The use of systemic chemotherapy for breast cancer has contributed to the recent decline in breast cancer mortality;however, an unacceptable number of patients fail systemic therapy and die of disseminated disease. Identifying factors important in resistance and directing patients towards more effective treatment is the translational goal of Project 3. Using quantitative PET imaging to measure glucose metabolism, and more recently dynamic contrast-enhanced (DCE) MRI to measure blood flow, we have identified an in vivo metabolic signature for locally advanced breast cancer (LABC) resistant to neoadjuvant chemotherapy as (1) a pre-therapy mismatch between metabolism and perfusion, (2) persistent or even increased tumor perfusion despite treatment, and (3) an altered pattern of glucose metabolism relative to glucose delivery after treatment. This pattern predicts incomplete response, early relapse and death independent of established prognostic factors, including pathologic primary tumor and nodal pathologic response. We have also found this pattern is more profoundly associated with triple-negative (TN, ER/PR/HER2 negative) tumors versus those that express ER/PR and/or over-express HER2. We now propose to translate our clinical, in vivo findings in patients back into the laboratory to identify the biologic features of tumors underlying these findings. In a cohort of LABC patients undergoing neoadjuvant chemotherapy, we will (1) compare imaging findings to tumor phenotype determined by IHC and expression microarrays to determine which molecular pathways are most involved In the resistant imaging phenotype, (2) determine the role ofthe tumor microenvironment, specifically tumor hypoxia measured by FMISO PET and the expression ofthe hypoxic tissue markers measured by IHC, and (3) relate macroscopic metabolic properties measured by imaging to cellular metabolism in biopsy specimens and a panel of cell lines using ToF-SIMS, with the goal of relating findings on metabolic pathways measured in cell lines to the resistant phenotype seen in patients. Successful completion of the studies will identify breast cancer patients likely to fail systemic chemotherapy and direct therapy towards those biologic targets most likely to overcome resistance.

Public Health Relevance

This Project will investigate quantitative in vivo imaging as a means of identifying breast cancers resistant to systemic therapy and directing the patient towards more effective therapy. The primary translational goal of the project is to relate in vivo imaging findings predictive of poor response and patient outcome to underlying tumor biology with the goal of directing treatment targeted to specific resistance factors.

Agency
National Institute of Health (NIH)
Institute
National Cancer Institute (NCI)
Type
Specialized Center (P50)
Project #
5P50CA138293-05
Application #
8728123
Study Section
Special Emphasis Panel (ZCA1-GRB-I)
Project Start
Project End
Budget Start
2014-09-01
Budget End
2015-08-31
Support Year
5
Fiscal Year
2014
Total Cost
$340,614
Indirect Cost
$141,528
Name
Fred Hutchinson Cancer Research Center
Department
Type
DUNS #
078200995
City
Seattle
State
WA
Country
United States
Zip Code
98109
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Sorace, Anna G; Partridge, Savannah C; Li, Xia et al. (2018) Distinguishing benign and malignant breast tumors: preliminary comparison of kinetic modeling approaches using multi-institutional dynamic contrast-enhanced MRI data from the International Breast MR Consortium 6883 trial. J Med Imaging (Bellingham) 5:011019
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Paszkiewicz, Paulina J; Fräßle, Simon P; Srivastava, Shivani et al. (2016) Targeted antibody-mediated depletion of murine CD19 CAR T cells permanently reverses B cell aplasia. J Clin Invest 126:4262-4272
Busch, Dirk H; Fräßle, Simon P; Sommermeyer, Daniel et al. (2016) Role of memory T cell subsets for adoptive immunotherapy. Semin Immunol 28:28-34
Robinson, Michael A; Graham, Daniel J; Morrish, Fionnuala et al. (2016) Lipid analysis of eight human breast cancer cell lines with ToF-SIMS. Biointerphases 11:02A303
Pinker, K; Marino, M A; Dr Meyer-Baese, A et al. (2016) [Multiparametric and molecular imaging of breast tumors with MRI and PET/MRI]. Radiologe 56:612-21
Bluestein, Blake M; Morrish, Fionnuala; Graham, Daniel J et al. (2016) An unsupervised MVA method to compare specific regions in human breast tumor tissue samples using ToF-SIMS. Analyst 141:1947-57

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