Research ArticleCancer

APOBEC mutation drives early-onset squamous cell carcinomas in recessive dystrophic epidermolysis bullosa

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Science Translational Medicine  22 Aug 2018:
Vol. 10, Issue 455, eaas9668
DOI: 10.1126/scitranslmed.aas9668
  • Fig. 1 RDEB SCC somatic mutation and DNA copy number alterations resemble those identified in HNSCC and UV SCC.

    (A) Significantly mutated genes in RDEB SCC. Bar graph indicates number of mutations affecting protein-coding sequence. Matrix rows represent significantly mutated genes (identified using the MuSiC algorithm; FDR, <0.001) ordered by P value. Each column represents a different sample (n = 27; number indicating samples RDEBSCC_01 through RDEBSCC_31) with box color-coded by mutation type; if more than one mutation is seen in a gene in a sample, then the most impactful mutation (activating Ras > nonsense > frameshift > splice site > missense) is coded. Dark blue bars on left show percentage of samples containing mutations for that gene. (B) Comparison of significantly mutated genes in RDEB SCC, UV SCC, and HNSCC. Color scale corresponds to MutSigCV algorithm q value. (C) Copy number alterations for 27 RDEB SCC. Red indicates regions of copy number gain, whereas blue indicates regions of copy number loss, with chromosome indicated at the top.

  • Fig. 2 Mutation signature analysis identifies prevalent APOBEC signatures in RDEB SCC.

    (A) The y axis displays number of mutations, and x axis organizes 24 RDEB SCC samples in descending order of total number of single-nucleotide variants, with colors representing mutation types. The pie chart shows the collective proportion of all mutational signatures across all 15,776 mutations in all 24 samples. (B) Pie charts show proportions of single-nucleotide variants corresponding to specific signatures for 36 UV SCC (15) and 191 HNSCC (16), separated by HPV status or presence of tobacco signature mutations. Only high-confidence samples, those showing cosine similarity of >0.85 between observed nucleotide context and reconstructed nucleotide context using the identified signatures and their activities, are shown.

  • Fig. 3 Increased signatures 2 and 13 mutations in RDEB SCC.

    (A to C) Each box plot represents the distribution of mutation proportions identified by exome sequencing (as a percentage) corresponding to specific mutational signatures for RDEB SCC (n = 24), UV SCC (n = 36), HNSCC (tobacco- and HPV-negative; n = 104), tobacco-positive HNSCC (Tob+; n = 62), and HPV-positive HNSCC (HPV+; n = 25). Solid box outlines show the second and third quartiles, and the median is shown by the red line. The first and fourth quartiles are denoted by black broken lines, with outliers (more or less than two-thirds of the maximum/minimum) represented by +.

  • Fig. 4 DNA nucleotide excision repair persists in RDEB SCCs.

    The x axis shows RDEBSCC_02 (EB2), RDEBSCC_03, and RDEBSCC_05, which have acquired sufficient numbers of UV damage–induced mutations to support the described analysis (n > 1000); DNA repair wild-type UV damage–induced SCCs (WTSCC); and SCCs obtained from individuals with defective nucleotide excision repair (xeroderma pigmentosum patients; XPC and XPD). The y axis shows, for each sample, the fold decrease in the untranscribed strand mutation rate in genes with greater than 38.1 reads per kilobase of transcript per million mapped reads (RPKM), compared to genes with an RPKM of 0, for regions of high H3K9me3 density [>10 median chromatin immunoprecipitation sequencing intensity (42)]. Note that RDEBSCC_05 has fewer mutations (73 nonsynonymous mutations) and a lower contribution of UV mutation (15%) compared with RDEBSCC_02 and RDEBSCC_03 (334 and 153 nonsynonymous mutations, >50% UV contribution).

  • Fig. 5 RNA sequencing identifies increased APOBEC3 gene expression in RDEB SCC.

    (A) The y axis displays number of log2 fold change compared to control housekeeping mRNA expression for 9 of 11 APOBEC-related genes. Norm, normal; Tum, tumor; Ctrl. avg, control average. (B) The y axis displays number of log2 fold change in SCC over normal, and x axis shows each of 11 APOBEC-related genes. RDEB SCC has the greatest increase in APOBEC3 gene expression comparing UV SCC and all other SCC cancers profiled in the Cancer Genome Atlas (TCGA). (C) qPCR measurement of APOBEC3A, APOBEC3B, and APOBEC3H mRNA expression relative to ACTB from three separate RDEB SCC and surrounding tissue.

