Microarray Gene Expression Approach May Help Predict Cancer Patient Survival

A new predictive genetic tool is being developed that could help cancer patients and their physicians decide whether follow-up treatments are likely to help.

A 3-year study led by Jerry Shay, professor at the Cell Biology Department of the University of Texas (UT) Southwestern Medical Center (Dallas, TX, USA), investigated effects of irradiation in a lung cancer-susceptible mouse model by looking at gene expression changes, then applying the results to examine disease outcome prediction for human patients with early stage lung or breast cancer. The researchers found that they could predict which patients had a high or low chance of survival.

Carcinogenesis is an adaptive process between nascent tumor cells and their microenvironment, including the modification of inflammatory responses from antitumorigenic to protumorigenic. Radiation exposure can stimulate inflammatory responses that inhibit or promote carcinogenesis, and can damage surrounding healthy tissue. Cancer survival statistics vary depending on the stage of the cancer and when it is diagnosed. The study, described by Delgado O. et al. in Clinical Cancer Research, March 15, 2014, examined the impact of radiation exposure on mouse lung cancer progression in vivo and assessed the clinical relevance of the results to predicting survival rates for human patients. The study offers insight into helping patients assess treatment risks.

“This finding could be relevant to the many thousands of individuals affected by these cancers and could prevent unnecessary therapy,” said Prof. Shay; “We’re trying to find better prognostic indicators of outcomes so that only patients who will benefit from additional therapy receive it.”

The research team found that some types of irradiation resulted in an increase in invasive, more malignant tumors. Gene expression changes in the mice were examined from well before development of advanced cancers. The mouse genes that correlated with poor outcomes were then matched with human genes. Upon comparing the mouse predictive signatures with more than 700 human cancer patient signatures, the overall survivability of the patients correlated with the predictive signature in the mice—the classifier that predicted invasive cancer in mice also predicted poor outcomes in humans. Immunohistochemical analyses suggested that tumor cells drive predictive signature.

The findings predicted overall survival in the patients with early-stage lung- or breast- adenocarcinomas, however the genes were not predictive for patients with lung squamous cell carcinoma. Other types of cancers have yet to be tested. “Personalized medicine is coming,” said Prof. Shay; “I think this is the future—patients looking at their risks of cancer recurrence and deciding what to do next. We can better tailor the treatment to fit the individual. That’s the goal.”

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University of Texas Southwestern Medical Center