Clients in the News – UCLA Researchers Observe Thermodynamics May Help to Better Understand Cancers

This figure illustrates that genes that up-regulated in the lung cancer state are down regulated in normal controls, and genes that are highly up-regulated in the normal controls are down-regulated in the lung cancer state. There is a clear, correlated gene-expression behavior present that not only characterizes the lung cancer state but can also be used to distinguish cancer patients from non-cancer patients. (Source: UCLA/PNAS/Sohila Zadran, Raphael Levine, Francoise Remacle)

When the “war on cancer” was declared with the signing of the National Cancer Act in 1971, identifying potential physical traits, or biomarkers, that would allow doctors to detect the disease early on was a significant goal. To this day, progress in the battle against cancer depends on understanding the underlying causes and molecular mechanisms of the disease.

In a new study, UCLA researchers analyzed the gene-expression profiles of more than 2,000 patients and were able to identify cancer-specific gene signatures for breast, lung, prostate and ovarian cancers. The study applied an innovative approach to gene-array analysis known as “surprisal analysis,” which uses the principles of thermodynamics— the study of the relationship between different forms of energy— to understand cellular processes in cancer.

The research appears in the early online edition of Proceedings of the National Academy of Sciences and will be published in an upcoming print edition.

Surprisal analysis allows researchers to observe how cellular energy is expended in cancer cells and how this process affects the way in which these cells choose to express certain genes. In particular, scientists can look at how cancer cells decide to use energy when expressing critical genes that allow them to persist and grow.

By identifying such cancer-specific gene signatures, scientists are able to distinguish, with high fidelity, the biopsy samples of cancer patients from control samples and potentially to identify novel cancer biomarkers for early detection of the disease and the development of new therapies.

Research co-author Raphael Levine, a UCLA distinguished professor of chemistry and biochemistry and of medical and molecular pharmacology, and his fellow researchers hope the cancer-specific signatures they identify using surprisal analysis will provide “thermodynamic targets” against cancer.

“We believe that this paper introduces a new hallmark of cancer— a thermodynamic signature— where the free energy redistributions among cellular biomolecules in the cancer state, not seen in the non-cancer state, sustain the disease,” says Levine, a faculty member in the UCLA College of Letters and Science. “A further, future power of surprisal analysis is in its ability to detect ‘patient potentials,’ meaning patient-specific differences can be detected in the analysis, reintroducing the possibility of personalized medicine to the cancer arena.”