Detailed Materials and Methods Section
This document contains a more detailed methods section than that provided in
Horvath S, Zhang B, Carlson M, Lu KV, Zhu S, Felciano RM, Laurance MF, Zhao
W, Shu, Q, Lee Y, Scheck AC, Liau LM, Wu H, Geschwind DH, Febbo PG,
Kornblum HI, Cloughesy TF, Nelson SF Mischel PS (2006) Analysis of Oncogenic
Signaling Networks in Glioblastoma Identifies ASPM as a Novel Molecular Target.
PNAS
METHODS:
Patient samples:
120 glioblastoma patient samples with sufficient high quality RNA were available
for molecular analysis. Tumor samples were obtained at the time of surgery in
accordance with a UCLA IRB-approved protocol. The diagnosis was confirmed by
frozen section and reviewed by subsequent at least two neuropathologists. Dataset 1,
consisted of 55 glioblastomas (21). Dataset 2 consisted of the next 65 glioblastoma
samples available.
Microarray Data
Gene expression profiling with Affymetrix high density oligonucleotide
microarrays was performed and analyzed as previously described(21). 10 g of total
RNA was reverse transcribed, second strand synthesis performed, and in vitro
transcription performed using biotinylated nucleotides using the ENZO BioArray High
Yield Kit (Enzo Diagnostics, Farmingdale, NY). 15 g of fragmented cRNA probe was
hybridized to Affymetrix HG-U133A arrays (Affymetrix) and stained with streptavidinphycoerythrin. Microarrays were scanned in the Genearray Scanner (Hewlett Packard) to
a normalized target intensity of 2500. Microarray Suite 5.0 was used to define
absent/present calls and generate cel files using the default settings. All grid placements
and microarrays were visually reviewed for accuracy and array defects. Data files (cel)
were uploaded into the dCHIP program (http://www.dchip.org/) and normalized to the
median intensity array. Quantification was performed using model-based expression and
the perfect match minus mismatch method implemented in dCHIP.
Software Tutorial and Data Availability
We provide the entire statistical code used for generating the weighted gene coexpression network results so that the reader can reproduce all of our findings. A
weighted gene co-expression network analysis tutorial and the data files can be found at
the following webpage:
http://www.genetics.ucla.edu/labs/horvath/CoexpressionNetwork/ASPMgene
Generation of weighted gene coexpression network:
Genes with expression levels that are highly correlated are biologically
interesting, since they imply common regulatory mechanisms or participation in similar
biological processes. To construct a network from microarray gene-expression data, we
begin by calculating the Pearson correlations for all pairs of genes in the network.
Because microarray data can be noisy and the number of samples is often small, we
weight the Pearson correlations by taking their absolute value and raising them to the
power ß. This step effectively serves to emphasize strong correlations and punish weak
correlations on an exponential scale. These weighted correlations, in turn, represent the
connection strengths between genes in the network. By adding up these connection
strengths for each gene, we produce a single number (called connectivity, or k) that
describes how strongly that gene is connected to all other genes in the network. The
weighted network construction was performed using R as described in (22). Briefly, the
absolute value of the Pearson correlation coefficient was calculated for all pair-wise
comparisons of gene-expression values across all microarray samples. The Pearson
correlation matrix was then transformed into an adjacency matrix A, i.e. a matrix of
connection strengths using a power function. Thus, the connection strength aij between
gene expressions xi and xj is defined as aij | cor ( xi , xu ) | . The resulting weighted
network represents an improvement over unweighted networks based on dichotomizing
the correlation matrix, since a) the continuous nature of the gene co-expression
information is preserved and b) the results of weighted network analyses are highly
robust with respect to the choice of the parameter ß, whereas unweighted networks
display sensitivity to the choice of the cutoff. Gene expression networks, like virtually all
types of biological networks, have been found to exhibit an approximate scale free
topology. To choose a particular power ß, we used the scale-free topology criterion on the
8000 most varying genes but our findings are highly robust with respect to the choice of
ß. We chose a power ß=6, which is large enough so that the resulting network exhibited
approximate scale free topology (model fitting index R-squared=0.95). The network
connectivity ki of the i-th gene expression profile xi is the sum of the connection
strengths with all other genes in the network, i.e. ki   aiu   | cor ( xi , xu ) |
u i
u i
The next step in network construction is to identify groups of genes with similar patterns
of connection strengths by searching for genes with high topological overlap (22, 63).
