Citations

Antimicrobial stewardship programs (ASPs) are an effective strategy for improving the quality and safety of antimicrobial prescribing for hospitalized patients. Pediatric ASPs are in their early stages of development, and there are unique issues relevant to children. The imperative to ensure that antimicrobials are prescribed judiciously is highlighted by the ongoing epidemic increase in antimicrobial‐resistant infections and the simultaneous decline in the rate of new drug development. In this review we describe the process of ASP development for pediatrics, review existing data regarding the impact of pediatric ASPs, and describe the priorities and challenges for ASP research including study design and appropriate end points.

Background— Hundreds of nonsynonymous single nucleotide variants (nsSNVs) have been identified in the 2 most common long-QT syndrome-susceptibility genes ( KCNQ1 and KCNH2 ). Unfortunately, an ≈3% background rate of rare KCNQ1 and KCNH2 nsSNVs amongst healthy individuals complicates the ability to distinguish rare pathogenic mutations from similarly rare yet presumably innocuous variants. Methods and Results— In this study, 4 tools [(1) conservation across species, (2) Grantham values, (3) sorting intolerant from tolerant, and (4) polymorphism phenotyping] were used to predict pathogenic or benign status for nsSNVs identified across 388 clinically definite long-QT syndrome cases and 1344 ostensibly healthy controls. From these data, estimated predictive values were determined for each tool independently, in concert with previously published protein topology–derived estimated predictive values, and synergistically when ≥3 tools were in agreement. Overall, all 4 tools displayed a statistically significant ability to distinguish between case-derived and control-derived nsSNVs in KCNQ1 , whereas each tool, except Grantham values, displayed a similar ability to differentiate KCNH2 nsSNVs. Collectively, when at least 3 of the 4 tools agreed on the pathogenic status of C-terminal nsSNVs located outside the KCNH2/Kv11.1 cyclic nucleotide−binding domain, the topology-specific estimated predictive value improved from 56% to 91%. Conclusions— Although in silico prediction tools should not be used to predict independently the pathogenicity of a novel, rare nSNV, our results support the potential clinical use of the synergistic utility of these tools to enhance the classification of nsSNVs, particularly for Kv11.1’s difficult to interpret C-terminal region.

Background: The results of surgical treatment for femoroacetabular impingement have been increasingly reported, and more advanced intra-articular disease has been identified as an important predictive factor of outcome. Yet, the reliability of arthroscopic hip disease classification has not been well defined. Purpose/Hypothesis: To determine the intraobserver and interobserver reliability of the Beck classification of labral and articular cartilage disease (anterior-superior acetabular rim) encountered in hip arthroscopy. Secondly, we identified the sources of poor reliability that may be improved with future disease classification schemes. Our hypothesis was that the Beck classification of labral and chondral lesions would demonstrate substantial reliability, while the differentiation of early forms of disease would be a common source of disagreement. Study Design: Cohort study; Level of evidence, 3. Methods: Four experienced hip arthroscopists reviewed standardized arthroscopic videos of 40 cases. Arthroscopic findings at the anterior-superior acetabular rim were classified using the Beck classification of labral and articular cartilage disease. Repeat classification of videos was performed at least 2 weeks later. The reliability of arthroscopic classification was defined using the average weighted Cohen κ values and agreement rates. Results: Arthroscopic classification of labral disease using the Beck classification demonstrated moderate to substantial interobserver reliability (average κ = .62; range, .48-.78) and an overall agreement rate of 81.7%. Intraobserver reliability showed a similar level of reliability (average κ = .65; agreement rate, 80.6%). The differentiation between labral degeneration and labral detachment was a common source of disagreement. Similarly, the Beck classification of articular cartilage disease had moderate to substantial interobserver reliability (average κ = .65; range, .49-.78) and overall agreement rate of 57.5%. Intraobserver reliability showed a slightly better level of reliability (average κ = .80; agreement rate, 77.5%). The differentiation between articular cartilage malacia and debonding was a common source of disagreement. Conclusion: The arthroscopic classification of acetabular rim disease with the Beck classification has substantial interobserver reliability. This level of reliability is similar to previously reported arthroscopic disease classifications in the knee and shoulder and seems appropriate for future outcome reporting. Future classifications that eliminate common sources of disagreement may further improve the reliability.

