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This Week in Cancer Cell: Jul 22, 2011

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In Cancer Cell this week, researchers in New York and Chicago write that somatic loss-of-function mutations in the ten-eleven translocation 2 gene can lead to increased hematopoietic stem cell self-renewal and myeloid transformation. The Tet2 mutations occur in many patients with myeloid malignancies, the authors add. In this study, the team found that Tet2 loss leads to a "progressive enlargement of the hematopoietic stem cell compartment and eventual myeloproliferation in vivo." In addition, the team saw that Tet2+/- mice displayed increased stem cell self-renewal and extramedullary hematopoiesis.

Also in Cancer Cell this week, Massachusetts researchers report their method for the selective killing of mixed lineage leukemia cells using a potent, small-molecule DOT1L inhibitor. Mislocated enzymatic activity of DOT1L has been suggested as a possible driver of leukemogenesis in mixed lineage leukemia, the authors write. Treatment of MLL cells with a compound called EPZ004777 — a selective inhibitor of DOT1L — resulted in the compound "selectively inhibit[ing] H3K79 methylation and block[ing] expression of leukemogenic genes," the team adds. Exposure to EPZ004777 results in selective killing of those specific cells that carry the MLL gene translocation, with little to no effect on normal cells. In vivo administration of the compound in mice MLL xenograft models increased survival rates in sick mice, the researchers write.

And finally in Cancer Cell this week, researchers in the US and Europe suggest that an "axis" between Pin1 and mutant p53 promotes aggressiveness in breast cancer. Pin1 enhances tumorigenesis in a mouse model and cooperates with mutant p53 in Ras-dependent transformation, the authors write. In breast cancer, Pin1 promotes mutant p53 dependent inhibition of the antimetastatic factor p63, and induces a mutant p53 transcriptional program to increase the aggressiveness of the tumor. "In a cohort of patients, Pin1 overexpression influenced the prognostic value of p53 mutation," the team adds. "These results define a Pin1/mutant p53 axis that conveys oncogenic signals to promote aggressiveness in human cancers."

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Computer Model Uses Genetics, Health Data to Predict Mental Disorders

A new model in JAMA Psychiatry finds combining genetic and health record data can predict a mental disorder diagnosis before one is made clinically.

Study Tracks Off-Target Gene Edits Linked to Epigenetic Features

Using machine learning, researchers characterize in BMC Genomics the potential off-target effects of 19 computed or experimentally determined epigenetic features during CRISPR-Cas9 editing.