The use of topological overlap serves as a filter to exclude spurious or isolated
connections during network construction.
To calculate the topological overlap for a pair of genes, we compare them in
terms of their connection strengths with all other genes in the network. For a network
represented by an adjacency matrix A  [aij ], aij  [0,1] , a well-known formula for
defining topological overlap for weighted networks is given by:
l ij  aij
 ij 
min{ k i , k j }  1  aij
where, lij 
and
a
u i , j
iu
auj denotes the number of nodes to which both i and j are connected,
k i   aiu denotes
the
connectivity.
Since
the
module
identification
is
u i
computationally intensive, only the 3600 most connected genes were considered for
module detection. Since module genes tend to have high connectivity this step does not
lead to a big loss in information. Using the topological overlap dissimilarity measure (1 –
topological overlap) in average linkage hierarchical clustering, five gene coexpression
modules were detected in the 55 training set samples. As we demonstrate in R tutorial
that can be found on our webpage, module identification is fairly robust with respect to
the dissimilarity measure; using the standard gene expression dissimilarity based on 1
minus the absolute value of the Pearson correlation produces roughly the same modules.
After identifying modules of co-expressed genes, each module in effect becomes
a new network, and a new measure of connectivity (intramodular connectivity, or kin), is
defined as the sum of a gene's connection strengths with all other genes in its module.
Scale-free networks
Scale free networks can be found in numerous domains and they are essential for
understanding gene and protein interactions. The following references may serve as
introductory reading on scale free networks: Barabási and Albert (1999), Albert and
Barabasi (2000), Albert et al (2000), Barabasi and Oltvai (2004), Jeong et al (2001).
Computing the module eigengene
To compute the module eigengene, we decomposed the standardized geneexpression profile of each module via the singular value decomposition (X=UDV T). The
first column V1 of V corresponded to the module eigengene (25).
Using GBM survival time to define a measure of prognostic gene significance
For each of the 120 glioblastoma samples (55 in dataset 1, 65 in dataset 2), patient
survival information was available. Since some of the survival times were censored, we
used a Cox proportional hazards model to regress survival time on individual gene
expression profiles. To identify a measure of prognostic gene significance for each gene,
we used a Cox proportional hazards regression model to compute a hazard ratio and a pvalue. We defined the (prognostic) gene significance of a gene by minus the logarithm of
its univariate Cox regression p-value.
To relate intramodular connectivity to prognostic gene significance we Spearman
correlation coefficients and the corresponding p-values (cor.test function in the R
software).
Breast cancer dataset
We used the Agilent microarray data that were used to find prognostic genes for breast
cancer recurrence in a published dataset (23). The processed data and a corresponding
network tutorial can be found at our webpage.
Using hierarchical clustering involving all genes, we identified one sample (S54) in the
metastasis group as a data outlier and excluded it from further analysis. Since we were
mainly interested in studying the preservation of the GBM module color assignment in
the breast cancer network, we mapped the 3600 most connected GBM genes into the
Agilent microarray data. Carrying out a distinct network analysis of the breast cancer data
is beyond the scope of this article and will be reported elsewhere. We assigned colors to
the breast cancer gene expression profiles according to the module assignment in the
GBM 55 data. Next we constructed a weighted gene co-expression network using the
power beta=6.Using the recurrence free survival time, we defined the gene significance
of the i-th gene expression profile xi as minus log (with base 10) of the Spearman
correlation test p-value, i.e. GSi=log10(Spearman p-value).
