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:

Joseph W. and Dorothy W. Beard Distinguished Professor of Experimental Surgery

Surgery, Surgical Sciences
School of Medicine

Professor in 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

Administered By
Surgical Oncology
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:

Genomic analysis defines clonal relationships of ductal carcinoma in situ and recurrent invasive breast cancer.

Ductal carcinoma in situ (DCIS) is the most common form of preinvasive breast cancer and, despite treatment, a small fraction (5-10%) of DCIS patients develop subsequent invasive disease. A fundamental biologic question is whether the invasive disease arises from tumor cells in the initial DCIS or represents new unrelated disease. To address this question, we performed genomic analyses on the initial DCIS lesion and paired invasive recurrent tumors in 95 patients together with single-cell DNA sequencing in a subset of cases. Our data show that in 75% of cases the invasive recurrence was clonally related to the initial DCIS, suggesting that tumor cells were not eliminated during the initial treatment. Surprisingly, however, 18% were clonally unrelated to the DCIS, representing new independent lineages and 7% of cases were ambiguous. This knowledge is essential for accurate risk evaluation of DCIS, treatment de-escalation strategies and the identification of predictive biomarkers.
Authors
Lips, EH; Kumar, T; Megalios, A; Visser, LL; Sheinman, M; Fortunato, A; Shah, V; Hoogstraat, M; Sei, E; Mallo, D; Roman-Escorza, M; Ahmed, AA; Xu, M; van den Belt-Dusebout, AW; Brugman, W; Casasent, AK; Clements, K; Davies, HR; Fu, L; Grigoriadis, A; Hardman, TM; King, LM; Krete, M; Kristel, P; de Maaker, M; Maley, CC; Marks, JR; Menegaz, BA; Mulder, L; Nieboer, F; Nowinski, S; Pinder, S; Quist, J; Salinas-Souza, C; Schaapveld, M; Schmidt, MK; Shaaban, AM; Shami, R; Sridharan, M; Zhang, J; Stobart, H; Collyar, D; Nik-Zainal, S; Wessels, LFA; Hwang, ES; Navin, NE; Futreal, PA; Grand Challenge PRECISION consortium,; Thompson, AM; Wesseling, J; Sawyer, EJ
MLA Citation
Lips, Esther H., et al. “Genomic analysis defines clonal relationships of ductal carcinoma in situ and recurrent invasive breast cancer.Nat Genet, vol. 54, no. 6, June 2022, pp. 850–60. Pubmed, doi:10.1038/s41588-022-01082-3.
URI
https://scholars.duke.edu/individual/pub1524174
PMID
35681052
Source
pubmed
Published In
Nat Genet
Volume
54
Published Date
Start Page
850
End Page
860
DOI
10.1038/s41588-022-01082-3

Anomaly Detection of Calcifications in Mammography Based on 11,000 Negative Cases.

In mammography, calcifications are one of the most common signs of breast cancer. Detection of such lesions is an active area of research for computer-aided diagnosis and machine learning algorithms. Due to limited numbers of positive cases, many supervised detection models suffer from overfitting and fail to generalize. We present a one-class, semi-supervised framework using a deep convolutional autoencoder trained with over 50,000 images from 11,000 negative-only cases. Since the model learned from only normal breast parenchymal features, calcifications produced large signals when comparing the residuals between input and reconstruction output images. As a key advancement, a structural dissimilarity index was used to suppress non-structural noises. Our selected model achieved pixel-based AUROC of 0.959 and AUPRC of 0.676 during validation, where calcification masks were defined in a semi-automated process. Although not trained directly on any cancers, detection performance of calcification lesions on 1,883 testing images (645 malignant and 1238 negative) achieved 75% sensitivity at 2.5 false positives per image. Performance plateaued early when trained with only a fraction of the cases, and greater model complexity or a larger dataset did not improve performance. This study demonstrates the potential of this anomaly detection approach to detect mammographic calcifications in a semi-supervised manner with efficient use of a small number of labeled images, and may facilitate new clinical applications such as computer-aided triage and quality improvement.
Authors
Hou, R; Peng, Y; Grimm, LJ; Ren, Y; Mazurowski, MA; Marks, JR; King, LM; Maley, CC; Hwang, ES; Lo, JY
MLA Citation
Hou, Rui, et al. “Anomaly Detection of Calcifications in Mammography Based on 11,000 Negative Cases.Ieee Trans Biomed Eng, vol. 69, no. 5, May 2022, pp. 1639–50. Pubmed, doi:10.1109/TBME.2021.3126281.
URI
https://scholars.duke.edu/individual/pub1502472
PMID
34788216
Source
pubmed
Published In
Ieee Trans Biomed Eng
Volume
69
Published Date
Start Page
1639
End Page
1650
DOI
10.1109/TBME.2021.3126281

Racial Differences in the Tumor Immune Landscape and Survival of Women with High-Grade Serous Ovarian Carcinoma.

