Jennifer Freedman

Positions:

Assistant Professor in Medicine

Medicine, Medical Oncology
School of Medicine

Member of the Duke Cancer Institute

Duke Cancer Institute
School of Medicine

Education:

Ph.D. 2001

Emory University

Grants:

Publications:

Melanoma

Authors
Augustine, CK; Freedman, JA; Beasley, GM; Tyler, DS
MLA Citation
Augustine, C. K., et al. Melanoma. Vol. 2, Aug. 2013, pp. 765–75. Scopus, doi:10.1016/B978-0-12-382227-7.00066-5.
URI
https://scholars.duke.edu/individual/pub967715
Source
scopus
Volume
2
Published Date
Start Page
765
End Page
775
DOI
10.1016/B978-0-12-382227-7.00066-5

Characterization of an oxaliplatin sensitivity predictor in a preclinical murine model of colorectal cancer.

Despite advances in contemporary chemotherapeutic strategies, long-term survival still remains elusive for patients with metastatic colorectal cancer. A better understanding of the molecular markers of drug sensitivity to match therapy with patient is needed to improve clinical outcomes. In this study, we used in vitro drug sensitivity data from the NCI-60 cell lines together with their Affymetrix microarray data to develop a gene expression signature to predict sensitivity to oxaliplatin. To validate our oxaliplatin sensitivity signature, patient-derived colorectal cancer explants (PDCCE) were developed in nonobese diabetic/severe combined immunodeficient (NOD/SCID) mice from resected human colorectal tumors. Analysis of gene expression profiles found similarities between the PDCCEs and their parental human tumors, suggesting their utility to study drug sensitivity in vivo. The oxaliplatin sensitivity signature was then validated in vivo with response data from 14 PDCCEs treated with oxaliplatin and was found to have an accuracy of 92.9% (sensitivity = 87.5%; specificity = 100%). Our findings suggest that PDCCEs can be a novel source to study drug sensitivity in colorectal cancer. Furthermore, genomic-based analysis has the potential to be incorporated into future strategies to optimize individual therapy for patients with metastatic colorectal cancer.
Authors
Kim, MK; Osada, T; Barry, WT; Yang, XY; Freedman, JA; Tsamis, KA; Datto, M; Clary, BM; Clay, T; Morse, MA; Febbo, PG; Lyerly, HK; Hsu, DS
MLA Citation
Kim, Mickey K., et al. “Characterization of an oxaliplatin sensitivity predictor in a preclinical murine model of colorectal cancer..” Mol Cancer Ther, vol. 11, no. 7, July 2012, pp. 1500–09. Pubmed, doi:10.1158/1535-7163.MCT-11-0937.
URI
https://scholars.duke.edu/individual/pub765588
PMID
22351745
Source
pubmed
Published In
Mol Cancer Ther
Volume
11
Published Date
Start Page
1500
End Page
1509
DOI
10.1158/1535-7163.MCT-11-0937

A methodology for utilization of predictive genomic signatures in FFPE samples.

BACKGROUND: Gene expression signatures developed to measure the activity of oncogenic signaling pathways have been used to dissect the heterogeneity of tumor samples and to predict sensitivity to various cancer drugs that target components of the relevant pathways, thus potentially identifying therapeutic options for subgroups of patients. To facilitate broad use, including in a clinical setting, the ability to generate data from formalin-fixed, paraffin-embedded (FFPE) tissues is essential. METHODS: Patterns of pathway activity in matched fresh-frozen and FFPE xenograft tumor samples were generated using the MessageAmp Premier methodology in combination with assays using Affymetrix arrays. Results generated were compared with those obtained from fresh-frozen samples using a standard Affymetrix assay. In addition, gene expression data from patient matched fresh-frozen and FFPE melanomas were also utilized to evaluate the consistency of predictions of oncogenic signaling pathway status. RESULTS: Significant correlation was observed between pathway activity predictions from paired fresh-frozen and FFPE xenograft tumor samples. In addition, significant concordance of pathway activity predictions was also observed between patient matched fresh-frozen and FFPE melanomas. CONCLUSIONS: Reliable and consistent predictions of oncogenic pathway activities can be obtained from FFPE tumor tissue samples. The ability to reliably utilize FFPE patient tumor tissue samples for genomic analyses will lead to a better understanding of the biology of disease progression and, in the clinical setting, will provide tools to guide the choice of therapeutics to those most likely to be effective in treating a patient's disease.
Authors
Freedman, JA; Augustine, CK; Selim, AM; Holshausen, KC; Wei, Z; Tsamis, KA; Hsu, DS; Dressman, HK; Barry, WT; Tyler, DS; Nevins, JR
MLA Citation
Freedman, Jennifer A., et al. “A methodology for utilization of predictive genomic signatures in FFPE samples..” Bmc Med Genomics, vol. 4, July 2011. Pubmed, doi:10.1186/1755-8794-4-58.
URI
https://scholars.duke.edu/individual/pub758438
PMID
21745407
Source
pubmed
Published In
Bmc Medical Genomics
Volume
4
Published Date
Start Page
58
DOI
10.1186/1755-8794-4-58