Statistical Robustness
Our results regarding the relationship between connectivity and prognostic gene
significance are highly robust with regard to how the connectivity measure is defined
(robust to choice of the power beta, the type of network used: weighted or unweighted)
and with respect to how the gene significance measure was defined (see our online
network tutorials).
Pathway analysis of hub genes:
The Ingenuity Pathways Knowledge Base (IPKB) was used to identify to the
subnetwork of potential interactions (64). This subnetwork was further partitioned into
smaller subnetworks by optimizing the local network density using the specificity
connectivity of each focus gene (the relative percentage of its network connections to
other focus genes). The initiation and growth of pathways proceeded from genes with the
highest specificity of connections, and expanded to include up to 35 genes per network
by iteratively selecting additional highly-specific network neighbors.
Genomic and functional analysis in glioblastoma cell lines:
The isogenic U87MG expressing PTEN, EGFR and EGFRvIII in varying
combinations have been previously reported (2). In brief, cell lines were grown in
duplicate cultures under serum free conditions for 48 hours, and RNA was isolated using
the Qiagen RNeasy Mini Kit Gene. Expression analysis using Affymetrix HG-U133A
arrays was performed and analyzed, as described above.
EGFR inhibitor treatment:
The EGFR tyrosine kinase inhibitor Erlotinib (Tarceva, OSI-774) was kindly
provided by Genentech, Inc. (South San Francisco, CA). 1x105 U87MG and U87EGFRvIII cells were seeded respectively in 100mm culture dishes and maintained in
DMEM medium supplemented with 10% FBS. Cells were incubated in 5% CO2, 95%
humidity incubator for 3 days to reach 50-70% confluency. Then all cells were switched
to serum-free medium. The next day U87-EGFRvIII cells were treated by 5uM OSI-774
while U87MG and U87-EGFRvIII control group receiving the equivalent vehicle. 24 hrs
later, cell total RNA was isolated by Qiagen RNeasy Mini Kit.
RT-PCR was performed for the following genes: ASPM, PRC1, AURKB, MELK,
PTTG1 and TOP2A. RNA expression level was detected by semi-quantitatively. The
PCR Primers used are:
ASPM: forward ATCTCAAACGCCATCAGG, reverse
CATTTTACGTTGCTTCCATTT; PRC1: forward AGCTCCACGATGCTGAGATT,
reverse
ACTATTGGCCGTAGCATTGG
;
AURKB:
forward
CTATCGCCGCATCGTCAAGGTG,
reverse
GCAGCCGTTCCGAGGGGTTAT;
MELK:
forward
CTCCGCCCCTCAGGTTCTTTTTCT,
reverse
AGCCACCTGTCCCAATAGTTTCAT;
PTTG1:
forward
ATGCCCCACCAGCCTTACCTA, reverse GCTTGGCTGTTTTTGTTTGAG; TOP2A:
forward CATTGGCTGTGGTATTGTAGAAAG, reverse GGCCCCCTGCATCATTGG.
siRNA mediated inhibition of ASPM
GM1600 low passage patient derived glioblastoma cells were established from a
glioblastoma patient resection and cultured as previously described. Cells were
maintained in IMEM medium supplied with 10% fetal bovine serum (FBS).
ASPM SiRNA sequences were designed by Dharmacon siDESIGN tool.
