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. 2012 Oct;44(10):1104-10.
doi: 10.1038/ng.2396. Epub 2012 Sep 2.

Integrative genome analyses identify key somatic driver mutations of small-cell lung cancer (V体育官网入口)

Martin Peifer  1 , Lynnette Fernández-Cuesta, Martin L Sos, Julie George, Danila Seidel, Lawryn H Kasper, Dennis Plenker, Frauke Leenders, Ruping Sun, Thomas Zander, Roopika Menon, Mirjam Koker, Ilona Dahmen, Christian Müller, Vincenzo Di Cerbo, Hans-Ulrich Schildhaus, Janine Altmüller, Ingelore Baessmann, Christian Becker, Bram de Wilde, Jo Vandesompele, Diana Böhm, Sascha Ansén, Franziska Gabler, Ines Wilkening, Stefanie Heynck, Johannes M Heuckmann, Xin Lu, Scott L Carter, Kristian Cibulskis, Shantanu Banerji, Gad Getz, Kwon-Sik Park, Daniel Rauh, Christian Grütter, Matthias Fischer, Laura Pasqualucci, Gavin Wright, Zoe Wainer, Prudence Russell, Iver Petersen, Yuan Chen, Erich Stoelben, Corinna Ludwig, Philipp Schnabel, Hans Hoffmann, Thomas Muley, Michael Brockmann, Walburga Engel-Riedel, Lucia A Muscarella, Vito M Fazio, Harry Groen, Wim Timens, Hannie Sietsma, Erik Thunnissen, Egbert Smit, Daniëlle A M Heideman, Peter J F Snijders, Federico Cappuzzo, Claudia Ligorio, Stefania Damiani, John Field, Steinar Solberg, Odd Terje Brustugun, Marius Lund-Iversen, Jörg Sänger, Joachim H Clement, Alex Soltermann, Holger Moch, Walter Weder, Benjamin Solomon, Jean-Charles Soria, Pierre Validire, Benjamin Besse, Elisabeth Brambilla, Christian Brambilla, Sylvie Lantuejoul, Philippe Lorimier, Peter M Schneider, Michael Hallek, William Pao, Matthew Meyerson, Julien Sage, Jay Shendure, Robert Schneider, Reinhard Büttner, Jürgen Wolf, Peter Nürnberg, Sven Perner, Lukas C Heukamp, Paul K Brindle, Stefan Haas, Roman K Thomas
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Integrative genome analyses identify key somatic driver mutations of small-cell lung cancer (VSports app下载)

Martin Peifer et al. Nat Genet. 2012 Oct.

"V体育官网" Abstract

Small-cell lung cancer (SCLC) is an aggressive lung tumor subtype with poor prognosis. We sequenced 29 SCLC exomes, 2 genomes and 15 transcriptomes and found an extremely high mutation rate of 7. 4±1 protein-changing mutations per million base pairs. Therefore, we conducted integrated analyses of the various data sets to identify pathogenetically relevant mutated genes. In all cases, we found evidence for inactivation of TP53 and RB1 and identified recurrent mutations in the CREBBP, EP300 and MLL genes that encode histone modifiers. Furthermore, we observed mutations in PTEN, SLIT2 and EPHA7, as well as focal amplifications of the FGFR1 tyrosine kinase gene. Finally, we detected many of the alterations found in humans in SCLC tumors from Tp53 and Rb1 double knockout mice. Our study implicates histone modification as a major feature of SCLC, reveals potentially therapeutically tractable genomic alterations and provides a generalizable framework for the identification of biologically relevant genes in the context of high mutational background. VSports手机版.

