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

Surgery, Surgical Sciences
School of Medicine

Professor of Pathology

Pathology
School of Medicine

Member of the Duke Cancer Institute

Duke Cancer Institute
School of Medicine

Education:

Ph.D. 1985

University of California - San Diego

Grants:

Genomic Diversity and the Microenvironment as Drivers of Progression in DCIS

Awarded By
Department of Defense
Role
Co Investigator
Start Date
End Date

(PQC3) Genomic Diversity and the Microenvironment as Drivers of Metastasis in DCIS

Awarded By
National Institutes of Health
Role
Co Investigator
Start Date
End Date

Developing Biomarker-Based Prognostics in Breast Cancer

Awarded By
National Institutes of Health
Role
Consultant
Start Date
End Date

Improving genomic prediction models in breast cancer.

Awarded By
National Institutes of Health
Role
Investigator
Start Date
End Date

PPARy: Biomarker for Breast Cancer in Older Women

Administered By
Medicine, Geriatrics
Awarded By
National Institutes of Health
Role
Mentor
Start Date
End Date

Publications:

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

Authors
Rosenberg, SM; Hendrix, LH; Schreiber, KL; Thompson, AM; Bedrosian, I; Hughes, KS; Lynch, T; Basila, D; Collyar, DE; Frank, ES; Darai, S; Lanahan, C; Marks, JR; Plichta, JK; Hyslop, T; Partridge, AH; Hwang, ES
URI
https://scholars.duke.edu/individual/pub1443610
Source
crossref
Published In
Poster Session Abstracts
Published Date
DOI
10.1158/1538-7445.sabcs19-p1-21-07

Abstract P2-10-18: Deciphering racial disparities in breast cancer collagen reorganization by targeted extracellular matrix proteomics

Authors
Angel, PM; Saunders, J; Jensen-Smith, H; Bruner, E; Ford, ME; Berkhiser, S; Boxall, B; Bethard, J; Ball, LE; Yeh, ES; Hollingsworth, MA; Mehta, AS; Marks, JR; Nakshatri, H; Drake, RR
MLA Citation
Angel, Peggi M., et al. “Abstract P2-10-18: Deciphering racial disparities in breast cancer collagen reorganization by targeted extracellular matrix proteomics.” Poster Session Abstracts, American Association for Cancer Research, 2020. Crossref, doi:10.1158/1538-7445.sabcs19-p2-10-18.
URI
https://scholars.duke.edu/individual/pub1444179
Source
crossref
Published In
Poster Session Abstracts
Published Date
DOI
10.1158/1538-7445.sabcs19-p2-10-18

Pan-cancer analysis of whole genomes.

Cancer is driven by genetic change, and the advent of massively parallel sequencing has enabled systematic documentation of this variation at the whole-genome scale1-3. Here we report the integrative analysis of 2,658 whole-cancer genomes and their matching normal tissues across 38 tumour types from the Pan-Cancer Analysis of Whole Genomes (PCAWG) Consortium of the International Cancer Genome Consortium (ICGC) and The Cancer Genome Atlas (TCGA). We describe the generation of the PCAWG resource, facilitated by international data sharing using compute clouds. On average, cancer genomes contained 4-5 driver mutations when combining coding and non-coding genomic elements; however, in around 5% of cases no drivers were identified, suggesting that cancer driver discovery is not yet complete. Chromothripsis, in which many clustered structural variants arise in a single catastrophic event, is frequently an early event in tumour evolution; in acral melanoma, for example, these events precede most somatic point mutations and affect several cancer-associated genes simultaneously. Cancers with abnormal telomere maintenance often originate from tissues with low replicative activity and show several mechanisms of preventing telomere attrition to critical levels. Common and rare germline variants affect patterns of somatic mutation, including point mutations, structural variants and somatic retrotransposition. A collection of papers from the PCAWG Consortium describes non-coding mutations that drive cancer beyond those in the TERT promoter4; identifies new signatures of mutational processes that cause base substitutions, small insertions and deletions and structural variation5,6; analyses timings and patterns of tumour evolution7; describes the diverse transcriptional consequences of somatic mutation on splicing, expression levels, fusion genes and promoter activity8,9; and evaluates a range of more-specialized features of cancer genomes8,10-18.
Authors
ICGC/TCGA Pan-Cancer Analysis of Whole Genomes Consortium,
MLA Citation
ICGC/TCGA Pan-Cancer Analysis of Whole Genomes Consortium, Australasian Soc Infect Dis. “Pan-cancer analysis of whole genomes.Nature, vol. 578, no. 7793, Feb. 2020, pp. 82–93. Pubmed, doi:10.1038/s41586-020-1969-6.
URI
https://scholars.duke.edu/individual/pub1431526
PMID
32025007
Source
pubmed
Published In
Nature
Volume
578
Published Date
Start Page
82
End Page
93
DOI
10.1038/s41586-020-1969-6

