Deep Learning-based Modeling for Preclinical Drug Safety Assessment.

In drug development, assessing the toxicity of candidate compounds is crucial for successfully transitioning from preclinical research to early-stage clinical trials. Drug safety is typically assessed using animal models with a manual histopathological examination of tissue sections to characterize the dose-response relationship of the compound - a time-intensive process prone to inter-observer variability and predominantly involving tedious review of cases without abnormalities. Artificial intelligence (AI) methods in pathology hold promise to accelerate this assessment and enhance reproducibility and objectivity.

AI-driven Discovery of Morphomolecular Signatures in Toxicology.

Early identification of drug toxicity is essential yet challenging in drug development. At the preclinical stage, toxicity is assessed with histopathological examination of tissue sections from animal models to detect morphological lesions. To complement this analysis, toxicogenomics is increasingly employed to understand the mechanism of action of the compound and ultimately identify lesion-specific safety biomarkers for which assays can be designed.

A multimodal generative AI copilot for human pathology.

Computational pathology has witnessed considerable progress in the development of both task-specific predictive models and task-agnostic self-supervised vision encoders. However, despite the explosive growth of generative artificial intelligence (AI), there have been few studies on building general-purpose multimodal AI assistants and copilots tailored to pathology. Here we present PathChat, a vision-language generalist AI assistant for human pathology.

A visual-language foundation model for computational pathology.

The accelerated adoption of digital pathology and advances in deep learning have enabled the development of robust models for various pathology tasks across a diverse array of diseases and patient cohorts. However, model training is often difficult due to label scarcity in the medical domain, and a model's usage is limited by the specific task and disease for which it is trained. Additionally, most models in histopathology leverage only image data, a stark contrast to how humans teach each other and reason about histopathologic entities.

A General-Purpose Self-Supervised Model for Computational Pathology.

Tissue phenotyping is a fundamental computational pathology (CPath) task in learning objective characterizations of histopathologic biomarkers in anatomic pathology. However, whole-slide imaging (WSI) poses a complex computer vision problem in which the large-scale image resolutions of WSIs and the enormous diversity of morphological phenotypes preclude large-scale data annotation. Current efforts have proposed using pretrained image encoders with either transfer learning from natural image datasets or self-supervised pretraining on publicly-available histopathology datasets, but have not been extensively developed and evaluated across diverse tissue types at scale.

Identification of fusions with potential clinical significance in melanoma.

Though uncommon in melanoma, gene fusions may have therapeutic implications. Next generation sequencing-based clinical assays, designed to detect relevant gene fusions, mutations, and copy number changes, were performed on 750 melanomas (375 primary and 375 metastases) at our institution from 2014-2021. These included 599 (80%) cutaneous, 38 (5%) acral, 11 (1.

Defining genome-wide CRISPR-Cas genome-editing nuclease activity with GUIDE-seq.

Genome-wide unbiased identification of double-stranded breaks enabled by sequencing (GUIDE-seq) is a sensitive, unbiased, genome-wide method for defining the activity of genome-editing nucleases in living cells. GUIDE-seq is based on the principle of efficient integration of an end-protected double-stranded oligodeoxynucleotide tag into sites of nuclease-induced DNA double-stranded breaks, followed by amplification of tag-containing genomic DNA molecules and high-throughput sequencing. Here we describe a detailed GUIDE-seq protocol including cell transfection, library preparation, sequencing and bioinformatic analysis.

t(4;12)(q12;p13) ETV6-rearranged AML without eosinophilia does not involve PDGFRA: relevance for imatinib insensitivity.

Acute myeloid leukemia (AML) with t(4;12)(q12;p13) translocation is rare and often associated with an aggressive clinical course and poor prognosis. Previous reports based on fluorescence in situ hybridization (FISH) analysis have suggested that ETV6::PDGFRA fusions are present in these patients, despite the absence of eosinophilia, which is typically found in other hematopoietic malignancies with PDGFRA-containing fusions. We first detected an ETV6-SCFD2 fusion by targeted RNA sequencing in a patient with t(4;12)(q12;p13) who had been diagnosed with an ETV6-PDGFRA fusion by FISH analysis but failed to respond to imatinib.

Digital Image Analysis for Estimating Stromal CD8+ Tumor-Infiltrating Lymphocytes in Lung Adenocarcinoma.

Background: Stromal CD8+ tumor-infiltrating lymphocytes (TILs) are an important prognostic and predictive indicator in non-small cell lung cancer (NSCLC). In this study, we aimed to develop and test the feasibility of a digital image analysis (DIA) workflow for estimating stromal CD8+ TIL density.

Methods: A DIA workflow developed in a software platform (QuPath) was applied to a specified region of interest (ROI) within the stromal compartment of dual PD-L1/CD8 immunostained slides from 50 lung adenocarcinoma patients.

Digital media archive for gross pathology images based on open-source tools and Fast Healthcare Interoperability Resources (FHIR).

