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:

Developing Biomarker-Based Prognostics in Breast Cancer

Administered By
Surgery, Surgical Sciences
Awarded By
National Institutes of Health
Role
Consultant
Start Date
End Date

Improving genomic prediction models in breast cancer.

Administered By
Surgery, Surgical Sciences
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

A Simple System for Early Detection of Breast Cancer

Administered By
Surgery, Surgical Sciences
Awarded By
Arizona State University
Role
Principal Investigator
Start Date
End Date

Surrogate Markers Of Tumor Specific Immunity

Administered By
Surgery
Awarded By
National Institutes of Health
Role
Co-Principal Investigator
Start Date
End Date

Publications:

Retina-Match: Ipsilateral Mammography Lesion Matching in a Single Shot Detection Pipeline

In mammography and tomosynthesis, radiologists use the geometric relationship of the four standard screening views to detect breast abnormalities. To date, computer aided detection methods focus on formulations based only on a single view. Recent multi-view methods are either black box approaches using methods such as relation blocks, or perform extensive, case-level feature aggregation requiring large data redundancy. In this study, we propose Retina-Match, an end-to-end trainable pipeline for detection, matching, and refinement that can effectively perform ipsilateral lesion matching in paired screening mammography images. We demonstrate effectiveness on a private, digital mammography data set with 1,016 biopsied lesions and 2,000 negative cases.
Authors
Ren, Y; Lu, J; Liang, Z; Grimm, LJ; Kim, C; Taylor-Cho, M; Yoon, S; Marks, JR; Lo, JY
MLA Citation
Ren, Y., et al. “Retina-Match: Ipsilateral Mammography Lesion Matching in a Single Shot Detection Pipeline.” Lecture Notes in Computer Science (Including Subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), vol. 12905 LNCS, 2021, pp. 345–54. Scopus, doi:10.1007/978-3-030-87240-3_33.
URI
https://scholars.duke.edu/individual/pub1499563
Source
scopus
Published In
Lecture Notes in Computer Science (Including Subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Volume
12905 LNCS
Published Date
Start Page
345
End Page
354
DOI
10.1007/978-3-030-87240-3_33

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 scale<sup>1-3</sup>. 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 promoter<sup>4</sup>; identifies new signatures of mutational processes that cause base substitutions, small insertions and deletions and structural variation<sup>5,6</sup>; analyses timings and patterns of tumour evolution<sup>7</sup>; describes the diverse transcriptional consequences of somatic mutation on splicing, expression levels, fusion genes and promoter activity<sup>8,9</sup>; and evaluates a range of more-specialized features of cancer genomes<sup>8,10-18</sup>.
Authors
ICGC/TCGA Pan-Cancer Analysis of Whole Genomes Consortium,
MLA Citation
ICGC/TCGA Pan-Cancer Analysis of Whole Genomes Consortium, Ken H. “Pan-cancer analysis of whole genomes.Nature, vol. 578, no. 7793, Feb. 2020, pp. 82–93. Epmc, doi:10.1038/s41586-020-1969-6.
URI
https://scholars.duke.edu/individual/pub1431526
PMID
32025007
Source
epmc
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

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