Posts Tagged ‘journal’

Rare stem cells hold potential for infertility treatments

Researchers studying infertility in mouse models found that, unlike similar types of cells that develop into sperm, the stem cells that express PAX7 can survive treatment with toxic drugs and radiation. If the findings hold true in people, they eventually could lead to new strategies to restore or protect fertility in men undergoing cancer treatment.

“Unfortunately, many cancer treatments negatively impact fertility, and men who receive such treatments are at high risk of losing their fertility. This is of great concern among cancer patients,” said Dr. Diego H. Castrillon, Associate Professor of Pathology and Director of Investigative Pathology. “The PAX7 stem cells we identified proved highly resistant to cancer treatments, suggesting that they may be the cells responsible for the recovery of fertility following such treatments.”

Infertility, which the Centers for Disease Control estimates affects as many as 4.7 million men in the United States, is a key complication of cancer treatments, such as chemotherapy and radiation therapy.

The new findings, presented in the Journal of Clinical Investigation, provide valuable insight into the process of sperm development. Known as spermatogenesis, sperm development is driven by a population of “immature” stem cells called progenitors in the testes. These cells gradually “mature” into fully differentiated sperm cells. Dr. Castrillon and his team tracked progenitor cells that express the protein PAX7 in mouse testes, and found that these cells gradually give rise to mature sperm.

“We have long known that male fertility is driven by rare stem cells within the testes, but the precise identity of these stem cells has been disputed,” said Dr. Castrillon, who holds the John H. Childers, M.D. Professorship in Pathology. “Our findings suggest that these rare PAX7 cells are the key cells within the testes that are ultimately responsible for male fertility.”

Importantly, even after exposure to toxic chemotherapy or radiation treatments, the PAX7-expressing cells continued to divide and thus could contribute to restoring sperm development.

source :

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

source :

Better classification to improve treatments for breast cancer

Cancer arises due to genetic changes which cause normal cells to develop into tumors. As we learn more about breast cancer, we are seeing that it is not one single disease — the mutations in the genes that cause different cancers are not alike, and this is why tumors respond differently to treatment and grow at different rates. Currently, there are two key markers that clinicians use to predict response to treatments.

Spotting the trends in tumor genetics and creating a system to diagnose tumor types is a primary objective of cancer scientists. To this end, researchers at Cancer Research UK and the University of Cambridge have been developing the IntClust system, which uses genomic technology to create a classification system with enough detail to more accurately pinpoint which type of breast cancer a patient has, and therefore what treatment would be most appropriate.

To test the system, the scientists looked at the 997 tumor samples they had used to develop the system, and 7,544 samples from public databases, along with the genomic and clinical data including data from The Cancer Genome Atlas. They classified these using their IntClust system, and the two main systems in use today — PAM50, which groups cancers into five types, and SCMGENE, which classifies cancer into four.

They found that IntClust was at least as good at predicting patients’ prognosis and response to treatment as the existing system. But the system identified a previously unnoticed subgroup of tumors in just 3.1% of women with very poor survival rates, which appeared to be resistant to treatment. Identifying the genomic signatures for this group could flag up these high risk cancers early, and having the genomic data for these could aid in the investigation of new avenues for treatments for this type of cancer.

At present, using this system to classify tumors would be costly for most clinicians, and interpreting the results requires training that many clinical settings don’t have access to. But the detail and accuracy of this system could be of great use to breast cancer researchers, who will be able to investigate the reasons that certain groups of cancer respond better to certain treatments, in order to find clinical markers, or to identify new targets for breast cancer treatments.

Raza Ali, lead author from Cancer Research UK Cambridge Institute, says: “We have developed an expression-based method for classification of breast tumours into the IntClust subtypes. Our findings highlight the potential of this approach in the era of targeted therapies, and lay the foundation for the generation of a clinical test to assign tumors to IntClust subtypes.”

source :