Macroscopic examination of surgical pathology and autopsy cases is a fundamental component of anatomic pathology. The photographic documentation of such clinical specimens is essential, and it may be required in certain instances. Our department began using consumer-grade digital cameras in 2005 to improve the practice of gross photography.

Recurrent PROC and novel PROS1 mutations in Vietnamese patients diagnosed with idiopathic deep venous thrombosis.

Introduction: Genetic mutations of PROC and PROS1 are well-known risk factors for deep venous thrombosis (DVT) in the Asian population. However, the genetic profile of Vietnamese patients with DVT remains elusive. This study aimed to investigate the spectrum of genetic mutations of these two genes in Vietnamese patients diagnosed with idiopathic DVT.

Targeted Informatics for Optimal Detection, Characterization, and Quantification of FLT3 Internal Tandem Duplications Across Multiple Next-Generation Sequencing Platforms.

Assessment of internal tandem duplications in FLT3 (FLT3-ITDs) and their allelic ratio (AR) is recommended by clinical guidelines for diagnostic workup of acute myeloid leukemia and traditionally performed through capillary electrophoresis (CE). Although significant progress has been made integrating FLT3-ITD detection within contemporary next-generation sequencing (NGS) panels, AR estimation is not routinely part of clinical NGS practice because of inherent biases and challenges. In this study, data from multiple NGS platforms-anchored multiplex PCR (AMP), amplicon [TruSeq Custom Amplicon (TSCA)], and hybrid-capture-were analyzed through a custom algorithm, including platform-specific measures of AR.

Novel and established EWSR1 gene fusions and associations identified by next-generation sequencing and fluorescence in-situ hybridization.

EWSR1 is a 'promiscuous' gene that can fuse with many different partner genes in phenotypically identical tumors or partner with the same genes in morphologically and behaviorally different neoplasms. Our study set out to examine the EWSR1 fusions identified at our institution over a 3-year period, using various methods, their association with specific entities and possible detection of novel partners and associations. Sixty-three consecutive cases investigated for EWSR1 gene fusions between 2015 and 2018 at our institution were included in this study.

Clinical Validation of a Cell-Free DNA Gene Panel.

The use of liquid biopsies to identify driver mutations in patients with solid tumors holds great promise for performing targeted therapy selection, monitoring disease progression, and detecting treatment resistance mechanisms. We describe herein the development and clinical validation of a 28-gene cell-free DNA panel that targets the most common genetic alterations in solid tumors. Bioinformatic and variant filtering solutions were developed to improve test sensitivity and specificity.

Clinically Integrated Molecular Diagnostics in Adenoid Cystic Carcinoma.

Background: Adenoid cystic carcinoma (ACC) is an aggressive salivary gland malignancy without effective systemic therapies. Delineation of molecular profiles in ACC has led to an increased number of biomarker-stratified clinical trials; however, the clinical utility and U.S.

VarGrouper: A Bioinformatic Tool for Local Haplotyping of Deletion-Insertion Variants from Next-Generation Sequencing Data after Variant Calling.

Accurate genetic variant representation through nomenclature and annotation is essential for understanding functional consequence and properly noting the presence of variants across time, assays, and laboratories. Current variant calling algorithms detect single deletion-insertion variants as multiple indel and/or substitution variants from next-generation sequencing data. Consequently, these variants are separately annotated in bioinformatics pipelines, leading to inaccurate variant representation.

Clinicopathological and molecular features of SF3B1-mutated myeloproliferative neoplasms.

The introduction of next-generation sequencing has broadened the genetic landscape of myeloproliferative neoplasms (MPNs) beyond JAK2, MPL, and CALR. However, the biological role and clinical impact of most other mutations are not well defined. We interrogated 101 genes in 143 BCR-ABL1-negative MPNs in chronic phase from 2 large institutions.

Implementing the DICOM Standard for Digital Pathology.

Background: Digital Imaging and Communications in Medicine (DICOM) is the standard for the representation, storage, and communication of medical images and related information. A DICOM file format and communication protocol for pathology have been defined; however, adoption by vendors and in the field is pending. Here, we implemented the essential aspects of the standard and assessed its capabilities and limitations in a multisite, multivendor healthcare network.

Clinical Utility of Rapid Genotyping in Advanced Lung Cancer.

Purpose: Targeted therapy is the cornerstone of treatment of advanced -mutant non-small-cell lung cancer (NSCLC). Next-generation sequencing (NGS), the preferred method for genotyping, typically requires several weeks. Here, we assessed workflows designed to rapidly identify patients with actionable mutations and reduce time to initiation (TTI) of epidermal growth factor receptor (EGFR)-directed therapy.

Artificial Intelligence Approach for Variant Reporting.

Purpose: Next-generation sequencing technologies are actively applied in clinical oncology. Bioinformatics pipeline analysis is an integral part of this process; however, humans cannot yet realize the full potential of the highly complex pipeline output. As a result, the decision to include a variant in the final report during routine clinical sign-out remains challenging.