Multiscale quantification of morphological heterogeneity with creation of a predictor of longer survival in glioblastoma

Intratumor heterogeneity is a main cause of the dismal prognosis of glioblastoma (GBM). Yet, there remains a lack of a uniform assessment of the degree of heterogeneity. With a multiscale approach, we addressed the hypothesis that intratumor heterogeneity exists on different levels comprising traditional regional analyses, but also innovative methods including computer-assisted analysis of tumor morphology combined with epigenomic data. With this aim, 157 biopsies of 37 patients with therapy-naive IDH-wildtype GBM were analyzed regarding the intratumor variance of protein expression of glial marker GFAP, microglia marker Iba1 and proliferation marker Mib1. Hematoxylin and eosin stained slides were evaluated for tumor vascularization. For the estimation of pixel intensity and nuclear profiling, automated analysis was used. Additionally, DNA methylation profiling was conducted separately for the single biopsies. Scoring systems were established to integrate several parameters into one score for the four examined modalities of heterogeneity (regional, cellular, pixel-level and epigenomic). As a result, we could show that heterogeneity was detected in all four modalities. Furthermore, for the regional, cellular and epigenomic level, we confirmed the results of earlier studies stating that a higher degree of heterogeneity is associated with poorer overall survival. To integrate all modalities into one score, we designed a predictor of longer survival, which showed a highly significant separation regarding the OS. In conclusion, multiscale intratumor heterogeneity exists in glioblastoma and its degree has an impact on overall survival. In future studies, the implementation of a broadly feasible heterogeneity index should be considered.     

Bench to bedside: recent advances in lung cancer pathological research with implications for diagnostic clinical practice

After decades of relative stagnation lung cancer is emerging as a disease type where rapid progress is being made in diagnosis and therapy, as well as in our understanding of disease biology. Much of this progress is of immediate impact to diagnosticians, and more is likely to affect diagnostic practice in the near future. In this review we seek to briefly summarize several key areas of active research of immediate or probable imminent value to trainee and consultant pulmonary pathologists alike. We cover some major changes in tumour classification, grading, and patient stratification, as well as considering the state of the art in machine-assisted interpretation of lung cancer histology, and the use of genetically modified lung cancer models.     

Deciphering the molecular landscape of microcephaly in 87 Indian families by exome sequencing

Microcephaly is a frequent feature of neurodevelopmental disorders (NDDs). Our study presents the heterogeneous spectrum of genetic disorders in patients with microcephaly either in isolated form or in association with other neurological and extra-neural abnormalities. We present data of 91 patients from 87 unrelated families referred to our clinic during 2016–2020 and provide a comprehensive clinical and genetic landscape in the studied cohort. Molecular diagnosis using exome sequencing was made in 45 families giving a yield of 51.7%. In 9 additional families probable causative variants were detected. We identified disease causing variations in 49 genes that are involved in different functional pathways Among these, 36 had an autosomal recessive pattern, 8 had an autosomal dominant pattern (all inherited de novo), and 5 had an X-linked pattern. In 41 probands where sequence variations in autosomal recessive genes were identified 31 were homozygotes (including 16 from non-consanguineous families). The study added 28 novel pathogenic/likely pathogenic variations. The study also calls attention to phenotypic variability and expansion in spectrum as well as uncovers genes where microcephaly is not reported previously or is a rare finding. We here report phenotypes associated with the genes for ultra-rare NDDs with microcephaly namely ATRIP, MINPP1, PNPLA8, AIMP2, ANKLE2, NCAPD2 and TRIT1.     

Can EGFR mutation status be reliably determined in pre‐operative needle biopsies from adenocarcinomas of the lung?

