def test_extract_cancer_types(self):
m = MatchEngine(get_db())
match_tree = match_tree_example
g = m.create_match_tree(match_tree)
pmt = ParseMatchTree(g)
cancer_type_dict = pmt.extract_cancer_types()
assert sorted(cancer_type_dict['diagnoses']) == sorted(
['Ocular Melanoma']), cancer_type_dict['diagnoses']
assert sorted(cancer_type_dict['cancer_types_expanded']) == sorted([
'Ocular Melanoma', 'Uveal Melanoma', 'Conjunctival Melanoma'
]), cancer_type_dict['cancer_types_expanded']
assert sorted(cancer_type_dict['excluded_cancer_types']
) == [], cancer_type_dict['excluded_cancer_types']
assert cancer_type_dict['primary_cancer_types'] == [
'Eye'
], cancer_type_dict['primary_cancer_types']
m = MatchEngine(get_db())
match_tree = match_tree_example2
g = m.create_match_tree(match_tree)
pmt = ParseMatchTree(g)
cancer_type_dict = pmt.extract_cancer_types()
assert sorted(cancer_type_dict['diagnoses']) == sorted(
['_SOLID_']), cancer_type_dict['diagnoses']
assert 'Acute Lymphoid Leukemia' not in cancer_type_dict[
'cancer_types_expanded'], cancer_type_dict['cancer_types_expanded']
assert sorted(cancer_type_dict['excluded_cancer_types']
) == [], cancer_type_dict['excluded_cancer_types']
assert cancer_type_dict['primary_cancer_types'] == [
'All Solid Tumors'
], cancer_type_dict['primary_cancer_types']
m = MatchEngine(get_db())
match_tree = match_tree_example3
g = m.create_match_tree(match_tree)
pmt = ParseMatchTree(g)
cancer_type_dict = pmt.extract_cancer_types()
assert sorted(cancer_type_dict['diagnoses']) == sorted(
['_LIQUID_']), cancer_type_dict['diagnoses']
assert 'Acute Lymphoid Leukemia' in cancer_type_dict[
'cancer_types_expanded'], cancer_type_dict['cancer_types_expanded']
assert sorted(cancer_type_dict['excluded_cancer_types']
) == [], cancer_type_dict['excluded_cancer_types']
assert cancer_type_dict['primary_cancer_types'] == [
'All Liquid Tumors'
], cancer_type_dict['primary_cancer_types']
def test_extract_variants(self):
m = MatchEngine(get_db())
match_tree = trial['treatment_list']['step'][0]['match'][0]
g = m.create_match_tree(match_tree)
pmt = ParseMatchTree(g)
v = pmt.extract_variants()
assert 'BRAF V600E' in v['variants']
assert 'BRAF V600K' in v['variants']
assert 'KRAS any' in v['variants']
assert 'EGFR wt' in v['wts']
assert len(v['variants']) == 3
assert len(v['wts']) == 1
match_tree = trial['treatment_list']['step'][0]['match'][1]
g = m.create_match_tree(match_tree)
pmt = ParseMatchTree(g)
v = pmt.extract_variants()
assert 'PTEN CNV' in v['cnvs']
assert 'BRCA1 SV' in v['svs']
assert 'BRAF V600' in v['exclusions']
assert len(v['variants']) == 0
assert len(v['cnvs']) == 1
assert len(v['svs']) == 1
assert len(v['wts']) == 0
assert len(v['exclusions']) == 1
match_tree = trial['treatment_list']['step'][0]['match'][2]
g = m.create_match_tree(match_tree)
pmt = ParseMatchTree(g)
v = pmt.extract_variants()
assert 'BRAF V600E' not in v['variants']
assert len(v['variants']) == 0
assert len(v['wts']) == 0
assert 'BRAF V600E' in v['exclusions']
match_tree = trial['treatment_list']['step'][0]['match'][4]
g = m.create_match_tree(match_tree)
pmt = ParseMatchTree(g)
v = pmt.extract_variants()
assert 'BRAF V600K' in v['variants']
assert 'EGFR any' in v['variants']
assert len(v['variants']) == 2
assert 'PTEN CNV' in v['cnvs']
assert len(v['cnvs']) == 1
assert 'KRAS' in v['exclusions']
assert 'NRAS' in v['exclusions']
assert len(v['exclusions']) == 2
assert 'NTRK1 wt' in v['wts']
assert len(v['wts']) == 1
def test_extract_variants(self):
m = MatchEngine(get_db())
