Prediction of Metastasis Using Second Harmonic Generation
Abstract
A breast cancer patient with estrogen receptor positive (ER+) invasive ductal carcinoma (IDC) typically has the tumor removed, possibly with nearby lymph nodes, and hormonal therapy is begun. Then a significant decision is made: should the patient receive adjuvant chemotherapy to attack cells that have escaped the tumor? In IDC ER+ patients whose cancer has spread to the lymph nodes, the choice is clear and virtually all are treated systemically. However, in the majority of IDC ER+ cases the cancer has not yet spread ("N0"), and the choice is unclear. Current data suggest that about half of patients that are systemically treated would not have metastasized, did not need to suffer the toxic effects of systemic therapy, and were "overtreated." Hence, there is a pressing need to predict who will, and will not, metastasize, to minimize overtreatment. This need is currently addressed with conventional pathology laboratory measures, as well as commercial products such as OncotypeDX. A 21-gene test, Oncotype is now the standard of care for many U.S. hospitals, including ours where it assists the clinician on our team with decisions for ~80% of cases. However, it is not perfect, and 69.5% of patients in the Oncotype "high-risk" category don t metastasize after 10 years. It is also expensive at $4K per sample. Consequently, we identified a need to improve the accuracy, speed, and cost of metastatic predictions. We next realized that Oncotype and conventional pathology analysis focuses on tumor cells, including their morphology and gene expression. Less attention is paid to the extracellular matrix through which metastasizing cells travel. This is akin to predicting traffic conditions in a city by studying the cars and their engine construction, but ignoring the quality of the roads upon which they travel. We and others have demonstrated that tumor collagen structure, as measured with the optical process called second harmonic generation (SHG), influences tumor metastasis. This suggests that collagen structure may provide prognostic information about metastasis that is "matrix-focused" and hence complementary, or even superior, to "cell-focused" genomic methods. Most recently, we found that one SHG measure of collagen structure, the SHG forward- to backward-scattering ratio (F/B), changed significantly with tumor invasiveness, consistent with our unpublished observation that tumor cells locomote faster in low F/B collagen gels. Consequently, we hypothesize that SHG F/B is a clinically useful predictor of metastatic outcome. In a preliminary study in a cohort of 125 ER+ patient samples from the Netherlands who did not undergo adjuvant therapy after tumor removal, and who were N0 upon tumor excision, we found that F/B measurement in primary tumor samples is a significant prognostic indicator of time to metastasis. This is a population in which key treatment decisions regarding adjuvant chemotherapy must be made and for whom improved prognostic information on metastasis is required to reduce overtreatment. In order to move this technology forward, we must now validate it in a retrospective study of a second population, based in the U.S., and which has undergone adjuvant therapy. With our clinical collaborators Drs. Skinner and Tang, we have identified a cohort of 429 IDC breast cancer patients from Strong Memorial Hospital who have archived tissue samples suitable for F/B analysis, are N0, who have ample follow-up data, and who encompass the full variety of treatment histories. With our collaborating biostatistician Dr. Salzman, we found that this cohort contains sufficient patients to answer the following questions: (1) Does F/B predict metastatic outcome in IDC patients? (2) Does F/B predict metastatic outcome in ER+ IDC patients? (3) Does F/B predict metastatic outcome in ER+ IDC patients treated with hormonal, chemo-, and/or radiotherapy? We will derive the most powerful predictive formula based upon F/B alone
Document Details
- Document Type
- DoD Grant Award
- Publication Date
- Apr 04, 2016
- Source ID
- W81XWH1510040
Entities
People
- Edward B Brown
Organizations
- United States Army
- University of Rochester