Use of gene expression and pathway signatures to characterize the complexity of human melanoma.

A defining characteristic of most human cancers is heterogeneity, resulting from the somatic acquisition of a complex array of genetic and genomic alterations. Dissecting this heterogeneity is critical to developing an understanding of the underlying mechanisms of disease and to paving the way toward personalized treatments of the disease. We used gene expression data sets from the analysis of primary and metastatic melanomas to develop a molecular description of the heterogeneity that characterizes this disease. Unsupervised hierarchical clustering, gene set enrichment analyses, and pathway activity analyses were used to describe the genetic heterogeneity of melanomas. Patterns of gene expression that revealed two distinct classes of primary melanoma, two distinct classes of in-transit melanoma, and at least three subgroups of metastatic melanoma were identified. Expression signatures developed to predict the status of oncogenic signaling pathways were used to explore the biological basis underlying these differential patterns of expression. This analysis of activities revealed unique pathways that distinguished the primary and metastatic subgroups of melanoma. Distinct patterns of gene expression across primary, in-transit, and metastatic melanomas underline the genetic heterogeneity of this disease. This heterogeneity can be described in terms of deregulation of signaling pathways, thus increasing the knowledge of the biological features underlying individual melanomas and potentially directing therapeutic opportunities to individual patients with melanoma.
Authors
Freedman, JA; Tyler, DS; Nevins, JR; Augustine, CK
MLA Citation
Freedman, Jennifer A., et al. “Use of gene expression and pathway signatures to characterize the complexity of human melanoma..” Am J Pathol, vol. 178, no. 6, June 2011, pp. 2513–22. Pubmed, doi:10.1016/j.ajpath.2011.02.037.
URI
https://scholars.duke.edu/individual/pub758436
PMID
21641377
Source
pubmed
Published In
The American Journal of Pathology
Volume
178
Published Date
Start Page
2513
End Page
2522
DOI
10.1016/j.ajpath.2011.02.037

A combinatorial mechanism for determining the specificity of E2F activation and repression.

Various studies have detailed the role of E2F proteins in both transcription activation and repression. Further study has shown that distinct promoter elements, but comprising the same E2F-recognition motif, confer positive or negative E2F control and that this reflects binding of either activator or repressor E2F proteins, respectively. We now show that the specificity of binding of an activator or repressor E2F protein is determined by adjacent sequences that bind a cooperating transcription factor. We propose that the functional E2F element is a module comprising not only the E2F-binding site but also the adjacent site for the cooperating transcription factor.
Authors
Freedman, JA; Chang, JT; Jakoi, L; Nevins, JR
MLA Citation
Freedman, J. A., et al. “A combinatorial mechanism for determining the specificity of E2F activation and repression..” Oncogene, vol. 28, no. 32, Aug. 2009, pp. 2873–81. Pubmed, doi:10.1038/onc.2009.153.
URI
https://scholars.duke.edu/individual/pub790352
PMID
19543322
Source
pubmed
Published In
Oncogene
Volume
28
Published Date
Start Page
2873
End Page
2881
DOI
10.1038/onc.2009.153