TCGA Data analysis Breast Cancer Analysis

Check for correlation between MYC amplified tumor samples and TP53 mutations MYC amplification is determined by two different ways:

MYC is in a segment having a copynumber > 4 (this likely excludes chromosome-arm level events)
"Our" definition of a focal amplification
- segment is small (smaller than 20Mbp)
- segment covering gene is increased (log2-ratio > 0.2)
- segment is focal (log2-ratio of segment is at least 0.2 above weighted mean of neighbouring 20Mbp)
- segment does not contain segmental duplications (less than 50%)
- segment doesn't overlap with known CNV from Database of genomic variants (start and endpoint are within 100Kbp from variants start and endpoint)

Importing from TCGA_analysis_BRCA_import and load raw data



In [1]:

    
import NotebookImport
from TCGA_analysis_BRCA_import import *









    



importing IPython notebook from TCGA_analysis_BRCA_import.ipynb
Loading Focal Amplification data
Loading RNASeq data
Loading CNV Data
Loading Somatic Mutation Data
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TCGA-AR-A0TU-01A not found
TCGA-AR-A1AT-01A not found
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Loading Clinical Data
Samples: 1086
  --Focal Data: 1086
  --CNV:        1086
  --RNASeq:     1081
  --Somatic:    968
  --Clinical:   1058

Check our focal amplification definition by checking expression of putative MYC amplified samples



In [2]:

    
MYC_amp_rnaseq_values=list()
   MYC_no_amp_rnaseq_values=list()
   for sample in samples.values():
     if sample.checkFocalGeneAmp("MYC") and (sample.getRNASeqFromGene("MYC") != "n/a"):
       MYC_amp_rnaseq_values.append(sample.getRNASeqFromGene("MYC"))
     if not sample.checkFocalGeneAmp("MYC") and (sample.getRNASeqFromGene("MYC") != "n/a"):
       MYC_no_amp_rnaseq_values.append(sample.getRNASeqFromGene("MYC"))

   MYC_amp_rnaseq = pandas.Series(MYC_amp_rnaseq_values)
   MYC_no_amp_rnaseq = pandas.Series(MYC_no_amp_rnaseq_values)
   t,p=scipy.stats.ttest_ind(MYC_amp_rnaseq_values,MYC_no_amp_rnaseq_values)
   t1,p1=scipy.stats.mannwhitneyu(MYC_amp_rnaseq_values,MYC_no_amp_rnaseq_values)
   print ""
   print " 1) test whether MYC is overexpressed in samples with focal MYC amplification"
   print "   MYC_amp mean: "+str(MYC_amp_rnaseq.mean())+ "   Samples: "+str(len(MYC_amp_rnaseq_values))
   print "   MYC_no_amp mean: "+str(MYC_no_amp_rnaseq.mean())+ "   Samples: "+str(len(MYC_no_amp_rnaseq_values))
   print "   RNASeq Expression in MYC vs. non-MYC: T-test: "+str(t)+"   p-value:"+str(p)
   print "   RNASeq Expression in MYC vs. non-MYC: Mann-W-U-test: "+str(t1)+"   p-value:"+str(p1)









    



 1) test whether MYC is overexpressed in samples with focal MYC amplification
   MYC_amp mean: 3671.48326508   Samples: 63
   MYC_no_amp mean: 2092.78970825   Samples: 1018
   RNASeq Expression in MYC vs. non-MYC: T-test: 6.19236738058   p-value:8.41114011757e-10
   RNASeq Expression in MYC vs. non-MYC: Mann-W-U-test: 19659.0   p-value:1.23730536313e-07

Check our focal amplification definition by checking expression of putative CCND1 amplified samples



In [4]:

    
# 2) test whether CCND1 is overexpressed in samples with focal CCND1 amplification"
   CCND1_amp_rnaseq_values=list()
   CCND1_no_amp_rnaseq_values=list()

   for sample in samples.values():
     if sample.checkFocalGeneAmp("CCND1") and (sample.getRNASeqFromGene("CCND1") != "n/a"):
       CCND1_amp_rnaseq_values.append(sample.getRNASeqFromGene("CCND1"))
       #print "CCND1 amplified\t"+str(sample.getRNASeqFromGene("CCND1"))
     if not sample.checkFocalGeneAmp("CCND1") and (sample.getRNASeqFromGene("CCND1") != "n/a"):
       CCND1_no_amp_rnaseq_values.append(sample.getRNASeqFromGene("CCND1"))
       #print "CCND1 not amplified\t"+str(sample.getRNASeqFromGene("CCND1"))