<h4>Background</h4>Tumor-infiltrating lymphocytes (TIL) confer a survival benefit among patients with ovarian cancer; however, little work has been conducted in racially diverse cohorts.<h4>Methods</h4>The current study investigated racial differences in the tumor immune landscape and survival of age- and stage-matched non-Hispanic Black and non-Hispanic White women with high-grade serous ovarian carcinoma (HGSOC) enrolled in two population-based studies (n = 121 in each racial group). We measured TILs (CD3+), cytotoxic T cells (CD3+CD8+), regulatory T cells (CD3+FoxP3+), myeloid cells (CD11b+), and neutrophils (CD11b+CD15+) via multiplex immunofluorescence. Multivariable Cox proportional hazard regression was used to estimate the association between immune cell abundance and survival overall and by race.<h4>Results</h4>Overall, higher levels of TILs, cytotoxic T cells, myeloid cells, and neutrophils were associated with better survival in the intratumoral and peritumoral region, irrespective of tissue compartment (tumor, stroma). Improved survival was noted for T-regulatory cells in the peritumoral region and in the stroma of the intratumoral region, but no association for intratumoral T-regulatory cells. Despite similar abundance of immune cells across racial groups, associations with survival among non-Hispanic White women were consistent with the overall findings, but among non-Hispanic Black women, most associations were attenuated and not statistically significant.<h4>Conclusions</h4>Our results add to the existing evidence that a robust immune infiltrate confers a survival advantage among women with HGSOC; however, non-Hispanic Black women may not experience the same survival benefit as non-Hispanic White women with HGSOC.<h4>Impact</h4>This study contributes to our understanding of the immunoepidemiology of HGSOC in diverse populations.
Authors
Peres, LC; Colin-Leitzinger, C; Sinha, S; Marks, JR; Conejo-Garcia, JR; Alberg, AJ; Bandera, EV; Berchuck, A; Bondy, ML; Christensen, BC; Cote, ML; Doherty, JA; Moorman, PG; Peters, ES; Moran Segura, C; Nguyen, JV; Schwartz, AG; Terry, PD; Wilson, CM; Fridley, BL; Schildkraut, JM
MLA Citation
Peres, Lauren C., et al. “Racial Differences in the Tumor Immune Landscape and Survival of Women with High-Grade Serous Ovarian Carcinoma.Cancer Epidemiology, Biomarkers & Prevention : A Publication of the American Association for Cancer Research, Cosponsored by the American Society of Preventive Oncology, vol. 31, no. 5, May 2022, pp. 1006–16. Epmc, doi:10.1158/1055-9965.epi-21-1334.
URI
https://scholars.duke.edu/individual/pub1512202
PMID
35244678
Source
epmc
Published In
Cancer Epidemiology, Biomarkers & Prevention : a Publication of the American Association for Cancer Research, Cosponsored by the American Society of Preventive Oncology
Volume
31
Published Date
Start Page
1006
End Page
1016
DOI
10.1158/1055-9965.epi-21-1334

A multi-modal exploration of heterogeneous physico-chemical properties of DCIS breast microcalcifications.

