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Add heme #14

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117 changes: 116 additions & 1 deletion geniesp/bpc_redcap_export_mapping.py
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Original file line number Diff line number Diff line change
Expand Up @@ -281,6 +281,102 @@ def create_regimens(syn, regimen_infodf, top_x_regimens=5, cohort="NSCLC"):
'used': regimen_synid}


def configure_heme(hemedf):
"""Configure heme onc file to cBioPortal standard"""
heme_onc_class = ["hemonc_cytotoxic", "hemonc_immuno", "hemonc_targeted",
"hemonc_endocrine"]
heme_onc_target = ["hemonc_EGFR", "hemonc_ALK", "hemonc_ROS1",
"hemonc_MET", "hemonc_RET", "hemonc_BRAF",
"hemonc_TRK", "hemonc_TKI"]
heme_onc_regimen = [
"Capecitabine monotherapy", "Gemcitabine monotherapy", "Gemcitabine and nab-Paclitaxel", "NO MATCH",
"Fluorouracil monotherapy", "Carboplatin and Pemetrexed",
"Erlotinib monotherapy", "Docetaxel monotherapy", "Nivolumab monotherapy",
"Carboplatin, Pemetrexed, Bevacizumab", "Pembrolizumab monotherapy",
"Carboplatin monotherapy", "Vinorelbine monotherapy", "Crizotinib monotherapy", "Cisplatin and Pemetrexed",
"Cannot be determined (>= 1 investigational drug)", "Cisplatin and Etoposide", "Carboplatin and Paclitaxel",
"Bevacizumab monotherapy", "Bicalutamide and Leuprolide", "Leuprolide monotherapy", "NA", "VH", "Pemetrexed monotherapy",
"Afatinib monotherapy", "Osimertinib monotherapy", "Cisplatin and Vinorelbine", "Cannot be determined (>= 1 other drug)",
"Alectinib monotherapy", "Brigatinib monotherapy", "Lorlatinib monotherapy", "Exemestane monotherapy",
"Anastrozole monotherapy", "FULV|Intraperitoneal 5-FU", "Carboplatin and Gemcitabine", "Tamoxifen monotherapy",
"Ceritinib monotherapy", "Pemetrexed and Bevacizumab", "Gefitinib monotherapy", "Atezolizumab monotherapy",
"FLOX|FOLFOX4|mFOLFOX6|FOLFOX2|FOLFOX 7/sLV5FU2|mFOLFOX7|Nordic FLOX|OXAFAFU|FOLFOX chronotherapy|OLF|FOLFOX",
"Cisplatin monotherapy", "Carboplatin, Paclitaxel, Bevacizumab", "AC-T|ddAC-T",
"Cisplatin and Paclitaxel", "Cisplatin and Gemcitabine", "CMF", "Letrozole monotherapy", "Gemcitabine and Paclitaxel", "Temozolomide monotherapy",
"Doxorubicin monotherapy", "Carboplatin and Etoposide",
"Carboplatin, Pemetrexed, Pembrolizumab",
"Cisplatin and Docetaxel", "Carboplatin and Docetaxel",
"Bacillus Calmette-Guerin (BCG) monotherapy",
"MVAC|MVAC, dose-dense", "Durvalumab monotherapy",
"nab-Paclitaxel monotherapy", "Carboplatin and nab-Paclitaxel",
"Flutamide and Leuprolide", "Imatinib monotherapy",
"Dabrafenib and Trametinib", "AC|ddAC", "Docetaxel and Ramucirumab",
"Sunitinib monotherapy", "Cisplatin, Pemetrexed, Bevacizumab",
"Gemcitabine and Vinorelbine", "Afatinib and Cetuximab",
"Cabozantinib monotherapy", "Degarelix monotherapy", "Paclitaxel and Ramucirumab",
"Vinorelbine and Bevacizumab", "Paclitaxel and Bevacizumab",
"Paclitaxel, nanoparticle albumin-bound and Bevacizumab", "Paclitaxel monotherapy",
"Vemurafenib monotherapy", "Dabrafenib monotherapy",
"Erlotinib and Bevacizumab", "Docetaxel and Vinorelbine",
"TC (Taxotere)", "Carboplatin, Paclitaxel, Pembrolizumab",
"Sorafenib monotherapy", "Ipilimumab and Nivolumab|Ipilimumab, then Nivolumab|Nivolumab, then Ipilimumab",
"FUOX and RT|FOLFOX 7/sLV5FU2 (L-Leucovorin)|FOLFOX4 (L-Leucovorin)|FLOX|FOLFOX4|mFOLFOX6|FOLFOX2|FOLFOX 7/sLV5FU2|mFOLFOX7|Nordic FLOX|OXAFAFU|FOLFOX chronotherapy|EOF|Fluorouracil, Oxaliplatin, RT|OLF|FOLFOX|mFOLFOX6 (L-Leucovorin)|mFOLFOX7 (L-Leucovorin)",
"Cetuximab monotherapy", "Docetaxel and Gemcitabine", "Gemcitabine and Trastuzumab",
"Mitomycin monotherapy", "Megestrol monotherapy", "Afatinib and Bevacizumab",
"BR", "Ipilimumab monotherapy", "Decitabine monotherapy",
"Carboplatin and Vinorelbine", "Gemcitabine and Bevacizumab",
"FUOX", "Capecitabine and Mitomycin", "Pomalidomide monotherapy",
"Daratumumab monotherapy", "Cisplatin, Gemcitabine, Necitumumab",
"IL-2 monotherapy", "Talimogene laherparepvec monotherapy",
"Dacarbazine monotherapy", "Pemetrexed and Pembrolizumab", "Carboplatin, Etoposide, Atezolizumab",
"Ado-trastuzumab emtansine monotherapy", "VR", "BVR", "EC|DI EC|ddEC",
"CISCA|CAP (Platinol)", "CapeOx and Erlotinib", "Capecitabine and Erlotinib", "Methotrexate monotherapy",
"Cyclophosphamide monotherapy", "DCF", "Trastuzumab monotherapy",
"Carboplatin, nab-Paclitaxel, Pembrolizumab"
]
all_hemedf = pd.DataFrame()
for _, row in hemedf.iterrows():
heme_classes_list = []
for heme_class in heme_onc_class:
heme_modified = heme_class.replace("hemonc_", "").capitalize()
heme_classes_list.extend([heme_modified] * row[heme_class])
# OBTAIN HEME_ONC_TARGET map
# Since sometimes records can have more than one target,
# Keep the binary and add each heme onc column
target_map = {
("HEME_ONC_TARGET_" + target.replace('hemonc_', "")): row[target]
for target in heme_onc_target
}
# target_cols = row[heme_onc_target]
# targets = [
# target.replace("hemonc_", "")
# for target in target_cols.index[target_cols == 1].tolist()
# ]
# target_str = ",".join(targets)
# Obtain HEME_ONC_REGIMEN
regimen_cols = row[heme_onc_regimen]
regimen = regimen_cols.index[regimen_cols == 1].tolist()
if regimen:
regimen = regimen[0]
else:
regimen = ""
# If a record doens't have any heme onc classes, it won't have any
# rows in the heme onc dataframe
new_heme_dict = {
'record_id': [row['record_id']] * len(heme_classes_list),
'redcap_repeat_instance': [row['redcap_repeat_instance']] * len(heme_classes_list),
'HEME_ONC_CLASS': heme_classes_list,
# 'HEME_ONC_TARGET': [target_str] * len(heme_classes_list),
'HEME_ONC_REGIMEN': [regimen] * len(heme_classes_list)
}
# Add target cols to heme
new_heme_dict.update(target_map)
new_hemedf = pd.DataFrame(new_heme_dict)
new_hemedf.drop_duplicates(inplace=True)
all_hemedf = all_hemedf.append(new_hemedf)
return all_hemedf


