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Ludlow, L. E.

Publications and source records attributed to Ludlow, L. E..

2 recordsLinked to original sources

Spatial transcriptomic profiling of human retinoblastoma

Retinoblastoma (RB) represents one of the most prevalent intraocular cancers in children. Understanding the tumor heterogeneity in RB is important to design better targeted therapies. Here we used spatial transcriptomic to profile human retina and RB tumor to comprehensively dissect the spatial cell-cell communication networks. We found high intratumoral heterogeneity in RB, consisting of 10 transcriptionally distinct subpopulations with varying levels of proliferation capacity. Our results uncovered a complex architecture of the tumor microenvironment that predominantly consisted of cone precursors, as well as glial cells and cancer-associated fibroblasts. We delineated the cell trajectory underlying malignant progression of RB, and identified key signaling pathways driving genetic regulation across RB progression. We also explored the signaling pathways mediating cell-cell communications in RB subpopulations, and mapped the spatial networks of RB subpopulations and region neighbors. Altogether, we constructed the first spatial gene atlas for RB, which allowed us to characterize the transcriptomic landscape in spatially-resolved RB subpopulations, providing novel insights into the complex spatial communications involved in RB progression.

systems biology↗

ALLSorts: a RNA-Seq classifier for B-Cell Acute Lymphoblastic Leukemia.

B-cell acute lymphoblastic leukemia (B-ALL) is the most common childhood cancer. Subtypes within B-ALL are distinguished by characteristic structural variants and mutations, which in some instances strongly correlate with responses to treatment. The World Health Organisation (WHO) recognises seven distinct classifications, or subtypes, as of 2016. However, recent studies have demonstrated that B-ALL can be segmented into 23 subtypes based on a combination of genomic features and gene expression profiles. A method to identify a patients subtype would have clear clinical utility. Despite this, no publically available classification methods using RNA-Seq exist for this purpose. Here we present ALLSorts: a publicly available method that uses RNA-Seq data to classify B-ALL samples to 18 known subtypes and five meta-subtypes. ALLSorts is the result of a hierarchical supervised machine learning algorithm applied to a training set of 1223 B-ALL samples aggregated from multiple cohorts. Validation revealed that ALLSorts can accurately attribute samples to subtypes and can attribute multiple subtypes to a sample. Furthermore, when applied to both paediatric and adult cohorts, ALLSorts was able to classify previously undefined samples into subtypes. ALLSorts is available and documented on GitHub (https://github.com/Oshlack/AllSorts/). Key PointsO_LIALLSorts is a gene expression classifier for B-cell acute lymphoblastic leukemia, which predicts 18 distinct genomic subtypes - including those designated by the World Health Organisation (WHO) and provisional entities. C_LIO_LITrained and validated on over 2300 B-ALL samples, representing each subtype and a variety of clinical features. C_LIO_LICorrectly identified subtypes in 91% of cases in a held-out dataset and between 82-93% across a newly combined cohort of paediatric and adult samples. C_LIO_LIALLSorts assigned subtypes to samples with previously unknown driver events. C_LI ALLsorts is an accurate, comprehensive and freely available classification tool that distinguishes subtypes of B-cell acute lymphoblastic leukemia from RNA-sequencing.

bioinformatics↗