Most prostate cancers are adenocarcinomas that develop from the gland cells that produce seminal fluid.

New oncology drugs only have a 3.4% success rate once making it from Phase I clinical trials to FDA approval. This is the lowest success rate among the 21 major disease indications.1 This poor success rate leaves everyone, from the patients to the benchtop scientists, questioning the validity of the models and the resulting data.

Growth of tumor cells within the bone marrow is a common phenomenon in hematologic malignancies.

In this Tech Spotlight we will present an overview of some of the different non-invasive mouse imaging modalities we offer and the advantages of these services in biodistribution studies, monitoring of treatment efficacy and molecular target assessment of disease in tumor models.

2D in vitro assays do not reflect the tumor architecture and environment found in vivo, resulting in differences in the outcomes and pharmacological potencies of test agents being screened when comparing 2D in vitro versus in vivo models.

Animal models can be very powerful tools for analyzing the development and progression of cancer. One example of this is the transgenic polyoma middle T oncoprotein (PyMT) model.

Given the poor prognosis (28 percent five-year survival rate) for patients with advanced stage disease (stage III or IV), predictive preclinical models are needed for reliably evaluating treatment options. In most cases of advanced prostate cancer surgical resection is the optimal treatment plan.

Because so many novel approaches to treat pancreatic cancer have met with disappointing clinical outcomes, many drug discovery scientists have turned to immunotherapy in pancreatic cancer based on the recent successes of harnessing the immune system to treat other cancers.

The in vivo efficacy of small molecule inhibitors is often assessed by measuring the phosphorylation state of cell signaling proteins within the pathways the drug targets. This can be a challenge when analysis of solid tumors is required.

While preclinical oncology studies aim to mimic human diseases in an animal setting, the greatest challenge researchers face is the translatability of the results. An ideal preclinical tumor model would not only employ the specific cancer subtype but would also simulate the complex microenvironment required for tumorigenesis.