A5034618262- Radiomics and Machine Learning in Medical Imaging
- Cancer Research and Treatments
- Cancer, Hypoxia, and Metabolism
- Cancer Immunotherapy and Biomarkers
- Cancer Risks and Factors
- Metabolism, Diabetes, and Cancer
- Colorectal Cancer Treatments and Studies
- Nutrition and Health in Aging
- Colorectal Cancer Screening and Detection
- Cancer, Lipids, and Metabolism
- Advanced X-ray and CT Imaging
- Gastric Cancer Management and Outcomes
- Cancer survivorship and care
- Melanoma and MAPK Pathways
- Genetic factors in colorectal cancer
- Cutaneous Melanoma Detection and Management
- Diet and metabolism studies
- Cancer Genomics and Diagnostics
- Nutritional Studies and Diet
- CAR-T cell therapy research
- Gut microbiota and health
- Multiple and Secondary Primary Cancers
- Colorectal Cancer Surgical Treatments
- Telomeres, Telomerase, and Senescence
- Esophageal Cancer Research and Treatment
University of Utah
2018-2025
Huntsman Cancer Institute
2018-2025
Fred Hutch Cancer Center
2014-2023
Cancer Research Center
2022
University of Washington
2010-2018
Seattle University
2017
Novartis (Singapore)
2012
The Ohio State University
2007-2008
Chinese University of Hong Kong
2008
Background Esophageal adenocarcinoma (EA) incidence in many developed countries has increased dramatically over four decades, while survival remains poor. Persons with Barrett's esophagus (BE), who experience substantially elevated EA risk, are typically followed surveillance involving periodic endoscopy biopsies, although few progress to EA. No medical, surgical or lifestyle interventions have been proven safely lower risk. Design We investigated whether smoking, obesity alcohol could...
Purpose Regular use of aspirin is associated with improved survival for patients colorectal cancer (CRC). However, the timing and subtype CRC that would benefit most from using other nonsteroidal anti-inflammatory drugs (NSAIDs) in relation to unclear. Patients Methods In all, 2,419 age 18 74 years incident invasive who were diagnosed 1997 2008 identified population-based registries United States, Canada, Australia. Detailed epidemiologic questionnaires administered at study enrollment...
Emerging evidence supports the important role of tumor microbiome in oncogenesis, cancer immune phenotype, progression, and treatment outcomes many malignancies. In this study, we investigated metastatic melanoma its potential roles association with clinical outcomes, such as survival, patients disease treated checkpoint inhibitors (ICI). Baseline samples were collected from 71 before ICIs. Bulk RNA sequencing (RNA-seq) was conducted on formalin-fixed, paraffin-embedded fresh frozen samples....
Abstract Tumor hypoxia has been shown to predict poor patient outcomes in several cancer types, partially because it reduces radiation’s ability kill cells. We hypothesized that some of the clinical effects could also be due its impact on tumor microbiome. Therefore, we examined RNA sequencing data from Oncology Research Information Exchange Network database patients with colorectal treated radiotherapy. identified microbial RNAs for each and related them hypoxic gene expression scores...
Objective: The objective of this study was to explore if the time day (AM vs PM) resistance exercise is performed influences glucose and insulin concentrations, body composition, muscular strength in adults with prediabetes. Methods: A secondary data analysis conducted using from "Resist Diabetes" study, a phase II intervention. Participants (Age:59.9±5.4 yrs; BMI:33±3.7 kg/m 2 ) prediabetes overweight or obesity were categorized into AM (N=73) PM (N=80) exercisers based on when they...
95 Background: The incidence of colorectal cancer among young adults aged 20-49 has continually increased in recent decades. Early-onset (EOCRC) patients are more likely to be diagnosed at a advanced stage disease, have hereditary syndromes, and different molecular pathological features disease than later-onset patients. Herein we compare the risk factors, treatment approaches, responses early-onset versus within. Methods: ColoCare Study is prospective cohort enrolling individuals with...
Colorectal cancer (CRC) is the second overall leading cause of death in United States, with recurrence being a frequent mortality. Approaches to improve disease-free survival (DFS) are urgently needed. The gut microbiome, reflected fecal samples, likely mechanistically linked CRC progression and may serve as non-invasive biomarker. Accordingly, we leveraged baseline samples from N = 166 stage I-III patients ColoCare Study, prospective cohort newly diagnosed patients. We sequenced V3 V4...