(http://www.dharmacon.com/sidesign/default.aspx?source=0). 5’ overhang sequences for
BglII and SalI were added for cloning. The ASPM SiRNA sequences were: forward
gatccccCTGGTTCAGTGGTATTAAAttcaagagaTTTAATACCACTGAACCAGtttttggaac,
reverse
tcgagttccaaaaaCTGGTTCAGTGGTATTAAAtctcttgaaTTTAATACCACTGAACCAGggg. The
ASPM SiRNA sequence was manually scrambled for generating scrambled ASPM
siRNA as a control, and the mock sequence was blasted with NCBI nucleotide database
yielding no significant alignments with other sequences. The mock SiRNA sequences
were:
forward
gatccccTGTAGATACGTAGCTATGTttcaagagaACATAGCTACGTATCTACAtttttggaac,
reverse
tcgagttccaaaaaTGTAGATACGTAGCTATGTtctcttgaaACATAGCTACGTATCTACAggg. The
SiRNA DNA oligos were synthesized and PAGE purified by IDT, Inc. The forward and
reverse oligos were annealed at the presence of 50mM NaCl and cloned into retroviral
vector LTRH1 (a gift from Dr. Medzhitov) BglII and SalI cloning site. The constructs
were retro-virally infected into CD and HEK293T cells. Infected cells were sparsely
seeded into 100mm dishes. Cells were maintained in 5% CO2, 95% humidity incubator
until individual clones grew up. Cell clones were individually picked and expanded. Total
RNA were isolated by Qiagen RNeasy Mini Kit and screened by semi-qRT-PCR for
ASPM RNA expression level. The ASPM primer sequences used in RT-PCR were:
forward ATCTCAAACGCCATCAGG, reverse CATTTTACGTTGCTTCCATTT.
For proliferation assays, 1500 cells/well in 8 replicates were seeded into 96 well
plates. Cells were fixed and stained by 0.25% crystal violet in methanol every day or
every other day. Stained plates were densitometry scored by AlphaImager 2200 software
and plot in Microsoft Excel.
Neurosphere cell culture and transfection
Cerebral cortex was isolated from E12 mice. Cells were dissociated and cultured
at 50,000 cells/ml in neurosphere formation medium (Neural Basal medium (Invitrogen)
with B27 (GIBCO BRL), bFGF (Peprotech), EGF (Chemicon), heparin (Sigma-Aldrich)
and penicillin-streptomycin (Gemini Bioproducts)) for a week. Growth factors were
added every three days. Neurospheres were dissociated and plated onto poly-L-ornithine
(Sigma)/ fibronectin coated 6-well plates in Neural Basal medium with 2% FBS (GIBCO
BRL). 6 hours later, the serum medium was removed and replaced with neurosphere
formation medium without heparin and penicillin-streptomycin. 24 hours later, cells were
transfected with 100nM siRNA targeting Aspm and 100nM control siRNA targeting
firefly luciferase using lipofectAMINE 2000 (Invitrogen). The cells were incubated with
reagents for 6 hours and passaged for secondary neurosphere formation assay.
Secondary neurosphere formation assay
Cells were lifted off the plate with TriplExpress (GIBCO BRL), and then placed
into neurosphere formation medium at 1000 cells/ml and 100 cells/ml. Neurospheres
were propagated for 1 week and the number and the size of the secondary neurospheres
formed were measured using Microcomputer Imaging Device program (MCID).
siRNA synthesis
siRNA was synthesized using the Silencer siRNA Construction Kit (Ambion).
Three contructs were generated targeting different coding regions of Aspm, and one of
them was demonstrated to knockdown Aspm efficiently and was chosen for further
analysis.
Aspm target sequence: AATACAAAGCACGTACAGGAT
Semiquantitative RT-PCR
Total RNA was isoalted using TRIzol (GIBCO BRL), and 1μg RNA was
converted to cDNA by reverse transcriptase (Imrpon). The amount of cDNA was
measured by RT-PCR using prmers for GADPH. After adjustment, equal amount of
cDNA for each sample was subjected to PCR analysis using gene-specific primers.
Some references on scale free networks
 Albert, R. and Barabasi, A. L (2000) Topology of evolving networks: local events
and universality. Phys Rev Lett, vol 85, No 24, pp5234-7.
 Albert, R. and Jeong, H. and Barabasi, A. L. (2000) Error and attack tolerance of
complex networks. Nature, vol 406, No.6794, pp378-82
 Barabási AL, Albert R. (1999). Emergence of scaling in random networks
science. Science, 286 (5439), 509-512.
 Barabasi AL and Oltvai ZN (2004) Network Biology: Understanding the Cells's
Functional Organization. Nature Reviews Genetics (5) p101-113
 H. Jeong and S. P. Mason and A. L. Barabási and Z. N. Oltvai (2001) Lethality
and centrality in protein networks. Nature. Vol 411, pp 41-43
REFERENCES
1.
