UCSD Researchers Use Cancer Avatars To Determine Best Treatment For Tumors

Researchers at the University of California, San Diego (UCSD) School of Medicine and Moores Cancer Center are working on virtual cell models that can help drug companies and doctors figure out the best course of action to follow in the treatment of cancer patients.

The researchers are using these models, which they are calling “cancer avatars” to find out which drugs are most effective in fighting a patient’s particular form of cancer. The researchers have created virtual simulations of healthy cells that can be distorted and turned into any kind of cancer cell, which they then dub a patient’s “cancer avatar.” A computer model is then used to predict the drugs that could be used to kill the real cancer. Researchers believe that this process will better help pharmaceutical companies recruit the appropriate cancer patients for their clinical trials.

In their studies, the researchers were able to match the genomic signatures of cancer patients — in particular, those with glioblastoma — and accurately predict sensitivities to various cancer drugs. The researchers have stated that this approach can help maximize patients’ exposure to drugs that is effective against their particular strain of cancer, while minimizing patient’s exposure to the toxic effects of the cancer drugs that would be ineffective.

Sandeep Pingle, who co-led the study, commented on the results and implications for pharmaceutical companies. “Genomics tells us that cancers are a lot like snowflakes. No two cancers are alike so it does not make sense to give all patients the same drugs. This is the idea behind personalizing therapies for cancer,” said Pingle. “With the virtual cell model, we can take into account all the complexity of cellular processes to predict which drugs will be the most effective against a particular tumor based on its genomic profile.”

Pingle went on to say that the results of the study could impact how pharmaceutical companies and doctors ultimately personalize treatments for cancer patients.  Santosh Kesari, and the senior author of the study, said that the computer models could make pharmaceutical research and drug treatment much more cost effective, and it could help clinics identify the best treatment for individual cancer patients.

The researchers at UCSD published their findings in the Journal of Translational Medicine.