The identification of EGFR mutations in non‐small‐cell lung cancer is important for selecting patients, who may benefit from treatment with EGFR tyrosine kinase inhibitors. The analysis is usually performed on cytological aspirates and/or histological needle biopsies, representing a small fraction of the tumour volume. The aim of the present investigation was to evaluate the diagnostic performance of this molecular test. We retrospectively included 201 patients with primary adenocarcinoma of the lung. EGFR mutation status (exon 19 deletions and exon 21 L858R point mutation) was evaluated on both pre‐operative biopsies (131 histological and 70 cytological) and on the surgical specimens, using PCR. Samples with low tumour cell fraction were assigned to laser micro‐dissection (LMD). We found nine (4.5%) patients with EGFR mutation in the lung tumour resections, but failed to identify mutation in one of the corresponding pre‐operative, cytological specimens. Several (18.4%) analyses of the pre‐operative biopsies were inconclusive, especially in case of biopsies undergoing LMD and regarding exon 21 analysis. Discrepancy of mutation status in one patient may reflect intra‐tumoural heterogeneity or technical issues. Moreover, several inconclusive results in the diagnostic biopsies reveal that attention must be paid on the suitability of pre‐operative biopsies for EGFR mutation analysis.     

Association of regional socioeconomic deprivation and rurality with global developmental delay in early childhood: Data from mandatory school entry examinations in Germany

BackgroundFrom birth to young adulthood, health and development of young people are strongly linked to their living situation, including their family’s socioeconomic position (SEP) and living environment. The impact of regional characteristics on development in early childhood beyond family SEP has been rarely investigated. This study aimed to identify regional predictors of global developmental delay at school entry taking family SEP into consideration.MethodWe used representative, population-based data from mandatory school entry examinations of the German federal state of Brandenburg in 2018/2019 with n=22,801 preschool children. By applying binary multilevel models, we hierarchically analyzed the effect of regional deprivation defined by the German Index of Socioeconomic Deprivation (GISD) and rurality operationalized as inverted population density of the children’s school district on global developmental delay (GDD) while adjusting for family SEP (low, medium and high).ResultsFamily SEP was significantly and strongly linked to GDD. Children with the highest family SEP showed a lower odds for GDD compared to a medium SEP (female: OR=4.26, male: OR=3.46) and low SEP (female: OR=16.58, male: OR=12.79). Furthermore, we discovered a smaller, but additional and independent effect of regional socioeconomic deprivation on GDD, with a higher odds for children from a more deprived school district (female: OR=1.35, male: OR=1.20). However, rurality did not show a significant link to GDD in preschool children beyond family SEP and regional deprivation.ConclusionFamily SEP and regional deprivation are risk factors for child development and of particular interest to promote health of children in early childhood and over the life course.     

Applications of geospatial analyses in health research among homeless people: A systematic scoping review of available evidence

BackgroundUnequal housing access resulted in more than 150 million homeless people worldwide, with millions more expected to be added every year due to the ongoing climate-related crises. Homeless population has a counterproductive effect on the social, psychological integration efforts by the community and exposure to other severe health-related issues. Geographic Information Systems (GIS) have long been applied in urban planning and policy, housing and homelessness, and health-related research.MethodsWe used the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) method to systematically review 24 articles collected from multiple databases (n = 10) that focused on health-related issues among homeless people and used geospatial analysis techniques in their research.ResultsOur findings indicated a geographic clustering of case study locations– 26 out of the 31 case study sites are from the USA and Canada. Studies used spatial analysis techniques to identify hotspots, clusters and patterns of patient location and population distribution. Studies also reported relationships among the location of homeless shelters and substance use, discarded needles, different infectious and non-infectious disease clusters.ConclusionMost studies were restricted in analyzing and visualizing the patterns and disease clusters; however, geospatial analyses techniques are useful and offer diverse techniques for a more sophisticated understanding of the spatial characteristics of the health issues among homeless people. Better integration of GIS in health research among the homeless would help formulate sensible policies to counter health inequities among this vulnerable population group.     

