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Principal Investigator: Rebecca Ivy Hartman
Organization: VA BOSTON HEALTH CARE SYSTEM
Fiscal Year: 2024
Funding agency: Veterans Affairs
Malignant melanoma is one of the top five cancers in Veterans. If diagnosed early, like the
majority of melanomas in the U.S., it is highly curable with local surgery alone in the outpatient
setting. In contrast, clinically aggressive melanoma, defined in this proposal as tumor stage
pT1b, requires sentinel lymph node biopsy under general anesthesia and has a worse
prognosis, accounting for a double the number of melanoma deaths compared to early-stage
disease. There are no melanoma screening guidelines, and thus, there is a critical need for
personalized easy-to-use approaches for melanoma screening to improve health outcomes and
reduce treatment costs. Dr. Rebecca Hartman is a clinical dermatologist and researcher in
epidemiology and health services research who seeks to improve melanoma screening
approaches through personalization of risk assessment in a clinically applicable manner. The
aims of this CDA-2 proposal are to (1) create and validate a clinical risk prediction model for
clinically aggressive melanoma in Veterans and (2) create a genetic risk prediction model for
clinically aggressive melanoma in Veterans and validate a combined genetic and clinical risk
prediction model in an external civilian cohort. Aim 1 will conduct logistic regression analyses to
examine for associations between potential phenotypic risk factors and clinically aggressive
melanoma, including demographics, co-morbidities, disease history, immunosuppression,
military history, environmental exposures, and healthcare utilization. This aim will produce an
easy-to-use clinical risk prediction model for clinically aggressive melanoma that is specific to
Veterans and can be integrated into the electronic health record. Aim 2 will conduct a candidate
gene analysis of previously established cutaneous melanoma SNP risk factors as well as
GWAS to examine for novel genetic risk factors for clinically aggressive melanoma. Cox
proportional hazards regression will be used to create a polygenic risk score for clinically
aggressive melanoma and model performance will be evaluated using decile percentiles of risk
and ROC curve. This aim will produce a polygenic risk score for clinically aggressive melanoma.
Subsequently, the combined clinical and genetic risk prediction model will be validated in an
independent civilian cohort using the Mass General Brigham Biobank. Future directions include
a clinical trial of the clinical risk prediction model, integrated into the electronic health record, to
examine prospective real-world model performance as well as examination of any novel genetic
associations found in GWAS for potential therapeutic applications. This clinical risk prediction
tool will help VA clinicians select patients to triage to dermatology for melanoma screening. The
mentorship, research, and training programs described in this CDA-2 application will propel Dr.
Hartman’s career as an independent VA-based dermato-epidemiologic researcher. Her
mentorship team includes Drs. Mary Brophy, Maryam Asgari, Michael Gaziano, Nathanael
Fillmore, and Luc Djousse, who are experts in oncology, dermato-epidemiology, epidemiology,
genetic epidemiology, and big data analysis from the Massachusetts Veterans Epidemiology
Research and Information Center (MAVERIC) and the Harvard Dermatology Program. Through
this CDA-2 application, Dr. Hartman will develop skills in big data analysis, risk prediction
modeling, and genetic epidemiology as well as undergoing professional development to prepare
her to apply for a VA Merit Award and continue toward becoming an independent VA-based
clinician researcher.
