Accession Number : AD1010649

Title :   Next-Generation Molecular Histology Using Highly Multiplexed Ion Beam Imaging (MIBI) of Breast Cancer Tissue Specimens for Enhanced Clinical Guidance

Descriptive Note : Technical Report,01 Jul 2014,30 Jun 2015

Corporate Author : University of California, Davis Davis United States

Personal Author(s) : Borowsky,Alexander

Full Text :

Report Date : 01 Jul 2015

Pagination or Media Count : 46

Abstract : Current breast cancer diagnosis includes predictive assays to guide therapy decisions, involving a minimum of 3assays: ER, PR,and HER2. Many labs also include a marker of proliferation (Ki67), and sometimes myoepithelial (SMA), epithelial (CK8/18), and lobular markers(ECAD). Recently, a host of new multi-marker panels developed. The Mammostrat assay (Clarient) uses a panel of five IHC markers (P53,SLC7A5,NRDG1, HTF9C, CEACAM5). Gene-expression assays using qRT-PCR, array hybridization, and RNA sequence assays have also been developed. The OncotypeDX, for example, uses a panel of 21 genes (16 analytical, 5 controls: Ki67, STK15, Survivin, CCNB1, MYBL2, MMP11,CTSL2, HER2, GRB7, GSTM1,CD68, BAG1, ER, PGR, BCL2, SCUBE2, ACTB, GAPDH, RPLPO, GUS, TFRC) to stratify risk of recurrence, and relative benefit of adjuvant chemotherapy. This explosion in biomarkers poses both cost and logical selection challenges. In addition, these assays generally lose all spatial context information (including heterogeneity). MIBI technology provides the potential to simultaneously assay all of the relevant analytes in an intact tissue architecture, with submicron resolution and a greatly expanded dynamic range of quantitation. We propose to develop assays and analysis tools to evaluate breast cancer tissues using formal fixed and paraffin embedded tumor tissues from the clinic, and we will compare the utility of the MIBI platform assays to the current assays. Our objective is to validate MIBI as an alternative to current standard multi-gene assays. We also hypothesize that MIBI breast cancer data will improve the ability to stratify risk and predict therapy responses by taking into account the distribution and heterogeneity of molecularly defined cell populations in breast cancer.

Descriptors :   histology , ion beams , Breast Cancer , tissues (biology) , Diagnosis (Medicine) , assaying , signs and symptoms , genes , neoplasms , pathology , morphology , ribonucleic acids , Hybridization , histochemistry , Immunochemistry , predictions , antigens

Distribution Statement : APPROVED FOR PUBLIC RELEASE