Biology subjects

Fisher, D. A.

Publications and source records attributed to Fisher, D. A..

3 recordsLinked to original sources

bioRxiv2022

IMC-Denoise: a content aware denoising pipeline to enhance Imaging Mass Cytometry

Imaging Mass Cytometry (IMC) is an emerging multiplexed imaging technology for analyzing complex microenvironments that has the ability to detect the spatial distribution of at least 40 cell markers. However, this new modality has unique image data processing requirements, particularly when applying this technology to patient tissue specimens. In these cases, signal-to-noise ratio for particular markers can be low despite optimization of staining conditions, and the presence of pixel intensity artifacts can deteriorate image quality and the subsequent performance of downstream analysis. Here we demonstrate an automated content-aware pipeline, IMC-Denoise, to restore IMC images. Specifically, we deploy a differential intensity map-based restoration (DIMR) algorithm for removing hot pixels and a self-supervised deep learning algorithm for filtering shot noise (DeepSNF). IMC-Denoise outperforms existing methods for adaptive hot pixel removal, and delivers significant image quality improvement and background noise removal to a diverse set of IMC channels and datasets. This includes a unique, technically challenging, human bone marrow IMC dataset; in which we achieve noise level reduction of 87% for a 5.6-fold higher contrast-to-noise ratio, and more accurate background noise removal with approximately two-fold improved F1 score. Our approach remarkably enhances both manual gating and automated phenotyping with cell-scale down-stream analysis on these complex data. We anticipate that IMC-Denoise will provide similar benefits in mass cytometry imaging domains to more deeply characterize the complex and diverse tissue microenvironment.

bioinformatics↗

bioRxiv2020

A Humanized Animal Model Predicts Clonal Evolution and Therapeutic Vulnerabilities in Myeloproliferative Neoplasms

Myeloproliferative neoplasms (MPNs) are chronic blood diseases with significant morbidity and mortality. While sequencing studies have elucidated the genetic mutations that drive these diseases, MPNs remain largely incurable with a significant proportion of patients progressing to rapidly fatal secondary acute myeloid leukemia (sAML). Therapeutic discovery has been hampered by the inability of genetically-engineered mouse models to generate key human pathologies such as bone marrow fibrosis. To circumvent these limitations, here we present a humanized animal model of myelofibrosis (MF) patient-derived xenografts (PDXs). These PDXs robustly engrafted patient cells which recapitulated the patients genetic hierarchy and pathologies such as reticulin fibrosis and propagation of MPN-initiating stem cells. The model can select for engraftment of rare leukemic subclones to identify MF patients at-risk for sAML transformation, and can be used as a platform for genetic target validation and therapeutic discovery. We present a novel but generalizable model to study human MPN biology. STATEMENT OF SIGNIFICANCEAlthough the genetic events driving myeloproliferative neoplasms (MPNs) are well-defined, therapeutic discovery has been hampered by the inability of murine models to replicate key patient pathologies. Here, we present a patient-derived xenograft (PDX) system to model human myelofibrosis that reproduces human pathologies and is amenable to genetic and pharmacological manipulation.

cancer biology↗

bioRxiv2020

Impact of Screening and Follow-up Colonoscopy Adenoma Sensitivity on Colorectal Cancer Screening Outcomes in the CRC-AIM Microsimulation Model

BackgroundReal-world data for patients with positive colorectal cancer (CRC) screening stool-tests demonstrates that adenoma detection rates are lower when endoscopists are blinded to the stool-test results. This suggests adenoma sensitivity may be lower for screening colonoscopy than for follow-up to a known positive stool-based test. Previous CRC microsimulation models assume identical sensitivities between screening and follow-up colonoscopies after positive stool-tests. The Colorectal Cancer and Adenoma Incidence and Mortality Microsimulation Model (CRC-AIM) was used to explore the impact on screening outcomes when assuming different adenoma sensitivity between screening and combined follow-up/surveillance colonoscopies. MethodsModeled screening strategies included colonoscopy every 10 years, triennial multitarget stool DNA (mt-sDNA), or annual fecal immunochemical test (FIT) from 50-75 years. Outcomes were reported per 1,000 individuals without diagnosed CRC at age 40. Base-case adenoma sensitivity values were identical for screening and follow-up/surveillance colonoscopies. Ranges of adenoma sensitivity values for colonoscopy performance were developed using different slopes of odds ratio adjustments and were designated as small, medium, or large impact scenarios. ResultsAs the differences in adenoma sensitivity for screening versus follow-up/surveillance colonoscopies became greater, life-years gained (LYG) and reductions in CRC-related incidence and mortality versus no screening increased for mt-sDNA and FIT and decreased for screening colonoscopy. The LYG relative to screening colonoscopy reached >90% with FIT in the base-case scenario and with mt-sDNA in a "medium impact" scenario. ConclusionsAssuming identical adenoma sensitivities for screening and follow-up/surveillance colonoscopies underestimates the potential benefits of stool-based screening strategies.

cancer biology↗