Post on 15-Mar-2020
Patología Molecular
Diagnóstica
• LIMS
• Gestión de peticiones electrónicas
• Visión web de informes
• Integración en la historia clínica
• Análisis de imagen
• … como las otras unidades del Servicio
Bioinformática
La aplicación de tecnología
de computación a la
gestión y análisis de datos
biológicos (Wikipedia)
Alineamiento E11
c-kit ACCTACAAAT ATTTACAGAA ACCCATGTAT GAAGTACAGT GGAAGGTTGT
E11 .......... ........AA ACCCATGTAT GAAGTACAGT GGAAGGTTGT
c3E11R CCTTTC...T CCCCACAGAA ACCCATGTAT GAAGTACAGC GGAAGGTTGT
c3E11F CCTTTC...T CCCCACAGAA ACCCATGTAT GAAGTACAGC GGAAGGTTGT
1q gain and CDT2 overexpression
underlie an aggressive and highly
proliferative form of Ewing Sarcoma
Carlos Mackintosh, José Luis Ordóñez, Daniel J. García, Victoria Sevillano, Antonio
Llombart-Bosch, Karoly Szuhai, Katia Scotlandi, Marco Alberghini, Raf Sciot, Friedl Sinnaeve, Pancras C.W. Hogendoorn, Piero
Picci, Sakari Knuutila, Uta Dirksen, Maria Debiec-Rychter, Karl-Ludwig Schaefer,
and Enrique de Álava
Oncogene 2011
Unpaired integrative analysis
Functional validation • CNA in EWS cases (EE99) - set 1 (n=67)
studied with arrayCGH and correlation with clinical data.
• Expression microarrays in EWS cases – set 2 (n=38).
– Unsupervised and supervised analysis in groups made along with their 1q - genomic status.
• Functional validation of a candidate gene located in 1q
aCGH Analysis
• Data acquisition: GenePix software
– Flagging with GPix scripts (removal of bad quality spots)
• Data processing: Segmentation, Gain/Loss Calling.
– Packages snapCGH, CGHcall (BioConductor)
• Reduction of complexity/Clustering: CGHregions, WECCA (BioC)
• Correlation with clinical data:
– CGHtest, CGHPermutations and CGHMultiarray (BioC)
• Evaluation of the overall size of genome altered
– Script “CGH altered genome”, written by myself in R language
Bioinformatics screening of CNA’s
clinical impact Test type: CGHPermutations
Test CNAs found enriched p-value / FDR q-value
#1.- Primary tumors with osseous location vs. non-
osseous NS
#2.- Primary tumors with axial location vs. those
with peripheral location NS
#3.- Relapsing tumors (metastasis and/or local
relapse) vs. non–relapsing
1q gain (proximal)
1q gain (distal)
0.015 / 0.048
0.009 / 0.048
#4.- Metastasic primary tumors vs. non-metastasic NS
#5.- Metastasis at diagnosis vs. new metastasis NS
#6.- Single metastasis vs. multiple metastases NS
#7.- Metastasis to sites including lung vs.
metastasis to sites not including lung
8p gain (entire arm)
8q gain (entire arm)
0.017 / 0.03
0.002 / 0.005
#8.- Patients younger than median age vs. older 16p loss (pter – 2Mb) 0.03 / 0.057
#9.- Cell lines vs. tumors with PGA > 1% 8q gain (124Mb – 129 Mb from pter)
3p loss (40Mb – 75Mb from pter) 9p loss (pter – 24 Mb from pter)
0.003 / 0.06
0.004 / 0.02 0.005 / 0.014
#10.- 1qGT vs. 1qNT Chr 12 gain
Chr 20 gain
16q loss (entire arm)
0.005 / 0.005
0.002 / 0.002
0.02 / 0.07
Test type: CGHlogrank
CNAs found significant OS (p-value/FDR q-value) DFS (p-value/FDR q-value)
1q gain (proximal) < 10-3 / < 10-3 < 10-3 / < 10-3
1q gain (distal) 0.002 / 0.021 0.004 / 0.03
Chr 20 gain 0.006 / 0.026 NS
Chr 22 loss 0.014 / 0.048 NS
Chr 10 loss NS 0.005 / 0.06
Unpaired integrative analysis
Functional validation • CNA in EWS cases (EE99) - set 1 (n=67)
studied with arrayCGH and correlation with clinical data.
• Expression microarrays in EWS cases – set 2 (n=38).
