Serine hydroxymethyltransferase 2 expression promotes tumorigenesis in rhabdomyosarcoma with 12q13-q14 amplification.

The 12q13-q14 chromosomal region is recurrently amplified in 25% of fusion-positive (FP) rhabdomyosarcoma (RMS) cases and is associated with a poor prognosis. To identify amplified oncogenes in FP RMS, we compared the size, gene composition, and expression of 12q13-q14 amplicons in FP RMS with those of other cancer categories (glioblastoma multiforme, lung adenocarcinoma, and liposarcoma) in which 12q13-q14 amplification frequently occurs. We uncovered a 0.2 Mb region that is commonly amplified across these cancers and includes CDK4 and 6 other genes that are overexpressed in amplicon-positive samples. Additionally, we identified a 0.5 Mb segment that is only recurrently amplified in FP RMS and includes 4 genes that are overexpressed in amplicon-positive RMS. Among these genes, only serine hydroxymethyltransferase 2 (SHMT2) was overexpressed at the protein level in an amplicon-positive RMS cell line. SHMT2 knockdown in amplicon-positive RMS cells suppressed growth, transformation, and tumorigenesis, whereas overexpression in amplicon-negative RMS cells promoted these phenotypes. High SHMT2 expression reduced sensitivity of FP RMS cells to SHIN1, a direct SHMT2 inhibitor, but sensitized cells to pemetrexed, an inhibitor of the folate cycle. In conclusion, our study demonstrates that SHMT2 contributes to tumorigenesis in FP RMS and that SHMT2 amplification predicts differential response to drugs targeting this metabolic pathway.

Imputing HbA1c from capillary blood glucose levels in patients with type 2 diabetes in Sri Lanka: a cross-sectional study.

OBJECTIVE: To develop a population-specific methodology for estimating glycaemic control that optimises resource allocation for patients with diabetes in rural Sri Lanka. DESIGN: Cross-sectional study. SETTING: Trincomalee, Sri Lanka. PARTICIPANTS: Patients with non-insulin-treated type 2 diabetes (n=220) from three hospitals in Trincomalee, Sri Lanka. OUTCOME MEASURE: Cross-validation was used to build and validate linear regression models to identify predictors of haemoglobin A1c (HbA1c). Validation of models that regress HbA1c on known determinants of glycaemic control was thus the major outcome. These models were then used to devise an algorithm for categorising the patients based on estimated levels of glycaemic control. RESULTS: Time since last oral intake other than water and capillary blood glucose were the statistically significant predictors of HbA1c and thus included in the final models. In order to minimise type II error (misclassifying a high-risk individual as low-risk or moderate-risk), an algorithm for interpreting estimated glycaemic control was created. With this algorithm, 97.2% of the diabetic patients with HbA1c ≥9.0% were correctly identified. CONCLUSIONS: Our calibrated algorithm represents a highly sensitive approach for detecting patients with high-risk diabetes while optimising the allocation of HbA1c testing. Implementation of these methods will optimise the usage of resources devoted to the management of diabetes in Trincomalee, Sri Lanka. Further external validation with diverse patient populations is required before applying our algorithm more widely.

The impact of maternal adiposity specialization on infant birthweight: upper versus lower body fat.

BACKGROUND: The specialization of human fat deposits is an inquiry of special importance in the study of fetal growth. It has been theorized that maternal lower-body fat is designated specifically for lactation and not for the growth of the fetus. OBJECTIVE: Our goal was to compare the contributions of maternal upper-body versus lower-body adiposity to infant birth weight. We hypothesized that upper-body adiposity would be strongly associated with infant birth weight and that lower-body adiposity would be weakly or negligibly associated with infant birth weight-after adjusting for known determinants. STUDY DESIGN: In this prospective cohort study, 355 women initiated medical pre-natal care during the first trimester of pregnancy at The University of Oklahoma Health Sciences Center during 1990-1993. Maternal anthropometric measurements were assessed at the first clinic visit: (a) height; (b) weight; (c) circumferences of the upper arm, forearm, and thigh; and, (d) skin-fold measurements of the bicep, subscapular region, and thigh. RESULTS: Infant birth weight was regressed on known major determinants to create the foundational model. Maternal anthropometric variables subsequently were added one at a time into this multiple regression model. The highest contribution by a single anthropometric variable to infant birthweight was, in order: subscapular skin-fold, forearm circumference, and thigh circumference. With one upper-body (subscapular skin-fold) and one lower-body (circumference of the thigh) adiposity measure in the model, the z-score regression coefficient (s.e.) was 85.7g (30.8) [p=0.0057] for maternal subscapular skin-fold and 19.0g (31.6) [p=0.5477] for circumference of the thigh. When the second-best upper-body contributor to infant birthweight (circumference of the forearm) was entered with one lower-body measure into the model, the z-score regression coefficient (s.e.) was 77.5g (38.5) [p=0.0451] for maternal forearm circumference and 14.1g (38.5) [p=0.7146] for circumference of the thigh. When both subscapular skinfold and forearm circumference were added to the model in place of BMI, the explained variance (r2=0.5478) was similar to the model using BMI (r2=0.5487). CONCLUSION: Upper-body adiposity - whether operationalized by subscapular skin-fold or circumference of the forearm - was a markedly larger determinant of infant birth weight than lower-body adiposity.