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Figures

Figure 1
Figure 1
a) Copy number analysis to detect significantly altered regions across 63 tumors. Statistical significance, expressed by q-values (x-axes), is computed for each genomic location (y-axis) (Supplementary Information). Deletions (blue lines, lower scale) and amplifications (red lines, upper scale) are analyzed independently and vertical dashed black lines indicate the significance threshold of 1%. Focally amplified and deleted regions were identified using narrow thresholds (upper quantile: 10%; lower quantile: 15%) to resolve CNAs down to candidate driver genes. b) CNAs of chromosome 8 containing FGFR1 (8p12). Samples are sorted according to the amplitude of FGFR1 amplification. c) FISH analysis to screen for FGFR1 amplifications in an independent set of 51 tumors. Quantification of green signals (FGFR1 specific probe) in comparison to red signals (centromere 8 probe) reveals three FGFR1 amplified samples. d) Copy number analysis based on array-CGH data of 20 SCLC tumors derived from p53/Rb1-deficient mice. Data was analyzed similar to the analysis presented in a). Due to the small sample size, we used a significance threshold of 5% (vertical dashed lines). e) Circos plot of all validated chimeric transcripts detected by transcriptome sequencing. f) Circos plot of validated genomic rearrangements obtained from whole genome sequencing. Both rearrangements affect only portions of the genome smaller than 500kbp. While the structural variant in sample S00841 affects non-coding DNA, the rearrangement in S00830 leads to a loss of exon 7 to 11 of the gene FOXP1.
Figure 2
Figure 2
a) Comparison of broad structural genome alterations between surgically resected and autopsy samples. The analysis is based on absolute copy numbers determined using a reconstruction of the allelic state (Supplementary Note). A broad alteration is assigned to be present if 1/4 of the chromosome arm is altered accordingly. Difference between resected and autopsy samples of broad CNAs in 3p, 3q, 5p, 13q, and 17p were statistically tested by a Fisher’s exact test. b) Distribution of the mutation frequency observed in SCLC (points: resected cases; squares: autopsy samples; diamonds: cell lines). The average of the mutation frequency in SCLC (red lines and label) is compared to various tumor types taken from recent large-scale sequencing studies of: melanoma (MEL), SCLC38, breast cancer (BC), ovarian cancer (OC), multiple myeloma (MM), ovarian clear cell carcinoma (OCC), prostate cancer (PC), renal cell cancer (RC), and chronic lymphocytic leukemia (CLL). c) A schema showing the various steps of our integrated analysis and filtering procedures. All candidate driver genes extracted from sequencing are filtered against gene expression derived from transcription sequencing. CNAs are identified from SNP arrays and candidate CNA regions that are entirely driven by a single SCLC sample were subsequently removed. d) Candidate driver genes identified by significance analysis, presence in the COSMIC database, clustered mutations, and genes that are also involved in fusion events. The type of each mutation is shown for every sample including the gene specific total number of mutated samples.
Figure 3
Figure 3
a) The spectrum of mutations affecting SLIT2. Red mutation labels indicate mutations detected by exome sequencing and black labels indicate the results of the extended screen using 454 sequencing. b) Mutations in CREBBP and EP300. Similar to a), red mutation labels indicate mutations discovered by whole exome sequencing, whereas black mutation labels show the results from the extended sequencing around the HAT domain. c) The structure of the chimeric transcript affecting RHBDF1 and CREBPP is shown. Note, that the genomic scale has been adapted to accommodate exons from both genes (axis break, dashes). Chimeric reads are shown below. d) Cell lines that show abnormal signals in the break-apart FISH assay of CREBBP/EP300. In case of CREBBP, both cell lines are showing a loss of the telomeric signal (red signal). For EP300 one cell line also showed a loss of the telomeric signal (here green signal). Break-apart FISH results for CREBBP in H209 are shown as a control38. e) Copy number status for CREBBP and EP300 of all samples that show a deletion in one of the two genes (copy numbers ≤ 1.6 are considered as being deleted). Copy numbers are sorted with respect to the minimal copy number between CREBBP and EP300.
Figure 4
Figure 4
a) CREBBP/EP300 mutations mapped to the crystal structure of the EP300 HAT domain. All mutations are positioned at the molecular interface involved in Lys-CoA inhibitor binding. In particular, Asp1399 and Gln1455 (equivalent to CREBBP Asp1435 and Gln1491) are located on the substrate-binding loop L1 (red). b) Immunofluorescence was applied to measure levels of acetylated lysine 18 on histone H3 (H3K18Ac) in wild-type MEFs, Crebbp/Ep300 dKO MEFs and dKO MEFs transduced with retroviruses expressing wild-type or SCLC-derived mutants of mouse Crebbp. Human mutations were made at the equivalent murine amino acid, but human numbering is shown in labels. Crebbp-HA signal, red (CY3); H3K18Ac, green (Alexa 488); nuclei, blue (DAPI). The functionally defective Trp1502Ala/Tyr1503Ser81 was included as a control. c) Quantification of H3K18Ac mean signal intensity per nucleus relative to the HA-tagged Crebbp mean signal intensity. P-values shown are from Bonferroni post test of one way ANOVA. * P<0.05, **** P<0.0001. d) Whole cell lysates of DMS114 cells stably infected with lentiviruses expressing shRNAs targeting CREBBP were analyzed for CREBBP protein levels by immunoblotting. e) DMS114 cells stably infected with lentiviral shRNAs targeting CREBBP or the indicated control cells were seeded in 6-well plates and counted as triplicates at the indicated time points (x-axis). Absolute numbers are given on the y-axis and error bars are showing one standard deviation of the mean.
Figure 5
Figure 5
a) Analysis of copy-neutral LOH events (CNLOH) in SCLC. The allelic state of each exome-sequenced sample was reconstructed by applying a detailed mathematical model (Supplementary Information). Genomic portions that showed a loss of heterozygosity (LOH) and an absolute copy number equal to the overall samples’ ploidy are classified CNLOH events. Only samples showing at least one CNLOH event are shown. An analysis of the allelic fraction of somatic mutations in CNLOH regions yields information about the timing of these mutational events. b) TP53 and RB1 mutations in CNLOH regions. c) Distribution of mutations (including rearrangements), hemizygous deletions, and LOH affecting TP53 and RB1 across all exome-sequenced samples. d) SCLC driver genes and their mutation frequency mapped to signaling pathways. We classified the occurring mutations into 5 major groups: receptor tyrosine kinase (RTK) alterations, PI3-kinase and p53 pathway, cell cycle control, histone modifiers, and regulation of actin cytoskeleton.
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References

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