match_tree = trial['treatment_list']['step'][0]['match'][0]
g = m.create_match_tree(match_tree)
pmt = ParseMatchTree(g)
v = pmt.extract_variants()
assert 'BRAF V600E' in v['variants']
assert 'BRAF V600K' in v['variants']
assert 'KRAS any' in v['variants']
assert 'EGFR wt' in v['wts']
assert len(v['variants']) == 3
assert len(v['wts']) == 1
match_tree = trial['treatment_list']['step'][0]['match'][1]
g = m.create_match_tree(match_tree)
pmt = ParseMatchTree(g)
v = pmt.extract_variants()
assert 'PTEN CNV' in v['cnvs']
assert 'BRCA1 SV' in v['svs']
assert 'BRAF V600' in v['exclusions']
assert len(v['variants']) == 0
assert len(v['cnvs']) == 1
assert len(v['svs']) == 1
assert len(v['wts']) == 0
assert len(v['exclusions']) == 1
match_tree = trial['treatment_list']['step'][0]['match'][2]
g = m.create_match_tree(match_tree)
pmt = ParseMatchTree(g)
v = pmt.extract_variants()
assert 'BRAF V600E' not in v['variants']
assert len(v['variants']) == 0
assert len(v['wts']) == 0
assert 'BRAF V600E' in v['exclusions']
match_tree = trial['treatment_list']['step'][0]['match'][4]
g = m.create_match_tree(match_tree)
pmt = ParseMatchTree(g)
v = pmt.extract_variants()
assert 'BRAF V600K' in v['variants']
assert 'EGFR any' in v['variants']
assert len(v['variants']) == 2
assert 'PTEN CNV' in v['cnvs']
assert len(v['cnvs']) == 1
assert 'KRAS' in v['exclusions']
assert 'NRAS' in v['exclusions']
assert len(v['exclusions']) == 2
assert 'NTRK1 wt' in v['wts']
assert len(v['wts']) == 1
def test_extract_hr_status(self):
m = MatchEngine(get_db())
match_tree = trial['treatment_list']['step'][0]['match'][5]
g = m.create_match_tree(match_tree)
pmt = ParseMatchTree(g)
hr = pmt.extract_hr_status()
assert sorted(hr) == sorted(['HER2 Negative', 'ER Negative', 'PR Positive'])
def test_extract_hr_status(self):
m = MatchEngine(get_db())
match_tree = trial['treatment_list']['step'][0]['match'][5]
g = m.create_match_tree(match_tree)
pmt = ParseMatchTree(g)
hr = pmt.extract_hr_status()
assert sorted(hr) == sorted(
['HER2 Negative', 'ER Negative', 'PR Positive'])
def test_extract_signatures(self):
m = MatchEngine(get_db())
match_tree = trial['treatment_list']['step'][0]['match'][3]
g = m.create_match_tree(match_tree)
pmt = ParseMatchTree(g)
s = pmt.extract_signatures()
assert 'MMR-D' in s[0]
assert 'MSI-H' in s[1]
def test_extract_genes(self):
m = MatchEngine(get_db())
match_tree = trial['treatment_list']['step'][0]['match'][0]
g = m.create_match_tree(match_tree)
pmt = ParseMatchTree(g)
genes = pmt.extract_genes()
assert 'BRAF' in genes, genes
assert 'KRAS' in genes, genes
assert 'EGFR' not in genes
assert 'test' not in genes, genes
assert len(genes) == 2, genes
match_tree = trial['treatment_list']['step'][0]['match'][2]
g = m.create_match_tree(match_tree)
pmt = ParseMatchTree(g)
genes = pmt.extract_genes()
assert 'BRAF' not in genes, genes
def test_extract_signatures(self):
m = MatchEngine(get_db())
match_tree = trial['treatment_list']['step'][0]['match'][3]
g = m.create_match_tree(match_tree)
pmt = ParseMatchTree(g)
s = pmt.extract_signatures()
assert 'MMR-D' in s[0]
assert 'MSI-H' in s[1]
def test_extract_cancer_types(self):
m = MatchEngine(get_db())
match_tree = match_tree_example
g = m.create_match_tree(match_tree)
pmt = ParseMatchTree(g)
cancer_type_dict = pmt.extract_cancer_types()
assert sorted(cancer_type_dict['diagnoses']) == sorted([
'Ocular Melanoma'
]), cancer_type_dict['diagnoses']
assert sorted(cancer_type_dict['cancer_types_expanded']) == sorted([
'Ocular Melanoma',
'Uveal Melanoma',
'Conjunctival Melanoma'
