• Fully annotated prostate cancer samples including patient outcome
• Low passage number primary human xenografts including: prostate tumors from advanced stages and CRPC, lymph node metastases, bone metastases from prostate tumors
• Matched plasma, serum, RNA stabilized buffy coat and urine
• Flash frozen primary tumor specimens
• All subjects consented for germline sequencing
• Close integration with mouse models, FACS and sequencing facilities
• FACS analysis and sorting of cancer stem cell populations, basal and luminal cell surface profiling.

Primary Tumor derived pancreatic cancer xenografts

• Orthotopic and subcutaneous position
• Matched flash frozen primary tumor specimens
• Novel tumor associated fibroblast cell lines

Orthotopic models

• Human
• Murine syngeneic (from KPC mice)
• Paired cell lines derived from primary/metastases
• Cell based assays for migration/invasion, apoptosis

GEMM models

• Pdx-cre/Kras-LSL (KC) model of pancreatic intraepithelial neoplasia and cancer
• KPC (Kras-p53-re) model of pancreatic cancer

Cell-based models

• Bone marrow stromal cell/ prostate cancer bone niche co-culture model
• Prostate cancer induced-Osteoblast differentiation as in vitro model for osteoblastic lesion formation that is unique to bone metastatic prostate cancer and has no treatment and is associated with significant morbidity and mortality.
• Anchorage-independent growth of primary tumors
• RNA (Q-RT-PCR) and protein (CB1000) expression and isoform analysis of differentiation and signal transduction pathways for prostate cancer stem cells and bone cells

Xenograft in vivo models

• In vivo model for prostate cancer bone metastatic growth and lesion formation: Intra-tibial bone transplantation of prostate tumors into immunodeficient mouse (SCID or Rag2-/-; gamma c chain -/- mice) xenograft models
  • Osteoblastic lesion forming prostate cancer cell lines
  • Primary human prostate cancer xenografts (grown as intra-tibial, subcutaneous or orthotopic tumors)
    • Tumor growth and spontaneous metastasis (Bioluminescence/IVIS)
    • Micro CT and Xray of bone lesion formation and growth
    • Immunofluoresence cytochemistry (IFC) and immunohistochemistry (IHC) of bone/tumor sections on cryosections using Cryojane and paraffin embedded/ EDTA decalcified protocols optimized for bone microenvironment immunostaining.

Genome-wide expression profiling:

• Tumors and niche cells isolated from cell based in vitro and xenograft in vivo based models and primary patient tumors including advanced stages and CRPC, lymph node metastases, bone metastases from prostate tumors.
• Three replicates will be run per sample category
• Total RNA will be extracted from tumors immediately at time of harvest by homogenization in TRIZol reagent followed by Qiagen RNeasy purification to remove residual organics.
• RNA concentration and total yield will be determined with Nanodrop ND1000. Quality, purity and integrity of total RNA will be assessed using Agilent's 2100 BioAnalyzer to measure RNA integrity number (RIN).
• Total RNA (200 ng) will be amplified, biotinylated and hybridized on the newest and most cost effective Human expression chip from Illumina; currently, the Sentrix Human HT-12 v4.0 gene expression BeadChip;
  • For querying the expression of 48,000 human gene targets including all RefSeq genes and known alternative splice regions.
  • Processes 12 samples per chip.
  • RNA labeling and hybridizations will be performed at the Biogem UCSD Illumina Beadchip core facility. Slides will be scanned using Illumina BeadStation and signal extracted using Illumina BeadStudio software (Illumina, San Diego, CA). Prices based on UCSD microarray core pricing
  • http://microarrays.ucsd.edu/illumina/pricing.php

Cell-based models

• Cell migration/invasion
• Anchorage-independent growth
• 2D or 3D tube formation

Zebrafish models

• Embryonic vascular development
• Tailfin regeneration
• Tumor growth

Chick chorioallantoic membrane (CAM) models

• Growth factor-induced angiogenesis
• Tumor growth and spontaneous metastasis

Mouse Models

• Angiogenesis

Physiological angiogenesis (retinal neovascularization)

Matrigel plug assay

Aortic ring sprouting assay

• Cancer

Orthotopic pancreatic, breast, and renal cancer models (syngeneic or xenograft)

Spontaneous pancreatic and breast cancer models (genetically engineered mice)

Primary human xenografts (grown as subcutaneous or orthotopic tumors)

Vascular hyperproliferation (hemangioma/endothelioma)

Experimental metastasis (tail vein or splenic injections)

• Life Technologies SOLiD™ System
• Illumina

• Raw data will be analyzed using Bioconductor packages, www.bioconductor.org. Quality assessment will be performed looking at the interarray Pearson correlation and clustering based on top variant genes to assess overall data coherence. Contrast analysis of differential expression will be performed using the LIMMA package (Smyth et al, [2005]). After linear model fitting, a Bayesian estimate of differential expression will be calculated. Data analysis will be aimed at (1) identifying transcriptional changes in the treated compared to untreated tumors, and (2) the effect of drug treatment on gene expression abnormalities. The threshold for statistical significance will be set at p < 0.005. Gene ontology and functional pathway analysis will be carried out using the Database for Annotation, Visualization and Integrated Discovery (DAVID) and Ingenuity Pathway Analysis, www.ingenuity.com.
  
  In addition, raw data files will be uploaded to an Acuity 4.0 relational database and data management platform for microarray data processing including quality control filtering based on signal threshold, signal-to-noise ratio, normalization, statistical analysis for significance and reproducibility of signal in replicates.
  
  Bioinformatic analysis will also include gene centric (eg. unsupervised hierarchical clustering, supervised k-means clustering, principal components analysis (PCA) and significance analysis of microarrays (SAM). Functional annotation and biopathway analyses using Ingenuity, STEM, DAVID and network module centric analyses using WGCNA, Forest network and hub-genes network modules to identify groups of potentially co-regulated genes.

Arthritis

• Disc Degeneration
• Rabbit anular puncture model (spine, rabbit, rat)
• Thrombin induced disc degeneration model (rabbit, rat)
• DRG implantation model
• Sciatica
• Facet Joint Inflammation model
• Thrombin facet joint model

Cancer

• Xenograft human murine models
• Chondrosarcoma (Subcutaneous and Orthotopic)
• Soft tissue sarcoma

Musculoskeletal Specimen Biorepository

• Bone and Soft tissue tumor collection
• Bone sarcoma specimens
• Metastatic specimen collection
• Normal tissue-skin, fat, muscle, tendon, bone