Biology subjects

Santoso, M.

Publications and source records attributed to Santoso, M..

2 recordsLinked to original sources

bioRxiv2023

Complex structural variation is prevalent and highly pathogenic in pediatric solid tumors

BackgroundIn pediatric cancer, structural variants (SVs) and copy number alterations can contribute to cancer initiation and progression, and hence aid diagnosis and treatment stratification. The few studies into complex rearrangements have found associations with tumor aggressiveness or poor outcome. Yet, their prevalence and biological relevance across pediatric solid tumors remains unknown. ResultsIn a cohort of 120 primary tumors, we systematically characterized patterns of extrachromosomal DNA, chromoplexy and chromothripsis across five pediatric solid cancer types: neuroblastoma, Ewing sarcoma, Wilms tumor, hepatoblastoma and rhabdomyosarcoma. Complex SVs were identified in 56 tumors (47%) and different classes occurred across multiple cancer types. Recurrently mutated regions tend to be cancer-type specific and overlap with cancer genes, suggesting that selection contributes to shaping the SV landscape. In total, we identified potentially pathogenic complex SVs in 42 tumors that affect cancer driver genes or result in unfavorable chromosomal alterations. Half of which were known drivers, e.g. MYCN amplifications due to ecDNA and EWSR1::FLI1 fusions due to chromoplexy. Recurrent novel candidate complex events include chromoplexy in WT1 in Wilms tumors, focal chromothripsis with 1p loss in hepatoblastomas and complex MDM2 amplifications in rhabdomyosarcomas. ConclusionsComplex SVs are prevalent and pathogenic in pediatric solid tumors. They represent a type of genomic variation which currently remains unexplored. Moreover, carrying complex SVs seems to be associated with adverse clinical events. Our study highlights the potential for complex SVs to be incorporated in risk stratification or exploited for targeted treatments.

cancer biology↗

bioRxiv2022

A Strategy to Quantify Myofibroblast Activation on a Continuous Spectrum

Myofibroblasts are a highly secretory and contractile phenotype most commonly identified by the de novo expression and assembly of alpha-smooth muscle actin stress fibers. Traditionally, this activation process has been thought of as a binary process, with cells being labeled as "activated" or "quiescent (non-activated)". More recently, this view has been expanded to consider activation on a continuous spectrum. However, there is no established method to quantify a cells position on this spectrum, and as a result, the binary labeling system is still widely used. While transcriptomic analyses provide a continuous measure of myofibroblast markers, a faster and more facile screening method is needed. To this end, we utilized optical microscopy and machine learning methods to quantify myofibroblast activation on a spectrum. We first measured size and shape features of over 1,000 individual cardiac fibroblasts and found that these features provide enough information to predict activation state, on the binary scale, with 94% accuracy as compared to manual classification. We next performed dimensionality reduction techniques on these features to create a continuous scale of activation. Importantly, this new classification system captures a range of fibroblast activation states, but still possesses inherent bias due to choice of morphological features. Thus, we next used self-supervised machine learning to create a second continuous labeling system free from biases associated with the manually measured features. Lastly, we compared our findings for mechanically activated cardiac fibroblasts to a distribution of cell phenotypes generated from transcriptomic data using single-cell RNA sequencing. Altogether, these results demonstrate a continuous spectrum of activation from fibroblast to myofibroblast and provide a strategy to quantify a cells position on that spectrum.

bioengineering↗