]), cancer_type_dict['cancer_types_expanded']
assert sorted(cancer_type_dict['excluded_cancer_types']) == [], cancer_type_dict['excluded_cancer_types']
assert cancer_type_dict['primary_cancer_types'] == ['Eye'], cancer_type_dict['primary_cancer_types']
m = MatchEngine(get_db())
match_tree = match_tree_example2
g = m.create_match_tree(match_tree)
pmt = ParseMatchTree(g)
cancer_type_dict = pmt.extract_cancer_types()
assert sorted(cancer_type_dict['diagnoses']) == sorted(['_SOLID_']), cancer_type_dict['diagnoses']
assert 'Acute Lymphoid Leukemia' not in cancer_type_dict['cancer_types_expanded'], cancer_type_dict['cancer_types_expanded']
assert sorted(cancer_type_dict['excluded_cancer_types']) == [], cancer_type_dict['excluded_cancer_types']
assert cancer_type_dict['primary_cancer_types'] == ['All Solid Tumors'], cancer_type_dict['primary_cancer_types']
m = MatchEngine(get_db())
match_tree = match_tree_example3
g = m.create_match_tree(match_tree)
pmt = ParseMatchTree(g)
cancer_type_dict = pmt.extract_cancer_types()
assert sorted(cancer_type_dict['diagnoses']) == sorted(['_LIQUID_']), cancer_type_dict['diagnoses']
assert 'Acute Lymphoid Leukemia' in cancer_type_dict['cancer_types_expanded'], cancer_type_dict[
'cancer_types_expanded']
assert sorted(cancer_type_dict['excluded_cancer_types']) == [], cancer_type_dict['excluded_cancer_types']
assert cancer_type_dict['primary_cancer_types'] == ['All Liquid Tumors'], cancer_type_dict['primary_cancer_types']
def test_extract_genes(self):
m = MatchEngine(get_db())
match_tree = trial['treatment_list']['step'][0]['match'][0]
g = m.create_match_tree(match_tree)
pmt = ParseMatchTree(g)
genes = pmt.extract_genes()
assert 'BRAF' in genes, genes
assert 'KRAS' in genes, genes
assert 'EGFR' not in genes
assert 'test' not in genes, genes
assert len(genes) == 2, genes
match_tree = trial['treatment_list']['step'][0]['match'][2]
g = m.create_match_tree(match_tree)
pmt = ParseMatchTree(g)
genes = pmt.extract_genes()
assert 'BRAF' not in genes, genes
def _get_signatures(self, item):
"""
Creates hormone receptor status and mutational signature summary lists
:param item: Trial document
"""
m = MatchEngine(get_db())
for step in item['treatment_list']['step']:
if 'match' in step:
g = m.create_match_tree(step['match'][0])
pmt = ParseMatchTree(g)
signatures = pmt.extract_signatures()
self.mmr.extend(signatures[0])
self.ms.extend(signatures[1])
self.sigs.extend(signatures[2])
self.hr.extend(pmt.extract_hr_status())
if 'arm' in step:
for arm in step['arm']:
if 'match' in arm:
g = m.create_match_tree(arm['match'][0])
pmt = ParseMatchTree(g)
signatures = pmt.extract_signatures()
self.mmr.extend(signatures[0])
self.ms.extend(signatures[1])
self.sigs.extend(signatures[2])
self.hr.extend(pmt.extract_hr_status())
if 'dose_level' in arm:
for dose in arm['dose_level']:
if 'match' in dose:
g = m.create_match_tree(dose['match'][0])
pmt = ParseMatchTree(g)
signatures = pmt.extract_signatures()
self.mmr.extend(signatures[0])
self.ms.extend(signatures[1])
self.sigs.extend(signatures[2])
self.hr.extend(pmt.extract_hr_status())
def _get_signatures(self, item):
"""
Creates hormone receptor status and mutational signature summary lists
:param item: Trial document
"""
m = MatchEngine(get_db())
for step in item['treatment_list']['step']:
if 'match' in step:
g = m.create_match_tree(step['match'][0])
pmt = ParseMatchTree(g)
signatures = pmt.extract_signatures()
self.mmr.extend(signatures[0])
self.ms.extend(signatures[1])
self.hr.extend(pmt.extract_hr_status())
