Publications by authors named "Caitlin Guccione"

As next-generation sequencing technologies produce deeper genome coverages at lower costs, there is a critical need for reliable computational host DNA removal in metagenomic data. We find that insufficient host filtration using prior human genome references can introduce false sex biases and inadvertently permit flow-through of host-specific DNA during bioinformatic analyses, which could be exploited for individual identification. To address these issues, we introduce and benchmark three host filtration methods of varying throughput, with concomitant applications across low biomass samples such as skin and high microbial biomass datasets including fecal samples.

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Article Synopsis
  • In 2020, researchers discovered cancer-specific microbial signals in The Cancer Genome Atlas (TCGA), leading to multiple papers confirming their findings.
  • They addressed concerns about batch correction and contamination affecting results, showing that their methods yielded consistent results despite these issues.
  • The development of a new method, Exhaustive, significantly improved sensitivity in data cleaning, reinforcing the validity of cancer type-specific microbial signatures in TCGA.
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Circular extrachromosomal DNA (ecDNA) in patient tumors is an important driver of oncogenic gene expression, evolution of drug resistance and poor patient outcomes. Applying computational methods for the detection and reconstruction of ecDNA across a retrospective cohort of 481 medulloblastoma tumors from 465 patients, we identify circular ecDNA in 82 patients (18%). Patients with ecDNA-positive medulloblastoma were more than twice as likely to relapse and three times as likely to die within 5 years of diagnosis.

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Quantifying the differential abundance (DA) of specific taxa among experimental groups in microbiome studies is challenging due to data characteristics (e.g., compositionality, sparsity) and specific study designs (e.

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Article Synopsis
  • Barrett's esophagus (BE) is a precursor to esophageal adenocarcinomas (EACs), but the mechanisms driving this transformation are not fully understood, prompting researchers to use an AI-driven approach for insight.
  • The study validated its predictions through various methods including human organoid models and genomic analyses, concluding that EACs derive exclusively from BE and identifying a specific immune environment involving CXCL8/IL8 and neutrophils as a key factor in this transformation.
  • Notably, this immune response is stronger in White individuals compared to African Americans, with findings suggesting that benign ethnic neutropenia (BEN) in Africans Americans might reduce the risk of progression from BE to EAC.
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The presence and role of microbes in human cancers has come full circle in the last century. Tumors are no longer considered aseptic, but implications for cancer biology and oncology remain underappreciated. Opportunities to identify and build translational diagnostics, prognostics, and therapeutics that exploit cancer's second genome-the metagenome-are manifold, but require careful consideration of microbial experimental idiosyncrasies that are distinct from host-centric methods.

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Esophageal adenocarcinoma (EAC) claims the lives of half of patients within the first year of diagnosis, and its incidence has rapidly increased since the 1970s despite extensive research into etiological factors. The changes in the microbiome within the distal esophagus in modern populations may help explain the growth in cases that other common EAC risk factors together cannot fully explain. The precursor to EAC is Barrett's esophagus (BE), a metaplasia adapted to a reflux-mediated microenvironment that can be challenging to diagnose in patients who do not undergo endoscopic screening.

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Summary: Clinical sequencing aims to identify somatic mutations in cancer cells for accurate diagnosis and treatment. However, most widely used clinical assays lack patient-matched control DNA and additional analysis is needed to distinguish somatic and unfiltered germline variants. Such computational analyses require accurate assessment of tumor cell content in individual specimens.

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