Jeffrey Marks
Overview:
I have been engaged in basic and applied cancer research for over 28 years beginning with my post-doctoral fellowship under Arnold Levine at Princeton. Since being appointed to the faculty in the Department of Surgery at Duke, my primary interest has been towards understanding breast and ovarian cancer. I am a charter member of the NCI-Early Detection Research Network (EDRN) and have been an integral scientist in the breast and gynecologic collaborative group for 15 years including leading this group for a 5 year period. I am also a major contributor to the Cancer Genome Atlas and have worked in this context for the past 4 years. My research interests are in the molecular etiology of these diseases and understanding how key genetic events contribute to their onset and progression. My work has been very multi-disciplinary incorporating quantitative, population, genetic, and behavioral approaches. I consider my specialty to be in the area of using human breast and ovarian cancer as the primary and only authentic model system to understand these diseases.
Positions:
Professor of Surgery
Professor of Pathology
Member of the Duke Cancer Institute
Education:
Ph.D. 1985
Grants:
Developing Biomarker-Based Prognostics in Breast Cancer
Improving genomic prediction models in breast cancer.
PPARy: Biomarker for Breast Cancer in Older Women
A Simple System for Early Detection of Breast Cancer
Surrogate Markers Of Tumor Specific Immunity
Publications:
Retina-Match: Ipsilateral Mammography Lesion Matching in a Single Shot Detection Pipeline
Abstract P1-21-07: The Patient-reported Outcomes after Routine Treatment of Atypical Lesions (PORTAL) study: Pain, psychosocial wellbeing, and quality of life among women undergoing guideline concordant care for DCIS vs. active surveillance for in situ an
Abstract P2-10-18: Deciphering racial disparities in breast cancer collagen reorganization by targeted extracellular matrix proteomics
Pan-cancer analysis of whole genomes.
Microcalcification localization and cluster detection using unsupervised convolutional autoencoders and structural similarity index