if 'arm' in step:
for arm in step['arm']:
if 'match' in arm:
g = m.create_match_tree(arm['match'][0])
pmt = ParseMatchTree(g)
signatures = pmt.extract_signatures()
self.mmr.extend(signatures[0])
self.ms.extend(signatures[1])
self.hr.extend(pmt.extract_hr_status())
if 'dose_level' in arm:
for dose in arm['dose_level']:
if 'match' in dose:
g = m.create_match_tree(dose['match'][0])
pmt = ParseMatchTree(g)
signatures = pmt.extract_signatures()
self.mmr.extend(signatures[0])
self.ms.extend(signatures[1])
self.hr.extend(pmt.extract_hr_status())
class Autocomplete:
def __init__(self, item):
"""
Creates data for ElasticSearch's autocomplete index
:param item: Trial info:
- treatment_list: Nested dictionary containing all match criteria
- summary: Summary object created by the API
"""
self.summary = item['_summary']
self.treatment_list = item['treatment_list']
self.vdict = {
'variants': [],
'wts': [],
'svs': [],
'cnvs': [],
'exclusions': []
}
self.genes = []
self.cancer_type_dict = None
self.m = MatchEngine(get_db())
@staticmethod
def _get_cancer_type_weight(cancer_type, hierarchy='default'):
"""
Sets the weights for ElasticSearch autocompletion on cancer types. Cancer type terms
are split so that autocomplete suggestions will populate regardless of which word in the
multi-word cancer type string is initially input. Higher weighted terms will populate the
top of the autocomplete dropdown list.
:param cancer_type: Text to display in the autocomplete dropdown list.
:param hierarchy: Weight to give the text.
:return: Dictionary specifying ElasticSearch rules.
"""
weight_dict = {'primary': 10, 'default': 5, 'bucket': 20}
if cancer_type == 'All Solid Tumors' or cancer_type == 'All Liquid Tumors':
hierarchy = 'bucket'
return {
'input':
list(
set([cancer_type] +
[i for i in cancer_type.split() if len(i) > 3])),
'output':
cancer_type,
'weight':
weight_dict[hierarchy]
}
@staticmethod
def _get_variants_weight(variant, esrule='variants'):
"""
Sets the weights for ElasticSearch autocompletion on gene variants. Higher weighted terms will populate the
top of the autocomplete dropdown list.
:param variant: Text to display in the autocomplete dropdown list.
:param esrule: Type of variant. This will determine the ElasticSearch parameters.
:return: Dictionary specifying ElasticSearch rules.
"""
weight_dict = {'variants': 1, 'wts': 5, 'svs': 3, 'cnvs': 3}
return {'input': variant, 'weight': weight_dict[esrule]}
@staticmethod
def _get_investigator_suggest(investigator, dfci_investigator):
"""
Creates a list of investigators from the _summary field of the trial collection
"""
iin = []
iout = ''
ispl = [i.strip() for i in investigator.split(',')]
if len(ispl) == 1:
iin = [ispl[0]]
iout = investigator
elif len(ispl) >= 2:
iin = [ispl[0], ispl[1]]
iout = '%s %s' % (ispl[1], ispl[0])
dfci_in = []
dfci_out = ''
if dfci_investigator is not None and 'first_name' in dfci_investigator:
dfci_in.append(dfci_investigator['first_name'].strip())
dfci_out += dfci_investigator['first_name'].strip()
if dfci_investigator is not None and 'last_name' in dfci_investigator:
dfci_in.append(dfci_investigator['last_name'].strip())
dfci_out += ' %s' % dfci_investigator['last_name'].strip()
inv_suggest = [{'input': [i for i in iin if i != ''], 'output': iout}]
if dfci_out != iout and dfci_out != '':
inv_suggest.append({'input': dfci_in, 'output': dfci_out.strip()})
return inv_suggest
@staticmethod
def _get_tumor_types_search(ct_suggest):
"""
Maps special cancer type text output to the values stored in the ElasticSearch index.