2.
3.
Sawyers, C. (2004) Nature 432, 294-7.
Mellinghoff, I. K., Wang, M. Y., Vivanco, I., Haas-Kogan, D. A., Zhu, S., Dia, E.
Q., Lu, K. V., Yoshimoto, K., Huang, J. H., Chute, D. J., Riggs, B. L., Horvath,
S., Liau, L. M., Cavenee, W. K., Rao, P. N., Beroukhim, R., Peck, T. C., Lee, J.
C., Sellers, W. R., Stokoe, D., Prados, M., Cloughesy, T. F., Sawyers, C. L. &
Mischel, P. S. (2005) N Engl J Med 353, 2012-24.
Valk, P. J., Verhaak, R. G., Beijen, M. A., Erpelinck, C. A., Barjesteh van
Waalwijk van Doorn-Khosrovani, S., Boer, J. M., Beverloo, H. B., Moorhouse,
M. J., van der Spek, P. J., Lowenberg, B. & Delwel, R. (2004) N Engl J Med 350,
1617-28.
4.
5.
6.
7.
8.
9.
10.
11.
12.
13.
14.
15.
16.
17.
18.
19.
20.
21.
22.
Shipp, M. A., Ross, K. N., Tamayo, P., Weng, A. P., Kutok, J. L., Aguiar, R. C.,
Gaasenbeek, M., Angelo, M., Reich, M., Pinkus, G. S., Ray, T. S., Koval, M. A.,
Last, K. W., Norton, A., Lister, T. A., Mesirov, J., Neuberg, D. S., Lander, E. S.,
Aster, J. C. & Golub, T. R. (2002) Nat Med 8, 68-74.
Perou, C. M., Sorlie, T., Eisen, M. B., van de Rijn, M., Jeffrey, S. S., Rees, C. A.,
Pollack, J. R., Ross, D. T., Johnsen, H., Akslen, L. A., Fluge, O.,
Pergamenschikov, A., Williams, C., Zhu, S. X., Lonning, P. E., Borresen-Dale, A.
L., Brown, P. O. & Botstein, D. (2000) Nature 406, 747-52.
Monti, S., Savage, K. J., Kutok, J. L., Feuerhake, F., Kurtin, P., Mihm, M., Wu,
B., Pasqualucci, L., Neuberg, D., Aguiar, R. C., Dal Cin, P., Ladd, C., Pinkus, G.
S., Salles, G., Harris, N. L., Dalla-Favera, R., Habermann, T. M., Aster, J. C.,
Golub, T. R. & Shipp, M. A. (2005) Blood 105, 1851-61.
Liu, E. T. & Karuturi, K. R. (2004) N Engl J Med 350, 1595-7.
Ebert, B. L. & Golub, T. R. (2004) Blood 104, 923-32.
Bullinger, L., Dohner, K., Bair, E., Frohling, S., Schlenk, R. F., Tibshirani, R.,
Dohner, H. & Pollack, J. R. (2004) N Engl J Med 350, 1605-16.
Hood, L., Heath, J. R., Phelps, M. E. & Lin, B. (2004) Science 306, 640-3.
Lin, B., White, J. T., Lu, W., Xie, T., Utleg, A. G., Yan, X., Yi, E. C., Shannon,
P., Khrebtukova, I., Lange, P. H., Goodlett, D. R., Zhou, D., Vasicek, T. J. &
Hood, L. (2005) Cancer Res 65, 3081-91.
Hood, L. & Perlmutter, R. M. (2004) Nat Biotechnol 22, 1215-7.
Bild, A. & Febbo, P. G. (2005) Proc Natl Acad Sci U S A 102, 15278-9.
Bild, A. H., Yao, G., Chang, J. T., Wang, Q., Potti, A., Chasse, D., Joshi, M. B.,
Harpole, D., Lancaster, J. M., Berchuck, A., Olson, J. A., Jr., Marks, J. R.,
Dressman, H. K., West, M. & Nevins, J. R. (2006) Nature 439, 353-7.