– Unsupervised and supervised analysis in groups made along with their 1q - genomic status.
• Functional validation of a candidate gene located in 1q
Unpaired integrative analysis
Functional validation • CNA in EWS cases (EE99) - set 1 (n=67)
studied with arrayCGH and correlation with clinical data.
• Expression microarrays in EWS cases – set 2 (n=38).
– Unsupervised and supervised analysis in groups made along with their 1q - genomic status.
• Functional validation of a candidate gene located in 1q
Classification of the ES set 2 according to
the 1qG signature (1qGSig)
• Signature Definition:74 genes located in 1q with the highest d-values and fold-change values (cut-off settled: 90th percentile, d-value > 4.9; fold change, R-fold > 1.5)
• 9 tumors out of 38 clearly positioned in a same cluster (including the 6 tumors with known 1qG)
1qG Sig
Unpaired integrative analysis
Functional validation • CNA in EWS cases (EE99) - set 1 (n=67)
studied with arrayCGH and correlation with clinical data.
• Expression microarrays in EWS cases – set 2 (n=38).
– Unsupervised and supervised analysis in groups made along with their 1q - genomic status.
• Functional validation of a candidate gene located in 1q
Classification of the ES set 2 according to
the 1qG signature (1qGSig)
• Signature Definition:74 genes located in 1q with the highest d-values and fold-change values (cut-off settled: 90th percentile, d-value > 4.9; fold change, R-fold > 1.5)
• 9 tumors out of 38 clearly positioned in a same cluster (including the 6 tumors with known 1qG)
1qG Sig
Prospective
clinical trial
Genomics
Proteomics
Functional validation
(in vitro/in vivo)
Tissue Validation xenografts
Cell lines
GE Animal
models
shRNA screens
Bioinformatics Biobanks
• MSigDB database C2: match with gene sets unveiling a lost of the cell
cycle controls
• MSigDB database C4: enrichment in hundreds of GO terms involved in
cell cycle regulation
• MSigDB database C3: enrichment in datasets belonging to genes with
promoters containing E2F - response elements
• MSigDB database C1: enrichment in genomic positional 1qG genes.
GSEA analysis: 1qGSig vs. non-
1qGSig tumors
GSEA genesets in 1qGSig tumors - IPA validation
C2 MSigDB Size NES NOM p-val FDR q-val
G1 to S cell cycle reactome 68 2.193 0.000 0.001
Pyrimidine metabolism 57 1.989 0.000 0.005
P21 any dn 34 1.967 0.000 0.006
Ren e2f1 targets 38 1.945 0.000 0.006
Schumacher myc up 49 1.938 0.000 0.006
C3 MSigDB Size NES NOM p-val FDR q-val
V$E2F Q4 01 185 2.128 0.000 0.002
V$E2F1DP1 01 185 2.009 0.002 0.002
V$E2F1 Q6 01 192 1.998 0.002 0.002
V$MYC Q2 152 1.799 0.000 0.010
V$MYCMAX 01 215 1.763 0.000 0.015
C5 MSigDB Size NES NOM p-val FDR q-val
Cell cycle go 0007049 294 2.114 0.000 0.015
Regulation of cell cycle 170 2.108 0.000 0.006
Mitotic cell cycle 140 2.098 0.000 0.004
Dna replication 93 2.095 0.000 0.004
G1 S transition of mitotic cell cycle 27 1.971 0.002 0.006
Regulation of cyclin dependent protein kinase activity
42 1.942 0.000 0.008
C1 MSigDB Size NES NOM p-val FDR q-val
CHR1Q44 42 2.333 0.000 0.000
CHR1Q42 102 2.187 0.000 0.006
CHR1Q25 73 2.022 0.002 0.034
CHR1Q22 67 1.960 0.002 0.053
CHR1Q21 212 1.915 0.002 0.057
CHR1Q41 36 1.779 0.005 0.105
CHR1Q24 48 1.720 0.014 0.147
CHR1Q32 143 1.666 0.014 0.210
CHR1Q23 78 1.630 0.014 0.235
SAM analysis to identify those 1q genes with
the highest change in expression
• ES set 2 SAM. D-value-ranked list
AFFY_ID SYMBOL CHR bp_position p.value d.value stdev q.value R.fold
222680_s_at DTL 1 210275541 0 11.8830232 0.18993904 0 4.78013054
