Genetic Variation and Drug Response

Imagine taking a patients skin cells, using them to derive induced pluripotent stem cells [iPSCs], differentiating the stem cells to produce cells of a particular type, and then exposing the differentiated cells to drugs that the patient might be given, suggests Russ B. Altman, M.D., Ph.D., professor of bioengineering, genetics, medicine, and biomedical data science at Stanford University. Such procedures might detect the potential for drug-induced toxicity and reduce the incidence of serious side-effects in the clinical setting.

The ability to predict adverse effects is particularly important for therapeutic agents that are associated with a high likelihood of failure or adverse effects. Predicting adverse effects could also help tailor treatments in a more rational manner.

An example of a drug with a challenging adverse effect profile is doxorubicin. This chemotherapeutic agent is known to be cardiotoxic in some patients, but predicting which patients are at risk is difficult. In fact, no reliable means of predicting doxorubicin-induced cardiotoxicity (DIC) exists, so the drug cannot be administered with confidence.

In a recent study conducted in collaboration with Dr. Paul Burridge from Northwestern University School of Medicine and Dr. Joseph Wu from Stanford Cardiovascular Institute, and other colleagues, bioinformatics analyses performed by Dr. Altmans group were critical to show that patient-specific human induced pluripotent stem cell-derived cardiomyocytes can recapitulate at the single-cell level the predilection to develop doxorubicin-induced cardiotoxicity.

It was pretty straightforward, on the informatics side, to show a correlation between the cellular responses and the clinical responses, asserts Dr. Altman. This correlation is incredibly exciting.

Human iPSCs obtained from female patients with breast cancer and matched with healthy volunteers were differentiated into cardiomyocytes. RNA-Seq and microarray analyses were subsequently used to profile and compare gene-expression changes in the cardiomyocytes derived from the healthy volunteers and in those from the breast cancer patients with and without clinical DIC. Cells derived from patients presenting clinical DIC were more sensitive to therapy, exhibited increased metabolic stress and reactive oxygen species, and had impaired intracellular calcium signaling, as compared to cells derived from patients who did not show clinical DIC.

Using microarray analyses to examine gene-expression perturbations in response to various doxorubicin concentrations, this study revealed that in vitro, the cardiomyocytes recapitulated patients predilection to DIC. The study also indicated that genetic and molecular analyses could provide a powerful tool to predict clinical toxicity to therapeutic agents.

The findings in the research setting are very intriguing, comments Dr. Altman. There is a lot of engineering to make them more reliable and reproducible.

Even though stem cell studies have shown a lot of promise, reproducibility has been particularly challenging, and results from different labs may vary depending on multiple factors, including small differences in experimental protocols and the versions of the stem cells used by various labs, for which it is very difficult to show equivalency.

The work is only half complete when the research is published, Dr. Altman concludes. Lots of details need to be addressed before this can be put into routine clinical use.

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Gene Expression's Big Rethink - Genetic Engineering & Biotechnology News