:param ct_suggest: Cancer type text to display.
:return: Cancer type text stored in th ElasticSearch index, which we will query.
"""
tts = []
for ct in ct_suggest:
if 'output' in ct and ct['output'] == 'All Solid Tumors':
tts.append('_SOLID_')
elif 'output' in ct and ct['output'] == 'All Liquid Tumors':
tts.append('_LIQUID_')
else:
tts.append(ct['output'])
return tts
def _extract_data_from_match(self, match):
"""
Extract Cancer Type, Gene, and Variant data from the given match tree
"""
g = self.m.create_match_tree(match)
pmt = ParseMatchTree(g)
self.cancer_type_dict = pmt.extract_cancer_types()
self.genes.extend(pmt.extract_genes())
vdict_tmp = pmt.extract_variants()
for k, v in self.vdict.iteritems():
v.extend(vdict_tmp[k])
def add_autocomplete(self):
"""
Recursively iterates through the treatment list and creates a list of genes contained within.
:return: Nested dictionary containing all genes referenced within this trial
"""
for step in self.treatment_list['step']:
if 'match' in step:
self._extract_data_from_match(step['match'][0])
if 'arm' in step:
for arm in step['arm']:
if 'match' in arm:
self._extract_data_from_match(arm['match'][0])
if 'dose_level' in arm:
for dose in arm['dose_level']:
if 'match' in dose:
self._extract_data_from_match(dose['match'][0])
if self.cancer_type_dict is None:
self.cancer_type_dict = {
'diagnoses': [],
'primary_cancer_types': [],
'cancer_types_expanded': [],
'excluded_cancer_types': []
}
weighted_cancer_types = []
for ct in self.cancer_type_dict['primary_cancer_types']:
suggestion = self._get_cancer_type_weight(ct, hierarchy='primary')
weighted_cancer_types.append(suggestion)
for ct in set(self.cancer_type_dict['cancer_types_expanded']) - set(
self.cancer_type_dict['primary_cancer_types']):
suggestion = self._get_cancer_type_weight(ct, hierarchy='default')
weighted_cancer_types.append(suggestion)
weighted_variants = {}
for key in ['variants', 'cnvs', 'svs', 'wts']:
weighted_variants[key] = []
for v in set(self.vdict[key]):
suggestion = self._get_variants_weight(v, esrule=key)
weighted_variants[key].append(suggestion)
suggestors = {
"cancer_type_suggest":
weighted_cancer_types,
"hugo_symbol_suggest": {
"input": list(set(self.genes))
},
"variant_suggest": [
i for i in weighted_variants['variants']
if not i['input'].endswith('any')
],
"wildtype_suggest":
weighted_variants['wts'],
"cnv_suggest":
weighted_variants['cnvs'],
"sv_suggest":
weighted_variants['svs'],
"protocol_no_suggest": {
'input': self.summary['protocol_number']
},
"disease_center_suggest": {
'input': [
i.replace('(', '').replace(')', '')
for i in self.summary['disease_center'].split()
],
'output':
self.summary['disease_center']
},
'disease_status_suggest': {
'input': self.summary['disease_status']
},
'drug_suggest': {
'input': [i.title() for i in self.summary['drugs']]
},
'investigator_suggest':
self._get_investigator_suggest(self.summary['investigator'],
self.summary['dfci_investigator']),
'mmr_status_suggest': {
'input': self.summary['mmr_status'] + self.summary['ms_status']
},
'nct_number_suggest': {
'input': self.summary['nct_number']
}
}
searchers = {
"tumor_types":
list(set(self._get_tumor_types_search(weighted_cancer_types))),
"genes":
list(set(self.genes)),
"variants":
list(set([i['input'] for i in weighted_variants['variants']])),
"wildtype_genes":
list(set([i['input'] for i in weighted_variants['wts']])),
"cnv_genes":
list(set([i['input'] for i in weighted_variants['cnvs']])),
"sv_genes":
list(set([i['input'] for i in weighted_variants['svs']])),
"exclusion_genes":
list(set(self.vdict['exclusions'])),
"protocol_no":
self.summary["protocol_number"],
"drugs":
self.summary["drugs"],
"age":
self.summary["age_summary"],