On estimands arising from misspecified semiparametric rate-based analysis of recurrent episodic conditions

Marginal rate-based analyses are widely used for the analysis of recurrent events in clinical trials. In many areas of application, the events are not instantaneous but rather signal the onset of a symptomatic episode representing a recurrent infection, respiratory exacerbation, or bout of acute depression. In rate-based analyses, it is unclear how to best handle the time during which individuals are experiencing symptoms and hence are not at risk. We derive the limiting value of the Nelson-Aalen estimator and estimators of the regression coefficients under a semiparametric rate-based model in terms of an underlying two-state process. We investigate the impact of the distribution of the episode durations, heterogeneity, and dependence on the asymptotic and finite sample properties of standard estimators. We also consider the impact of these features on power in trials designed to test intervention effects on rate functions. An application to a trial of individuals with herpes simplex virus is given for illustration.     

Heterogeneity in amount of growth factors secreted by platelets in platelet-rich plasma samples from alopecia patients

Platelet α-granules release growth factors (GFs) that promote healing and tissue regeneration. Platelet-rich plasma (PRP) is shown to be beneficial in treating alopecia, and however, clinical response can be inconsistent. Due to several fold enrichment of platelets secreting large quantities of GFs following PRP injections, heterogeneity in amounts of GFs secreted by platelets may contribute to inconsistent clinical responses. Herein, we evaluated factors that could potentially contribute to heterogeneous secretion of GFs by platelets. We measured platelet secretion of transforming growth factor beta1 (TGFβ1), platelet-derived growth factor (PDGF-BB), epidermal growth factor (EGF), vascular endothelial growth factor (VEGF) and fibroblast growth factor (FGF2) in aliquots of de-identified PRP samples from female patients undergoing therapy in the hair disease clinic. Although secretion of GFs by platelets was comparable in PRP samples of patients with non-cicatricial and cicatricial alopecia, a Shapiro-Wilk test for normal distribution indicated significant variability across all patient samples. The amount of GF secreted by platelets was comparable when PRP prepared from two FDA-cleared devices with distinct techniques were compared. We provide evidence of platelets secreting heterogeneous amounts of GFs within each sample as high and low secretion of random factors could be simultaneously detected. These results suggest inherent heterogeneity in secretion of GFs by platelets in patient samples that are not influenced by the device used to prepare PRP. Since some GFs could have antagonistic effects on hair growth, a balance between amounts of growth promoting and inhibiting factors may be crucial in determining clinical response to PRP therapy.     

Nonproportional hazards and unobserved heterogeneity in clustered survival data: When can we tell the difference?

Multivariate survival data are frequently encountered in biomedical applications in the form of clustered failures (or recurrent events data). A popular way of analyzing such data is by using shared frailty models, which assume that the proportional hazards assumption holds conditional on an unobserved cluster-specific random effect. Such models are often incorporated in more complicated joint models in survival analysis. If the random effect distribution has finite expectation, then the conditional proportional hazards assumption does not carry over to the marginal models. It has been shown that, for univariate data, this makes it impossible to distinguish between the presence of unobserved heterogeneity (eg, due to missing covariates) and marginal nonproportional hazards. We show that time-dependent covariate effects may falsely appear as evidence in favor of a frailty model also in the case of clustered failures or recurrent events data, when the cluster size or number of recurrent events is small. When true unobserved heterogeneity is present, the presence of nonproportional hazards leads to overestimating the frailty effect. We show that this phenomenon is somewhat mitigated as the cluster size grows. We carry out a simulation study to assess the behavior of test statistics and estimators for frailty models in such contexts. The gamma, inverse Gaussian, and positive stable shared frailty models are contrasted using a novel software implementation for estimating semiparametric shared frailty models. Two main questions are addressed in the contexts of clustered failures and recurrent events: whether covariates with a time-dependent effect may appear as indication of unobserved heterogeneity and whether the additional presence of unobserved heterogeneity can be detected in this case. Finally, the practical implications are illustrated in a real-world data analysis example.