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Principal Investigator: Felicia Gomez
Organization: WASHINGTON UNIVERSITY
Fiscal Year: 2024
Award: $194,367
Funding agency: National Cancer Institute
Hodgkin lymphoma (HL) accounts for ten percent of all lymphomas in the western world and remains a
substantial clinical problem. Relapsed and refractory (R/R) HL presents a particularly critical unmet clinical
need because approximately 25% of HL patients will be refractory to the standard of care or will relapse while
receiving treatment, and the overall survival of R/R HL patients is ~50%. New targeted treatment options and
improved risk assessment are needed to improve the therapeutic options for these patients. HL is defined by a
rare malignant B cell (Hodgkin-Reed-Sternberg , HRS cells) that occupies a small fraction of the tumor
microenvironment. Despite many advances in next generation sequencing technologies, the identification of
somatic variants in cancers characterized by rare cell populations remains technically and analytically
challenging. These obstacles have resulted in a limited number of studies that have used high throughput
genome-wide technologies to characterize the genomic landscape of newly diagnosed HL, and far fewer have
attempted to use these technologies to address the genomic landscape of R/R HL. Here, we address this
shortfall by asking three questions: 1) Can ultra-deep genome-wide sequencing, paired with analysis and
filtering strategies optimized for low VAF variants identify somatic variants driving treatment resistance in R/R
HL? 2) Can we use somatic mutation status in a large cohort of newly diagnosed and R/R HL to identify genes
that are predictive of response to therapy? 3) Can somatic mutations identified in genomic and/or cell free
DNA describe clonal heterogeneity among HRS cells and can cell free DNA serve as biomarker of HL at
diagnosis or relapse? This research will be broadly impactful because: 1) it will describe the genomic
landscape of R/R HL and will identify biomarkers that are predictive of poor response to treatment; 2) it will
begin to address the relationship between clonal architecture in HL, disease outcome, and prognosis; 3) it will
further define cfDNA as a suitable target for HL disease monitoring
By completing this proposal Dr. Felicia Gomez will gain advanced training in genomics, bioinformatics,
lymphoma biology, and cancer biology in general. This proposal takes advantage of the cutting edge genomics
and cancer research at Washington University School of Medicine (WUSM). WUSM is an ideal location to
complete the experiments and training described in this proposal. Dr. Gomez is a well-trained scholar who is
poised to develop a program of research that will be innovative and responsive to the need for diversity in the
biomedical workforce. When Dr. Gomez transitions to independence she will continue to focus on cancer
genomics and will broaden her research to address the interactions between somatic and germline variation
and how this interaction affects the development and progression of lymphoma and other malignancies.
Terms: <Address><Affect><After Care><After-Treatment><Aftercare><Architecture><Automobile Driving><B blood cells><B cell><B cells><B-Cells><B-Lymphocytes><B-cell><Bio-Informatics><Bioinformatics><Biological Markers><Biology><Blood Plasma><Cancer Biology><Cancers><Cell Body><Cell Communication and Signaling><Cell Signaling><Cells><Clinical><Collection><DNA><Data><Deoxyribonucleic Acid><Development><Diagnosis><Disease><Disease Outcome><Disorder><Engineering / Architecture><Evolution><Genes><Genetic Alteration><Genetic Change><Genetic defect><Genome><Genomics><Germ Lines><Germinoblastic Sarcoma><Germinoblastoma><Goals><Heterogeneity><Hodgkin Disease><Hodgkin Disorder><Hodgkin lymphoma><Hodgkin's><Hodgkin's Lymphoma><Hodgkin's disease><Hodgkins lymphoma><Intracellular Communication and Signaling><Location><Lymph Node Reticuloendothelial System><Lymph node proper><Lymphatic nodes><Lymphoma><Malignant><Malignant - descriptor><Malignant Cell><Malignant Lymphogranuloma><Malignant Lymphoma><Malignant Neoplasms><Malignant Tumor><Methodology><Methods><Molecular><Monitor><Morphology><Mutate><Mutation><Mutation Detection><NGS Method><NGS system><Needs Assessment><Newly Diagnosed><Normal Tissue><Normal tissue morphology><Outcome><Pathway interactions><Patients><Plasma><Plasma Serum><Population><Prediction of Response to Therapy><Predictive Factor><Primary Neoplasm><Primary Tumor><Prognosis><Reagent><Recurrence><Recurrent><Reed-Sternberg Cells><Refractory><Relapse><Research><Resistance><Reticuloendothelial System, Serum, Plasma><Reticulolymphosarcoma><Risk Assessment><Salvage Therapy><Salvage-Tx><Sampling><Signal Transduction><Signal Transduction Systems><Signaling><Somatic Mutation><Source><Statistical Methods><Techniques><Technology><Testing><Therapeutic><Training><Tumor Biology><Universities><Variant><Variation><Washington><Western World><anti-cancer research><bio-markers><biologic marker><biological signal transduction><biomarker><biomarker identification><cancer cell><cancer genomics><cancer microenvironment><cancer research><cancer type><cell free DNA><cell free circulating DNA><cell type><clinical relevance><clinically relevant><cohort><deep sequencing><design><designing><developmental><driving><exome><exome sequencing><exome-seq><exomes><experiment><experimental research><experimental study><experiments><genome mutation><genome scale><genome-wide><genomewide><identification of biomarkers><identification of new biomarkers><improved><innovate><innovation><innovative><large data sets><large datasets><lymph gland><lymph nodes><lymphnodes><malignancy><marker identification><medical college><medical schools><mutation status><mutational status><neoplasm/cancer><new drug target><new druggable target><new pharmacotherapy target><new therapeutic target><new therapy target><next gen sequencing><next generation sequencing><nextgen sequencing><novel drug target><novel druggable target><novel pharmacotherapy target><novel therapeutic target><novel therapy target><oncogenomics><pathway><post treatment><predict therapeutic response><predict therapy response><predictive biomarkers><predictive marker><predictive molecular biomarker><programs><resistance to therapy><resistant><resistant to therapy><response to therapy><response to treatment><school of medicine><somatic variant><standard of care><statistic methods><targeted drug therapy><targeted drug treatments><targeted therapeutic><targeted therapeutic agents><targeted therapy><targeted treatment><therapeutic resistance><therapeutic response><therapy prediction><therapy resistant><therapy response><tool><treatment prediction><treatment resistance><treatment response><treatment response prediction><treatment responsiveness><tumor><tumor microenvironment>