  • Fig. 6 M-WES in RDEB SCC.

    Separate regions of five different tumors (A and B) were compared with respect to mutation type and mutation signature. Heat maps show the presence (color indicated by key) or absence (white) of a somatic mutation in each tumor region (T). Each gene is arranged in a row, and putative driver mutations are indicated. The pie chart indicates the proportion of somatic mutations attributed to a given mutation signature, as described. Trunk refers to those somatic mutations shared by different tumor regions, and branch indicates private mutations, which were analyzed together for mutation signatures. (C) Primary (T1, RDEBSCC_20) and rapid recurrence (T2, RDEBSCC_25) were compared with respect to mutation type and mutation signature.

  • Fig. 7 RDEB SCC transcriptomes resemble HNSCC.

    (A) Normalized enrichment scores for each SCC signature compared to normal tissue were determined, and cancer types ranked by similarity clockwise on a CIRCOS plot. The outer ring shows enrichment of up-regulated transcripts, and the inner ring shows enrichment of down-regulated transcripts. Using this analysis, RDEB SCC was most closely related to the basal and mesenchymal subtypes of HNSCC, whereas UV SCC was more closely related to the atypical subtype of HNSCC and esophageal (ESCA) SCC. LUSCC, lung SCC; CSCC, cervical SCC. (B) KEGG (Kyoto Encyclopedia of Genes and Genomes) analysis identified inflammatory-associated pathways (red) increased in RDEB SCC. Pathways shown P < 0.001, increasing from left (P = 2.8 × 10−19) to right (P = 0.00064). ECM, extracellular matrix; PI3K, phosphatidylinositol 3-kinase; Jak, Janus kinase; STAT, signal transducers and activators of transcription.

Supplementary Materials

  • www.sciencetranslationalmedicine.org/cgi/content/full/10/455/eaas9668/DC1

    Materials and Methods

    Fig. S1. Exome and genome mutation signature profiles and evidence of strand bias support identification of signatures 7 and 5.

    Fig. S2. Signature 5 correlates with age in RDEB SCC.

    Fig. S3. RNA sequencing analysis of RDEB SCC tumors.

    Fig. S4. APOBEC gene expression in HNSCC subtypes and UV SCC.

    Fig. S5. APOBEC3A mutation motifs are enriched in RDEB SCC.

    Fig. S6. Histology of tumors used for M-WES.

    Table S1. RDEB SCC tumor sample details.

    Table S2. Somatic mutations identified across 27 RDEB SCC tumors.

    Table S3. MuSiC output analysis of 27 RDEB SCC tumors.

    Table S4. MutSigCV output analysis of 27 RDEB SCC tumors.

    Table S5. Copy number variations identified across 27 RDEB SCC tumors.

    References (4355)

  • The PDF file includes:

    • Materials and Methods
    • Fig. S1. Exome and genome mutation signature profiles and evidence of strand bias support identification of signatures 7 and 5.
    • Fig. S2. Signature 5 correlates with age in RDEB SCC.
    • Fig. S3. RNA sequencing analysis of RDEB SCC tumors.
    • Fig. S4. APOBEC gene expression in HNSCC subtypes and UV SCC.
    • Fig. S5. APOBEC3A mutation motifs are enriched in RDEB SCC.
    • Fig. S6. Histology of tumors used for M-WES.
    • References (4355)

    [Download PDF]

    Other Supplementary Material for this manuscript includes the following:

    • Table S1 (Microsoft Excel format). RDEB SCC tumor sample details.
    • Table S2 (Microsoft Excel format). Somatic mutations identified across 27 RDEB SCC tumors.
    • Table S3 (Microsoft Excel format). MuSiC output analysis of 27 RDEB SCC tumors.
    • Table S4 (Microsoft Excel format). MutSigCV output analysis of 27 RDEB SCC tumors.
    • Table S5 (Microsoft Excel format). Copy number variations identified across 27 RDEB SCC tumors.

    [Download Tables S1 to S5]

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