A5008403556- Cancer Genomics and Diagnostics
- Genomics and Rare Diseases
- Molecular Biology Techniques and Applications
- BRCA gene mutations in cancer
- Genetic factors in colorectal cancer
- Global Cancer Incidence and Screening
- Health Systems, Economic Evaluations, Quality of Life
- Machine Learning in Healthcare
- Health, Environment, Cognitive Aging
- Biomedical Ethics and Regulation
- Ethics in Clinical Research
- Colorectal Cancer Screening and Detection
- Science, Research, and Medicine
- Multiple Myeloma Research and Treatments
- Biomedical Text Mining and Ontologies
- Health Literacy and Information Accessibility
- Acute Lymphoblastic Leukemia research
- Migration, Health and Trauma
- Cervical Cancer and HPV Research
- Colorectal Cancer Treatments and Studies
University of California, Davis
2022-2024
University of Southern California
2024
University of California, Los Angeles
2023
Cancer is the leading cause of death among Latinos, largest minority population in United States (US). To address cancer challenges experienced by we conducted a catchment area assessment (CAPA) using validated questions from National Institute (NCI) health supplement at our NCI-designated center California.A mixed-methods CAPA was administered bilingual-bicultural staff, with focus on understanding differences between foreign-born and US-born Latinos.255 Latinos responded to survey August...
Abstract To understand multiple myeloma (MM) disparities we established the Precision MEDicine, EqUity and Disparities Research in MultipLe MyeLomA (MEDULLA) study, a population-based study California. Our contacted, via Cancer Registry of Greater California (CRGC), 100 adult MM patients from four racial/ethnic groups: Non-Hispanic Black (NHB), White (NHW), Latinos, Asians. Data collection included variables reported to CRGC, self-administered survey, biospecimen saliva collection. The...
Abstract Precision medicine holds great promise for improving cancer outcomes. Yet, there are large inequities in the demographics of patients from whom genomic data and models, including patient-derived xenografts (PDX), developed treatments optimized. In this study, we a genetic ancestry pipeline Cancer Genomics Cloud, which used to assess diversity models currently available National Institute–supported PDX Development Trial Centers Research Network (PDXNet). We showed that is an...
<p>Figure S4 Shows the Power to identify at least 5 patients with a known driver mutation that is present in populations varying low frequencies.</p>
<div>Abstract<p>Precision medicine holds great promise for improving cancer outcomes. Yet, there are large inequities in the demographics of patients from whom genomic data and models, including patient-derived xenografts (PDX), developed treatments optimized. In this study, we a genetic ancestry pipeline Cancer Genomics Cloud, which used to assess diversity models currently available National Institute–supported PDX Development Trial Centers Research Network (PDXNet). We showed...
<div>Abstract<p>Precision medicine holds great promise for improving cancer outcomes. Yet, there are large inequities in the demographics of patients from whom genomic data and models, including patient-derived xenografts (PDX), developed treatments optimized. In this study, we a genetic ancestry pipeline Cancer Genomics Cloud, which used to assess diversity models currently available National Institute–supported PDX Development Trial Centers Research Network (PDXNet). We showed...
<p>Top 10 female age-adjusted mortality (per 100,000) and 5-year number of deaths (count) in NLW, AAs, Latinos. Priority cancer health disparity malignancies have ratio, DR, >1). Incidence-based data from SEER (18 Registries, November 2019 Sub, 2000–2017)</p>
<p>Number of available models for priority cancer health disparity malignancies</p>
<p>Number of available models for priority cancer health disparity malignancies</p>
<p>Populations of origin for unrelated individuals from the 1000 Genomes Phase III, GenomeAsia, and INMEGEN data used as reference populations to design a new SNPweights panel.</p>
<p>Mean and Stdev of differences for 5 continental ancestral estimations between SNPWeights Panel 1000Genomes K=10 Admixture Estimates by individual's super-population designation.</p>
<p>Top 10 female age-adjusted mortality (per 100,000) and 5-year number of deaths (count) in NLW, AAs, Latinos. Priority cancer health disparity malignancies have ratio, DR, >1). Incidence-based data from SEER (18 Registries, November 2019 Sub, 2000–2017)</p>
<p>1000Genomes K=10 Admixture Estimates downloaded from ftp://ftp.1000genomes.ebi.ac.uk/vol1/ftp/release/20130502/supporting/admixture_files/ALL.wgs.phase3_shapeit2_filtered.20141217.maf0.05.10.Q. Super-population designations were added to the partitions by sorting each partition and determining corresponding designation for partition.</p>
<p>Populations of origin for unrelated individuals from the 1000 Genomes Phase III, GenomeAsia, and INMEGEN data used as reference populations to design a new SNPweights panel.</p>
<p>PDXnet metadata for samples which genetic ancestry was estimated and were assigned into African (AFR), European (EUR), East Asian (EAS), American (AMR), South (SAS) continental ancestry.</p>
<p>1000Genomes K=10 Admixture Estimates downloaded from ftp://ftp.1000genomes.ebi.ac.uk/vol1/ftp/release/20130502/supporting/admixture_files/ALL.wgs.phase3_shapeit2_filtered.20141217.maf0.05.10.Q. Super-population designations were added to the partitions by sorting each partition and determining corresponding designation for partition.</p>
<p>Figure S2 shows Diversity of genetic ancestry estimates from PDXNet breast cancer models. A: Inferred for 115 models in PDXnet. B: Top two categories 26 "MIXED" samp</p>
<p>Top 10 male age-adjusted mortality rates (per 100,000) and 5-year number of deaths (count) in NLWs, AAs, Latinos. Priority cancer health disparity malignancies have ratio, DR, >1). Incidence-based data from SEER (18 Registries, November 2019 Sub, 2000–2017)</p>
<p>Figure S1 shows the Clustering of 1,990 non-admixed reference samples and 2,387 admixed samples(brown) individuals based on first three principal components. Colors represent continental ances</p>
<p>Ancestral estimates 1000Genomes individuals (n=929) not used in reference panel generation. Estimates calculated from K=10 admixture data, SNPWeights Panel, and the differences between these for each individual.</p>
<p>The full list of collaborators for PDXNet Consortium and Supplementary Data Methods</p>
<p>Figure S3 Shows the Power to detect a driver mutation that is absent in EUR but present non-EUR category at varying low frequencies.</p>
<p>Ancestral estimates 1000Genomes individuals (n=929) not used in reference panel generation. Estimates calculated from K=10 admixture data, SNPWeights Panel, and the differences between these for each individual.</p>
<p>Figure S4 Shows the Power to identify at least 5 patients with a known driver mutation that is present in populations varying low frequencies.</p>