BACKGROUND A cancer diagnosis during adolescence or young adulthood may negatively influence social well-being. The existing literature concerning the social well-being of adolescents and young adults (AYAs) with cancer was reviewed to identify gaps in current research and highlight priority areas for future research. METHODS A systematic review of the scientific literature published in English from 2000 through 2014 was performed. Eligible studies included patients and survivors diagnosed between the ages of 15 to 39 years that reported on social well-being domains in the City of Hope Cancer Survivor Quality of Life Model. Each article was reviewed for relevance using a standardized template. A total of 253 potential articles were identified. After exclusions, a final sample of 26 articles identified domains of social well-being that are believed to be understudied among AYAs with cancer: 1) educational attainment, employment, and financial burden; 2) social relationships; and 3) supportive care. Articles were read in their entirety, single coded, and summarized according to domain. RESULTS AYAs with cancer report difficulties related to employment, educational attainment, and financial stability. They also report problems with the maintenance and development of peer and family relationships, intimate and marital relationships, and peer support. Supportive services are desired among AYAs. Few studies have reported results in reference to comparison samples or by cancer subtypes. CONCLUSIONS Future research studies on AYAs with cancer should prioritize the inclusion of underserved AYA populations, more heterogeneous cancer samples, and comparison groups to inform the development of supportive services. Priority areas for potential intervention include education and employment reintegration, and social support networks.

A B S T R A C T PurposeBecause adolescent and young adult (AYA) patients with cancer have experienced variable improvement in survival over the past two decades, enhancing the quality and timeliness of cancer care in this population has emerged as a priority area. To identify current trends in AYA care, we examined patterns of clinical trial participation, time to treatment, and provider characteristics in a population-based sample of AYA patients with cancer. MethodsUsing the National Cancer Institute Patterns of Care Study, we used multivariate logistic regression to evaluate demographic and provider characteristics associated with clinical trial enrollment and time to treatment among 1,358 AYA patients with cancer (age 15 to 39 years) identified through the Surveillance, Epidemiology, and End Results Program. ResultsIn our study, 14% of patients age 15 to 39 years had enrolled onto a clinical trial; participation varied by type of cancer, with the highest participation in those diagnosed with acute lymphoblastic leukemia (37%) and sarcoma (32%). Multivariate analyses demonstrated that uninsured, older patients and those treated by nonpediatric oncologists were less likely to enroll onto clinical trials. Median time from pathologic confirmation to first treatment was 3 days, but this varied by race/ethnicity and cancer site. In multivariate analyses, advanced cancer stage and outpatient treatment alone were associated with longer time from pathologic confirmation to treatment. ConclusionOur study identified factors associated with low clinical trial participation in AYA patients with cancer. These findings support the continued need to improve access to clinical trials and innovative treatments for this population, which may ultimately translate into improved survival.

Although most AYA patients with cancer return to work after cancer, treatment intensity, not having insurance, and quitting work/school directly after diagnosis can influence work/educational outcomes. Future research should investigate underlying causes for these differences and best practices for effective transition of these cancer survivors to the workplace/school after treatment.

In contrast to single-institution studies, our population-based analysis found a decrease in unilateral mastectomy rates from 2000 to 2006 in the United States. Variations in referral patterns and patient selection are potential explanations for these differences between single institutions and national trends.

Background: Classifying nuclear magnetic resonance (NMR) spectra is a crucial step in many metabolomics experiments. Since several multivariate classification techniques depend upon the variance of the data, it is important to first minimise any contribution from unwanted technical variance arising from sample preparation and analytical measurements, and thereby maximise any contribution from wanted biological variance between different classes. The generalised logarithm (glog) transform was developed to stabilise the variance in DNA microarray datasets, but has rarely been applied to metabolomics data. In particular, it has not been rigorously evaluated against other scaling techniques used in metabolomics, nor tested on all forms of NMR spectra including 1-dimensional (1D) 1 H, projections of 2D 1 H, 1 H J-resolved (pJRES), and intact 2D J-resolved (JRES).

Introduction: Cancer for adolescents and young adults (AYA) differs from younger and older patients; AYA face medical challenges while navigating social and developmental transitions. Research suggests that these patients are under or inadequately served by current support services, which may affect health-related quality of life (HRQOL).Methods: We examined unmet service needs and HRQOL in the National Cancer Institute’s Adolescent and Young Adult Health Outcomes and Patient Experience (AYA HOPE) study, a population-based cohort (n = 484), age 15–39, diagnosed with cancer 6–14 months prior, in 2007–2009. Unmet service needs were psychosocial, physical, spiritual, and financial services where respondents endorsed that they needed, but did not receive, a listed service. Linear regression models tested associations between any or specific unmet service needs and HRQOL, adjusting for demographic, medical, and health insurance variables.Results: Over one-third of respondents reported at least one unmet service need. The most common were financial (16%), mental health (15%), and support group (14%) services. Adjusted models showed that having any unmet service need was associated with worse overall HRQOL, fatigue, physical, emotional, social, and school/work functioning, and mental health (p’s < 0.0001). Specific unmet services were related to particular outcomes [e.g., needing pain management was associated with worse overall HRQOL, physical and social functioning (p’s < 0.001)]. Needing mental health services had the strongest associations with worse HRQOL outcomes; needing physical/occupational therapy was most consistently associated with poorer functioning across domains.Discussion: Unmet service needs in AYAs recently diagnosed with cancer are associated with worse HRQOL. Research should examine developmentally appropriate, relevant practices to improve access to services demonstrated to adversely impact HRQOL, particularly physical therapy and mental health services.

The number of lymph nodes evaluated for colon cancer has markedly increased in the past 2 decades but was not associated with an overall shift toward higher-staged cancers, questioning the upstaging mechanism as the primary basis for improved survival in patients with more lymph nodes evaluated.

Metabolomics datasets, by definition, comprise of measurements of large numbers of metabolites. Both technical (analytical) and biological factors will induce variation within these measurements that is not consistent across all metabolites. Consequently, criteria are required to assess the reproducibility of metabolomics datasets that are derived from all the detected metabolites. Here we calculate spectrum-wide relative standard deviations (RSDs; also termed coefficient of variation, CV) for ten metabolomics datasets, spanning a variety of sample types from mammals, fish, invertebrates and a cell line, and display them succinctly as boxplots. We demonstrate multiple applications of spectral RSDs for characterising technical as well as inter-individual biological variation: for optimising metabolite extractions, comparing analytical techniques, investigating matrix effects, and comparing biofluids and tissue extracts from single and multiple species for optimising experimental design. Technical variation within metabolomics datasets, recorded using one- and two-dimensional NMR and mass spectrometry, ranges from 1.6 to 20.6% (reported as the median spectral RSD). Inter-individual biological variation is typically larger, ranging from as low as 7.2% for tissue extracts from laboratory-housed rats to 58.4% for fish plasma. In addition, for some of the datasets we confirm that the spectral RSD values are largely invariant across different spectral processing methods, such as baseline correction, normalisation and binning resolution. In conclusion, we propose spectral RSDs and their median values contained herein as practical benchmarks for metabolomics studies.