AbstractSrc tyrosine kinases are overexpressed in pancreatic cancers, and the oral Src inhibitor saracatinib has shown antitumor activity in preclinical models of pancreas cancer. We performed a CTEP‐sponsored Phase II clinical trial of saracatinib in previously treated pancreas cancer patients, with a primary endpoint of 6‐month survival. A Simon MinMax two‐stage phase II design was used. Saracatinib (175 mg/day) was administered orally continuously in 28‐day cycles. In the unselected portion of the study, 18 patients were evaluable. Only two (11%) patients survived for at least 6 months, and three 6‐month survivors were required to move to second stage of study as originally designed. The study was amended as a biomarker‐driven trial (leucine rich repeat containing protein 19 [LRRC19] > insulin‐like growth factor‐binding protein 2 [IGFBP2] “top scoring pairs” polymerase chain reaction [PCR] assay, and PIK3CA mutant) based on preclinical data in a human pancreas tumor explant model. In the biomarker study, archival tumor tissue or fresh tumor biopsies were tested. Biomarker‐positive patients were eligible for the study. Only one patient was PIK3CA mutant in a 3′ untranslated region (UTR) portion of the gene. This patient was enrolled in the study and failed to meet the 6‐month survival endpoint. As the frequency of biomarker‐positive patients was very low (<3%), the study was closed. Although we were unable to conclude whether enriching for a subset of second/third line pancreatic cancer patients treated with a Src inhibitor based on a biomarker would improve 6‐month survival, we demonstrate that testing pancreatic tumor samples for a biomarker‐driven, multicenter study in metastatic pancreas cancer is feasible.

Abstract Background: Estrogen receptors are over-expressed in around 70% of breast cancer cases. The genetic changes that occur during aromatase inhibitor (AI) treatment are not well understood and may differ depending upon the patient's response phenotype. Methods: We performed whole genome sequencing (WGS) of matched blood, pre-treatment, and post-treatment biopsy samples from 22 estrogen receptor positive breast cancer patients treated with neoadjuvant aromatase inhibitors. For 5 cases, we performed the whole genome sequencing (WGS) on patients’ matched normal, two pre AI-treatment, and two post AI-treatment DNA isolates from biopsy samples. We validated all putative coding and non-coding somatic mutations using deep sequencing. By comparing the validated somatic mutations from pre- and post- AI treatment biopsy samples, we were able to determine the alterations in the tumor genomes. In every case we defined the clonal architecture of each pair of pre-treatment and post-treatment biopsy samples by comparing the variant allele frequencies from thousands of validated somatic mutations. Results: Comparisons of the two pre AI-treatment biopsy samples from the same patient indicates that the variant allele frequencies of mutations showed high concordances in all 5 cases, 0.74 to 0.95 range of correlation coefficient. Only a small percentage of somatic mutations were detected in one pre-treatment sample and not the other (4.65% overall). In comparing the somatic variations between pre-treatment and matched post-treatment biopsy samples in 22 cases, we found that patients with good clinical response to AI treatment retained known driver mutations only in their pre-treatment tumors. Conversely, those patients with poor clinical response presented new driver mutations in their post-treatment samples. Furthermore, the variant allele frequency for most mutated genes decreased in post AI treatment samples for patients with good AI treatment response; on the contrary, the variant allele frequency increased for patients with poor clinical response. Conclusions: From WGS of matched normal, pre-treatment, and post-treatment biopsy samples, we identified new driver genes mutated in patients with poor clinical response, while patients with good clinical response had lost mutated driver genes in their post-treatment biopsy samples. The genetic landscape revealed by WGS of pre-treatment and post-treatment biopsy samples reveals mutational repertoires are remodeled by AI therapy. This finding suggests deep sequencing of AI treated samples will be necessary to reveal the complete complement of mutations present in a patient's tumor. Citation Format: {Authors}. {Abstract title} [abstract]. In: Proceedings of the 103rd Annual Meeting of the American Association for Cancer Research; 2012 Mar 31-Apr 4; Chicago, IL. Philadelphia (PA): AACR; Cancer Res 2012;72(8 Suppl):Abstract nr LB-423. doi:1538-7445.AM2012-LB-423