<p>Supplemental Table 3. Association between metabolic phenotypes defined by ATP III criteria with obesity-related cancer risk in the Women’s Health Initiative cohort (N= 23,915).</p>
<p>Supplemental Figure 3. Kaplan–Meier cumulative incidence curves for obesity-related cancer risk among Women’s Health Initiative (WHI) participants according to metabolic phenotype defined by (A) Wildman criteria, (B) ATP III (C) HOMA-IR, and (D) hs-CRP ≥ 3 mg/L.</p>
<p>Supplemental Figure 4. Hazard ratios and 95% confidence intervals for the association of metabolic phenotype defined by Wildman criteria with obesity-related cancer risk, stratified type among postmenopausal women in Women’s Health Initiative. Model adjusted demographics (age, education, race/ethnicity, marital status), lifestyle (smoking status, physical activity, alcohol use, fruits vegetable intake, fiber read meat intake), hormonal factors female cancers (parity, hormone therapy...
<p>Supplemental Table 2. Association between metabolic phenotypes defined by Wildman criteria with obesity-related cancer risk in the Women’s Health Initiative cohort (N= 20,593).</p>
<p>Supplemental Table 6. Association between metabolic phenotypes defined by Homeostatic Model Assessment for Insulin Resistance (HOMA-IR) with obesity-related cancer risk in the Women’s Health Initiative cohort (N= 23,232).</p>
<p>Supplemental Figure 7. Hazard ratios and 95% confidence intervals for the association of metabolic phenotype defined by Homeostatic Model Assessment Insulin Resistance (HOMA-IR) with obesity-related cancer risk, stratified type among postmenopausal women in Women’s Health Initiative. adjusted demographics (age, education, race/ethnicity, marital status), lifestyle (smoking status, physical activity, alcohol use, fruits vegetable intake, fiber read meat intake), hormonal factors...
<p>Supplemental Table 7. Association between metabolic phenotypes defined by Wildman criteria with obesity-related cancer (ORC) risk after excluding participants diagnosed ORC within the first three years of follow-up in Women’s Health Initiative cohort (N= 20,320).</p>
<p>Supplemental Table 1. Criteria for metabolic phenotype classification. HDL high density lipoprotein cholesterol; HOMA-IR Homeostatic Model Assessment Insulin Resistance ; hs-CRP sensitive C-reactive protein</p>
<p>Supplemental Figure 6. Hazard ratios and 95% confidence intervals for the association of metabolic phenotype defined by C-Reactive Protein (hs-CRP) (A) ≥ 3 mg/L, (B) 10 mg/L with obesity-related cancer risk, stratified type among postmenopausal women in Women’s Health Initiative. Model adjusted demographics (age, education, race/ethnicity, marital status), lifestyle (smoking status, physical activity, alcohol use, fruits vegetable intake, fiber read meat intake), hormonal factors...
<p>Supplemental Table 4. Association between metabolic phenotypes defined by high sensitivity C-Reactive Protein (hs-CRP) ≥ 3 mg/L with obesity-related cancer risk in the Women’s Health Initiative cohort (N= 21,245).</p>
<p>Supplemental Figure 5. Hazard ratios and 95% confidence intervals for the association of metabolic phenotype defined by ATP III criteria with obesity-related cancer risk, stratified type among postmenopausal women in Women’s Health Initiative. Model adjusted demographics (age, education, race/ethnicity, marital status), lifestyle (smoking status, physical activity, alcohol use, fruits vegetable intake, fiber read meat intake), hormonal factors female cancers (parity, hormone therapy...
<p>Supplemental Figure 1. Study Schematic of Women’s Health Initiative Participant Selection. Flowchart detailing the selection process study participants, and final cohort size for study.</p>
<div>Abstract<p>Body mass index (BMI) may misclassify obesity-related cancer (ORC) risk, as metabolic dysfunction can occur across BMI levels. We hypothesized that at any increases ORC risk compared with normal without dysfunction. Postmenopausal women (<i>n</i> = 20,593) in the Women’s Health Initiative baseline biomarkers [blood pressure, fasting triglycerides, high-density lipoprotein cholesterol, glucose, homeostatic model assessment for insulin resistance...