FIGURE

Fig. 2

ID

ZDB-FIG-200406-130

Publication

Almstedt et al., 2020 - Integrative discovery of treatments for high-risk neuroblastoma

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Fig. 2

Detection of multiple targets linked to neuroblastoma risk factors.

a Validation of neuroblastoma risk signatures by each signature's agreement across three independent cohorts (R2, TARGET and SEQC). Dark grey = signatures constructed by TargetTranslator, using high variance genes; grey = TargetTranslator, L1000 landmark genes; white = L1000 landmark genes, using the Characteristic direction algorithm. b Principal component analysis of the 10 most reproducible signatures, showing risk factors at unit length and marker genes as points; note collinearity between signatures and the distinct/opposing direction of differentiation signatures. Blue: signatures associated with disease pathways or poor outcome. Red: signatures associated with differentiation. c Left: Matching compounds and drugs in L1000 to neuroblastoma signatures; score empirical cumulative distribution function (ecdf) for 19763 drugs, based on L1000 from 14 cell lines (dashed line = permutation control). Right: Detection of enriched targets by shifts in the ecdf curve. L1000 compounds were mapped to drug targets using the STITCH database. Examples of drugs with a common target are highlighted (coloured curve). Note examples of confirmed (AKT1, MTOR, HDAC1), predicted (HMGCR, MAPK8, CNR2) and control targets (MYC, RARB, associated to MYCN and differentiation signatures, respectively). q are FDR-controlled p-values of a Kolmogorov–Smirnoff test that compares target-specific compounds (coloured curve) vs other compounds. d Visualization of pathway dependencies for all targets with q-value < 0.0001, mapped to the minimal spanning tree (MST) of all STRING links between targets. (MST facilitates visualization, by removing redundant links).

Expression Data

Expression Detail

Antibody Labeling

Phenotype Data

Phenotype Detail

Acknowledgments

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