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Integrated exome and RNA sequencing of dedifferentiated liposarcoma - PubMed

  • ️Tue Jan 01 2019

. 2019 Dec 12;10(1):5683.

doi: 10.1038/s41467-019-13286-z.

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Integrated exome and RNA sequencing of dedifferentiated liposarcoma

Makoto Hirata et al. Nat Commun. 2019.

Erratum in

  • Publisher Correction: Integrated exome and RNA sequencing of dedifferentiated liposarcoma.

    Hirata M, Asano N, Katayama K, Yoshida A, Tsuda Y, Sekimizu M, Mitani S, Kobayashi E, Komiyama M, Fujimoto H, Goto T, Iwamoto Y, Naka N, Iwata S, Nishida Y, Hiruma T, Hiraga H, Kawano H, Motoi T, Oda Y, Matsubara D, Fujita M, Shibata T, Nakagawa H, Nakayama R, Kondo T, Imoto S, Miyano S, Kawai A, Yamaguchi R, Ichikawa H, Matsuda K. Hirata M, et al. Nat Commun. 2020 Feb 19;11(1):1024. doi: 10.1038/s41467-020-14680-8. Nat Commun. 2020. PMID: 32075980 Free PMC article.

Abstract

The genomic characteristics of dedifferentiated liposarcoma (DDLPS) that are associated with clinical features remain to be identified. Here, we conduct integrated whole exome and RNA sequencing analysis in 115 DDLPS tumors and perform comparative genomic analysis of well-differentiated and dedifferentiated components from eight DDLPS samples. Several somatic copy-number alterations (SCNAs), including the gain of 12q15, are identified as frequent genomic alterations. CTDSP1/2-DNM3OS fusion genes are identified in a subset of DDLPS tumors. Based on the association of SCNAs with clinical features, the DDLPS tumors are clustered into three groups. This clustering can predict the clinical outcome independently. The comparative analysis between well-differentiated and dedifferentiated components identify two categories of genomic alterations: shared alterations, associated with tumorigenesis, and dedifferentiated-specific alterations, associated with malignant transformation. This large-scale genomic analysis reveals the mechanisms underlying the development and progression of DDLPS and provides insights that could contribute to the refinement of DDLPS management.

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Conflict of interest statement

The authors declare no competing interests.

Figures

Fig. 1
Fig. 1

Characteristics of the somatic mutations and copy-number alterations in DDLPS. a Frequency of nonsynonymous SNVs and short INDELs identified by exome sequencing for each DDLPS sample. b Mean mutation frequency per megabase of coding sequence for each autosomal chromosome. Light blue and orange bars represent the frequency of SNVs and short INDELs, respectively. c 96 substitution classification for DDLPS samples. SNVs were classified according to six base substitution patterns, C > A, C > G, C > T, T > A, T > C, and T > G, and also based on the identity of the bases immediately 5′ and 3′ to each mutated base. d Mutation signature analysis for 119 DDLPS samples. The values represent the contribution of each signature (left) and the signature number (right). e Chromosomal regions with gained (red) and lost (blue) SCNAs identified in 119 DDLPS samples using GISTIC 2.0. The genes in each region are listed in Supplementary Data 1 and 2.

Fig. 2
Fig. 2

Chromosomal rearrangements and fusion genes in DDLPS. a Circos plot of chromosomal rearrangements across 103 DDLPS tumors. The central circle displays structural rearrangements of the fusion genes. The red and blue lines indicate intra- and interchromosomal rearrangements, respectively, which were recurrently observed in at least six cases, while the gray lines show those which were observed in less than six cases. Case count at each gene was performed by referring to the number of partner-genes; a gene rearranged with multiple positions in one case was recognized as different cases. The second and third inner circles represent the histograms of the cases with intra- and interchromosomal rearrangements, respectively, of the genes at the indicated positions. The range of the axis for the counts of the cases in each histogram is from 0 to 60. b, c Schematic of CTDSP1-DNM3OS (b) and CTDSP2-DNM3OS- (c) fusion genes. The region in the red box includes the genomic breakpoint, and the red vertical bars denote the breakpoints in the mRNA (upper panel). Supporting reads (middle) and sequencing chromatogram (lower) of CTDSP1-DNM3OS from three DDLPS tumors. The red arrow represents the breakpoint. d DNM3OS expression in DDLPS. DDLPS tumors from the JSGC-NCC cohort were classified according to their DNM3OS-fusion status and the high-level copy-number gain (HL-Gain) of DNM3OS. The box signifies the upper and lower quartiles; the center bold line within the box, median; the upper and lower whiskers, upper and lower quartiles +/− interquartile ranges, respectively. *P < 0.05 and **P < 0.01 by Steel-Dwass test. e, f Pearson correlation tests and scatter plots showing the relationship between the expression of MIR214 and DNM3OS in JSGC-NCC (e) and TCGA (f) tumors. The expression of MIR214 and DNM3OS in 30 and 52 DDLPS tumors from JSGC-NCC and TCGA, respectively, was analyzed. The red dots represent DNM3OS-fusion-positive samples. P-values, derived from Pearson’s rank correlation test.

Fig. 3
Fig. 3

Mutational landscape of DDLPS and survival analysis by genomic clustering. a Mutational landscape of DDLPS. The status of the SCNAs, which were independently associated with disease-specific or progression-free survival, TP53 and ATRX driver mutations, and DNM3OS-fusion genes is depicted in the landscape. Clustering was performed based on the SCNA status of 1p32.1 and 12p13.32 in DDLPS with a high-level gain of 12q15. The bar graph on the right side represents the ratio of the affected samples to all examined samples. b, c Impact of genomic clustering on disease-specific (b) and progression-free (c) survival of patients with DDLPS. Disease-specific and progression-free survival was analyzed using Kaplan–Meier methods. P-values derived from log-rank analysis are included on each panel.

Fig. 4
Fig. 4

Comparative analysis of well-differentiated (WD) and dedifferentiated (DD) components from DDLPS. a–c Representative somatic mutations (nonsynonymous SNVs and INDELs) (a), SCNAs (b), and SVs (c) in WD and DD components from the same patients. In a, the circle size and numbers indicate the number of somatic mutations in WD or DD. A boxed gene, OTP1, indicates a common somatic mutation in the sample. In b, copy numbers were plotted according to the order of the chromosomal regions, from chromosome 1 (top) to 22 (bottom) and chromosome X. Red lines indicate segmented exome circular binary segmentation calls. The segmentation size is based on the exome capture kit bed file. Solid arrows indicate 12q15; empty arrows indicate 1p32.1. In c, two representative cases are presented, with others presented in Supplementary Fig. 9. d Multidimensional scaling analysis of expression profiles of paired WD and DD. Six pairs of WD and DD were analyzed. e, f GSEA analysis comparing the expression profiles of DD and WD. The gene sets most enriched in DD (e) and WD (f) are shown. g, h Volcano plots of the DD-specific gain (g) and loss (h) of genes. The red and blue dots denote large magnitude fold-changes (more than 2 or less than ½; horizontal axis) and high statistical significance (more than 1.301 of −log10 of P-value by one-sided paired T test; vertical axis), respectively.

Fig. 5
Fig. 5

Scheme of genomic events during DDLPS progression.

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