66% of PRS), men with low (0%-33%) and high (66%-100%) PRS had Odds Ratios (ORs) for overall prostate cancer of 2.

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  • The paper examines the current and potential uses of Large Language Models (LLMs) in medical informatics, focusing on clinical and anatomic pathology.
  • It highlights various considerations for using LLMs in healthcare, including identifying suitable applications and assessing their benefits and drawbacks.
  • The discussion also covers essential infrastructure, education, security, bias, privacy concerns, and the necessity for a strong framework to navigate regulatory and legal issues related to LLMs in clinical settings.
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  • Colorectal cancer (CRC) is the second most common cancer in the U.S., and genetic testing is important for early detection and tailored treatment, improving patient survival rates.
  • The tissue slide review (TSR), essential for genetic testing, presents challenges due to pathologist discrepancies and a lack of quality metrics.
  • An advanced method called progressive context encoder anomaly detection (P-CEAD) offers an automated and more reliable approach for segmenting CRC tumors from whole slide images, outperforming traditional manual methods in terms of reproducible quality.
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Quantifying tumor-infiltrating lymphocytes (TILs) in breast cancer tumors is a challenging task for pathologists. With the advent of whole slide imaging that digitizes glass slides, it is possible to apply computational models to quantify TILs for pathologists. Development of computational models requires significant time, expertise, consensus, and investment.

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The clinical significance of the tumor-immune interaction in breast cancer is now established, and tumor-infiltrating lymphocytes (TILs) have emerged as predictive and prognostic biomarkers for patients with triple-negative (estrogen receptor, progesterone receptor, and HER2-negative) breast cancer and HER2-positive breast cancer. How computational assessments of TILs might complement manual TIL assessment in trial and daily practices is currently debated. Recent efforts to use machine learning (ML) to automatically evaluate TILs have shown promising results.

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Modern histologic imaging platforms coupled with machine learning methods have provided new opportunities to map the spatial distribution of immune cells in the tumor microenvironment. However, there exists no standardized method for describing or analyzing spatial immune cell data, and most reported spatial analyses are rudimentary. In this review, we provide an overview of two approaches for reporting and analyzing spatial data (raster versus vector-based).

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Mutations that predispose to familial pheochromocytoma and paraganglioma include inherited variants in the four genes (, , and ) encoding subunits of succinate dehydrogenase (SDH), an enzyme of the mitochondrial tricarboxylic acid cycle and complex II of the electron transport chain. In heterozygous variant carriers, somatic loss of heterozygosity is thought to result in tumorigenic accumulation of succinate and reactive oxygen species. Inexplicably, variants affecting the SDHB subunit predict worse clinical outcomes.

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Unlabelled: Pathogenic protein-truncating variants of RAD51C, which plays an integral role in promoting DNA damage repair, increase the risk of breast and ovarian cancer. A large number of RAD51C missense variants of uncertain significance (VUS) have been identified, but the effects of the majority of these variants on RAD51C function and cancer predisposition have not been established. Here, analysis of 173 missense variants by a homology-directed repair (HDR) assay in reconstituted RAD51C-/- cells identified 30 nonfunctional (deleterious) variants, including 18 in a hotspot within the ATP-binding region.

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Article Synopsis
  • This study investigates the risk of contralateral breast cancer (CBC) among women with specific germline pathogenic variants (PVs) after being treated for breast cancer.
  • It involved a large cohort of over 15,000 women, examining differences in CBC risk based on factors like race, age, and tumor characteristics.
  • Findings indicate that certain PV carriers face significantly higher risks of developing CBC, especially those with ER-negative breast cancer, suggesting a need for closer monitoring and preventive measures for these high-risk groups.
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Background: Network-connected medical devices have rapidly proliferated in the wake of recent global catalysts, leaving clinical laboratories and healthcare organizations vulnerable to malicious actors seeking to ransom sensitive healthcare information. As organizations become increasingly dependent on integrated systems and data-driven patient care operations, a sudden cyberattack and the associated downtime can have a devastating impact on patient care and the institution as a whole. Cybersecurity, information security, and information assurance principles are, therefore, vital for clinical laboratories to fully prepare for what has now become inevitable, future cyberattacks.

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  • - The study investigates the causal relationship between physical activity, sedentary behavior, and breast cancer risk using Mendelian randomization, analyzing data from over 130,000 European women.
  • - Findings suggest that higher levels of genetic predisposition to physical activity are linked to a significantly lower overall breast cancer risk, particularly for pre/perimenopausal cases, while increased sedentary time correlates with a higher risk of certain types of tumors.
  • - The results are consistent across various test groups and indicate that promoting physical activity and reducing sedentary behavior might be beneficial in mitigating breast cancer risks.
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