HNSCC HPV- Cohort TP53 Mutation Exploration
Here we conduct a general expration of TP53 mutations within the HNSCC discovery cohort. While we try and remain unbiased in our screen for molecular coorelates of survival, we do have much more information on TP53 mutations than most others.
In Poeta, a TP53 mutation is labeled as disruptive if it is either a stop mutation, or if is located at a binding site and induces a change in polarity of the encoded amino acid. Interestingly, we found that the polarity of the substitution had little effect on prognosis and that patients with a mutation to the L2 binding site had worse outcomes than patients with a mutation to the L3 binding site. In addition, within the context of the framework we set forth for biomarker discovery, we chose to ignore the classification of mutations (past silent/non-silent) in order to keep sample size high at the risk of false positives. For these reasons we elected to simply display the functional assignment of the mutations in Figure 1 rather than obscure these results with a classification scheme.
In [1]:
import NotebookImport
from Imports import *
TP53 Mutation Clinical Coorelates¶
In [2]:
p53_mut = mut.df.ix['TP53'].ix[keepers_o].dropna().astype(int)
In [3]:
survival_and_stats(p53_mut, surv, figsize=(5,4), order=[2,1,0])
In [4]:
screen_feature(p53_mut>0, kruskal_pandas, clinical.processed.T).head()
Out[4]:
In [5]:
ecs = clinical.clinical.presenceofpathologicalnodalextracapsularspread
ecs.name = 'Extra Capsular Spread'
pd.crosstab(p53_mut>0, ecs).T.plot(kind='bar', rot=15)
Out[5]:
TP53 Mutation Functional Assessment¶
Classification by functional domains¶
It is important to note, that here a patient with multiple mutation is counted multiple times.
In [6]:
import re as re
get_nums = lambda s: re.findall(r'\d+', s)
def is_disruptive(v):
c = v.Variant_Classification
if c != 'Missense_Mutation':
if 'Ins' in c or 'Del' in c:
return 'InDel'
else:
return v.Variant_Classification.split('_')[0]
else:
s = v.Protein_Change
aa = int(get_nums(s)[0])
if int(aa) in range(163,196):
return 'L2'
if int(aa) in range(236, 252):
return 'L3'
return 'other'
In [7]:
p53 = FH.get_submaf(run.data_path, cancer.name, ['TP53'], fields='All').ix['TP53']
In [8]:
dd = p53.apply(is_disruptive, 1)
dd = dd.replace('Silent',nan).dropna()
p53 = p53.ix[dd.index]
others = keepers_o.diff(p53.Tumor_Sample_Barcode.ix[dd.index]).intersection(mut.df.columns)
dd.index = p53.Tumor_Sample_Barcode.ix[dd.index]
dd = pd.concat([pd.Series('WT', others), dd])
dd = dd[[i in keepers_o for i in dd.index]]
pc = pd.Series(list(p53.Protein_Change), index=p53.Tumor_Sample_Barcode)
pc = pd.concat([pd.Series('WT', others), pc])
pc = pc[[i in keepers_o for i in pc.index]]
In [9]:
s2 = surv.unstack().ix[dd.index]
s2.index = range(len(dd))
s2 = s2.stack()
pats = pd.Series(dd.index, range(len(dd)))
dd.index = range(len(dd))
pc.index = range(len(dd))
In [10]:
df = pd.concat([pats, pc, dd, s2[:,'days'], s2[:,'event']],
keys=['patient ID','Functional Class','Protien Change',
'Days to Death/Censoring', 'Death Indicator'],
axis=1).sort(['patient ID'])
df = df.set_index('patient ID')
df.to_csv(FIGDIR + 'fig2b.csv')
Figure 2b¶
In [11]:
fig, ax = subplots(figsize=(3.5,2.7))
c={'WT': 'grey', 'Splice':colors[0], 'other': colors[5], 'L3': colors[1], 'L2':colors[2],
'Nonsense': colors[3], 'InDel': colors[4]}
draw_survival_curve(dd, s2, colors=c, ax=ax)
ax.legend().set_visible(False)
prettify_ax(ax)
fig.tight_layout()
fig.savefig(FIGDIR + 'fig2b.pdf', transparent=True)
In [12]:
survival_and_stats(dd, s2, colors=colors[:6] + ['grey'] + colors[6:], figsize=(4.5,6))
Differing survival rates were observed depending on the TP53 protein domain affected by the mutation or its predicted functional status16 (log-rank P = .04, Extended Data Fig. 2).