   CCND1_amp_rnaseq = pandas.Series(CCND1_amp_rnaseq_values)
   CCND1_no_amp_rnaseq = pandas.Series(CCND1_no_amp_rnaseq_values)
   t,p=scipy.stats.ttest_ind(CCND1_amp_rnaseq_values,CCND1_no_amp_rnaseq_values)
   t1,p1=scipy.stats.mannwhitneyu(CCND1_amp_rnaseq_values,CCND1_no_amp_rnaseq_values)
   print ""
   print " 2) test whether CCND1 is overexpressed in samples with focal CCND1 amplification"
   print "   CCND1_amp mean: "+str(CCND1_amp_rnaseq.mean())+ "   Samples: "+str(len(CCND1_amp_rnaseq_values))
   print "   CCND1_no_amp mean: "+str(CCND1_no_amp_rnaseq.mean())+ "   Samples: "+str(len(CCND1_no_amp_rnaseq_values))
   print "   RNASeq Expression in CCND1 vs. non-CCND1: T-test: "+str(t)+"   p-value:"+str(p)
   print "   RNASeq Expression in CCND1 vs. non-CCND1: Mann-W-U-test: "+str(t1)+"   p-value:"+str(p1)









    



 2) test whether CCND1 is overexpressed in samples with focal CCND1 amplification
   CCND1_amp mean: 28852.97017   Samples: 213
   CCND1_no_amp mean: 8463.57061233   Samples: 868
   RNASeq Expression in CCND1 vs. non-CCND1: T-test: 17.7647590371   p-value:3.9205517323e-62
   RNASeq Expression in CCND1 vs. non-CCND1: Mann-W-U-test: 25601.0   p-value:1.54838269276e-60

Test whether MYC is overexpressed in samples with MYC copynumber > 4



In [5]:

    
# 3) test whether MYC is overexpressed in samples with MYC copynumber > 4
   # get Log2-ratio values from MYC (hg19: chr8:128,748,315-128,753,680)
   MYC_cnover4_rnaseq=list()
   MYC_cnunder4_rnaseq = list()

   for sample in samples.values():
     log2 = sample.getLog2FromGene(gene_positions['MYC'].chrom,gene_positions['MYC'].start, gene_positions['MYC'].end)
     if (log2 != None):
       copynumber = 2.0 * 2.0 ** log2
     else:
       copynumber="n/a"
     if copynumber != "n/a" and copynumber > 4 and sample.getRNASeqFromGene("MYC") != "n/a":
       MYC_cnover4_rnaseq.append(sample.getRNASeqFromGene("MYC"))
     elif copynumber != "n/a" and copynumber < 4 and sample.getRNASeqFromGene("MYC") != "n/a":
       MYC_cnunder4_rnaseq.append(sample.getRNASeqFromGene("MYC"))

   t,p=scipy.stats.ttest_ind(MYC_cnover4_rnaseq,MYC_cnunder4_rnaseq)
   t1,p1=scipy.stats.mannwhitneyu(MYC_cnover4_rnaseq,MYC_cnunder4_rnaseq)
   print ""
   print " 3) test whether MYC is overexpressed in samples with MYC copynumber > 4"
   print "   MYC_CN>4 mean: "+str(numpy.mean(MYC_cnover4_rnaseq))+ "   Samples: "+str(len(MYC_cnover4_rnaseq))
   print "   MYC_CN<4 mean: "+str(numpy.mean(MYC_cnunder4_rnaseq))+ "   Samples: "+str(len(MYC_cnunder4_rnaseq))
   print "   RNASeq Expression in MYC (CN>4) vs. MYC (CN<4): T-test: "+str(t)+"   p-value:"+str(p)
   print "   RNASeq Expression in MYC (CN>4) vs. MYC (CN<4): Mann-W-U: "+str(t1)+"   p-value:"+str(p1)









    



 3) test whether MYC is overexpressed in samples with MYC copynumber > 4
   MYC_CN>4 mean: 3444.19124   Samples: 115
   MYC_CN<4 mean: 2036.45864549   Samples: 965
   RNASeq Expression in MYC (CN>4) vs. MYC (CN<4): T-test: 7.31522747153   p-value:5.00593992126e-13
   RNASeq Expression in MYC (CN>4) vs. MYC (CN<4): Mann-W-U: 35210.0   p-value:7.12888221259e-11

Test whether CCND1 is overexpressed in samples with CCND1 copynumber > 4



In [6]:

