Robert S. Steinfelder
A5055269341- Genetic factors in colorectal cancer
- Colorectal Cancer Screening and Detection
- Nutritional Studies and Diet
- Cancer Genomics and Diagnostics
- Colorectal Cancer Treatments and Studies
- Nutrition and Health in Aging
- Genetic Associations and Epidemiology
- Plant-based Medicinal Research
- Animal testing and alternatives
- BRCA gene mutations in cancer
- Molecular Biology Techniques and Applications
- Ferroptosis and cancer prognosis
- Gut microbiota and health
- AI in cancer detection
- Nutrition, Genetics, and Disease
- Genomics and Rare Diseases
- Radiomics and Machine Learning in Medical Imaging
- Pancreatic and Hepatic Oncology Research
- RNA modifications and cancer
- Cancer Research and Treatments
- Epigenetics and DNA Methylation
- Helicobacter pylori-related gastroenterology studies
- Metabolism, Diabetes, and Cancer
- Erythrocyte Function and Pathophysiology
- Diverticular Disease and Complications
Fred Hutch Cancer Center
2018-2025
Cancer Research Center
2023
Cape Town HVTN Immunology Laboratory / Hutchinson Centre Research Institute of South Africa
2023
Source BioScience (United Kingdom)
2017
Colorectal cancer (CRC) is a biologically heterogeneous disease. To characterize its mutational profile, we conduct targeted sequencing of 205 genes for 2,105 CRC cases with survival data. Our data shows several findings in addition to enhancing the existing knowledge CRC. We identify PRKCI, SPZ1, MUTYH, MAP2K4, FETUB, and TGFBR2 as additional significantly mutated find that among hypermutated tumors, an increased mutation burden associated improved CRC-specific (HR = 0.42, 95% CI:...
Abstract Carriers of germline biallelic pathogenic variants in the MUTYH gene have a high risk colorectal cancer. We test 5649 cancers to evaluate discriminatory potential tumor mutational signature specific for identifying carriers and classifying uncertain clinical significance (VUS). Using matched targeted multi-gene panel approach, our classifier identifies all known non-carriers an independent set 3019 (accuracy = 100% (95% confidence interval 99.87–100%)). All monoallelic are...
Abstract Protective associations of fruits, vegetables, and fiber intake with colorectal cancer risk have been shown in many, but not all epidemiologic studies. One possible reason for study heterogeneity is that dietary factors may distinct effects by molecular subtypes. Here, we investigate the association fruit, four well-established subtypes separately combination. Nine observational studies including 9,592 cases microsatellite instability (MSI), CpG island methylator phenotype (CIMP),...
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Abstract Polygenic risk scores (PRS) have great potential to guide precision colorectal cancer (CRC) prevention by identifying those at higher undertake targeted screening. However, current PRS using European ancestry data sub-optimal performance in non-European populations, limiting their utility among these populations. Towards addressing this deficiency, we expand development for CRC incorporating Asian (21,731 cases; 47,444 controls) into training datasets (78,473 107,143 controls). The...
Abstract Background Waist circumference (WC) and its allometric counterpart, “a body shape index” (ABSI), are risk factors for colorectal cancer (CRC); however, it is uncertain whether associations with these measurements limited to specific molecular subtypes of the disease. Methods Data from 2,772 CRC cases 3,521 controls were pooled four cohort studies within Genetics Epidemiology Colorectal Cancer Consortium. Four markers (BRAF mutation, KRAS CpG island methylator phenotype,...
Abstract Background Deep Learning (DL) has emerged as a powerful tool to predict genetic biomarkers directly from digitized Hematoxylin and Eosin (H&E) slides in colorectal cancer (CRC). However, few studies have systematically investigated the predictability of beyond routinely available alterations such microsatellite instability (MSI), BRAF KRAS mutations. Methods Our primary dataset comprised H&E CRC tumors across five cohorts totaling 1,376 patients who underwent comprehensive...
Artificial intelligence (AI) has transformed digital pathology by enabling biomarker prediction from high-resolution whole slide images (WSIs). However, current methods are computationally inefficient, processing thousands of redundant tiles per WSI and requiring complex aggregator models. We introduce EAGLE (Efficient Approach for Guided Local Examination), a deep learning framework that emulates pathologists selectively analyzing informative regions. incorporates two foundation models:...
<p>Association between waist circumference, proximal colon cancer, and its molecular subtypes.</p>
<p>Summary of study-specific assessment microsatellite instability (MSI) status.</p>
<p>Association between ABSI, colorectal cancer, and its molecular subtypes after conducting multiple imputation.</p>
<p>Association between waist circumference, colorectal cancer, and its molecular subtypes after conducting multiple imputation.</p>
<p>Association between waist circumference, distal colon cancer, and its molecular subtypes.</p>
<p>Association between A Body Shape Index (ABSI), distal colon cancer, and its molecular subtypes.</p>
<p>Association between A Body Shape Index (ABSI), proximal colon cancer, and its molecular subtypes.</p>
<p>Association between waist circumference, rectal cancer, and its molecular subtypes.</p>
<p>Association between A Body Shape Index (ABSI), rectal cancer, and its molecular subtypes.</p>
<p>Summary of study-specific assessment CpG Island Methylation Phenotype (CIMP) status.</p>
<div>AbstractBackground:<p>Waist circumference (WC) and its allometric counterpart, “a body shape index” (ABSI), are risk factors for colorectal cancer; however, it is uncertain whether associations with these measurements limited to specific molecular subtypes of the disease.</p>Methods:<p>Data from 2,772 cancer cases 3,521 controls were pooled four cohort studies within Genetics Epidemiology Colorectal Cancer Consortium. Four markers (<i>BRAF</i>...
<p>Diagnostic proponsity plots.</p>
<p>Association between waist circumference, colorectal cancer, and its molecular subtypes stratifying by early later-onset.</p>
<p>Forest plots. Association between (top) waist circumference (per 5 cm), (bottom) a body shape index 1-SD increase) and Jass classified types of colorectal cancer after conducting multiple imputation.</p>
<p>Association between waist circumference, ABSI, colorectal cancer, and its molecular subtypes adjusting for further covariates.</p>
<p>Diagnostic density plots.</p>