"phase":
self.summary["phase_summary"],
"disease_status":
self.summary["disease_status"],
"nct_number":
self.summary["nct_number"],
"disease_center":
self.summary["disease_center"],
"mmr_status":
self.summary["mmr_status"],
"ms_status":
self.summary["ms_status"],
"mutational_signatures":
self.summary["mutational_signatures"],
"investigator":
[i['output'] for i in suggestors['investigator_suggest']],
"short_title":
self.summary["short_title"]
}
return suggestors, searchers, parse_primary_cancer_types(
self.cancer_type_dict['primary_cancer_types'])
class Autocomplete:
def __init__(self, item):
"""
Creates data for ElasticSearch's autocomplete index
:param item: Trial info:
- treatment_list: Nested dictionary containing all match criteria
- summary: Summary object created by the API
"""
self.summary = item['_summary']
self.treatment_list = item['treatment_list']
self.vdict = {
'variants': [],
'wts': [],
'svs': [],
'cnvs': [],
'exclusions': []
}
self.genes = []
self.cancer_type_dict = None
self.m = MatchEngine(get_db())
@staticmethod
def _get_cancer_type_weight(cancer_type, hierarchy='default'):
"""
Sets the weights for ElasticSearch autocompletion on cancer types. Cancer type terms
are split so that autocomplete suggestions will populate regardless of which word in the
multi-word cancer type string is initially input. Higher weighted terms will populate the
top of the autocomplete dropdown list.
:param cancer_type: Text to display in the autocomplete dropdown list.
:param hierarchy: Weight to give the text.
:return: Dictionary specifying ElasticSearch rules.
"""
weight_dict = {'primary': 10, 'default': 5, 'bucket': 20}
if cancer_type == 'All Solid Tumors' or cancer_type == 'All Liquid Tumors':
hierarchy = 'bucket'
return {
'input': list(set([cancer_type] + [i for i in cancer_type.split() if len(i) > 3])),
'output': cancer_type,
'weight': weight_dict[hierarchy]
}
@staticmethod
def _get_variants_weight(variant, esrule='variants'):
"""
Sets the weights for ElasticSearch autocompletion on gene variants. Higher weighted terms will populate the
top of the autocomplete dropdown list.
:param variant: Text to display in the autocomplete dropdown list.
:param esrule: Type of variant. This will determine the ElasticSearch parameters.
:return: Dictionary specifying ElasticSearch rules.
"""
weight_dict = {
'variants': 1,
'wts': 5,
'svs': 3,
'cnvs': 3
}
return {'input': variant, 'weight': weight_dict[esrule]}
@staticmethod
def _get_investigator_suggest(investigator, dfci_investigator):
"""
Creates a list of investigators from the _summary field of the trial collection
"""
iin = []
iout = ''
ispl = [i.strip() for i in investigator.split(',')]
if len(ispl) == 1:
iin = [ispl[0]]
iout = investigator
elif len(ispl) >= 2:
iin = [ispl[0], ispl[1]]
iout = '%s %s' % (ispl[1], ispl[0])
dfci_in = []
dfci_out = ''
if dfci_investigator is not None and 'first_name' in dfci_investigator:
dfci_in.append(dfci_investigator['first_name'].strip())
dfci_out += dfci_investigator['first_name'].strip()
if dfci_investigator is not None and 'last_name' in dfci_investigator:
dfci_in.append(dfci_investigator['last_name'].strip())
dfci_out += ' %s' % dfci_investigator['last_name'].strip()
inv_suggest = [{
'input': [i for i in iin if i != ''],
'output': iout
}]
if dfci_out != iout and dfci_out != '':
inv_suggest.append({
'input': dfci_in,
'output': dfci_out.strip()
})
return inv_suggest
@staticmethod
def _get_tumor_types_search(ct_suggest):
"""
Maps special cancer type text output to the values stored in the ElasticSearch index.
:param ct_suggest: Cancer type text to display.
:return: Cancer type text stored in th ElasticSearch index, which we will query.
