Toward More Predictive Tests for Cancer Therapies

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Understanding Clinical Drug Resistance at the Cellular Level

Ninety-five percent of experimental cancer treatments fail during clinical development, with seventy percent of them proving to be ineffective in late phase II clinical trials. The scientific community agrees that a major reason for this failure is the lack of preclinical models that can accurately mimic cancer and predict how well a compound may work in people.

Historically, preclinical testing of therapies has used xenografts initiated with tumor cell lines that are extensively passaged in the artificial environment of in vitro culture. The xenografts become highly uniform clones that do not reflect the complicated and heterogeneous nature of cancer. When cultured, cancer cells no longer behave as they did inside a body and do not experience any of the intricate influences that affect natural tumor cells, such as interactions with other cell types. Tumor models based on cancer cell lines, therefore, may not allow researchers to understand the complex nature of how tumor cells function and how to thwart them.

Patient-derived xenograft (PDX) models are allowing researchers to better reflect the natural composition of tumors. PDX models are generated using freshly excised patient tumor biopsies that are transplanted directly into immunocompromised mice—without any intervening cell culture step. In addition, PDX models replicate normal tumor surroundings, including blood vessels, support cells, and oxygen and nutrient levels. Despite these advantages, PDX models are far from perfect. The implanted fragments are from different parts of the tumor biopsy and do not contain the entire diversity of tumor cell types.

SRI Biosciences researchers have developed a more advanced version of this model, called PDCellX™, using dissociated tumor cells from the entire biopsy sample instead of tumor fragments. By using primary tumor cell suspensions, PDCellX models better reconstitute the diverse cellular composition of human cancer and enable investigators to understand tumor cell subpopulations that naturally occur in every cancer. PDCellX models retain the histological and molecular phenotypes of the patient biopsy and retain primary tumor properties and metastatic behavior.

PDCellX models offer a number of advantages over standard, fragment-initiated PDX models. These advantages simulate a more realistic tumor composition for testing novel therapies before progressing to patients. PDCellX models allow researchers to investigate mechanisms of drug resistance, which are notoriously difficult to study due to their complexity. The models also permit researchers to measure and characterize the tumor cells that escape into the circulatory system or migrate to other tissues, and compare their phenotype to the original tumor cells at the primary site.

The search for effective cancer therapies requires preclinical investigation to select the best candidates to advance into the clinic. PDCellX models allow extensive testing of antitumor agents at the cellular level to determine if a drug is hitting its target—before it is advanced to expensive, time-consuming clinical trials that potentially expose patients to ineffective therapies and risk. Moreover, PDCellX models enable optimized tumor engraftment and growth rate with shorter study timeframes than fragment-based PDX models, shortening schedules and reducing costs.

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