High Prevalence of Chromosomal Rearrangements and LINE Retrotranspositions Detected in Formalin-Fixed, Paraffin-Embedded Colorectal Cancer Tissue.

Structural variants (SVs) caused by chromosomal rearrangements in common fragile sites or long interspersed nuclear element (LINE) retrotranspositions are highly prevalent in colorectal cancer. However, methodology for the targeted detection of these SVs is lacking. This article reports the use of formalin-fixed paraffin-embedded targeted-locus capture (FFPE-TLC) sequencing as a novel technology for the targeted detection of tumor-specific SVs. Analysis of 29 FFPE colorectal tumor samples and 8 matched normal samples revealed tumor-specific SVs in 24 patients (83%), with a median of 2 SVs per patient (range, 1 to 21). A total of 104 SVs were found in the common fragile site-associated genes MACROD2, PRKN, FHIT, and WWOX in 18 patients (62%), and 39 SVs caused by three LINE transposable elements were found in 15 patients (52%). Tumor specificity of SVs was independently verified by Droplet Digital PCR of tumor tissue DNA, and their applicability as plasma circulating tumor DNA biomarkers was demonstrated. It was concluded that FFPE-TLC sequencing enables the detection of tumor-specific SVs caused by chromosomal rearrangements and LINE retrotranspositions in FFPE tissue. Therefore, FFPE-TLC sequencing facilitates the investigation of the biological and clinical effects of SVs using FFPE material from (retrospective) cohorts of cancer patients and has potential clinical applicability in the detection of SV biomarkers in the routine molecular diagnostics setting.

Disentangling the heterogeneity of multiple sclerosis through identification of independent neuropathological dimensions.

Multiple sclerosis (MS) is a heterogeneous neurological disorder with regards to clinical presentation and pathophysiology. Here, we investigated the heterogeneity of MS by performing an exploratory factor analysis on quantitative and qualitative neuropathology data collected for 226 MS donors in the Netherlands Brain Bank autopsy cohort. Three promising dimensions were identified and subsequently validated with clinical, neuropathological, and genetic data. Dimension 1 ranged from a predominance of remyelinated and inactive lesions to extensive pathological changes, higher proportions of active and mixed lesions, and foamy microglia morphology. This pattern was positively correlated with more severe disease, the presence of B and T cells, and neuroaxonal damage. Scoring high on dimension 2 was associated with active lesions, reactive sites, and the presence of nodules. These donors had less severe disease, a specific pattern of cortical lesions, and MS risk variants in the human leukocyte antigen region, the latter indicating a connection between disease onset and this neuropathological dimension. Donors scoring high on dimension 3 showed increased lesional pathology with relatively more mixed and inactive lesions and ramified microglia morphology. This pattern was associated with longer disease duration, subpial cortical lesions, less involvement of the adaptive immune system, and less axonal damage. Taken together, the three dimensions may represent (1) demyelination and immune cell activity associated with pathological and clinical progression, (2) microglia (re)activity and possibly lesion initiation, and (3) loss of lesion activity and scar formation. Our findings highlight that a thorough understanding of the interplay between multiple pathological characteristics is crucial to understand the heterogeneity of MS pathology, as well as its association with genetic predictors and disease outcomes. The scores of donors on the dimensions can serve as an important starting point for further disentanglement of MS heterogeneity and translation into observations and interventions in living cohorts with MS.

Identification of clinical disease trajectories in neurodegenerative disorders with natural language processing.

Neurodegenerative disorders exhibit considerable clinical heterogeneity and are frequently misdiagnosed. This heterogeneity is often neglected and difficult to study. Therefore, innovative data-driven approaches utilizing substantial autopsy cohorts are needed to address this complexity and improve diagnosis, prognosis and fundamental research. We present clinical disease trajectories from 3,042 Netherlands Brain Bank donors, encompassing 84 neuropsychiatric signs and symptoms identified through natural language processing. This unique resource provides valuable new insights into neurodegenerative disorder symptomatology. To illustrate, we identified signs and symptoms that differed between frequently misdiagnosed disorders. In addition, we performed predictive modeling and identified clinical subtypes of various brain disorders, indicative of neural substructures being differently affected. Finally, integrating clinical diagnosis information revealed a substantial proportion of inaccurately diagnosed donors that masquerade as another disorder. The unique datasets allow researchers to study the clinical manifestation of signs and symptoms across neurodegenerative disorders, and identify associated molecular and cellular features.