Memorial Sloan Kettering Cancer Center

About: Memorial Sloan Kettering Cancer Center is a healthcare organization based out in New York, New York, United States. It is known for research contribution in the topics: Cancer & Population. The organization has 30293 authors who have published 65381 publications receiving 4462534 citations. The organization is also known as: MSKCC & New York Cancer Hospital.

PRDM16 controls a brown fat/skeletal muscle switch

Abstract: Brown fat can increase energy expenditure and protect against obesity through a specialized program of uncoupled respiration. Here we show by in vivo fate mapping that brown, but not white, fat cells arise from precursors that express Myf5, a gene previously thought to be expressed only in the myogenic lineage. We also demonstrate that the transcriptional regulator PRDM16 (PRD1-BF1-RIZ1 homologous domain containing 16) controls a bidirectional cell fate switch between skeletal myoblasts and brown fat cells. Loss of PRDM16 from brown fat precursors causes a loss of brown fat characteristics and promotes muscle differentiation. Conversely, ectopic expression of PRDM16 in myoblasts induces their differentiation into brown fat cells. PRDM16 stimulates brown adipogenesis by binding to PPAR-gamma (peroxisome-proliferator-activated receptor-gamma) and activating its transcriptional function. Finally, Prdm16-deficient brown fat displays an abnormal morphology, reduced thermogenic gene expression and elevated expression of muscle-specific genes. Taken together, these data indicate that PRDM16 specifies the brown fat lineage from a progenitor that expresses myoblast markers and is not involved in white adipogenesis.

Structure of the MDM2 oncoprotein bound to the p53 tumor suppressor transactivation domain.

Abstract: The MDM2 oncoprotein is a cellular inhibitor of the p53 tumor suppressor in that it can bind the transactivation domain of p53 and downregulate its ability to activate transcription. In certain cancers, MDM2 amplification is a common event and contributes to the inactivation of p53. The crystal structure of the 109-residue amino-terminal domain of MDM2 bound to a 15-residue transactivation domain peptide of p53 revealed that MDM2 has a deep hydrophobic cleft on which the p53 peptide binds as an amphipathic alpha helix. The interface relies on the steric complementarity between the MDM2 cleft and the hydrophobic face of the p53 alpha helix and, in particular, on a triad of p53 amino acids-Phe19, Trp23, and Leu26-which insert deep into the MDM2 cleft. These same p53 residues are also involved in transactivation, supporting the hypothesis that MDM2 inactivates p53 by concealing its transactivation domain. The structure also suggests that the amphipathic alpha helix may be a common structural motif in the binding of a diverse family of transactivation factors to the TATA-binding protein-associated factors.

A coding-independent function of gene and pseudogene mRNAs regulates tumour biology

Abstract: The canonical role of messenger RNA (mRNA) is to deliver protein-coding information to sites of protein synthesis. However, given that microRNAs bind to RNAs, we hypothesized that RNAs could possess a regulatory role that relies on their ability to compete for microRNA binding, independently of their protein-coding function. As a model for the protein-coding-independent role of RNAs, we describe the functional relationship between the mRNAs produced by the PTEN tumour suppressor gene and its pseudogene PTENP1 and the critical consequences of this interaction. We find that PTENP1 is biologically active as it can regulate cellular levels of PTEN and exert a growth-suppressive role. We also show that the PTENP1 locus is selectively lost in human cancer. We extended our analysis to other cancer-related genes that possess pseudogenes, such as oncogenic KRAS. We also demonstrate that the transcripts of protein-coding genes such as PTEN are biologically active. These findings attribute a novel biological role to expressed pseudogenes, as they can regulate coding gene expression, and reveal a non-coding function for mRNAs.

Multiclass cancer diagnosis using tumor gene expression signatures

Abstract: The optimal treatment of patients with cancer depends on establishing accurate diagnoses by using a complex combination of clinical and histopathological data. In some instances, this task is difficult or impossible because of atypical clinical presentation or histopathology. To determine whether the diagnosis of multiple common adult malignancies could be achieved purely by molecular classification, we subjected 218 tumor samples, spanning 14 common tumor types, and 90 normal tissue samples to oligonucleotide microarray gene expression analysis. The expression levels of 16,063 genes and expressed sequence tags were used to evaluate the accuracy of a multiclass classifier based on a support vector machine algorithm. Overall classification accuracy was 78%, far exceeding the accuracy of random classification (9%). Poorly differentiated cancers resulted in low-confidence predictions and could not be accurately classified according to their tissue of origin, indicating that they are molecularly distinct entities with dramatically different gene expression patterns compared with their well differentiated counterparts. Taken together, these results demonstrate the feasibility of accurate, multiclass molecular cancer classification and suggest a strategy for future clinical implementation of molecular cancer diagnostics.