Elizabeth Richardson

Gene expression profiling of the tumor microenvironment during breast cancer progression.

IntroductionThe importance of the tumor microenvironment in breast cancer has been increasingly recognized. Critical molecular changes in the tumor stroma accompanying cancer progression, however, remain largely unknown. We conducted a comparative analysis of global gene expression changes in the stromal and epithelial compartments during breast cancer progression from normal to preinvasive to invasive ductal carcinoma.MethodsWe combined laser capture microdissection and gene expression microarrays to analyze 14 patient-matched normal epithelium, normal stroma, tumor epithelium and tumor-associated stroma specimens. Differential gene expression and gene ontology analyses were performed.ResultsTumor-associated stroma undergoes extensive gene expression changes during cancer progression, to a similar extent as that seen in the malignant epithelium. Highly upregulated genes in the tumor-associated stroma include constituents of the extracellular matrix and matrix metalloproteases, and cell-cycle-related genes. Decreased expression of cytoplasmic ribosomal proteins and increased expression of mitochondrial ribosomal proteins were observed in both the tumor epithelium and the stroma. The transition from preinvasive to invasive growth was accompanied by increased expression of several matrix metalloproteases (MMP2, MMP11 and MMP14). Furthermore, as observed in malignant epithelium, a gene expression signature of histological tumor grade also exists in the stroma, with high-grade tumors associated with increased expression of genes involved in immune response.ConclusionsOur results suggest that the tumor microenvironment participates in tumorigenesis even before tumor cells invade into stroma, and that it may play important roles in the transition from preinvasive to invasive growth. The immune cells in the tumor stroma may be exploited by the malignant epithelial cells in high-grade tumors for aggressive invasive growth.

In Situ Proteomic Analysis of Human Breast Cancer Epithelial Cells Using Laser Capture Microdissection: Annotation by Protein Set Enrichment Analysis and Gene Ontology

Identification of molecular signatures that allow detection of the transition from normal breast epithelial cells to malignant invasive cells is a critical component in the development of diagnostic, therapeutic, and preventative strategies for human breast cancer. Substantial efforts have been devoted to deciphering breast cancer etiology at the genome level, but only a limited number of studies have appeared at the proteome level. In this work, we compared individual in situ proteome profiles of nonpatient matched nine noncancerous, normal breast epithelial (NBE) samples with nine estrogen receptor (ER)-positive (luminal subtype), invasive malignant breast epithelial (MBE) samples by combining laser capture microdissection (LCM) and quantitative shotgun proteomics. A total of 12,970 unique peptides were identified from the 18 samples, and 1623 proteins were selected for quantitative analysis using spectral index (SpI) as a measure of protein abundance. A total of 298 proteins were differentially expressed between NBE and MBE at 95% confidence level, and this differential expression correlated well with immunohistochemistry (IHC) results reported in the Human Protein Atlas (HPA) database. To assess pathway level patterns in the observed expression changes, we developed protein set enrichment analysis (PSEA), a modification of a well-known approach in gene expression analysis, Gene Set Enrichment Analysis (GSEA). Unlike single gene-based functional term enrichment analyses that only examines pathway overrepresentation of proteins above a given significance threshold, PSEA applies a weighted running sum statistic to the entire expression data to discover significantly enriched protein groups. Application of PSEA to the expression data in this study revealed not only well-known ER-dependent and cellular morphology-dependent protein abundance changes, but also significant alterations of downstream targets for multiple transcription factors (TFs), suggesting a role for specific gene regulatory pathways in breast tumorigenesis. A parallel GOMiner analysis revealed both confirmatory and complementary data to PSEA. The combination of the two annotation approaches yielded extensive biological feature mapping for in depth analysis of the quantitative proteomic data.

Microproteomic analysis of 10,000 laser captured microdissected breast tumor cells using short-range sodium dodecyl sulfate-polyacrylamide gel electrophoresis and porous layer open tubular liquid chromatography tandem mass spectrometry

Precise proteomic profiling of limited levels of disease tissue represents an extremely challenging task. Here, we present an effective and reproducible microproteomic workflow for sample sizes of only 10,000 cells that integrates selective sample procurement via laser capture microdissection (LCM), sample clean-up and protein level fractionation using short-range SDS-PAGE, followed by ultrasensitive LC-MS/MS analysis using a 10 μm i.d. porous layer open tubular (PLOT) column. With 10,000 LCM captured mouse hepatocytes for method development and performance assessment, only 10% of the in-gel digest, equivalent to ∼1000 cells, was needed per LC-MS/MS analysis. The optimized workflow was applied to the differential proteomic analysis of 10,000 LCM collected primary and metastatic breast cancer cells from the same patient. More than 1100 proteins were identified from each injection with >1700 proteins identified from three LCM samples of 10,000 cells from the same patient (1123 with at least two unique peptides). Label free quantitation (spectral counting) was performed to identify differential protein expression between the primary and metastatic cell populations. Informatics analysis of the resulting data indicated that vesicular transport and extracellular remodeling processes were significantly altered between the two cell types. The ability to extract meaningful biological information from limited, but highly informative cell populations demonstrates the significant benefits of the described microproteomic workflow.