"""
tts = []
for ct in ct_suggest:
if 'output' in ct and ct['output'] == 'All Solid Tumors':
tts.append('_SOLID_')
elif 'output' in ct and ct['output'] == 'All Liquid Tumors':
tts.append('_LIQUID_')
else:
tts.append(ct['output'])
return tts
def _extract_data_from_match(self, match):
"""
Extract Cancer Type, Gene, and Variant data from the given match tree
"""
g = self.m.create_match_tree(match)
pmt = ParseMatchTree(g)
self.cancer_type_dict = pmt.extract_cancer_types()
self.genes.extend(pmt.extract_genes())
vdict_tmp = pmt.extract_variants()
for k, v in self.vdict.iteritems():
v.extend(vdict_tmp[k])
def add_autocomplete(self):
"""
Recursively iterates through the treatment list and creates a list of genes contained within.
:return: Nested dictionary containing all genes referenced within this trial
"""
for step in self.treatment_list['step']:
if 'match' in step:
self._extract_data_from_match(step['match'][0])
if 'arm' in step:
for arm in step['arm']:
if 'match' in arm:
self._extract_data_from_match(arm['match'][0])
if 'dose_level' in arm:
for dose in arm['dose_level']:
if 'match' in dose:
self._extract_data_from_match(dose['match'][0])
if self.cancer_type_dict is None:
self.cancer_type_dict = {
'diagnoses': [],
'primary_cancer_types': [],
'cancer_types_expanded': [],
'excluded_cancer_types': []
}
weighted_cancer_types = []
for ct in self.cancer_type_dict['primary_cancer_types']:
suggestion = self._get_cancer_type_weight(ct, hierarchy='primary')
weighted_cancer_types.append(suggestion)
for ct in set(self.cancer_type_dict['cancer_types_expanded']) - set(self.cancer_type_dict['primary_cancer_types']):
suggestion = self._get_cancer_type_weight(ct, hierarchy='default')
weighted_cancer_types.append(suggestion)
weighted_variants = {}
for key in ['variants', 'cnvs', 'svs', 'wts']:
weighted_variants[key] = []
for v in set(self.vdict[key]):
suggestion = self._get_variants_weight(v, esrule=key)
weighted_variants[key].append(suggestion)
suggestors = {
"cancer_type_suggest": weighted_cancer_types,
"hugo_symbol_suggest": {"input": list(set(self.genes))},
"variant_suggest": [i for i in weighted_variants['variants'] if not i['input'].endswith('any')],
"wildtype_suggest": weighted_variants['wts'],
"cnv_suggest": weighted_variants['cnvs'],
"sv_suggest": weighted_variants['svs'],
"protocol_no_suggest": {'input': self.summary['protocol_number']},
"disease_center_suggest": {
'input': [i.replace('(', '').replace(')', '') for i in self.summary['disease_center'].split()],
'output': self.summary['disease_center']
},
'disease_status_suggest': {'input': self.summary['disease_status']},
'drug_suggest': {'input': [i.title() for i in self.summary['drugs']]},
'investigator_suggest': self._get_investigator_suggest(self.summary['investigator'],
self.summary['dfci_investigator']),
'mmr_status_suggest': {'input': self.summary['mmr_status'] + self.summary['ms_status']},
'nct_number_suggest': {'input': self.summary['nct_number']}
}
searchers = {
"tumor_types": list(set(self._get_tumor_types_search(weighted_cancer_types))),
"genes": list(set(self.genes)),
"variants": list(set([i['input'] for i in weighted_variants['variants']])),
"wildtype_genes": list(set([i['input'] for i in weighted_variants['wts']])),
"cnv_genes": list(set([i['input'] for i in weighted_variants['cnvs']])),
"sv_genes": list(set([i['input'] for i in weighted_variants['svs']])),
"exclusion_genes": list(set(self.vdict['exclusions'])),
"protocol_no": self.summary["protocol_number"],
"drugs": self.summary["drugs"],
"age": self.summary["age_summary"],
"phase": self.summary["phase_summary"],
"disease_status": self.summary["disease_status"],
"nct_number": self.summary["nct_number"],
"disease_center": self.summary["disease_center"],
"mmr_status": self.summary["mmr_status"],
"ms_status": self.summary["ms_status"],
"mutational_signatures": self.summary["mutational_signatures"],
"investigator": [i['output'] for i in suggestors['investigator_suggest']],
"short_title": self.summary["short_title"]
}
return suggestors, searchers, parse_primary_cancer_types(self.cancer_type_dict['primary_cancer_types'])