Copy-number dosage regulates telomere maintenance and disease-associated pathways in neuroblastoma

Burkert, Martin; Blanc, Eric; Thiessen, Nina; Weber, Christiane; Toedling, Joern; Monti, Remo; Dombrowe, Victoria M; Biase, Maria Stella de; Kaufmann, Tom L.; Haase, Kerstin; Waszak, Sebastian M.; Eggert, Angelika; Beule, Dieter; Schulte, Johannes H.; Ohler, Uwe; Schwarz, Roland F.

doi:10.1101/2022.08.16.504100

Cited by 2 publications

(1 citation statement)

References 80 publications

(127 reference statements)

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“…RAS pathway mutations have previously been identified in relapsed neuroblastomas 35 . More recently, upregulation of H3F3B has been associated with the alternative lengthening of telomeres (ALT) phenotype in neuroblastoma, which is associated with poor outcomes 36 . Moreover, tumors harboring RAS pathway mutations in combination with telomere maintenance mechanisms were shown to have extremely poor survival rates 37 .…”

Section: Resultsmentioning

confidence: 99%

Effective methods for bulk RNA-Seq deconvolution using scnRNA-Seq transcriptomes

Cobos

Panah

Epps

et al. 2022

Preprint

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RNA profiling technologies at single-cell resolutions, including single-cell and single-nuclei RNA sequencing (scRNA-Seq and snRNA-Seq, scnRNA-Seq for short), can help characterize the composition of tissues and reveal cells that influence key functions in both healthy and disease tissues. However, the use of these technologies is operationally challenging because of high costs and stringent sample-collection requirements. Computational deconvolution methods that infer the composition of bulk-profiled samples using scnRNA-Seq-characterized cell types can broaden scnRNA-Seq applications, but their effectiveness remains controversial. We produced the first systematic evaluation of deconvolution methods on datasets with either known or scnRNA-Seq-estimated compositions. Our analyses revealed biases that are common to scnRNA-Seq 10X Genomics assays and illustrated the importance of accurate and properly controlled data preprocessing and method selection and optimization. Moreover, our results suggested that concurrent RNA-Seq and scnRNA-Seq profiles can help improve the accuracy of both scnRNA-Seq preprocessing and the deconvolution methods that employ them. Indeed, our proposed method, Single-cell RNA Quantity Informed Deconvolution (SQUID), combined RNA-Seq transformation and dampened weighted least-squares deconvolution approaches to consistently outperform other methods in predicting the composition of cell mixtures and tissue samples. Furthermore, our analysis suggested that only SQUID could identify outcomes-predictive cancer cell subclones in pediatric acute myeloid leukemia and neuroblastoma datasets, suggesting that deconvolution accuracy improvements are vital to enabling its applications in the life sciences.

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Section: Resultsmentioning

confidence: 99%

Effective methods for bulk RNA-Seq deconvolution using scnRNA-Seq transcriptomes

Cobos

Panah

Epps

et al. 2022

Preprint

View full text Add to dashboard Cite

show abstract

Effective methods for bulk RNA-seq deconvolution using scnRNA-seq transcriptomes

Cobos,

Panah,

Epps

et al. 2023

Genome Biol

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Background RNA profiling technologies at single-cell resolutions, including single-cell and single-nuclei RNA sequencing (scRNA-seq and snRNA-seq, scnRNA-seq for short), can help characterize the composition of tissues and reveal cells that influence key functions in both healthy and disease tissues. However, the use of these technologies is operationally challenging because of high costs and stringent sample-collection requirements. Computational deconvolution methods that infer the composition of bulk-profiled samples using scnRNA-seq-characterized cell types can broaden scnRNA-seq applications, but their effectiveness remains controversial. Results We produced the first systematic evaluation of deconvolution methods on datasets with either known or scnRNA-seq-estimated compositions. Our analyses revealed biases that are common to scnRNA-seq 10X Genomics assays and illustrated the importance of accurate and properly controlled data preprocessing and method selection and optimization. Moreover, our results suggested that concurrent RNA-seq and scnRNA-seq profiles can help improve the accuracy of both scnRNA-seq preprocessing and the deconvolution methods that employ them. Indeed, our proposed method, Single-cell RNA Quantity Informed Deconvolution (SQUID), which combines RNA-seq transformation and dampened weighted least-squares deconvolution approaches, consistently outperformed other methods in predicting the composition of cell mixtures and tissue samples. Conclusions We showed that analysis of concurrent RNA-seq and scnRNA-seq profiles with SQUID can produce accurate cell-type abundance estimates and that this accuracy improvement was necessary for identifying outcomes-predictive cancer cell subclones in pediatric acute myeloid leukemia and neuroblastoma datasets. These results suggest that deconvolution accuracy improvements are vital to enabling its applications in the life sciences.

show abstract

Copy-number dosage regulates telomere maintenance and disease-associated pathways in neuroblastoma

Cited by 2 publications

References 80 publications

Effective methods for bulk RNA-Seq deconvolution using scnRNA-Seq transcriptomes

Effective methods for bulk RNA-Seq deconvolution using scnRNA-Seq transcriptomes

Effective methods for bulk RNA-seq deconvolution using scnRNA-seq transcriptomes

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