Ductal carcinoma in situ (DCIS) is frequently associated with breast calcification. This study combines multiple analytical techniques to investigate the heterogeneity of these calcifications at the micrometre scale. X-ray diffraction, scanning electron microscopy and Raman and Fourier-transform infrared spectroscopy were used to determine the physicochemical and crystallographic properties of type II breast calcifications located in formalin fixed paraffin embedded DCIS breast tissue samples. Multiple calcium phosphate phases were identified across the calcifications, distributed in different patterns. Hydroxyapatite was the dominant mineral, with magnesium whitlockite found at the calcification edge. Amorphous calcium phosphate and octacalcium phosphate were also identified close to the calcification edge at the apparent mineral/matrix barrier. Crystallographic features of hydroxyapatite also varied across the calcifications, with higher crystallinity centrally, and highest carbonate substitution at the calcification edge. Protein was also differentially distributed across the calcification and the surrounding soft tissue, with collagen and β-pleated protein features present to differing extents. Combination of analytical techniques in this study was essential to understand the heterogeneity of breast calcifications and how this may link crystallographic and physicochemical properties of calcifications to the surrounding tissue microenvironment.
Authors
Gosling, S; Calabrese, D; Nallala, J; Greenwood, C; Pinder, S; King, L; Marks, J; Pinto, D; Lynch, T; Lyburn, ID; Hwang, ES; Grand Challenge Precision Consortium,; Rogers, K; Stone, N
MLA Citation
Gosling, Sarah, et al. “A multi-modal exploration of heterogeneous physico-chemical properties of DCIS breast microcalcifications.Analyst, vol. 147, no. 8, Apr. 2022, pp. 1641–54. Pubmed, doi:10.1039/d1an01548f.
URI
https://scholars.duke.edu/individual/pub1513187
PMID
35311860
Source
pubmed
Published In
Analyst
Volume
147
Published Date
Start Page
1641
End Page
1654
DOI
10.1039/d1an01548f

Prediction of Upstaging in Ductal Carcinoma in Situ Based on Mammographic Radiomic Features.

Background Improving diagnosis of ductal carcinoma in situ (DCIS) before surgery is important in choosing optimal patient management strategies. However, patients may harbor occult invasive disease not detected until definitive surgery. Purpose To assess the performance and clinical utility of mammographic radiomic features in the prediction of occult invasive cancer among women diagnosed with DCIS on the basis of core biopsy findings. Materials and Methods In this Health Insurance Portability and Accountability Act-compliant retrospective study, digital magnification mammographic images were collected from women who underwent breast core-needle biopsy for calcifications that was performed at a single institution between September 2008 and April 2017 and yielded a diagnosis of DCIS. The database query was directed at asymptomatic women with calcifications without a mass, architectural distortion, asymmetric density, or palpable disease. Logistic regression with regularization was used. Differences across training and internal test set by upstaging rate, age, lesion size, and estrogen and progesterone receptor status were assessed by using the Kruskal-Wallis or χ2 test. Results The study consisted of 700 women with DCIS (age range, 40-89 years; mean age, 59 years ± 10 [standard deviation]), including 114 with lesions (16.3%) upstaged to invasive cancer at subsequent surgery. The sample was split randomly into 400 women for the training set and 300 for the testing set (mean ages: training set, 59 years ± 10; test set, 59 years ± 10; P = .85). A total of 109 radiomic and four clinical features were extracted. The best model on the test set by using all radiomic and clinical features helped predict upstaging with an area under the receiver operating characteristic curve of 0.71 (95% CI: 0.62, 0.79). For a fixed high sensitivity (90%), the model yielded a specificity of 22%, a negative predictive value of 92%, and an odds ratio of 2.4 (95% CI: 1.8, 3.2). High specificity (90%) corresponded to a sensitivity of 37%, positive predictive value of 41%, and odds ratio of 5.0 (95% CI: 2.8, 9.0). Conclusion Machine learning models that use radiomic features applied to mammographic calcifications may help predict upstaging of ductal carcinoma in situ, which can refine clinical decision making and treatment planning. © RSNA, 2022.
Authors
Hou, R; Grimm, LJ; Mazurowski, MA; Marks, JR; King, LM; Maley, CC; Lynch, T; van Oirsouw, M; Rogers, K; Stone, N; Wallis, M; Teuwen, J; Wesseling, J; Hwang, ES; Lo, JY
MLA Citation
Hou, Rui, et al. “Prediction of Upstaging in Ductal Carcinoma in Situ Based on Mammographic Radiomic Features.Radiology, vol. 303, no. 1, Apr. 2022, pp. 54–62. Pubmed, doi:10.1148/radiol.210407.
URI
https://scholars.duke.edu/individual/pub1505281
PMID
34981975
Source
pubmed
Published In
Radiology
Volume
303
Published Date
Start Page
54
End Page
62
DOI
10.1148/radiol.210407