In the era of omics-driven research, it remains a common dilemma to stratify individual patients based on the molecular characteristics of their tumors. To improve molecular stratification of patients with breast cancer, we developed the Gaussian mixture model (GMM)–based classifier. This probabilistic classifier was built on mRNA expression data from more than 300 clinical samples of breast cancer and healthy tissue and was validated on datasets of ESR1, PGR, and ERBB2, which encode standard clinical markers and therapeutic targets. To demonstrate how a GMM approach could be exploited for multiclass classification using data from a candidate marker, we analyzed the insulin-like growth factor I receptor (IGF1R), a promising target, but a marker of uncertain importance in breast cancer. The GMM defined subclasses with downregulated (40%), unchanged (39%), upregulated (19%), and overexpressed (2%) IGF1R levels; inter- and intrapatient analyses of IGF1R transcript and protein levels supported these predictions. Overexpressed IGF1R was observed in a small percentage of tumors. Samples with unchanged and upregulated IGF1R were differentiated tumors, and downregulation of IGF1R correlated with poorly differentiated, high-risk hormone receptor–negative and HER2-positive tumors. A similar correlation was found in the independent cohort of carcinoma in situ, suggesting that loss or low expression of IGF1R is a marker of aggressiveness in subsets of preinvasive and invasive breast cancer. These results demonstrate the importance of probabilistic modeling that delves deeper into molecular data and aims to improve diagnostic classification, prognostic assessment, and treatment selection. Significance: A GMM classifier demonstrates potential use for clinical validation of markers and determination of target populations, particularly when availability of specimens for marker development is low.

Resilience is a dynamic process through which people adjust to adversity and buffer anxiety and depression. The COVID-19 global pandemic has introduced a shared source of adversity for people across the world, with detrimental implications for mental health. Despite the pronounced vulnerability of autistic adults to anxiety and depression during the COVID-19 pandemic, relationships among autism-related traits, resilience, and mental health outcomes have not been examined. As such, we aimed to describe the relationships between these traits in a sample enriched in autism spectrum traits during the COVID-19 pandemic. We also aimed to investigate the impact of demographic and social factors on these relationships. Across three independent samples of adults, we assessed resilience factors, autism-related traits, anxiety symptoms, and depression symptoms during the COVID-19 pandemic. One sample (recruited via the Autism Spectrum Program of Excellence, n = 201) was enriched for autism traits while the other two (recruited via Amazon Mechanical Turk, n = 624 and Facebook, n = 929) drew from the general population. We found resilience factors and quantitative autism-related traits to be inversely related, regardless of the resilience measure used. Additionally, we found that resilience factors moderate the relationship between autism-related traits and depression symptoms such that resilience appears to be protective. Across the neurodiversity spectrum, resilience factors may be targets to improve mental health outcomes. This approach may be especially important during the ongoing COVID-19 pandemic and in its aftermath.

Autism spectrum disorder (ASD) comprises a multi‐dimensional set of quantitative behavioral traits expressed along a continuum in autistic and neurotypical individuals. ASD diagnosis—a dichotomous trait—is known to be highly heritable and has been used as the phenotype for most ASD genetic studies. But less is known about the heritability of autism spectrum quantitative traits, especially in adults, an important prerequisite for gene discovery. We sought to measure the heritability of many autism‐relevant quantitative traits in adults high in autism spectrum traits and their extended family members. Among adults high in autism spectrum traits (n = 158) and their extended family members (n = 245), we calculated univariate and bivariate heritability estimates for 19 autism spectrum traits across several behavioral domains. We found nearly all tested autism spectrum quantitative traits to be significantly heritable (h2 = 0.24–0.79), including overall ASD traits, restricted repetitive behaviors, broader autism phenotype traits, social anxiety, and executive functioning. The degree of shared heritability varied based on method and specificity of the assessment measure. We found high shared heritability for the self‐report measures and for most of the informant‐report measures, with little shared heritability among performance‐based cognition tasks. These findings suggest that many autism spectrum quantitative traits would be good, feasible candidates for future genetics studies, allowing for an increase in the power of autism gene discovery. Our findings suggest that the degree of shared heritability between traits depends on the assessment method (self‐report vs. informant‐report vs. performance‐based tasks), as well as trait‐specificity. Lay Summary We found that the scores from questionnaires and tasks measuring different types of behaviors and abilities related to autism spectrum disorder (ASD) were heritable (strongly influenced by gene variants passed down through a family) among autistic adults and their family members. These findings mean that these scores can be used in future studies interested in identifying specific genes and gene variants that are associated with different behaviors and abilities related with ASD.

There is uncertainty among researchers and clinicians about how to best measure autism spectrum dimensional traits in adults. In a sample of adults with high levels of autism spectrum traits and without intellectual disability (probands, n = 103) and their family members (n = 96), we sought to compare self vs. informant reports of autism spectrum-related traits and possible effects of sex on discrepancies. Using correlational analysis, we found poor agreement between self- and informant-report measures for probands, yet moderate agreement for family members. We found reporting discrepancy was greatest for female probands, often self-reporting more autism-related behaviors. Our findings suggest that autism spectrum traits are often underrecognized by informants, making self-report data important to collect in clinical and research settings. Supplementary Information The online version contains supplementary material available at 10.1007/s10803-022-05822-6.