In [13]:
get_surv_fit_lr(s2, dd[dd!='WT'])
Out[13]:
Bar Plot of Hazard Ratios for Supplement
In [14]:
dd = dd.replace('WT', 'aWT')
f = get_cox_ph(s2, dd, interactions=False)
ci = convert_robj(robjects.r.summary(f)[7])
ci.index = map(lambda s: s[7:], ci.index)
n = ci.ix[0]*0 +1
n.name = 'WT'
ci = ci.append(n)
In [15]:
fig, ax = subplots(figsize=(7,4))
ci = ci.sort('exp(coef)')
haz = ci['exp(coef)']
b = haz.plot(kind='bar', ax=ax,
yerr=[haz - ci['lower .95'], ci['upper .95'] - haz], ecolor='black',
rot=0, color=['grey', colors[5], colors[4], colors[0], colors[3],
colors[2], colors[1]])
prettify_ax(ax)
ax.set_ylabel('Hazard Ratio')
Out[15]:
P-values for Bar Comparisons
In [16]:
from itertools import combinations
In [17]:
sig = pd.Series({c: get_cox_ph_ms(s2, dd[dd.isin(c)], interactions=False)['LR']
for c in combinations(dd.unique(),2)})
sig.order()
Out[17]:
Classification of Mutations via Poeta et. al.¶
- http://www.ncbi.nlm.nih.gov/pubmed/18094376
- In this paper the authors sequence TP53 for 420 patients.
- They classify a mutation as disruptive or non-disruptive if it:
a) contains a stop mutation
b) occurs at a binding site AND encodes an amino acid with a different polarity
In [18]:
lo = pd.read_csv('../Extra_Data/amino_acids.csv', index_col=1)
lo = lo.groupby(level=0).first()
def is_disruptive(s):
if s.endswith('*'):
return True
if s.endswith('splice'):
return False
if 'fs' in s:
return False
aa = s[3:-1]
try:
if int(aa) in range(163,196) + range(236, 252):
if lo.Polarity[s[2]] != lo.Polarity[s[-1]]:
return True
except:
pass
return False
In [19]:
p53 = FH.get_submaf(run.data_path, cancer.name, ['TP53'], fields='All').ix['TP53']
status = pd.concat([combine(p53.Protein_Change.map(is_disruptive), p53.is_silent==0),
p53.Tumor_Sample_Barcode], axis=1,
keys=['status','barcode'])
status = status.set_index('barcode')['status']
status = (status == 'both').groupby(level=0).sum().clip_upper(1.)
status = status.ix[mut.df.columns].fillna(-1).map({-1:'WT',0:'Non-Disruptive',1:'Disruptive'})
status = status.ix[keepers_o]
In [20]:
survival_and_stats(status, surv, colors=colors[:6] + ['grey'] + colors[6:], figsize=(7,5))
In [21]:
get_surv_fit_lr(surv, status[status.isin(['Non-Disruptive', 'WT'])])
Out[21]:
Modified Classification of Mutations via Poeta et. al.¶
- I find that the polarity of the mutation has no effect
- I am also including splice-sites as disruptive
- This introduces many more disruptive mutations and results in a larger effect size
- In the manuscript we note that "Notably, patients with mutations predicted as non-disruptive of function nonetheless had worse prognosis than patients with wild-type TP53", thus we feel that using this modified metric is more conservative for this particular statistic, the p-value would be more extreme for the traditional definition
In [22]:
def is_disruptive_mod(s):
if s.endswith('*'):
return True
if s.endswith('splice'):
return True
if 'fs' in s:
return False
aa = s[3:-1]
try:
if int(aa) in range(163,196) + range(236, 252):
return True
except:
pass
return False
In [23]:
p53 = FH.get_submaf(run.data_path, cancer.name, ['TP53'], fields='All').ix['TP53']
status = pd.concat([combine(p53.Protein_Change.map(is_disruptive_mod), p53.is_silent==0),
p53.Tumor_Sample_Barcode], axis=1,
keys=['status','barcode'])
status = status.set_index('barcode')['status']
status = (status == 'both').groupby(level=0).sum().clip_upper(1.)
status = status.ix[mut.df.columns].fillna(-1).map({-1:'WT',0:'Non-Disruptive',1:'Disruptive'})
status = status.ix[keepers_o]
In [24]:
survival_and_stats(status, surv, colors=colors[:6] + ['grey'] + colors[6:], figsize=(7,5))
Notably, patients with mutations predicted as non-disruptive of function nonetheless had worse prognosis than patients with wild-type TP53.
In [25]:
f = get_cox_ph(surv, status[status.isin(['Non-Disruptive', 'WT'])]=='Non-Disruptive', interactions=False,
print_desc=True);
In [26]:
exp(.79), exp(.79) - exp(.79 - .353)
Out[26]:
TP53 Mutation Hotspots in HNSCC¶
- I also threw in a plot of the frequent mutations as well, can't get alot from it but it shows that they do matter, even though we don't have the power to really say anything else.
- When we start looking across multiple cancers, or have 1000 person cohorts, we may be able to extract some meaningfull information by looking at the effects of individual mutations but that is a ways off.
In [27]:
cc = p53.set_index('Tumor_Sample_Barcode').Protein_Change
cc = pd.concat([pd.Series('WT', others), cc])
cc = cc[cc.isin(true_index(cc.value_counts() > 5))]
s2 = surv.unstack().ix[cc.index]
s2.index = range(len(cc))
s2 = s2.stack()
cc.index = range(len(cc))
In [28]:
survival_and_stats(cc, s2, colors=['grey'] + colors, figsize=(7,5))
