Rutgers researcher receives $1.65M to study cancer mutations, growth


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Photo by Courtesy of Michele Fischer |

X.F. Steven Zheng, a researcher in the Rutgers Cancer Institute of New Jersey, received a $1.65 million grant to study the mechanisms behind cancer mutation and growth. Zheng says only 7 percent of all applications pass.


The National Institutes of Health awarded a Rutgers researcher $1.65 million to explore the mechanisms behind cancer mutation and growth.

“This is considered a major grant for cancer research,” said recipient X.F. Steven Zheng, a researcher in the Rutgers Cancer Institute of New Jersey. “Only 7 percent of all applications pass.”

The grant will mainly be used to research a newly discovered encoding gene, said Zheng, a professor in the Department of Pharmacology at Robert Wood Johnson Medical School?.

This gene, which is expressed in cancer cells, can turn normal cells into malignant cells, he said. Malignant cells are the type of cancer cells that cause harm to the host.

“We did a random screening on yeast and found out how genes are affected by drugs,” he said. “We saw that similar genes existed in humans.”

They use baker’s yeast as a modeling organism to isolate genes that may cause uncontrolled cell growth, he said. Yeast is used because it shares fundamental genetic building blocks with humans.

Yeast has about 6,000 genes encoded in its genome, Zheng said. Humans have about 10 times as many genes.

“In recent years, we realized that amino acids are key chemical signals,” he said. “We call those signals mitogenic signals. A cell has the ability to detect the presence of an amino acid in its surroundings.”

The detection occurs when proteins allow signals to pass using chemical communication in a process called signal transduction.

Amino acid capacity can be used to increase protein synthesis, he said. They use this to experiment on signals that pass through tumor tissues from colon cancer patients.

“Through this, we identified a protein that is over-expressed in tumor cells: Rab1,” he said. “Rab1 is there when the cell is overproduced and enhances certain amino acid signals.”

Quick and uncontrollable growth is a key indicator of a cancer cell and the presence of Rab1, he said. The boosted amino acid signals tell the tumor to grow faster.

They study colon cancer because it is a common form of cancer, he said. Not many effective drugs can treat it, and remedies may be generalized for other types of cancer.

According to the National Cancer Institute website, in 2013, an estimated 102,480 will be diagnosed with colon cancer and 50,830 will die from colon or rectal cancer.

“Colon cancer is one model we started with, but we found evidence this protein is overproduced in other types of cancer, for instance, breast cancer,” Zheng said.

According to the American Cancer Society website, colorectal cancer is the third leading cause of cancer death.

Understanding molecular mechanisms behind tumor formation may make way for diagnosis and therapy, he said. He thinks it is important to find the pathological basis for tumor formation.

The overproduction of Rab1 leaves tumor cells more sensitive to a new class of drugs, he said. These anti-tumor drugs are called mTOR inhibitors.

The mTOR inhibitor is another protein involved in the amino acid signal transduction pathway, he said. Not all tumor cells overproduce mTOR, so the efficiency of drugs targeting this protein is relatively low.

Tumors benefit from using a class of proteins known as cancer drives, he said, and mTOR falls under classification of proteins.

“Tumor cells use cancer drivers similarly to how a car uses a certain type of fuel,” he said. “Highly active concentrations of certain proteins keep the growth engine running smoothly and efficiently.”

Many tumor types can be treated with similar drugs, but the response is dependent on the individual, he said. Each patient has a different profile of mutations and gene expressions.

By finding the mutations, one is able to pinpoint the weakness of a certain type of cancer, he said.

 “It’s a matter of effectively utilizing drugs,” he said. “Before a treatment starts, we want to be able to predict how a patient may respond to a particular treatment so we can avoid unnecessary treatment.”


By Andrew Rodriguez

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