Microcalcification localization and cluster detection using unsupervised convolutional autoencoders and structural similarity index

© 2020 SPIE. Detecting microcalcification clusters in mammograms is important to the diagnosis of breast diseases. Previous studies which mainly focused on supervised methods require abundant annotated training data but these data are usually hard to acquire. In this work, we leverage unsupervised convolutional autoencoders and structural similarity (SSIM) based post-processing to detect and localize microcalcification clusters in full-field digital mammograms (FFDMs). Our models were trained by patches extracted from 3,632 normal cases, in total with 16,702 mammograms. Evaluations were conducted in three aspects, including patch-based anomaly detection, pixel-wise microcalcification localization, and microcalcification cluster detection. Specifically, the receiver operating characteristic (ROC) analysis was used for patch-based anomaly detection. Then, a pixel-wise ROC analysis and a cluster-based free-response ROC (FROC) analysis were performed to assess our detection algorithms of individual microcalcifications and microcalcification clusters, respectively. We achieved a pixel-wise AUC of 0.97 as well as a cluster-based sensitivity of 0.62 at 1 false positive per image and 0.75 at 2.5 false positives per image. Both qualitative and quantitative results demonstrated the effectiveness of our method.
Authors
Peng, Y; Hou, R; Ren, Y; Grimm, LJ; Marks, JR; Hwang, ES; Lo, JY
MLA Citation
Peng, Y., et al. “Microcalcification localization and cluster detection using unsupervised convolutional autoencoders and structural similarity index.” Progress in Biomedical Optics and Imaging  Proceedings of Spie, vol. 11314, 2020. Scopus, doi:10.1117/12.2551263.
URI
https://scholars.duke.edu/individual/pub1447091
Source
scopus
Published In
Progress in Biomedical Optics and Imaging Proceedings of Spie
Volume
11314
Published Date
DOI
10.1117/12.2551263

A multitask deep learning method in simultaneously predicting occult invasive disease in ductal carcinoma in-situ and segmenting microcalcifications in mammography

© 2020 SPIE. We proposed a two-branch multitask learning convolutional neural network to solve two different but related tasks at the same time. Our main task is to predict occult invasive disease in biopsy proven Ductal Carcinoma in-situ (DCIS), with an auxiliary task of segmenting microcalcifications (MCs). In this study, we collected digital mammography from 604 patients, 400 of which were DCIS. The model used patches with size of 512×512 extracted within a radiologist masked ROIs as input, with outputs including noisy MC segmentations obtained from our previous algorithms, and classification labels from final diagnosis at patients' definite surgery. We utilized a deep multitask model by combining both Unet segmentation networks and prediction classification networks, by sharing first several convolutional layers. The model achieved a patch-based ROC-AUC of 0.69, with a case-based ROC-AUC of 0.61. Segmentation results achieved a dice coefficient of 0.49.
Authors
Hou, R; Grimm, LJ; Mazurowski, MA; Marks, JR; King, LM; Maley, CC; Hwang, ES; Lo, JY
MLA Citation
Hou, R., et al. “A multitask deep learning method in simultaneously predicting occult invasive disease in ductal carcinoma in-situ and segmenting microcalcifications in mammography.” Progress in Biomedical Optics and Imaging  Proceedings of Spie, vol. 11314, 2020. Scopus, doi:10.1117/12.2549669.
URI
https://scholars.duke.edu/individual/pub1447092
Source
scopus
Published In
Progress in Biomedical Optics and Imaging Proceedings of Spie
Volume
11314
Published Date
DOI
10.1117/12.2549669