Posts Tagged ‘real’

Enzyme controlling metastasis of breast cancer identified

“The take-home message of the study is that we have found a way to target breast cancer metastasis through a pathway regulated by an enzyme,” said lead author Xuefeng Wu, PhD, a postdoctoral researcher at UC San Diego.

The enzyme, called UBC13, was found to be present in breast cancer cells at two to three times the levels of normal healthy cells. Although the enzyme’s role in regulating normal cell growth and healthy immune system function is well-documented, the study is among the first to show a link to the spread of breast cancer.

Specifically, Wu and colleagues with the UC San Diego Moores Cancer Center found that the enzyme regulates cancer cells’ ability to transmit signals that stimulate cell growth and survival by regulating the activity of a protein called p38 which when “knocked down” prevents metastasis. Of clinical note, the researchers said a compound that inhibits the activation of p38 is already being tested for treatment of rheumatoid arthritis.

In their experiments, scientists took human breast cancer cell lines and used a lentivirus to silence the expression of both the UBC13 and p38 proteins. These altered cancer cells were then injected into the mammary tissues of mice. Although the primary tumors grew in these mice, their cancers did not spread.

“Primary tumors are not normally lethal,” Wu said. “The real danger is cancer cells that have successfully left the primary site, escaped through the blood vessels and invaded new organs. It may be only a few cells that escape, but they are aggressive. Our study shows we may be able to block these cells and save lives.”

Researchers have also defined a metastasis gene signature that can be used to evaluate clinical responses to cancer therapies that target the metastasis pathway.

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New tool aids stem cell engineering for medical research

“This free platform has a broad range of uses for all types of cell-based investigations and can potentially offer help to people working on all types of cancer,” says Hu Li, Ph.D., investigator in the Mayo Clinic Center for Individualized Medicine and Department of Molecular Pharmacology & Experimental Therapeutics, and co-lead investigator in the two works. “CellNet will indicate how closely an engineered cell resembles the real counterpart and even suggests ways to adjust the engineering.”

The network biology platform contains data on a wide range of cells and details on what is known about those cell types. Researchers say the platform can be applied to almost any study and allows users to refine the engineering process. In the long term, it should provide a reliable short cut to the early phases of drug development, individualized cancer therapies, and pharmacogenetics.

CellNet uses 21 cell types and tissues and data from 56 published human and mouse engineering studies as a basis for analyzing and predicting cell fate and corresponding engineering strategies. The platform also offers classification scores to determine differentiation and conversion of induced pluripotent stem cells. It reveals incomplete conversion of engineered microphages and hepatocytes. CellNet can be used for interrogation of cell fate following expression profiling, by classifying input by cell type, quantifying gene regulatory network status, and identifying aberrant regulators affecting the engineering process. All this is valuable in predicting success of engraftment of cancer tumors in mouse avatars for cancer and drug development research. CellNet can be accessed at

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RNA sequence could help doctors to tailor unique prostate cancer treatment programs

Colin Collins and Alexander Wyatt, and other researchers from the Vancouver Prostate Centre at the Vancouver Coastal Health Research Institute, matched 25 patients’ treatment outcomes with the RNA sequence of their prostate cancer tumors. They suggest that similarities between the RNA of some of the patients’ tumors could open up new avenues of treatment.

Prostate cancer is the fourth most common cancer worldwide, but can be effectively managed. Doctors normally recommend a combination of therapies, because patients’ reaction to treatment varies considerably. The side-effects of these treatments can be significant, so current research is focused around precision medicine — classifying patients on their tumor’s molecular changes, and only giving them the treatments that are expected to be most effective.

To investigate variations between the highest risk cases of prostate cancer, researchers conducted a range of genomic analyses, including sequencing the RNA in 25 patients’ prostate tumors. The RNA molecules direct which proteins the cell produces, so the RNA sequences show how tumor cells behave differently to normal cells.

Alexander Wyatt, Vancouver Prostate Centre, says: “Most genomic sequencing studies have focused on the DNA, which gives us important information about a tumor’s history. In our study we examined RNA, which tells us which genes are being used and are disrupted at the time the tumor was collected.”

They then matched up this data with the detailed follow-up information that they had for each of the patients. They were then able to see what sequence disruptions were associated with a positive reaction to different therapies, and they believe this could aid personalized medicine.

Alexander Wyatt says: “We were surprised by the sheer number of genomic differences between patients. This complexity may help explain why patients respond differently to treatment, and why some tumors grow faster than others. The more we understand tumor-to-tumor variability, the closer we come to accurately tailoring a patient’s management specifically for his own tumor. Overall, this is a very exciting time for cancer research, as global sequencing efforts mean we are advancing towards precision oncology.”

Another potential use of this information is that in certain groups, there was a similarity in the type of genes and pathways that were disrupted in the tumors. This might indicate an underlying cancer mechanism that could be exploited to create new cancer treatments.

Alexander Wyatt says: “Despite the enormous complexity between patients at the individual gene level, when we examined the functions of affected genes, clear commonalities between groups of patients emerged. Ultimately it may be possible to exploit this convergent biology.”

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