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PurposeTissue-based comprehensive genomic profiling (CGP) is increasingly utilized for treatment selection in patients with advanced solid tumors, however real-world tissue availability may limit widespread implementation. Here we established real-world CGP tissue availability and assessed CGP performance on consecutively received samples.Patients and MethodPost-hoc, non-prespecified analysis of 32,048 consecutive tumor tissue samples received for StrataNGS, a multiplex PCR based-CGP (PCR-CGP) test, as part of an ongoing observational trial (NCT03061305). Tumor tissue sample characteristics and PCR-CGP performance were assessed across all tested tumor samples, including exception samples not meeting minimum input requirements (2 tumor surface area [TSA], DNA or RNA yield 5yrs). Tests reporting at least one prioritized alteration or meeting all sequencing QC metrics (and ≥20% TC) were considered successful. For prostate carcinoma and lung adenocarcinoma, tests reporting at least one actionable/informative alteration or those meeting all sequencing QC metrics (and ≥20% TC) were considered actionable.ResultsPCR-CGP was attempted in 31,165 of 32,048 (97.2%) consecutively received solid tumor tissue samples. Among the 31,165 tested samples, 10.7% had low (2 TSA), highlighting the challenging nature of samples received for CGP. Of the 31,101 samples evaluable for input requirements, 8,079 (26.0%) were exceptions not meeting requirements. However, 94.2% of the 31,101 tested samples were successfully reported, including 80.6% of exception samples. Importantly, 80.6% of 1,344 tested prostate carcinomas and 87.8% of 1,144 tested lung adenocarcinomas yielded results informing treatment selection.ConclusionMost real-world tumor tissue samples from patients with advanced cancer desiring CGP are limited, requiring optimized CGP approaches to produce meaningful results. An optimized PCR-CGP test, coupled with an inclusive exception testing policy, delivered reportable results for >94% of samples, potentially expanding the proportion of CGP-testable patients, and thus the impact of biomarker-guided targeted and immunotherapies.

Background Heterogeneity in the phenotypic presentation of autism spectrum disorder (ASD) is apparent in the profile and the severity of sensory features. Here, we applied factor mixture modelling (FMM) to test a multidimensional factor model of sensory processing in ASD. We aimed to identify homogeneous sensory subgroups in ASD that differ intrinsically in their severity along continuous factor scores. We also investigated sensory subgroups in relation to clinical variables: sex, age, IQ, social-communication symptoms, restricted and repetitive behaviours, adaptive functioning and symptoms of anxiety and attention-deficit/hyperactivity disorder. Methods Three hundred thirty-two children and adults with ASD between the ages of 6 and 30 years with IQs varying between 40 and 148 were included. First, three different confirmatory factor models were fit to the 38 items of the Short Sensory Profile (SSP). Then, latent class models (with two-to-six subgroups) were evaluated. The best performing factor model, the 7-factor structure, was subsequently used in two FMMs that varied in the number of subgroups: a two-subgroup, seven-factor model and a three-subgroup and seven-factor model. Results The ‘three-subgroup/seven-factor’ FMM was superior to all other models based on different fit criteria. Identified subgroups differed in sensory severity from severe, moderate to low. Accounting for the potential confounding effects of age and IQ, participants in these sensory subgroups had different levels of social-communicative symptoms, restricted and repetitive behaviours, adaptive functioning skills and symptoms of inattention and anxiety. Limitations Results were derived using a single parent-report measure of sensory features, the SSP, which limits the generalisability of findings. Conclusion Sensory features can be best described by three homogeneous sensory subgroups that differ in sensory severity gradients along seven continuous factor scores. Identified sensory subgroups were further differentiated by the severity of core and co-occurring symptoms, and level of adaptive functioning, providing novel evidence on the associated clinical correlates of sensory subgroups. These sensory subgroups provide a platform to further interrogate the neurobiological and genetic correlates of altered sensory processing in ASD.

Based on prior research, the presence of more residents diagnosed with mental illness in a neighborhood may indicate the presence of more offenders or more targets. We suggest that it may also indicate reduced guardianship and lower informal social control. Using data from blocks in 108 cities in Southern California, we examined the association between two types of mental disorders (affective disorders and substance use disorders) and crime rates. Even after controlling for more traditional predictors of neighborhood crime, we found a strong positive relationship between certain mental disorders and aggravated assault and motor vehicle theft rates. We also tested nonlinear interactions with economic disadvantage and found that a higher proportion of mentally ill residents in a zip code was associated with higher block-level crime rates in zip codes with lower levels of concentrated economic disadvantage. The findings suggest that mental illness is a unique ecological factor by which to understand neighborhood crime.