Subramanian, A., Tamayo, P., Mootha, V. K., Mukherjee, S., Ebert, B. L.,
Gillette, M. A., Paulovich, A., Pomeroy, S. L., Golub, T. R., Lander, E. S. &
Mesirov, J. P. (2005) Proc Natl Acad Sci U S A 102, 15545-50.
Segal, E., Friedman, N., Koller, D. & Regev, A. (2004) Nat Genet 36, 1090-8.
Aggarwal, A., Guo, D. L., Hoshida, Y., Yuen, S. T., Chu, K. M., So, S.,
Boussioutas, A., Chen, X., Bowtell, D., Aburatani, H., Leung, S. Y. & Tan, P.
(2006) Cancer Res 66, 232-41.
Mootha, V. K., Lindgren, C. M., Eriksson, K. F., Subramanian, A., Sihag, S.,
Lehar, J., Puigserver, P., Carlsson, E., Ridderstrale, M., Laurila, E., Houstis, N.,
Daly, M. J., Patterson, N., Mesirov, J. P., Golub, T. R., Tamayo, P., Spiegelman,
B., Lander, E. S., Hirschhorn, J. N., Altshuler, D. & Groop, L. C. (2003) Nat
Genet 34, 267-73.
Lamb, J., Ramaswamy, S., Ford, H. L., Contreras, B., Martinez, R. V., Kittrell, F.
S., Zahnow, C. A., Patterson, N., Golub, T. R. & Ewen, M. E. (2003) Cell 114,
323-34.
Kang, Y. (2005) Expert Rev Mol Diagn 5, 385-95.
Freije, W. A., Castro-Vargas, F. E., Fang, Z., Horvath, S., Cloughesy, T., Liau, L.
M., Mischel, P. S. & Nelson, S. F. (2004) Cancer Res 64, 6503-10.
Zhang, B. a. H. S. (2005) Statistical Applications in Genetics and Molecular
Biology 4.
23.
24.
25.
26.
27.
28.
29.
30.
31.
32.
33.
34.
35.
36.
37.
38.
39.
40.
41.
42.
43.
44.
van 't Veer, L. J., Dai, H., van de Vijver, M. J., He, Y. D., Hart, A. A., Mao, M.,
Peterse, H. L., van der Kooy, K., Marton, M. J., Witteveen, A. T., Schreiber, G. J.,
Kerkhoven, R. M., Roberts, C., Linsley, P. S., Bernards, R. & Friend, S. H. (2002)
Nature 415, 530-6.
Rhodes, D. R., Yu, J., Shanker, K., Deshpande, N., Varambally, R., Ghosh, D.,
Barrette, T., Pandey, A. & Chinnaiyan, A. M. (2004) Proc Natl Acad Sci U S A
101, 9309-14.
Alter, O., Brown, P. O. & Botstein, D. (2000) Proc Natl Acad Sci U S A 97,
10101-6.
Quinones-Hinojosa, A., Sanai, N., Smith, J. S. & McDermott, M. W. (2005) J
Neurooncol 74, 19-30.
Han, J. D., Bertin, N., Hao, T., Goldberg, D. S., Berriz, G. F., Zhang, L. V.,
Dupuy, D., Walhout, A. J., Cusick, M. E., Roth, F. P. & Vidal, M. (2004) Nature
430, 88-93.
Jeong, H., Mason, S. P., Barabasi, A. L. & Oltvai, Z. N. (2001) Nature 411, 41-2.
Carlson, M. R., Zhang, B., Fang, Z., Mischel, P. S., Horvath, S. & Nelson, S. F.
(2006) BMC Genomics 7, 40.
Basso, K., Margolin, A. A., Stolovitzky, G., Klein, U., Dalla-Favera, R. &
Califano, A. (2005) Nat Genet 37, 382-90.
Barabasi, A. L. & Oltvai, Z. N. (2004) Nat Rev Genet 5, 101-13.