204603_at EXO1 1 240078157 0 10.7803037 0.20495983 0 4.62524311
206102_at GINS1 20 25336322 0 10.4319029 0.1801058 0 3.67780813
208644_at PARP1 1 224615014 0 10.3203701 0.10240054 0 2.08032868
200750_s_at RAN 12 129922520 0 10.1432607 0.10119041 0 2.03693604
202420_s_at DHX9 1 181075073 0 9.65949114 0.07362746 0 1.63716957
210415_s_at ODF2 9
c(130258252,
130259107) 0 9.52624618 0.08992072 0 1.81077454
209825_s_at UCK2 1 164063513 0 9.51261952 0.15449537 0 2.76955779
211594_s_at MRPL9 1 149998746 0 9.21492519 0.0871298 0 1.74459795
207828_s_at CENPF 1 212843154 0 9.12066268 0.21432169 0 3.87650316
211519_s_at KIF2C 1 44978076 0 9.0995555 0.18756768 0 3.26431996
223229_at UBE2T 1 200567407 0 8.95465463 0.20409681 0 3.54950146
203316_s_at SNRPE 1 202097362 0 8.95076944 0.16118798 0 2.71839749
207332_s_at TFRC 3
c(-197260551, -
197260551) 0 8.9270326 0.22422384 0 4.00458714
First term of the list: high and consistent change
Fold change markedly higher than the expected due to
genomic dosage (1.5)
Unpaired integrative analysis
Functional validation • CNA in EWS cases - set 1 (n=67) and 16
EWS cell lines studied with arrayCGH and correlation with clinical data.
• Expression microarrays in EWS cases – set 2 (n=38).
– Unsupervised and supervised analysis in groups made along with their 1q - genomic status.
• Functional validation of a candidate gene located in 1q
DTL belongs to the CUL4/DDB1 ubiquitin ligase complex.
It selects the targets to be tagged for degradation
-Cdt1
-P53
-P21 & p27
E2f1
Kim, Y., Starostina, N. G., and Kipreos, E. T. (2008). The CRL4Cdt2 ubiquitin
ligase targets the degradation of p21Cip1 to control replication licensing.
Genes Dev 22, 2507-2519.
Cell Cycle 5:15, 1719-1729, August 2006
GENES & DEVELOPMENT 22:2496–2506, Sept 2008
GENES & DEVELOPMENT 22:2507–2519, Sept 2008
THE JOURNAL OF BIOLOGICAL CHEMISTRY 283: 43,
29045–29052, August 2008
Developmental Cell 15, 890–900, December 2008
Cell Cycle 5:15, 1675-1680, August 2006
Molecular Cell 23, 709–721, September 1, 2006
GENES & DEVELOPMENT 20:3117–3129, Nov 2006
Ongoing work: preclinical validation in ES of a specific
inhibitor of the CRL protein-ubiquitin-ligase complexes
XS Puente et al. Nature 000, 1-5 (2011) doi:10.1038/nature10113
Profile of somatic mutations in four CLL genomes.
XS Puente et al. Nature 000, 1-5 (2011) doi:10.1038/nature10113
Mutational and functional analysis of MYD88 in CLL.
Aspectos relevantes
• Análisis de secuencias
• Anotación de genomas
• Análisis de la expresión génica
• Análisis de mutaciones en el cáncer
• Análisis de la expresión de proteínas
• Modelado de sistemas biológicos
• Análisis de imagen de alto rendimiento
Esquema
• Introducción: dos consideraciones
• Aplicación: Tres ejemplos
• Reflexión final. Tiempo de cambio
BIOMEDICINA Y ASISTENCIA SANITARIA: CONCEPTO ACTUAL
ESTILO DE VIDA
SALUD ------------- ENFERMEDAD
AMBIENTE
DOTACIÓN GENÉTICA
RESISTENCIA ------------- SENSIBILIDAD
GENOMA PROTEOMA
MEDICINA PERSONALIZADA
TRATAMIENTO
2500 5000 7500 10000 12500 15000 17500 20000
• Prepare matrix coated tissue section (on ITO slide)
• Obtain MALDI spectra from defined positions (pixels)
200 µm pixels
MALDI imaging
integrando proteómica e imagen
Momento de cambio
• Progreso tecnológico rápido con
producción masiva de datos
• Progreso en bioinformática-computación
• Biología de sistemas frente a
reduccionismo
• Acceso a la personalización (genoma)
• Globalización
After “The Anatomy Lecture of Dr. Nicolaes Tulp” – Rembrandt, 1632
(Cortesía Dr. Carlos Cordón Cardó)