Terms: <2,4,D/2,4,5,T><65 and older><65 or older><65 years of age and older><65 years of age or more><65 years of age or older><65+ years><65+ years old><> 65 years><Accounting><Actinic Rays><Age><Aged 65 and Over><Altitude><Armed Forces Personnel><Award><Big Data><BigData><Cancers><Candidate Disease Gene><Candidate Gene><Case Study><Cessation of life><Chronic><Clinical><Clinical Trials><Cutaneous Melanoma><Data><Data Analyses><Data Analysis><Data Set><Death><Dermatologist><Dermatology><Detection><Development><Diagnosis><Disease><Disorder><Drugs><Early Diagnosis><Electronic Health Record><Environmental Exposure><Environmental Factor><Environmental Risk Factor><Epidemiologic Research><Epidemiologic Studies><Epidemiological Studies><Epidemiological data><Epidemiology><Epidemiology Research><Epidemiology data><Ethnic Origin><Ethnicity><Future><GWA study><GWAS><General Anesthesia><Genetic Risk><Genetic analyses><Genetic predisposing factor><Goals><Health><Health Care Systems><Health Care Utilization><Health Services Evaluation><Health Services Research><Healthcare Systems><History><Immunosuppression><Immunosuppression Effect><Immunosuppressive Effect><Information Centers><Investigators><Ionizing Electromagnetic Radiation><Ionizing radiation><Knowledge><Logistic Regressions><Machine Learning><Malignant Cutaneous Melanoma><Malignant Melanoma><Malignant Melanoma of Skin><Malignant Neoplasms><Malignant Skin Neoplasm><Malignant Tumor><Massachusetts><Medical Care Research><Medication><Melanoma><Melanoma Skin><Melanoma patient><Melanotic Nevus><Mentors><Mentorship><Military><Military Personnel><Modeling><Nevus><Oncology><Oncology Cancer><Operative Procedures><Operative Surgical Procedures><Out-patients><Outcome><Outpatients><Patient Selection><Patient risk><Patients><Performance><Pharmaceutical Preparations><Phenotype><Photosensitizers><Photosensitizing Agents><Population><Predisposition><Prognosis><Program Description><ROC Analyses><ROC Curve><Race><Races><Radiation-Ionizing Total><Recording of previous events><Regression Analyses><Regression Analysis><Regression Diagnostics><Research><Research Design><Research Personnel><Research Priority><Research Proposals><Researchers><Retrospective cohort study><Risk><Risk Assessment><Risk Factors><Sample Size><Sentinel Lymph Node Biopsy><Sentinel Node Biopsy><Services><Severities><Single Base Polymorphism><Single Nucleotide Polymorphism><Skin Cancer><Statistical Regression><Study Type><Sun Exposure><Surgical><Surgical Interventions><Surgical Procedure><Susceptibility><T-Stage><Testing><Therapeutic><Training><Training Programs><Treatment Cost><Triage><Tumor stage><UV light><UV radiation><UV rays><Ultraviolet Rays><Veterans><Work><above age 65><after age 65><age 65 and greater><age 65 and older><age 65 or older><age > 65><age of 65 years onward><aged 65 and greater><aged 65+><aged ≥65><agent orange><ages><big-data science><biobank><biorepository><cancer epidemiology><career><case report><clinical applicability><clinical application><clinical decision support><clinical predictive model><clinical risk><co-morbid><co-morbidity><cohort><comorbidity><data interpretation><data warehouse><demographics><dermal melanoma><developmental><diagnostic approach><diagnostic strategy><drug/agent><early detection><electronic health care record><electronic health medical record><electronic health plan record><electronic health registry><electronic medical health record><environmental risk><epidemiologic><epidemiologic data><epidemiologic investigation><epidemiological><epidemiology study><genetic analysis><genetic association><genetic epidemiologic study><genetic epidemiology><genetic risk factor><genome wide association><genome wide association scan><genome wide association studies><genome wide association study><genomewide association scan><genomewide association studies><genomewide association study><genomic data><genomic data-set><genomic dataset><hazard><health care service use><health care service utilization><healthcare service use><healthcare service utilization><healthcare utilization><high risk><histories><human old age (65+)><immune suppression><immune suppressive activity><immune suppressive function><immunosuppressive activity><immunosuppressive function><immunosuppressive response><improved><individualized cancer care><individualized oncology><inherited factor><insight><ionizing output><machine based learning><male><malignancy><malignant skin tumor><military population><neoplasm/cancer><new drug treatments><new drugs><new pharmacological therapeutic><new therapeutics><new therapy><next generation therapeutics><novel><novel drug treatments><novel drugs><novel pharmaco-therapeutic><novel pharmacological therapeutic><novel therapeutics><novel therapy><old age><oncology program><over 65 years><personalized oncology><photosensitizer><polygenic risk score><precision cancer care><precision cancer medicine><precision oncology><predictive tools><primary care provider><programs><prospective><prospective test><providers from primary care><providers of primary care><racial><racial background><racial origin><receiver operating characteristic analyses><receiver operating characteristic curve><recommended screening><risk prediction algorithm><risk prediction model><risk stratification><screening><screening guidelines><screening recommendations><screenings><services research><sex><single nucleotide variant><skills><skin mole><solar exposure><stratify risk><study design><sun light exposure><sunlight exposure><support tools><surgery><tool><treatment strategy><tumor><ultra violet light><ultra violet radiation><ultra violet rays><ultraviolet light><ultraviolet radiation><whole genome association analysis><whole genome association studies><whole genome association study><≥65 years>