Integrated Proteomic, Transcriptomic, and Biological Network Analysis of Breast Carcinoma Reveals Molecular Features of Tumorigenesis and Clinical Relapse

Gene and protein expression changes observed with tumorigenesis are often interpreted independently of each other and out of context of biological networks. To address these limitations, this study examined several approaches to integrate transcriptomic and proteomic data with known protein-protein and signaling interactions in estrogen receptor positive (ER+) breast cancer tumors. An approach that built networks from differentially expressed proteins and identified among them networks enriched in differentially expressed genes yielded the greatest success. This method identified a set of genes and proteins linking pathways of cellular stress response, cancer metabolism, and tumor microenvironment. The proposed network underscores several biologically intriguing events not previously studied in the context of ER+ breast cancer, including the overexpression of p38 mitogen-activated protein kinase and the overexpression of poly(ADP-ribose) polymerase 1. A gene-based expression signature biomarker built from this network was significantly predictive of clinical relapse in multiple independent cohorts of ER+ breast cancer patients, even after correcting for standard clinicopathological variables. The results of this study demonstrate the utility and power of an integrated quantitative proteomic, transcriptomic, and network analysis approach to discover robust and clinically meaningful molecular changes in tumors.

Abstract P6-01-10: Prognostic significance of breast cancer index (BCI) in node-positive hormone receptor positive early breast cancer: NCIC CTG MA.14

Background: The continuous linear Breast Cancer Index (BCI) risk index combines the ratio of genes HOXB13 to IL17BR (H/I) and the molecular grade index (MGI) (Zhang et al, Clinical Cancer Research, 2013). The BCI signature was developed for node-negative breast cancer patients treated with tamoxifen. We examine here whether linear BCI is prognostic for node-positive hormone-receptor positive tamoxifen-treated patients. Methods: MA.14 randomly assigned 667 hormone positive (HR+), postmenopausal women to 5 years of tamoxifen (TAM) +/- 2 years of octreotide LAR (TAM-OCT). A representative subgroup of 299 patients underwent gene expression profiling by RT-PCR for linear BCI. We performed exploratory analyses restricted to node positive patients. The primary objective was to assess the prognostic effect of BCI on relapse-free survival (RFS). RFS was defined as the time from randomization to the time of recurrence of the primary disease alone, including local and ipsilateral nodal recurrence and metastatic disease, and censoring at longest follow-up or death from another cause. With a median 9.8 years follow-up, the association of BCI with RFS was assessed by multivariate Cox regression including treatment, stratification factors (other than nodal status), and baseline patient and tumor characteristics. Patients were defined to be low risk based on BCI if the adjusted Cox survival was >95%, where adjustment was by trial treatment, stratification factors, and baseline patient and tumor characteristics, including IGF-1, IGFBP-3, and C-peptide. Results: 292 of 299 patient samples passed internal analytical quality control; 116 node positive ER+ve patients had 34 (29.3%) relapses, with adjusted Cox survival at 9.6 years of 87.8%. Fifty-two of the 116 patients (45%) did not receive adjuvant chemotherapy, and experienced 11 (21%) RFS events. In the 116 patients, higher continuous BCI value was associated with shorter RFS (p=0.002): hazard ratio (HR) 1.49 (95% CI 1.16-1.91). Smaller pathologic T had significantly (p=0.03) better RFS HR=0.39, (95%CI 0.17-0.90). With MA.14 patient mean BCI of 5.09532, Cox survival at 4.1 years was 95.2%; 17/34 (50%) who recurred had failed by this time. Discussion: In this subgroup analysis, we found that BCI and tumor size were significant prognostic factors for node-positive hormone-receptor positive patients who were treated with tamoxifen. Citation Format: Dennis Sgroi, Paul Goss, Judy-Anne Chapman, Elizabeth Richardson, Shemeica Binns, Yi Zhang, Cathy Schnabel, Mark Erlander, Kathy Pritchard, Lei Han, Lois Sheperd, Michael Pollack. Prognostic significance of breast cancer index (BCI) in node-positive hormone receptor positive early breast cancer: NCIC CTG MA.14 [abstract]. In: Proceedings of the Thirty-Seventh Annual CTRC-AACR San Antonio Breast Cancer Symposium: 2014 Dec 9-13; San Antonio, TX. Philadelphia (PA): AACR; Cancer Res 2015;75(9 Suppl):Abstract nr P6-01-10.

Developing and Implementing Performance Outcome Assessments: Evidentiary, Methodologic, and Operational Considerations

The use of performance outcome (PerfO) assessments to measure cognitive or physical function in drug trials presents several challenges for both sponsors and regulators, owing in part to a relative lack of scientific guidance on their development, implementation, and interpretation. In December 2016, the Duke-Margolis Center for Health Policy convened a 2-day workshop to explore the evidentiary, methodologic, and operational challenges associated with PerfO measures, and to identify potential paths to addressing these challenges. This paper presents both a summary of the discussion as well as additional input from a working group of experts from FDA, industry, academia, and public-private consortia. It is intended to advance the discussion around the development and use of PerfO measures to assess patient functioning in clinical trials intended to support registration of new treatments, and to highlight the key gaps in knowledge where additional research, collaboration, and discussion are needed.

Regional Approaches to Expedited Drug Development and Review: Can Regulatory Harmonization Improve Outcomes?

Drug regulatory agencies around the world have implemented programs to expedite drug development and review for promising new products for serious diseases. These programs are all intended to minimize delays in patient access to innovative medicines, and have used broadly similar strategies to shorten drug development and review timelines. However, they differ in many key respects, and some stakeholders have suggested that these differences create unnecessary barriers in the development and approval process, possibly leading to delays in access. In collaboration with FDA, the Duke-Margolis Center for Health Policy convened an expert workshop to elicit feedback from a broad range of stakeholders as to whether a lack of harmonization across expedited programs is interfering with the efficient development of new products and, if so, to explore strategies for addressing these challenges. This report provides a summary of key themes and major findings from that discussion.