    
# 4) test whether CCND1 is overexpressed in samples with CCND1 copynumber > 4
   CCND1_cnover4_rnaseq=list()
   CCND1_cnunder4_rnaseq = list()

   for sample in samples.values():
     log2 = sample.getLog2FromGene(gene_positions['CCND1'].chrom,gene_positions['CCND1'].start, gene_positions['CCND1'].end)
     if (log2 != None):
       copynumber = 2.0 * 2.0 ** log2
     else:
       copynumber="n/a"
     if copynumber != "n/a" and copynumber > 4 and sample.getRNASeqFromGene("CCND1") != "n/a":
       CCND1_cnover4_rnaseq.append(sample.getRNASeqFromGene("CCND1"))
     elif copynumber != "n/a" and copynumber < 4 and sample.getRNASeqFromGene("CCND1") != "n/a":
       CCND1_cnunder4_rnaseq.append(sample.getRNASeqFromGene("CCND1"))

   t,p=scipy.stats.ttest_ind(CCND1_cnover4_rnaseq,CCND1_cnunder4_rnaseq)
   t1,p1=scipy.stats.mannwhitneyu(CCND1_cnover4_rnaseq,CCND1_cnunder4_rnaseq)
   print ""
   print " 4) test whether CCND1 is overexpressed in samples with CCND1 copynumber > 4"
   print "   CCND1_CN>4 mean: "+str(numpy.mean(CCND1_cnover4_rnaseq))+ "Samples: "+str(len(CCND1_cnover4_rnaseq))
   print "   CCND1_CN<4 mean: "+str(numpy.mean(CCND1_cnunder4_rnaseq))+ "Samples: "+str(len(CCND1_cnunder4_rnaseq))
   print "   RNASeq Expression in CCND1 (CN>4) vs. CCND1 (CN<4): T-test: "+str(t)+"   p-value:"+str(p)
   print "   RNASeq Expression in CCND1 (CN>4) vs. CCND1 (CN<4): Mann-W-U: "+str(t1)+"   p-value:"+str(p1)









    



 4) test whether CCND1 is overexpressed in samples with CCND1 copynumber > 4
   CCND1_CN>4 mean: 37713.5077093Samples: 140
   CCND1_CN<4 mean: 8647.4043937Samples: 936
   RNASeq Expression in CCND1 (CN>4) vs. CCND1 (CN<4): T-test: 22.9215740841   p-value:5.64677655276e-95
   RNASeq Expression in CCND1 (CN>4) vs. CCND1 (CN<4): Mann-W-U: 11335.0   p-value:1.5566493917e-56

Test whether focally amplified genes of Beroukhim et al. are overexpressed



In [2]:

    
focal_amp_rnaseq_values=list()
focal_no_amp_rnaseq_values=list()
beroukhim_genes=["MYC","CCND1","ERBB2","CDK4","NKX2-1","MDM2","EGFR","MCL1","FGFR1","KRAS","CCNE1","CRKL","HMGA2","TERT","PRKCI","IGF1R","MYCL","MYCN","CDK6","BCL2L1","MYB","MET","JUN","BIRC2","YAP1","PDGFRA","KIT","PIK3CA","MDM4","AR"]

for sample in samples.values():
   if not sample.rnaseq_data:
      continue
   for gene in beroukhim_genes:
      if sample.checkFocalGeneAmp(gene) and (sample.getRNASeqFromGene(gene) != "n/a"):
         focal_amp_rnaseq_values.append(sample.getRNASeqFromGene(gene))
      if not sample.checkFocalGeneAmp(gene) and (sample.getRNASeqFromGene(gene) != "n/a"):
         focal_no_amp_rnaseq_values.append(sample.getRNASeqFromGene(gene))

focal_amp_rnaseq = pandas.Series(focal_amp_rnaseq_values)
focal_no_amp_rnaseq = pandas.Series(focal_no_amp_rnaseq_values)
t,p=scipy.stats.ttest_ind(focal_amp_rnaseq_values,focal_no_amp_rnaseq_values)
t1,p1=scipy.stats.mannwhitneyu(focal_amp_rnaseq_values,focal_no_amp_rnaseq_values)

print ""
print " 5) test whether focally amplified genes are overexpressed in samples with focal amplifications"
print "   MYC_amp mean: "+str(focal_amp_rnaseq.mean())+ "   Samples: "+str(len(focal_amp_rnaseq_values))
print "   MYC_no_amp mean: "+str(focal_no_amp_rnaseq.mean())+ "   Samples: "+str(len(focal_no_amp_rnaseq_values))
print "   RNASeq Expression in MYC vs. non-MYC: T-test: "+str(t)+"   p-value:"+str(p)
print "   RNASeq Expression in MYC vs. non-MYC: Mann-W-U-test: "+str(t1)+"   p-value:"+str(p1)









    



 5) test whether focally amplified genes are overexpressed in samples with focal amplifications
   MYC_amp mean: 23508.2963466   Samples: 1008
   MYC_no_amp mean: 2101.94084578   Samples: 30341
   RNASeq Expression in MYC vs. non-MYC: T-test: 73.7539358248   p-value:0.0
   RNASeq Expression in MYC vs. non-MYC: Mann-W-U-test: 6485968.0   p-value:2.26374723341e-213



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