Choe, G., Horvath, S., Cloughesy, T. F., Crosby, K., Seligson, D., Palotie, A.,
Inge, L., Smith, B. L., Sawyers, C. L. & Mischel, P. S. (2003) Cancer Res 63,
2742-6.
Ermoian, R. P., Furniss, C. S., Lamborn, K. R., Basila, D., Berger, M. S.,
Gottschalk, A. R., Nicholas, M. K., Stokoe, D. & Haas-Kogan, D. A. (2002) Clin
Cancer Res 8, 1100-6.
Smith, J. S., Tachibana, I., Passe, S. M., Huntley, B. K., Borell, T. J., Iturria, N.,
O'Fallon, J. R., Schaefer, P. L., Scheithauer, B. W., James, C. D., Buckner, J. C.
& Jenkins, R. B. (2001) J Natl Cancer Inst 93, 1246-56.
Hamid, T., Malik, M. T. & Kakar, S. S. (2005) Mol Cancer 4, 3.
Horwitz, G. A., Miklovsky, I., Heaney, A. P., Ren, S. G. & Melmed, S. (2003)
Mol Endocrinol 17, 600-9.
Larsen, A. K. & Gobert, C. (1999) Pathol Oncol Res 5, 171-8.
Sellers, W. R. & Loda, M. (2002) Cancer Cell 2, 349-50.
Andrews, P. D. (2005) Oncogene 24, 5005-15.
Altieri, D. C. (2003) Oncogene 22, 8581-9.
Bond, J., Roberts, E., Mochida, G. H., Hampshire, D. J., Scott, S., Askham, J. M.,
Springell, K., Mahadevan, M., Crow, Y. J., Markham, A. F., Walsh, C. A. &
Woods, C. G. (2002) Nat Genet 32, 316-20.
Bond, J., Scott, S., Hampshire, D. J., Springell, K., Corry, P., Abramowicz, M. J.,
Mochida, G. H., Hennekam, R. C., Maher, E. R., Fryns, J. P., Alswaid, A., Jafri,
H., Rashid, Y., Mubaidin, A., Walsh, C. A., Roberts, E. & Woods, C. G. (2003)
Am J Hum Genet 73, 1170-7.
Woods, C. G., Bond, J. & Enard, W. (2005) Am J Hum Genet 76, 717-28.
Mekel-Bobrov, N., Gilbert, S. L., Evans, P. D., Vallender, E. J., Anderson, J. R.,
Hudson, R. R., Tishkoff, S. A. & Lahn, B. T. (2005) Science 309, 1720-2.
45.
46.
47.
48.
49.
50.
51.
52.
53.
54.
55.
56.
57.
58.
59.
60.
61.
62.
Ponting, C. & Jackson, A. P. (2005) Curr Opin Genet Dev 15, 241-8.
Kouprina, N., Pavlicek, A., Mochida, G. H., Solomon, G., Gersch, W., Yoon, Y.
H., Collura, R., Ruvolo, M., Barrett, J. C., Woods, C. G., Walsh, C. A., Jurka, J.
& Larionov, V. (2004) PLoS Biol 2, E126.
Kouprina, N., Pavlicek, A., Collins, N. K., Nakano, M., Noskov, V. N., Ohzeki,
J., Mochida, G. H., Risinger, J. I., Goldsmith, P., Gunsior, M., Solomon, G.,
Gersch, W., Kim, J. H., Barrett, J. C., Walsh, C. A., Jurka, J., Masumoto, H. &
Larionov, V. (2005) Hum Mol Genet 14, 2155-65.
Hemmati, H. D., Nakano, I., Lazareff, J. A., Masterman-Smith, M., Geschwind,
D. H., Bronner-Fraser, M. & Kornblum, H. I. (2003) Proc Natl Acad Sci U S A
100, 15178-83.
Singh, S. K., Clarke, I. D., Terasaki, M., Bonn, V. E., Hawkins, C., Squire, J. &
Dirks, P. B. (2003) Cancer Res 63, 5821-8.
