# Developing Diagnostic and Therapeutic Strategies to Reduce Hispanic Cancer Disparities Using Genetic, Molecular and Physiological Signatures from Mexican origin Cancer Patients > **NIH NIH U54** · UNIVERSITY OF TEXAS EL PASO · 2022 · $499,296 ## Abstract ABSTRACT Hispanics are the largest and fastest growing US ethnic demographic, and it is projected that the Hispanic share of the US population will increase to nearly 30% by 2050. Hispanics suffer from marked cancer health-related disparities and cancer is the leading cause of death for Hispanics in the US. Despite these considerable cancer health-related issues, Hispanics are underrepresented in cancer research in two significant areas. First, there is little research focused on understanding the genetic, molecular, and physiological aberrations in Hispanic cancer tissue, which may be particularly important for explaining Hispanic cancer health disparities. Second, there is a lack of availability of cancer tissue from Hispanic patients, a profound barrier for developing targeted therapeutic interventions. The objectives of the present Research Plan will directly address each of these deficiencies by establishing the first comprehensive Mexican Origin Cancer Tissue Biorepository and by developing, for the first time, novel genetic, molecular, and cellular signatures from primarily Mexican origin cancer patients as platforms to inform diagnostic and therapeutic strategies. Our focus on the Mexican origin population stems from the fact that the El Paso TX USA/Juárez CH MX borderplex is one of the most dynamic international borders in the world, with a population of more than 2.5 million residents. In El Paso County, TX 82% of residents are Hispanic, 96% of whom are of Mexican origin. The proposed studies will test the novel HYPOTHESIS that the reduction and elimination of cancer health disparities in Mexican origin individuals is dependent on identifying, understanding, and therapeutically-targeting the unique molecular, genetic and physiological signatures of cancer from these patients. Specific Aim 1 is focused on establishing the first comprehensive Mexican Origin Cancer Tissue Biorepository at The University of Texas at El Paso (UTEP) through our established collaborations with area hospitals in El Paso and Ciudad Juarez CH MX. This will allow local investigators access to Hispanic cancer patient samples for investigative purposes. Specific Aim 2 will address the fact that Hispanics are underrepresented in cancer research and clinical trials. Clinical observations such as poor survival may be attributed to a lack of unknown genetic abnormalities. Therefore, we propose to perform the first FDA-approved drug assay with comprehensive multiplex proteomic analysis and Whole Exome Sequencing (WES) on patient samples obtained from the Mexican Origin Biorepository. Specific Aim 3 will develop the first diagnostic tools using molecular, multi-omics, and bioinformatic approaches to address the growing incidences of liver and prostate cancer within the Hispanic community. The current research findings will exert a sustained and powerful influence on the field by providing novel insight and direction for reducing the deleterious... ## Key facts - **NIH application ID:** 10357591 - **Project number:** 5U54MD007592-29 - **Recipient organization:** UNIVERSITY OF TEXAS EL PASO - **Principal Investigator:** JIANYING ZHANG - **Activity code:** U54 (R01, R21, SBIR, etc.) - **Funding institute:** NIH - **Fiscal year:** 2022 - **Award amount:** $499,296 - **Award type:** 5 - **Project period:** 1998-06-15 → 2024-02-29 ## Primary source NIH RePORTER: https://reporter.nih.gov/project-details/10357591 ## Citation > US National Institutes of Health, RePORTER application 10357591, Developing Diagnostic and Therapeutic Strategies to Reduce Hispanic Cancer Disparities Using Genetic, Molecular and Physiological Signatures from Mexican origin Cancer Patients (5U54MD007592-29). Retrieved via AI Analytics 2026-05-27 from https://api.ai-analytics.org/grant/nih/10357591. Licensed CC0. --- *[NIH grants dataset](/datasets/nih-grants) · CC0 1.0*