class BpcProjectRunner(metaclass=ABCMeta):
"""BPC redcap to cbioportal export"""
# Sponsorted project name
Expand Down Expand Up @@ -435,6 +531,10 @@ def write_clinical_file(self, clinicaldf, redcap_to_cbiomappingdf,
def make_timeline_treatmentdf(self, infodf, sample_type):
"""Make timeline treatment dataframe"""
subset_infodf = infodf[infodf['sampleType'] == sample_type]
# Get heme info
heme_infodf = subset_infodf[
subset_infodf['data_type'] == 'heme'
]
# Exclude heme onc columns
subset_infodf = subset_infodf[
~subset_infodf['data_type'].isin(['portal_value', 'heme'])
Expand All @@ -451,6 +551,10 @@ def make_timeline_treatmentdf(self, infodf, sample_type):
timelinedf = timelinedf[
timelinedf['redcap_ca_index'] == "Yes"
]
# Get heme onc mapping
heme_synid = heme_infodf['id'].unique()[0]
heme_ent = self.syn.get(heme_synid)
used_heme_entity = f'{heme_synid}.{heme_ent.versionNumber}'
# Flatten multiple columns values into multiple rows
multiple_cols_idx = subset_infodf['code'].str.contains("[*]")
final_timelinedf = pd.DataFrame()
Expand Down Expand Up @@ -481,6 +585,7 @@ def make_timeline_treatmentdf(self, infodf, sample_type):
]
non_multi_cols = subset_infodf[~multiple_cols_idx]['code'].tolist()
non_multi_cols.append("record_id")
non_multi_cols.append("redcap_repeat_instance")

# Merge final timeline
final_timelinedf = final_timelinedf.merge(
Expand All @@ -502,14 +607,24 @@ def make_timeline_treatmentdf(self, infodf, sample_type):
# Must add in PATIENT_ID
mapping['record_id'] = 'PATIENT_ID'
final_timelinedf = final_timelinedf.rename(columns=mapping)
hemedf = pd.read_csv(heme_ent.path)
hemedf = configure_heme(hemedf)
final_timelinedf = final_timelinedf.merge(
hemedf,
left_on=["PATIENT_ID", "redcap_repeat_instance"],
right_on=['record_id', 'redcap_repeat_instance'],
how="left"
)
final_timelinedf.drop(columns=['record_id', "redcap_repeat_instance"],
inplace=True)
# timeline file must be in this order
cols_to_order = ['PATIENT_ID', 'START_DATE', 'STOP_DATE',
'EVENT_TYPE', 'TREATMENT_TYPE', 'AGENT']
cols_to_order.extend(
final_timelinedf.columns.drop(cols_to_order).tolist()
)
return {'df': final_timelinedf[cols_to_order].drop_duplicates(),
'used': [used_entity]}
'used': [used_entity, used_heme_entity]}

def write_and_storedf(self, df, filepath, used_entities=[]):
"""Write and store dataframe
Expand Down