Singh, S. K., Hawkins, C., Clarke, I. D., Squire, J. A., Bayani, J., Hide, T.,
Henkelman, R. M., Cusimano, M. D. & Dirks, P. B. (2004) Nature 432, 396-401.
Galli, R., Binda, E., Orfanelli, U., Cipelletti, B., Gritti, A., De Vitis, S., Fiocco,
R., Foroni, C., Dimeco, F. & Vescovi, A. (2004) Cancer Res 64, 7011-21.
Whitfield, M. L., George, L. K., Grant, G. D. & Perou, C. M. (2006) Nat Rev
Cancer 6, 99-106.
Mischel, P. S., Cloughesy, T. F. & Nelson, S. F. (2004) Nat Rev Neurosci 5, 78292.
Bae, I., Rih, J. K., Kim, H. J., Kang, H. J., Haddad, B., Kirilyuk, A., Fan, S.,
Avantaggiati, M. L. & Rosen, E. M. (2005) Cell Cycle 4, 1641-66.
Rosty, C., Sheffer, M., Tsafrir, D., Stransky, N., Tsafrir, I., Peter, M., de
Cremoux, P., de La Rochefordiere, A., Salmon, R., Dorval, T., Thiery, J. P.,
Couturier, J., Radvanyi, F., Domany, E. & Sastre-Garau, X. (2005) Oncogene 24,
7094-104.
Sotiriou, C., Wirapati, P., Loi, S., Harris, A., Fox, S., Smeds, J., Nordgren, H.,
Farmer, P., Praz, V., Haibe-Kains, B., Desmedt, C., Larsimont, D., Cardoso, F.,
Peterse, H., Nuyten, D., Buyse, M., Van de Vijver, M. J., Bergh, J., Piccart, M. &
Delorenzi, M. (2006) J Natl Cancer Inst 98, 262-72.
Fish, J. L., Kosodo, Y., Enard, W., Paabo, S. & Huttner, W. B. (2006) Proc Natl
Acad Sci U S A 103, 10438-43.
Sanai, N., Alvarez-Buylla, A. & Berger, M. S. (2005) N Engl J Med 353, 811-22.
Dougherty, J. D., Garcia, A. D., Nakano, I., Livingstone, M., Norris, B.,
Polakiewicz, R., Wexler, E. M., Sofroniew, M. V., Kornblum, H. I. & Geschwind,
D. H. (2005) J Neurosci 25, 10773-85.
Nakano, I., Paucar, A. A., Bajpai, R., Dougherty, J. D., Zewail, A., Kelly, T. K.,
Kim, K. J., Ou, J., Groszer, M., Imura, T., Freije, W. A., Nelson, S. F., Sofroniew,
M. V., Wu, H., Liu, X., Terskikh, A. V., Geschwind, D. H. & Kornblum, H. I.
(2005) J Cell Biol 170, 413-27.
Karsten, S. L., Kudo, L. C., Jackson, R., Sabatti, C., Kornblum, H. I. &
Geschwind, D. H. (2003) Dev Biol 261, 165-82.
Groszer, M., Erickson, R., Scripture-Adams, D. D., Dougherty, J. D., Le Belle, J.,
Zack, J. A., Geschwind, D. H., Liu, X., Kornblum, H. I. & Wu, H. (2006) Proc
Natl Acad Sci U S A 103, 111-6.
63.
64.
Ravasz, E., Somera, A. L., Mongru, D. A., Oltvai, Z. N. & Barabasi, A. L. (2002)
Science 297, 1551-5.
Calvano, S. E., Xiao, W., Richards, D. R., Felciano, R. M., Baker, H. V., Cho, R.
J., Chen, R. O., Brownstein, B. H., Cobb, J. P., Tschoeke, S. K., Miller-Graziano,
C., Moldawer, L. L., Mindrinos, M. N., Davis, R. W., Tompkins, R. G. & Lowry,
S. F. (2005) Nature 437, 1032-7.